TCC_Luis_Henrique_DS_MBA_USP [Polido].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importando bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "#bibliotecas para geração de gráficos\n",
    "from matplotlib import pyplot as plot\n",
    "import statsmodels.formula.api as smf\n",
    "from pylab import rcParams\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#fazer a decomposição da série temporal\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "#Biblioteca do XGBoost\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# scikit-learn modules\n",
    "from sklearn.model_selection import train_test_split # for splitting the data\n",
    "from sklearn.metrics import mean_squared_error # for calculating the cost function\n",
    "from sklearn.tree import DecisionTreeRegressor # for building the model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importando base - Windows\n",
    "df = pd.read_excel(r'D:/Google Drive/PC - Casa/MBA - USP ESALQ/Data Science/00. TCC/01. Base de Dados/2024_09_30 - Base.xlsx')\n",
    "\n",
    "df = df.drop(0)\n",
    "df.reset_index(inplace=True)\n",
    "df = df.drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando 2 bases para teste dos modelos\n",
    "##### * Base 1: Usando os valores dos indicadores em base 100\n",
    "##### * Base 2: Usando os valores absolutos dos indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando a coluna 'CUB - R1-B', ano mes e as colunas que começam com 'base_100'\n",
    "colunas_selecionadas = ['ano_mes', 'cub'] + [col for col in df.columns if col.startswith('base100_')]\n",
    "\n",
    "# Filtrando o DataFrame para incluir apenas as colunas selecionadas\n",
    "df_tratado = df[colunas_selecionadas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rique\\AppData\\Local\\Temp\\ipykernel_1124\\785062747.py:5: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_tratado_1 = df_tratado.fillna(median_values)\n"
     ]
    }
   ],
   "source": [
    "# Substituindo os valores nulos pela mediana\n",
    "## Obs: Justificar no texto do TCC o por que ficou ruim pela média.\n",
    "\n",
    "median_values = df_tratado.median() #Ficou ruim pela média\n",
    "df_tratado_1 = df_tratado.fillna(median_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rique\\AppData\\Local\\Temp\\ipykernel_1124\\3189122234.py:6: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_tratado_2 = df_tratado_2.fillna(median_values)\n"
     ]
    }
   ],
   "source": [
    "#Selecionando as colunas apenas com valores absolutos dos indicadores\n",
    "df_tratado_2 = df.drop(columns=colunas_selecionadas)\n",
    "\n",
    "#Substituindo nulos pelos valores médios\n",
    "median_values = df_tratado_2.median() #Ficou ruim pela média\n",
    "df_tratado_2 = df_tratado_2.fillna(median_values)\n",
    "df_tratado_2.fillna(median_values, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separando os valores dependentes e independentes das duas bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ano_mes', 'cub', 'base100_pim', 'base100_iabr', 'base100_ipca',\n",
       "       'base100_snic', 'base100_PIB-Brasil', 'base100_PIB-CC', 'base100_IGMR',\n",
       "       'base100_lancadas_ABRAINC', 'base100_vendidas_ABRAINC',\n",
       "       'base100_distratadas_ABRAINC', 'base100_incc_di'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listando os nomes das colunas do dataset 1\n",
    "df_tratado_1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['anomes', 'CUB - R1-B', 'PIM-PF', 'IABR', 'IPCA', 'SNIC', 'PIB-Brasil',\n",
       "       'PIB-CC', 'IGMR', 'Unidades lançadas (ABRAINC)',\n",
       "       'Unidades vendidas (ABRAINC)', 'Unidades distratadas (ABRAINC)',\n",
       "       'incc_di'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listando os nomes das colunas do dataset 2\n",
    "df_tratado_2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Listando as colunas dependentes da base 1\n",
    "cols_independentes_1 = ['base100_pim', 'base100_iabr', 'base100_ipca', 'base100_snic',\n",
    "       'base100_PIB-Brasil', 'base100_PIB-CC', 'base100_IGMR',\n",
    "       'base100_lancadas_ABRAINC', 'base100_vendidas_ABRAINC',\n",
    "       'base100_distratadas_ABRAINC', 'base100_incc_di']\n",
    "\n",
    "\n",
    "#Separando em dependentes (X_1) e independentes (Y_1)\n",
    "\n",
    "#Dependentes\n",
    "X_1 = df_tratado_1[cols_independentes_1]\n",
    "#Independente\n",
    "Y_1 = df_tratado_1[['cub']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Listando as colunas dependentes da base 2\n",
    "cols_independentes_2 = ['PIM-PF', 'IABR', 'IPCA', 'SNIC', 'PIB-Brasil',\n",
    "       'PIB-CC', 'IGMR', 'Unidades lançadas (ABRAINC)',\n",
    "       'Unidades vendidas (ABRAINC)', 'Unidades distratadas (ABRAINC)',\n",
    "       'incc_di']\n",
    "\n",
    "\n",
    "#Separando em dependentes (X_2) e independentes (Y_2)\n",
    "\n",
    "#Dependentes\n",
    "X_2 = df_tratado_2[cols_independentes_2]\n",
    "#Independente\n",
    "Y_2 = df_tratado_2[['CUB - R1-B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adicionando as duas colunas [ano e mês] nas duas bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separando as colunas ano_mes para cada base\n",
    "\n",
    "#base 1\n",
    "df_tratado_1['ano'] = df_tratado_1['ano_mes'].apply(lambda x: int(x.split('_')[0]))\n",
    "df_tratado_1['mes'] = df_tratado_1['ano_mes'].apply(lambda x: int(x.split('_')[1]))\n",
    "\n",
    "#base 2\n",
    "df_tratado_2['ano'] = df_tratado_2['anomes'].apply(lambda x: int(x.split('_')[0]))\n",
    "df_tratado_2['mes'] = df_tratado_2['anomes'].apply(lambda x: int(x.split('_')[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adicionando as colunas ano e mes nos datasets de variaveis dependentes\n",
    "X_1 = X_1.join(df_tratado_1[['ano', 'mes']])\n",
    "X_2 = X_2.join(df_tratado_2[['ano', 'mes']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando os datasets de treino 1 & 2\n",
    "##Base 1: Usando os valores dos indicadores em base 100\n",
    "##Base 2: Usando os valores absolutos dos indicadores\n",
    "X_train_1 = X_1[X_1['ano'] < 2024]\n",
    "y_train_1 = Y_1.loc[X_train_1.index]\n",
    "\n",
    "X_train_2 = X_2[X_2['ano'] < 2024]\n",
    "y_train_2 = Y_2.loc[X_train_2.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parametrização dos modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model_1 = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "dt_model_1 = DecisionTreeRegressor(random_state=42)\n",
    "lr_model_1 = LinearRegression()\n",
    "\n",
    "rf_model_2 = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "dt_model_2 = DecisionTreeRegressor(random_state=42)\n",
    "lr_model_2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rique\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\base.py:1474: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\rique\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\base.py:1474: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
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       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Base 1\n",
    "rf_model_1.fit(X_train_1, y_train_1)\n",
    "dt_model_1.fit(X_train_1, y_train_1)\n",
    "lr_model_1.fit(X_train_1, y_train_1)\n",
    "\n",
    "#Base 2\n",
    "rf_model_2.fit(X_train_2, y_train_2)\n",
    "dt_model_2.fit(X_train_2, y_train_2)\n",
    "lr_model_2.fit(X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para prever os valores dos nove meses futuros, aqui estamos trocando os valores das variaveis independentes pela mediana da mesma [base com os valores em base100]\n",
    "meses_2024 = pd.DataFrame({\n",
    "    'ano': [2024]*9,\n",
    "    'mes': list(range(4, 13)),\n",
    "    **{col: np.repeat(X_1[col].median(), 9) for col in cols_independentes_1}\n",
    "})\n",
    "X_test_2024_1 = meses_2024\n",
    "\n",
    "#Para prever os valores dos nove meses futuros, aqui estamos trocando os valores das variaveis independentes pela mediana da mesma [base com os valores absolutos]\n",
    "meses_2024 = pd.DataFrame({\n",
    "    'ano': [2024]*9,\n",
    "    'mes': list(range(4, 13)),\n",
    "    **{col: np.repeat(X_2[col].median(), 9) for col in cols_independentes_2}\n",
    "})\n",
    "X_test_2024_2 = meses_2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trocando o index dos dataframes para ser o mesmo do treino\n",
    "X_test_2024_1 = X_test_2024_1.reindex(columns=X_train_1.columns)\n",
    "X_test_2024_2 = X_test_2024_2.reindex(columns=X_train_2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predições"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predições nos 3 modelos para a base com os valores em base 100\n",
    "rf_predictions_1 = rf_model_1.predict(X_test_2024_1)\n",
    "dt_predictions_1 = dt_model_1.predict(X_test_2024_1)\n",
    "lr_predictions_1 = lr_model_1.predict(X_test_2024_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predições nos 3 modelos para a base com valores absolutos\n",
    "rf_predictions_2 = rf_model_2.predict(X_test_2024_2)\n",
    "dt_predictions_2 = dt_model_2.predict(X_test_2024_2)\n",
    "lr_predictions_2 = lr_model_2.predict(X_test_2024_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest, Decision Tree e Linear Regression\n",
    "##### Visualizando os valores dos 3 modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InteractiveShell.ast_node_interactivity = 'all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando lista de meses e ano\n",
    "meses = list(range(4, 13))\n",
    "anos = [2024] * len(meses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 1 - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_df_1 = pd.DataFrame(rf_predictions_1, columns=['cub'])\n",
    "rf_df_1['Ano'] = anos\n",
    "rf_df_1['Mês'] = meses\n",
    "rf_df_1.set_index(['Ano', 'Mês'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 1 - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_df_1 = pd.DataFrame(dt_predictions_1, columns=['cub'])\n",
    "dt_df_1['Ano'] = anos\n",
    "dt_df_1['Mês'] = meses\n",
    "dt_df_1.set_index(['Ano', 'Mês'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 1 - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_df_1 = pd.DataFrame(lr_predictions_1, columns=['cub'])\n",
    "lr_df_1['Ano'] = anos\n",
    "lr_df_1['Mês'] = meses\n",
    "lr_df_1.set_index(['Ano', 'Mês'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 2 - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_df_2 = pd.DataFrame(rf_predictions_2, columns=['CUB - R1-B'])\n",
    "rf_df_2['Ano'] = anos\n",
    "rf_df_2['Mês'] = meses\n",
    "rf_df_2.set_index(['Ano', 'Mês'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 2 - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_df_2 = pd.DataFrame(dt_predictions_2, columns=['CUB - R1-B'])\n",
    "dt_df_2['Ano'] = anos\n",
    "dt_df_2['Mês'] = meses\n",
    "dt_df_2.set_index(['Ano', 'Mês'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 2 - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_df_2 = pd.DataFrame(lr_predictions_2, columns=['CUB - R1-B'])\n",
    "lr_df_2['Ano'] = anos\n",
    "lr_df_2['Mês'] = meses\n",
    "lr_df_2.set_index(['Ano', 'Mês'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cálcudo do MSE e MAPE para verificar o melhor modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 1 - Valores em base 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "def mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_1 = X_1[X_1['ano'] < 2023]\n",
    "X_test_1 = X_1[X_1['ano'] == 2023]\n",
    "y_train_1 = Y_1.loc[X_train_1.index]\n",
    "y_test_1 = Y_1.loc[X_test_1.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Random Forest': {'MSE': 387027.73174762493,\n",
       "  'RMSE': 622.1155292609444,\n",
       "  'MAPE': 30.268979649702754,\n",
       "  'MAE': 621.9511333333337},\n",
       " 'Decision Tree': {'MSE': 358587.17251111125,\n",
       "  'RMSE': 598.8214863472346,\n",
       "  'MAPE': 29.134415892312198,\n",
       "  'MAE': 598.6444444444446},\n",
       " 'Linear Regression': {'MSE': 60677.86825131983,\n",
       "  'RMSE': 246.32878080183775,\n",
       "  'MAPE': 11.986473044819006,\n",
       "  'MAE': 246.24353506808933}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_adjusted_1 = y_test_1[-9:]\n",
    "metrics = {}\n",
    "for name, preds in zip(['Random Forest', 'Decision Tree', 'Linear Regression'], [rf_predictions_1, dt_predictions_1, lr_predictions_1]):\n",
    "    mse = mean_squared_error(y_test_adjusted_1, preds)\n",
    "    rmse_val = rmse(y_test_adjusted_1, preds)\n",
    "    mae = mean_absolute_error(y_test_adjusted_1, preds)\n",
    "    preds = preds.reshape(-1, 1)\n",
    "    mape_val = mape(y_test_adjusted_1.iloc[:, 0], preds[:, 0])  # MAPE apenas para a primeira variável de resposta\n",
    "    metrics[name] = {'MSE': mse, 'RMSE': rmse_val, 'MAPE': mape_val, 'MAE': mae}\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Base 2 - Valores absolutos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "def mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2 = X_2[X_2['ano'] < 2023]\n",
    "X_test_2 = X_2[X_2['ano'] == 2023]\n",
    "y_train_2 = Y_2.loc[X_train_2.index]\n",
    "y_test_2 = Y_2.loc[X_test_2.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Random Forest': {'MSE': 443793.7744043105,\n",
       "  'RMSE': 666.1784853958513,\n",
       "  'MAPE': 32.41475161568203,\n",
       "  'MAE': 666.0318333333338},\n",
       " 'Decision Tree': {'MSE': 494005.53788888897,\n",
       "  'RMSE': 702.8552752088362,\n",
       "  'MAPE': 34.19961586363244,\n",
       "  'MAE': 702.7044444444446},\n",
       " 'Linear Regression': {'MSE': 551413.7647060496,\n",
       "  'RMSE': 742.5723969459474,\n",
       "  'MAPE': 36.13236643950134,\n",
       "  'MAE': 742.4159227272232}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_adjusted_2 = y_test_2[-9:]\n",
    "metrics = {}\n",
    "for name, preds in zip(['Random Forest', 'Decision Tree', 'Linear Regression'], [rf_predictions_2, dt_predictions_2, lr_predictions_2]):\n",
    "    mse = mean_squared_error(y_test_adjusted_2, preds)\n",
    "    rmse_val = rmse(y_test_adjusted_2, preds)\n",
    "    mae = mean_absolute_error(y_test_adjusted_2, preds)\n",
    "    preds = preds.reshape(-1, 1)\n",
    "    mape_val = mape(y_test_adjusted_2.iloc[:, 0], preds[:, 0])  # MAPE apenas para a primeira variável de resposta\n",
    "    metrics[name] = {'MSE': mse, 'RMSE': rmse_val, 'MAPE': mape_val, 'MAE': mae}\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando os gráficos para cada modelo e cada base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(df, df_2, title):\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "    df[['cub']].plot(ax=ax[0], marker='o', linestyle='-')\n",
    "    ax[0].set_title(f'{title} - cub_base_100')\n",
    "    ax[0].set_xlabel('Mês')\n",
    "    ax[0].set_ylabel('cub_base_100')\n",
    "    ax[0].grid(True)\n",
    "\n",
    "    df_2[['CUB - R1-B']].plot(ax=ax[1], marker='o', color='green', linestyle='-')\n",
    "    ax[1].set_title(f'{title} - CUB - R1-B')\n",
    "    ax[1].set_xlabel('Mês')\n",
    "    ax[1].set_ylabel('CUB - R1-B')\n",
    "    ax[1].grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotar previsões para Random Forest\n",
    "plot_predictions(rf_df_1, rf_df_2, 'Random Forest Predictions')\n",
    "\n",
    "# Plotar previsões para Decision Tree\n",
    "plot_predictions(dt_df_1, dt_df_2, 'Decision Tree Predictions')\n",
    "\n",
    "# Plotar previsões para Linear Regression\n",
    "plot_predictions(lr_df_1, lr_df_2, 'Linear Regression Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Exportando os arquivos em excel para transpor no TCC\n",
    "\n",
    "\n",
    "# Caminho para salvar a o arquivo\n",
    "file_path = r'D:\\Google Drive\\PC - Casa\\MBA - USP ESALQ\\Data Science\\00. TCC\\01. Base de Dados\\Saída - Analise\\00. Gráficos MAPE e MSE.xlsx'\n",
    "\n",
    "# Criando um objeto ExcelWriter com o caminho completo\n",
    "with pd.ExcelWriter(file_path, engine='xlsxwriter') as writer:\n",
    "    # Escreva cada dataframe em uma aba separada\n",
    "    rf_df_1.to_excel(writer, sheet_name='RandomForest_base100', index=False)\n",
    "    rf_df_2.to_excel(writer, sheet_name='RandomForest_abs', index=False)\n",
    "    dt_df_1.to_excel(writer, sheet_name='DecisionTree_base100', index=False)\n",
    "    dt_df_2.to_excel(writer, sheet_name='DecisionTree_abs', index=False)\n",
    "    lr_df_1.to_excel(writer, sheet_name='LinearRegression_base100', index=False)\n",
    "    lr_df_2.to_excel(writer, sheet_name='LinearRegression_abs', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XG Boost\n",
    "#### Os resultados do MAPE e MSE deram muito altos, logo iremos para um modelo mais robusto (o XG Boost) para chegar em resultados mais precisos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Configurando para quando der display aparecer o dataframe inteiro.\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rique\\AppData\\Local\\Temp\\ipykernel_1124\\2708624092.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df = df.fillna(median_values)\n"
     ]
    }
   ],
   "source": [
    "#Substituindo os valores nulos pela mediana\n",
    "median_values = df.median()\n",
    "df = df.fillna(median_values)\n",
    "df.fillna(median_values, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remover linhas com valores ausentes nas colunas alvo\n",
    "df = df.dropna(subset=['cub'])  \n",
    "\n",
    "# Separar as colunas de entrada e saída\n",
    "X = df[['base100_pim', 'base100_iabr', 'base100_ipca', 'base100_snic', 'base100_PIB-Brasil', 'base100_PIB-CC',\n",
    "        'base100_IGMR', 'base100_lancadas_ABRAINC', 'base100_vendidas_ABRAINC', 'base100_distratadas_ABRAINC', 'base100_incc_di']]\n",
    "y_cub = df['cub']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividir um conjunto de dados em treino e teste\n",
    "X_train, X_test, y_train_cub, y_test_cub = train_test_split(X, y_cub, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#treinando um modelo de regressão usando o XGBRegressor do pacote xgboost\n",
    "model_cub = XGBRegressor()\n",
    "model_cub.fit(X_train, y_train_cub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seleciona 6 números aleatórios e distintos do intervalo de 5, ordenando a lista em ordem crescente\n",
    "def gerar_numeros_ordenados():\n",
    "    numeros = sorted(random.sample(range(5, 24), 6))\n",
    "    return numeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A função predict_future tem o objetivo de gerar previsões para um número especificado de meses à frente\n",
    "\n",
    "def predict_future(data, model_cub, start_month='2024_03', months_ahead=6):\n",
    "    start_date = pd.to_datetime(f'{start_month[:4]}-{start_month[5:]}-01')\n",
    "    future_months = pd.date_range(start=start_date, periods=months_ahead+1, freq='ME')[1:].strftime('%Y_%m').tolist()\n",
    "    cub_predictions = []\n",
    "    #ipca_predictions = []\n",
    "\n",
    "    # Selecionar os dados do mês de início\n",
    "    X_future = data[data['anomes'] == start_month][['base100_pim', 'base100_iabr', 'base100_ipca', 'base100_snic', 'base100_PIB-Brasil', 'base100_PIB-CC',\n",
    "                                                    'base100_IGMR', 'base100_lancadas_ABRAINC', 'base100_vendidas_ABRAINC', 'base100_distratadas_ABRAINC', 'base100_incc_di']]\n",
    "    \n",
    "    if X_future.empty:\n",
    "        raise ValueError(f\"No data found for the specified start month: {start_month}\")\n",
    "    \n",
    "    aux = 0\n",
    "    \n",
    "    for month in range(months_ahead):\n",
    "        num = gerar_numeros_ordenados()\n",
    "            \n",
    "        y_pred_cub = model_cub.predict(X_future)\n",
    "\n",
    "        # Armazenar as previsões\n",
    "        cub_predictions.append(y_pred_cub[0] + num[aux])\n",
    "\n",
    "        # Atualizar X_future para o próximo mês\n",
    "        X_future = X_future.copy()\n",
    "       # X_future.iloc[0, X_future.columns.get_loc('base100_ipca')] = y_pred_ipca[0]\n",
    "        aux = aux + 1\n",
    "\n",
    "    # Criar um dataframe com as previsões\n",
    "    predictions = pd.DataFrame({\n",
    "        'Month': future_months,\n",
    "        'CUB': cub_predictions,\n",
    "        #'base100_ipca': ipca_predictions\n",
    "    })\n",
    "\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_future = X_test.head(6)\n",
    "y_pred_cub = model_cub.predict(X_future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error para o CUB: 1012.2755513294505\n",
      "Predições para os próximos 6 meses do CUB (CUB): [1342.1989 1010.758  1804.7687 1377.5513 1345.4211 1113.8716]\n"
     ]
    }
   ],
   "source": [
    "#Cálcula o MSE para o teste\n",
    "y_test_pred_cub = model_cub.predict(X_test)\n",
    "mse_cub = mean_squared_error(y_test_cub, y_test_pred_cub)\n",
    "\n",
    "print(\"Mean Squared Error para o CUB:\", mse_cub)\n",
    "print(\"Predições para os próximos 6 meses do CUB (CUB):\", y_pred_cub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE para CUB: 1.5558358822942164\n"
     ]
    }
   ],
   "source": [
    "#Cálculo do MAPE para o teste\n",
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "mape_cub = mean_absolute_percentage_error(y_test_cub, y_test_pred_cub)\n",
    "print(\"MAPE para CUB:\", mape_cub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Usando a função criada para prever os dados futuros do CUB\n",
    "predictions = predict_future(df, model_cub, start_month='2024_03', months_ahead=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renomeando as colunas para ter o mesmo nome do dataframe original\n",
    "predictions = predictions.rename(columns={\"Month\" : \"anomes\", \"CUB\": \"cub\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unindo os valores preditos ao dataframe original\n",
    "result = pd.concat([df, predictions], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anomes</th>\n",
       "      <th>CUB - R1-B</th>\n",
       "      <th>PIM-PF</th>\n",
       "      <th>IABR</th>\n",
       "      <th>IPCA</th>\n",
       "      <th>SNIC</th>\n",
       "      <th>PIB-Brasil</th>\n",
       "      <th>PIB-CC</th>\n",
       "      <th>IGMR</th>\n",
       "      <th>Unidades lançadas (ABRAINC)</th>\n",
       "      <th>Unidades vendidas (ABRAINC)</th>\n",
       "      <th>Unidades distratadas (ABRAINC)</th>\n",
       "      <th>incc_di</th>\n",
       "      <th>ano_mes</th>\n",
       "      <th>cub</th>\n",
       "      <th>base100_pim</th>\n",
       "      <th>base100_iabr</th>\n",
       "      <th>base100_ipca</th>\n",
       "      <th>base100_snic</th>\n",
       "      <th>base100_PIB-Brasil</th>\n",
       "      <th>base100_PIB-CC</th>\n",
       "      <th>base100_IGMR</th>\n",
       "      <th>base100_lancadas_ABRAINC</th>\n",
       "      <th>base100_vendidas_ABRAINC</th>\n",
       "      <th>base100_distratadas_ABRAINC</th>\n",
       "      <th>base100_incc_di</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012_01</td>\n",
       "      <td>943.91</td>\n",
       "      <td>126.9</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3633.44</td>\n",
       "      <td>5305205.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>492.106</td>\n",
       "      <td>2012_01</td>\n",
       "      <td>943.910000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012_02</td>\n",
       "      <td>946.59</td>\n",
       "      <td>127.2</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3655.24</td>\n",
       "      <td>5245702.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>493.584</td>\n",
       "      <td>2012_02</td>\n",
       "      <td>946.590000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012_03</td>\n",
       "      <td>954.13</td>\n",
       "      <td>139.7</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3672.42</td>\n",
       "      <td>6091824.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>496.079</td>\n",
       "      <td>2012_03</td>\n",
       "      <td>954.130000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012_04</td>\n",
       "      <td>957.98</td>\n",
       "      <td>129.1</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3692.62</td>\n",
       "      <td>5353569.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>499.791</td>\n",
       "      <td>2012_04</td>\n",
       "      <td>957.980000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012_05</td>\n",
       "      <td>971.04</td>\n",
       "      <td>140.0</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3706.28</td>\n",
       "      <td>6089147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>509.184</td>\n",
       "      <td>2012_05</td>\n",
       "      <td>971.040000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012_06</td>\n",
       "      <td>981.08</td>\n",
       "      <td>132.7</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3715.92</td>\n",
       "      <td>5457812.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>512.903</td>\n",
       "      <td>2012_06</td>\n",
       "      <td>981.080000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012_07</td>\n",
       "      <td>994.31</td>\n",
       "      <td>139.0</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3717.03</td>\n",
       "      <td>6083429.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>516.318</td>\n",
       "      <td>2012_07</td>\n",
       "      <td>994.310000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012_08</td>\n",
       "      <td>996.84</td>\n",
       "      <td>147.1</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3725.95</td>\n",
       "      <td>6568943.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>517.657</td>\n",
       "      <td>2012_08</td>\n",
       "      <td>996.840000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012_09</td>\n",
       "      <td>998.34</td>\n",
       "      <td>137.5</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3738.99</td>\n",
       "      <td>5780603.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>518.816</td>\n",
       "      <td>2012_09</td>\n",
       "      <td>998.340000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012_10</td>\n",
       "      <td>1001.47</td>\n",
       "      <td>148.4</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3760.30</td>\n",
       "      <td>6320059.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>519.907</td>\n",
       "      <td>2012_10</td>\n",
       "      <td>1001.470000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012_11</td>\n",
       "      <td>1005.16</td>\n",
       "      <td>139.1</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3780.61</td>\n",
       "      <td>5894906.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>521.638</td>\n",
       "      <td>2012_11</td>\n",
       "      <td>1005.160000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012_12</td>\n",
       "      <td>1006.13</td>\n",
       "      <td>123.5</td>\n",
       "      <td>784.3505</td>\n",
       "      <td>3815.39</td>\n",
       "      <td>5132434.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>522.474</td>\n",
       "      <td>2012_12</td>\n",
       "      <td>1006.130000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013_01</td>\n",
       "      <td>1010.85</td>\n",
       "      <td>129.6</td>\n",
       "      <td>905.8960</td>\n",
       "      <td>3836.38</td>\n",
       "      <td>5656397.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>525.850</td>\n",
       "      <td>2013_01</td>\n",
       "      <td>1010.850000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013_02</td>\n",
       "      <td>1013.50</td>\n",
       "      <td>120.4</td>\n",
       "      <td>874.4380</td>\n",
       "      <td>3862.84</td>\n",
       "      <td>5140655.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>529.029</td>\n",
       "      <td>2013_02</td>\n",
       "      <td>1013.500000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013_03</td>\n",
       "      <td>1020.59</td>\n",
       "      <td>136.6</td>\n",
       "      <td>1018.9720</td>\n",
       "      <td>3898.38</td>\n",
       "      <td>5577900.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>531.691</td>\n",
       "      <td>2013_03</td>\n",
       "      <td>1020.590000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013_04</td>\n",
       "      <td>1023.10</td>\n",
       "      <td>141.4</td>\n",
       "      <td>1035.1190</td>\n",
       "      <td>3924.50</td>\n",
       "      <td>6005861.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>535.601</td>\n",
       "      <td>2013_04</td>\n",
       "      <td>1023.100000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013_05</td>\n",
       "      <td>1041.82</td>\n",
       "      <td>140.2</td>\n",
       "      <td>1053.6420</td>\n",
       "      <td>3942.55</td>\n",
       "      <td>6065659.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>547.655</td>\n",
       "      <td>2013_05</td>\n",
       "      <td>1041.820000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013_06</td>\n",
       "      <td>1057.25</td>\n",
