[6]Vectorized String Operations, Date Time.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorized String Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performing vectorized string operation on strings using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are vectorized operations\n",
    "a = np.array([1,2,3,4])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  8, 12, 16])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * 4 # an eg of vectorized operation... allow you to apply a \"single\" command to an entire array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'startswith'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# problem in vectorized opertions in vanilla python\u001b[39;00m\n\u001b[0;32m      2\u001b[0m s \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcat\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmat\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrat\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 4\u001b[0m [i\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m s]\n",
      "Cell \u001b[1;32mIn[5], line 4\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# problem in vectorized opertions in vanilla python\u001b[39;00m\n\u001b[0;32m      2\u001b[0m s \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcat\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmat\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrat\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 4\u001b[0m [i\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m s]\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'startswith'"
     ]
    }
   ],
   "source": [
    "# problem in vectorized opertions in vanilla python\n",
    "s = ['cat','mat',None,'rat']\n",
    "\n",
    "[i.startswith('c') for i in s]\n",
    "\n",
    "#'NoneType' object has no attribute 'startswith'. only 'string' has attribute 'startswith'.\n",
    "# vectorized opertions wont work well in normal python. so there is separayte fn in pandas.\n",
    "# i.e if there is missing values or None, python cant handle this functionalities.\n",
    "# also, process is slow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How pandas solves this issue?\n",
    "\n",
    "s = pd.Series(['cat','mat',None,'rat'])\n",
    "# 'str' is string accessor\n",
    "s.str.startswith('c')\n",
    "# here none is detected & ignored\n",
    "# fast and optimized\n",
    "\n",
    "#So pandas built its own functionality. it is fast, bcoz it is built on numpy, from C language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, Mr. Owen Harris\n",
       "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                                 Heikkinen, Miss. Laina\n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "886                                Montvila, Rev. Juozas\n",
       "887                         Graham, Miss. Margaret Edith\n",
       "888             Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "889                                Behr, Mr. Karl Howell\n",
       "890                                  Dooley, Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import titanic\n",
    "df = pd.read_csv('titanic.csv')\n",
    "df['Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Functions\n",
    "### lower/upper/capitalize/title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, mr. owen harris\n",
       "1      Cumings, mrs. john bradley (florence briggs th...\n",
       "2                                 Heikkinen, miss. laina\n",
       "3           Futrelle, mrs. jacques heath (lily may peel)\n",
       "4                               Allen, mr. william henry\n",
       "                             ...                        \n",
       "886                                Montvila, rev. juozas\n",
       "887                         Graham, miss. margaret edith\n",
       "888             Johnston, miss. catherine helen \"carrie\"\n",
       "889                                Behr, mr. karl howell\n",
       "890                                  Dooley, mr. patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.lower()\n",
    "df['Name'].str.upper()\n",
    "df['Name'].str.title()\n",
    "df['Name'].str.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.len() #no. of characters in each passengers name\n",
    "df['Name'].str.len().max() #max length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.len()==82\n",
    "df['Name'][df['Name'].str.len()==82].values[0]  #name with max length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### strip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hiii'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"                   Hiii                              \".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, Mr. Owen Harris\n",
       "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                                 Heikkinen, Miss. Laina\n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "886                                Montvila, Rev. Juozas\n",
       "887                         Graham, Miss. Margaret Edith\n",
       "888             Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "889                                Behr, Mr. Karl Howell\n",
       "890                                  Dooley, Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split,get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Name'].str.split(',')\n",
    "df['lastname'] = df['Name'].str.split(',').str.get(0) #if u want only surname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>lastname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>S</td>\n",
       "      <td>Braund</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cumings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Futrelle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Allen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked   lastname  \n",
       "0      0         A/5 21171   7.2500   NaN        S     Braund  \n",
       "1      0          PC 17599  71.2833   C85        C    Cumings  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  Heikkinen  \n",
       "3      0            113803  53.1000  C123        S   Futrelle  \n",
       "4      0            373450   8.0500   NaN        S      Allen  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                  Mr. Owen Harris\n",
       "1       Mrs. John Bradley (Florence Briggs Thayer)\n",
       "2                                      Miss. Laina\n",
       "3               Mrs. Jacques Heath (Lily May Peel)\n",
       "4                                Mr. William Henry\n",
       "                          ...                     \n",
       "886                                    Rev. Juozas\n",
       "887                           Miss. Margaret Edith\n",
       "888                 Miss. Catherine Helen \"Carrie\"\n",
       "889                                Mr. Karl Howell\n",
       "890                                    Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.split(',').str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                  Mr. Owen Harris\n",
       "1       Mrs. John Bradley (Florence Briggs Thayer)\n",
       "2                                      Miss. Laina\n",
       "3               Mrs. Jacques Heath (Lily May Peel)\n",
       "4                                Mr. William Henry\n",
       "                          ...                     \n",
       "886                                    Rev. Juozas\n",
       "887                           Miss. Margaret Edith\n",
       "888                 Miss. Catherine Helen \"Carrie\"\n",
       "889                                Mr. Karl Howell\n",
       "890                                    Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str.split(',').str.get(1).str.strip('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['title','firstname']] = df['Name'].str.split(',').str.get(1).str.strip().str.split(' ', n=1, expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>lastname</th>\n",
       "      <th>title</th>\n",
       "      <th>firstname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>John Bradley (Florence Briggs Thayer)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>William Henry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked   lastname  title  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S     Braund    Mr.   \n",
       "1      0          PC 17599  71.2833   C85        C    Cumings   Mrs.   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  Heikkinen  Miss.   \n",
       "3      0            113803  53.1000  C123        S   Futrelle   Mrs.   \n",
       "4      0            373450   8.0500   NaN        S      Allen    Mr.   \n",
       "\n",
       "                               firstname  \n",
       "0                            Owen Harris  \n",
       "1  John Bradley (Florence Briggs Thayer)  \n",
       "2                                  Laina  \n",
       "3          Jacques Heath (Lily May Peel)  \n",
       "4                          William Henry  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr.          517\n",
       "Miss.        182\n",
       "Mrs.         125\n",
       "Master.       40\n",
       "Dr.            7\n",
       "Rev.           6\n",
       "Mlle.          2\n",
       "Major.         2\n",
       "Col.           2\n",
       "the            1\n",
       "Capt.          1\n",
       "Ms.            1\n",
