tutorials/tutorial_01_ML/tutorial_01_ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical ML: Scikit-learn\n",
    "## Introducing Pandas and Scikit-learn\n",
    "### Juan Antonio Cortés (jacortes@ugr.es)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, cross_val_predict, GridSearchCV, StratifiedKFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import tree\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC, SVR\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\n",
    "built on top of the Python programming language.\n",
    "\n",
    "It is the perfect tool to explore, clean and process tabular data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas tables (DataFrames from now on) are comprised of series (one-dimensional ndarray with axis labels). Below, an example of a series with a missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrames can be built from different data types. For example, we can build a DataFrame with a date column (it will be the index of the DataFrame in this example) and four numerical columns (the data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('20201101', periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.016871</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-1.150457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>0.227719</td>\n",
       "      <td>-0.184575</td>\n",
       "      <td>-0.221846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01  0.822360  1.157473 -0.799502  1.150930\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048\n",
       "2020-11-04 -1.016871 -0.218234  0.855766 -1.150457\n",
       "2020-11-05 -1.386138  0.178950 -0.448507  0.408281\n",
       "2020-11-06  2.153887  0.227719 -0.184575 -0.221846"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also build a DataFrame from a Python dictionary. Thanks to the flexibility of Pandas we can mix different data types inside the same DataFrame. For example: integers, time stamps, floats, text..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'A': 1.,\n",
    "                    'B': pd.Timestamp('20201101'),\n",
    "                    'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "                    'D': np.array([3] * 4, dtype='int32'),\n",
    "                    'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "                    'F': 'foo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2020-11-01  1.0  3   test  foo\n",
       "1  1.0 2020-11-01  1.0  3  train  foo\n",
       "2  1.0 2020-11-01  1.0  3   test  foo\n",
       "3  1.0 2020-11-01  1.0  3  train  foo"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have our DataFrame we can use different approaches to view it. Pandas offers multiples ways to accomplish this task:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can peek at the beginning of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.016871</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-1.150457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01  0.822360  1.157473 -0.799502  1.150930\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048\n",
       "2020-11-04 -1.016871 -0.218234  0.855766 -1.150457\n",
       "2020-11-05 -1.386138  0.178950 -0.448507  0.408281"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.016871</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-1.150457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>0.227719</td>\n",
       "      <td>-0.184575</td>\n",
       "      <td>-0.221846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048\n",
       "2020-11-04 -1.016871 -0.218234  0.855766 -1.150457\n",
       "2020-11-05 -1.386138  0.178950 -0.448507  0.408281\n",
       "2020-11-06  2.153887  0.227719 -0.184575 -0.221846"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also list the name of the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing the data is a trivial task with Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>2020-11-01</th>\n",
       "      <th>2020-11-02</th>\n",
       "      <th>2020-11-03</th>\n",
       "      <th>2020-11-04</th>\n",
       "      <th>2020-11-05</th>\n",
       "      <th>2020-11-06</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>0.350608</td>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.016871</td>\n",
       "      <td>-1.386138</td>\n",
       "      <td>2.153887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.157473</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>0.227719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.799502</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>-0.184575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.150930</td>\n",
       "      <td>-0.745286</td>\n",
       "      <td>-1.429048</td>\n",
       "      <td>-1.150457</td>\n",
       "      <td>0.408281</td>\n",
       "      <td>-0.221846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2020-11-01  2020-11-02  2020-11-03  2020-11-04  2020-11-05  2020-11-06\n",
       "A    0.822360    0.350608   -0.269674   -1.016871   -1.386138    2.153887\n",
       "B    1.157473   -1.262714   -1.238298   -0.218234    0.178950    0.227719\n",
       "C   -0.799502   -0.461467    0.898560    0.855766   -0.448507   -0.184575\n",
       "D    1.150930   -0.745286   -1.429048   -1.150457    0.408281   -0.221846"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as sort the DataFrame. For example, we can sort it by column in descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>1.150930</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>0.822360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>-0.745286</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>0.350608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-1.429048</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>-0.269674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.150457</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>-1.016871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>0.408281</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>-1.386138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>-0.221846</td>\n",
       "      <td>-0.184575</td>\n",
       "      <td>0.227719</td>\n",
       "      <td>2.153887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2020-11-01  1.150930 -0.799502  1.157473  0.822360\n",
       "2020-11-02 -0.745286 -0.461467 -1.262714  0.350608\n",
       "2020-11-03 -1.429048  0.898560 -1.238298 -0.269674\n",
       "2020-11-04 -1.150457  0.855766 -0.218234 -1.016871\n",
       "2020-11-05  0.408281 -0.448507  0.178950 -1.386138\n",
