dates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!DOCTYPE html>\n",
    "<html>\n",
    "<body>\n",
    "<div align=\"center\">\n",
    "<h2>Prepared by Asif Bhat</h2>\n",
    "<h1>Working with Dates in Pandas</h1>\n",
    "</div>\n",
    "    \n",
    "</body>\n",
    "</html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>3/20/1960</td>\n",
       "      <td>E453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>3/19/1981</td>\n",
       "      <td>E983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>9/12/1999</td>\n",
       "      <td>E675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>7/12/1967</td>\n",
       "      <td>E120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name        DOB EmpID\n",
       "0   Asif  3/20/1960  E453\n",
       "1  Basit  3/19/1981  E983\n",
       "2   John  9/12/1999  E675\n",
       "3   Hary  7/12/1967  E120"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Name     object\n",
       "DOB      object\n",
       "EmpID    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person = pd.DataFrame({'Name':['Asif' , 'Basit' , 'John', 'Hary'],\n",
    "                       'DOB': ['3/20/1960', '3/19/1981', '9/12/1999' , '7/12/1967'],\n",
    "                       'EmpID': ['E453', 'E983', 'E675','E120']})\n",
    "person\n",
    "person.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             object\n",
       "DOB      datetime64[ns]\n",
       "EmpID            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>1960-03-20</td>\n",
       "      <td>E453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>E983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>1999-09-12</td>\n",
       "      <td>E675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>1967-07-12</td>\n",
       "      <td>E120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name        DOB EmpID\n",
       "0   Asif 1960-03-20  E453\n",
       "1  Basit 1981-03-19  E983\n",
       "2   John 1999-09-12  E675\n",
       "3   Hary 1967-07-12  E120"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the datatype of the column to Datetime\n",
    "person['DOB']=pd.to_datetime(person['DOB']) \n",
    "person.dtypes\n",
    "person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>1960-03-20</td>\n",
       "      <td>E453</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1960</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>1999-09-12</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>1967-07-12</td>\n",
       "      <td>E120</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1967</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name        DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "0   Asif 1960-03-20  E453      3   20  1960           11            6   \n",
       "1  Basit 1981-03-19  E983      3   19  1981           12            3   \n",
       "2   John 1999-09-12  E675      9   12  1999           36            6   \n",
       "3   Hary 1967-07-12  E120      7   12  1967           28            2   \n",
       "\n",
       "    Day Name Month Name  \n",
       "0     Sunday      March  \n",
       "1   Thursday      March  \n",
       "2     Sunday  September  \n",
       "3  Wednesday       July  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Month , Day , Year , 'Day of Week' and 'Week of Year' from the Date field \n",
    "person['Month'] = person.DOB.dt.month\n",
    "person['Day'] = person.DOB.dt.day\n",
    "person['Year'] = person.DOB.dt.year\n",
    "person['Week Number'] =person.DOB.dt.isocalendar().week\n",
    "person['Day Of Week'] = person.DOB.dt.dayofweek\n",
    "person['Day Name']=pd.to_datetime(person['DOB']).dt.day_name()\n",
    "person['Month Name']=pd.to_datetime(person['DOB']).dt.month_name()\n",
    "person "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>20/03/1960</td>\n",
       "      <td>E453</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1960</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>19/03/1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12/09/1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>12/07/1967</td>\n",
       "      <td>E120</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1967</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "0   Asif  20/03/1960  E453      3   20  1960           11            6   \n",
       "1  Basit  19/03/1981  E983      3   19  1981           12            3   \n",
       "2   John  12/09/1999  E675      9   12  1999           36            6   \n",
       "3   Hary  12/07/1967  E120      7   12  1967           28            2   \n",
       "\n",
       "    Day Name Month Name  \n",
       "0     Sunday      March  \n",
       "1   Thursday      March  \n",
       "2     Sunday  September  \n",
       "3  Wednesday       July  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Name           object\n",
       "DOB            object\n",
       "EmpID          object\n",
       "Month           int64\n",
       "Day             int64\n",
       "Year            int64\n",
       "Week Number    UInt32\n",
       "Day Of Week     int64\n",
       "Day Name       object\n",
       "Month Name     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing Datetime format to '%d/%m/%Y' using strftime()\n",
    "person['DOB']=pd.to_datetime(person['DOB']).dt.strftime('%d/%m/%Y') # Note : This will change the datatype back to object\n",