       "Sir.           1\n",
       "Lady.          1\n",
       "Mme.           1\n",
       "Don.           1\n",
       "Jonkheer.      1\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_6028\\438541295.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['title'] = df['title'].str.replace('Ms.','Miss.')\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_6028\\438541295.py:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['title'] = df['title'].str.replace('Mlle.','Miss.')\n"
     ]
    }
   ],
   "source": [
    "df['title'] = df['title'].str.replace('Ms.','Miss.')\n",
    "df['title'] = df['title'].str.replace('Mlle.','Miss.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr.          517\n",
       "Miss.        185\n",
       "Mrs.         125\n",
       "Master.       40\n",
       "Dr.            7\n",
       "Rev.           6\n",
       "Major.         2\n",
       "Col.           2\n",
       "Don.           1\n",
       "Mme.           1\n",
       "Lady.          1\n",
       "Sir.           1\n",
       "Capt.          1\n",
       "the            1\n",
       "Jonkheer.      1\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filtering...\n",
    "### startswith/endswith......isdigit/isalpha...contains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>lastname</th>\n",
       "      <th>title</th>\n",
       "      <th>firstname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stewart, Mr. Albert A</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17605</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Albert A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>304</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Keane, Miss. Nora A</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>226593</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>E101</td>\n",
       "      <td>Q</td>\n",
       "      <td>Keane</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Nora A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                   Name     Sex  Age  SibSp  \\\n",
       "64            65         0       1  Stewart, Mr. Albert A    male  NaN      0   \n",
       "303          304         1       2    Keane, Miss. Nora A  female  NaN      0   \n",
       "\n",
       "     Parch    Ticket     Fare Cabin Embarked lastname  title firstname  \n",
       "64       0  PC 17605  27.7208   NaN        C  Stewart    Mr.  Albert A  \n",
       "303      0    226593  12.3500  E101        Q    Keane  Miss.    Nora A  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['firstname'].str.endswith('A')\n",
    "df[df['firstname'].str.endswith('A')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>lastname</th>\n",
       "      <th>title</th>\n",
       "      <th>firstname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked, lastname, title, firstname]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['firstname'].str.isdigit()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>42</td>\n",
       "      <td>0</td>\n",
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       "      <th>45</th>\n",
       "      <td>46</td>\n",
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       "      <td>Doling, Mrs. John T (Ada Julia Bone)</td>\n",
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       "      <td>231919</td>\n",
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       "      <th>112</th>\n",
       "      <td>113</td>\n",
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       "      <td>Barton, Mr. David John</td>\n",
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       "      <td>0</td>\n",
       "      <td>324669</td>\n",
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       "      <td>118</td>\n",
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       "      <td>161</td>\n",
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       "      <td>163</td>\n",
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       "      <td>166</td>\n",
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       "      <td>Master.</td>\n",
       "      <td>Frank John William \"Frankie\"</td>\n",
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       "      <th>168</th>\n",
       "      <td>169</td>\n",
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       "      <td>Baumann, Mr. John D</td>\n",
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       "      <td>Mr.</td>\n",
       "      <td>John D</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>189</td>\n",
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       "      <td>Bourke, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>364849</td>\n",
       "      <td>15.5000</td>\n",
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       "      <td>John</td>\n",
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       "      <td>Perkin, Mr. John Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21174</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Perkin</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mellors, Mr. William John</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SW/PP 751</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Mellors</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>William John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lovell, Mr. John Hall (\"Henry\")</td>\n",
       "      <td>male</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21173</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Lovell</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Hall (\"Henry\")</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Sage</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>George John Jr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>329</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Goldsmith, Mrs. Frank John (Emily Alice Brown)</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>363291</td>\n",
       "      <td>20.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Goldsmith</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Frank John (Emily Alice Brown)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>402</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Adams, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>341826</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Adams</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>419</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Matthews, Mr. William John</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28228</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Matthews</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>William John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>468</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Smart, Mr. John Montgomery</td>\n",
       "      <td>male</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113792</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Smart</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Montgomery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>528</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Farthing, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17483</td>\n",
       "      <td>221.7792</td>\n",
       "      <td>C95</td>\n",
       "      <td>S</td>\n",
       "      <td>Farthing</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>549</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Goldsmith, Mr. Frank John</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>363291</td>\n",
       "      <td>20.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Goldsmith</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Frank John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Davies, Master. John Morgan Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 33112</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Davies</td>\n",
       "      <td>Master.</td>\n",
       "      <td>John Morgan Jr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>551</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thayer, Mr. John Borland Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17421</td>\n",
       "      <td>110.8833</td>\n",
       "      <td>C70</td>\n",
       "      <td>C</td>\n",
       "      <td>Thayer</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Borland Jr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Simmons, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392082</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Simmons</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>573</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Flynn, Mr. John Irwin (\"Irving\")</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17474</td>\n",
       "      <td>26.3875</td>\n",
       "      <td>E25</td>\n",
       "      <td>S</td>\n",
       "      <td>Flynn</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Irwin (\"Irving\")</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>575</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rush, Mr. Alfred George John</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4. 20589</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Rush</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Alfred George John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>582</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thayer, Mrs. John Borland (Marian Longstreth M...</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17421</td>\n",