       "2020-11-06 -0.221846 -0.184575  0.227719  2.153887"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.016871</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-1.150457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>0.227719</td>\n",
       "      <td>-0.184575</td>\n",
       "      <td>-0.221846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048\n",
       "2020-11-04 -1.016871 -0.218234  0.855766 -1.150457\n",
       "2020-11-05 -1.386138  0.178950 -0.448507  0.408281\n",
       "2020-11-06  2.153887  0.227719 -0.184575 -0.221846\n",
       "2020-11-01  0.822360  1.157473 -0.799502  1.150930"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas also offers a quick statistic summary of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.109028</td>\n",
       "      <td>-0.192517</td>\n",
       "      <td>-0.023288</td>\n",
       "      <td>-0.331238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.295781</td>\n",
       "      <td>0.935596</td>\n",
       "      <td>0.724413</td>\n",
       "      <td>0.980145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.830072</td>\n",
       "      <td>-0.983282</td>\n",
       "      <td>-0.458227</td>\n",
       "      <td>-1.049164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.040467</td>\n",
       "      <td>-0.019642</td>\n",
       "      <td>-0.316541</td>\n",
       "      <td>-0.483566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.704422</td>\n",
       "      <td>0.215527</td>\n",
       "      <td>0.595680</td>\n",
       "      <td>0.250750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.109028 -0.192517 -0.023288 -0.331238\n",
       "std    1.295781  0.935596  0.724413  0.980145\n",
       "min   -1.386138 -1.262714 -0.799502 -1.429048\n",
       "25%   -0.830072 -0.983282 -0.458227 -1.049164\n",
       "50%    0.040467 -0.019642 -0.316541 -0.483566\n",
       "75%    0.704422  0.215527  0.595680  0.250750\n",
       "max    2.153887  1.157473  0.898560  1.150930"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another usual task whilst working with data is the selection. Pandas offers a \"pythonic\" selection interface so we can slice our DataFrame in multiple ways. With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-11-01    0.822360\n",
       "2020-11-02    0.350608\n",
       "2020-11-03   -0.269674\n",
       "2020-11-04   -1.016871\n",
       "2020-11-05   -1.386138\n",
       "2020-11-06    2.153887\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>-0.799502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-0.461467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>0.898560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.016871</td>\n",
       "      <td>0.855766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>-0.448507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>-0.184575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2020-11-01  0.822360 -0.799502\n",
       "2020-11-02  0.350608 -0.461467\n",
       "2020-11-03 -0.269674  0.898560\n",
       "2020-11-04 -1.016871  0.855766\n",
       "2020-11-05 -1.386138 -0.448507\n",
       "2020-11-06  2.153887 -0.184575"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A', 'C']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01  0.822360  1.157473 -0.799502  1.150930\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by a combination of the previous method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>1.157473</td>\n",
       "      <td>-0.799502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.855766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>0.178950</td>\n",
       "      <td>-0.448507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>0.227719</td>\n",
       "      <td>-0.184575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2020-11-01  1.157473 -0.799502\n",
       "2020-11-02 -1.262714 -0.461467\n",
       "2020-11-03 -1.238298  0.898560\n",
       "2020-11-04 -0.218234  0.855766\n",
       "2020-11-05  0.178950 -0.448507\n",
       "2020-11-06  0.227719 -0.184575"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-11-01', '2020-11-02', '2020-11-03', '2020-11-04',\n",
       "               '2020-11-05', '2020-11-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>-0.269674</td>\n",
       "      <td>-1.238298</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>-1.429048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>-1.016871</td>\n",
       "      <td>-0.218234</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>-1.150457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>-1.386138</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-03 -0.269674 -1.238298  0.898560 -1.429048\n",
       "2020-11-04 -1.016871 -0.218234  0.855766 -1.150457\n",
       "2020-11-05 -1.386138  0.178950 -0.448507  0.408281"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2020-11-3':'2020-11-5']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas also supports boolean indexing. With this feature we can select data based on conditions, complex or simple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we can obtain the boolean mask that satisfies the following condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D\n",
       "2020-11-01   True   True  False   True\n",
       "2020-11-02   True  False  False  False\n",
       "2020-11-03  False  False   True  False\n",
       "2020-11-04  False  False   True  False\n",
       "2020-11-05  False   True  False   True\n",
       "2020-11-06   True   True  False  False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df > 0\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.898560</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.855766</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>0.227719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01  0.822360  1.157473       NaN  1.150930\n",
       "2020-11-02  0.350608       NaN       NaN       NaN\n",
       "2020-11-03       NaN       NaN  0.898560       NaN\n",
       "2020-11-04       NaN       NaN  0.855766       NaN\n",
       "2020-11-05       NaN  0.178950       NaN  0.408281\n",
       "2020-11-06  2.153887  0.227719       NaN       NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also apply these conditions over a subset of the data. For example, we can get all the values that satisfy the next constraint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>0.822360</td>\n",
       "      <td>1.157473</td>\n",
       "      <td>-0.799502</td>\n",
       "      <td>1.150930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>0.350608</td>\n",
       "      <td>-1.262714</td>\n",
       "      <td>-0.461467</td>\n",
       "      <td>-0.745286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>2.153887</td>\n",