    "person\n",
    "person.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>03-20-1960</td>\n",
       "      <td>E453</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1960</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>03-19-1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12-09-1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>12-07-1967</td>\n",
       "      <td>E120</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1967</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "0   Asif  03-20-1960  E453      3   20  1960           11            6   \n",
       "1  Basit  03-19-1981  E983      3   19  1981           12            3   \n",
       "2   John  12-09-1999  E675      9   12  1999           36            6   \n",
       "3   Hary  12-07-1967  E120      7   12  1967           28            2   \n",
       "\n",
       "    Day Name Month Name  \n",
       "0     Sunday      March  \n",
       "1   Thursday      March  \n",
       "2     Sunday  September  \n",
       "3  Wednesday       July  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Name           object\n",
       "DOB            object\n",
       "EmpID          object\n",
       "Month           int64\n",
       "Day             int64\n",
       "Year            int64\n",
       "Week Number    UInt32\n",
       "Day Of Week     int64\n",
       "Day Name       object\n",
       "Month Name     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing Datetime format to ''%m-%d-%Y' using strftime()\n",
    "person['DOB']=pd.to_datetime(person['DOB']).dt.strftime('%m-%d-%Y') # Note : This will change the datatype back to object\n",
    "person\n",
    "person.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
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       "      <th>Month Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>03-19-1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12-09-1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "1  Basit  03-19-1981  E983      3   19  1981           12            3   \n",
       "2   John  12-09-1999  E675      9   12  1999           36            6   \n",
       "\n",
       "   Day Name Month Name  \n",
       "1  Thursday      March  \n",
       "2    Sunday  September  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find employees who are born after 12-20-1980\n",
    "from datetime import date\n",
    "person[pd.to_datetime(person['DOB']) > pd.Timestamp(date(1980,12,20))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>03-19-1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12-09-1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "1  Basit  03-19-1981  E983      3   19  1981           12            3   \n",
       "2   John  12-09-1999  E675      9   12  1999           36            6   \n",
       "\n",
       "   Day Name Month Name  \n",
       "1  Thursday      March  \n",
       "2    Sunday  September  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find employees who are born after 12-20-1980\n",
    "from datetime import date\n",
    "person[pd.to_datetime(person['DOB']) > pd.Timestamp('1980/12/20')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>03-19-1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12-09-1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "1  Basit  03-19-1981  E983      3   19  1981           12            3   \n",
       "2   John  12-09-1999  E675      9   12  1999           36            6   \n",
       "\n",
       "   Day Name Month Name  \n",
       "1  Thursday      March  \n",
       "2    Sunday  September  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all records where DOB is between \"12-20-1980\" - \"12-20-2000\"\n",
    "from datetime import date\n",
    "person[(pd.to_datetime(person['DOB']) > pd.Timestamp(date(1980,12,20))) & \n",
    "       (pd.to_datetime(person['DOB']) < pd.Timestamp(date(2000,12,20)))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1960-03-20 00:00:00')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Min Date in a dataframe column\n",
    "pd.to_datetime(person['DOB']).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1999-12-09 00:00:00')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Max Date in a dataframe column\n",
    "pd.to_datetime(person['DOB']).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timestamp :2021-03-26 22:17:00.173312\n",
      "Current Date : 2021-03-26\n",
      "Yesterday: 2021-03-25\n",
      "Tomorrow: 2021-03-27\n",
      "Tomorrow: 2021-03-27 00:00:00\n",
      "Date after adding Business Day: 2021-03-29 00:00:00\n",
      "Date after adding 1 month to current date: 2021-04-26 00:00:00\n",
      "Date Difference in hours: 610.0\n"
     ]
    }
   ],
   "source": [
    "# Current timestamp\n",
    "timestamp = pd.to_datetime('now')\n",
    "print('Timestamp :{}'.format(timestamp))\n",
    "\n",
    "# Current Date (Today)\n",
    "current_date=pd.to_datetime('now').date()\n",
    "print('Current Date : {}'.format(current_date))\n",
    "\n",
    "# Yesterday\n",
    "yesterday = pd.to_datetime('now').date()- pd.Timedelta('1 day')\n",
    "print('Yesterday: {}'.format(yesterday))\n",
    "\n",
    "# tomorrow\n",
    "tomorrow = pd.to_datetime('now').date() + pd.Timedelta('1 day')\n",
    "print('Tomorrow: {}'.format(tomorrow))\n",
    "#OR \n",