       "      <td>110.8833</td>\n",
       "      <td>C68</td>\n",
       "      <td>C</td>\n",
       "      <td>Thayer</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>John Borland (Marian Longstreth Morris)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>584</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ross, Mr. John Hugo</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13049</td>\n",
       "      <td>40.1250</td>\n",
       "      <td>A10</td>\n",
       "      <td>C</td>\n",
       "      <td>Ross</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Hugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>587</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Jarvis, Mr. John Denzil</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>237565</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Jarvis</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Denzil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>595</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Chapman, Mr. John Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/AH 29037</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Chapman</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Horgan, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370377</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>Horgan</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Bowen, Mr. David John \"Dai\"</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54636</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Bowen</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>David John \"Dai\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>658</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Bourke, Mrs. John (Catherine)</td>\n",
       "      <td>female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>364849</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>Bourke</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>John (Catherine)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>695</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Weir, Col. John</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113800</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Weir</td>\n",
       "      <td>Col.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thayer, Mr. John Borland</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17421</td>\n",
       "      <td>110.8833</td>\n",
       "      <td>C68</td>\n",
       "      <td>C</td>\n",
       "      <td>Thayer</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Borland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>701</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
       "      <td>Astor</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>John Jacob (Madeleine Talmadge Force)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>734</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Berriman, Mr. William John</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28425</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Berriman</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>William John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>761</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Garfirth, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>358585</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Garfirth</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>766</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogeboom, Mrs. John C (Anna Andrews)</td>\n",
       "      <td>female</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D11</td>\n",
       "      <td>S</td>\n",
       "      <td>Hogeboom</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>John C (Anna Andrews)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>819</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Holm, Mr. John Fredrik Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C 7075</td>\n",
       "      <td>6.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Holm</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John Fredrik Alexander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>823</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19972</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Reuchlin</td>\n",
       "      <td>Jonkheer.</td>\n",
       "      <td>John George</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>826</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Flynn, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>368323</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>Flynn</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>849</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Harper, Rev. John</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>248727</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Harper</td>\n",
       "      <td>Rev.</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Gill</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>John William</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "41            42         0       2   \n",
       "45            46         0       3   \n",
       "98            99         1       2   \n",
       "112          113         0       3   \n",
       "117          118         0       2   \n",
       "160          161         0       3   \n",
       "162          163         0       3   \n",
       "165          166         1       3   \n",
       "168          169         0       1   \n",
       "188          189         0       3   \n",
       "212          213         0       3   \n",
       "226          227         1       2   \n",
       "227          228         0       3   \n",
       "324          325         0       3   \n",
       "328          329         1       3   \n",
       "401          402         0       3   \n",
       "418          419         0       2   \n",
       "467          468         0       1   \n",
       "527          528         0       1   \n",
       "548          549         0       3   \n",
       "549          550         1       2   \n",
       "550          551         1       1   \n",
       "563          564         0       3   \n",
       "572          573         1       1   \n",
       "574          575         0       3   \n",
       "581          582         1       1   \n",
       "583          584         0       1   \n",
       "586          587         0       2   \n",
       "594          595         0       2   \n",
       "613          614         0       3   \n",
       "624          625         0       3   \n",
       "657          658         0       3   \n",
       "694          695         0       1   \n",
       "698          699         0       1   \n",
       "700          701         1       1   \n",
       "733          734         0       2   \n",
       "760          761         0       3   \n",
       "765          766         1       1   \n",
       "818          819         0       3   \n",
       "822          823         0       1   \n",
       "825          826         0       3   \n",
       "848          849         0       2   \n",
       "864          865         0       2   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "41   Turpin, Mrs. William John Robert (Dorothy Ann ...  female  27.0      1   \n",
       "45                            Rogers, Mr. William John    male   NaN      0   \n",
       "98                Doling, Mrs. John T (Ada Julia Bone)  female  34.0      0   \n",
       "112                             Barton, Mr. David John    male  22.0      0   \n",
       "117                    Turpin, Mr. William John Robert    male  29.0      1   \n",
       "160                           Cribb, Mr. John Hatfield    male  44.0      0   \n",
       "162                         Bengtsson, Mr. John Viktor    male  26.0      0   \n",
       "165    Goldsmith, Master. Frank John William \"Frankie\"    male   9.0      0   \n",
       "168                                Baumann, Mr. John D    male   NaN      0   \n",
       "188                                   Bourke, Mr. John    male  40.0      1   \n",
       "212                             Perkin, Mr. John Henry    male  22.0      0   \n",
       "226                          Mellors, Mr. William John    male  19.0      0   \n",
       "227                    Lovell, Mr. John Hall (\"Henry\")    male  20.5      0   \n",
       "324                           Sage, Mr. George John Jr    male   NaN      8   \n",
       "328     Goldsmith, Mrs. Frank John (Emily Alice Brown)  female  31.0      1   \n",
       "401                                    Adams, Mr. John    male  26.0      0   \n",
       "418                         Matthews, Mr. William John    male  30.0      0   \n",
       "467                         Smart, Mr. John Montgomery    male  56.0      0   \n",
       "527                                 Farthing, Mr. John    male   NaN      0   \n",
       "548                          Goldsmith, Mr. Frank John    male  33.0      1   \n",
       "549                     Davies, Master. John Morgan Jr    male   8.0      1   \n",
       "550                        Thayer, Mr. John Borland Jr    male  17.0      0   \n",
       "563                                  Simmons, Mr. John    male   NaN      0   \n",
       "572                   Flynn, Mr. John Irwin (\"Irving\")    male  36.0      0   \n",
       "574                       Rush, Mr. Alfred George John    male  16.0      0   \n",