       "      <td>0.227719</td>\n",
       "      <td>-0.184575</td>\n",
       "      <td>-0.221846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-11-01  0.822360  1.157473 -0.799502  1.150930\n",
       "2020-11-02  0.350608 -1.262714 -0.461467 -0.745286\n",
       "2020-11-06  2.153887  0.227719 -0.184575 -0.221846"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['A'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course the logical operators (and, or, exclusive-or) are supported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C, D]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['A'] > 0) & (df['C'] > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can handle missing values without hassle. Given the Series introduced at the beginning of the notebook which contains one missing value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can obtain which values are missing (NaN):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we stated previously Pandas allows boolean indexing..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[pd.isna(s)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we are left with three choices: do nothing, delete the missing values or replace them with a value. The first one is not an issue and with Pandas neither of them are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily delete them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or replace it with another value, in this case 5, but Pandas offers multiple options: previous/next valid value, mean or whichever function we pass to the `fillna` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    5.0\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas offers a shortcut to `matplotlib` so we can easily plot our data with the call of a method. Let's build a random time series dataset and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('11/1/2020', periods=1000))\n",
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default plot type is the `line` plot, perfect for the data at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot multivariate data and Pandas will handle the labels, legends, colors..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or change the plot type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zNg6VXYIV7Nhjj2X58uWFr9dDOmZmiXDDNzNLhBu+mVkiPIZv9VHCs0XNmsRH+GZmifARfpP4CNjMxuGGb2Y2jqHZcwpd30ROo+3p6WHu3Lm8+OKLHHjggcyfP5/Pfe5zHHBAvkEZN3wzs4o5+OCDWbduHQDbtm3joosu4tlnn+XLX/5yrvV6DN/MrMKOOuoolixZwtVXX01EvmdCueGbmVXcG9/4Rnbt2sW2bdtyrccN38wsEW74ZmYV98gjj9DT08NRRx2Vaz3+0NbMCuOHtBdvZGSESy+9lMsuuwxJudblhm9mNo4y7kb6/PPPc+qpp75yWubFF1/M5z//+dzrdcO3xvPtg61udu3aNSnrzfsQ8wsk3S/pZUl94yy3WdJ6SeskDebZppmZ7Z+8R/gbgI8AfzeBZc+KiCdzbs/MzPZTroYfEUNA7g8SzMyqJCIq39f25yKsbp2WGcCPJa2RtGC8BSUtkDQoaXBkZKRL5ZmZtUydOpXt27fnvqp1MkUE27dvZ+rUqfv0fXs9wpf0U+D1Y8z6YkTcNMHtvDMitko6CviJpI0RcdtYC0bEEmAJQF9fX3X/xs2skWbOnMnw8DBVP+CcOnUqM2fO3Kfv2WvDj4j37HdFr65ja/bnNkk3AKcDYzZ8M7MyTZkyhVmzZpVdxqSY9CEdSYdIOnT3a+D3aX3Ya2ZmXZT3tMzzJQ0DZwA3S1qVTT9W0spssaOBOyTdC9wN3BwRP8qzXTMz23d5z9K5AbhhjOn/Crw/e/0IcEqe7ZiZWX6q8ifRkkaAR7u0uSOBJl8n4Hz15nz11e1sb4iIGWPNqHTD7yZJgxHR8WrhunO+enO++qpSNt8e2cwsEW74ZmaJcMN/1ZKyC5hkzldvzldflcnmMXwzs0T4CN/MLBFu+GZmiXDDNzNLRJKPOFTrRtenA8dlk7YCd0dDPtBoej4ASUfTli8iniiznqI1OV+T98+qZ0vuQ1tJvw98E3iY1g8DYCbwJuC/RcSPy6qtCAnkOxW4BpjGnvmeppVvbTmVFSOBfI3dP2uRLSKS+gKGgN4xps8Chsquz/n2mm8d8NYxpr8NuLfs+pxvr/kau3/WIVuKY/gHAsNjTN8KTOlyLZOh6fkOiYi7Rk+MiDuBQ0qop2hNz9fk/bPy2VIcw/8OcI+kZcCWbNrxwIXAtaVVVZym5/uhpJuB77FnvvlAE2673fR8Td4/K58tuTF8AElvBj7Enh+srIiIB8qrqjgJ5DsXOI/X5lvZ+bvqI4F8jd0/q54tyYa/m6QjACJiR9m1TIam57N6a/L+WdVsyY3hSzpB0jJJ24C7gLslbcum9ZZcXm4J5JsmaZGkIUk7JG3PXi+SdFjZ9eWVQL7G7p91yJZcwweuo/WUrmMi4sSIeBNwDHAjsKzMwgrS9HzLgaeAsyLiiIiYDpxF67TF5WUWVpCm52vy/ln5bMkN6Uh6OCJO3Nd5dZFAvgcj4nf2dV5dJJCvsftnHbKleJbOGknfBJay5yfplwC/LK2q4jQ936OS/gxYGtnVp9lVqZ/i1bx11vR8Td4/K58txSP8g4A/Ys+zIIaBHwDXRsQLZdVWhATyHQ4spJXvaCCAJ4AVwFVV+5BsXyWQr7H7Zx2yJdfwzcxSleKHtq+Q9MHx3tddAvlOG+993SWQr7H7Z1WzJd3wgd/by/u6a3q+P97L+7prer4m75+VzOYhHTOzRKR4lg6SZjP2petD5VVVnATyTQPOYc98qyLi6dKKKlAC+Rq7f1Y9W3JDOpKuoHURhIC7sy8BA5IWlllbERLINx9YC5wJ/Gb2dRatU+Lml1haIRLI19j9sw7ZkhvSkfQQcHJEvDhq+kHA/VW4OCKPBPI9SOt+8U+Pmn44cFdEnFRKYQVJIF9j9886ZEvuCB94GTh2jOnHZPPqrun5ROvc9NFezubVXdPzNXn/rHy2FMfw+4GfSXqYV6+GO4HWY8guK6uoAvXT7Hx/BayV9GP2zPde4CulVVWcpufrp7n7Zz8Vz5bckA6ApAN47YOG74mIXeVVVZwE8h0OvI/Xfqj5VHlVFSeBfI3dP6ueLcUjfGj9yrz7a/f7SvzKVZBG54uIpyStpu0fVVOaITQ/H83ePyudLbkjfNXhyfI5JJDvVOAaYBqt+5SIVr6naeVbW1pxBUggX2P3z1pkK/sp6t3+ogZPlne+cfOto3UWy+jpbwPuLbs+59trvsbun3XIluJZOpV/snxOTc93SETcNXpiRNwJHFJCPUVrer4m75+Vz5biGH7lnyyfU9Pz/VDSzcD32DPffOBHpVVVnKbna/L+WflsyY3hA0iaw9iXP1fiyfJ5JZDvXMbOt7K8qoqTQL7G7p9Vz5ZkwzczS1GKY/ivkHTleO/rLoF8C8Z7X3cJ5LtyvPd1VtVsSTd8YM1e3tdd0/ONvtVAE2490K7p+Zq8f1Yym4d0zMwSkdxZOpIOpPWg4fN59UZHW4GbaD1o+MVO31sHTc8HIOl9wIfZ84OxmyKiCWexNDpfk/fPOmRL7ghf0gCtqxaX8uo5szOBS4AjIuITJZVWiATyLQZOonXaYnu++cDDEfHZkkorRAL5Grt/1iFbig3/oehwT/Hx5tVFqvkkCXgoKnDP8TxSzbe3eXVQh2wpfmi7Q9IF2V3tgNYd7iR9AmjCDaqanm+npLEeCP17wM5uFzMJmp6vyftn5bOleITfC1wFvJvWD0HAYcAtwMKI+JfSiitAAvlOA74FHMqrvzYfDzwDfCYiKnE2xP5KIF8vDd0/65AtuYbfTtJ0gIjYXnYtk6HJ+SS9nj1vH/x4mfUUren5oPH7ZyWzJXeWDrz2yfKSdp8FsbHUwgqSQL5pwH+krSFKWhWjngNbVwnka+z+WfVsyY3hj/Nk+WVVebJ8Hgnkmw+sBc4EfjP7OgtYk82rtQTyNXb/rEO25IZ06vBk+TwSyPcgrfvFPz1q+uHAXVU4EyKPBPI1dv+sQ7bkjvCpwZPlc2p6PvHq4+PavUwzbj3Q9HxN3j8rny3FMfx+Kv5k+Zz6aXa+vwLWSvoxe+Z7L/CV0qoqTtPz9dPc/bOfimdLbkgHqv9k+bwSyHc48D72zLcqGvKg7wTyNXb/rHq2JBu+mVmKUhzDNzNLkhu+mVki3PDNKi673YLVjKTfkjQv+0ymEtzw20haX3YNeUk6XtIySbdL+nNJU9rm3VhiaYWQNFvSDyXdLOm3JX1X0tOS7lbrAdK1Jum0UV/zgBWS3tKExi/pP7e9ninpZ9nP7+eS6n6Nwd9LOjJ7/T5gA61766yTdEGpxWWS+9BW0kc6zQKuiYgZ3aynaJJ+AnwfuJPWwxjmAf8pIrZL+mVEvKXUAnOSdBvwNeB1wCLgCuA64INAf0ScXWJ5uUl6mdbP7oW2yW/LpkVEvLuUwgoiaW1EnJa9Xg78FPg2rdsRXFbnn5+k9RExN3v9c+CiiNic/Sfws4g4pdwK0zwP/zrgHxj74papXa5lMsyIiGuy138i6Q+A2yR9iLEz182hEfEDAElfiYhl2fQfSPpyiXUV5QLgcuCrEfFDAEn/EhFnlVvWpDgpIj6evb5B0pdKrSa/AyT9VkQ8S+tCq18BRMSTaj0Nq3SVKKLL7gP+JiI2jJ4h6T0l1FO0KZKmRsROgIj4e0mPA6uAQ8otrRA9ba//dtS8g7pZyGSIiO9LWgV8JRv++FOa8R/1bjMlfZ3Wb9QzJE1puxXBlHG+rw6+DKyW9A3gn4H/K2kFrXshVeLxlCk2/H7g2Q7zzu9iHZPl28BbgVt3T4iIn2ZjiF8trarifEPS6yLiuYj45u6Jkt5Ea3ig9iLiOeBz2Zj9Ulr3xm+K/972epDW0NxT2e2gV5RTUjEiYrmkXwKfpvWYygNpDccNRMSqUovLJDeGb1YnkkRrGKvTQYrZhCXX8FWDJ8vn4XzOV2Vt+T7MnrcfqH2+OmRLseFX/snyeTif81VZk/PVIVuKDb/yT5bPw/mcr8qanK8O2VK88KryT5bPyfnqzfnqq/LZUmz4FwIfA56Q9JBaT6l5AvhINq/unK/enK++Kp8tuSGddqrok+WL4nz15nz1VdVsKR7hvyIitme3HFhSdi2Twfnqzfnqq6rZkm74bfrKLmCSOV+9OV99VSqbG37LtrILmGTOV2/OV1+Vypb0GL6ZWUp8hN+mauNtRXO+enO++qpKtuRunibpiE6zgPd3s5bJ4Hz15nz1VYdsyQ3pSNoFPErrh7BbZO+Pi4ha32LX+Zyvypqcrw7ZkjvCBx4Bzo6IX42eIWlLCfUUzfnqzfnqq/LZUhzDXwx0eqhwE+4Xvxjnq7PFOF9dLabi2ZIb0jEzS1WKR/gdSXpv2TVMJuerN+err6pk8xF+G0m/iogTyq5jsjhfvTlffVUlW3If2mYPFR5zFjC9m7VMBuerN+errzpkS67hA/8B+APguVHTBZze/XIK53z15nz1VflsKTb8O4FfR8Sto2dIerCEeormfPXmfPVV+WwewzczS4TP0gEkfbDsGiaT89Wb89VX1bL5CB+QtDYiTiu7jsnifPXmfPVVtWw+wm/R3hepNeerN+err0plc8Nv+a9lFzDJnK/enK++KpUtxbN0kDQbOA84Lpu0VdK/RcRQiWUVxvnqzfnqq+rZkjvCl3QFsIzWr1p3Z18CBiQtLLO2IjhfvTlffdUhW3If2kp6CDg5Il4cNf0g4P6IOLGcyorhfM5XZU3OV4dsyR3hAy8Dx44x/ZhsXt05X705X31VPluKY/j9wM8kPQzsfijBCcCbgMvKKqpA/ThfnfXjfHXVT8WzJTekAyDpAFr3tnjlgxXgnojYVV5VxXG+enO++qp6tiQbvplZipIbw5f0u5LulLRF0hJJh7fNu7vM2orgfPXmfPVVh2zJNXzgm8CVwFzgIeAOSb+dzZtSVlEFcr56c776qny2FD+0PTQifpS9/htJa4AfSboYaML4lvPVm/PVV+WzpdjwkTQtIp4BiIjVkj4KfB84otzKiuF89eZ89VX1bCkO6VwFzGmfEBH3AWcD/1hKRcVyvnpzvvqqfDafpWNmlojkjvAlTZO0SNJGSTskbZc0lE07rOz68nK+enO++qpDtuQaPrAceAo4MyKOiIjpwFnZtOWlVlYM56s356uvymdLbkhH0oMR8Tv7Oq8unM/5qqzJ+eqQLcUj/Ecl/Zmko3dPkHS0Wrc23TLO99WF89Wb89VX5bOl2PA/AUwHbs3G2XYA/0TrtKmPl1lYQZyv3pyvviqfLbkhHTOzVKV4hI+k2ZLOlnTIqOnnlFVTkZyv3pyvvqqeLbmGL+ly4CbgT4D7JZ3XNvt/lFNVcZyv3pyvvuqQLcVbK/wXYF5EPCepF7heUm9E/C9az5+sO+erN+err8pnS7HhHxARzwFExGZJZ9L6wbyBivxQcnK+enO++qp8tuSGdIAnJJ26+032A/ogcCSt25rWnfPVm/PVV+WzJXeWjqSZwEsR8fgY894REf9cQlmFcT7nq7Im56tDtuQavplZqlIc0jEzS5IbvplZItzwzcwS4YZvZpaI/w9pCjJPZQ4DxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation \"plot\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases we don't even need to use the binding with Matplotlib. For example, another usual plot we can make with Pandas and Pandas alone is the correlation plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>-0.360596</td>\n",
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       "      <td>-0.528606</td>\n",
       "      <td>0.857962</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.360596</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.854416</td>\n",
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       "      <th>D</th>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "A  1.000000  0.236466 -0.360596  0.222564\n",
       "B  0.236466  1.000000 -0.528606  0.857962\n",
       "C -0.360596 -0.528606  1.000000 -0.854416\n",
       "D  0.222564  0.857962 -0.854416  1.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = df.corr()\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas supports styles (even with HTML and CSS) so creating a colored correlation plot is really easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >A</th>        <th class=\"col_heading level0 col1\" >B</th>        <th class=\"col_heading level0 col2\" >C</th>        <th class=\"col_heading level0 col3\" >D</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row0_col1\" class=\"data row0 col1\" >0.24</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row0_col2\" class=\"data row0 col2\" >-0.36</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row0_col3\" class=\"data row0 col3\" >0.22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45level0_row1\" class=\"row_heading level0 row1\" >B</th>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row1_col0\" class=\"data row1 col0\" >0.24</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row1_col2\" class=\"data row1 col2\" >-0.53</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row1_col3\" class=\"data row1 col3\" >0.86</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45level0_row2\" class=\"row_heading level0 row2\" >C</th>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row2_col0\" class=\"data row2 col0\" >-0.36</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row2_col1\" class=\"data row2 col1\" >-0.53</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row2_col3\" class=\"data row2 col3\" >-0.85</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45level0_row3\" class=\"row_heading level0 row3\" >D</th>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row3_col0\" class=\"data row3 col0\" >0.22</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row3_col1\" class=\"data row3 col1\" >0.86</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row3_col2\" class=\"data row3 col2\" >-0.85</td>\n",
       "                        <td id=\"T_fa41ea14_2e27_11eb_a122_73dddeb70e45row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f4bed930490>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you are wondering Pandas can also read (and write) from multiple file types: CSV, Excel, HDF5, Parquet... Let's load the SDSS (Sloan Digital Sky Survey) dataset from https://www.sdss.org/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.read_csv('data/sdss.csv')\n",
    "_ = df.pop('objid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this is not all about Pandas. Pandas is a huge library which offers endless resources to handle our data. Do not forget to visit the official documentation to take a glance of all that Pandas has to offer: https://pandas.pydata.org/docs/reference/index.html#api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start by taking a peek at the head of the data to get a notion of the data at hand. The data consists of 10000 observations of space taken by the SDSS (Sloan Digital Sky Survey). Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy or quasar.\n",
    "\n",
    "Data from: https://www.sdss.org/\n",
    "\n",
    "The Sloan Digital Sky Survey has created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one third of the sky, and spectra for more than three million astronomical objects. Learn and explore all phases and surveys—past, present, and future—of the SDSS.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run</th>\n",
       "      <th>rerun</th>\n",
       "      <th>camcol</th>\n",
       "      <th>field</th>\n",
       "      <th>specobjid</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>mjd</th>\n",
       "      <th>fiberid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183.531326</td>\n",
       "      <td>0.089693</td>\n",
       "      <td>19.47406</td>\n",
       "      <td>17.04240</td>\n",
       "      <td>15.94699</td>\n",
       "      <td>15.50342</td>\n",
       "      <td>15.22531</td>\n",
       "      <td>752</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>267</td>\n",
       "      <td>3722360139651588096</td>\n",
       "      <td>STAR</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>3306</td>\n",
       "      <td>54922</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>183.598371</td>\n",
       "      <td>0.135285</td>\n",
       "      <td>18.66280</td>\n",
       "      <td>17.21449</td>\n",
       "      <td>16.67637</td>\n",
       "      <td>16.48922</td>\n",
       "      <td>16.39150</td>\n",
       "      <td>752</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>267</td>\n",
       "      <td>363814405953054720</td>\n",
       "      <td>STAR</td>\n",
