26-DataFrameOperations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dictionary = {\"James\" : [40,30,np.nan], \"Kirk\" : [20,np.nan,50], \"Lars\" : [30,50,40]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_frame = pd.DataFrame(my_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>James</th>\n",
       "      <th>Kirk</th>\n",
       "      <th>Lars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>40</td>\n",
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      "text/plain": [
       "   James  Kirk  Lars\n",
       "0   40.0  20.0    30\n",
       "1   30.0   NaN    50\n",
       "2    NaN  50.0    40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>James</th>\n",
       "      <th>Kirk</th>\n",
       "      <th>Lars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
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       "      <td>30</td>\n",
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      "text/plain": [
       "   James  Kirk  Lars\n",
       "0   40.0  20.0    30"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_frame.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>James</th>\n",
       "      <th>Kirk</th>\n",
       "      <th>Lars</th>\n",
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       "  </thead>\n",
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       "      <td>30</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   James  Kirk  Lars\n",
       "0   40.0  20.0    30\n",
       "1   30.0   NaN    50\n",
       "2    NaN  50.0    40"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
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       "      <th>2</th>\n",
       "      <td>40</td>\n",
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      ],
      "text/plain": [
       "   Lars\n",
       "0    30\n",
       "1    50\n",
       "2    40"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_frame.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_new_dictionary = {\"James\" : [40,30,np.nan], \"Kirk\" : [20,np.nan,50], \"Lars\" : [30,50,40], \"Rob\" : [45,np.nan,np.nan]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_new_data_frame = pd.DataFrame(my_new_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Rob</th>\n",
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       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
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       "      <td>45.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "   James  Kirk  Lars   Rob\n",
       "0   40.0  20.0    30  45.0\n",
       "1   30.0   NaN    50   NaN\n",
       "2    NaN  50.0    40   NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "my_new_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>2</th>\n",
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       "      <td>40</td>\n",
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      "text/plain": [
       "   James  Kirk  Lars\n",
       "0   40.0  20.0    30\n",
       "1   30.0   NaN    50\n",
       "2    NaN  50.0    40"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_new_data_frame.dropna(axis=1, thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>James</th>\n",
       "      <th>Kirk</th>\n",
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       "      <th>Rob</th>\n",
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       "      <td>45.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   James  Kirk  Lars   Rob\n",
       "0   40.0  20.0    30  45.0\n",
       "1   30.0   NaN    50   NaN\n",
       "2    NaN  50.0    40   NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "my_new_data_frame"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Lars</th>\n",
       "      <th>Rob</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30</td>\n",
       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "      <td>20.0</td>\n",
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      "text/plain": [
       "   James  Kirk  Lars   Rob\n",
       "0   40.0  20.0    30  45.0\n",
       "1   30.0  20.0    50  20.0\n",
       "2   20.0  50.0    40  20.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_new_data_frame.fillna(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_dict = {\"Programming Language\" : [\"Python\",\"Python\",\"Python\",\"Java\",\"Java\",\"Swift\"], \"Name\" : [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\"], \"Salary\" : [100,90,80,70,60,50]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_frame = pd.DataFrame(salary_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>A</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python</td>\n",
       "      <td>B</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Python</td>\n",
       "      <td>C</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Java</td>\n",
       "      <td>D</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Java</td>\n",
       "      <td>E</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Swift</td>\n",
       "      <td>F</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Programming Language Name  Salary\n",
       "0               Python    A     100\n",
       "1               Python    B      90\n",
       "2               Python    C      80\n",
       "3                 Java    D      70\n",
       "4                 Java    E      60\n",
       "5                Swift    F      50"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_object = salary_frame.groupby(\"Programming Language\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Java</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swift</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  Salary\n",
       "Programming Language              \n",
       "Java                     2       2\n",
       "Python                   3       3\n",
       "Swift                    1       1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Java</th>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>90</td>\n",
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       "      <th>Swift</th>\n",
       "      <td>50</td>\n",
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      "text/plain": [
       "                      Salary\n",
       "Programming Language        \n",
       "Java                      65\n",
       "Python                    90\n",
       "Swift                     50"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
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       "      <th>Programming Language</th>\n",
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       "      <th>Java</th>\n",
       "      <td>D</td>\n",
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       "      <th>Python</th>\n",
       "      <td>A</td>\n",
       "      <td>80</td>\n",
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       "      <th>Swift</th>\n",
       "      <td>F</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "                     Name  Salary\n",
       "Programming Language             \n",
       "Java                    D      60\n",
       "Python                  A      80\n",
       "Swift                   F      50"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
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       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Java</th>\n",
       "      <td>E</td>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>C</td>\n",
       "      <td>100</td>\n",
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       "      <th>Swift</th>\n",
       "      <td>F</td>\n",
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      "text/plain": [
       "                     Name  Salary\n",
       "Programming Language             \n",
       "Java                    E      70\n",
       "Python                  C     100\n",
       "Swift                   F      50"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "group_object.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
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       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>Java</th>\n",
       "      <td>60</td>\n",
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       "      <td>80</td>\n",
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       "      <td>50</td>\n",
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      "text/plain": [
       "                      Salary\n",
       "Programming Language        \n",
       "Java                      60\n",
       "Python                    80\n",
       "Swift                     50"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.min(\"Salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
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       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
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       "      <th>Java</th>\n",
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       "      <td>100</td>\n",
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       "      <th>Swift</th>\n",
       "      <td>50</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                      Salary\n",
       "Programming Language        \n",
       "Java                      70\n",
       "Python                   100\n",