       "581  Thayer, Mrs. John Borland (Marian Longstreth M...  female  39.0      1   \n",
       "583                                Ross, Mr. John Hugo    male  36.0      0   \n",
       "586                            Jarvis, Mr. John Denzil    male  47.0      0   \n",
       "594                            Chapman, Mr. John Henry    male  37.0      1   \n",
       "613                                   Horgan, Mr. John    male   NaN      0   \n",
       "624                        Bowen, Mr. David John \"Dai\"    male  21.0      0   \n",
       "657                      Bourke, Mrs. John (Catherine)  female  32.0      1   \n",
       "694                                    Weir, Col. John    male  60.0      0   \n",
       "698                           Thayer, Mr. John Borland    male  49.0      1   \n",
       "700  Astor, Mrs. John Jacob (Madeleine Talmadge Force)  female  18.0      1   \n",
       "733                         Berriman, Mr. William John    male  23.0      0   \n",
       "760                                 Garfirth, Mr. John    male   NaN      0   \n",
       "765               Hogeboom, Mrs. John C (Anna Andrews)  female  51.0      1   \n",
       "818                   Holm, Mr. John Fredrik Alexander    male  43.0      0   \n",
       "822                    Reuchlin, Jonkheer. John George    male  38.0      0   \n",
       "825                                    Flynn, Mr. John    male   NaN      0   \n",
       "848                                  Harper, Rev. John    male  28.0      0   \n",
       "864                             Gill, Mr. John William    male  24.0      0   \n",
       "\n",
       "     Parch           Ticket      Fare    Cabin Embarked   lastname      title  \\\n",
       "1        0         PC 17599   71.2833      C85        C    Cumings       Mrs.   \n",
       "41       0            11668   21.0000      NaN        S     Turpin       Mrs.   \n",
       "45       0  S.C./A.4. 23567    8.0500      NaN        S     Rogers        Mr.   \n",
       "98       1           231919   23.0000      NaN        S     Doling       Mrs.   \n",
       "112      0           324669    8.0500      NaN        S     Barton        Mr.   \n",
       "117      0            11668   21.0000      NaN        S     Turpin        Mr.   \n",
       "160      1           371362   16.1000      NaN        S      Cribb        Mr.   \n",
       "162      0           347068    7.7750      NaN        S  Bengtsson        Mr.   \n",
       "165      2           363291   20.5250      NaN        S  Goldsmith    Master.   \n",
       "168      0         PC 17318   25.9250      NaN        S    Baumann        Mr.   \n",
       "188      1           364849   15.5000      NaN        Q     Bourke        Mr.   \n",
       "212      0        A/5 21174    7.2500      NaN        S     Perkin        Mr.   \n",
       "226      0        SW/PP 751   10.5000      NaN        S    Mellors        Mr.   \n",
       "227      0        A/5 21173    7.2500      NaN        S     Lovell        Mr.   \n",
       "324      2         CA. 2343   69.5500      NaN        S       Sage        Mr.   \n",
       "328      1           363291   20.5250      NaN        S  Goldsmith       Mrs.   \n",
       "401      0           341826    8.0500      NaN        S      Adams        Mr.   \n",
       "418      0            28228   13.0000      NaN        S   Matthews        Mr.   \n",
       "467      0           113792   26.5500      NaN        S      Smart        Mr.   \n",
       "527      0         PC 17483  221.7792      C95        S   Farthing        Mr.   \n",
       "548      1           363291   20.5250      NaN        S  Goldsmith        Mr.   \n",
       "549      1       C.A. 33112   36.7500      NaN        S     Davies    Master.   \n",
       "550      2            17421  110.8833      C70        C     Thayer        Mr.   \n",
       "563      0  SOTON/OQ 392082    8.0500      NaN        S    Simmons        Mr.   \n",
       "572      0         PC 17474   26.3875      E25        S      Flynn        Mr.   \n",
       "574      0       A/4. 20589    8.0500      NaN        S       Rush        Mr.   \n",
       "581      1            17421  110.8833      C68        C     Thayer       Mrs.   \n",
       "583      0            13049   40.1250      A10        C       Ross        Mr.   \n",
       "586      0           237565   15.0000      NaN        S     Jarvis        Mr.   \n",
       "594      0      SC/AH 29037   26.0000      NaN        S    Chapman        Mr.   \n",
       "613      0           370377    7.7500      NaN        Q     Horgan        Mr.   \n",
       "624      0            54636   16.1000      NaN        S      Bowen        Mr.   \n",
       "657      1           364849   15.5000      NaN        Q     Bourke       Mrs.   \n",
       "694      0           113800   26.5500      NaN        S       Weir       Col.   \n",
       "698      1            17421  110.8833      C68        C     Thayer        Mr.   \n",
       "700      0         PC 17757  227.5250  C62 C64        C      Astor       Mrs.   \n",
       "733      0            28425   13.0000      NaN        S   Berriman        Mr.   \n",
       "760      0           358585   14.5000      NaN        S   Garfirth        Mr.   \n",
       "765      0            13502   77.9583      D11        S   Hogeboom       Mrs.   \n",
       "818      0           C 7075    6.4500      NaN        S       Holm        Mr.   \n",
       "822      0            19972    0.0000      NaN        S   Reuchlin  Jonkheer.   \n",
       "825      0           368323    6.9500      NaN        Q      Flynn        Mr.   \n",
       "848      1           248727   33.0000      NaN        S     Harper       Rev.   \n",
       "864      0           233866   13.0000      NaN        S       Gill        Mr.   \n",
       "\n",
       "                                       firstname  \n",
       "1          John Bradley (Florence Briggs Thayer)  \n",
       "41   William John Robert (Dorothy Ann Wonnacott)  \n",
       "45                                  William John  \n",
       "98                       John T (Ada Julia Bone)  \n",
       "112                                   David John  \n",
       "117                          William John Robert  \n",
       "160                                John Hatfield  \n",
       "162                                  John Viktor  \n",
       "165                 Frank John William \"Frankie\"  \n",
       "168                                       John D  \n",
       "188                                         John  \n",
       "212                                   John Henry  \n",
       "226                                 William John  \n",
       "227                          John Hall (\"Henry\")  \n",
       "324                               George John Jr  \n",
       "328               Frank John (Emily Alice Brown)  \n",
       "401                                         John  \n",
       "418                                 William John  \n",
       "467                              John Montgomery  \n",
       "527                                         John  \n",
       "548                                   Frank John  \n",
       "549                               John Morgan Jr  \n",
       "550                              John Borland Jr  \n",
       "563                                         John  \n",
       "572                        John Irwin (\"Irving\")  \n",
       "574                           Alfred George John  \n",
       "581      John Borland (Marian Longstreth Morris)  \n",
       "583                                    John Hugo  \n",
       "586                                  John Denzil  \n",
       "594                                   John Henry  \n",
       "613                                         John  \n",
       "624                             David John \"Dai\"  \n",
       "657                             John (Catherine)  \n",
       "694                                         John  \n",
       "698                                 John Borland  \n",
       "700        John Jacob (Madeleine Talmadge Force)  \n",
       "733                                 William John  \n",
       "760                                         John  \n",
       "765                        John C (Anna Andrews)  \n",
       "818                       John Fredrik Alexander  \n",
       "822                                  John George  \n",
       "825                                         John  \n",
       "848                                         John  \n",
       "864                                 John William  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applying regex\n",
    "# contains\n",
    "# search john -> both case\n",
    "\n",
    "df[df['firstname'].str.contains('john',case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>lastname</th>\n",
       "      <th>title</th>\n",