       "      <td>-0.000055</td>\n",
       "      <td>323</td>\n",
       "      <td>51615</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.680207</td>\n",
       "      <td>0.126185</td>\n",
       "      <td>19.38298</td>\n",
       "      <td>18.19169</td>\n",
       "      <td>17.47428</td>\n",
       "      <td>17.08732</td>\n",
       "      <td>16.80125</td>\n",
       "      <td>752</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>268</td>\n",
       "      <td>323274319570429952</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.123111</td>\n",
       "      <td>287</td>\n",
       "      <td>52023</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183.870529</td>\n",
       "      <td>0.049911</td>\n",
       "      <td>17.76536</td>\n",
       "      <td>16.60272</td>\n",
       "      <td>16.16116</td>\n",
       "      <td>15.98233</td>\n",
       "      <td>15.90438</td>\n",
       "      <td>752</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>269</td>\n",
       "      <td>3722365362331820032</td>\n",
       "      <td>STAR</td>\n",
       "      <td>-0.000111</td>\n",
       "      <td>3306</td>\n",
       "      <td>54922</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183.883288</td>\n",
       "      <td>0.102557</td>\n",
       "      <td>17.55025</td>\n",
       "      <td>16.26342</td>\n",
       "      <td>16.43869</td>\n",
       "      <td>16.55492</td>\n",
       "      <td>16.61326</td>\n",
       "      <td>752</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>269</td>\n",
       "      <td>3722365912087633920</td>\n",
       "      <td>STAR</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>3306</td>\n",
       "      <td>54922</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "           ra       dec         u         g         r         i         z  \\\n",
       "0  183.531326  0.089693  19.47406  17.04240  15.94699  15.50342  15.22531   \n",
       "1  183.598371  0.135285  18.66280  17.21449  16.67637  16.48922  16.39150   \n",
       "2  183.680207  0.126185  19.38298  18.19169  17.47428  17.08732  16.80125   \n",
       "3  183.870529  0.049911  17.76536  16.60272  16.16116  15.98233  15.90438   \n",
       "4  183.883288  0.102557  17.55025  16.26342  16.43869  16.55492  16.61326   \n",
       "\n",
       "   run  rerun  camcol  field            specobjid   class  redshift  plate  \\\n",
       "0  752    301       4    267  3722360139651588096    STAR -0.000009   3306   \n",
       "1  752    301       4    267   363814405953054720    STAR -0.000055    323   \n",
       "2  752    301       4    268   323274319570429952  GALAXY  0.123111    287   \n",
       "3  752    301       4    269  3722365362331820032    STAR -0.000111   3306   \n",
       "4  752    301       4    269  3722365912087633920    STAR  0.000590   3306   \n",
       "\n",
       "     mjd  fiberid  \n",
       "0  54922      491  \n",
       "1  51615      541  \n",
       "2  52023      513  \n",
       "3  54922      510  \n",
       "4  54922      512  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns description is as follows:\n",
    "* `ra`: right ascension.\n",
    "* `dec`: declination.\n",
    "* `u`: ultraviolet.\n",
    "* `g`: green.\n",
    "* `r`: red.\n",
    "* `i`: near infrared.\n",
    "* `z`: infrared.\n",
    "* `run`: run number.\n",
    "* `rerun`: rerun number.\n",
    "* `camcol`: camera column.\n",
    "* `field`: field number.\n",
    "* `specobjid`: object Identifier.\n",
    "* `class`: object class (galaxy, star or quasar).\n",
    "* `redshift`: final redshift.\n",
    "* `plate`: plate number.\n",
    "* `mjd`: MJD of observation.\n",
    "* `fiberid`: fiber ID.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run</th>\n",
       "      <th>rerun</th>\n",
       "      <th>camcol</th>\n",
       "      <th>field</th>\n",
       "      <th>specobjid</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>mjd</th>\n",
       "      <th>fiberid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>175.529987</td>\n",
       "      <td>14.836148</td>\n",
       "      <td>18.619355</td>\n",
       "      <td>17.371931</td>\n",
       "      <td>16.840963</td>\n",
       "      <td>16.583579</td>\n",
       "      <td>16.422833</td>\n",
       "      <td>981.034800</td>\n",
       "      <td>301.0</td>\n",
       "      <td>3.648700</td>\n",
       "      <td>302.380100</td>\n",
       "      <td>1.645022e+18</td>\n",
       "      <td>0.143726</td>\n",
       "      <td>1460.986400</td>\n",
       "      <td>52943.533300</td>\n",
       "      <td>353.069400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47.783439</td>\n",
       "      <td>25.212207</td>\n",
       "      <td>0.828656</td>\n",
       "      <td>0.945457</td>\n",
       "      <td>1.067764</td>\n",
       "      <td>1.141805</td>\n",
       "      <td>1.203188</td>\n",
       "      <td>273.305024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.666183</td>\n",
       "      <td>162.577763</td>\n",
       "      <td>2.013998e+18</td>\n",
       "      <td>0.388774</td>\n",
       "      <td>1788.778371</td>\n",
       "      <td>1511.150651</td>\n",
       "      <td>206.298149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.235100</td>\n",
       "      <td>-5.382632</td>\n",
       "      <td>12.988970</td>\n",
       "      <td>12.799550</td>\n",
       "      <td>12.431600</td>\n",
       "      <td>11.947210</td>\n",
       "      <td>11.610410</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>301.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.995782e+17</td>\n",
       "      <td>-0.004136</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>51578.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>157.370946</td>\n",
       "      <td>-0.539035</td>\n",
       "      <td>18.178035</td>\n",
       "      <td>16.815100</td>\n",
       "      <td>16.173333</td>\n",
       "      <td>15.853705</td>\n",
       "      <td>15.618285</td>\n",
       "      <td>752.000000</td>\n",
       "      <td>301.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>3.389250e+17</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>51900.000000</td>\n",
       "      <td>186.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>180.394514</td>\n",
       "      <td>0.404166</td>\n",
       "      <td>18.853095</td>\n",
       "      <td>17.495135</td>\n",
       "      <td>16.858770</td>\n",
       "      <td>16.554985</td>\n",
       "      <td>16.389945</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>301.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>4.966580e+17</td>\n",
       "      <td>0.042591</td>\n",
