Dummy_Locations-Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tjP8cJt-eRqb"
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YHvIElYri1fk"
   },
   "source": [
    "This is a stripped file, using only the dates from the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ia3eXGEonBgH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['LN', 'TIME_EVENT', 'COUNTRY'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "file_path = r\"D:\\Study\\Coding\\Twitter\\ESWB\\sub_France.txt\"\n",
    "dummy_df = pd.read_csv(file_path, sep=\";\")\n",
    "\n",
    "print(dummy_df .columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>LN</th>\n",
       "      <th>TIME_EVENT</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>14/10/2019 15:00</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-10-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>14/10/2019 14:00</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-10-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228</td>\n",
       "      <td>18/08/2019 20:00</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-08-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>229</td>\n",
       "      <td>18/08/2019 19:00</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-08-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230</td>\n",
       "      <td>18/08/2019 19:00</td>\n",
       "      <td>FR</td>\n",
       "      <td>2019-08-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LN        TIME_EVENT COUNTRY       Date\n",
       "0   23  14/10/2019 15:00      FR 2019-10-14\n",
       "1   24  14/10/2019 14:00      FR 2019-10-14\n",
       "2  228  18/08/2019 20:00      FR 2019-08-18\n",
       "3  229  18/08/2019 19:00      FR 2019-08-18\n",
       "4  230  18/08/2019 19:00      FR 2019-08-18"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_df['Date'] = pd.to_datetime(dummy_df.TIME_EVENT.str[:10])\n",
    "dummy_df.head()\n",
    "#hail_france.info()\n",
    "\n",
    "# df['New_Sample'] = df.Sample.str[:1]\n",
    "# df['Date']= pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>DATE</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  Count\n",
       "0 2017-01-09      2\n",
       "1 2017-02-05      1\n",
       "2 2017-03-02      1\n",
       "3 2017-03-23      6\n",
       "4 2017-03-31      2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_df_date = dummy_df['Date'].dt.date.value_counts().sort_index().reset_index()\n",
    "dummy_df_date.columns = ['DATE','Count']\n",
    "dummy_df_date['DATE'] = pd.to_datetime(dummy_df_date['DATE'], dayfirst=True)\n",
    "dummy_df_date.head()\n",
    "# hail_france_date.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 108 entries, 0 to 107\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   DATE    108 non-null    datetime64[ns]\n",
      " 1   Count   108 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(1)\n",
      "memory usage: 1.8 KB\n"
     ]
    }
   ],
   "source": [
    "r = pd.date_range(start=dummy_df_date.DATE.min(), end=dummy_df_date.DATE.max())\n",
    "dummy_df_date.set_index('DATE').reindex(r).fillna(0.0).rename_axis('DATE').reset_index()\n",
    "dummy_df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_df_date = dummy_df_date.set_index(['DATE'])\n",
    "dummy_df_date.index = pd.to_datetime(dummy_df_date.index, unit='d')\n",
    "dummy_df_dateW = dummy_df_date.resample(\"W\").sum()\n",
    "dummy_df_dateW.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>DATE</th>\n",
       "      <th>Count</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-05</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-02-12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  Count  week\n",
       "0 2017-01-15      2     1\n",
       "1 2017-01-22      0     2\n",
       "2 2017-01-29      0     3\n",
       "3 2017-02-05      1     4\n",
       "4 2017-02-12      0     5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_df_dateW['week'] = dummy_df_dateW.index + 1\n",
    "dummy_df_dateW.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "years_fmt = mdates.DateFormatter('%Y')\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "#sns.barplot(x=hail_france_dateW.DATE,y='Count',data=hail_france_dateW)\n",
    "ax.bar(dummy_df_dateW.DATE, dummy_df_dateW.Count,  width =6, color='green')\n",
    "\n",
    "# format the ticks\n",
    "ax.xaxis.set_major_locator(years)\n",
    "ax.xaxis.set_major_formatter(years_fmt)\n",
    "ax.xaxis.set_minor_locator(months)\n",
    "\n",
    "datemin = datetime.date(dummy_df_dateW.DATE.min().year, 1, 1)\n",
    "datemax = datetime.date(dummy_df_dateW.DATE.max().year + 1, 1, 1)\n",
    "ax.set_xlim(datemin, datemax)\n",
    "\n",
    "ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "\n",
    "ax.set_ylabel('Count by Week')\n",
    "ax.set_title('Reports in France (2017-2019)')\n",
    "\n",
    "ax.tick_params(which='both', width=2)\n",
    "ax.tick_params(which='major', length=8)\n",
    "ax.tick_params(which='minor', length=5, color='r')\n",
    "\n",
    "ax.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Hail-Locations.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}