       "      <th>firstname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>Braund</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>John Bradley (Florence Briggs Thayer)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>Moran</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>McCarthy</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Timothy J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Sutehall</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Henry Jr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>Graham</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Margaret Edith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Johnston</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Catherine Helen \"Carrie\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>Behr</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Karl Howell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>Dooley</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Patrick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "..           ...       ...     ...   \n",
       "884          885         0       3   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "5                                     Moran, Mr. James    male   NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked   lastname  title  \\\n",
       "0        0         A/5 21171   7.2500   NaN        S     Braund    Mr.   \n",
       "1        0          PC 17599  71.2833   C85        C    Cumings   Mrs.   \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  Heikkinen  Miss.   \n",
       "5        0            330877   8.4583   NaN        Q      Moran    Mr.   \n",
       "6        0             17463  51.8625   E46        S   McCarthy    Mr.   \n",
       "..     ...               ...      ...   ...      ...        ...    ...   \n",
       "884      0   SOTON/OQ 392076   7.0500   NaN        S   Sutehall    Mr.   \n",
       "887      0            112053  30.0000   B42        S     Graham  Miss.   \n",
       "888      2        W./C. 6607  23.4500   NaN        S   Johnston  Miss.   \n",
       "889      0            111369  30.0000  C148        C       Behr    Mr.   \n",
       "890      0            370376   7.7500   NaN        Q     Dooley    Mr.   \n",
       "\n",
       "                                 firstname  \n",
       "0                              Owen Harris  \n",
       "1    John Bradley (Florence Briggs Thayer)  \n",
       "2                                    Laina  \n",
       "5                                    James  \n",
       "6                                Timothy J  \n",
       "..                                     ...  \n",
       "884                               Henry Jr  \n",
       "887                         Margaret Edith  \n",
       "888               Catherine Helen \"Carrie\"  \n",
       "889                            Karl Howell  \n",
       "890                                Patrick  \n",
       "\n",
       "[671 rows x 15 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find lastnames with start and end char vowel\n",
    "df[df['lastname'].str.contains('^[^aeiouAEIOU].+[^aeiouAEIOU]$')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Brau\n",
       "1      Cumi\n",
       "2      Heik\n",
       "3      Futr\n",
       "4      Alle\n",
       "       ... \n",
       "886    Mont\n",
       "887    Grah\n",
       "888    John\n",
       "889    Behr\n",
       "890    Dool\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#slicing\n",
    "df['Name'].str[:4] #first 4 letters of every name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    Ban,M.Oe ars\n",
       "1      Cmns r.Jh rde Foec rgsTae)\n",
       "2                     Hiknn is an\n",
       "3          Ftel,Ms aqe et Ll a el\n",
       "4                    Aln r ila er\n",
       "                  ...            \n",
       "886                   Mnvl,Rv uzs\n",
       "887                Gaa,Ms.Mrae dt\n",
       "888          Jhso,Ms.CteieHln\"are\n",
       "889                   Bh,M.Kr oel\n",
       "890                    Doe,M.Ptik\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str[::2] #alternate characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                sirraH newO .rM ,dnuarB\n",
       "1      )reyahT sggirB ecnerolF( yeldarB nhoJ .srM ,sg...\n",
       "2                                 aniaL .ssiM ,nenikkieH\n",
       "3           )leeP yaM yliL( htaeH seuqcaJ .srM ,ellertuF\n",
       "4                               yrneH mailliW .rM ,nellA\n",
       "                             ...                        \n",
       "886                                sazouJ .veR ,alivtnoM\n",
       "887                         htidE teragraM .ssiM ,maharG\n",
       "888             \"eirraC\" neleH enirehtaC .ssiM ,notsnhoJ\n",
       "889                                llewoH lraK .rM ,rheB\n",
       "890                                  kcirtaP .rM ,yelooD\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].str[::-1] #reverse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATE,TIME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timestamp Object:-\n",
    "Time stamps reference particular moments in time (e.g., Oct 24th, 2022 at 7:00pm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Timestamp objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-02-07 00:00:00')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2023/2/7')  #year-month-day hour:minute:sec, since time is not specified, midnight time is assumed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.Timestamp('2023/2/7'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-02-07 00:00:00')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2023-2-7')          #diff. formats\n",
    "pd.Timestamp('2023, 2, 7')          #provide space after comma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 00:00:00')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2023') #if only year is specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-03-07 00:00:00')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('7th March 2023') #using texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-05 09:21:00')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# providing time also\n",
    "pd.Timestamp('5th January 2023 9:21AM')\n",
    "# pd.Timestamp('2023/1/5/9/21')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-05 21:21:00')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AM and PM\n",
    "pd.Timestamp('5th January 2023 9:21AM')\n",
    "pd.Timestamp('5th January 2023 9:21PM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 1, 5, 9, 21, 56)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using datetime.datetime object in python\n",
    "import datetime as dt\n",
    "\n",
    "dt.datetime(2023,1,5,9,21,56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-05 09:21:56')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Timestamp(dt.datetime(2023,1,5,9,21,56))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adv. of timestamp\n",
    "# fetching attributes\n",
    "x.year\n",
    "x.month\n",
    "x.day\n",
    "x.hour\n",
    "x.minute\n",
    "x.second"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# why separate objects to handle data and time when python already has datetime functionality?\n",
    "\n",
    "syntax wise datetime is very convenient\n",
    "But the performance takes a hit while working with huge data. List vs Numpy Array\n",
    "The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy.\n",
    "The datetime64 dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('2015-07-04', dtype='datetime64[D]')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "date = np.array('2015-07-04', dtype=np.datetime64)\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
       "       '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
       "       '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
       "      dtype='datetime64[D]')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + np.arange(12) #support vectorised opersations"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Because of the uniform type in NumPy datetime64 arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's datetime objects, especially as arrays get large\n",
    "\n",
    "Pandas Timestamp object combines the ease-of-use of python datetime with the efficient storage and vectorized interface of numpy.datetime64\n",
    "\n",
    "From a group of these Timestamp objects, Pandas can construct a DatetimeIndex that can be used to index data in a Series or DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DatetimeIndex Object"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "A collection of pandas timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-01', '2022-01-01', '2021-01-01'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from strings\n",
    "pd.DatetimeIndex(['2023/1/1','2022/1/1','2021/1/1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.DatetimeIndex(['2023/1/1','2022/1/1','2021/1/1']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-01', '2022-01-01', '2021-01-01'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using python datetime object\n",
    "pd.DatetimeIndex([dt.datetime(2023,1,1),dt.datetime(2022,1,1),dt.datetime(2021,1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using pd.timestamps\n",
    "dt_index = pd.DatetimeIndex([pd.Timestamp(2023,1,1),pd.Timestamp(2022,1,1),pd.Timestamp(2021,1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023-01-01    1\n",
       "2022-01-01    2\n",
       "2021-01-01    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using datatimeindex as series index\n",
    "\n",
    "pd.Series([1,2,3],index=dt_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## date_range function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04',\n",
       "               '2023-01-05', '2023-01-06', '2023-01-07', '2023-01-08',\n",