       "      <td>441.000000</td>\n",
       "      <td>51997.000000</td>\n",
       "      <td>351.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>201.547279</td>\n",
       "      <td>35.649397</td>\n",
       "      <td>19.259232</td>\n",
       "      <td>18.010145</td>\n",
       "      <td>17.512675</td>\n",
       "      <td>17.258550</td>\n",
       "      <td>17.141447</td>\n",
       "      <td>1331.000000</td>\n",
       "      <td>301.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>2.881300e+18</td>\n",
       "      <td>0.092579</td>\n",
       "      <td>2559.000000</td>\n",
       "      <td>54468.000000</td>\n",
       "      <td>510.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.884382</td>\n",
       "      <td>68.542265</td>\n",
       "      <td>19.599900</td>\n",
       "      <td>19.918970</td>\n",
       "      <td>24.802040</td>\n",
       "      <td>28.179630</td>\n",
       "      <td>22.833060</td>\n",
       "      <td>1412.000000</td>\n",
       "      <td>301.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>9.468834e+18</td>\n",
       "      <td>5.353854</td>\n",
       "      <td>8410.000000</td>\n",
       "      <td>57481.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ra           dec             u             g             r  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean     175.529987     14.836148     18.619355     17.371931     16.840963   \n",
       "std       47.783439     25.212207      0.828656      0.945457      1.067764   \n",
       "min        8.235100     -5.382632     12.988970     12.799550     12.431600   \n",
       "25%      157.370946     -0.539035     18.178035     16.815100     16.173333   \n",
       "50%      180.394514      0.404166     18.853095     17.495135     16.858770   \n",
       "75%      201.547279     35.649397     19.259232     18.010145     17.512675   \n",
       "max      260.884382     68.542265     19.599900     19.918970     24.802040   \n",
       "\n",
       "                  i             z           run    rerun        camcol  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.0  10000.000000   \n",
       "mean      16.583579     16.422833    981.034800    301.0      3.648700   \n",
       "std        1.141805      1.203188    273.305024      0.0      1.666183   \n",
       "min       11.947210     11.610410    308.000000    301.0      1.000000   \n",
       "25%       15.853705     15.618285    752.000000    301.0      2.000000   \n",
       "50%       16.554985     16.389945    756.000000    301.0      4.000000   \n",
       "75%       17.258550     17.141447   1331.000000    301.0      5.000000   \n",
       "max       28.179630     22.833060   1412.000000    301.0      6.000000   \n",
       "\n",
       "              field     specobjid      redshift         plate           mjd  \\\n",
       "count  10000.000000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
       "mean     302.380100  1.645022e+18      0.143726   1460.986400  52943.533300   \n",
       "std      162.577763  2.013998e+18      0.388774   1788.778371   1511.150651   \n",
       "min       11.000000  2.995782e+17     -0.004136    266.000000  51578.000000   \n",
       "25%      184.000000  3.389250e+17      0.000081    301.000000  51900.000000   \n",
       "50%      299.000000  4.966580e+17      0.042591    441.000000  51997.000000   \n",
       "75%      414.000000  2.881300e+18      0.092579   2559.000000  54468.000000   \n",
       "max      768.000000  9.468834e+18      5.353854   8410.000000  57481.000000   \n",
       "\n",
       "            fiberid  \n",
       "count  10000.000000  \n",
       "mean     353.069400  \n",
       "std      206.298149  \n",
       "min        1.000000  \n",
       "25%      186.750000  \n",
       "50%      351.000000  \n",
       "75%      510.000000  \n",
       "max     1000.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After knowing a bit about the data we can check for missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run</th>\n",
       "      <th>rerun</th>\n",
       "      <th>camcol</th>\n",
       "      <th>field</th>\n",
       "      <th>specobjid</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>mjd</th>\n",
       "      <th>fiberid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ra    dec      u      g      r      i      z    run  rerun  camcol  \\\n",
       "0     False  False  False  False  False  False  False  False  False   False   \n",
       "1     False  False  False  False  False  False  False  False  False   False   \n",
       "2     False  False  False  False  False  False  False  False  False   False   \n",
       "3     False  False  False  False  False  False  False  False  False   False   \n",
       "4     False  False  False  False  False  False  False  False  False   False   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "9995  False  False  False  False  False  False  False  False  False   False   \n",
       "9996  False  False  False  False  False  False  False  False  False   False   \n",
       "9997  False  False  False  False  False  False  False  False  False   False   \n",
       "9998  False  False  False  False  False  False  False  False  False   False   \n",
       "9999  False  False  False  False  False  False  False  False  False   False   \n",
       "\n",
       "      field  specobjid  class  redshift  plate    mjd  fiberid  \n",
       "0     False      False  False     False  False  False    False  \n",
       "1     False      False  False     False  False  False    False  \n",
       "2     False      False  False     False  False  False    False  \n",
       "3     False      False  False     False  False  False    False  \n",
       "4     False      False  False     False  False  False    False  \n",
       "...     ...        ...    ...       ...    ...    ...      ...  \n",
       "9995  False      False  False     False  False  False    False  \n",
       "9996  False      False  False     False  False  False    False  \n",
       "9997  False      False  False     False  False  False    False  \n",
       "9998  False      False  False     False  False  False    False  \n",
       "9999  False      False  False     False  False  False    False  \n",
       "\n",
       "[10000 rows x 17 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df) #.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another basic task during the EDA phase is to check the class distribution. We can easily plot the histogram with Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['class'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is clearly imbalanced towards the GALAXY and STAR classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GALAXY    4998\n",