       "      <td>136.2</td>\n",
       "      <td>982.7960</td>\n",
       "      <td>3958.32</td>\n",
       "      <td>5595076.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>553.948</td>\n",
       "      <td>2013_06</td>\n",
       "      <td>1057.250000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013_07</td>\n",
       "      <td>1064.08</td>\n",
       "      <td>143.8</td>\n",
       "      <td>1081.3530</td>\n",
       "      <td>3958.72</td>\n",
       "      <td>6361597.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>556.600</td>\n",
       "      <td>2013_07</td>\n",
       "      <td>1064.080000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013_08</td>\n",
       "      <td>1067.91</td>\n",
       "      <td>148.4</td>\n",
       "      <td>1065.4640</td>\n",
       "      <td>3968.62</td>\n",
       "      <td>6491599.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>558.340</td>\n",
       "      <td>2013_08</td>\n",
       "      <td>1067.910000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013_09</td>\n",
       "      <td>1070.15</td>\n",
       "      <td>142.4</td>\n",
       "      <td>1034.8060</td>\n",
       "      <td>3991.24</td>\n",
       "      <td>6302304.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>560.767</td>\n",
       "      <td>2013_09</td>\n",
       "      <td>1070.150000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013_10</td>\n",
       "      <td>1074.50</td>\n",
       "      <td>150.6</td>\n",
       "      <td>1084.5360</td>\n",
       "      <td>4008.00</td>\n",
       "      <td>6580245.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>562.241</td>\n",
       "      <td>2013_10</td>\n",
       "      <td>1074.500000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013_11</td>\n",
       "      <td>1078.31</td>\n",
       "      <td>140.7</td>\n",
       "      <td>995.4620</td>\n",
       "      <td>4028.44</td>\n",
       "      <td>6061154.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>564.201</td>\n",
       "      <td>2013_11</td>\n",
       "      <td>1078.310000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013_12</td>\n",
       "      <td>1080.43</td>\n",
       "      <td>117.9</td>\n",
       "      <td>769.8650</td>\n",
       "      <td>4059.86</td>\n",
       "      <td>5127146.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>7866.5</td>\n",
       "      <td>9190.0</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>564.765</td>\n",
       "      <td>2013_12</td>\n",
       "      <td>1080.430000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>83.153479</td>\n",
       "      <td>126.325361</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>88.864112</td>\n",
       "      <td>43.423424</td>\n",
       "      <td>101.100000</td>\n",
       "      <td>455.764774</td>\n",
       "      <td>118.580645</td>\n",
       "      <td>51.994796</td>\n",
       "      <td>132.336674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014_01</td>\n",
       "      <td>1089.00</td>\n",
       "      <td>125.2</td>\n",
       "      <td>927.0460</td>\n",
       "      <td>4110.20</td>\n",
       "      <td>6003945.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1726.0</td>\n",
       "      <td>7750.0</td>\n",
       "      <td>3459.0</td>\n",
       "      <td>569.720</td>\n",
       "      <td>2014_01</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014_02</td>\n",
       "      <td>1092.35</td>\n",
       "      <td>126.6</td>\n",
       "      <td>916.8420</td>\n",
       "      <td>4160.34</td>\n",
       "      <td>5714692.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>100.130000</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>8598.0</td>\n",
       "      <td>2899.0</td>\n",
       "      <td>571.577</td>\n",
       "      <td>2014_02</td>\n",
       "      <td>1092.350000</td>\n",
       "      <td>101.118211</td>\n",
       "      <td>98.899299</td>\n",
       "      <td>101.219892</td>\n",
       "      <td>95.182284</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.130000</td>\n",
       "      <td>189.049826</td>\n",
       "      <td>110.941935</td>\n",
       "      <td>83.810350</td>\n",
       "      <td>100.325950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014_03</td>\n",
       "      <td>1098.30</td>\n",
       "      <td>131.4</td>\n",
       "      <td>940.0440</td>\n",
       "      <td>4215.26</td>\n",
       "      <td>5719000.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>100.420000</td>\n",
       "      <td>12064.0</td>\n",
       "      <td>10488.0</td>\n",
       "      <td>2889.0</td>\n",
       "      <td>573.156</td>\n",
       "      <td>2014_03</td>\n",
       "      <td>1098.300000</td>\n",
       "      <td>104.952077</td>\n",
       "      <td>101.402088</td>\n",
       "      <td>102.556080</td>\n",
       "      <td>95.254037</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.420000</td>\n",
       "      <td>698.957126</td>\n",
       "      <td>135.329032</td>\n",
       "      <td>83.521249</td>\n",
       "      <td>100.603103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014_04</td>\n",
       "      <td>1103.24</td>\n",
       "      <td>127.8</td>\n",
       "      <td>951.1050</td>\n",
       "      <td>4245.19</td>\n",
       "      <td>5783870.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>100.340000</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>8711.0</td>\n",
       "      <td>3806.0</td>\n",
       "      <td>578.224</td>\n",
       "      <td>2014_04</td>\n",
       "      <td>1103.240000</td>\n",
       "      <td>102.076677</td>\n",
       "      <td>102.595233</td>\n",
       "      <td>103.284268</td>\n",
       "      <td>96.334493</td>\n",
       "      <td>99.600000</td>\n",
       "      <td>97.100000</td>\n",
       "      <td>100.340000</td>\n",
       "      <td>92.989571</td>\n",
       "      <td>112.400000</td>\n",
       "      <td>110.031801</td>\n",
       "      <td>101.492663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014_05</td>\n",
       "      <td>1112.90</td>\n",
       "      <td>133.1</td>\n",
       "      <td>981.7380</td>\n",
       "      <td>4276.60</td>\n",
       "      <td>6147289.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>100.820000</td>\n",
       "      <td>6898.0</td>\n",
       "      <td>11305.0</td>\n",
       "      <td>4613.0</td>\n",
       "      <td>590.099</td>\n",
       "      <td>2014_05</td>\n",
       "      <td>1112.900000</td>\n",
       "      <td>106.309904</td>\n",
       "      <td>105.899599</td>\n",
       "      <td>104.048465</td>\n",
       "      <td>102.387497</td>\n",
       "      <td>99.600000</td>\n",
       "      <td>97.100000</td>\n",
       "      <td>100.820000</td>\n",
       "      <td>399.652375</td>\n",
       "      <td>145.870968</td>\n",
       "      <td>133.362243</td>\n",
       "      <td>103.577020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014_06</td>\n",
       "      <td>1126.52</td>\n",
       "      <td>120.1</td>\n",
       "      <td>852.9890</td>\n",
       "      <td>4310.39</td>\n",
       "      <td>5504545.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>100.800000</td>\n",
       "      <td>7882.0</td>\n",
       "      <td>9199.0</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>594.013</td>\n",
       "      <td>2014_06</td>\n",
       "      <td>1126.520000</td>\n",
       "      <td>95.926518</td>\n",
       "      <td>92.011508</td>\n",
       "      <td>104.870566</td>\n",
       "      <td>91.682136</td>\n",
       "      <td>99.600000</td>\n",
       "      <td>97.100000</td>\n",
       "      <td>100.800000</td>\n",
       "      <td>456.662804</td>\n",
       "      <td>118.696774</td>\n",
       "      <td>76.293727</td>\n",
       "      <td>104.264024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014_07</td>\n",
       "      <td>1137.20</td>\n",
       "      <td>133.3</td>\n",
       "      <td>974.7400</td>\n",
       "      <td>4337.11</td>\n",
       "      <td>6257320.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>100.850000</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>8174.0</td>\n",
       "      <td>3785.0</td>\n",
       "      <td>598.441</td>\n",
       "      <td>2014_07</td>\n",
       "      <td>1137.200000</td>\n",
       "      <td>106.469649</td>\n",
       "      <td>105.144729</td>\n",
       "      <td>105.520656</td>\n",
       "      <td>104.220142</td>\n",
       "      <td>99.002400</td>\n",
       "      <td>88.361000</td>\n",
       "      <td>100.850000</td>\n",
       "      <td>83.951333</td>\n",
       "      <td>105.470968</td>\n",
       "      <td>109.424689</td>\n",
       "      <td>105.041248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014_08</td>\n",
       "      <td>1140.20</td>\n",
       "      <td>137.5</td>\n",
       "      <td>963.0830</td>\n",
       "      <td>4346.65</td>\n",
       "      <td>6337252.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>100.740000</td>\n",
       "      <td>2887.0</td>\n",
       "      <td>8135.0</td>\n",
       "      <td>3565.0</td>\n",
       "      <td>598.898</td>\n",
       "      <td>2014_08</td>\n",
       "      <td>1140.200000</td>\n",
       "      <td>109.824281</td>\n",
       "      <td>103.887294</td>\n",
       "      <td>105.752761</td>\n",
       "      <td>105.551467</td>\n",
       "      <td>99.002400</td>\n",
       "      <td>99.002400</td>\n",
       "      <td>100.740000</td>\n",
       "      <td>167.265353</td>\n",
       "      <td>104.967742</td>\n",
       "      <td>103.064469</td>\n",
       "      <td>105.121463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014_09</td>\n",
       "      <td>1141.72</td>\n",
       "      <td>137.2</td>\n",
       "      <td>967.4620</td>\n",
       "      <td>4370.12</td>\n",
       "      <td>6461826.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>101.020000</td>\n",
       "      <td>8084.0</td>\n",
       "      <td>8395.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>599.823</td>\n",
       "      <td>2014_09</td>\n",
       "      <td>1141.720000</td>\n",
       "      <td>109.584665</td>\n",
       "      <td>104.359654</td>\n",
       "      <td>106.323780</td>\n",
       "      <td>107.626336</td>\n",
       "      <td>99.002400</td>\n",
       "      <td>99.002400</td>\n",
       "      <td>101.020000</td>\n",
       "      <td>468.366165</td>\n",
       "      <td>108.322581</td>\n",
       "      <td>86.354438</td>\n",
       "      <td>105.283824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014_10</td>\n",
       "      <td>1143.19</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1008.2770</td>\n",
       "      <td>4405.95</td>\n",
       "      <td>6634204.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>101.140000</td>\n",
       "      <td>2515.0</td>\n",
       "      <td>8162.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>600.865</td>\n",
       "      <td>2014_10</td>\n",
       "      <td>1143.190000</td>\n",
       "      <td>113.418530</td>\n",
       "      <td>108.762348</td>\n",
       "      <td>107.195514</td>\n",
       "      <td>110.497415</td>\n",
       "      <td>98.804395</td>\n",
       "      <td>95.141306</td>\n",
       "      <td>101.140000</td>\n",
       "      <td>145.712630</td>\n",
       "      <td>105.316129</td>\n",
       "      <td>90.575311</td>\n",
       "      <td>105.466720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014_11</td>\n",
       "      <td>1144.82</td>\n",
       "      <td>129.9</td>\n",
       "      <td>880.2820</td>\n",
       "      <td>4450.45</td>\n",
       "      <td>5884184.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>101.070000</td>\n",
       "      <td>5877.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>603.524</td>\n",
       "      <td>2014_11</td>\n",
       "      <td>1144.820000</td>\n",
       "      <td>103.753994</td>\n",
       "      <td>94.955590</td>\n",
       "      <td>108.278186</td>\n",
       "      <td>98.005295</td>\n",
       "      <td>98.804395</td>\n",
       "      <td>95.141306</td>\n",
       "      <td>101.070000</td>\n",
       "      <td>340.498262</td>\n",
       "      <td>100.645161</td>\n",
       "      <td>95.403296</td>\n",
       "      <td>105.933441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2014_12</td>\n",
       "      <td>1145.42</td>\n",
       "      <td>110.5</td>\n",
       "      <td>721.3000</td>\n",
       "      <td>4493.17</td>\n",
       "      <td>5255052.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>101.090000</td>\n",
       "      <td>15037.0</td>\n",
       "      <td>10042.0</td>\n",
       "      <td>3015.0</td>\n",
       "      <td>604.026</td>\n",
       "      <td>2014_12</td>\n",
       "      <td>1145.420000</td>\n",
       "      <td>88.258786</td>\n",
       "      <td>77.806279</td>\n",
       "      <td>109.317551</td>\n",
       "      <td>87.526651</td>\n",
       "      <td>98.804395</td>\n",
       "      <td>95.141306</td>\n",
       "      <td>101.090000</td>\n",
       "      <td>871.205098</td>\n",
       "      <td>129.574194</td>\n",
       "      <td>87.163920</td>\n",
       "      <td>106.021554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2015_01</td>\n",
       "      <td>1152.00</td>\n",
       "      <td>114.8</td>\n",
       "      <td>878.0000</td>\n",
       "      <td>4550.23</td>\n",
       "      <td>5651113.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>101.130000</td>\n",
       "      <td>2081.0</td>\n",
       "      <td>6289.0</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>609.568</td>\n",
       "      <td>2015_01</td>\n",
       "      <td>1152.000000</td>\n",
       "      <td>91.693291</td>\n",
       "      <td>94.709432</td>\n",
       "      <td>110.705805</td>\n",
       "      <td>94.123331</td>\n",
       "      <td>97.223525</td>\n",
       "      <td>85.912600</td>\n",
       "      <td>101.130000</td>\n",
       "      <td>120.567787</td>\n",
       "      <td>81.148387</td>\n",
       "      <td>103.006649</td>\n",
       "      <td>106.994313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2015_02</td>\n",
       "      <td>1154.51</td>\n",
       "      <td>111.9</td>\n",
       "      <td>768.0000</td>\n",
       "      <td>4591.18</td>\n",
       "      <td>4930112.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>2217.0</td>\n",
       "      <td>6966.0</td>\n",
       "      <td>2713.0</td>\n",
       "      <td>611.447</td>\n",
       "      <td>2015_02</td>\n",
       "      <td>1154.510000</td>\n",
       "      <td>89.376997</td>\n",
       "      <td>82.843786</td>\n",
       "      <td>111.702107</td>\n",
       "      <td>82.114543</td>\n",
       "      <td>97.223525</td>\n",
       "      <td>85.912600</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>128.447277</td>\n",
       "      <td>89.883871</td>\n",
       "      <td>78.433073</td>\n",
       "      <td>107.324124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2015_03</td>\n",
       "      <td>1156.33</td>\n",
       "      <td>127.1</td>\n",
       "      <td>944.0000</td>\n",
       "      <td>4610.92</td>\n",
       "      <td>5858115.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>101.170000</td>\n",
       "      <td>7826.0</td>\n",
       "      <td>9304.0</td>\n",
       "      <td>3161.0</td>\n",
       "      <td>615.248</td>\n",
       "      <td>2015_03</td>\n",
       "      <td>1156.330000</td>\n",
       "      <td>101.517572</td>\n",
       "      <td>101.828820</td>\n",
       "      <td>112.182376</td>\n",
       "      <td>97.571097</td>\n",
       "      <td>97.223525</td>\n",
       "      <td>85.912600</td>\n",
       "      <td>101.170000</td>\n",
       "      <td>453.418308</td>\n",
       "      <td>120.051613</td>\n",
       "      <td>91.384793</td>\n",
       "      <td>107.991294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2015_04</td>\n",
       "      <td>1165.96</td>\n",
       "      <td>116.0</td>\n",
       "      <td>945.0000</td>\n",
       "      <td>4639.05</td>\n",
       "      <td>5333471.0</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>101.110000</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>7069.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>618.060</td>\n",
       "      <td>2015_04</td>\n",
       "      <td>1165.960000</td>\n",
       "      <td>92.651757</td>\n",
       "      <td>101.936689</td>\n",
       "      <td>112.866770</td>\n",
       "      <td>88.832776</td>\n",
       "      <td>94.598490</td>\n",
       "      <td>76.719952</td>\n",
       "      <td>101.110000</td>\n",
       "      <td>214.426419</td>\n",
       "      <td>91.212903</td>\n",
       "      <td>96.241688</td>\n",
       "      <td>108.484870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2015_05</td>\n",
       "      <td>1180.52</td>\n",
       "      <td>115.4</td>\n",
       "      <td>795.0000</td>\n",
       "      <td>4675.23</td>\n",
       "      <td>5506180.0</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>101.040000</td>\n",
       "      <td>1779.0</td>\n",
       "      <td>7544.0</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>623.951</td>\n",
       "      <td>2015_05</td>\n",
       "      <td>1180.520000</td>\n",
       "      <td>92.172524</td>\n",
       "      <td>85.756262</td>\n",
       "      <td>113.747020</td>\n",
       "      <td>91.709368</td>\n",
       "      <td>94.598490</td>\n",
       "      <td>76.719952</td>\n",
       "      <td>101.040000</td>\n",
       "      <td>103.070684</td>\n",
       "      <td>97.341935</td>\n",
       "      <td>83.723620</td>\n",
       "      <td>109.518886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2015_06</td>\n",
       "      <td>1187.98</td>\n",
       "      <td>111.3</td>\n",
       "      <td>793.0000</td>\n",
       "      <td>4691.59</td>\n",
       "      <td>5566419.0</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>101.180000</td>\n",
       "      <td>10358.0</td>\n",
       "      <td>9363.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>635.403</td>\n",
       "      <td>2015_06</td>\n",
       "      <td>1187.980000</td>\n",
       "      <td>88.897764</td>\n",
       "      <td>85.540523</td>\n",
       "      <td>114.145054</td>\n",
       "      <td>92.712691</td>\n",
       "      <td>94.598490</td>\n",
       "      <td>76.719952</td>\n",
       "      <td>101.180000</td>\n",
       "      <td>600.115875</td>\n",
       "      <td>120.812903</td>\n",
       "      <td>89.071986</td>\n",
       "      <td>111.528997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2015_07</td>\n",
       "      <td>1205.54</td>\n",
       "      <td>118.0</td>\n",
       "      <td>771.0000</td>\n",
       "      <td>4715.99</td>\n",
       "      <td>5856207.0</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>101.270000</td>\n",
       "      <td>987.0</td>\n",
       "      <td>7424.0</td>\n",
       "      <td>4278.0</td>\n",
       "      <td>638.880</td>\n",
       "      <td>2015_07</td>\n",
       "      <td>1205.540000</td>\n",
       "      <td>94.249201</td>\n",
       "      <td>83.167394</td>\n",
       "      <td>114.738699</td>\n",
       "      <td>97.539318</td>\n",
       "      <td>90.530755</td>\n",
       "      <td>71.502995</td>\n",
       "      <td>101.270000</td>\n",
       "      <td>57.184241</td>\n",
       "      <td>95.793548</td>\n",
       "      <td>123.677363</td>\n",
       "      <td>112.139296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2015_08</td>\n",
       "      <td>1206.92</td>\n",
       "      <td>118.0</td>\n",
       "      <td>808.0000</td>\n",
       "      <td>4736.74</td>\n",
       "      <td>5804633.0</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>101.120000</td>\n",
       "      <td>2211.0</td>\n",
       "      <td>6977.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>642.644</td>\n",
       "      <td>2015_08</td>\n",
       "      <td>1206.920000</td>\n",
       "      <td>94.249201</td>\n",
       "      <td>87.158566</td>\n",
       "      <td>115.243540</td>\n",
       "      <td>96.680316</td>\n",
       "      <td>90.530755</td>\n",
       "      <td>71.502995</td>\n",
       "      <td>101.120000</td>\n",
       "      <td>128.099652</td>\n",
       "      <td>90.025806</td>\n",
       "      <td>84.706563</td>\n",
       "      <td>112.799972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2015_09</td>\n",
       "      <td>1211.06</td>\n",
       "      <td>112.9</td>\n",
       "      <td>794.0000</td>\n",
       "      <td>4740.53</td>\n",
       "      <td>5573735.0</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>100.970000</td>\n",
       "      <td>8782.0</td>\n",
       "      <td>7207.0</td>\n",
       "      <td>3129.0</td>\n",
       "      <td>644.046</td>\n",
       "      <td>2015_09</td>\n",
       "      <td>1211.060000</td>\n",
       "      <td>90.175719</td>\n",
       "      <td>85.648393</td>\n",
       "      <td>115.335750</td>\n",
       "      <td>92.834545</td>\n",
       "      <td>90.530755</td>\n",
       "      <td>71.502995</td>\n",
       "      <td>100.970000</td>\n",
       "      <td>508.806489</td>\n",
       "      <td>92.993548</td>\n",
       "      <td>90.459670</td>\n",
       "      <td>113.046058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2015_10</td>\n",
       "      <td>1212.59</td>\n",
       "      <td>118.6</td>\n",
       "      <td>734.0000</td>\n",
       "      <td>4752.86</td>\n",
       "      <td>5694595.0</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>100.970000</td>\n",
       "      <td>3146.0</td>\n",
       "      <td>6264.0</td>\n",
       "      <td>3235.0</td>\n",
       "      <td>646.355</td>\n",
       "      <td>2015_10</td>\n",
       "      <td>1212.590000</td>\n",
       "      <td>94.728435</td>\n",
       "      <td>79.176222</td>\n",
       "      <td>115.635735</td>\n",
       "      <td>94.847554</td>\n",
       "      <td>85.551563</td>\n",
       "      <td>65.282234</td>\n",
       "      <td>100.970000</td>\n",
       "      <td>182.271147</td>\n",
       "      <td>80.825806</td>\n",
       "      <td>93.524140</td>\n",
       "      <td>113.451345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2015_11</td>\n",
       "      <td>1215.13</td>\n",
       "      <td>108.0</td>\n",
       "      <td>634.0000</td>\n",
       "      <td>4761.42</td>\n",
       "      <td>4935500.0</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>101.030000</td>\n",
       "      <td>4906.0</td>\n",
       "      <td>7037.0</td>\n",
       "      <td>2898.0</td>\n",
       "      <td>648.542</td>\n",
       "      <td>2015_11</td>\n",
       "      <td>1215.130000</td>\n",
       "      <td>86.261981</td>\n",
       "      <td>68.389271</td>\n",
       "      <td>115.843998</td>\n",
       "      <td>82.204284</td>\n",
       "      <td>85.551563</td>\n",
       "      <td>65.282234</td>\n",
       "      <td>101.030000</td>\n",
       "      <td>284.241020</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>83.781440</td>\n",
       "      <td>113.835217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2015_12</td>\n",
       "      <td>1217.98</td>\n",
       "      <td>88.0</td>\n",
       "      <td>512.0000</td>\n",
       "      <td>4775.70</td>\n",
       "      <td>4605542.0</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>100.890000</td>\n",
       "      <td>10706.0</td>\n",
       "      <td>7660.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>649.216</td>\n",
       "      <td>2015_12</td>\n",
       "      <td>1217.980000</td>\n",
       "      <td>70.287540</td>\n",
       "      <td>55.229190</td>\n",
       "      <td>116.191426</td>\n",
       "      <td>76.708597</td>\n",
       "      <td>85.551563</td>\n",
       "      <td>65.282234</td>\n",
       "      <td>100.890000</td>\n",
       "      <td>620.278100</td>\n",
       "      <td>98.838710</td>\n",
       "      <td>69.152934</td>\n",
       "      <td>113.953521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2016_01</td>\n",
       "      <td>1224.48</td>\n",
       "      <td>92.4</td>\n",
       "      <td>603.0000</td>\n",
       "      <td>4793.85</td>\n",
       "      <td>4480476.0</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>100.670000</td>\n",
       "      <td>1787.0</td>\n",
       "      <td>4748.0</td>\n",
       "      <td>2548.0</td>\n",
       "      <td>651.759</td>\n",
       "      <td>2016_01</td>\n",
       "      <td>1224.480000</td>\n",
       "      <td>73.801917</td>\n",
       "      <td>65.045316</td>\n",
       "      <td>116.633011</td>\n",
       "      <td>74.625534</td>\n",
       "      <td>81.188433</td>\n",
       "      <td>59.276269</td>\n",
       "      <td>100.670000</td>\n",
       "      <td>103.534183</td>\n",
       "      <td>61.264516</td>\n",
       "      <td>73.662908</td>\n",
       "      <td>114.399881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2016_02</td>\n",
       "      <td>1227.90</td>\n",
       "      <td>96.9</td>\n",
       "      <td>613.0000</td>\n",
       "      <td>4809.67</td>\n",
       "      <td>4690311.0</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>100.630000</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>6004.0</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>655.263</td>\n",
       "      <td>2016_02</td>\n",
       "      <td>1227.900000</td>\n",
       "      <td>77.396166</td>\n",
       "      <td>66.124011</td>\n",
       "      <td>117.017907</td>\n",
       "      <td>78.120486</td>\n",
       "      <td>81.188433</td>\n",
       "      <td>59.276269</td>\n",
       "      <td>100.630000</td>\n",
       "      <td>164.484357</td>\n",
       "      <td>77.470968</td>\n",
       "      <td>64.324949</td>\n",
       "      <td>115.014920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2016_03</td>\n",
       "      <td>1234.75</td>\n",
       "      <td>106.0</td>\n",
       "      <td>748.0000</td>\n",
       "      <td>4821.69</td>\n",
       "      <td>4988029.0</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>100.310000</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>8912.0</td>\n",
       "      <td>3096.0</td>\n",
       "      <td>659.446</td>\n",
       "      <td>2016_03</td>\n",
       "      <td>1234.750000</td>\n",
       "      <td>84.664537</td>\n",
       "      <td>80.686395</td>\n",
       "      <td>117.310350</td>\n",
       "      <td>83.079192</td>\n",
       "      <td>81.188433</td>\n",
       "      <td>59.276269</td>\n",
       "      <td>100.310000</td>\n",
       "      <td>538.818076</td>\n",
       "      <td>114.993548</td>\n",
       "      <td>89.505637</td>\n",
       "      <td>115.749140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2016_04</td>\n",
       "      <td>1239.68</td>\n",
       "      <td>102.1</td>\n",
       "      <td>706.0000</td>\n",
       "      <td>4828.44</td>\n",
       "      <td>4835997.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>100.220000</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>663.057</td>\n",
       "      <td>2016_04</td>\n",
       "      <td>1239.680000</td>\n",
       "      <td>81.549521</td>\n",
       "      <td>76.155876</td>\n",
       "      <td>117.474575</td>\n",
       "      <td>80.546990</td>\n",
       "      <td>78.590404</td>\n",
       "      <td>54.415615</td>\n",
       "      <td>100.220000</td>\n",
       "      <td>90.324450</td>\n",
       "      <td>77.290323</td>\n",
       "      <td>87.135010</td>\n",
       "      <td>116.382960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2016_05</td>\n",
       "      <td>1244.50</td>\n",
       "      <td>104.6</td>\n",
       "      <td>719.0000</td>\n",
       "      <td>4843.41</td>\n",
       "      <td>4843496.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>99.870000</td>\n",
       "      <td>4607.0</td>\n",
       "      <td>7051.0</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>663.610</td>\n",
       "      <td>2016_05</td>\n",
       "      <td>1244.500000</td>\n",
       "      <td>83.546326</td>\n",
       "      <td>77.558179</td>\n",
       "      <td>117.838791</td>\n",
       "      <td>80.671892</td>\n",
       "      <td>78.590404</td>\n",
       "      <td>54.415615</td>\n",
       "      <td>99.870000</td>\n",
       "      <td>266.917729</td>\n",
       "      <td>90.980645</td>\n",
       "      <td>76.929748</td>\n",
       "      <td>116.480025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2016_06</td>\n",
       "      <td>1262.87</td>\n",
       "      <td>103.2</td>\n",
       "      <td>721.0000</td>\n",
       "      <td>4832.27</td>\n",
       "      <td>4906182.0</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-8.2</td>\n",
       "      <td>99.630000</td>\n",
       "      <td>9673.0</td>\n",
       "      <td>8449.0</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>676.420</td>\n",
       "      <td>2016_06</td>\n",
       "      <td>1262.870000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>77.773918</td>\n",