       "               '2023-01-09', '2023-01-10', '2023-01-11', '2023-01-12',\n",
       "               '2023-01-13', '2023-01-14', '2023-01-15', '2023-01-16',\n",
       "               '2023-01-17', '2023-01-18', '2023-01-19', '2023-01-20',\n",
       "               '2023-01-21', '2023-01-22', '2023-01-23', '2023-01-24',\n",
       "               '2023-01-25', '2023-01-26', '2023-01-27', '2023-01-28',\n",
       "               '2023-01-29', '2023-01-30', '2023-01-31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generates dates in a range\n",
    "pd.date_range(start='2023/1/1',end='2023/1/31') #by default, freq='D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-01', '2023-01-03', '2023-01-05', '2023-01-07',\n",
       "               '2023-01-09', '2023-01-11', '2023-01-13', '2023-01-15',\n",
       "               '2023-01-17', '2023-01-19', '2023-01-21', '2023-01-23',\n",
       "               '2023-01-25', '2023-01-27', '2023-01-29', '2023-01-31'],\n",
       "              dtype='datetime64[ns]', freq='2D')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2023/1/1',end='2023/1/31',freq='2D') #alternate days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-01', '2023-01-05', '2023-01-09', '2023-01-13',\n",
       "               '2023-01-17', '2023-01-21', '2023-01-25', '2023-01-29',\n",
       "               '2023-02-02', '2023-02-06', '2023-02-10', '2023-02-14',\n",
       "               '2023-02-18', '2023-02-22', '2023-02-26'],\n",
       "              dtype='datetime64[ns]', freq='4D')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2023/1/1',end='2023/2/28',freq='4D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-05', '2023-01-06', '2023-01-09', '2023-01-10',\n",
       "               '2023-01-11', '2023-01-12', '2023-01-13', '2023-01-16',\n",
       "               '2023-01-17', '2023-01-18', '2023-01-19', '2023-01-20',\n",
       "               '2023-01-23', '2023-01-24', '2023-01-25', '2023-01-26',\n",
       "               '2023-01-27', '2023-01-30', '2023-01-31', '2023-02-01',\n",
       "               '2023-02-02', '2023-02-03', '2023-02-06', '2023-02-07',\n",
       "               '2023-02-08', '2023-02-09', '2023-02-10', '2023-02-13',\n",
       "               '2023-02-14', '2023-02-15', '2023-02-16', '2023-02-17',\n",
       "               '2023-02-20', '2023-02-21', '2023-02-22', '2023-02-23',\n",
       "               '2023-02-24', '2023-02-27', '2023-02-28'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B -> business days.. mon to fri only\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-08', '2023-01-15', '2023-01-22', '2023-01-29',\n",
       "               '2023-02-05', '2023-02-12', '2023-02-19', '2023-02-26'],\n",
       "              dtype='datetime64[ns]', freq='W-SUN')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# W -> one week per day\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='W') #sundays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-05', '2023-01-12', '2023-01-19', '2023-01-26',\n",
       "               '2023-02-02', '2023-02-09', '2023-02-16', '2023-02-23'],\n",
       "              dtype='datetime64[ns]', freq='W-THU')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# W -> one week per day\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='W-THU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-05 00:00:00', '2023-01-05 01:00:00',\n",
       "               '2023-01-05 02:00:00', '2023-01-05 03:00:00',\n",
       "               '2023-01-05 04:00:00', '2023-01-05 05:00:00',\n",
       "               '2023-01-05 06:00:00', '2023-01-05 07:00:00',\n",
       "               '2023-01-05 08:00:00', '2023-01-05 09:00:00',\n",
       "               ...\n",
       "               '2023-02-27 15:00:00', '2023-02-27 16:00:00',\n",
       "               '2023-02-27 17:00:00', '2023-02-27 18:00:00',\n",
       "               '2023-02-27 19:00:00', '2023-02-27 20:00:00',\n",
       "               '2023-02-27 21:00:00', '2023-02-27 22:00:00',\n",
       "               '2023-02-27 23:00:00', '2023-02-28 00:00:00'],\n",
       "              dtype='datetime64[ns]', length=1297, freq='H')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# H -> Hourly data(factor)\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='H') #timestamp in each hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-05 00:00:00', '2023-01-05 06:00:00',\n",
       "               '2023-01-05 12:00:00', '2023-01-05 18:00:00',\n",
       "               '2023-01-06 00:00:00', '2023-01-06 06:00:00',\n",
       "               '2023-01-06 12:00:00', '2023-01-06 18:00:00',\n",
       "               '2023-01-07 00:00:00', '2023-01-07 06:00:00',\n",
       "               ...\n",
       "               '2023-02-25 18:00:00', '2023-02-26 00:00:00',\n",
       "               '2023-02-26 06:00:00', '2023-02-26 12:00:00',\n",
       "               '2023-02-26 18:00:00', '2023-02-27 00:00:00',\n",
       "               '2023-02-27 06:00:00', '2023-02-27 12:00:00',\n",
       "               '2023-02-27 18:00:00', '2023-02-28 00:00:00'],\n",
       "              dtype='datetime64[ns]', length=217, freq='6H')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# H -> Hourly data(factor)\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='6H') #in every 6 hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-31', '2023-02-28'], dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# M -> Month end\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-02-01'], dtype='datetime64[ns]', freq='MS')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# M -> Month start\n",
    "pd.date_range(start='2023/1/5',end='2023/2/28',freq='MS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-12-31', '2024-12-31', '2025-12-31', '2026-12-31',\n",
       "               '2027-12-31', '2028-12-31', '2029-12-31'],\n",
       "              dtype='datetime64[ns]', freq='A-DEC')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A -> Year end\n",
    "pd.date_range(start='2023/1/5',end='2030/2/28',freq='A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2024-01-01', '2025-01-01', '2026-01-01', '2027-01-01',\n",
       "               '2028-01-01', '2029-01-01', '2030-01-01'],\n",
       "              dtype='datetime64[ns]', freq='AS-JAN')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A -> Year start\n",
    "pd.date_range(start='2023/1/5',end='2030/2/28',freq='AS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2023-01-31', '2023-02-28', '2023-03-31', '2023-04-30',\n",
       "               '2023-05-31', '2023-06-30', '2023-07-31', '2023-08-31',\n",
       "               '2023-09-30', '2023-10-31', '2023-11-30', '2023-12-31',\n",
       "               '2024-01-31', '2024-02-29', '2024-03-31', '2024-04-30',\n",
       "               '2024-05-31', '2024-06-30', '2024-07-31', '2024-08-31',\n",
       "               '2024-09-30', '2024-10-31', '2024-11-30', '2024-12-31',\n",
       "               '2025-01-31'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using periods(number of results)\n",
    "pd.date_range(start='2023/1/5',periods=25,freq='M') #instead of end, specify period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to_datetime function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converts an existing objects to pandas timestamp/datetimeindex object... mostly used...impt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2023/1/1\n",
       "1    2022/4/2\n",
       "2    2021/1/5\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simple series example\n",
    "\n",
    "s = pd.Series(['2023/1/1','2022/4/2','2021/1/5'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2023\n",
       "1    2022\n",
       "2    2021\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here date is object/string... cant fetch attributes directly\n",
    "# eg) to fetch year,\n",
    "s.str.split('/').str.get(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2023-01-01\n",
       "1   2022-04-02\n",
       "2   2021-01-05\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#so convert series 'to_datetime'\n",
    "pd.to_datetime(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2023\n",
       "1    2022\n",
       "2    2021\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(s).dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(s).dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(s).dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    January\n",
       "1      April\n",
       "2    January\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(s).dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Sunday\n",
       "1    Saturday\n",
       "2     Tuesday\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(s).dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2023/1/1\n",
       "1      2022/1/1\n",
       "2    2021/130/1\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with errors\n",
    "s = pd.Series(['2023/1/1','2022/1/1','2021/130/1'])\n",