       "STAR      4152\n",
       "QSO        850\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next figure shows the basic workflow while approaching a Machine Learning problem. We can distinguish two tasks, separate at first but intertwined in the last stages:\n",
    "\n",
    "* Optimization of the machine learning model (left) which will make the prediction using the data.\n",
    "* Construction of the dataset (accomplished during the EDA phase) and partitioning in training and test data (right)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/grid_search_workflow.png)\n",
    "From: https://scikit-learn.org/stable/modules/cross_validation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must first split the data in two sets: `X` and `y`. These names are a common convention. `X` for the variables and `y` for the target (class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.pop('class')\n",
    "X = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to split the data, one more time, into train and test partitions. Scikit-learn can easily do that for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=24112020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the train partition (80% of our data) and the test partition (20% of our data) randomly selected from all the data and stratified (both partitions has the same class distribution) for better results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we train and optimize the desired model via the cross validation strategy. The next figure shows a graphical representation of this strategy:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/grid_search_cross_validation.png)\n",
    "From: https://scikit-learn.org/stable/modules/cross_validation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/knn_example.png)\n",
    "From: Cheng, Debo, et al. \"kNN algorithm with data-driven k value.\" International Conference on Advanced Data Mining and Applications. Springer, Cham, 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to define the parameters which will be tested. In this case, the number of neighbors (`k`) that the algorithm will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_neighbors': [x for x in range (3, 16)]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we instantiate our classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can search for the best `k`. Sklearn offers the `GridSearchCV` method to accomplish this. This method will test all the combination of parameters via cross validation and will return the best parameters obtained during this training phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=KNeighborsClassifier(), n_jobs=4,\n",
       "             param_grid={'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                         14, 15]})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we get `k=15`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=15)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the training step we need to check how the model performs against data that has not been \"seen\" by the model (the test data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we get the predictions we compare them to the ground-truth to check the model's performance. Once again, sklearn has a classification report with all the common metrics used in this type of problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.75      0.96      0.85      1000\n",
      "         QSO       0.57      0.02      0.05       170\n",
      "        STAR       0.91      0.78      0.84       830\n",
      "\n",
      "    accuracy                           0.81      2000\n",
      "   macro avg       0.74      0.59      0.58      2000\n",
      "weighted avg       0.80      0.81      0.78      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what if we use a more complex (yet simple) model? The next figure shows a simple decision tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/dt_example.png)\n",
    "From: https://towardsai.net/p/programming/decision-trees-explained-with-a-practical-example-fe47872d3b53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion':('gini', 'entropy'), \n",
    "              'max_depth':[5, 10, 15, None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier(random_state=24112020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=DecisionTreeClassifier(random_state=24112020), n_jobs=4,\n",
       "             param_grid={'criterion': ('gini', 'entropy'),\n",
       "                         'max_depth': [5, 10, 15, None]})"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5, random_state=24112020)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.98      0.99      0.99      1000\n",
      "         QSO       0.94      0.91      0.93       170\n",
      "        STAR       1.00      1.00      1.00       830\n",
      "\n",
      "    accuracy                           0.99      2000\n",
      "   macro avg       0.97      0.97      0.97      2000\n",
      "weighted avg       0.99      0.99      0.99      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the perks of this model is the interpretability of it. Thanks to sklearn, we can plot the final decision tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,20))\n",
    "tree.plot_tree(clf, feature_names=X.columns, class_names=['GALAXY', 'QSAR', 'STAR']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/rf_example.png)\n",
    "From: https://rpubs.com/Avalos42/randomforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "              'n_estimators': [5, 10, 15, 20],\n",
    "              'max_depth': [5, 10, 15, None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(random_state=24112020)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=RandomForestClassifier(random_state=24112020), n_jobs=4,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [5, 10, 15, None],\n",
       "                         'n_estimators': [5, 10, 15, 20]})"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=15, n_estimators=15, random_state=24112020)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.99      0.99      0.99      1000\n",
      "         QSO       0.96      0.93      0.94       170\n",
      "        STAR       1.00      1.00      1.00       830\n",
      "\n",
      "    accuracy                           0.99      2000\n",
      "   macro avg       0.98      0.97      0.98      2000\n",
