python3/notebooks/pandas-datetimeindex/datetime-index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1.3.2', '1.21.2', '3.4.3')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__,np.__version__, matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>name</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>john</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mary</td>\n",
       "      <td>1999-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peter</td>\n",
       "      <td>2000-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jeff</td>\n",
       "      <td>1995-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bill</td>\n",
       "      <td>1992-06-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name date_of_birth\n",
       "0   john    2000-01-01\n",
       "1   mary    1999-12-20\n",
       "2  peter    2000-11-01\n",
       "3   jeff    1995-02-25\n",
       "4   bill    1992-06-30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'name':[\n",
    "        'john','mary','peter','jeff','bill'\n",
    "    ],\n",
    "    'date_of_birth':[\n",
    "        '2000-01-01', '1999-12-20', '2000-11-01', '1995-02-25', '1992-06-30',\n",
    "    ],\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use date column as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>john</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-12-20</th>\n",
       "      <td>mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-11-01</th>\n",
       "      <td>peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-25</th>\n",
       "      <td>jeff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-06-30</th>\n",
       "      <td>bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name\n",
       "2000-01-01   john\n",
       "1999-12-20   mary\n",
       "2000-11-01  peter\n",
       "1995-02-25   jeff\n",
       "1992-06-30   bill"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_series = pd.to_datetime(df['date_of_birth'])\n",
    "\n",
    "datetime_index = pd.DatetimeIndex(datetime_series.values)\n",
    "\n",
    "df2=df.set_index(datetime_index)\n",
    "\n",
    "df2.drop('date_of_birth',axis=1,inplace=True)\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01', '1999-12-20', '2000-11-01', '1995-02-25',\n",
       "               '1992-06-30'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fill empty periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>name</th>\n",
       "      <th>day_born</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>john</td>\n",
       "      <td>1988-12-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mary</td>\n",
       "      <td>1988-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peter</td>\n",
       "      <td>1988-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jeff</td>\n",
       "      <td>1988-12-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bill</td>\n",
       "      <td>1988-12-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name    day_born\n",
       "0   john  1988-12-28\n",
       "1   mary  1988-12-25\n",
       "2  peter  1988-12-24\n",
       "3   jeff  1988-12-26\n",
       "4   bill  1988-12-30"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'name':[\n",
    "        'john','mary','peter','jeff','bill'\n",
    "    ],\n",
    "    'day_born':[\n",
    "        '1988-12-28', '1988-12-25', '1988-12-24', '1988-12-26',  '1988-12-30'\n",
    "    ],\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-12-24</th>\n",
       "      <td>peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-25</th>\n",
       "      <td>mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-26</th>\n",
       "      <td>jeff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-28</th>\n",
       "      <td>john</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-30</th>\n",
       "      <td>bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name\n",
       "1988-12-24  peter\n",
       "1988-12-25   mary\n",
       "1988-12-26   jeff\n",
       "1988-12-28   john\n",
       "1988-12-30   bill"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_series = pd.to_datetime(df['day_born'])\n",
    "\n",
    "datetime_index = pd.DatetimeIndex(datetime_series.values)\n",
    "\n",
    "df3=df.set_index(datetime_index)\n",
    "\n",
    "df3.drop('day_born',axis=1,inplace=True)\n",
    "\n",
    "df3.sort_index(inplace=True)\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1988-12-24', '1988-12-25', '1988-12-26', '1988-12-28',\n",
       "               '1988-12-30'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-12-24</th>\n",
       "      <td>peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-25</th>\n",
       "      <td>mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-26</th>\n",
       "      <td>jeff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-27</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-28</th>\n",
       "      <td>john</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-29</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-30</th>\n",
       "      <td>bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name\n",
       "1988-12-24  peter\n",
       "1988-12-25   mary\n",
       "1988-12-26   jeff\n",