    "tomorrow = pd.to_datetime('now').date() + pd.DateOffset(days=1)\n",
    "print('Tomorrow: {}'.format(tomorrow))\n",
    "\n",
    "#Add Business Day to current date\n",
    "add_buss_day=pd.to_datetime('now').date()+pd.offsets.BDay(1)\n",
    "print('Date after adding Business Day: {}'.format(add_buss_day)) # Saturday & Sunday will be excluded\n",
    "\n",
    "#Add 1 month to current date\n",
    "add_month=pd.to_datetime('now').date()+pd.DateOffset(months=1)\n",
    "print('Date after adding 1 month to current date: {}'.format(add_month)) \n",
    "\n",
    "# Date Difference in hours\n",
    "diff_hrs= (pd.to_datetime('2021-03-26 21:11:13') - pd.to_datetime('2021-03-01 11:11:13')).total_seconds()//3600\n",
    "print('Date Difference in hours: {}'.format(diff_hrs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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",
       "      <th>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>03-20-1960</td>\n",
       "      <td>E453</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1960</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>March</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>03-19-1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12-09-1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>12-07-1967</td>\n",
       "      <td>E120</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1967</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>July</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "0   Asif  03-20-1960  E453      3   20  1960           11            6   \n",
       "1  Basit  03-19-1981  E983      3   19  1981           12            3   \n",
       "2   John  12-09-1999  E675      9   12  1999           36            6   \n",
       "3   Hary  12-07-1967  E120      7   12  1967           28            2   \n",
       "\n",
       "    Day Name Month Name  Age  \n",
       "0     Sunday      March   61  \n",
       "1   Thursday      March   40  \n",
       "2     Sunday  September   22  \n",
       "3  Wednesday       July   54  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>DOB</th>\n",
       "      <th>EmpID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asif</td>\n",
       "      <td>03-20-1960</td>\n",
       "      <td>E453</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1960</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>March</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basit</td>\n",
       "      <td>03-19-1981</td>\n",
       "      <td>E983</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1981</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>12-09-1999</td>\n",
       "      <td>E675</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1999</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>September</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hary</td>\n",
       "      <td>12-07-1967</td>\n",
       "      <td>E120</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1967</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>July</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name         DOB EmpID  Month  Day  Year  Week Number  Day Of Week  \\\n",
       "0   Asif  03-20-1960  E453      3   20  1960           11            6   \n",
       "1  Basit  03-19-1981  E983      3   19  1981           12            3   \n",
       "2   John  12-09-1999  E675      9   12  1999           36            6   \n",
       "3   Hary  12-07-1967  E120      7   12  1967           28            2   \n",
       "\n",
       "    Day Name Month Name  Age  \n",
       "0     Sunday      March   61  \n",
       "1   Thursday      March   40  \n",
       "2     Sunday  September   22  \n",
       "3  Wednesday       July   54  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age of the person (Extract year from current time and subtract from Year column)\n",
    "person['Age'] = pd.to_datetime('now').year - person['Year']\n",
    "person\n",
    "\n",
    "# OR \n",
    "person['Age'] = pd.to_datetime('now').year - pd.to_datetime(person['DOB']).dt.year\n",
    "person "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959-01-01</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-01-02</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-01-04</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-01-05</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1959-01-06</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1959-01-07</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1959-01-08</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-01-09</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-01-10</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Births\n",
       "0  1959-01-01      35\n",
       "1  1959-01-02      32\n",
       "2  1959-01-03      30\n",
       "3  1959-01-04      31\n",
       "4  1959-01-05      44\n",
       "5  1959-01-06      29\n",
       "6  1959-01-07      45\n",
       "7  1959-01-08      43\n",
       "8  1959-01-09      38\n",
       "9  1959-01-10      27"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets work on simple dataset (Female birth Dataset)\n",
    "# The source of the dataset is credited to Newton (1988).\n",
    "female = pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-total-female-births.csv')\n",
    "female.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('364 days 00:00:00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min & max date to get the date range\n",