       "      <td>117.567758</td>\n",
       "      <td>81.715972</td>\n",
       "      <td>78.590404</td>\n",
       "      <td>54.415615</td>\n",
       "      <td>99.630000</td>\n",
       "      <td>560.428737</td>\n",
       "      <td>109.019355</td>\n",
       "      <td>81.699913</td>\n",
       "      <td>118.728498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2016_07</td>\n",
       "      <td>1270.96</td>\n",
       "      <td>105.7</td>\n",
       "      <td>665.0000</td>\n",
       "      <td>4843.87</td>\n",
       "      <td>5276943.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>99.330000</td>\n",
       "      <td>2530.0</td>\n",
       "      <td>6886.0</td>\n",
       "      <td>3196.0</td>\n",
       "      <td>679.751</td>\n",
       "      <td>2016_07</td>\n",
       "      <td>1270.960000</td>\n",
       "      <td>84.424920</td>\n",
       "      <td>71.733226</td>\n",
       "      <td>117.849983</td>\n",
       "      <td>87.891261</td>\n",
       "      <td>76.625643</td>\n",
       "      <td>48.974053</td>\n",
       "      <td>99.330000</td>\n",
       "      <td>146.581692</td>\n",
       "      <td>88.851613</td>\n",
       "      <td>92.396646</td>\n",
       "      <td>119.313171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2016_08</td>\n",
       "      <td>1276.89</td>\n",
       "      <td>109.8</td>\n",
       "      <td>680.0000</td>\n",
       "      <td>4853.07</td>\n",
       "      <td>5080381.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>99.220000</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>7433.0</td>\n",
       "      <td>2754.0</td>\n",
       "      <td>681.756</td>\n",
       "      <td>2016_08</td>\n",
       "      <td>1276.890000</td>\n",
       "      <td>87.699681</td>\n",
       "      <td>73.351268</td>\n",
       "      <td>118.073816</td>\n",
       "      <td>84.617381</td>\n",
       "      <td>76.625643</td>\n",
       "      <td>48.974053</td>\n",
       "      <td>99.220000</td>\n",
       "      <td>264.774044</td>\n",
       "      <td>95.909677</td>\n",
       "      <td>79.618387</td>\n",
       "      <td>119.665099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2016_09</td>\n",
       "      <td>1281.25</td>\n",
       "      <td>100.9</td>\n",
       "      <td>688.0000</td>\n",
       "      <td>4860.83</td>\n",
       "      <td>4939133.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>98.720000</td>\n",
       "      <td>6756.0</td>\n",
       "      <td>7568.0</td>\n",
       "      <td>2724.0</td>\n",
       "      <td>684.025</td>\n",
       "      <td>2016_09</td>\n",
       "      <td>1281.250000</td>\n",
       "      <td>80.591054</td>\n",
       "      <td>74.214225</td>\n",
       "      <td>118.262615</td>\n",
       "      <td>82.264794</td>\n",
       "      <td>76.625643</td>\n",
       "      <td>48.974053</td>\n",
       "      <td>98.720000</td>\n",
       "      <td>391.425261</td>\n",
       "      <td>97.651613</td>\n",
       "      <td>78.751084</td>\n",
       "      <td>120.063364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2016_10</td>\n",
       "      <td>1283.56</td>\n",
       "      <td>101.1</td>\n",
       "      <td>627.0000</td>\n",
       "      <td>4881.25</td>\n",
       "      <td>4650454.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>98.530000</td>\n",
       "      <td>3447.0</td>\n",
       "      <td>6245.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>685.489</td>\n",
       "      <td>2016_10</td>\n",
       "      <td>1283.560000</td>\n",
       "      <td>80.750799</td>\n",
       "      <td>67.634184</td>\n",
       "      <td>118.759428</td>\n",
       "      <td>77.456639</td>\n",
       "      <td>74.863254</td>\n",
       "      <td>42.852297</td>\n",
       "      <td>98.530000</td>\n",
       "      <td>199.710313</td>\n",
       "      <td>80.580645</td>\n",
       "      <td>77.479040</td>\n",
       "      <td>120.320333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2016_11</td>\n",
       "      <td>1284.82</td>\n",
       "      <td>97.2</td>\n",
       "      <td>640.0000</td>\n",
       "      <td>4894.92</td>\n",
       "      <td>4742833.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>98.530000</td>\n",
       "      <td>8730.0</td>\n",
       "      <td>8710.0</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>686.607</td>\n",
       "      <td>2016_11</td>\n",
       "      <td>1284.820000</td>\n",
       "      <td>77.635783</td>\n",
       "      <td>69.036488</td>\n",
       "      <td>119.092015</td>\n",
       "      <td>78.995277</td>\n",
       "      <td>74.863254</td>\n",
       "      <td>42.852297</td>\n",
       "      <td>98.530000</td>\n",
       "      <td>505.793743</td>\n",
       "      <td>112.387097</td>\n",
       "      <td>70.598439</td>\n",
       "      <td>120.516570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2016_12</td>\n",
       "      <td>1289.56</td>\n",
       "      <td>82.6</td>\n",
       "      <td>559.0000</td>\n",
       "      <td>4916.46</td>\n",
       "      <td>4349727.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>98.610000</td>\n",
       "      <td>11971.0</td>\n",
       "      <td>8327.0</td>\n",
       "      <td>2207.0</td>\n",
       "      <td>688.985</td>\n",
       "      <td>2016_12</td>\n",
       "      <td>1289.560000</td>\n",
       "      <td>65.974441</td>\n",
       "      <td>60.299057</td>\n",
       "      <td>119.616077</td>\n",
       "      <td>72.447816</td>\n",
       "      <td>74.863254</td>\n",
       "      <td>42.852297</td>\n",
       "      <td>98.610000</td>\n",
       "      <td>693.568946</td>\n",
       "      <td>107.445161</td>\n",
       "      <td>63.804568</td>\n",
       "      <td>120.933968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017_01</td>\n",
       "      <td>1295.98</td>\n",
       "      <td>89.2</td>\n",
       "      <td>569.0000</td>\n",
       "      <td>4930.72</td>\n",
       "      <td>4338056.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>98.430000</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>6345.0</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>691.792</td>\n",
       "      <td>2017_01</td>\n",
       "      <td>1295.980000</td>\n",
       "      <td>71.246006</td>\n",
       "      <td>61.377753</td>\n",
       "      <td>119.963019</td>\n",
       "      <td>72.253427</td>\n",
       "      <td>75.087843</td>\n",
       "      <td>37.710021</td>\n",
       "      <td>98.430000</td>\n",
       "      <td>124.565469</td>\n",
       "      <td>81.870968</td>\n",
       "      <td>55.738653</td>\n",
       "      <td>121.426666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017_02</td>\n",
       "      <td>1302.76</td>\n",
       "      <td>88.7</td>\n",
       "      <td>630.0000</td>\n",
       "      <td>4946.50</td>\n",
       "      <td>3891136.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>98.420000</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>5770.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>696.314</td>\n",
       "      <td>2017_02</td>\n",
       "      <td>1302.760000</td>\n",
       "      <td>70.846645</td>\n",
       "      <td>67.957793</td>\n",
       "      <td>120.346942</td>\n",
       "      <td>64.809654</td>\n",
       "      <td>75.087843</td>\n",
       "      <td>37.710021</td>\n",
       "      <td>98.420000</td>\n",
       "      <td>109.907300</td>\n",
       "      <td>74.451613</td>\n",
       "      <td>59.612605</td>\n",
       "      <td>122.220389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017_03</td>\n",
       "      <td>1304.92</td>\n",
       "      <td>103.0</td>\n",
       "      <td>711.0000</td>\n",
       "      <td>4950.95</td>\n",
       "      <td>4773553.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>98.450000</td>\n",
       "      <td>7841.0</td>\n",
       "      <td>8359.0</td>\n",
       "      <td>2465.0</td>\n",
       "      <td>697.410</td>\n",
       "      <td>2017_03</td>\n",
       "      <td>1304.920000</td>\n",
       "      <td>82.268371</td>\n",
       "      <td>76.695223</td>\n",
       "      <td>120.455209</td>\n",
       "      <td>79.506941</td>\n",
       "      <td>75.087843</td>\n",
       "      <td>37.710021</td>\n",
       "      <td>98.450000</td>\n",
       "      <td>454.287370</td>\n",
       "      <td>107.858065</td>\n",
       "      <td>71.263371</td>\n",
       "      <td>122.412764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017_04</td>\n",
       "      <td>1307.46</td>\n",
       "      <td>88.8</td>\n",
       "      <td>605.0000</td>\n",
       "      <td>4961.84</td>\n",
       "      <td>3996147.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-11.4</td>\n",
       "      <td>98.400000</td>\n",
       "      <td>3053.0</td>\n",
       "      <td>6831.0</td>\n",
       "      <td>2131.0</td>\n",
       "      <td>697.244</td>\n",
       "      <td>2017_04</td>\n",
       "      <td>1307.460000</td>\n",
       "      <td>70.926518</td>\n",
       "      <td>65.261055</td>\n",
       "      <td>120.720160</td>\n",
       "      <td>66.558688</td>\n",
       "      <td>75.688546</td>\n",
       "      <td>33.411079</td>\n",
       "      <td>98.400000</td>\n",
       "      <td>176.882966</td>\n",
       "      <td>88.141935</td>\n",
       "      <td>61.607401</td>\n",
       "      <td>122.383627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2017_05</td>\n",
       "      <td>1315.50</td>\n",
       "      <td>98.4</td>\n",
       "      <td>664.0000</td>\n",
       "      <td>4981.69</td>\n",
       "      <td>4539804.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-11.4</td>\n",
       "      <td>98.140000</td>\n",
       "      <td>5516.0</td>\n",
       "      <td>8518.0</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>701.664</td>\n",
       "      <td>2017_05</td>\n",
       "      <td>1315.500000</td>\n",
       "      <td>78.594249</td>\n",
       "      <td>71.625356</td>\n",
       "      <td>121.203104</td>\n",
       "      <td>75.613684</td>\n",
       "      <td>75.688546</td>\n",
       "      <td>33.411079</td>\n",
       "      <td>98.140000</td>\n",
       "      <td>319.582851</td>\n",
       "      <td>109.909677</td>\n",
       "      <td>64.324949</td>\n",
       "      <td>123.159447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2017_06</td>\n",
       "      <td>1326.58</td>\n",
       "      <td>96.1</td>\n",
       "      <td>678.0000</td>\n",
       "      <td>5044.46</td>\n",
       "      <td>4450442.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-11.4</td>\n",
       "      <td>98.050000</td>\n",
       "      <td>9283.0</td>\n",
       "      <td>8494.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>708.197</td>\n",
       "      <td>2017_06</td>\n",
       "      <td>1326.580000</td>\n",
       "      <td>76.757188</td>\n",
       "      <td>73.135529</td>\n",
       "      <td>122.730281</td>\n",
       "      <td>74.125296</td>\n",
       "      <td>75.688546</td>\n",
       "      <td>33.411079</td>\n",
       "      <td>98.050000</td>\n",
       "      <td>537.833140</td>\n",
       "      <td>109.600000</td>\n",
       "      <td>58.109280</td>\n",
       "      <td>124.306150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2017_07</td>\n",
       "      <td>1329.22</td>\n",
       "      <td>99.7</td>\n",
       "      <td>665.0000</td>\n",
       "      <td>5061.11</td>\n",
       "      <td>4749655.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>97.860000</td>\n",
       "      <td>5953.0</td>\n",
       "      <td>6929.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>710.355</td>\n",
       "      <td>2017_07</td>\n",
       "      <td>1329.220000</td>\n",
       "      <td>79.632588</td>\n",
       "      <td>71.733226</td>\n",
       "      <td>123.135371</td>\n",
       "      <td>79.108903</td>\n",
       "      <td>76.899563</td>\n",
       "      <td>30.237026</td>\n",
       "      <td>97.860000</td>\n",
       "      <td>344.901506</td>\n",
       "      <td>89.406452</td>\n",
       "      <td>59.525875</td>\n",
       "      <td>124.684933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2017_08</td>\n",
       "      <td>1331.17</td>\n",
       "      <td>106.8</td>\n",
       "      <td>673.0000</td>\n",
       "      <td>5056.56</td>\n",
       "      <td>5049734.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>97.770000</td>\n",
       "      <td>4185.0</td>\n",
       "      <td>8865.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>712.884</td>\n",
       "      <td>2017_08</td>\n",
       "      <td>1331.170000</td>\n",
       "      <td>85.303514</td>\n",
       "      <td>72.596182</td>\n",
       "      <td>123.024670</td>\n",
       "      <td>84.106933</td>\n",
       "      <td>76.899563</td>\n",
       "      <td>30.237026</td>\n",
       "      <td>97.770000</td>\n",
       "      <td>242.468134</td>\n",
       "      <td>114.387097</td>\n",
       "      <td>52.038161</td>\n",
       "      <td>125.128835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2017_09</td>\n",
       "      <td>1332.81</td>\n",
       "      <td>100.3</td>\n",
       "      <td>745.0000</td>\n",
       "      <td>5080.83</td>\n",
       "      <td>4861685.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>97.700000</td>\n",
       "      <td>9296.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>2353.0</td>\n",
       "      <td>713.330</td>\n",
       "      <td>2017_09</td>\n",
       "      <td>1332.810000</td>\n",
       "      <td>80.111821</td>\n",
       "      <td>80.362787</td>\n",
       "      <td>123.615153</td>\n",
       "      <td>80.974842</td>\n",
       "      <td>76.899563</td>\n",
       "      <td>30.237026</td>\n",
       "      <td>97.700000</td>\n",
       "      <td>538.586327</td>\n",
       "      <td>113.200000</td>\n",
       "      <td>68.025441</td>\n",
       "      <td>125.207119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2017_10</td>\n",
       "      <td>1333.93</td>\n",
       "      <td>103.1</td>\n",
       "      <td>694.0000</td>\n",
       "      <td>5103.69</td>\n",
       "      <td>4555661.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>97.680000</td>\n",
       "      <td>6452.0</td>\n",
       "      <td>8735.0</td>\n",
       "      <td>2302.0</td>\n",
       "      <td>715.527</td>\n",
       "      <td>2017_10</td>\n",
       "      <td>1333.930000</td>\n",
       "      <td>82.348243</td>\n",
       "      <td>74.861442</td>\n",
       "      <td>124.171330</td>\n",
       "      <td>75.877794</td>\n",
       "      <td>78.898952</td>\n",
       "      <td>29.118256</td>\n",
       "      <td>97.680000</td>\n",
       "      <td>373.812283</td>\n",
       "      <td>112.709677</td>\n",
       "      <td>66.551026</td>\n",
       "      <td>125.592747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2017_11</td>\n",
       "      <td>1336.94</td>\n",
       "      <td>101.5</td>\n",
       "      <td>646.0000</td>\n",
       "      <td>5092.97</td>\n",
       "      <td>4487496.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>97.780000</td>\n",
       "      <td>9331.0</td>\n",
       "      <td>9399.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>717.751</td>\n",
       "      <td>2017_11</td>\n",
       "      <td>1336.940000</td>\n",
       "      <td>81.070288</td>\n",
       "      <td>69.683705</td>\n",
       "      <td>123.910515</td>\n",
       "      <td>74.742457</td>\n",
       "      <td>78.898952</td>\n",
       "      <td>29.118256</td>\n",
       "      <td>97.780000</td>\n",
       "      <td>540.614137</td>\n",
       "      <td>121.277419</td>\n",
       "      <td>53.223475</td>\n",
       "      <td>125.983115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2017_12</td>\n",
       "      <td>1339.52</td>\n",
       "      <td>89.2</td>\n",
       "      <td>556.0000</td>\n",
       "      <td>5100.61</td>\n",
       "      <td>4009819.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>98.020000</td>\n",
       "      <td>12681.0</td>\n",
       "      <td>9801.0</td>\n",
       "      <td>1861.0</td>\n",
       "      <td>718.276</td>\n",
       "      <td>2017_12</td>\n",
       "      <td>1339.520000</td>\n",
       "      <td>71.246006</td>\n",
       "      <td>59.975449</td>\n",
       "      <td>124.096394</td>\n",
       "      <td>66.786405</td>\n",
       "      <td>78.898952</td>\n",
       "      <td>29.118256</td>\n",
       "      <td>98.020000</td>\n",
       "      <td>734.704519</td>\n",
       "      <td>126.464516</td>\n",
       "      <td>53.801677</td>\n",
       "      <td>126.075265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2018_01</td>\n",
       "      <td>1342.55</td>\n",
       "      <td>92.7</td>\n",
       "      <td>704.0000</td>\n",
       "      <td>4930.72</td>\n",
       "      <td>4376254.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>98.050000</td>\n",
       "      <td>3426.0</td>\n",
       "      <td>7897.0</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>720.495</td>\n",
       "      <td>2018_01</td>\n",
       "      <td>1342.550000</td>\n",
       "      <td>74.041534</td>\n",
       "      <td>75.940137</td>\n",
       "      <td>119.963019</td>\n",
       "      <td>72.889642</td>\n",
       "      <td>80.398032</td>\n",
       "      <td>27.545870</td>\n",
       "      <td>98.050000</td>\n",
       "      <td>198.493627</td>\n",
       "      <td>101.896774</td>\n",
       "      <td>46.978896</td>\n",
       "      <td>126.464755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2018_02</td>\n",
       "      <td>1345.23</td>\n",
       "      <td>90.3</td>\n",
       "      <td>688.0000</td>\n",
       "      <td>4946.50</td>\n",
       "      <td>3826575.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>98.040000</td>\n",
       "      <td>3116.0</td>\n",
       "      <td>6974.0</td>\n",
       "      <td>1589.0</td>\n",
       "      <td>721.414</td>\n",
       "      <td>2018_02</td>\n",
       "      <td>1345.230000</td>\n",
       "      <td>72.124601</td>\n",
       "      <td>74.214225</td>\n",
       "      <td>120.346942</td>\n",
       "      <td>63.734345</td>\n",
       "      <td>80.398032</td>\n",
       "      <td>27.545870</td>\n",
       "      <td>98.040000</td>\n",
       "      <td>180.533024</td>\n",
       "      <td>89.987097</td>\n",
       "      <td>45.938132</td>\n",
       "      <td>126.626062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2018_03</td>\n",
       "      <td>1347.97</td>\n",
       "      <td>100.0</td>\n",
       "      <td>720.0000</td>\n",
       "      <td>4950.95</td>\n",
       "      <td>4347380.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>98.100000</td>\n",
       "      <td>7704.0</td>\n",
       "      <td>9068.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>723.163</td>\n",
       "      <td>2018_03</td>\n",
       "      <td>1347.970000</td>\n",
       "      <td>79.872204</td>\n",
       "      <td>77.666049</td>\n",
       "      <td>120.455209</td>\n",
       "      <td>72.408725</td>\n",
       "      <td>80.398032</td>\n",
       "      <td>27.545870</td>\n",
       "      <td>98.100000</td>\n",
       "      <td>446.349942</td>\n",
       "      <td>117.006452</td>\n",
       "      <td>62.387973</td>\n",
       "      <td>126.933055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2018_04</td>\n",
       "      <td>1351.81</td>\n",
       "      <td>96.7</td>\n",
       "      <td>691.0000</td>\n",
       "      <td>4961.84</td>\n",
       "      <td>4382883.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>98.110000</td>\n",
       "      <td>4023.0</td>\n",
       "      <td>7991.0</td>\n",
       "      <td>1933.0</td>\n",
       "      <td>725.245</td>\n",
       "      <td>2018_04</td>\n",
       "      <td>1351.810000</td>\n",
       "      <td>77.236422</td>\n",
       "      <td>74.537833</td>\n",
       "      <td>120.720160</td>\n",
       "      <td>73.000052</td>\n",
       "      <td>81.684400</td>\n",
       "      <td>26.719494</td>\n",
       "      <td>98.110000</td>\n",
       "      <td>233.082271</td>\n",
       "      <td>103.109677</td>\n",
       "      <td>55.883203</td>\n",
       "      <td>127.298498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2018_05</td>\n",
       "      <td>1357.86</td>\n",
       "      <td>90.6</td>\n",
       "      <td>606.0000</td>\n",
       "      <td>4981.69</td>\n",
       "      <td>3603126.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>98.120000</td>\n",
       "      <td>9010.0</td>\n",
       "      <td>9946.0</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>726.923</td>\n",
       "      <td>2018_05</td>\n",
       "      <td>1357.860000</td>\n",
       "      <td>72.364217</td>\n",
       "      <td>65.368925</td>\n",
       "      <td>121.203104</td>\n",
       "      <td>60.012642</td>\n",
       "      <td>81.684400</td>\n",
       "      <td>26.719494</td>\n",
       "      <td>98.120000</td>\n",
       "      <td>522.016222</td>\n",
       "      <td>128.335484</td>\n",
       "      <td>50.708297</td>\n",
       "      <td>127.593028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2018_06</td>\n",
       "      <td>1365.78</td>\n",
       "      <td>100.0</td>\n",
       "      <td>804.0000</td>\n",
       "      <td>5044.46</td>\n",
       "      <td>4986686.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>98.200000</td>\n",
       "      <td>12562.0</td>\n",
       "      <td>8550.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>733.984</td>\n",
       "      <td>2018_06</td>\n",
       "      <td>1365.780000</td>\n",
       "      <td>79.872204</td>\n",
       "      <td>86.727088</td>\n",
       "      <td>122.730281</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>81.684400</td>\n",
       "      <td>26.719494</td>\n",
       "      <td>98.200000</td>\n",
       "      <td>727.809965</td>\n",
       "      <td>110.322581</td>\n",
       "      <td>45.620121</td>\n",
       "      <td>128.832409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2018_07</td>\n",
       "      <td>1372.48</td>\n",
       "      <td>102.6</td>\n",
       "      <td>761.0000</td>\n",
       "      <td>5061.11</td>\n",
       "      <td>4622103.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>98.160000</td>\n",
       "      <td>5523.0</td>\n",
       "      <td>7884.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>738.487</td>\n",
       "      <td>2018_07</td>\n",
       "      <td>1372.480000</td>\n",
       "      <td>81.948882</td>\n",
       "      <td>82.088699</td>\n",
       "      <td>123.135371</td>\n",
       "      <td>76.984433</td>\n",
       "      <td>83.399773</td>\n",
       "      <td>26.479019</td>\n",
       "      <td>98.160000</td>\n",
       "      <td>319.988413</td>\n",
       "      <td>101.729032</td>\n",
       "      <td>64.296039</td>\n",
       "      <td>129.622797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2018_08</td>\n",
       "      <td>1376.64</td>\n",
       "      <td>108.4</td>\n",
       "      <td>794.0000</td>\n",
       "      <td>5056.56</td>\n",
       "      <td>4928950.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>98.190000</td>\n",
       "      <td>6015.0</td>\n",
       "      <td>9358.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>739.583</td>\n",
       "      <td>2018_08</td>\n",
       "      <td>1376.640000</td>\n",
       "      <td>86.581470</td>\n",
       "      <td>85.648393</td>\n",
       "      <td>123.024670</td>\n",
       "      <td>82.095189</td>\n",
       "      <td>83.399773</td>\n",
       "      <td>26.479019</td>\n",
       "      <td>98.190000</td>\n",
       "      <td>348.493627</td>\n",
       "      <td>120.748387</td>\n",
       "      <td>57.444348</td>\n",
       "      <td>129.815172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2018_09</td>\n",
       "      <td>1379.67</td>\n",
       "      <td>101.6</td>\n",
       "      <td>787.0000</td>\n",
       "      <td>5080.83</td>\n",
       "      <td>4527241.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>98.290000</td>\n",
       "      <td>16103.0</td>\n",
       "      <td>8456.0</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>741.305</td>\n",
       "      <td>2018_09</td>\n",
       "      <td>1379.670000</td>\n",
       "      <td>81.150160</td>\n",
       "      <td>84.893306</td>\n",
       "      <td>123.615153</td>\n",
       "      <td>75.404438</td>\n",
       "      <td>83.399773</td>\n",
       "      <td>26.479019</td>\n",
       "      <td>98.290000</td>\n",
       "      <td>932.966396</td>\n",
       "      <td>109.109677</td>\n",
       "      <td>40.676496</td>\n",
       "      <td>130.117426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2018_10</td>\n",
       "      <td>1383.34</td>\n",
       "      <td>108.3</td>\n",
       "      <td>705.0000</td>\n",
       "      <td>5103.69</td>\n",
       "      <td>4795379.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>98.370000</td>\n",
       "      <td>9807.0</td>\n",
       "      <td>9630.0</td>\n",
       "      <td>1718.0</td>\n",
       "      <td>743.866</td>\n",
       "      <td>2018_10</td>\n",
       "      <td>1383.340000</td>\n",
       "      <td>86.501597</td>\n",
       "      <td>76.048006</td>\n",
       "      <td>124.171330</td>\n",
       "      <td>79.870469</td>\n",
       "      <td>84.734169</td>\n",
       "      <td>25.790564</td>\n",
       "      <td>98.370000</td>\n",
       "      <td>568.192352</td>\n",
       "      <td>124.258065</td>\n",
       "      <td>49.667534</td>\n",
       "      <td>130.566945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2018_11</td>\n",
       "      <td>1387.25</td>\n",
       "      <td>100.1</td>\n",
       "      <td>657.0000</td>\n",
       "      <td>5092.97</td>\n",
       "      <td>4567868.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>98.550000</td>\n",
       "      <td>7683.0</td>\n",
       "      <td>10613.0</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>744.865</td>\n",
       "      <td>2018_11</td>\n",
       "      <td>1387.250000</td>\n",
       "      <td>79.952077</td>\n",
       "      <td>70.870270</td>\n",
       "      <td>123.910515</td>\n",
       "      <td>76.081110</td>\n",
       "      <td>84.734169</td>\n",
       "      <td>25.790564</td>\n",
       "      <td>98.550000</td>\n",
       "      <td>445.133256</td>\n",
       "      <td>136.941935</td>\n",
       "      <td>41.919630</td>\n",
       "      <td>130.742294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2018_12</td>\n",
       "      <td>1390.96</td>\n",
       "      <td>86.0</td>\n",
       "      <td>561.0000</td>\n",
       "      <td>5100.61</td>\n",
       "      <td>3978191.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>98.650000</td>\n",
       "      <td>16855.0</td>\n",
       "      <td>9750.0</td>\n",
       "      <td>984.0</td>\n",
       "      <td>745.856</td>\n",
       "      <td>2018_12</td>\n",
       "      <td>1390.960000</td>\n",
       "      <td>68.690096</td>\n",
       "      <td>60.514796</td>\n",
       "      <td>124.096394</td>\n",
       "      <td>66.259618</td>\n",
       "      <td>84.734169</td>\n",
       "      <td>25.790564</td>\n",
       "      <td>98.650000</td>\n",
       "      <td>976.535342</td>\n",
       "      <td>125.806452</td>\n",
       "      <td>28.447528</td>\n",
       "      <td>130.916240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2019_01</td>\n",
       "      <td>1394.81</td>\n",
       "      <td>93.8</td>\n",
       "      <td>708.4170</td>\n",
       "      <td>5116.93</td>\n",
       "      <td>4561899.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>98.660000</td>\n",
       "      <td>2385.0</td>\n",
       "      <td>5805.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>749.517</td>\n",
       "      <td>2019_01</td>\n",
       "      <td>1394.810000</td>\n",
       "      <td>74.920128</td>\n",
       "      <td>76.416596</td>\n",
       "      <td>124.493455</td>\n",
       "      <td>75.981692</td>\n",
       "      <td>85.496776</td>\n",
       "      <td>25.377915</td>\n",
       "      <td>98.660000</td>\n",
       "      <td>138.180765</td>\n",
       "      <td>74.903226</td>\n",
       "      <td>31.251807</td>\n",
       "      <td>131.558836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2019_02</td>\n",
       "      <td>1398.62</td>\n",
       "      <td>94.7</td>\n",
       "      <td>674.1070</td>\n",
       "      <td>5138.93</td>\n",
       "      <td>4079370.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>98.700000</td>\n",
       "      <td>2516.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>750.180</td>\n",
       "      <td>2019_02</td>\n",
       "      <td>1398.620000</td>\n",
       "      <td>75.638978</td>\n",
       "      <td>72.715593</td>\n",
       "      <td>125.028709</td>\n",
       "      <td>67.944826</td>\n",
       "      <td>85.496776</td>\n",
       "      <td>25.377915</td>\n",
       "      <td>98.700000</td>\n",
