day9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.25.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "pandas.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it is an open source, BSD-licensed library providing high-performance,easy-to -data structure and data analysis tools for the python programming language"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## creating data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- understanding data frame format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number\n",
       "0      11\n",
       "1      22\n",
       "2      33\n",
       "3      44\n",
       "4      55"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data=[11,22,33,44,55]\n",
    "df=pd.DataFrame(data, columns=['number'])\n",
    "print(type(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- list of list method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Subject  Marks\n",
      "0    maths   85.0\n",
      "1  english   80.0\n",
      "2  science   82.0\n"
     ]
    }
   ],
   "source": [
    "data=[['maths',85],['english','80'],['science','82']]\n",
    "df=pd.DataFrame(data,columns=['Subject','Marks'],dtype=float)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- dictionary of list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Subject  Marks\n",
      "0    maths   85.0\n",
      "1  english   80.0\n",
      "2  science   82.0\n"
     ]
    }
   ],
   "source": [
    "data={'Subject':['maths','english','science'],'Marks':[85,80,82]}\n",
    "df=pd.DataFrame(data,dtype=float)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- list of dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Subject</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maths</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>English</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Science</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subject  marks\n",
       "0    Maths   85.0\n",
       "1  English   80.0\n",
       "2  Science   82.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[{'Subject':'Maths','marks':85},{'Subject':'English','marks':80},\\\n",
    "     {'Subject':'Science','marks':82}]\n",
    "df=pd.DataFrame(data,dtype=float)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- understanding data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Names  birthdates\n",
      "0      Bob        1968\n",
      "1  Jessica        1955\n",
      "2     Mary        1977\n",
      "3     John        1978\n",
      "4      Mel        1973\n",
      "\n",
      "     Names  birthdates\n",
      "1  Jessica        1955\n",
      "2     Mary        1977\n",
      "3     John        1978\n",
      "4      Mel        1973\n",
      "5      Max        2001\n",
      "\n",
      "     Names  birthdates\n",
      "0      Bob        1968\n",
      "1  Jessica        1955\n",
      "2     Mary        1977\n",
      "3     John        1978\n",
      "4      Mel        1973\n",
      "5      Max        2001\n"
     ]
    }
   ],
   "source": [
    "d={'Names':['Bob','Jessica','Mary','John','Mel',\"Max\"],'birthdates':\\\n",
    "   [1968,1955,1977,1978,1973,2001]}\n",
    "df=pd.DataFrame(data=d)\n",
    "print(df.head())\n",
    "print()\n",
    "print(df.tail())\n",
    "print()\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Names', 'birthdates']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        Bob\n",
       "1    Jessica\n",
       "Name: Names, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(df['Names']))\n",
    "df['Names'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df[['Names']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>birthdates</th>\n",
       "      <th>Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birthdates    Names\n",
       "0        1968      Bob\n",
       "1        1955  Jessica\n",
       "2        1977     Mary"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['birthdates','Names']].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- rename rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Names', 'birthdates'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Names</th>\n",
       "      <th>Birthdates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Birthdates\n",
       "0      Bob        1968\n",
       "1  Jessica        1955\n",
       "2     Mary        1977\n",
       "3     John        1978\n",
       "4      Mel        1973\n",
       "5      Max        2001"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=['Names','Birthdates']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Appending"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Names</th>\n",
       "      <th>Birthdates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mark</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Birthdates\n",
       "0      Bob        1968\n",
       "1  Jessica        1955\n",
       "2     Mary        1977\n",
       "3     John        1978\n",
       "4      Mel        1973\n",
       "5      Max        2001\n",
       "6     Mark        1951"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method 1\n",
    "new_data=pd.DataFrame([['Mark',1951]],columns=['Names','Birthdates'],index=[6])\n",
    "df=df.append(new_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Names</th>\n",
       "      <th>Birthdates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mark</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Birthdates\n",
       "0      Bob        1968\n",
       "1  Jessica        1955\n",
       "2     Mary        1977\n",
       "3     John        1978\n",
       "4      Mel        1973\n",
       "5      Max        2001\n",
       "6     Mark        1951"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=[0,1,2,3,4,5,6]\n",
    "df.index=i\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Names</th>\n",
       "      <th>Birthdates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mark</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stark</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Birthdates\n",
       "0      Bob        1968\n",
       "1  Jessica        1955\n",
       "2     Mary        1977\n",
       "3     John        1978\n",
       "4      Mel        1973\n",
       "5      Max        2001\n",
       "6     Mark        1951\n",
       "7    Stark        1973"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 2\n",
    "df.loc[7]=['Stark',1973]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shaikh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    },
    {