    "pd.to_datetime(female['Date']).max()-pd.to_datetime(female['Date']).min()  # This is one year of dataset that we need to anlyse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change datatype of Date column to Datetime\n",
    "female['Date'] = pd.to_datetime(female['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Date</th>\n",
       "      <th>Births</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-01-02</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-01-04</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-01-05</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Births  Month  Day  Year  Week Number  Day Of Week  Day Name  \\\n",
       "0 1959-01-01      35      1    1  1959            1            3  Thursday   \n",
       "1 1959-01-02      32      1    2  1959            1            4    Friday   \n",
       "2 1959-01-03      30      1    3  1959            1            5  Saturday   \n",
       "3 1959-01-04      31      1    4  1959            1            6    Sunday   \n",
       "4 1959-01-05      44      1    5  1959            2            0    Monday   \n",
       "\n",
       "  Month Name  \n",
       "0    January  \n",
       "1    January  \n",
       "2    January  \n",
       "3    January  \n",
       "4    January  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create helper columns\n",
    "female['Month'] = female.Date.dt.month\n",
    "female['Day'] = female.Date.dt.day\n",
    "female['Year'] = female.Date.dt.year\n",
    "female['Week Number'] =female.Date.dt.isocalendar().week\n",
    "female['Day Of Week'] = female.Date.dt.dayofweek\n",
    "female['Day Name']=pd.to_datetime(female['Date']).dt.day_name()\n",
    "female['Month Name']=pd.to_datetime(female['Date']).dt.month_name()\n",
    "\n",
    "# OR We can use below lines of code as well\n",
    "female['Month'] = female.Date.apply(lambda x:x.month)\n",
    "female['Day'] = female.Date.apply(lambda x:x.day)\n",
    "female['Year'] = female.Date.apply(lambda x:x.year)\n",
    "female['Week Number'] =female.Date.apply(lambda x:x.week)\n",
    "female['Day Of Week'] = female.Date.apply(lambda x:x.dayofweek)\n",
    "female['Day Name']=pd.to_datetime(female['Date']).apply(lambda x:x.day_name())\n",
    "female['Month Name']=pd.to_datetime(female['Date']).apply(lambda x:x.month_name())\n",
    "\n",
    "female.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1213"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total female births in the month of January\n",
    "female[female['Month Name'] =='January']['Births'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female births in January : 1213\n",
      "Female births in February : 1148\n",
      "Female births in March : 1218\n",
      "Female births in April : 1195\n",
      "Female births in May : 1208\n",
      "Female births in June : 1212\n",
      "Female births in July : 1300\n",
      "Female births in August : 1351\n",
      "Female births in September : 1446\n",
      "Female births in October : 1368\n",
      "Female births in November : 1350\n",
      "Female births in December : 1314\n"
     ]
    }
   ],
   "source": [
    "# Total female births in each month using for loop\n",
    "for i in female['Month Name'].unique():\n",
    "    print('Female births in {0} : {1}'.format(i,female[female['Month Name'] ==i]['Births'].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births\n",
       "Month Name        \n",
       "April         1195\n",
       "August        1351\n",
       "December      1314\n",
       "February      1148\n",
       "January       1213\n",
       "July          1300\n",
       "June          1212\n",
       "March         1218\n",
       "May           1208\n",
       "November      1350\n",
       "October       1368\n",
       "September     1446"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using \"group by\" to get female births in each month\n",
    "female.groupby('Month Name').sum()[['Births']] # Month Name column data is not in ascending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births\n",
       "Month Name        \n",
       "April         1195\n",
       "August        1351\n",
       "December      1314\n",
       "February      1148\n",
       "January       1213\n",
       "July          1300\n",
       "June          1212\n",
       "March         1218\n",
       "May           1208\n",
       "November      1350\n",
       "October       1368\n",
       "September     1446"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month Name'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "# Use Pivot table to get female births in each month\n",
    "pd.pivot_table(female,values=['Births'],index=['Month Name'],aggfunc=np.sum) # Month Name data is not in proper order.\n",
    "pd.pivot_table(female,values=['Births'],index=['Month Name'],aggfunc=np.sum).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will convert \"Month Name\" column into Categorical variable and specify the ordering\n",
    "order = ['January','February','March','April','May','June',\n",
    "         'July','August','September','October','November','December']\n",
    " \n",
    "female['Month Name']=pd.Categorical(female['Month Name'],order)"
   ]
  },
  {
   "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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births\n",