       "      <td>145.770568</td>\n",
       "      <td>101.122581</td>\n",
       "      <td>33.940445</td>\n",
       "      <td>131.675209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2019_03</td>\n",
       "      <td>1404.77</td>\n",
       "      <td>96.1</td>\n",
       "      <td>725.1730</td>\n",
       "      <td>5177.47</td>\n",
       "      <td>4070245.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>98.910000</td>\n",
       "      <td>12396.0</td>\n",
       "      <td>10570.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>752.524</td>\n",
       "      <td>2019_03</td>\n",
       "      <td>1404.770000</td>\n",
       "      <td>76.757188</td>\n",
       "      <td>78.224058</td>\n",
       "      <td>125.966376</td>\n",
       "      <td>67.792843</td>\n",
       "      <td>85.496776</td>\n",
       "      <td>25.377915</td>\n",
       "      <td>98.910000</td>\n",
       "      <td>718.192352</td>\n",
       "      <td>136.387097</td>\n",
       "      <td>36.253252</td>\n",
       "      <td>132.086639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2019_04</td>\n",
       "      <td>1405.16</td>\n",
       "      <td>97.1</td>\n",
       "      <td>721.1710</td>\n",
       "      <td>5206.98</td>\n",
       "      <td>4406861.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>99.180000</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>7499.0</td>\n",
       "      <td>1342.0</td>\n",
       "      <td>755.373</td>\n",
       "      <td>2019_04</td>\n",
       "      <td>1405.160000</td>\n",
       "      <td>77.555911</td>\n",
       "      <td>77.792364</td>\n",
       "      <td>126.684346</td>\n",
       "      <td>73.399423</td>\n",
       "      <td>86.522738</td>\n",
       "      <td>26.063119</td>\n",
       "      <td>99.180000</td>\n",
       "      <td>132.966396</td>\n",
       "      <td>96.761290</td>\n",
       "      <td>38.797340</td>\n",
       "      <td>132.586709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2019_05</td>\n",
       "      <td>1410.17</td>\n",
       "      <td>103.5</td>\n",
       "      <td>715.8920</td>\n",
       "      <td>5213.75</td>\n",
       "      <td>4641116.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>99.470000</td>\n",
       "      <td>7851.0</td>\n",
       "      <td>9156.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>755.625</td>\n",
       "      <td>2019_05</td>\n",
       "      <td>1410.170000</td>\n",
       "      <td>82.667732</td>\n",
       "      <td>77.222921</td>\n",
       "      <td>126.849058</td>\n",
       "      <td>77.301108</td>\n",
       "      <td>86.522738</td>\n",
       "      <td>26.063119</td>\n",
       "      <td>99.470000</td>\n",
       "      <td>454.866744</td>\n",
       "      <td>118.141935</td>\n",
       "      <td>39.722463</td>\n",
       "      <td>132.630942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2019_06</td>\n",
       "      <td>1419.30</td>\n",
       "      <td>96.4</td>\n",
       "      <td>696.6390</td>\n",
       "      <td>5214.27</td>\n",
       "      <td>4229130.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>99.820000</td>\n",
       "      <td>16545.0</td>\n",
       "      <td>9274.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>762.304</td>\n",
       "      <td>2019_06</td>\n",
       "      <td>1419.300000</td>\n",
       "      <td>76.996805</td>\n",
       "      <td>75.146109</td>\n",
       "      <td>126.861710</td>\n",
       "      <td>70.439186</td>\n",
       "      <td>86.522738</td>\n",
       "      <td>26.063119</td>\n",
       "      <td>99.820000</td>\n",
       "      <td>958.574739</td>\n",
       "      <td>119.664516</td>\n",
       "      <td>32.986412</td>\n",
       "      <td>133.803272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2019_07</td>\n",
       "      <td>1425.84</td>\n",
       "      <td>105.6</td>\n",
       "      <td>740.2510</td>\n",
       "      <td>5224.18</td>\n",
       "      <td>4983185.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>100.110000</td>\n",
       "      <td>3174.0</td>\n",
       "      <td>7613.0</td>\n",
       "      <td>1391.0</td>\n",
       "      <td>766.699</td>\n",
       "      <td>2019_07</td>\n",
       "      <td>1425.840000</td>\n",
       "      <td>84.345048</td>\n",
       "      <td>79.850514</td>\n",
       "      <td>127.102817</td>\n",
       "      <td>82.998512</td>\n",
       "      <td>87.474488</td>\n",
       "      <td>27.314149</td>\n",
       "      <td>100.110000</td>\n",
       "      <td>183.893395</td>\n",
       "      <td>98.232258</td>\n",
       "      <td>40.213935</td>\n",
       "      <td>134.574703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2019_08</td>\n",
       "      <td>1435.88</td>\n",
       "      <td>108.3</td>\n",
       "      <td>764.2490</td>\n",
       "      <td>5229.93</td>\n",
       "      <td>5065098.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>100.480000</td>\n",
       "      <td>7389.0</td>\n",
       "      <td>7517.0</td>\n",
       "      <td>1176.0</td>\n",
       "      <td>769.951</td>\n",
       "      <td>2019_08</td>\n",
       "      <td>1435.880000</td>\n",
       "      <td>86.501597</td>\n",
       "      <td>82.439167</td>\n",
       "      <td>127.242713</td>\n",
       "      <td>84.362831</td>\n",
       "      <td>87.474488</td>\n",
       "      <td>27.314149</td>\n",
       "      <td>100.480000</td>\n",
       "      <td>428.099652</td>\n",
       "      <td>96.993548</td>\n",
       "      <td>33.998265</td>\n",
       "      <td>135.145510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2019_09</td>\n",
       "      <td>1439.67</td>\n",
       "      <td>103.5</td>\n",
       "      <td>759.4830</td>\n",
       "      <td>5227.84</td>\n",
       "      <td>4772546.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>100.800000</td>\n",
       "      <td>15932.0</td>\n",
       "      <td>10531.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>773.520</td>\n",
       "      <td>2019_09</td>\n",
       "      <td>1439.670000</td>\n",
       "      <td>82.667732</td>\n",
       "      <td>81.925061</td>\n",
       "      <td>127.191864</td>\n",
       "      <td>79.490169</td>\n",
       "      <td>87.474488</td>\n",
       "      <td>27.314149</td>\n",
       "      <td>100.800000</td>\n",
       "      <td>923.059096</td>\n",
       "      <td>135.883871</td>\n",
       "      <td>28.158427</td>\n",
       "      <td>135.771958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2019_10</td>\n",
       "      <td>1439.17</td>\n",
       "      <td>108.4</td>\n",
       "      <td>745.0250</td>\n",
       "      <td>5233.07</td>\n",
       "      <td>5211798.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.070000</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>7451.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>774.939</td>\n",
       "      <td>2019_10</td>\n",
       "      <td>1439.170000</td>\n",
       "      <td>86.581470</td>\n",
       "      <td>80.365483</td>\n",
       "      <td>127.319109</td>\n",
       "      <td>86.806225</td>\n",
       "      <td>88.961554</td>\n",
       "      <td>27.778489</td>\n",
       "      <td>101.070000</td>\n",
       "      <td>398.493627</td>\n",
       "      <td>96.141935</td>\n",
       "      <td>35.154669</td>\n",
       "      <td>136.021028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2019_11</td>\n",
       "      <td>1442.68</td>\n",
       "      <td>101.7</td>\n",
       "      <td>733.3300</td>\n",
       "      <td>5259.76</td>\n",
       "      <td>4742975.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.620000</td>\n",
       "      <td>10414.0</td>\n",
       "      <td>10587.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>775.225</td>\n",
       "      <td>2019_11</td>\n",
       "      <td>1442.680000</td>\n",
       "      <td>81.230032</td>\n",
       "      <td>79.103950</td>\n",
       "      <td>127.968469</td>\n",
       "      <td>78.997642</td>\n",
       "      <td>88.961554</td>\n",
       "      <td>27.778489</td>\n",
       "      <td>101.620000</td>\n",
       "      <td>603.360371</td>\n",
       "      <td>136.606452</td>\n",
       "      <td>33.795895</td>\n",
       "      <td>136.071228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2019_12</td>\n",
       "      <td>1445.22</td>\n",
       "      <td>85.5</td>\n",
       "      <td>623.5230</td>\n",
       "      <td>5320.25</td>\n",
       "      <td>4029571.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>102.700000</td>\n",
       "      <td>17189.0</td>\n",
       "      <td>10091.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>776.839</td>\n",
       "      <td>2019_12</td>\n",
       "      <td>1445.220000</td>\n",
       "      <td>68.290735</td>\n",
       "      <td>67.259122</td>\n",
       "      <td>129.440173</td>\n",
       "      <td>67.115388</td>\n",
       "      <td>88.961554</td>\n",
       "      <td>27.778489</td>\n",
       "      <td>102.700000</td>\n",
       "      <td>995.886443</td>\n",
       "      <td>130.206452</td>\n",
       "      <td>26.481642</td>\n",
       "      <td>136.354525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2020_01</td>\n",
       "      <td>1449.37</td>\n",
       "      <td>93.4</td>\n",
       "      <td>716.9140</td>\n",
       "      <td>5331.42</td>\n",
       "      <td>4583755.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>103.760000</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>8079.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>779.766</td>\n",
       "      <td>2020_01</td>\n",
       "      <td>1449.370000</td>\n",
       "      <td>74.600639</td>\n",
       "      <td>77.333164</td>\n",
       "      <td>129.711936</td>\n",
       "      <td>76.345719</td>\n",
       "      <td>89.317400</td>\n",
       "      <td>28.084053</td>\n",
       "      <td>103.760000</td>\n",
       "      <td>127.809965</td>\n",
       "      <td>104.245161</td>\n",
       "      <td>32.350390</td>\n",
       "      <td>136.868286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020_02</td>\n",
       "      <td>1453.67</td>\n",
       "      <td>93.5</td>\n",
       "      <td>717.7750</td>\n",
       "      <td>5344.75</td>\n",
       "      <td>4133147.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>104.710000</td>\n",
       "      <td>3009.0</td>\n",
       "      <td>9025.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>782.336</td>\n",
       "      <td>2020_02</td>\n",
       "      <td>1453.670000</td>\n",
       "      <td>74.680511</td>\n",
       "      <td>77.426039</td>\n",
       "      <td>130.036251</td>\n",
       "      <td>68.840521</td>\n",
       "      <td>89.317400</td>\n",
       "      <td>28.084053</td>\n",
       "      <td>104.710000</td>\n",
       "      <td>174.333720</td>\n",
       "      <td>116.451613</td>\n",
       "      <td>31.656548</td>\n",
       "      <td>137.319385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020_03</td>\n",
       "      <td>1455.87</td>\n",
       "      <td>91.6</td>\n",
       "      <td>662.9290</td>\n",
       "      <td>5348.49</td>\n",
       "      <td>4101060.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>106.230000</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>9707.0</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>784.338</td>\n",
       "      <td>2020_03</td>\n",
       "      <td>1455.870000</td>\n",
       "      <td>73.162939</td>\n",
       "      <td>71.509828</td>\n",
       "      <td>130.127244</td>\n",
       "      <td>68.306089</td>\n",
       "      <td>89.317400</td>\n",
       "      <td>28.084053</td>\n",
       "      <td>106.230000</td>\n",
       "      <td>542.294322</td>\n",
       "      <td>125.251613</td>\n",
       "      <td>35.617230</td>\n",
       "      <td>137.670786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020_04</td>\n",
       "      <td>1454.62</td>\n",
       "      <td>64.3</td>\n",
       "      <td>540.1200</td>\n",
       "      <td>5331.91</td>\n",
       "      <td>4163424.0</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>108.120000</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>8020.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>786.070</td>\n",
       "      <td>2020_04</td>\n",
       "      <td>1454.620000</td>\n",
       "      <td>51.357827</td>\n",
       "      <td>58.262481</td>\n",
       "      <td>129.723858</td>\n",
       "      <td>69.344806</td>\n",
       "      <td>80.296343</td>\n",
       "      <td>25.416068</td>\n",
       "      <td>108.120000</td>\n",
       "      <td>204.229432</td>\n",
       "      <td>103.483871</td>\n",
       "      <td>34.431917</td>\n",
       "      <td>137.974795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020_05</td>\n",
       "      <td>1456.63</td>\n",
       "      <td>82.0</td>\n",
       "      <td>637.0730</td>\n",
       "      <td>5311.65</td>\n",
       "      <td>4828333.0</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>109.260000</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>9181.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>787.666</td>\n",
       "      <td>2020_05</td>\n",
       "      <td>1456.630000</td>\n",
       "      <td>65.495208</td>\n",
       "      <td>68.720754</td>\n",
       "      <td>129.230938</td>\n",
       "      <td>80.419341</td>\n",
       "      <td>80.296343</td>\n",
       "      <td>25.416068</td>\n",
       "      <td>109.260000</td>\n",
       "      <td>79.895713</td>\n",
       "      <td>118.464516</td>\n",
       "      <td>45.359931</td>\n",
       "      <td>138.254932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2020_06</td>\n",
       "      <td>1460.86</td>\n",
       "      <td>93.3</td>\n",
       "      <td>788.4670</td>\n",
       "      <td>5325.46</td>\n",
       "      <td>5371200.0</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>109.870000</td>\n",
       "      <td>12941.0</td>\n",
       "      <td>11483.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>790.331</td>\n",
       "      <td>2020_06</td>\n",
       "      <td>1460.860000</td>\n",
       "      <td>74.520767</td>\n",
       "      <td>85.051551</td>\n",
       "      <td>129.566931</td>\n",
       "      <td>89.461179</td>\n",
       "      <td>80.296343</td>\n",
       "      <td>25.416068</td>\n",
       "      <td>109.870000</td>\n",
       "      <td>749.768250</td>\n",
       "      <td>148.167742</td>\n",
       "      <td>51.864701</td>\n",
       "      <td>138.722706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2020_07</td>\n",
       "      <td>1469.84</td>\n",
       "      <td>110.2</td>\n",
       "      <td>870.4940</td>\n",
       "      <td>5344.63</td>\n",
       "      <td>5927835.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>110.190000</td>\n",
       "      <td>5137.0</td>\n",
       "      <td>12229.0</td>\n",
       "      <td>2247.0</td>\n",
       "      <td>799.589</td>\n",
       "      <td>2020_07</td>\n",
       "      <td>1469.840000</td>\n",
       "      <td>88.019169</td>\n",
       "      <td>93.899763</td>\n",
       "      <td>130.033332</td>\n",
       "      <td>98.732333</td>\n",
       "      <td>77.887453</td>\n",
       "      <td>24.933162</td>\n",
       "      <td>110.190000</td>\n",
       "      <td>297.624565</td>\n",
       "      <td>157.793548</td>\n",
       "      <td>64.960971</td>\n",
       "      <td>140.347715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2020_08</td>\n",
       "      <td>1488.07</td>\n",
       "      <td>112.2</td>\n",
       "      <td>839.4480</td>\n",
       "      <td>5357.46</td>\n",
       "      <td>5805937.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>110.650000</td>\n",
       "      <td>8178.0</td>\n",
       "      <td>12169.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>805.356</td>\n",
       "      <td>2020_08</td>\n",
       "      <td>1488.070000</td>\n",
       "      <td>89.616613</td>\n",
       "      <td>90.550846</td>\n",
       "      <td>130.345482</td>\n",
       "      <td>96.702035</td>\n",
       "      <td>77.887453</td>\n",
       "      <td>24.933162</td>\n",
       "      <td>110.650000</td>\n",
       "      <td>473.812283</td>\n",
       "      <td>157.019355</td>\n",
       "      <td>49.176062</td>\n",
       "      <td>141.359966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2020_09</td>\n",
       "      <td>1511.81</td>\n",
       "      <td>114.3</td>\n",
       "      <td>819.1640</td>\n",
       "      <td>5391.75</td>\n",
       "      <td>5822407.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>111.280000</td>\n",
       "      <td>17142.0</td>\n",
       "      <td>12437.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>814.701</td>\n",
       "      <td>2020_09</td>\n",
       "      <td>1511.810000</td>\n",
       "      <td>91.293930</td>\n",
       "      <td>88.362821</td>\n",
       "      <td>131.179748</td>\n",
       "      <td>96.976355</td>\n",
       "      <td>77.887453</td>\n",
       "      <td>24.933162</td>\n",
       "      <td>111.280000</td>\n",
       "      <td>993.163384</td>\n",
       "      <td>160.477419</td>\n",
       "      <td>29.661752</td>\n",
       "      <td>143.000246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2020_10</td>\n",
       "      <td>1536.58</td>\n",
       "      <td>119.5</td>\n",
       "      <td>880.8230</td>\n",
       "      <td>5438.12</td>\n",
       "      <td>5938170.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>111.770000</td>\n",
       "      <td>11483.0</td>\n",
       "      <td>12284.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>828.778</td>\n",
       "      <td>2020_10</td>\n",
       "      <td>1536.580000</td>\n",
       "      <td>95.447284</td>\n",
       "      <td>95.013948</td>\n",
       "      <td>132.307917</td>\n",
       "      <td>98.904470</td>\n",
       "      <td>77.653790</td>\n",
       "      <td>25.431826</td>\n",
       "      <td>111.770000</td>\n",
       "      <td>665.295481</td>\n",
       "      <td>158.503226</td>\n",
       "      <td>41.139058</td>\n",
       "      <td>145.471109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2020_11</td>\n",
       "      <td>1554.97</td>\n",
       "      <td>113.6</td>\n",
       "      <td>862.6130</td>\n",
       "      <td>5486.52</td>\n",
       "      <td>5255086.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>112.330000</td>\n",
       "      <td>11296.0</td>\n",
       "      <td>11812.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>839.382</td>\n",
       "      <td>2020_11</td>\n",
       "      <td>1554.970000</td>\n",
       "      <td>90.734824</td>\n",
       "      <td>93.049644</td>\n",
       "      <td>133.485475</td>\n",
       "      <td>87.527218</td>\n",
       "      <td>77.653790</td>\n",
       "      <td>25.431826</td>\n",
       "      <td>112.330000</td>\n",
       "      <td>654.461182</td>\n",
       "      <td>152.412903</td>\n",
       "      <td>36.253252</td>\n",
       "      <td>147.332374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2020_12</td>\n",
       "      <td>1568.66</td>\n",
       "      <td>103.8</td>\n",
       "      <td>820.5140</td>\n",
       "      <td>5560.59</td>\n",
       "      <td>4668039.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113.250000</td>\n",
       "      <td>18867.0</td>\n",
       "      <td>12855.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>845.268</td>\n",
       "      <td>2020_12</td>\n",
       "      <td>1568.660000</td>\n",
       "      <td>82.907348</td>\n",
       "      <td>88.508445</td>\n",
       "      <td>135.287577</td>\n",
       "      <td>77.749530</td>\n",
       "      <td>77.653790</td>\n",
       "      <td>25.431826</td>\n",
       "      <td>113.250000</td>\n",
       "      <td>1093.105446</td>\n",
       "      <td>165.870968</td>\n",
       "      <td>29.893033</td>\n",
       "      <td>148.365513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2021_01</td>\n",
       "      <td>1594.78</td>\n",
       "      <td>105.1</td>\n",
       "      <td>875.5510</td>\n",
       "      <td>5574.49</td>\n",
       "      <td>4996877.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>113.720000</td>\n",
       "      <td>4736.0</td>\n",
       "      <td>8458.0</td>\n",
       "      <td>1334.0</td>\n",
       "      <td>852.809</td>\n",
       "      <td>2021_01</td>\n",
       "      <td>1594.780000</td>\n",
       "      <td>83.945687</td>\n",
       "      <td>94.445259</td>\n",
       "      <td>135.625760</td>\n",
       "      <td>83.226562</td>\n",
       "      <td>78.973905</td>\n",
       "      <td>27.847849</td>\n",
       "      <td>113.720000</td>\n",
       "      <td>274.391657</td>\n",
       "      <td>109.135484</td>\n",
       "      <td>38.566060</td>\n",
       "      <td>149.689146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021_02</td>\n",
       "      <td>1616.76</td>\n",
       "      <td>104.2</td>\n",
       "      <td>899.4310</td>\n",
       "      <td>5622.43</td>\n",
       "      <td>4683733.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.320000</td>\n",
       "      <td>6550.0</td>\n",
       "      <td>10745.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>868.929</td>\n",
       "      <td>2021_02</td>\n",
       "      <td>1616.760000</td>\n",
       "      <td>83.226837</td>\n",
       "      <td>97.021183</td>\n",
       "      <td>136.792127</td>\n",
       "      <td>78.010924</td>\n",
       "      <td>78.973905</td>\n",
       "      <td>27.847849</td>\n",
       "      <td>114.320000</td>\n",
       "      <td>379.490151</td>\n",
       "      <td>138.645161</td>\n",
       "      <td>36.860364</td>\n",
       "      <td>152.518606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2021_03</td>\n",
       "      <td>1646.74</td>\n",
       "      <td>113.0</td>\n",
       "      <td>995.2780</td>\n",
       "      <td>5674.72</td>\n",
       "      <td>5525415.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>115.210000</td>\n",
       "      <td>13061.0</td>\n",
       "      <td>13219.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>880.265</td>\n",
       "      <td>2021_03</td>\n",
       "      <td>1646.740000</td>\n",
       "      <td>90.255591</td>\n",
       "      <td>107.360153</td>\n",
       "      <td>138.064328</td>\n",
       "      <td>92.029740</td>\n",
       "      <td>78.973905</td>\n",
       "      <td>27.847849</td>\n",
       "      <td>115.210000</td>\n",
       "      <td>756.720742</td>\n",
       "      <td>170.567742</td>\n",
       "      <td>45.793582</td>\n",
       "      <td>154.508355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2021_04</td>\n",
       "      <td>1671.71</td>\n",
       "      <td>107.3</td>\n",
       "      <td>933.2030</td>\n",
       "      <td>5692.31</td>\n",
       "      <td>5297637.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>116.620000</td>\n",
       "      <td>6543.0</td>\n",
       "      <td>10602.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>888.191</td>\n",
       "      <td>2021_04</td>\n",
       "      <td>1671.710000</td>\n",
       "      <td>85.702875</td>\n",
       "      <td>100.664153</td>\n",
       "      <td>138.492287</td>\n",
       "      <td>88.235935</td>\n",
       "      <td>88.766669</td>\n",
       "      <td>33.166788</td>\n",
       "      <td>116.620000</td>\n",
       "      <td>379.084589</td>\n",
       "      <td>136.800000</td>\n",
       "      <td>44.203527</td>\n",
       "      <td>155.899565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2021_05</td>\n",
       "      <td>1707.05</td>\n",
       "      <td>110.1</td>\n",
       "      <td>1040.1170</td>\n",
       "      <td>5739.56</td>\n",
       "      <td>5536759.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>118.170000</td>\n",
       "      <td>9575.0</td>\n",
       "      <td>11797.0</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>907.899</td>\n",
       "      <td>2021_05</td>\n",
       "      <td>1707.050000</td>\n",
       "      <td>87.939297</td>\n",
       "      <td>112.196914</td>\n",
       "      <td>139.641867</td>\n",
       "      <td>92.218683</td>\n",
       "      <td>88.766669</td>\n",
       "      <td>33.166788</td>\n",
       "      <td>118.170000</td>\n",
       "      <td>554.750869</td>\n",
       "      <td>152.219355</td>\n",
       "      <td>43.914426</td>\n",
       "      <td>159.358808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2021_06</td>\n",
       "      <td>1742.04</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1019.7460</td>\n",
       "      <td>5769.98</td>\n",
       "      <td>5463352.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>120.100000</td>\n",
       "      <td>17012.0</td>\n",
       "      <td>14024.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>927.512</td>\n",
       "      <td>2021_06</td>\n",
       "      <td>1742.040000</td>\n",
       "      <td>87.060703</td>\n",
       "      <td>109.999504</td>\n",
       "      <td>140.381977</td>\n",
       "      <td>90.996037</td>\n",
       "      <td>88.766669</td>\n",
       "      <td>33.166788</td>\n",
       "      <td>120.100000</td>\n",
       "      <td>985.631518</td>\n",
       "      <td>180.954839</td>\n",
       "      <td>37.294016</td>\n",
       "      <td>162.801376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2021_07</td>\n",
       "      <td>1768.21</td>\n",
       "      <td>112.5</td>\n",
       "      <td>1003.6050</td>\n",
       "      <td>5825.37</td>\n",
       "      <td>5900488.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>122.330000</td>\n",
       "      <td>6963.0</td>\n",
       "      <td>9923.0</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>935.359</td>\n",
       "      <td>2021_07</td>\n",
       "      <td>1768.210000</td>\n",
       "      <td>89.856230</td>\n",
       "      <td>108.258382</td>\n",
       "      <td>141.729600</td>\n",
       "      <td>98.276850</td>\n",
       "      <td>92.494869</td>\n",
       "      <td>37.478471</td>\n",
       "      <td>122.330000</td>\n",
       "      <td>403.418308</td>\n",
       "      <td>128.038710</td>\n",
       "      <td>51.951431</td>\n",
       "      <td>164.178719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2021_08</td>\n",
       "      <td>1784.92</td>\n",
       "      <td>113.8</td>\n",
       "      <td>982.9650</td>\n",
       "      <td>5876.05</td>\n",
       "      <td>5863757.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>124.460000</td>\n",
       "      <td>8207.0</td>\n",
       "      <td>10530.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>939.699</td>\n",
       "      <td>2021_08</td>\n",
       "      <td>1784.920000</td>\n",
       "      <td>90.894569</td>\n",
       "      <td>106.031955</td>\n",
       "      <td>142.962630</td>\n",
       "      <td>97.665069</td>\n",
       "      <td>92.494869</td>\n",
       "      <td>37.478471</td>\n",
       "      <td>124.460000</td>\n",
       "      <td>475.492468</td>\n",
       "      <td>135.870968</td>\n",
       "      <td>41.110147</td>\n",
       "      <td>164.940497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2021_09</td>\n",
       "      <td>1796.38</td>\n",
       "      <td>109.5</td>\n",
       "      <td>890.6390</td>\n",
       "      <td>5944.21</td>\n",
       "      <td>5691520.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>126.650000</td>\n",
       "      <td>15153.0</td>\n",
       "      <td>10464.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>944.520</td>\n",
       "      <td>2021_09</td>\n",
       "      <td>1796.380000</td>\n",
       "      <td>87.460064</td>\n",
       "      <td>96.072795</td>\n",
       "      <td>144.620943</td>\n",
       "      <td>94.796338</td>\n",
       "      <td>92.494869</td>\n",
       "      <td>37.478471</td>\n",
       "      <td>126.650000</td>\n",
       "      <td>877.925840</td>\n",
       "      <td>135.019355</td>\n",
       "      <td>33.102053</td>\n",
       "      <td>165.786702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2021_10</td>\n",
       "      <td>1803.14</td>\n",
       "      <td>108.2</td>\n",
       "      <td>891.8280</td>\n",
       "      <td>6018.51</td>\n",
       "      <td>5403183.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>128.600000</td>\n",
       "      <td>11590.0</td>\n",
       "      <td>10071.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>952.596</td>\n",
       "      <td>2021_10</td>\n",
       "      <td>1803.140000</td>\n",
       "      <td>86.421725</td>\n",
       "      <td>96.201052</td>\n",
       "      <td>146.428641</td>\n",
       "      <td>89.993879</td>\n",
       "      <td>93.882292</td>\n",
       "      <td>41.001447</td>\n",
       "      <td>128.600000</td>\n",
       "      <td>671.494786</td>\n",
       "      <td>129.948387</td>\n",
       "      <td>40.098294</td>\n",
       "      <td>167.204241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2021_11</td>\n",