      "weighted avg       0.99      0.99      0.99      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model offers an useful trait: the feature importance. The higher the value the more important is the feature (variable) to the model so we can discard useless variables and improve the performance of the model. This is called \"feature selection\" and is one of the most powerful preprocessing techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00391372, 0.0032789 , 0.02165442, 0.02865361, 0.01082761,\n",
       "       0.10353049, 0.0497029 , 0.00244479, 0.        , 0.00107068,\n",
       "       0.00400284, 0.14607243, 0.56476699, 0.03300232, 0.02383341,\n",
       "       0.0032449 ])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance = clf.feature_importances_\n",
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>redshift</td>\n",
       "      <td>0.564767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>specobjid</td>\n",
       "      <td>0.146072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>i</td>\n",
       "      <td>0.10353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>z</td>\n",
       "      <td>0.0497029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plate</td>\n",
       "      <td>0.0330023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>g</td>\n",
       "      <td>0.0286536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mjd</td>\n",
       "      <td>0.0238334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>u</td>\n",
       "      <td>0.0216544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>r</td>\n",
       "      <td>0.0108276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>field</td>\n",
       "      <td>0.00400284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ra</td>\n",
       "      <td>0.00391372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dec</td>\n",
       "      <td>0.0032789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fiberid</td>\n",
       "      <td>0.0032449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>run</td>\n",
       "      <td>0.00244479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>camcol</td>\n",
       "      <td>0.00107068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rerun</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Importance\n",
       "12   redshift    0.564767\n",
       "11  specobjid    0.146072\n",
       "5           i     0.10353\n",
       "6           z   0.0497029\n",
       "13      plate   0.0330023\n",
       "3           g   0.0286536\n",
       "14        mjd   0.0238334\n",
       "2           u   0.0216544\n",
       "4           r   0.0108276\n",
       "10      field  0.00400284\n",
       "0          ra  0.00391372\n",
       "1         dec   0.0032789\n",
       "15    fiberid   0.0032449\n",
       "7         run  0.00244479\n",
       "9      camcol  0.00107068\n",
       "8       rerun           0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_df = pd.DataFrame([X.columns, feature_importance]).T\n",
    "fs_df.columns = ['Feature', 'Importance']\n",
    "fs_df.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the `redshift` is the more important feature by a huge margin. Let's see what happens when we train another model with only this feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A support-vector machine (SVM) constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](data/svm_example.png)\n",
    "From: Ulas, Cagdas. (2013). INCORPORATION OF A LANGUAGE MODEL INTO A BRAIN COMPUTER INTERFACE BASED SPELLER. 10.13140/2.1.2017.6326. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'svc__kernel': ['rbf', 'linear', 'poly']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = make_pipeline(StandardScaler(), SVC(random_state=24112020))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(clf, parameters, refit=True, cv=StratifiedKFold(), n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('svc', SVC(random_state=24112020))]),\n",
       "             n_jobs=4, param_grid={'svc__kernel': ['rbf', 'linear', 'poly']})"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train['redshift'].values.reshape(-1, 1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('svc', SVC(random_state=24112020))])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test['redshift'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see we obtain \"nearly perfect\" results using only one feature. This is a huge improvement in terms of training speed, prediction speed and model complexity (less complex models are often preferred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      GALAXY       0.98      0.98      0.98      1000\n",
      "         QSO       0.96      0.88      0.92       170\n",
      "        STAR       0.99      1.00      0.99       830\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.98      0.95      0.96      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering (K-Means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, lets try an unsupervised learning (a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision) model for clustering: K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing the best number of cluster: the elbow method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow curve')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distorsions = []\n",
    "labels = []\n",
    "centroids = []\n",
    "X = X[['ra', 'dec', 'redshift']]\n",
    "X_test = X_test[['ra', 'dec', 'redshift']]\n",
    "for k in range(2, 10):\n",
    "    clf = KMeans(n_clusters=k, random_state=24112020)\n",
    "    clf.fit(X)\n",
    "    labels.append(clf.predict(X_test))\n",
    "    distorsions.append(clf.inertia_)\n",
    "    centroids.append(clf.cluster_centers_)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(2, 10), distorsions)\n",
    "plt.grid(True)\n",
    "plt.title('Elbow curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4c2f52a2e0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure('K-Means on SDSS Dataset', figsize=(10,10))\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter(centroids[1][0][0], centroids[1][0][1], centroids[1][0][2], c='r', s=100)\n",
    "ax.scatter(centroids[1][1][0], centroids[1][1][1], centroids[1][1][2], c='r', s=100)\n",
    "ax.scatter(centroids[1][2][0], centroids[1][2][1], centroids[1][2][2], c='r', s=100)\n",
    "scatter = ax.scatter(X_test['ra'], X_test['dec'], X_test['redshift'], c=labels[1])\n",
    "fig.legend(handles=scatter.legend_elements()[0], labels=['GALAXY', 'QSO', 'STAR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jmoldon/dev/somachine2020/jacortes\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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     "output_type": "display_data"
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}