       "Month Name        \n",
       "January       1213\n",
       "February      1148\n",
       "March         1218\n",
       "April         1195\n",
       "May           1208\n",
       "June          1212\n",
       "July          1300\n",
       "August        1351\n",
       "September     1446\n",
       "October       1368\n",
       "November      1350\n",
       "December      1314"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female.groupby('Month Name').sum()[['Births']] # Now the output is much better after custom ordering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x432 with 0 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to get monthly female births using matplotlib library\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.bar(female.groupby('Month Name').sum().index,female.groupby('Month Name').sum()['Births'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month Name'>"
      ]
     },
     "execution_count": 25,
     "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": [
    "# Bar plot to get monthly female births using Pandas\n",
    "pd.pivot_table(female,values=['Births'],index=['Month Name'],aggfunc=np.sum).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>2283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>2182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>2142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Births\n",
       "Day Name         \n",
       "Monday       2139\n",
       "Tuesday      2275\n",
       "Wednesday    2280\n",
       "Thursday     2283\n",
       "Friday       2182\n",
       "Saturday     2142\n",
       "Sunday       2022"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same way we can implement custom ordering for Day Name field\n",
    "order=[ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "female['Day Name']=pd.Categorical(female['Day Name'],order)\n",
    "female.groupby('Day Name').sum()[['Births']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x432 with 0 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Bar Graph to show female births on day basis.\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.bar(female.groupby('Day Name').sum().index,female.groupby('Day Name').sum()['Births'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ffff7ae460>]"
      ]
     },
     "execution_count": 28,
     "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": [
    "# Daily female births\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(female['Date'],female['Births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Births</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-01-02</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-01-04</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-01-05</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1959-01-06</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1959-01-07</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1959-01-08</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-01-09</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-01-10</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1959-01-11</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1959-01-12</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1959-01-13</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1959-01-14</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1959-01-15</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1959-01-16</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1959-01-17</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1959-01-18</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1959-01-19</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1959-01-20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1959-01-21</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1959-01-22</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1959-01-23</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1959-01-24</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1959-01-25</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1959-01-26</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1959-01-27</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1959-01-28</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1959-01-29</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1959-01-30</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Births  Month  Day  Year  Week Number  Day Of Week   Day Name  \\\n",
       "1  1959-01-02      32      1    2  1959            1            4     Friday   \n",
       "2  1959-01-03      30      1    3  1959            1            5   Saturday   \n",
       "3  1959-01-04      31      1    4  1959            1            6     Sunday   \n",
       "4  1959-01-05      44      1    5  1959            2            0     Monday   \n",
       "5  1959-01-06      29      1    6  1959            2            1    Tuesday   \n",
       "6  1959-01-07      45      1    7  1959            2            2  Wednesday   \n",
       "7  1959-01-08      43      1    8  1959            2            3   Thursday   \n",
       "8  1959-01-09      38      1    9  1959            2            4     Friday   \n",
       "9  1959-01-10      27      1   10  1959            2            5   Saturday   \n",