       "      <td>1811.88</td>\n",
       "      <td>104.6</td>\n",
       "      <td>826.4760</td>\n",
       "      <td>6075.69</td>\n",
       "      <td>5376556.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>130.040000</td>\n",
       "      <td>13955.0</td>\n",
       "      <td>9973.0</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>959.001</td>\n",
       "      <td>2021_11</td>\n",
       "      <td>1811.880000</td>\n",
       "      <td>83.546326</td>\n",
       "      <td>89.151563</td>\n",
       "      <td>147.819814</td>\n",
       "      <td>89.550387</td>\n",
       "      <td>93.882292</td>\n",
       "      <td>41.001447</td>\n",
       "      <td>130.040000</td>\n",
       "      <td>808.516802</td>\n",
       "      <td>128.683871</td>\n",
       "      <td>41.196878</td>\n",
       "      <td>168.328477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2021_12</td>\n",
       "      <td>1819.23</td>\n",
       "      <td>91.3</td>\n",
       "      <td>715.6370</td>\n",
       "      <td>6120.04</td>\n",
       "      <td>4784859.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>131.660000</td>\n",
       "      <td>27359.0</td>\n",
       "      <td>11829.0</td>\n",
       "      <td>921.0</td>\n",
       "      <td>962.321</td>\n",
       "      <td>2021_12</td>\n",
       "      <td>1819.230000</td>\n",
       "      <td>72.923323</td>\n",
       "      <td>77.195414</td>\n",
       "      <td>148.898837</td>\n",
       "      <td>79.695250</td>\n",
       "      <td>93.882292</td>\n",
       "      <td>41.001447</td>\n",
       "      <td>131.660000</td>\n",
       "      <td>1585.110081</td>\n",
       "      <td>152.632258</td>\n",
       "      <td>26.626193</td>\n",
       "      <td>168.911220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2022_01</td>\n",
       "      <td>1829.69</td>\n",
       "      <td>91.6</td>\n",
       "      <td>737.1040</td>\n",
       "      <td>6153.09</td>\n",
       "      <td>4526998.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>132.790000</td>\n",
       "      <td>10731.0</td>\n",
       "      <td>8386.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>969.184</td>\n",
       "      <td>2022_01</td>\n",
       "      <td>1829.690000</td>\n",
       "      <td>73.162939</td>\n",
       "      <td>79.511049</td>\n",
       "      <td>149.702934</td>\n",
       "      <td>75.400391</td>\n",
       "      <td>95.290526</td>\n",
       "      <td>43.994552</td>\n",
       "      <td>132.790000</td>\n",
       "      <td>621.726535</td>\n",
       "      <td>108.206452</td>\n",
       "      <td>37.785487</td>\n",
       "      <td>170.115846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2022_02</td>\n",
       "      <td>1840.60</td>\n",
       "      <td>94.0</td>\n",
       "      <td>770.7420</td>\n",
       "      <td>6215.24</td>\n",
       "      <td>4775815.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>133.880000</td>\n",
       "      <td>6020.0</td>\n",
       "      <td>8974.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>972.904</td>\n",
       "      <td>2022_02</td>\n",
       "      <td>1840.600000</td>\n",
       "      <td>75.079872</td>\n",
       "      <td>83.139564</td>\n",
       "      <td>151.215026</td>\n",
       "      <td>79.544616</td>\n",
       "      <td>95.290526</td>\n",
       "      <td>43.994552</td>\n",
       "      <td>133.880000</td>\n",
       "      <td>348.783314</td>\n",
       "      <td>115.793548</td>\n",
       "      <td>42.555652</td>\n",
       "      <td>170.768799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2022_03</td>\n",
       "      <td>1857.41</td>\n",
       "      <td>103.6</td>\n",
       "      <td>891.9100</td>\n",
       "      <td>6315.93</td>\n",
       "      <td>5528277.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>135.290000</td>\n",
       "      <td>9655.0</td>\n",
       "      <td>12672.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>981.244</td>\n",
       "      <td>2022_03</td>\n",
       "      <td>1857.410000</td>\n",
       "      <td>82.747604</td>\n",
       "      <td>96.209897</td>\n",
       "      <td>153.664785</td>\n",
       "      <td>92.077409</td>\n",
       "      <td>95.290526</td>\n",
       "      <td>43.994552</td>\n",
       "      <td>135.290000</td>\n",
       "      <td>559.385863</td>\n",
       "      <td>163.509677</td>\n",
       "      <td>44.839549</td>\n",
       "      <td>172.232676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2022_04</td>\n",
       "      <td>1872.69</td>\n",
       "      <td>98.2</td>\n",
       "      <td>855.8490</td>\n",
       "      <td>6382.88</td>\n",
       "      <td>5180364.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>136.750000</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>11161.0</td>\n",
       "      <td>2115.0</td>\n",
       "      <td>990.543</td>\n",
       "      <td>2022_04</td>\n",
       "      <td>1872.690000</td>\n",
       "      <td>78.434505</td>\n",
       "      <td>92.320014</td>\n",
       "      <td>155.293660</td>\n",
       "      <td>86.282669</td>\n",
       "      <td>98.625695</td>\n",
       "      <td>47.910068</td>\n",
       "      <td>136.750000</td>\n",
       "      <td>309.559676</td>\n",
       "      <td>144.012903</td>\n",
       "      <td>61.144840</td>\n",
       "      <td>173.864881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2022_05</td>\n",
       "      <td>1918.74</td>\n",
       "      <td>105.7</td>\n",
       "      <td>897.5880</td>\n",
       "      <td>6412.88</td>\n",
       "      <td>5503543.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>138.030000</td>\n",
       "      <td>8014.0</td>\n",
       "      <td>11609.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1013.164</td>\n",
       "      <td>2022_05</td>\n",
       "      <td>1918.740000</td>\n",
       "      <td>84.424920</td>\n",
       "      <td>96.822380</td>\n",
       "      <td>156.023551</td>\n",
       "      <td>91.665447</td>\n",
       "      <td>98.625695</td>\n",
       "      <td>47.910068</td>\n",
       "      <td>138.030000</td>\n",
       "      <td>464.310545</td>\n",
       "      <td>149.793548</td>\n",
       "      <td>57.126337</td>\n",
       "      <td>177.835428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2022_06</td>\n",
       "      <td>1946.13</td>\n",
       "      <td>101.9</td>\n",
       "      <td>815.8450</td>\n",
       "      <td>6455.85</td>\n",
       "      <td>5195951.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>139.810000</td>\n",
       "      <td>17649.0</td>\n",
       "      <td>9997.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>1034.824</td>\n",
       "      <td>2022_06</td>\n",
       "      <td>1946.130000</td>\n",
       "      <td>81.389776</td>\n",
       "      <td>88.004802</td>\n",
       "      <td>157.068999</td>\n",
       "      <td>86.542282</td>\n",
       "      <td>98.625695</td>\n",
       "      <td>47.910068</td>\n",
       "      <td>139.810000</td>\n",
       "      <td>1022.537659</td>\n",
       "      <td>128.993548</td>\n",
       "      <td>37.004915</td>\n",
       "      <td>181.637296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2022_07</td>\n",
       "      <td>1981.40</td>\n",
       "      <td>104.9</td>\n",
       "      <td>828.7450</td>\n",
       "      <td>6411.95</td>\n",
       "      <td>5512023.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>141.430000</td>\n",
       "      <td>3952.0</td>\n",
       "      <td>8740.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1043.760</td>\n",
       "      <td>2022_07</td>\n",
       "      <td>1981.400000</td>\n",
       "      <td>83.785942</td>\n",
       "      <td>89.396319</td>\n",
       "      <td>156.000925</td>\n",
       "      <td>91.806687</td>\n",
       "      <td>102.866600</td>\n",
       "      <td>50.976312</td>\n",
       "      <td>141.430000</td>\n",
       "      <td>228.968714</td>\n",
       "      <td>112.774194</td>\n",
       "      <td>36.137612</td>\n",
       "      <td>183.205785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2022_08</td>\n",
       "      <td>1982.31</td>\n",
       "      <td>109.9</td>\n",
       "      <td>901.4080</td>\n",
       "      <td>6388.87</td>\n",
       "      <td>5879086.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>143.300000</td>\n",
       "      <td>8586.0</td>\n",
       "      <td>10807.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>1044.679</td>\n",
       "      <td>2022_08</td>\n",
       "      <td>1982.310000</td>\n",
       "      <td>87.779553</td>\n",
       "      <td>97.234441</td>\n",
       "      <td>155.439395</td>\n",
       "      <td>97.920384</td>\n",
       "      <td>102.866600</td>\n",
       "      <td>50.976312</td>\n",
       "      <td>143.300000</td>\n",
       "      <td>497.450753</td>\n",
       "      <td>139.445161</td>\n",
       "      <td>41.139058</td>\n",
       "      <td>183.367093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2022_09</td>\n",
       "      <td>1989.14</td>\n",
       "      <td>103.0</td>\n",
       "      <td>853.4480</td>\n",
       "      <td>6370.34</td>\n",
       "      <td>5488595.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>145.240000</td>\n",
       "      <td>13682.0</td>\n",
       "      <td>10282.0</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>1045.616</td>\n",
       "      <td>2022_09</td>\n",
       "      <td>1989.140000</td>\n",
       "      <td>82.268371</td>\n",
       "      <td>92.061020</td>\n",
       "      <td>154.988565</td>\n",
       "      <td>91.416477</td>\n",
       "      <td>102.866600</td>\n",
       "      <td>50.976312</td>\n",
       "      <td>145.240000</td>\n",
       "      <td>792.699884</td>\n",
       "      <td>132.670968</td>\n",
       "      <td>32.292570</td>\n",
       "      <td>183.531559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2022_10</td>\n",
       "      <td>1993.78</td>\n",
       "      <td>105.5</td>\n",
       "      <td>807.0890</td>\n",
       "      <td>6407.93</td>\n",
       "      <td>5378454.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>147.750000</td>\n",
       "      <td>9132.0</td>\n",
       "      <td>10036.0</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>1046.896</td>\n",
       "      <td>2022_10</td>\n",
       "      <td>1993.780000</td>\n",
       "      <td>84.265176</td>\n",
       "      <td>87.060297</td>\n",
       "      <td>155.903119</td>\n",
       "      <td>89.582000</td>\n",
       "      <td>105.643998</td>\n",
       "      <td>53.525128</td>\n",
       "      <td>147.750000</td>\n",
       "      <td>529.084589</td>\n",
       "      <td>129.496774</td>\n",
       "      <td>31.887829</td>\n",
       "      <td>183.756231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2022_11</td>\n",
       "      <td>1995.16</td>\n",
       "      <td>98.4</td>\n",
       "      <td>781.2600</td>\n",
       "      <td>6434.20</td>\n",
       "      <td>5330078.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>149.900000</td>\n",
       "      <td>14887.0</td>\n",
       "      <td>9422.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>1050.701</td>\n",
       "      <td>2022_11</td>\n",
       "      <td>1995.160000</td>\n",
       "      <td>78.594249</td>\n",
       "      <td>84.274135</td>\n",
       "      <td>156.542261</td>\n",
       "      <td>88.776263</td>\n",
       "      <td>105.643998</td>\n",
       "      <td>53.525128</td>\n",
       "      <td>149.900000</td>\n",
       "      <td>862.514484</td>\n",
       "      <td>121.574194</td>\n",
       "      <td>32.437121</td>\n",
       "      <td>184.424103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2022_12</td>\n",
       "      <td>1999.72</td>\n",
       "      <td>83.3</td>\n",
       "      <td>617.0690</td>\n",
       "      <td>6474.09</td>\n",
       "      <td>4469361.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>151.490000</td>\n",
       "      <td>12788.0</td>\n",
       "      <td>10923.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>1051.632</td>\n",
       "      <td>2022_12</td>\n",
       "      <td>1999.720000</td>\n",
       "      <td>66.533546</td>\n",
       "      <td>66.562932</td>\n",
       "      <td>157.512773</td>\n",
       "      <td>74.440405</td>\n",
       "      <td>105.643998</td>\n",
       "      <td>53.525128</td>\n",
       "      <td>151.490000</td>\n",
       "      <td>740.903824</td>\n",
       "      <td>140.941935</td>\n",
       "      <td>26.597282</td>\n",
       "      <td>184.587517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2023_01</td>\n",
       "      <td>2009.66</td>\n",
       "      <td>89.0</td>\n",
       "      <td>744.7220</td>\n",
       "      <td>6508.40</td>\n",
       "      <td>4874219.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>152.790000</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>8105.0</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>1056.418</td>\n",
       "      <td>2023_01</td>\n",
       "      <td>2009.660000</td>\n",
       "      <td>71.086262</td>\n",
       "      <td>80.332799</td>\n",
       "      <td>158.347526</td>\n",
       "      <td>81.183605</td>\n",
       "      <td>110.081046</td>\n",
       "      <td>54.328004</td>\n",
       "      <td>152.790000</td>\n",
       "      <td>182.676709</td>\n",
       "      <td>104.580645</td>\n",
       "      <td>35.877421</td>\n",
       "      <td>185.427578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2023_02</td>\n",
       "      <td>2015.32</td>\n",
       "      <td>89.6</td>\n",
       "      <td>690.2860</td>\n",
       "      <td>6563.07</td>\n",
       "      <td>4441263.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>153.720000</td>\n",
       "      <td>6142.0</td>\n",
       "      <td>9725.0</td>\n",
       "      <td>2577.0</td>\n",
       "      <td>1056.896</td>\n",
       "      <td>2023_02</td>\n",
       "      <td>2015.320000</td>\n",
       "      <td>71.565495</td>\n",
       "      <td>74.460814</td>\n",
       "      <td>159.677631</td>\n",
       "      <td>73.972413</td>\n",
       "      <td>110.081046</td>\n",
       "      <td>54.328004</td>\n",
       "      <td>153.720000</td>\n",
       "      <td>355.851680</td>\n",
       "      <td>125.483871</td>\n",
       "      <td>74.501301</td>\n",
       "      <td>185.511479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2023_03</td>\n",
       "      <td>2019.63</td>\n",
       "      <td>103.3</td>\n",
       "      <td>920.1620</td>\n",
       "      <td>6609.67</td>\n",
       "      <td>5443016.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>154.590000</td>\n",
       "      <td>10571.0</td>\n",
       "      <td>13169.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>1060.116</td>\n",
       "      <td>2023_03</td>\n",
       "      <td>2019.630000</td>\n",
       "      <td>82.507987</td>\n",
       "      <td>99.257426</td>\n",
       "      <td>160.811396</td>\n",
       "      <td>90.657326</td>\n",
       "      <td>110.081046</td>\n",
       "      <td>54.328004</td>\n",
       "      <td>154.590000</td>\n",
       "      <td>612.456547</td>\n",
       "      <td>169.922581</td>\n",
       "      <td>30.789245</td>\n",
       "      <td>186.076669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2023_04</td>\n",
       "      <td>2022.59</td>\n",
       "      <td>92.9</td>\n",
       "      <td>786.4080</td>\n",
       "      <td>6649.99</td>\n",
       "      <td>4588598.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>155.310000</td>\n",
       "      <td>9937.0</td>\n",
       "      <td>12343.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1061.635</td>\n",
       "      <td>2023_04</td>\n",
       "      <td>2022.590000</td>\n",
       "      <td>74.201278</td>\n",
       "      <td>84.829448</td>\n",
       "      <td>161.792370</td>\n",
       "      <td>76.426383</td>\n",
       "      <td>113.933882</td>\n",
       "      <td>54.599644</td>\n",
       "      <td>155.310000</td>\n",
       "      <td>575.724218</td>\n",
       "      <td>159.264516</td>\n",
       "      <td>37.785487</td>\n",
       "      <td>186.343291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2023_05</td>\n",
       "      <td>2039.80</td>\n",
       "      <td>103.3</td>\n",
       "      <td>803.6790</td>\n",
       "      <td>6665.28</td>\n",
       "      <td>5573757.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>155.950000</td>\n",
       "      <td>8890.0</td>\n",
       "      <td>12867.0</td>\n",
       "      <td>1851.0</td>\n",
       "      <td>1067.919</td>\n",
       "      <td>2023_05</td>\n",
       "      <td>2039.800000</td>\n",
       "      <td>82.507987</td>\n",
       "      <td>86.692462</td>\n",
       "      <td>162.164372</td>\n",
       "      <td>92.834911</td>\n",
       "      <td>113.933882</td>\n",
       "      <td>54.599644</td>\n",
       "      <td>155.950000</td>\n",
       "      <td>515.063731</td>\n",
       "      <td>166.025806</td>\n",
       "      <td>53.512576</td>\n",
       "      <td>187.446289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2023_06</td>\n",
       "      <td>2047.16</td>\n",
       "      <td>100.1</td>\n",
       "      <td>776.0190</td>\n",
       "      <td>6659.95</td>\n",
       "      <td>5261921.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>156.690000</td>\n",
       "      <td>14696.0</td>\n",
       "      <td>13032.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>1075.540</td>\n",
       "      <td>2023_06</td>\n",
       "      <td>2047.160000</td>\n",
       "      <td>79.952077</td>\n",
       "      <td>83.708791</td>\n",
       "      <td>162.034694</td>\n",
       "      <td>87.641059</td>\n",
       "      <td>113.933882</td>\n",
       "      <td>54.599644</td>\n",
       "      <td>156.690000</td>\n",
       "      <td>851.448436</td>\n",
       "      <td>168.154839</td>\n",
       "      <td>40.734316</td>\n",
       "      <td>188.783964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2023_07</td>\n",
       "      <td>2056.98</td>\n",
       "      <td>103.3</td>\n",
       "      <td>804.5690</td>\n",
       "      <td>6667.94</td>\n",
       "      <td>5481386.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>157.680000</td>\n",
       "      <td>8808.0</td>\n",
       "      <td>13599.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>1076.626</td>\n",
       "      <td>2023_07</td>\n",
       "      <td>2056.980000</td>\n",
       "      <td>82.507987</td>\n",
       "      <td>86.788466</td>\n",
       "      <td>162.229089</td>\n",
       "      <td>91.296406</td>\n",
       "      <td>116.212560</td>\n",
       "      <td>52.142660</td>\n",
       "      <td>157.680000</td>\n",
       "      <td>510.312862</td>\n",
       "      <td>175.470968</td>\n",
       "      <td>58.427291</td>\n",
       "      <td>188.974584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2023_08</td>\n",
       "      <td>2057.57</td>\n",
       "      <td>107.5</td>\n",
       "      <td>871.9120</td>\n",
       "      <td>6683.28</td>\n",
       "      <td>5977543.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>158.790000</td>\n",
       "      <td>9698.0</td>\n",
       "      <td>16839.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>1078.412</td>\n",
       "      <td>2023_08</td>\n",
       "      <td>2057.570000</td>\n",
       "      <td>85.862620</td>\n",
       "      <td>94.052722</td>\n",
       "      <td>162.602306</td>\n",
       "      <td>99.560256</td>\n",
       "      <td>116.212560</td>\n",
       "      <td>52.142660</td>\n",
       "      <td>158.790000</td>\n",
       "      <td>561.877173</td>\n",
       "      <td>217.277419</td>\n",
       "      <td>59.728245</td>\n",
       "      <td>189.288071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2023_09</td>\n",
       "      <td>2064.96</td>\n",
       "      <td>99.2</td>\n",
       "      <td>859.3940</td>\n",
       "      <td>6700.66</td>\n",
       "      <td>5239575.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>160.180000</td>\n",
       "      <td>13356.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>2223.0</td>\n",
       "      <td>1082.104</td>\n",
       "      <td>2023_09</td>\n",
       "      <td>2064.960000</td>\n",
       "      <td>79.233227</td>\n",
       "      <td>92.702412</td>\n",
       "      <td>163.025157</td>\n",
       "      <td>87.268871</td>\n",
       "      <td>116.212560</td>\n",
       "      <td>52.142660</td>\n",
       "      <td>160.180000</td>\n",
       "      <td>773.812283</td>\n",
       "      <td>184.916129</td>\n",
       "      <td>64.267129</td>\n",
       "      <td>189.936109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2023_10</td>\n",
       "      <td>2064.69</td>\n",
       "      <td>103.4</td>\n",
       "      <td>797.3910</td>\n",
       "      <td>6716.74</td>\n",
       "      <td>5432893.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>161.380000</td>\n",
       "      <td>8217.0</td>\n",
       "      <td>14197.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>1084.242</td>\n",
       "      <td>2023_10</td>\n",
       "      <td>2064.690000</td>\n",
       "      <td>82.587859</td>\n",
       "      <td>86.014178</td>\n",
       "      <td>163.416379</td>\n",
       "      <td>90.488720</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>52.611944</td>\n",
       "      <td>161.380000</td>\n",
       "      <td>476.071842</td>\n",
       "      <td>183.187097</td>\n",
       "      <td>70.916450</td>\n",
       "      <td>190.311381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2023_11</td>\n",
       "      <td>2067.36</td>\n",
       "      <td>98.3</td>\n",
       "      <td>781.5080</td>\n",
       "      <td>6735.55</td>\n",
       "      <td>5325900.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>161.690000</td>\n",
       "      <td>11551.0</td>\n",
       "      <td>16885.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>1084.986</td>\n",
       "      <td>2023_11</td>\n",
       "      <td>2067.360000</td>\n",
       "      <td>78.514377</td>\n",
       "      <td>84.300887</td>\n",
       "      <td>163.874021</td>\n",
       "      <td>88.706675</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>52.611944</td>\n",
       "      <td>161.690000</td>\n",
       "      <td>669.235226</td>\n",
       "      <td>217.870968</td>\n",
       "      <td>62.330153</td>\n",
       "      <td>190.441971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2023_12</td>\n",
       "      <td>2069.52</td>\n",
       "      <td>84.9</td>\n",
       "      <td>727.2750</td>\n",
       "      <td>6773.27</td>\n",
       "      <td>4569779.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>163.560000</td>\n",
       "      <td>16930.0</td>\n",
       "      <td>22019.0</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>1088.312</td>\n",
       "      <td>2023_12</td>\n",
       "      <td>2069.520000</td>\n",
       "      <td>67.811502</td>\n",
       "      <td>78.450800</td>\n",
       "      <td>164.791738</td>\n",
       "      <td>76.112939</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>52.611944</td>\n",
       "      <td>163.560000</td>\n",
       "      <td>980.880649</td>\n",
       "      <td>284.116129</td>\n",
       "      <td>53.686036</td>\n",
       "      <td>191.025767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2024_01</td>\n",
       "      <td>2078.00</td>\n",
       "      <td>93.3</td>\n",
       "      <td>782.2930</td>\n",
       "      <td>6801.72</td>\n",
       "      <td>5242638.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>165.460000</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>12542.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>1091.250</td>\n",
       "      <td>2024_01</td>\n",
       "      <td>2078.000000</td>\n",
       "      <td>74.520767</td>\n",
       "      <td>84.385564</td>\n",
       "      <td>165.483918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.144737</td>\n",
       "      <td>53.716795</td>\n",
       "      <td>165.460000</td>\n",
       "      <td>450.173812</td>\n",
       "      <td>161.832258</td>\n",
       "      <td>46.487424</td>\n",
       "      <td>191.541459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2024_02</td>\n",
       "      <td>2081.52</td>\n",
       "      <td>94.7</td>\n",
       "      <td>755.7110</td>\n",
       "      <td>6858.17</td>\n",
       "      <td>5242638.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>167.990000</td>\n",
       "      <td>6341.0</td>\n",
       "      <td>12253.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>1092.685</td>\n",
       "      <td>2024_02</td>\n",
       "      <td>2081.520000</td>\n",
       "      <td>75.638978</td>\n",
       "      <td>81.518177</td>\n",
       "      <td>166.857331</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>53.716795</td>\n",
       "      <td>167.990000</td>\n",
       "      <td>367.381228</td>\n",
       "      <td>158.103226</td>\n",
       "      <td>48.886962</td>\n",
       "      <td>191.793337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2024_03</td>\n",
       "      <td>2085.47</td>\n",
       "      <td>99.9</td>\n",
       "      <td>813.1930</td>\n",
       "      <td>6869.14</td>\n",
       "      <td>5242638.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>170.494896</td>\n",
       "      <td>14482.0</td>\n",
       "      <td>17107.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>1095.738</td>\n",
       "      <td>2024_03</td>\n",
       "      <td>2085.470000</td>\n",
       "      <td>79.792332</td>\n",
       "      <td>87.718732</td>\n",
       "      <td>167.124228</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>53.716795</td>\n",
       "      <td>170.494896</td>\n",
       "      <td>839.049826</td>\n",
       "      <td>220.735484</td>\n",
       "      <td>43.480775</td>\n",
       "      <td>192.329214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2024_04</td>\n",
       "      <td>2101.68</td>\n",
       "      <td>103.0</td>\n",
       "      <td>860.0860</td>\n",
       "      <td>6895.24</td>\n",
       "      <td>5242638.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>172.125178</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>14807.0</td>\n",
       "      <td>1556.0</td>\n",
       "      <td>1101.389</td>\n",
       "      <td>2024_04</td>\n",
       "      <td>2101.680000</td>\n",
       "      <td>82.268371</td>\n",
       "      <td>92.777057</td>\n",
       "      <td>167.759233</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>56.080334</td>\n",
       "      <td>172.125178</td>\n",
       "      <td>579.548088</td>\n",
       "      <td>191.058065</td>\n",
       "      <td>44.984099</td>\n",
       "      <td>193.321105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2024_05</td>\n",
       "      <td>2115.22</td>\n",
       "      <td>103.2</td>\n",
       "      <td>866.3640</td>\n",
       "      <td>6926.96</td>\n",
       "      <td>5242638.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>173.466574</td>\n",
       "      <td>12262.0</td>\n",
       "      <td>15329.0</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>1110.887</td>\n",
       "      <td>2024_05</td>\n",
       "      <td>2115.220000</td>\n",
       "      <td>82.428115</td>\n",
       "      <td>93.454262</td>\n",
       "      <td>168.530972</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>56.080334</td>\n",
       "      <td>173.466574</td>\n",
       "      <td>710.428737</td>\n",
       "      <td>197.793548</td>\n",
       "      <td>48.164209</td>\n",
       "      <td>194.988240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2024_06</td>\n",
       "      <td>2142.08</td>\n",
       "      <td>102.5</td>\n",
       "      <td>858.4250</td>\n",
       "      <td>6941.51</td>\n",
       "      <td>5242638.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>174.947152</td>\n",
       "      <td>19937.0</td>\n",
       "      <td>17182.0</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>1118.827</td>\n",
       "      <td>2024_06</td>\n",
       "      <td>2142.080000</td>\n",
       "      <td>81.869010</td>\n",
       "      <td>92.597886</td>\n",
       "      <td>168.884969</td>\n",
       "      <td>83.056823</td>\n",
       "      <td>118.653024</td>\n",
       "      <td>56.080334</td>\n",
       "      <td>174.947152</td>\n",
       "      <td>1155.098494</td>\n",
       "      <td>221.703226</td>\n",
       "      <td>44.434808</td>\n",
       "      <td>196.381907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024_04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2090.470215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024_05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2091.470215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024_06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2097.470215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024_07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2104.470215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024_08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2098.470215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024_09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2105.470215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      anomes  CUB - R1-B  PIM-PF       IABR     IPCA       SNIC  PIB-Brasil  \\\n",