       "Swift                     50"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.max(\"Salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method min in module pandas.core.groupby.groupby:\n",
      "\n",
      "min(numeric_only: bool = False, min_count: int = -1) method of pandas.core.groupby.generic.DataFrameGroupBy instance\n",
      "    Compute min of group values.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    numeric_only : bool, default False\n",
      "        Include only float, int, boolean columns. If None, will attempt to use\n",
      "        everything, then use only numeric data.\n",
      "    min_count : int, default -1\n",
      "        The required number of valid values to perform the operation. If fewer\n",
      "        than ``min_count`` non-NA values are present the result will be NA.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    Series or DataFrame\n",
      "        Computed min of values within each group.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(group_object.min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Java</th>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>80</td>\n",
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       "      <th>Swift</th>\n",
       "      <td>50</td>\n",
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      ],
      "text/plain": [
       "                      Salary\n",
       "Programming Language        \n",
       "Java                      60\n",
       "Python                    80\n",
       "Swift                     50"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.min(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Programming Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Java</th>\n",
       "      <td>2.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>67.5</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Python</th>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swift</th>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Salary                                                \n",
       "                      count  mean        std   min   25%   50%   75%    max\n",
       "Programming Language                                                       \n",
       "Java                    2.0  65.0   7.071068  60.0  62.5  65.0  67.5   70.0\n",
       "Python                  3.0  90.0  10.000000  80.0  85.0  90.0  95.0  100.0\n",
       "Swift                   1.0  50.0        NaN  50.0  50.0  50.0  50.0   50.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_object.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict1 = {\"Name\" : [\"A\",\"B\",\"C\",\"D\"],\n",
    "           \"Sports\" : [\"Basketball\", \"Football\", \"Tennis\", \"Running\"],\n",
    "            \"Calories\" : [100,200,300,400]\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_frame1 = pd.DataFrame(my_dict1, index=[0,1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>Football</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>Running</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name      Sports  Calories\n",
       "0    A  Basketball       100\n",
       "1    B    Football       200\n",
       "2    C      Tennis       300\n",
       "3    D     Running       400"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_frame1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict2 = {\"Name\" : [\"E\",\"F\",\"G\",\"H\"],\n",
    "           \"Sports\" : [\"Basketball\", \"Football\", \"Tennis\", \"Running\"],\n",
    "            \"Calories\" : [200,50,330,440]\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_frame2 = pd.DataFrame(my_dict2, index=[4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>E</td>\n",
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       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>Football</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>Running</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name      Sports  Calories\n",
       "4    E  Basketball       200\n",
       "5    F    Football        50\n",
       "6    G      Tennis       330\n",
       "7    H     Running       440"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict3 = {\"Name\" : [\"I\",\"J\",\"K\",\"L\"],\n",
    "           \"Sports\" : [\"Basketball\", \"Football\", \"Tennis\", \"Running\"],\n",
    "            \"Calories\" : [300,150,320,410]\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_frame3 = pd.DataFrame(my_dict3, index=[8,9,10,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Sports</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>J</td>\n",
       "      <td>Football</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L</td>\n",
       "      <td>Running</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name      Sports  Calories\n",
       "8     I  Basketball       300\n",
       "9     J    Football       150\n",
       "10    K      Tennis       320\n",
       "11    L     Running       410"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Calories</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Football</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>Running</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>Football</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>Running</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>J</td>\n",
       "      <td>Football</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L</td>\n",
       "      <td>Running</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name      Sports  Calories\n",
       "0     A  Basketball       100\n",
       "1     B    Football       200\n",
       "2     C      Tennis       300\n",
       "3     D     Running       400\n",
       "4     E  Basketball       200\n",
       "5     F    Football        50\n",
       "6     G      Tennis       330\n",
       "7     H     Running       440\n",
       "8     I  Basketball       300\n",
       "9     J    Football       150\n",
       "10    K      Tennis       320\n",
       "11    L     Running       410"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([my_frame1,my_frame2,my_frame3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Running</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name      Sports  Calories\n",
       "0    A  Basketball       100\n",
       "1    B    Football       200\n",
       "2    C      Tennis       300\n",
       "3    D     Running       400"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_frame1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "  <tbody>\n",
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       "      <th>4</th>\n",
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       "      <td>200</td>\n",
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       "      <th>5</th>\n",
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       "      <td>Football</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>Running</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name      Sports  Calories\n",
       "4    E  Basketball       200\n",
       "5    F    Football        50\n",
       "6    G      Tennis       330\n",
       "7    H     Running       440"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name_x</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Calories_x</th>\n",
       "      <th>Name_y</th>\n",
       "      <th>Calories_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>100</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>Football</td>\n",
       "      <td>200</td>\n",
       "      <td>F</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>300</td>\n",
       "      <td>G</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>Running</td>\n",
       "      <td>400</td>\n",
       "      <td>H</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name_x      Sports  Calories_x Name_y  Calories_y\n",
       "0      A  Basketball         100      E         200\n",
       "1      B    Football         200      F          50\n",
       "2      C      Tennis         300      G         330\n",
       "3      D     Running         400      H         440"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(my_frame1,my_frame2,on=\"Sports\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_salary_frame = pd.DataFrame({\"Name\" : [\"James\",\"Kirk\",\"Lars\"], \"Salary\" : [10,20,30], \"Age\" : [60,65,70]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kirk</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lars</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Salary  Age\n",
       "0  James      10   60\n",
       "1   Kirk      20   65\n",
       "2   Lars      30   70"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_salary_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['James', 'Kirk', 'Lars'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_salary_frame[\"Name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_salary_frame[\"Name\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grossToNet(salary):\n",
    "    return salary * 0.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6.5\n",
       "1    13.0\n",
       "2    19.5\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_salary_frame[\"Salary\"].apply(grossToNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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