       "10 1959-01-11      38      1   11  1959            2            6     Sunday   \n",
       "11 1959-01-12      33      1   12  1959            3            0     Monday   \n",
       "12 1959-01-13      55      1   13  1959            3            1    Tuesday   \n",
       "13 1959-01-14      47      1   14  1959            3            2  Wednesday   \n",
       "14 1959-01-15      45      1   15  1959            3            3   Thursday   \n",
       "15 1959-01-16      37      1   16  1959            3            4     Friday   \n",
       "16 1959-01-17      50      1   17  1959            3            5   Saturday   \n",
       "17 1959-01-18      43      1   18  1959            3            6     Sunday   \n",
       "18 1959-01-19      41      1   19  1959            4            0     Monday   \n",
       "19 1959-01-20      52      1   20  1959            4            1    Tuesday   \n",
       "20 1959-01-21      34      1   21  1959            4            2  Wednesday   \n",
       "21 1959-01-22      53      1   22  1959            4            3   Thursday   \n",
       "22 1959-01-23      39      1   23  1959            4            4     Friday   \n",
       "23 1959-01-24      32      1   24  1959            4            5   Saturday   \n",
       "24 1959-01-25      37      1   25  1959            4            6     Sunday   \n",
       "25 1959-01-26      43      1   26  1959            5            0     Monday   \n",
       "26 1959-01-27      39      1   27  1959            5            1    Tuesday   \n",
       "27 1959-01-28      35      1   28  1959            5            2  Wednesday   \n",
       "28 1959-01-29      44      1   29  1959            5            3   Thursday   \n",
       "29 1959-01-30      38      1   30  1959            5            4     Friday   \n",
       "\n",
       "   Month Name  \n",
       "1     January  \n",
       "2     January  \n",
       "3     January  \n",
       "4     January  \n",
       "5     January  \n",
       "6     January  \n",
       "7     January  \n",
       "8     January  \n",
       "9     January  \n",
       "10    January  \n",
       "11    January  \n",
       "12    January  \n",
       "13    January  \n",
       "14    January  \n",
       "15    January  \n",
       "16    January  \n",
       "17    January  \n",
       "18    January  \n",
       "19    January  \n",
       "20    January  \n",
       "21    January  \n",
       "22    January  \n",
       "23    January  \n",
       "24    January  \n",
       "25    January  \n",
       "26    January  \n",
       "27    January  \n",
       "28    January  \n",
       "29    January  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all records for the month of Janaury(1959-01-01 - 1959-01-31). \n",
    "# Using boolean is not good method when we are dealing with large datasets.\n",
    "female[(pd.to_datetime(female['Date']) > pd.Timestamp(date(1959,1,1))) & \n",
    "       (pd.to_datetime(female['Date']) < pd.Timestamp(date(1959,1,31)))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Births</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-02</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-03</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-04</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-05</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-27</th>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>1959</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-28</th>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-29</th>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-30</th>\n",
       "      <td>55</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31</th>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births  Month  Day  Year  Week Number  Day Of Week   Day Name  \\\n",
       "Date                                                                        \n",
       "1959-01-01      35      1    1  1959            1            3   Thursday   \n",
       "1959-01-02      32      1    2  1959            1            4     Friday   \n",
       "1959-01-03      30      1    3  1959            1            5   Saturday   \n",
       "1959-01-04      31      1    4  1959            1            6     Sunday   \n",
       "1959-01-05      44      1    5  1959            2            0     Monday   \n",
       "...            ...    ...  ...   ...          ...          ...        ...   \n",
       "1959-12-27      37     12   27  1959           52            6     Sunday   \n",
       "1959-12-28      52     12   28  1959           53            0     Monday   \n",
       "1959-12-29      48     12   29  1959           53            1    Tuesday   \n",
       "1959-12-30      55     12   30  1959           53            2  Wednesday   \n",
       "1959-12-31      50     12   31  1959           53            3   Thursday   \n",
       "\n",
       "           Month Name  \n",
       "Date                   \n",
       "1959-01-01    January  \n",
       "1959-01-02    January  \n",
       "1959-01-03    January  \n",
       "1959-01-04    January  \n",
       "1959-01-05    January  \n",
       "...               ...  \n",
       "1959-12-27   December  \n",
       "1959-12-28   December  \n",
       "1959-12-29   December  \n",
       "1959-12-30   December  \n",
       "1959-12-31   December  \n",
       "\n",