     "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names\n",
       "0  NaN       1968      Bob\n",
       "1  NaN       1955  Jessica\n",
       "2  NaN       1977     Mary\n",
       "3  NaN       1978     John\n",
       "4  NaN       1973      Mel\n",
       "5  NaN       2001      Max\n",
       "6  NaN       1951     Mark\n",
       "7  NaN       1973    Stark"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 1\n",
    "new=pd.DataFrame(columns=['Names','Birthdates','Age'])\n",
    "df=df.append(new)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names   Sex\n",
       "0  NaN       1968      Bob  male\n",
       "1  NaN       1955  Jessica  male\n",
       "2  NaN       1977     Mary  male\n",
       "3  NaN       1978     John  male\n",
       "4  NaN       1973      Mel  male\n",
       "5  NaN       2001      Max  male\n",
       "6  NaN       1951     Mark  male\n",
       "7  NaN       1973    Stark  male"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method2\n",
    "df['Sex']='male'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names     Sex\n",
       "0   50       1968      Bob    male\n",
       "1   63       1955  Jessica  female\n",
       "2  NaN       1977     Mary  female\n",
       "3   40       1978     John    male\n",
       "4  NaN       1973      Mel  female\n",
       "5   17       2001      Max    male\n",
       "6   67       1951     Mark    male\n",
       "7  NaN       1973    Stark    male"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0,'Age']=50\n",
    "df.loc[3,'Age']=40\n",
    "df.loc[5,'Age']=17\n",
    "df.loc[6,'Age']=67\n",
    "df.loc[1,'Age']=63\n",
    "df.loc[1,'Sex']='female'\n",
    "df.loc[2,'Sex']='female'\n",
    "df.loc[4,'Sex']='female'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Birthdates  Names     Sex\n",
       "4       1973    Mel  female\n",
       "5       2001    Max    male\n",
       "6       1951   Mark    male\n",
       "7       1973  Stark    male"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[4:7,'Birthdates':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names     Sex  Age\n",
       "0      Bob    male   50\n",
       "1  Jessica  female   63\n",
       "2     Mary  female  NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.index[:3],['Names','Sex','Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates Names     Sex\n",
       "0   50       1968   Bob    male\n",
       "2  NaN       1977  Mary  female\n",
       "4  NaN       1973   Mel  female"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:6:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names     Sex\n",
       "7  NaN       1973    Stark    male\n",
       "6   67       1951     Mark    male\n",
       "5   17       2001      Max    male\n",
       "4  NaN       1973      Mel  female\n",
       "3   40       1978     John    male\n",
       "2  NaN       1977     Mary  female\n",
       "1   63       1955  Jessica  female\n",
       "0   50       1968      Bob    male"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates Names     Sex\n",
       "0   50       1968   Bob    male\n",
       "2  NaN       1977  Mary  female\n",
       "3   40       1978  John    male"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydf=df.head()\n",
    "mydf[[True,False,True,True,False]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7    False\n",
       "Name: Age, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_filter=df['Age']>30\n",
    "age_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Birthdates    Names     Sex\n",
       "0  50       1968      Bob    male\n",
       "1  63       1955  Jessica  female\n",
       "3  40       1978     John    male\n",
       "6  67       1951     Mark    male"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[age_filter]"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Birthdates    Names     Sex\n",
       "0  50       1968      Bob    male\n",
       "1  63       1955  Jessica  female\n",
       "3  40       1978     John    male\n",
       "6  67       1951     Mark    male"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']>30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Birthdates Names   Sex\n",
       "5  17       2001   Max  male"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']<30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Birthdates Names   Sex\n",
       "0  50       1968   Bob  male\n",
       "3  40       1978  John  male\n",
       "6  67       1951  Mark  male"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Sex']=='male')& (df['Age']>30)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.08233054204616"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6    False\n",
       "7     True\n",
       "Name: Age, dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates  Names     Sex\n",
       "2  NaN       1977   Mary  female\n",
       "4  NaN       1973    Mel  female\n",
       "7  NaN       1973  Stark    male"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names     Sex\n",
       "0   50       1968      Bob    male\n",
       "1   63       1955  Jessica  female\n",
       "2  NaN       1977     Mary  female\n",
       "3   40       1978     John    male\n",
       "4  NaN       1973      Mel  female\n",
       "5   17       2001      Max    male\n",
       "6   67       1951     Mark    male\n",
       "7  NaN       1973    Stark    male"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birthdates'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1973    2\n",
       "1951    1\n",
       "1978    1\n",
       "1977    1\n",
       "1955    1\n",
       "2001    1\n",
       "1968    1\n",
       "Name: Birthdates, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birthdates'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 1, 1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df['Birthdates'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1973, 1951, 1978, 1977, 1955, 2001, 1968]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df['Birthdates'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef03f81198>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Birthdates'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names     Sex  Gender\n",
       "0   50       1968      Bob    male       0\n",
       "1   63       1955  Jessica  female       1\n",
       "2  NaN       1977     Mary  female       1\n",
       "3   40       1978     John    male       0\n",
       "4  NaN       1973      Mel  female       1\n",
       "5   17       2001      Max    male       0\n",
       "6   67       1951     Mark    male       0\n",