    "s #cant identity wrong dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Unknown string format: 2021/130/1 present at position 2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslib.pyx:605\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslibs\\parsing.pyx:318\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\dateutil\\parser\\_parser.py:1368\u001b[0m, in \u001b[0;36mparse\u001b[1;34m(timestr, parserinfo, **kwargs)\u001b[0m\n\u001b[0;32m   1367\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1368\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m DEFAULTPARSER\u001b[38;5;241m.\u001b[39mparse(timestr, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\dateutil\\parser\\_parser.py:643\u001b[0m, in \u001b[0;36mparser.parse\u001b[1;34m(self, timestr, default, ignoretz, tzinfos, **kwargs)\u001b[0m\n\u001b[0;32m    642\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 643\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ParserError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown string format: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, timestr)\n\u001b[0;32m    645\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(res) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[1;31mParserError\u001b[0m: Unknown string format: 2021/130/1",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslib.pyx:616\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: invalid string coercion to datetime for \"2021/130/1\" at position 2",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[74], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m pd\u001b[38;5;241m.\u001b[39mto_datetime(s)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1068\u001b[0m, in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m   1066\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[0;32m   1067\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1068\u001b[0m         values \u001b[38;5;241m=\u001b[39m convert_listlike(arg\u001b[38;5;241m.\u001b[39m_values, \u001b[38;5;28mformat\u001b[39m)\n\u001b[0;32m   1069\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m   1070\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:438\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    436\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m infer_datetime_format\n\u001b[0;32m    437\u001b[0m utc \u001b[38;5;241m=\u001b[39m tz \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 438\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64ns(\n\u001b[0;32m    439\u001b[0m     arg,\n\u001b[0;32m    440\u001b[0m     dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m    441\u001b[0m     yearfirst\u001b[38;5;241m=\u001b[39myearfirst,\n\u001b[0;32m    442\u001b[0m     utc\u001b[38;5;241m=\u001b[39mutc,\n\u001b[0;32m    443\u001b[0m     errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m    444\u001b[0m     require_iso8601\u001b[38;5;241m=\u001b[39mrequire_iso8601,\n\u001b[0;32m    445\u001b[0m     allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    446\u001b[0m )\n\u001b[0;32m    448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    449\u001b[0m     \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[0;32m    450\u001b[0m     \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[0;32m    451\u001b[0m     dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\datetimes.py:2177\u001b[0m, in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object, allow_mixed)\u001b[0m\n\u001b[0;32m   2175\u001b[0m order: Literal[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m flags\u001b[38;5;241m.\u001b[39mf_contiguous \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2176\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2177\u001b[0m     result, tz_parsed \u001b[38;5;241m=\u001b[39m tslib\u001b[38;5;241m.\u001b[39marray_to_datetime(\n\u001b[0;32m   2178\u001b[0m         data\u001b[38;5;241m.\u001b[39mravel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   2179\u001b[0m         errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m   2180\u001b[0m         utc\u001b[38;5;241m=\u001b[39mutc,\n\u001b[0;32m   2181\u001b[0m         dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m   2182\u001b[0m         yearfirst\u001b[38;5;241m=\u001b[39myearfirst,\n\u001b[0;32m   2183\u001b[0m         require_iso8601\u001b[38;5;241m=\u001b[39mrequire_iso8601,\n\u001b[0;32m   2184\u001b[0m         allow_mixed\u001b[38;5;241m=\u001b[39mallow_mixed,\n\u001b[0;32m   2185\u001b[0m     )\n\u001b[0;32m   2186\u001b[0m     result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mreshape(data\u001b[38;5;241m.\u001b[39mshape, order\u001b[38;5;241m=\u001b[39morder)\n\u001b[0;32m   2187\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   2188\u001b[0m     \u001b[38;5;66;03m# Exception is raised when a part of date is greater than 32 bit signed int\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslib.pyx:427\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslib.pyx:683\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslib.pyx:829\u001b[0m, in \u001b[0;36mpandas._libs.tslib._array_to_datetime_object\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslib.pyx:819\u001b[0m, in \u001b[0;36mpandas._libs.tslib._array_to_datetime_object\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\tslibs\\parsing.pyx:318\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\dateutil\\parser\\_parser.py:1368\u001b[0m, in \u001b[0;36mparse\u001b[1;34m(timestr, parserinfo, **kwargs)\u001b[0m\n\u001b[0;32m   1366\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser(parserinfo)\u001b[38;5;241m.\u001b[39mparse(timestr, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1367\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1368\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m DEFAULTPARSER\u001b[38;5;241m.\u001b[39mparse(timestr, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\dateutil\\parser\\_parser.py:643\u001b[0m, in \u001b[0;36mparser.parse\u001b[1;34m(self, timestr, default, ignoretz, tzinfos, **kwargs)\u001b[0m\n\u001b[0;32m    640\u001b[0m res, skipped_tokens \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parse(timestr, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    642\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 643\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ParserError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown string format: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, timestr)\n\u001b[0;32m    645\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(res) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    646\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ParserError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mString does not contain a date: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, timestr)\n",
      "\u001b[1;31mParserError\u001b[0m: Unknown string format: 2021/130/1 present at position 2"
     ]
    }
   ],
   "source": [
    "pd.to_datetime(s)  #crashes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_datetime(s,errors='coerce') #skip & ignore\n",
    "#NaT... not a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_datetime(s,errors='coerce').dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 277 entries, 0 to 276\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Date            277 non-null    object \n",
      " 1   Account         277 non-null    object \n",
      " 2   Category        277 non-null    object \n",
      " 3   Subcategory     0 non-null      float64\n",
      " 4   Note            273 non-null    object \n",
      " 5   INR             277 non-null    float64\n",
      " 6   Income/Expense  277 non-null    object \n",
      " 7   Note.1          0 non-null      float64\n",
      " 8   Amount          277 non-null    float64\n",
      " 9   Currency        277 non-null    object \n",
      " 10  Account.1       277 non-null    float64\n",
      "dtypes: float64(5), object(6)\n",
      "memory usage: 23.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('expense_data.csv')\n",
    "df.info() #Date is object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 277 entries, 0 to 276\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Date            277 non-null    datetime64[ns]\n",
      " 1   Account         277 non-null    object        \n",
      " 2   Category        277 non-null    object        \n",
      " 3   Subcategory     0 non-null      float64       \n",
      " 4   Note            273 non-null    object        \n",
      " 5   INR             277 non-null    float64       \n",
      " 6   Income/Expense  277 non-null    object        \n",
      " 7   Note.1          0 non-null      float64       \n",
      " 8   Amount          277 non-null    float64       \n",
      " 9   Currency        277 non-null    object        \n",
      " 10  Account.1       277 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(5), object(5)\n",
      "memory usage: 23.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dt accessor"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Accessor object for datetimelike properties of the Series values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "272    False\n",
       "273    False\n",
       "274    False\n",
       "275    False\n",
       "276    False\n",
       "Name: Date, Length: 277, dtype: bool"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].dt.year\n",
    "df['Date'].dt.month\n",
    "df['Date'].dt.day\n",
    "df['Date'].dt.month_name()\n",
    "df['Date'].dt.day_name()\n",
    "df['Date'].dt.is_month_end\n",
    "df['Date'].dt.is_month_start\n",
    "df['Date'].dt.is_quarter_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26fa924e810>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot graph\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df['Date'],df['INR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# day name wise bar chart\n",
    "df['day_name'] = df['Date'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Account</th>\n",