       "[365 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1959-01-01', '1959-01-02', '1959-01-03', '1959-01-04',\n",
       "               '1959-01-05', '1959-01-06', '1959-01-07', '1959-01-08',\n",
       "               '1959-01-09', '1959-01-10',\n",
       "               ...\n",
       "               '1959-12-22', '1959-12-23', '1959-12-24', '1959-12-25',\n",
       "               '1959-12-26', '1959-12-27', '1959-12-28', '1959-12-29',\n",
       "               '1959-12-30', '1959-12-31'],\n",
       "              dtype='datetime64[ns]', name='Date', length=365, freq=None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert date column into Datetime index for faster selection.\n",
    "female = female.set_index(['Date'])\n",
    "female\n",
    "female.index # DatetimeIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Births</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Week Number</th>\n",
       "      <th>Day Of Week</th>\n",
       "      <th>Day Name</th>\n",
       "      <th>Month Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-02</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-03</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-04</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-05</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-27</th>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>1959</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-28</th>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-29</th>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-30</th>\n",
       "      <td>55</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31</th>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>1959</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births  Month  Day  Year  Week Number  Day Of Week   Day Name  \\\n",
       "Date                                                                        \n",
       "1959-01-01      35      1    1  1959            1            3   Thursday   \n",
       "1959-01-02      32      1    2  1959            1            4     Friday   \n",
       "1959-01-03      30      1    3  1959            1            5   Saturday   \n",
       "1959-01-04      31      1    4  1959            1            6     Sunday   \n",
       "1959-01-05      44      1    5  1959            2            0     Monday   \n",
       "...            ...    ...  ...   ...          ...          ...        ...   \n",
       "1959-12-27      37     12   27  1959           52            6     Sunday   \n",
       "1959-12-28      52     12   28  1959           53            0     Monday   \n",
       "1959-12-29      48     12   29  1959           53            1    Tuesday   \n",
       "1959-12-30      55     12   30  1959           53            2  Wednesday   \n",
       "1959-12-31      50     12   31  1959           53            3   Thursday   \n",
       "\n",
       "           Month Name  \n",
       "Date                   \n",
       "1959-01-01    January  \n",
       "1959-01-02    January  \n",
       "1959-01-03    January  \n",
       "1959-01-04    January  \n",
       "1959-01-05    January  \n",
       "...               ...  \n",
       "1959-12-27   December  \n",
       "1959-12-28   December  \n",
       "1959-12-29   December  \n",
       "1959-12-30   December  \n",
       "1959-12-31   December  \n",
       "\n",
       "[365 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now lets select the data\n",
    "female.loc['1959'] # Get all data for year 1959"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'1960'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: -315619200000000000",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: Timestamp('1960-01-01 00:00:00')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    624\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: Timestamp('1960-01-01 00:00:00')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-9af28ea95a40>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfemale\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'1960'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# There is no data for 1960.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    877\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1108\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1109\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1110\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1057\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1058\u001b[0m         \u001b[1;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1059\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1061\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   3489\u001b[0m             \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3490\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3491\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3493\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    625\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    626\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 627\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig_key\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    628\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    629\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_maybe_cast_for_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTimestamp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '1960'"