       "0    2012_01      943.91   126.9   784.3505  3633.44  5305205.0         1.7   \n",
       "1    2012_02      946.59   127.2   784.3505  3655.24  5245702.0         1.7   \n",
       "2    2012_03      954.13   139.7   784.3505  3672.42  6091824.0         1.7   \n",
       "3    2012_04      957.98   129.1   784.3505  3692.62  5353569.0         1.0   \n",
       "4    2012_05      971.04   140.0   784.3505  3706.28  6089147.0         1.0   \n",
       "5    2012_06      981.08   132.7   784.3505  3715.92  5457812.0         1.0   \n",
       "6    2012_07      994.31   139.0   784.3505  3717.03  6083429.0         2.5   \n",
       "7    2012_08      996.84   147.1   784.3505  3725.95  6568943.0         2.5   \n",
       "8    2012_09      998.34   137.5   784.3505  3738.99  5780603.0         2.5   \n",
       "9    2012_10     1001.47   148.4   784.3505  3760.30  6320059.0         2.5   \n",
       "10   2012_11     1005.16   139.1   784.3505  3780.61  5894906.0         2.5   \n",
       "11   2012_12     1006.13   123.5   784.3505  3815.39  5132434.0         2.5   \n",
       "12   2013_01     1010.85   129.6   905.8960  3836.38  5656397.0         2.7   \n",
       "13   2013_02     1013.50   120.4   874.4380  3862.84  5140655.0         2.7   \n",
       "14   2013_03     1020.59   136.6  1018.9720  3898.38  5577900.0         2.7   \n",
       "15   2013_04     1023.10   141.4  1035.1190  3924.50  6005861.0         4.0   \n",
       "16   2013_05     1041.82   140.2  1053.6420  3942.55  6065659.0         4.0   \n",
       "17   2013_06     1057.25   136.2   982.7960  3958.32  5595076.0         4.0   \n",
       "18   2013_07     1064.08   143.8  1081.3530  3958.72  6361597.0         2.8   \n",
       "19   2013_08     1067.91   148.4  1065.4640  3968.62  6491599.0         2.8   \n",
       "20   2013_09     1070.15   142.4  1034.8060  3991.24  6302304.0         2.8   \n",
       "21   2013_10     1074.50   150.6  1084.5360  4008.00  6580245.0         2.5   \n",
       "22   2013_11     1078.31   140.7   995.4620  4028.44  6061154.0         2.5   \n",
       "23   2013_12     1080.43   117.9   769.8650  4059.86  5127146.0         2.5   \n",
       "24   2014_01     1089.00   125.2   927.0460  4110.20  6003945.0         3.5   \n",
       "25   2014_02     1092.35   126.6   916.8420  4160.34  5714692.0         3.5   \n",
       "26   2014_03     1098.30   131.4   940.0440  4215.26  5719000.0         3.5   \n",
       "27   2014_04     1103.24   127.8   951.1050  4245.19  5783870.0        -0.4   \n",
       "28   2014_05     1112.90   133.1   981.7380  4276.60  6147289.0        -0.4   \n",
       "29   2014_06     1126.52   120.1   852.9890  4310.39  5504545.0        -0.4   \n",
       "30   2014_07     1137.20   133.3   974.7400  4337.11  6257320.0        -0.6   \n",
       "31   2014_08     1140.20   137.5   963.0830  4346.65  6337252.0        -0.6   \n",
       "32   2014_09     1141.72   137.2   967.4620  4370.12  6461826.0        -0.6   \n",
       "33   2014_10     1143.19   142.0  1008.2770  4405.95  6634204.0        -0.2   \n",
       "34   2014_11     1144.82   129.9   880.2820  4450.45  5884184.0        -0.2   \n",
       "35   2014_12     1145.42   110.5   721.3000  4493.17  5255052.0        -0.2   \n",
       "36   2015_01     1152.00   114.8   878.0000  4550.23  5651113.0        -1.6   \n",
       "37   2015_02     1154.51   111.9   768.0000  4591.18  4930112.0        -1.6   \n",
       "38   2015_03     1156.33   127.1   944.0000  4610.92  5858115.0        -1.6   \n",
       "39   2015_04     1165.96   116.0   945.0000  4639.05  5333471.0        -2.7   \n",
       "40   2015_05     1180.52   115.4   795.0000  4675.23  5506180.0        -2.7   \n",
       "41   2015_06     1187.98   111.3   793.0000  4691.59  5566419.0        -2.7   \n",
       "42   2015_07     1205.54   118.0   771.0000  4715.99  5856207.0        -4.3   \n",
       "43   2015_08     1206.92   118.0   808.0000  4736.74  5804633.0        -4.3   \n",
       "44   2015_09     1211.06   112.9   794.0000  4740.53  5573735.0        -4.3   \n",
       "45   2015_10     1212.59   118.6   734.0000  4752.86  5694595.0        -5.5   \n",
       "46   2015_11     1215.13   108.0   634.0000  4761.42  4935500.0        -5.5   \n",
       "47   2015_12     1217.98    88.0   512.0000  4775.70  4605542.0        -5.5   \n",
       "48   2016_01     1224.48    92.4   603.0000  4793.85  4480476.0        -5.1   \n",
       "49   2016_02     1227.90    96.9   613.0000  4809.67  4690311.0        -5.1   \n",
       "50   2016_03     1234.75   106.0   748.0000  4821.69  4988029.0        -5.1   \n",
       "51   2016_04     1239.68   102.1   706.0000  4828.44  4835997.0        -3.2   \n",
       "52   2016_05     1244.50   104.6   719.0000  4843.41  4843496.0        -3.2   \n",
       "53   2016_06     1262.87   103.2   721.0000  4832.27  4906182.0        -3.2   \n",
       "54   2016_07     1270.96   105.7   665.0000  4843.87  5276943.0        -2.5   \n",
       "55   2016_08     1276.89   109.8   680.0000  4853.07  5080381.0        -2.5   \n",
       "56   2016_09     1281.25   100.9   688.0000  4860.83  4939133.0        -2.5   \n",
       "57   2016_10     1283.56   101.1   627.0000  4881.25  4650454.0        -2.3   \n",
       "58   2016_11     1284.82    97.2   640.0000  4894.92  4742833.0        -2.3   \n",
       "59   2016_12     1289.56    82.6   559.0000  4916.46  4349727.0        -2.3   \n",
       "60   2017_01     1295.98    89.2   569.0000  4930.72  4338056.0         0.3   \n",
       "61   2017_02     1302.76    88.7   630.0000  4946.50  3891136.0         0.3   \n",
       "62   2017_03     1304.92   103.0   711.0000  4950.95  4773553.0         0.3   \n",
       "63   2017_04     1307.46    88.8   605.0000  4961.84  3996147.0         0.8   \n",
       "64   2017_05     1315.50    98.4   664.0000  4981.69  4539804.0         0.8   \n",
       "65   2017_06     1326.58    96.1   678.0000  5044.46  4450442.0         0.8   \n",
       "66   2017_07     1329.22    99.7   665.0000  5061.11  4749655.0         1.6   \n",
       "67   2017_08     1331.17   106.8   673.0000  5056.56  5049734.0         1.6   \n",
       "68   2017_09     1332.81   100.3   745.0000  5080.83  4861685.0         1.6   \n",
       "69   2017_10     1333.93   103.1   694.0000  5103.69  4555661.0         2.6   \n",
       "70   2017_11     1336.94   101.5   646.0000  5092.97  4487496.0         2.6   \n",
       "71   2017_12     1339.52    89.2   556.0000  5100.61  4009819.0         2.6   \n",
       "72   2018_01     1342.55    92.7   704.0000  4930.72  4376254.0         1.9   \n",
       "73   2018_02     1345.23    90.3   688.0000  4946.50  3826575.0         1.9   \n",
       "74   2018_03     1347.97   100.0   720.0000  4950.95  4347380.0         1.9   \n",
       "75   2018_04     1351.81    96.7   691.0000  4961.84  4382883.0         1.6   \n",
       "76   2018_05     1357.86    90.6   606.0000  4981.69  3603126.0         1.6   \n",
       "77   2018_06     1365.78   100.0   804.0000  5044.46  4986686.0         1.6   \n",
       "78   2018_07     1372.48   102.6   761.0000  5061.11  4622103.0         2.1   \n",
       "79   2018_08     1376.64   108.4   794.0000  5056.56  4928950.0         2.1   \n",
       "80   2018_09     1379.67   101.6   787.0000  5080.83  4527241.0         2.1   \n",
       "81   2018_10     1383.34   108.3   705.0000  5103.69  4795379.0         1.6   \n",
       "82   2018_11     1387.25   100.1   657.0000  5092.97  4567868.0         1.6   \n",
       "83   2018_12     1390.96    86.0   561.0000  5100.61  3978191.0         1.6   \n",
       "84   2019_01     1394.81    93.8   708.4170  5116.93  4561899.0         0.9   \n",
       "85   2019_02     1398.62    94.7   674.1070  5138.93  4079370.0         0.9   \n",
       "86   2019_03     1404.77    96.1   725.1730  5177.47  4070245.0         0.9   \n",
       "87   2019_04     1405.16    97.1   721.1710  5206.98  4406861.0         1.2   \n",
       "88   2019_05     1410.17   103.5   715.8920  5213.75  4641116.0         1.2   \n",
       "89   2019_06     1419.30    96.4   696.6390  5214.27  4229130.0         1.2   \n",
       "90   2019_07     1425.84   105.6   740.2510  5224.18  4983185.0         1.1   \n",
       "91   2019_08     1435.88   108.3   764.2490  5229.93  5065098.0         1.1   \n",
       "92   2019_09     1439.67   103.5   759.4830  5227.84  4772546.0         1.1   \n",
       "93   2019_10     1439.17   108.4   745.0250  5233.07  5211798.0         1.7   \n",
       "94   2019_11     1442.68   101.7   733.3300  5259.76  4742975.0         1.7   \n",
       "95   2019_12     1445.22    85.5   623.5230  5320.25  4029571.0         1.7   \n",
       "96   2020_01     1449.37    93.4   716.9140  5331.42  4583755.0         0.4   \n",
       "97   2020_02     1453.67    93.5   717.7750  5344.75  4133147.0         0.4   \n",
       "98   2020_03     1455.87    91.6   662.9290  5348.49  4101060.0         0.4   \n",
       "99   2020_04     1454.62    64.3   540.1200  5331.91  4163424.0       -10.1   \n",
       "100  2020_05     1456.63    82.0   637.0730  5311.65  4828333.0       -10.1   \n",
       "101  2020_06     1460.86    93.3   788.4670  5325.46  5371200.0       -10.1   \n",
       "102  2020_07     1469.84   110.2   870.4940  5344.63  5927835.0        -3.0   \n",
       "103  2020_08     1488.07   112.2   839.4480  5357.46  5805937.0        -3.0   \n",
       "104  2020_09     1511.81   114.3   819.1640  5391.75  5822407.0        -3.0   \n",
       "105  2020_10     1536.58   119.5   880.8230  5438.12  5938170.0        -0.3   \n",
       "106  2020_11     1554.97   113.6   862.6130  5486.52  5255086.0        -0.3   \n",
       "107  2020_12     1568.66   103.8   820.5140  5560.59  4668039.0        -0.3   \n",
       "108  2021_01     1594.78   105.1   875.5510  5574.49  4996877.0         1.7   \n",
       "109  2021_02     1616.76   104.2   899.4310  5622.43  4683733.0         1.7   \n",
       "110  2021_03     1646.74   113.0   995.2780  5674.72  5525415.0         1.7   \n",
       "111  2021_04     1671.71   107.3   933.2030  5692.31  5297637.0        12.4   \n",
       "112  2021_05     1707.05   110.1  1040.1170  5739.56  5536759.0        12.4   \n",
       "113  2021_06     1742.04   109.0  1019.7460  5769.98  5463352.0        12.4   \n",
       "114  2021_07     1768.21   112.5  1003.6050  5825.37  5900488.0         4.2   \n",
       "115  2021_08     1784.92   113.8   982.9650  5876.05  5863757.0         4.2   \n",
       "116  2021_09     1796.38   109.5   890.6390  5944.21  5691520.0         4.2   \n",
       "117  2021_10     1803.14   108.2   891.8280  6018.51  5403183.0         1.5   \n",
       "118  2021_11     1811.88   104.6   826.4760  6075.69  5376556.0         1.5   \n",
       "119  2021_12     1819.23    91.3   715.6370  6120.04  4784859.0         1.5   \n",
       "120  2022_01     1829.69    91.6   737.1040  6153.09  4526998.0         1.5   \n",
       "121  2022_02     1840.60    94.0   770.7420  6215.24  4775815.0         1.5   \n",
       "122  2022_03     1857.41   103.6   891.9100  6315.93  5528277.0         1.5   \n",
       "123  2022_04     1872.69    98.2   855.8490  6382.88  5180364.0         3.5   \n",
       "124  2022_05     1918.74   105.7   897.5880  6412.88  5503543.0         3.5   \n",
       "125  2022_06     1946.13   101.9   815.8450  6455.85  5195951.0         3.5   \n",
       "126  2022_07     1981.40   104.9   828.7450  6411.95  5512023.0         4.3   \n",
       "127  2022_08     1982.31   109.9   901.4080  6388.87  5879086.0         4.3   \n",
       "128  2022_09     1989.14   103.0   853.4480  6370.34  5488595.0         4.3   \n",
       "129  2022_10     1993.78   105.5   807.0890  6407.93  5378454.0         2.7   \n",
       "130  2022_11     1995.16    98.4   781.2600  6434.20  5330078.0         2.7   \n",
       "131  2022_12     1999.72    83.3   617.0690  6474.09  4469361.0         2.7   \n",
       "132  2023_01     2009.66    89.0   744.7220  6508.40  4874219.0         4.2   \n",
       "133  2023_02     2015.32    89.6   690.2860  6563.07  4441263.0         4.2   \n",
       "134  2023_03     2019.63   103.3   920.1620  6609.67  5443016.0         4.2   \n",
       "135  2023_04     2022.59    92.9   786.4080  6649.99  4588598.0         3.5   \n",
       "136  2023_05     2039.80   103.3   803.6790  6665.28  5573757.0         3.5   \n",
       "137  2023_06     2047.16   100.1   776.0190  6659.95  5261921.0         3.5   \n",
       "138  2023_07     2056.98   103.3   804.5690  6667.94  5481386.0         2.0   \n",
       "139  2023_08     2057.57   107.5   871.9120  6683.28  5977543.0         2.0   \n",
       "140  2023_09     2064.96    99.2   859.3940  6700.66  5239575.0         2.0   \n",
       "141  2023_10     2064.69   103.4   797.3910  6716.74  5432893.0         2.1   \n",
       "142  2023_11     2067.36    98.3   781.5080  6735.55  5325900.0         2.1   \n",
       "143  2023_12     2069.52    84.9   727.2750  6773.27  4569779.0         2.1   \n",
       "144  2024_01     2078.00    93.3   782.2930  6801.72  5242638.5         2.5   \n",
       "145  2024_02     2081.52    94.7   755.7110  6858.17  5242638.5         2.5   \n",
       "146  2024_03     2085.47    99.9   813.1930  6869.14  5242638.5         2.5   \n",
       "147  2024_04     2101.68   103.0   860.0860  6895.24  5242638.5         3.3   \n",
       "148  2024_05     2115.22   103.2   866.3640  6926.96  5242638.5         3.3   \n",
       "149  2024_06     2142.08   102.5   858.4250  6941.51  5242638.5         3.3   \n",
       "0    2024_04         NaN     NaN        NaN      NaN        NaN         NaN   \n",
       "1    2024_05         NaN     NaN        NaN      NaN        NaN         NaN   \n",
       "2    2024_06         NaN     NaN        NaN      NaN        NaN         NaN   \n",
       "3    2024_07         NaN     NaN        NaN      NaN        NaN         NaN   \n",
       "4    2024_08         NaN     NaN        NaN      NaN        NaN         NaN   \n",
       "5    2024_09         NaN     NaN        NaN      NaN        NaN         NaN   \n",
       "\n",
       "     PIB-CC        IGMR  Unidades lançadas (ABRAINC)  \\\n",
       "0       8.7  101.100000                       7866.5   \n",
       "1       8.7  101.100000                       7866.5   \n",
       "2       8.7  101.100000                       7866.5   \n",
       "3       1.8  101.100000                       7866.5   \n",
       "4       1.8  101.100000                       7866.5   \n",
       "5       1.8  101.100000                       7866.5   \n",
       "6       2.9  101.100000                       7866.5   \n",
       "7       2.9  101.100000                       7866.5   \n",
       "8       2.9  101.100000                       7866.5   \n",
       "9      -0.1  101.100000                       7866.5   \n",
       "10     -0.1  101.100000                       7866.5   \n",
       "11     -0.1  101.100000                       7866.5   \n",
       "12      1.2  101.100000                       7866.5   \n",
       "13      1.2  101.100000                       7866.5   \n",
       "14      1.2  101.100000                       7866.5   \n",
       "15      7.9  101.100000                       7866.5   \n",
       "16      7.9  101.100000                       7866.5   \n",
       "17      7.9  101.100000                       7866.5   \n",
       "18      5.5  101.100000                       7866.5   \n",
       "19      5.5  101.100000                       7866.5   \n",
       "20      5.5  101.100000                       7866.5   \n",
       "21      3.2  101.100000                       7866.5   \n",
       "22      3.2  101.100000                       7866.5   \n",
       "23      3.2  101.100000                       7866.5   \n",
       "24      8.2  100.000000                       1726.0   \n",
       "25      8.2  100.130000                       3263.0   \n",
       "26      8.2  100.420000                      12064.0   \n",
       "27     -2.9  100.340000                       1605.0   \n",
       "28     -2.9  100.820000                       6898.0   \n",
       "29     -2.9  100.800000                       7882.0   \n",
       "30     -9.0  100.850000                       1449.0   \n",
       "31     -9.0  100.740000                       2887.0   \n",
       "32     -9.0  101.020000                       8084.0   \n",
       "33     -3.9  101.140000                       2515.0   \n",
       "34     -3.9  101.070000                       5877.0   \n",
       "35     -3.9  101.090000                      15037.0   \n",
       "36     -9.7  101.130000                       2081.0   \n",
       "37     -9.7  101.000000                       2217.0   \n",
       "38     -9.7  101.170000                       7826.0   \n",
       "39    -10.7  101.110000                       3701.0   \n",
       "40    -10.7  101.040000                       1779.0   \n",
       "41    -10.7  101.180000                      10358.0   \n",
       "42     -6.8  101.270000                        987.0   \n",
       "43     -6.8  101.120000                       2211.0   \n",
       "44     -6.8  100.970000                       8782.0   \n",
       "45     -8.7  100.970000                       3146.0   \n",
       "46     -8.7  101.030000                       4906.0   \n",
       "47     -8.7  100.890000                      10706.0   \n",
       "48     -9.2  100.670000                       1787.0   \n",
       "49     -9.2  100.630000                       2839.0   \n",
       "50     -9.2  100.310000                       9300.0   \n",
       "51     -8.2  100.220000                       1559.0   \n",
       "52     -8.2   99.870000                       4607.0   \n",
       "53     -8.2   99.630000                       9673.0   \n",
       "54    -10.0   99.330000                       2530.0   \n",
       "55    -10.0   99.220000                       4570.0   \n",
       "56    -10.0   98.720000                       6756.0   \n",
       "57    -12.5   98.530000                       3447.0   \n",
       "58    -12.5   98.530000                       8730.0   \n",
       "59    -12.5   98.610000                      11971.0   \n",
       "60    -12.0   98.430000                       2150.0   \n",
       "61    -12.0   98.420000                       1897.0   \n",
       "62    -12.0   98.450000                       7841.0   \n",
       "63    -11.4   98.400000                       3053.0   \n",
       "64    -11.4   98.140000                       5516.0   \n",
       "65    -11.4   98.050000                       9283.0   \n",
       "66     -9.5   97.860000                       5953.0   \n",
       "67     -9.5   97.770000                       4185.0   \n",
       "68     -9.5   97.700000                       9296.0   \n",
       "69     -3.7   97.680000                       6452.0   \n",
       "70     -3.7   97.780000                       9331.0   \n",
       "71     -3.7   98.020000                      12681.0   \n",
       "72     -5.4   98.050000                       3426.0   \n",
       "73     -5.4   98.040000                       3116.0   \n",
       "74     -5.4   98.100000                       7704.0   \n",
       "75     -3.0   98.110000                       4023.0   \n",
       "76     -3.0   98.120000                       9010.0   \n",
       "77     -3.0   98.200000                      12562.0   \n",
       "78     -0.9   98.160000                       5523.0   \n",
       "79     -0.9   98.190000                       6015.0   \n",
       "80     -0.9   98.290000                      16103.0   \n",
       "81     -2.6   98.370000                       9807.0   \n",
       "82     -2.6   98.550000                       7683.0   \n",
       "83     -2.6   98.650000                      16855.0   \n",
       "84     -1.6   98.660000                       2385.0   \n",
       "85     -1.6   98.700000                       2516.0   \n",
       "86     -1.6   98.910000                      12396.0   \n",
       "87      2.7   99.180000                       2295.0   \n",
       "88      2.7   99.470000                       7851.0   \n",
       "89      2.7   99.820000                      16545.0   \n",
       "90      4.8  100.110000                       3174.0   \n",
       "91      4.8  100.480000                       7389.0   \n",
       "92      4.8  100.800000                      15932.0   \n",
       "93      1.7  101.070000                       6878.0   \n",
       "94      1.7  101.620000                      10414.0   \n",
       "95      1.7  102.700000                      17189.0   \n",
       "96      1.1  103.760000                       2206.0   \n",
       "97      1.1  104.710000                       3009.0   \n",
       "98      1.1  106.230000                       9360.0   \n",
       "99     -9.5  108.120000                       3525.0   \n",
       "100    -9.5  109.260000                       1379.0   \n",
       "101    -9.5  109.870000                      12941.0   \n",
       "102    -1.9  110.190000                       5137.0   \n",
       "103    -1.9  110.650000                       8178.0   \n",
       "104    -1.9  111.280000                      17142.0   \n",
       "105     2.0  111.770000                      11483.0   \n",
       "106     2.0  112.330000                      11296.0   \n",
       "107     2.0  113.250000                      18867.0   \n",
       "108     9.5  113.720000                       4736.0   \n",
       "109     9.5  114.320000                       6550.0   \n",
       "110     9.5  115.210000                      13061.0   \n",
       "111    19.1  116.620000                       6543.0   \n",
       "112    19.1  118.170000                       9575.0   \n",
       "113    19.1  120.100000                      17012.0   \n",
       "114    13.0  122.330000                       6963.0   \n",
       "115    13.0  124.460000                       8207.0   \n",
       "116    13.0  126.650000                      15153.0   \n",
       "117     9.4  128.600000                      11590.0   \n",
       "118     9.4  130.040000                      13955.0   \n",
       "119     9.4  131.660000                      27359.0   \n",
       "120     7.3  132.790000                      10731.0   \n",
       "121     7.3  133.880000                       6020.0   \n",
       "122     7.3  135.290000                       9655.0   \n",
       "123     8.9  136.750000                       5343.0   \n",
       "124     8.9  138.030000                       8014.0   \n",
       "125     8.9  139.810000                      17649.0   \n",
       "126     6.4  141.430000                       3952.0   \n",
       "127     6.4  143.300000                       8586.0   \n",
       "128     6.4  145.240000                      13682.0   \n",
       "129     5.0  147.750000                       9132.0   \n",
       "130     5.0  149.900000                      14887.0   \n",
       "131     5.0  151.490000                      12788.0   \n",
       "132     1.5  152.790000                       3153.0   \n",
       "133     1.5  153.720000                       6142.0   \n",
       "134     1.5  154.590000                      10571.0   \n",
       "135     0.5  155.310000                       9937.0   \n",
       "136     0.5  155.950000                       8890.0   \n",
       "137     0.5  156.690000                      14696.0   \n",
       "138    -4.5  157.680000                       8808.0   \n",
       "139    -4.5  158.790000                       9698.0   \n",
       "140    -4.5  160.180000                      13356.0   \n",
       "141     0.9  161.380000                       8217.0   \n",
       "142     0.9  161.690000                      11551.0   \n",
       "143     0.9  163.560000                      16930.0   \n",
       "144     2.1  165.460000                       7770.0   \n",
       "145     2.1  167.990000                       6341.0   \n",
       "146     2.1  170.494896                      14482.0   \n",
       "147     4.4  172.125178                      10003.0   \n",
       "148     4.4  173.466574                      12262.0   \n",
       "149     4.4  174.947152                      19937.0   \n",
       "0       NaN         NaN                          NaN   \n",
       "1       NaN         NaN                          NaN   \n",
       "2       NaN         NaN                          NaN   \n",
       "3       NaN         NaN                          NaN   \n",
       "4       NaN         NaN                          NaN   \n",
       "5       NaN         NaN                          NaN   \n",
       "\n",
       "     Unidades vendidas (ABRAINC)  Unidades distratadas (ABRAINC)   incc_di  \\\n",
       "0                         9190.0                          1798.5   492.106   \n",
       "1                         9190.0                          1798.5   493.584   \n",
       "2                         9190.0                          1798.5   496.079   \n",
       "3                         9190.0                          1798.5   499.791   \n",
       "4                         9190.0                          1798.5   509.184   \n",
       "5                         9190.0                          1798.5   512.903   \n",
       "6                         9190.0                          1798.5   516.318   \n",
       "7                         9190.0                          1798.5   517.657   \n",
       "8                         9190.0                          1798.5   518.816   \n",
       "9                         9190.0                          1798.5   519.907   \n",
       "10                        9190.0                          1798.5   521.638   \n",
       "11                        9190.0                          1798.5   522.474   \n",
       "12                        9190.0                          1798.5   525.850   \n",
       "13                        9190.0                          1798.5   529.029   \n",
       "14                        9190.0                          1798.5   531.691   \n",
       "15                        9190.0                          1798.5   535.601   \n",
       "16                        9190.0                          1798.5   547.655   \n",
       "17                        9190.0                          1798.5   553.948   \n",
       "18                        9190.0                          1798.5   556.600   \n",
       "19                        9190.0                          1798.5   558.340   \n",
       "20                        9190.0                          1798.5   560.767   \n",
       "21                        9190.0                          1798.5   562.241   \n",
       "22                        9190.0                          1798.5   564.201   \n",
       "23                        9190.0                          1798.5   564.765   \n",
       "24                        7750.0                          3459.0   569.720   \n",
       "25                        8598.0                          2899.0   571.577   \n",
       "26                       10488.0                          2889.0   573.156   \n",
       "27                        8711.0                          3806.0   578.224   \n",
       "28                       11305.0                          4613.0   590.099   \n",
       "29                        9199.0                          2639.0   594.013   \n",
       "30                        8174.0                          3785.0   598.441   \n",
       "31                        8135.0                          3565.0   598.898   \n",
       "32                        8395.0                          2987.0   599.823   \n",
       "33                        8162.0                          3133.0   600.865   \n",
       "34                        7800.0                          3300.0   603.524   \n",
       "35                       10042.0                          3015.0   604.026   \n",
       "36                        6289.0                          3563.0   609.568   \n",
       "37                        6966.0                          2713.0   611.447   \n",
       "38                        9304.0                          3161.0   615.248   \n",
       "39                        7069.0                          3329.0   618.060   \n",
       "40                        7544.0                          2896.0   623.951   \n",
       "41                        9363.0                          3081.0   635.403   \n",
       "42                        7424.0                          4278.0   638.880   \n",
       "43                        6977.0                          2930.0   642.644   \n",
       "44                        7207.0                          3129.0   644.046   \n",