       "7  NaN       1973    Stark    male       0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping={'male':0,'female':1}\n",
    "df['Gender']=df['Sex'].apply(lambda x:mapping[x])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ef06c6b470>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.subplot(2,1,1)\n",
    "plt.bar(df['Gender'],df['Age'])\n",
    "plt.subplot(2,1,2)\n",
    "plt.scatter(df['Gender'],df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ef06d11390>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Birthdates'],df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shaikh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: Support for uppercase single-letter colors is deprecated since Matplotlib 3.1 and will be removed in 3.3; please use lowercase instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 8 artists>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(df['Gender'],['male','female'])\n",
    "plt.bar(df['Gender'],df['Age'],align='center',color='RGB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0    1968\n",
       "1    1955\n",
       "2    1977\n",
       "3    1978\n",
       "4    1973\n",
       "5    2001\n",
       "6    1951\n",
       "7    1973\n",
       "Name: Birthdates, dtype: object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birthdates'].unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## deleting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names     Sex  Gender\n",
       "0   50       1968      Bob    male       0\n",
       "1   63       1955  Jessica  female       1\n",
       "2  NaN       1977     Mary  female       1\n",
       "3   40       1978     John    male       0\n",
       "4  NaN       1973      Mel  female       1\n",
       "5   17       2001      Max    male       0\n",
       "6   67       1951     Mark    male       0\n",
       "7  NaN       1973    Stark    male       0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Sex'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Birthdates    Names  Gender\n",
       "0   50       1968      Bob       0\n",
       "1   63       1955  Jessica       1\n",
       "2  NaN       1977     Mary       1\n",
       "3   40       1978     John       0\n",
       "4  NaN       1973      Mel       1\n",
       "5   17       2001      Max       0\n",
       "6   67       1951     Mark       0\n",
       "7  NaN       1973    Stark       0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Birthdates    Names  Gender\n",
       "0  50       1968      Bob       0\n",
       "1  63       1955  Jessica       1\n",
       "3  40       1978     John       0\n",
       "5  17       2001      Max       0\n",
       "6  67       1951     Mark       0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.dropna(subset=['Age'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Birthdates    Names  Gender\n",
       "0   50        1968      Bob       0\n",
       "1   63        1955  Jessica       1\n",
       "2   30        1977     Mary       1\n",
       "3   40        1978     John       0\n",
       "4   30        1973      Mel       1\n",
       "5   17        2001      Max       0\n",
       "6   67        1951     Mark       0\n",
       "7   30        1973    Stark       0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Age</th>\n",
       "      <th>Birthdates</th>\n",
       "      <th>Names</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1968</td>\n",
       "      <td>Bob</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.4</td>\n",
       "      <td>1977</td>\n",
       "      <td>Mary</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>John</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47.4</td>\n",
       "      <td>1973</td>\n",
       "      <td>Mel</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>Max</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>47.4</td>\n",
       "      <td>1973</td>\n",
       "      <td>Stark</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Birthdates    Names  Gender\n",
       "0  50.0        1968      Bob       0\n",
       "1  63.0        1955  Jessica       1\n",
       "2  47.4        1977     Mary       1\n",
       "3  40.0        1978     John       0\n",
       "4  47.4        1973      Mel       1\n",
       "5  17.0        2001      Max       0\n",
       "6  67.0        1951     Mark       0\n",
       "7  47.4        1973    Stark       0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## groupby function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1951\n",
      "*******\n",
      "  Age Birthdates Names  Gender\n",
      "6  67       1951  Mark       0\n",
      "********\n",
      "1955\n",
      "*******\n",
      "  Age Birthdates    Names  Gender\n",
      "1  63       1955  Jessica       1\n",
      "********\n",
      "1968\n",
      "*******\n",
      "  Age Birthdates Names  Gender\n",
      "0  50       1968   Bob       0\n",
      "********\n",
      "1973\n",
      "*******\n",
      "   Age Birthdates  Names  Gender\n",
      "4  NaN       1973    Mel       1\n",
      "7  NaN       1973  Stark       0\n",
      "********\n",
      "1977\n",
      "*******\n",
      "   Age Birthdates Names  Gender\n",
      "2  NaN       1977  Mary       1\n",
      "********\n",
      "1978\n",
      "*******\n",
      "  Age Birthdates Names  Gender\n",
      "3  40       1978  John       0\n",
      "********\n",
      "2001\n",
      "*******\n",
      "  Age Birthdates Names  Gender\n",
      "5  17       2001   Max       0\n",
      "********\n"
     ]
    }
   ],
   "source": [
    "for names,value in df.groupby('Birthdates'):\n",
    "    print(names)\n",
    "    print('*******')\n",
    "    print(value)\n",
    "    print('********')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "*******\n",
      "   Age Birthdates  Names  Gender\n",
      "0   50       1968    Bob       0\n",
      "3   40       1978   John       0\n",
      "5   17       2001    Max       0\n",
      "6   67       1951   Mark       0\n",
      "7  NaN       1973  Stark       0\n",
      "********\n",
      "1\n",
      "*******\n",
      "   Age Birthdates    Names  Gender\n",
      "1   63       1955  Jessica       1\n",
      "2  NaN       1977     Mary       1\n",
      "4  NaN       1973      Mel       1\n",
      "********\n"
     ]
    }
   ],
   "source": [
    "for names,value in df.groupby('Gender'):\n",
    "    print(names)\n",
    "    print('*******')\n",
    "    print(value)\n",
    "    print('********')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}