       "1988-12-27    NaN\n",
       "1988-12-28   john\n",
       "1988-12-29    NaN\n",
       "1988-12-30   bill"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=df3.asfreq('D')\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1988-12-24', '1988-12-25', '1988-12-26', '1988-12-27',\n",
       "               '1988-12-28', '1988-12-29', '1988-12-30'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lag columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>31.474293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>18.360855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>45.865392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>13.293545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>10.255298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>53.480229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>34.337437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>16.192655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>68.528084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>99.677619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              reading\n",
       "2019-01-01  31.474293\n",
       "2019-01-02  18.360855\n",
       "2019-01-03  45.865392\n",
       "2019-01-04  13.293545\n",
       "2019-01-05  10.255298\n",
       "2019-01-06  53.480229\n",
       "2019-01-07  34.337437\n",
       "2019-01-08  16.192655\n",
       "2019-01-09  68.528084\n",
       "2019-01-10  99.677619"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    data={'reading': np.random.uniform(high=100,size=10)},\n",
    "    index=pd.to_datetime([date(2019,1,d) for d in range(1,11)])\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['reading_d_minus_1']=df['reading'].shift(1,freq='D')\n",
    "\n",
    "df['reading_d_minus_2']=df['reading'].shift(2,freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reading</th>\n",
       "      <th>reading_d_minus_1</th>\n",
       "      <th>reading_d_minus_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>45.865392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>53.480229</td>\n",
       "      <td>10.255298</td>\n",
       "      <td>13.293545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>34.337437</td>\n",
       "      <td>53.480229</td>\n",
       "      <td>10.255298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>16.192655</td>\n",
       "      <td>34.337437</td>\n",
       "      <td>53.480229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>68.528084</td>\n",
       "      <td>16.192655</td>\n",
       "      <td>34.337437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>99.677619</td>\n",
       "      <td>68.528084</td>\n",
       "      <td>16.192655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              reading  reading_d_minus_1  reading_d_minus_2\n",
       "2019-01-01  31.474293                NaN                NaN\n",
       "2019-01-02  18.360855          31.474293                NaN\n",
       "2019-01-03  45.865392          18.360855          31.474293\n",
       "2019-01-04  13.293545          45.865392          18.360855\n",
       "2019-01-05  10.255298          13.293545          45.865392\n",
       "2019-01-06  53.480229          10.255298          13.293545\n",
       "2019-01-07  34.337437          53.480229          10.255298\n",
       "2019-01-08  16.192655          34.337437          53.480229\n",
       "2019-01-09  68.528084          16.192655          34.337437\n",
       "2019-01-10  99.677619          68.528084          16.192655"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot counts by day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import date,datetime,timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# create a dummy dataset\n",
    "df = pd.DataFrame(\n",
    "    index=pd.to_datetime(\n",
    "        [datetime(2019,1,1)+timedelta(hours=h+10*np.random.uniform()) for h in range(0,1000)]\n",
    "    ),\n",
    "    data={\n",
    "        'browser': np.random.choice([\n",
    "            'chrome',\n",
    "            'firefox',\n",
    "            'safari',\n",
    "            'edge'\n",
    "        ],size=1000,replace=True)\n",
    "    },\n",
    "    \n",
    ")\n",
    "\n",
    "print(type(df.index))\n",
    "# 'pandas.core.indexes.datetimes.DatetimeIndex'\n",
    "\n",
    "# just to make it more intuitive\n",
    "df.index.name= 'login_datetime'\n",
    "\n",
    "df.groupby(by=[lambda dt: dt.strftime('%Y-%m-%d'),'browser']).size().groupby(level=0).apply(\n",
    "    lambda x: 100 * x / x.sum()\n",
    ").unstack().plot(kind='bar',stacked=True,legend='reverse',width=0.8)\n",
    "\n",
    "current_handles, current_labels = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "# sort or reorder the labels and handles\n",
    "reversed_handles = list(reversed(current_handles))\n",
    "reversed_labels = list(reversed(current_labels))\n",
    "\n",
    "# call plt.legend() with the new values\n",
    "plt.legend(reversed_handles,reversed_labels,loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.gcf().set_size_inches(12,5)\n",
    "plt.title('Agreggated number of visits to website, per day and browser')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}