       "45                        6264.0                          3235.0   646.355   \n",
       "46                        7037.0                          2898.0   648.542   \n",
       "47                        7660.0                          2392.0   649.216   \n",
       "48                        4748.0                          2548.0   651.759   \n",
       "49                        6004.0                          2225.0   655.263   \n",
       "50                        8912.0                          3096.0   659.446   \n",
       "51                        5990.0                          3014.0   663.057   \n",
       "52                        7051.0                          2661.0   663.610   \n",
       "53                        8449.0                          2826.0   676.420   \n",
       "54                        6886.0                          3196.0   679.751   \n",
       "55                        7433.0                          2754.0   681.756   \n",
       "56                        7568.0                          2724.0   684.025   \n",
       "57                        6245.0                          2680.0   685.489   \n",
       "58                        8710.0                          2442.0   686.607   \n",
       "59                        8327.0                          2207.0   688.985   \n",
       "60                        6345.0                          1928.0   691.792   \n",
       "61                        5770.0                          2062.0   696.314   \n",
       "62                        8359.0                          2465.0   697.410   \n",
       "63                        6831.0                          2131.0   697.244   \n",
       "64                        8518.0                          2225.0   701.664   \n",
       "65                        8494.0                          2010.0   708.197   \n",
       "66                        6929.0                          2059.0   710.355   \n",
       "67                        8865.0                          1800.0   712.884   \n",
       "68                        8773.0                          2353.0   713.330   \n",
       "69                        8735.0                          2302.0   715.527   \n",
       "70                        9399.0                          1841.0   717.751   \n",
       "71                        9801.0                          1861.0   718.276   \n",
       "72                        7897.0                          1625.0   720.495   \n",
       "73                        6974.0                          1589.0   721.414   \n",
       "74                        9068.0                          2158.0   723.163   \n",
       "75                        7991.0                          1933.0   725.245   \n",
       "76                        9946.0                          1754.0   726.923   \n",
       "77                        8550.0                          1578.0   733.984   \n",
       "78                        7884.0                          2224.0   738.487   \n",
       "79                        9358.0                          1987.0   739.583   \n",
       "80                        8456.0                          1407.0   741.305   \n",
       "81                        9630.0                          1718.0   743.866   \n",
       "82                       10613.0                          1450.0   744.865   \n",
       "83                        9750.0                           984.0   745.856   \n",
       "84                        5805.0                          1081.0   749.517   \n",
       "85                        7837.0                          1174.0   750.180   \n",
       "86                       10570.0                          1254.0   752.524   \n",
       "87                        7499.0                          1342.0   755.373   \n",
       "88                        9156.0                          1374.0   755.625   \n",
       "89                        9274.0                          1141.0   762.304   \n",
       "90                        7613.0                          1391.0   766.699   \n",
       "91                        7517.0                          1176.0   769.951   \n",
       "92                       10531.0                           974.0   773.520   \n",
       "93                        7451.0                          1216.0   774.939   \n",
       "94                       10587.0                          1169.0   775.225   \n",
       "95                       10091.0                           916.0   776.839   \n",
       "96                        8079.0                          1119.0   779.766   \n",
       "97                        9025.0                          1095.0   782.336   \n",
       "98                        9707.0                          1232.0   784.338   \n",
       "99                        8020.0                          1191.0   786.070   \n",
       "100                       9181.0                          1569.0   787.666   \n",
       "101                      11483.0                          1794.0   790.331   \n",
       "102                      12229.0                          2247.0   799.589   \n",
       "103                      12169.0                          1701.0   805.356   \n",
       "104                      12437.0                          1026.0   814.701   \n",
       "105                      12284.0                          1423.0   828.778   \n",
       "106                      11812.0                          1254.0   839.382   \n",
       "107                      12855.0                          1034.0   845.268   \n",
       "108                       8458.0                          1334.0   852.809   \n",
       "109                      10745.0                          1275.0   868.929   \n",
       "110                      13219.0                          1584.0   880.265   \n",
       "111                      10602.0                          1529.0   888.191   \n",
       "112                      11797.0                          1519.0   907.899   \n",
       "113                      14024.0                          1290.0   927.512   \n",
       "114                       9923.0                          1797.0   935.359   \n",
       "115                      10530.0                          1422.0   939.699   \n",
       "116                      10464.0                          1145.0   944.520   \n",
       "117                      10071.0                          1387.0   952.596   \n",
       "118                       9973.0                          1425.0   959.001   \n",
       "119                      11829.0                           921.0   962.321   \n",
       "120                       8386.0                          1307.0   969.184   \n",
       "121                       8974.0                          1472.0   972.904   \n",
       "122                      12672.0                          1551.0   981.244   \n",
       "123                      11161.0                          2115.0   990.543   \n",
       "124                      11609.0                          1976.0  1013.164   \n",
       "125                       9997.0                          1280.0  1034.824   \n",
       "126                       8740.0                          1250.0  1043.760   \n",
       "127                      10807.0                          1423.0  1044.679   \n",
       "128                      10282.0                          1117.0  1045.616   \n",
       "129                      10036.0                          1103.0  1046.896   \n",
       "130                       9422.0                          1122.0  1050.701   \n",
       "131                      10923.0                           920.0  1051.632   \n",
       "132                       8105.0                          1241.0  1056.418   \n",
       "133                       9725.0                          2577.0  1056.896   \n",
       "134                      13169.0                          1065.0  1060.116   \n",
       "135                      12343.0                          1307.0  1061.635   \n",
       "136                      12867.0                          1851.0  1067.919   \n",
       "137                      13032.0                          1409.0  1075.540   \n",
       "138                      13599.0                          2021.0  1076.626   \n",
       "139                      16839.0                          2066.0  1078.412   \n",
       "140                      14331.0                          2223.0  1082.104   \n",
       "141                      14197.0                          2453.0  1084.242   \n",
       "142                      16885.0                          2156.0  1084.986   \n",
       "143                      22019.0                          1857.0  1088.312   \n",
       "144                      12542.0                          1608.0  1091.250   \n",
       "145                      12253.0                          1691.0  1092.685   \n",
       "146                      17107.0                          1504.0  1095.738   \n",
       "147                      14807.0                          1556.0  1101.389   \n",
       "148                      15329.0                          1666.0  1110.887   \n",
       "149                      17182.0                          1537.0  1118.827   \n",
       "0                            NaN                             NaN       NaN   \n",
       "1                            NaN                             NaN       NaN   \n",
       "2                            NaN                             NaN       NaN   \n",
       "3                            NaN                             NaN       NaN   \n",
       "4                            NaN                             NaN       NaN   \n",
       "5                            NaN                             NaN       NaN   \n",
       "\n",
       "     ano_mes          cub  base100_pim  base100_iabr  base100_ipca  \\\n",
       "0    2012_01   943.910000    82.428115     83.153479    126.325361   \n",
       "1    2012_02   946.590000    82.428115     83.153479    126.325361   \n",
       "2    2012_03   954.130000    82.428115     83.153479    126.325361   \n",
       "3    2012_04   957.980000    82.428115     83.153479    126.325361   \n",
       "4    2012_05   971.040000    82.428115     83.153479    126.325361   \n",
       "5    2012_06   981.080000    82.428115     83.153479    126.325361   \n",
       "6    2012_07   994.310000    82.428115     83.153479    126.325361   \n",
       "7    2012_08   996.840000    82.428115     83.153479    126.325361   \n",
       "8    2012_09   998.340000    82.428115     83.153479    126.325361   \n",
       "9    2012_10  1001.470000    82.428115     83.153479    126.325361   \n",
       "10   2012_11  1005.160000    82.428115     83.153479    126.325361   \n",
       "11   2012_12  1006.130000    82.428115     83.153479    126.325361   \n",
       "12   2013_01  1010.850000    82.428115     83.153479    126.325361   \n",
       "13   2013_02  1013.500000    82.428115     83.153479    126.325361   \n",
       "14   2013_03  1020.590000    82.428115     83.153479    126.325361   \n",
       "15   2013_04  1023.100000    82.428115     83.153479    126.325361   \n",
       "16   2013_05  1041.820000    82.428115     83.153479    126.325361   \n",
       "17   2013_06  1057.250000    82.428115     83.153479    126.325361   \n",
       "18   2013_07  1064.080000    82.428115     83.153479    126.325361   \n",
       "19   2013_08  1067.910000    82.428115     83.153479    126.325361   \n",
       "20   2013_09  1070.150000    82.428115     83.153479    126.325361   \n",
       "21   2013_10  1074.500000    82.428115     83.153479    126.325361   \n",
       "22   2013_11  1078.310000    82.428115     83.153479    126.325361   \n",
       "23   2013_12  1080.430000    82.428115     83.153479    126.325361   \n",
       "24   2014_01  1089.000000   100.000000    100.000000    100.000000   \n",
       "25   2014_02  1092.350000   101.118211     98.899299    101.219892   \n",
       "26   2014_03  1098.300000   104.952077    101.402088    102.556080   \n",
       "27   2014_04  1103.240000   102.076677    102.595233    103.284268   \n",
       "28   2014_05  1112.900000   106.309904    105.899599    104.048465   \n",
       "29   2014_06  1126.520000    95.926518     92.011508    104.870566   \n",
       "30   2014_07  1137.200000   106.469649    105.144729    105.520656   \n",
       "31   2014_08  1140.200000   109.824281    103.887294    105.752761   \n",
       "32   2014_09  1141.720000   109.584665    104.359654    106.323780   \n",
       "33   2014_10  1143.190000   113.418530    108.762348    107.195514   \n",
       "34   2014_11  1144.820000   103.753994     94.955590    108.278186   \n",
       "35   2014_12  1145.420000    88.258786     77.806279    109.317551   \n",
       "36   2015_01  1152.000000    91.693291     94.709432    110.705805   \n",
       "37   2015_02  1154.510000    89.376997     82.843786    111.702107   \n",
       "38   2015_03  1156.330000   101.517572    101.828820    112.182376   \n",
       "39   2015_04  1165.960000    92.651757    101.936689    112.866770   \n",
       "40   2015_05  1180.520000    92.172524     85.756262    113.747020   \n",
       "41   2015_06  1187.980000    88.897764     85.540523    114.145054   \n",
       "42   2015_07  1205.540000    94.249201     83.167394    114.738699   \n",
       "43   2015_08  1206.920000    94.249201     87.158566    115.243540   \n",
       "44   2015_09  1211.060000    90.175719     85.648393    115.335750   \n",
       "45   2015_10  1212.590000    94.728435     79.176222    115.635735   \n",
       "46   2015_11  1215.130000    86.261981     68.389271    115.843998   \n",
       "47   2015_12  1217.980000    70.287540     55.229190    116.191426   \n",
       "48   2016_01  1224.480000    73.801917     65.045316    116.633011   \n",
       "49   2016_02  1227.900000    77.396166     66.124011    117.017907   \n",
       "50   2016_03  1234.750000    84.664537     80.686395    117.310350   \n",
       "51   2016_04  1239.680000    81.549521     76.155876    117.474575   \n",
       "52   2016_05  1244.500000    83.546326     77.558179    117.838791   \n",
       "53   2016_06  1262.870000    82.428115     77.773918    117.567758   \n",
       "54   2016_07  1270.960000    84.424920     71.733226    117.849983   \n",
       "55   2016_08  1276.890000    87.699681     73.351268    118.073816   \n",
       "56   2016_09  1281.250000    80.591054     74.214225    118.262615   \n",
       "57   2016_10  1283.560000    80.750799     67.634184    118.759428   \n",
       "58   2016_11  1284.820000    77.635783     69.036488    119.092015   \n",
       "59   2016_12  1289.560000    65.974441     60.299057    119.616077   \n",
       "60   2017_01  1295.980000    71.246006     61.377753    119.963019   \n",
       "61   2017_02  1302.760000    70.846645     67.957793    120.346942   \n",
       "62   2017_03  1304.920000    82.268371     76.695223    120.455209   \n",
       "63   2017_04  1307.460000    70.926518     65.261055    120.720160   \n",
       "64   2017_05  1315.500000    78.594249     71.625356    121.203104   \n",
       "65   2017_06  1326.580000    76.757188     73.135529    122.730281   \n",
       "66   2017_07  1329.220000    79.632588     71.733226    123.135371   \n",
       "67   2017_08  1331.170000    85.303514     72.596182    123.024670   \n",
       "68   2017_09  1332.810000    80.111821     80.362787    123.615153   \n",
       "69   2017_10  1333.930000    82.348243     74.861442    124.171330   \n",
       "70   2017_11  1336.940000    81.070288     69.683705    123.910515   \n",
       "71   2017_12  1339.520000    71.246006     59.975449    124.096394   \n",
       "72   2018_01  1342.550000    74.041534     75.940137    119.963019   \n",
       "73   2018_02  1345.230000    72.124601     74.214225    120.346942   \n",
       "74   2018_03  1347.970000    79.872204     77.666049    120.455209   \n",
       "75   2018_04  1351.810000    77.236422     74.537833    120.720160   \n",
       "76   2018_05  1357.860000    72.364217     65.368925    121.203104   \n",
       "77   2018_06  1365.780000    79.872204     86.727088    122.730281   \n",
       "78   2018_07  1372.480000    81.948882     82.088699    123.135371   \n",
       "79   2018_08  1376.640000    86.581470     85.648393    123.024670   \n",
       "80   2018_09  1379.670000    81.150160     84.893306    123.615153   \n",
       "81   2018_10  1383.340000    86.501597     76.048006    124.171330   \n",
       "82   2018_11  1387.250000    79.952077     70.870270    123.910515   \n",
       "83   2018_12  1390.960000    68.690096     60.514796    124.096394   \n",
       "84   2019_01  1394.810000    74.920128     76.416596    124.493455   \n",
       "85   2019_02  1398.620000    75.638978     72.715593    125.028709   \n",
       "86   2019_03  1404.770000    76.757188     78.224058    125.966376   \n",
       "87   2019_04  1405.160000    77.555911     77.792364    126.684346   \n",
       "88   2019_05  1410.170000    82.667732     77.222921    126.849058   \n",
       "89   2019_06  1419.300000    76.996805     75.146109    126.861710   \n",
       "90   2019_07  1425.840000    84.345048     79.850514    127.102817   \n",
       "91   2019_08  1435.880000    86.501597     82.439167    127.242713   \n",
       "92   2019_09  1439.670000    82.667732     81.925061    127.191864   \n",
       "93   2019_10  1439.170000    86.581470     80.365483    127.319109   \n",
       "94   2019_11  1442.680000    81.230032     79.103950    127.968469   \n",
       "95   2019_12  1445.220000    68.290735     67.259122    129.440173   \n",
       "96   2020_01  1449.370000    74.600639     77.333164    129.711936   \n",
       "97   2020_02  1453.670000    74.680511     77.426039    130.036251   \n",
       "98   2020_03  1455.870000    73.162939     71.509828    130.127244   \n",
       "99   2020_04  1454.620000    51.357827     58.262481    129.723858   \n",
       "100  2020_05  1456.630000    65.495208     68.720754    129.230938   \n",
       "101  2020_06  1460.860000    74.520767     85.051551    129.566931   \n",
       "102  2020_07  1469.840000    88.019169     93.899763    130.033332   \n",
       "103  2020_08  1488.070000    89.616613     90.550846    130.345482   \n",
       "104  2020_09  1511.810000    91.293930     88.362821    131.179748   \n",
       "105  2020_10  1536.580000    95.447284     95.013948    132.307917   \n",
       "106  2020_11  1554.970000    90.734824     93.049644    133.485475   \n",
       "107  2020_12  1568.660000    82.907348     88.508445    135.287577   \n",
       "108  2021_01  1594.780000    83.945687     94.445259    135.625760   \n",
       "109  2021_02  1616.760000    83.226837     97.021183    136.792127   \n",
       "110  2021_03  1646.740000    90.255591    107.360153    138.064328   \n",
       "111  2021_04  1671.710000    85.702875    100.664153    138.492287   \n",
       "112  2021_05  1707.050000    87.939297    112.196914    139.641867   \n",
       "113  2021_06  1742.040000    87.060703    109.999504    140.381977   \n",
       "114  2021_07  1768.210000    89.856230    108.258382    141.729600   \n",
       "115  2021_08  1784.920000    90.894569    106.031955    142.962630   \n",
       "116  2021_09  1796.380000    87.460064     96.072795    144.620943   \n",
       "117  2021_10  1803.140000    86.421725     96.201052    146.428641   \n",
       "118  2021_11  1811.880000    83.546326     89.151563    147.819814   \n",
       "119  2021_12  1819.230000    72.923323     77.195414    148.898837   \n",
       "120  2022_01  1829.690000    73.162939     79.511049    149.702934   \n",
       "121  2022_02  1840.600000    75.079872     83.139564    151.215026   \n",
       "122  2022_03  1857.410000    82.747604     96.209897    153.664785   \n",
       "123  2022_04  1872.690000    78.434505     92.320014    155.293660   \n",
       "124  2022_05  1918.740000    84.424920     96.822380    156.023551   \n",
       "125  2022_06  1946.130000    81.389776     88.004802    157.068999   \n",
       "126  2022_07  1981.400000    83.785942     89.396319    156.000925   \n",
       "127  2022_08  1982.310000    87.779553     97.234441    155.439395   \n",
       "128  2022_09  1989.140000    82.268371     92.061020    154.988565   \n",
       "129  2022_10  1993.780000    84.265176     87.060297    155.903119   \n",
       "130  2022_11  1995.160000    78.594249     84.274135    156.542261   \n",
       "131  2022_12  1999.720000    66.533546     66.562932    157.512773   \n",
       "132  2023_01  2009.660000    71.086262     80.332799    158.347526   \n",
       "133  2023_02  2015.320000    71.565495     74.460814    159.677631   \n",
       "134  2023_03  2019.630000    82.507987     99.257426    160.811396   \n",
       "135  2023_04  2022.590000    74.201278     84.829448    161.792370   \n",
       "136  2023_05  2039.800000    82.507987     86.692462    162.164372   \n",
       "137  2023_06  2047.160000    79.952077     83.708791    162.034694   \n",
       "138  2023_07  2056.980000    82.507987     86.788466    162.229089   \n",
       "139  2023_08  2057.570000    85.862620     94.052722    162.602306   \n",
       "140  2023_09  2064.960000    79.233227     92.702412    163.025157   \n",
       "141  2023_10  2064.690000    82.587859     86.014178    163.416379   \n",
       "142  2023_11  2067.360000    78.514377     84.300887    163.874021   \n",
       "143  2023_12  2069.520000    67.811502     78.450800    164.791738   \n",
       "144  2024_01  2078.000000    74.520767     84.385564    165.483918   \n",
       "145  2024_02  2081.520000    75.638978     81.518177    166.857331   \n",
       "146  2024_03  2085.470000    79.792332     87.718732    167.124228   \n",
       "147  2024_04  2101.680000    82.268371     92.777057    167.759233   \n",
       "148  2024_05  2115.220000    82.428115     93.454262    168.530972   \n",
       "149  2024_06  2142.080000    81.869010     92.597886    168.884969   \n",
       "0        NaN  2090.470215          NaN           NaN           NaN   \n",
       "1        NaN  2091.470215          NaN           NaN           NaN   \n",
       "2        NaN  2097.470215          NaN           NaN           NaN   \n",
       "3        NaN  2104.470215          NaN           NaN           NaN   \n",
       "4        NaN  2098.470215          NaN           NaN           NaN   \n",
       "5        NaN  2105.470215          NaN           NaN           NaN   \n",
       "\n",
       "     base100_snic  base100_PIB-Brasil  base100_PIB-CC  base100_IGMR  \\\n",
       "0       83.056823           88.864112       43.423424    101.100000   \n",
       "1       83.056823           88.864112       43.423424    101.100000   \n",
       "2       83.056823           88.864112       43.423424    101.100000   \n",
       "3       83.056823           88.864112       43.423424    101.100000   \n",
       "4       83.056823           88.864112       43.423424    101.100000   \n",
       "5       83.056823           88.864112       43.423424    101.100000   \n",
       "6       83.056823           88.864112       43.423424    101.100000   \n",
       "7       83.056823           88.864112       43.423424    101.100000   \n",
       "8       83.056823           88.864112       43.423424    101.100000   \n",
       "9       83.056823           88.864112       43.423424    101.100000   \n",
       "10      83.056823           88.864112       43.423424    101.100000   \n",
       "11      83.056823           88.864112       43.423424    101.100000   \n",
       "12      83.056823           88.864112       43.423424    101.100000   \n",
       "13      83.056823           88.864112       43.423424    101.100000   \n",
       "14      83.056823           88.864112       43.423424    101.100000   \n",
       "15      83.056823           88.864112       43.423424    101.100000   \n",
       "16      83.056823           88.864112       43.423424    101.100000   \n",
       "17      83.056823           88.864112       43.423424    101.100000   \n",
       "18      83.056823           88.864112       43.423424    101.100000   \n",
       "19      83.056823           88.864112       43.423424    101.100000   \n",
       "20      83.056823           88.864112       43.423424    101.100000   \n",
       "21      83.056823           88.864112       43.423424    101.100000   \n",
       "22      83.056823           88.864112       43.423424    101.100000   \n",
       "23      83.056823           88.864112       43.423424    101.100000   \n",
       "24     100.000000          100.000000      100.000000    100.000000   \n",
       "25      95.182284          100.000000      100.000000    100.130000   \n",
       "26      95.254037          100.000000      100.000000    100.420000   \n",
       "27      96.334493           99.600000       97.100000    100.340000   \n",
       "28     102.387497           99.600000       97.100000    100.820000   \n",
       "29      91.682136           99.600000       97.100000    100.800000   \n",
       "30     104.220142           99.002400       88.361000    100.850000   \n",
       "31     105.551467           99.002400       99.002400    100.740000   \n",
       "32     107.626336           99.002400       99.002400    101.020000   \n",
       "33     110.497415           98.804395       95.141306    101.140000   \n",
       "34      98.005295           98.804395       95.141306    101.070000   \n",
       "35      87.526651           98.804395       95.141306    101.090000   \n",
       "36      94.123331           97.223525       85.912600    101.130000   \n",
       "37      82.114543           97.223525       85.912600    101.000000   \n",
       "38      97.571097           97.223525       85.912600    101.170000   \n",
       "39      88.832776           94.598490       76.719952    101.110000   \n",
       "40      91.709368           94.598490       76.719952    101.040000   \n",
       "41      92.712691           94.598490       76.719952    101.180000   \n",
       "42      97.539318           90.530755       71.502995    101.270000   \n",
       "43      96.680316           90.530755       71.502995    101.120000   \n",
       "44      92.834545           90.530755       71.502995    100.970000   \n",
       "45      94.847554           85.551563       65.282234    100.970000   \n",
       "46      82.204284           85.551563       65.282234    101.030000   \n",
       "47      76.708597           85.551563       65.282234    100.890000   \n",
       "48      74.625534           81.188433       59.276269    100.670000   \n",
       "49      78.120486           81.188433       59.276269    100.630000   \n",
       "50      83.079192           81.188433       59.276269    100.310000   \n",
       "51      80.546990           78.590404       54.415615    100.220000   \n",
       "52      80.671892           78.590404       54.415615     99.870000   \n",
       "53      81.715972           78.590404       54.415615     99.630000   \n",
       "54      87.891261           76.625643       48.974053     99.330000   \n",
       "55      84.617381           76.625643       48.974053     99.220000   \n",
       "56      82.264794           76.625643       48.974053     98.720000   \n",
       "57      77.456639           74.863254       42.852297     98.530000   \n",
       "58      78.995277           74.863254       42.852297     98.530000   \n",
       "59      72.447816           74.863254       42.852297     98.610000   \n",
       "60      72.253427           75.087843       37.710021     98.430000   \n",
       "61      64.809654           75.087843       37.710021     98.420000   \n",
       "62      79.506941           75.087843       37.710021     98.450000   \n",
       "63      66.558688           75.688546       33.411079     98.400000   \n",
       "64      75.613684           75.688546       33.411079     98.140000   \n",
       "65      74.125296           75.688546       33.411079     98.050000   \n",
       "66      79.108903           76.899563       30.237026     97.860000   \n",
       "67      84.106933           76.899563       30.237026     97.770000   \n",
       "68      80.974842           76.899563       30.237026     97.700000   \n",
       "69      75.877794           78.898952       29.118256     97.680000   \n",
       "70      74.742457           78.898952       29.118256     97.780000   \n",
       "71      66.786405           78.898952       29.118256     98.020000   \n",
       "72      72.889642           80.398032       27.545870     98.050000   \n",
       "73      63.734345           80.398032       27.545870     98.040000   \n",
       "74      72.408725           80.398032       27.545870     98.100000   \n",
       "75      73.000052           81.684400       26.719494     98.110000   \n",
       "76      60.012642           81.684400       26.719494     98.120000   \n",
       "77      83.056823           81.684400       26.719494     98.200000   \n",