     ]
    }
   ],
   "source": [
    "female.loc['1960'] # There is no data for 1960. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-02</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-03</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-04</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-05</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-06</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-07</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-08</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-09</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-10</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-11</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-12</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-13</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-14</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-15</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-16</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-17</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-18</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-19</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-20</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-21</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-22</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-23</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-24</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-25</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-26</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-27</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-28</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-29</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-30</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-31</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births\n",
       "Date              \n",
       "1959-01-01      35\n",
       "1959-01-02      32\n",
       "1959-01-03      30\n",
       "1959-01-04      31\n",
       "1959-01-05      44\n",
       "1959-01-06      29\n",
       "1959-01-07      45\n",
       "1959-01-08      43\n",
       "1959-01-09      38\n",
       "1959-01-10      27\n",
       "1959-01-11      38\n",
       "1959-01-12      33\n",
       "1959-01-13      55\n",
       "1959-01-14      47\n",
       "1959-01-15      45\n",
       "1959-01-16      37\n",
       "1959-01-17      50\n",
       "1959-01-18      43\n",
       "1959-01-19      41\n",
       "1959-01-20      52\n",
       "1959-01-21      34\n",
       "1959-01-22      53\n",
       "1959-01-23      39\n",
       "1959-01-24      32\n",
       "1959-01-25      37\n",
       "1959-01-26      43\n",
       "1959-01-27      39\n",
       "1959-01-28      35\n",
       "1959-01-29      44\n",
       "1959-01-30      38\n",
       "1959-01-31      24"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all rows for the month of Janaury(1959-01-01 - 1959-01-31).\n",
    "female.loc['1959-01-01':'1959-01-31'][['Births']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Births    1213\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total births in the month of January \n",
    "female.loc['1959-01-01':'1959-01-31'][['Births']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot line chart for January data using Pandas visualization\n",
    "female.loc['1959-01-01':'1959-01-31'][['Births']].plot(figsize=(13,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 936x360 with 0 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fffff43c70>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot line chart for January data using Matplotlib library\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.plot(female.loc['1959-01-01':'1959-01-31'][['Births']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>SampleData</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1920-01-31</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920-02-29</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920-03-31</th>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920-04-30</th>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920-05-31</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-31</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SampleData\n",
       "Date                  \n",
       "1920-01-31          11\n",
       "1920-02-29          62\n",
       "1920-03-31         192\n",
       "1920-04-30         152\n",
       "1920-05-31         146\n",
       "...                ...\n",
       "2019-08-31         198\n",
       "2019-09-30          83\n",
       "2019-10-31          29\n",
       "2019-11-30         194\n",
       "2019-12-31          51\n",
       "\n",
       "[1200 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate Test Dataset using date_range() and random.randint()\n",
    "df = pd.DataFrame({'Date':pd.date_range('1/19/1920', periods = 1200, freq ='M') ,'SampleData':np.random.randint(0,200,size=1200)})\n",
    "df=df.set_index(['Date']) # Set Date column as index\n",
    "df\n",
    "df.loc['2010':'2012'].plot(figsize=(16,5)) # Plot line chart for data between 2010-2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End"
   ]
  },
  {
   "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": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}