       "      <th>Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Note</th>\n",
       "      <th>INR</th>\n",
       "      <th>Income/Expense</th>\n",
       "      <th>Note.1</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Account.1</th>\n",
       "      <th>day_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-03-02 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brownie</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-03-02 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To lended people</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-01 19:50:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-01 18:56:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Metro</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01 18:22:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snacks</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date               Account        Category  Subcategory  \\\n",
       "0 2022-03-02 10:11:00  CUB - online payment            Food          NaN   \n",
       "1 2022-03-02 10:11:00  CUB - online payment           Other          NaN   \n",
       "2 2022-03-01 19:50:00  CUB - online payment            Food          NaN   \n",
       "3 2022-03-01 18:56:00  CUB - online payment  Transportation          NaN   \n",
       "4 2022-03-01 18:22:00  CUB - online payment            Food          NaN   \n",
       "\n",
       "               Note    INR Income/Expense  Note.1  Amount Currency  Account.1  \\\n",
       "0           Brownie   50.0        Expense     NaN    50.0      INR       50.0   \n",
       "1  To lended people  300.0        Expense     NaN   300.0      INR      300.0   \n",
       "2            Dinner   78.0        Expense     NaN    78.0      INR       78.0   \n",
       "3             Metro   30.0        Expense     NaN    30.0      INR       30.0   \n",
       "4            Snacks   67.0        Expense     NaN    67.0      INR       67.0   \n",
       "\n",
       "    day_name  \n",
       "0  Wednesday  \n",
       "1  Wednesday  \n",
       "2    Tuesday  \n",
       "3    Tuesday  \n",
       "4    Tuesday  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='day_name'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('day_name')['INR'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#/month wise bar chart\n",
    "df['Month'] = df['Date'].dt.month_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Account</th>\n",
       "      <th>Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Note</th>\n",
       "      <th>INR</th>\n",
       "      <th>Income/Expense</th>\n",
       "      <th>Note.1</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Account.1</th>\n",
       "      <th>day_name</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-03-02 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brownie</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-03-02 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To lended people</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-01 19:50:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-01 18:56:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Metro</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-01 18:22:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snacks</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>INR</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date               Account        Category  Subcategory  \\\n",
       "0 2022-03-02 10:11:00  CUB - online payment            Food          NaN   \n",
       "1 2022-03-02 10:11:00  CUB - online payment           Other          NaN   \n",
       "2 2022-03-01 19:50:00  CUB - online payment            Food          NaN   \n",
       "3 2022-03-01 18:56:00  CUB - online payment  Transportation          NaN   \n",
       "4 2022-03-01 18:22:00  CUB - online payment            Food          NaN   \n",
       "\n",
       "               Note    INR Income/Expense  Note.1  Amount Currency  Account.1  \\\n",
       "0           Brownie   50.0        Expense     NaN    50.0      INR       50.0   \n",
       "1  To lended people  300.0        Expense     NaN   300.0      INR      300.0   \n",
       "2            Dinner   78.0        Expense     NaN    78.0      INR       78.0   \n",
       "3             Metro   30.0        Expense     NaN    30.0      INR       30.0   \n",
       "4            Snacks   67.0        Expense     NaN    67.0      INR       67.0   \n",
       "\n",
       "    day_name  Month  \n",
       "0  Wednesday  March  \n",
       "1  Wednesday  March  \n",
       "2    Tuesday  March  \n",
       "3    Tuesday  March  \n",
       "4    Tuesday  March  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Month'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Month')['INR'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Account</th>\n",
       "      <th>Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Note</th>\n",
       "      <th>INR</th>\n",
       "      <th>Income/Expense</th>\n",
       "      <th>Note.1</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Account.1</th>\n",
       "      <th>day_name</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-02-28 11:56:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>339.15</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>339.15</td>\n",
       "      <td>INR</td>\n",
       "      <td>339.15</td>\n",
       "      <td>Monday</td>\n",
       "      <td>February</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-02-28 11:45:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>From kumara</td>\n",
       "      <td>200.00</td>\n",
       "      <td>Income</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>200.00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>February</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2022-01-31 08:44:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vnr to apk</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2022-01-31 08:27:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To vicky</td>\n",
       "      <td>200.00</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>200.00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2022-01-31 08:26:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To ksr station</td>\n",
       "      <td>153.00</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>153.00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2021-11-30 14:24:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Gift</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bharath birthday</td>\n",
       "      <td>115.00</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>115.00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2021-11-30 14:17:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lunch with company</td>\n",
       "      <td>128.00</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>128.00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2021-11-30 10:11:00</td>\n",
       "      <td>CUB - online payment</td>\n",
       "      <td>Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Expense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.00</td>\n",
       "      <td>INR</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date               Account        Category  Subcategory  \\\n",
       "7   2022-02-28 11:56:00  CUB - online payment            Food          NaN   \n",
       "8   2022-02-28 11:45:00  CUB - online payment           Other          NaN   \n",
       "61  2022-01-31 08:44:00  CUB - online payment  Transportation          NaN   \n",
       "62  2022-01-31 08:27:00  CUB - online payment           Other          NaN   \n",
       "63  2022-01-31 08:26:00  CUB - online payment  Transportation          NaN   \n",
       "242 2021-11-30 14:24:00  CUB - online payment            Gift          NaN   \n",
       "243 2021-11-30 14:17:00  CUB - online payment            Food          NaN   \n",
       "244 2021-11-30 10:11:00  CUB - online payment            Food          NaN   \n",
       "\n",
       "                   Note     INR Income/Expense  Note.1  Amount Currency  \\\n",
       "7                 Pizza  339.15        Expense     NaN  339.15      INR   \n",
       "8           From kumara  200.00         Income     NaN  200.00      INR   \n",
       "61           Vnr to apk   50.00        Expense     NaN   50.00      INR   \n",
       "62             To vicky  200.00        Expense     NaN  200.00      INR   \n",
       "63       To ksr station  153.00        Expense     NaN  153.00      INR   \n",
       "242    Bharath birthday  115.00        Expense     NaN  115.00      INR   \n",
       "243  Lunch with company  128.00        Expense     NaN  128.00      INR   \n",
       "244           Breakfast   70.00        Expense     NaN   70.00      INR   \n",
       "\n",
       "     Account.1 day_name     Month  \n",
       "7       339.15   Monday  February  \n",
       "8       200.00   Monday  February  \n",
       "61       50.00   Monday   January  \n",
       "62      200.00   Monday   January  \n",
       "63      153.00   Monday   January  \n",
       "242     115.00  Tuesday  November  \n",
       "243     128.00  Tuesday  November  \n",
       "244      70.00  Tuesday  November  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Date'].dt.is_month_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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