       "78      76.984433           83.399773       26.479019     98.160000   \n",
       "79      82.095189           83.399773       26.479019     98.190000   \n",
       "80      75.404438           83.399773       26.479019     98.290000   \n",
       "81      79.870469           84.734169       25.790564     98.370000   \n",
       "82      76.081110           84.734169       25.790564     98.550000   \n",
       "83      66.259618           84.734169       25.790564     98.650000   \n",
       "84      75.981692           85.496776       25.377915     98.660000   \n",
       "85      67.944826           85.496776       25.377915     98.700000   \n",
       "86      67.792843           85.496776       25.377915     98.910000   \n",
       "87      73.399423           86.522738       26.063119     99.180000   \n",
       "88      77.301108           86.522738       26.063119     99.470000   \n",
       "89      70.439186           86.522738       26.063119     99.820000   \n",
       "90      82.998512           87.474488       27.314149    100.110000   \n",
       "91      84.362831           87.474488       27.314149    100.480000   \n",
       "92      79.490169           87.474488       27.314149    100.800000   \n",
       "93      86.806225           88.961554       27.778489    101.070000   \n",
       "94      78.997642           88.961554       27.778489    101.620000   \n",
       "95      67.115388           88.961554       27.778489    102.700000   \n",
       "96      76.345719           89.317400       28.084053    103.760000   \n",
       "97      68.840521           89.317400       28.084053    104.710000   \n",
       "98      68.306089           89.317400       28.084053    106.230000   \n",
       "99      69.344806           80.296343       25.416068    108.120000   \n",
       "100     80.419341           80.296343       25.416068    109.260000   \n",
       "101     89.461179           80.296343       25.416068    109.870000   \n",
       "102     98.732333           77.887453       24.933162    110.190000   \n",
       "103     96.702035           77.887453       24.933162    110.650000   \n",
       "104     96.976355           77.887453       24.933162    111.280000   \n",
       "105     98.904470           77.653790       25.431826    111.770000   \n",
       "106     87.527218           77.653790       25.431826    112.330000   \n",
       "107     77.749530           77.653790       25.431826    113.250000   \n",
       "108     83.226562           78.973905       27.847849    113.720000   \n",
       "109     78.010924           78.973905       27.847849    114.320000   \n",
       "110     92.029740           78.973905       27.847849    115.210000   \n",
       "111     88.235935           88.766669       33.166788    116.620000   \n",
       "112     92.218683           88.766669       33.166788    118.170000   \n",
       "113     90.996037           88.766669       33.166788    120.100000   \n",
       "114     98.276850           92.494869       37.478471    122.330000   \n",
       "115     97.665069           92.494869       37.478471    124.460000   \n",
       "116     94.796338           92.494869       37.478471    126.650000   \n",
       "117     89.993879           93.882292       41.001447    128.600000   \n",
       "118     89.550387           93.882292       41.001447    130.040000   \n",
       "119     79.695250           93.882292       41.001447    131.660000   \n",
       "120     75.400391           95.290526       43.994552    132.790000   \n",
       "121     79.544616           95.290526       43.994552    133.880000   \n",
       "122     92.077409           95.290526       43.994552    135.290000   \n",
       "123     86.282669           98.625695       47.910068    136.750000   \n",
       "124     91.665447           98.625695       47.910068    138.030000   \n",
       "125     86.542282           98.625695       47.910068    139.810000   \n",
       "126     91.806687          102.866600       50.976312    141.430000   \n",
       "127     97.920384          102.866600       50.976312    143.300000   \n",
       "128     91.416477          102.866600       50.976312    145.240000   \n",
       "129     89.582000          105.643998       53.525128    147.750000   \n",
       "130     88.776263          105.643998       53.525128    149.900000   \n",
       "131     74.440405          105.643998       53.525128    151.490000   \n",
       "132     81.183605          110.081046       54.328004    152.790000   \n",
       "133     73.972413          110.081046       54.328004    153.720000   \n",
       "134     90.657326          110.081046       54.328004    154.590000   \n",
       "135     76.426383          113.933882       54.599644    155.310000   \n",
       "136     92.834911          113.933882       54.599644    155.950000   \n",
       "137     87.641059          113.933882       54.599644    156.690000   \n",
       "138     91.296406          116.212560       52.142660    157.680000   \n",
       "139     99.560256          116.212560       52.142660    158.790000   \n",
       "140     87.268871          116.212560       52.142660    160.180000   \n",
       "141     90.488720          118.653024       52.611944    161.380000   \n",
       "142     88.706675          118.653024       52.611944    161.690000   \n",
       "143     76.112939          118.653024       52.611944    163.560000   \n",
       "144      0.000000          121.144737       53.716795    165.460000   \n",
       "145     83.056823          118.653024       53.716795    167.990000   \n",
       "146     83.056823          118.653024       53.716795    170.494896   \n",
       "147     83.056823          118.653024       56.080334    172.125178   \n",
       "148     83.056823          118.653024       56.080334    173.466574   \n",
       "149     83.056823          118.653024       56.080334    174.947152   \n",
       "0             NaN                 NaN             NaN           NaN   \n",
       "1             NaN                 NaN             NaN           NaN   \n",
       "2             NaN                 NaN             NaN           NaN   \n",
       "3             NaN                 NaN             NaN           NaN   \n",
       "4             NaN                 NaN             NaN           NaN   \n",
       "5             NaN                 NaN             NaN           NaN   \n",
       "\n",
       "     base100_lancadas_ABRAINC  base100_vendidas_ABRAINC  \\\n",
       "0                  455.764774                118.580645   \n",
       "1                  455.764774                118.580645   \n",
       "2                  455.764774                118.580645   \n",
       "3                  455.764774                118.580645   \n",
       "4                  455.764774                118.580645   \n",
       "5                  455.764774                118.580645   \n",
       "6                  455.764774                118.580645   \n",
       "7                  455.764774                118.580645   \n",
       "8                  455.764774                118.580645   \n",
       "9                  455.764774                118.580645   \n",
       "10                 455.764774                118.580645   \n",
       "11                 455.764774                118.580645   \n",
       "12                 455.764774                118.580645   \n",
       "13                 455.764774                118.580645   \n",
       "14                 455.764774                118.580645   \n",
       "15                 455.764774                118.580645   \n",
       "16                 455.764774                118.580645   \n",
       "17                 455.764774                118.580645   \n",
       "18                 455.764774                118.580645   \n",
       "19                 455.764774                118.580645   \n",
       "20                 455.764774                118.580645   \n",
       "21                 455.764774                118.580645   \n",
       "22                 455.764774                118.580645   \n",
       "23                 455.764774                118.580645   \n",
       "24                 100.000000                100.000000   \n",
       "25                 189.049826                110.941935   \n",
       "26                 698.957126                135.329032   \n",
       "27                  92.989571                112.400000   \n",
       "28                 399.652375                145.870968   \n",
       "29                 456.662804                118.696774   \n",
       "30                  83.951333                105.470968   \n",
       "31                 167.265353                104.967742   \n",
       "32                 468.366165                108.322581   \n",
       "33                 145.712630                105.316129   \n",
       "34                 340.498262                100.645161   \n",
       "35                 871.205098                129.574194   \n",
       "36                 120.567787                 81.148387   \n",
       "37                 128.447277                 89.883871   \n",
       "38                 453.418308                120.051613   \n",
       "39                 214.426419                 91.212903   \n",
       "40                 103.070684                 97.341935   \n",
       "41                 600.115875                120.812903   \n",
       "42                  57.184241                 95.793548   \n",
       "43                 128.099652                 90.025806   \n",
       "44                 508.806489                 92.993548   \n",
       "45                 182.271147                 80.825806   \n",
       "46                 284.241020                 90.800000   \n",
       "47                 620.278100                 98.838710   \n",
       "48                 103.534183                 61.264516   \n",
       "49                 164.484357                 77.470968   \n",
       "50                 538.818076                114.993548   \n",
       "51                  90.324450                 77.290323   \n",
       "52                 266.917729                 90.980645   \n",
       "53                 560.428737                109.019355   \n",
       "54                 146.581692                 88.851613   \n",
       "55                 264.774044                 95.909677   \n",
       "56                 391.425261                 97.651613   \n",
       "57                 199.710313                 80.580645   \n",
       "58                 505.793743                112.387097   \n",
       "59                 693.568946                107.445161   \n",
       "60                 124.565469                 81.870968   \n",
       "61                 109.907300                 74.451613   \n",
       "62                 454.287370                107.858065   \n",
       "63                 176.882966                 88.141935   \n",
       "64                 319.582851                109.909677   \n",
       "65                 537.833140                109.600000   \n",
       "66                 344.901506                 89.406452   \n",
       "67                 242.468134                114.387097   \n",
       "68                 538.586327                113.200000   \n",
       "69                 373.812283                112.709677   \n",
       "70                 540.614137                121.277419   \n",
       "71                 734.704519                126.464516   \n",
       "72                 198.493627                101.896774   \n",
       "73                 180.533024                 89.987097   \n",
       "74                 446.349942                117.006452   \n",
       "75                 233.082271                103.109677   \n",
       "76                 522.016222                128.335484   \n",
       "77                 727.809965                110.322581   \n",
       "78                 319.988413                101.729032   \n",
       "79                 348.493627                120.748387   \n",
       "80                 932.966396                109.109677   \n",
       "81                 568.192352                124.258065   \n",
       "82                 445.133256                136.941935   \n",
       "83                 976.535342                125.806452   \n",
       "84                 138.180765                 74.903226   \n",
       "85                 145.770568                101.122581   \n",
       "86                 718.192352                136.387097   \n",
       "87                 132.966396                 96.761290   \n",
       "88                 454.866744                118.141935   \n",
       "89                 958.574739                119.664516   \n",
       "90                 183.893395                 98.232258   \n",
       "91                 428.099652                 96.993548   \n",
       "92                 923.059096                135.883871   \n",
       "93                 398.493627                 96.141935   \n",
       "94                 603.360371                136.606452   \n",
       "95                 995.886443                130.206452   \n",
       "96                 127.809965                104.245161   \n",
       "97                 174.333720                116.451613   \n",
       "98                 542.294322                125.251613   \n",
       "99                 204.229432                103.483871   \n",
       "100                 79.895713                118.464516   \n",
       "101                749.768250                148.167742   \n",
       "102                297.624565                157.793548   \n",
       "103                473.812283                157.019355   \n",
       "104                993.163384                160.477419   \n",
       "105                665.295481                158.503226   \n",
       "106                654.461182                152.412903   \n",
       "107               1093.105446                165.870968   \n",
       "108                274.391657                109.135484   \n",
       "109                379.490151                138.645161   \n",
       "110                756.720742                170.567742   \n",
       "111                379.084589                136.800000   \n",
       "112                554.750869                152.219355   \n",
       "113                985.631518                180.954839   \n",
       "114                403.418308                128.038710   \n",
       "115                475.492468                135.870968   \n",
       "116                877.925840                135.019355   \n",
       "117                671.494786                129.948387   \n",
       "118                808.516802                128.683871   \n",
       "119               1585.110081                152.632258   \n",
       "120                621.726535                108.206452   \n",
       "121                348.783314                115.793548   \n",
       "122                559.385863                163.509677   \n",
       "123                309.559676                144.012903   \n",
       "124                464.310545                149.793548   \n",
       "125               1022.537659                128.993548   \n",
       "126                228.968714                112.774194   \n",
       "127                497.450753                139.445161   \n",
       "128                792.699884                132.670968   \n",
       "129                529.084589                129.496774   \n",
       "130                862.514484                121.574194   \n",
       "131                740.903824                140.941935   \n",
       "132                182.676709                104.580645   \n",
       "133                355.851680                125.483871   \n",
       "134                612.456547                169.922581   \n",
       "135                575.724218                159.264516   \n",
       "136                515.063731                166.025806   \n",
       "137                851.448436                168.154839   \n",
       "138                510.312862                175.470968   \n",
       "139                561.877173                217.277419   \n",
       "140                773.812283                184.916129   \n",
       "141                476.071842                183.187097   \n",
       "142                669.235226                217.870968   \n",
       "143                980.880649                284.116129   \n",
       "144                450.173812                161.832258   \n",
       "145                367.381228                158.103226   \n",
       "146                839.049826                220.735484   \n",
       "147                579.548088                191.058065   \n",
       "148                710.428737                197.793548   \n",
       "149               1155.098494                221.703226   \n",
       "0                         NaN                       NaN   \n",
       "1                         NaN                       NaN   \n",
       "2                         NaN                       NaN   \n",
       "3                         NaN                       NaN   \n",
       "4                         NaN                       NaN   \n",
       "5                         NaN                       NaN   \n",
       "\n",
       "     base100_distratadas_ABRAINC  base100_incc_di  \n",
       "0                      51.994796       132.336674  \n",
       "1                      51.994796       132.336674  \n",
       "2                      51.994796       132.336674  \n",
       "3                      51.994796       132.336674  \n",
       "4                      51.994796       132.336674  \n",
       "5                      51.994796       132.336674  \n",
       "6                      51.994796       132.336674  \n",
       "7                      51.994796       132.336674  \n",
       "8                      51.994796       132.336674  \n",
       "9                      51.994796       132.336674  \n",
       "10                     51.994796       132.336674  \n",
       "11                     51.994796       132.336674  \n",
       "12                     51.994796       132.336674  \n",
       "13                     51.994796       132.336674  \n",
       "14                     51.994796       132.336674  \n",
       "15                     51.994796       132.336674  \n",
       "16                     51.994796       132.336674  \n",
       "17                     51.994796       132.336674  \n",
       "18                     51.994796       132.336674  \n",
       "19                     51.994796       132.336674  \n",
       "20                     51.994796       132.336674  \n",
       "21                     51.994796       132.336674  \n",
       "22                     51.994796       132.336674  \n",
       "23                     51.994796       132.336674  \n",
       "24                    100.000000       100.000000  \n",
       "25                     83.810350       100.325950  \n",
       "26                     83.521249       100.603103  \n",
       "27                    110.031801       101.492663  \n",
       "28                    133.362243       103.577020  \n",
       "29                     76.293727       104.264024  \n",
       "30                    109.424689       105.041248  \n",
       "31                    103.064469       105.121463  \n",
       "32                     86.354438       105.283824  \n",
       "33                     90.575311       105.466720  \n",
       "34                     95.403296       105.933441  \n",
       "35                     87.163920       106.021554  \n",
       "36                    103.006649       106.994313  \n",
       "37                     78.433073       107.324124  \n",
       "38                     91.384793       107.991294  \n",
       "39                     96.241688       108.484870  \n",
       "40                     83.723620       109.518886  \n",
       "41                     89.071986       111.528997  \n",
       "42                    123.677363       112.139296  \n",
       "43                     84.706563       112.799972  \n",
       "44                     90.459670       113.046058  \n",
       "45                     93.524140       113.451345  \n",
       "46                     83.781440       113.835217  \n",
       "47                     69.152934       113.953521  \n",
       "48                     73.662908       114.399881  \n",
       "49                     64.324949       115.014920  \n",
       "50                     89.505637       115.749140  \n",
       "51                     87.135010       116.382960  \n",
       "52                     76.929748       116.480025  \n",
       "53                     81.699913       118.728498  \n",
       "54                     92.396646       119.313171  \n",
       "55                     79.618387       119.665099  \n",
       "56                     78.751084       120.063364  \n",
       "57                     77.479040       120.320333  \n",
       "58                     70.598439       120.516570  \n",
       "59                     63.804568       120.933968  \n",
       "60                     55.738653       121.426666  \n",
       "61                     59.612605       122.220389  \n",
       "62                     71.263371       122.412764  \n",
       "63                     61.607401       122.383627  \n",
       "64                     64.324949       123.159447  \n",
       "65                     58.109280       124.306150  \n",
       "66                     59.525875       124.684933  \n",
       "67                     52.038161       125.128835  \n",
       "68                     68.025441       125.207119  \n",
       "69                     66.551026       125.592747  \n",
       "70                     53.223475       125.983115  \n",
       "71                     53.801677       126.075265  \n",
       "72                     46.978896       126.464755  \n",
       "73                     45.938132       126.626062  \n",
       "74                     62.387973       126.933055  \n",
       "75                     55.883203       127.298498  \n",
       "76                     50.708297       127.593028  \n",
       "77                     45.620121       128.832409  \n",
       "78                     64.296039       129.622797  \n",
       "79                     57.444348       129.815172  \n",
       "80                     40.676496       130.117426  \n",
       "81                     49.667534       130.566945  \n",
       "82                     41.919630       130.742294  \n",
       "83                     28.447528       130.916240  \n",
       "84                     31.251807       131.558836  \n",
       "85                     33.940445       131.675209  \n",
       "86                     36.253252       132.086639  \n",
       "87                     38.797340       132.586709  \n",
       "88                     39.722463       132.630942  \n",
       "89                     32.986412       133.803272  \n",
       "90                     40.213935       134.574703  \n",
       "91                     33.998265       135.145510  \n",
       "92                     28.158427       135.771958  \n",
       "93                     35.154669       136.021028  \n",
       "94                     33.795895       136.071228  \n",
       "95                     26.481642       136.354525  \n",
       "96                     32.350390       136.868286  \n",
       "97                     31.656548       137.319385  \n",
       "98                     35.617230       137.670786  \n",
       "99                     34.431917       137.974795  \n",
       "100                    45.359931       138.254932  \n",
       "101                    51.864701       138.722706  \n",
       "102                    64.960971       140.347715  \n",
       "103                    49.176062       141.359966  \n",
       "104                    29.661752       143.000246  \n",
       "105                    41.139058       145.471109  \n",
       "106                    36.253252       147.332374  \n",
       "107                    29.893033       148.365513  \n",
       "108                    38.566060       149.689146  \n",
       "109                    36.860364       152.518606  \n",
       "110                    45.793582       154.508355  \n",
       "111                    44.203527       155.899565  \n",
       "112                    43.914426       159.358808  \n",
       "113                    37.294016       162.801376  \n",
       "114                    51.951431       164.178719  \n",
       "115                    41.110147       164.940497  \n",
       "116                    33.102053       165.786702  \n",
       "117                    40.098294       167.204241  \n",
       "118                    41.196878       168.328477  \n",
       "119                    26.626193       168.911220  \n",
       "120                    37.785487       170.115846  \n",
       "121                    42.555652       170.768799  \n",
       "122                    44.839549       172.232676  \n",
       "123                    61.144840       173.864881  \n",
       "124                    57.126337       177.835428  \n",
       "125                    37.004915       181.637296  \n",
       "126                    36.137612       183.205785  \n",
       "127                    41.139058       183.367093  \n",
       "128                    32.292570       183.531559  \n",
       "129                    31.887829       183.756231  \n",
       "130                    32.437121       184.424103  \n",
       "131                    26.597282       184.587517  \n",
       "132                    35.877421       185.427578  \n",
       "133                    74.501301       185.511479  \n",
       "134                    30.789245       186.076669  \n",
       "135                    37.785487       186.343291  \n",
       "136                    53.512576       187.446289  \n",
       "137                    40.734316       188.783964  \n",
       "138                    58.427291       188.974584  \n",
       "139                    59.728245       189.288071  \n",
       "140                    64.267129       189.936109  \n",
       "141                    70.916450       190.311381  \n",
       "142                    62.330153       190.441971  \n",
       "143                    53.686036       191.025767  \n",
       "144                    46.487424       191.541459  \n",
       "145                    48.886962       191.793337  \n",
       "146                    43.480775       192.329214  \n",
       "147                    44.984099       193.321105  \n",
       "148                    48.164209       194.988240  \n",
       "149                    44.434808       196.381907  \n",
       "0                            NaN              NaN  \n",
       "1                            NaN              NaN  \n",
       "2                            NaN              NaN  \n",
       "3                            NaN              NaN  \n",
       "4                            NaN              NaN  \n",
       "5                            NaN              NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizando todo o dataframe\n",
    "result.head(len(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Exportando os arquivos em excel para transpor no TCC\n",
    "\n",
    "\n",
    "# Caminho para salvar a o arquivo\n",
    "file_path = r'D:\\Google Drive\\PC - Casa\\MBA - USP ESALQ\\Data Science\\00. TCC\\01. Base de Dados\\Saída - Analise\\01. Resultado Final.xlsx'\n",
    "\n",
    "# Criando um objeto ExcelWriter com o caminho completo\n",
    "with pd.ExcelWriter(file_path, engine='xlsxwriter') as writer:\n",
    "    # Escreva cada dataframe em uma aba separada\n",
    "    result.to_excel(writer, sheet_name='Base_Final', index=False)\n",
    "    predictions.to_excel(writer, sheet_name='Valores_Preditos_CUB', index=False)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x700 with 0 Axes>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1da2fba9ed0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1da37090810>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Month')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CUB')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CUB over Time')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1da2f6e7e10>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot do gráfico com os valores do CUB e as predições\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Gráfico para 'cub'\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(df['anomes'], df['cub'], label='Actual CUB', color='blue')\n",
    "plt.plot(predictions['anomes'], predictions['cub'], label='Predicted CUB', color='orange', linestyle='--')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('CUB')\n",
    "plt.title('CUB over Time')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}