pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "arctic-collect",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "composed-death",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#In case of numpy we were essentially creating n dimentional arrays but in case of pandas the core data structure is\n",
    "#series\n",
    "#To create series\n",
    "x=pd.Series([1,2,3,4,5]) #passing a list in it\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "lonely-cheese",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    101\n",
      "1    102\n",
      "2    103\n",
      "3    104\n",
      "4    105\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Why series? Because we can do a bunch of different operations if we represent list as a series\n",
    "#say you have to add 100 to all of the elements we only have to do\n",
    "print (x+100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "intellectual-hampshire",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n",
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3     True\n",
      "4     True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "#we can also check which are the numbers greater than 2 for eg\n",
    "print(x)\n",
    "print(x>2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "french-academy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3     True\n",
      "4     True\n",
      "dtype: bool\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#any function\n",
    "larger_than_2 = x > 2\n",
    "print(larger_than_2)\n",
    "larger_than_2.any()\n",
    "#here any() will search in the whole Series if any of the value is true it will return true else false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "informed-detective",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3     True\n",
      "4     True\n",
      "dtype: bool\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all function\n",
    "print(larger_than_2)\n",
    "larger_than_2.all()\n",
    "#similarly here all() will search in the whole Series if any of the value is true it will return true else false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "committed-observation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      4\n",
      "2     27\n",
      "3     16\n",
      "4    125\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#apply\n",
    "#Say you have a list and to each of the element of the list you have to apply a function \n",
    "#for example you need to square each of the elements, etc .i.e., you have to perform complex operation that \n",
    "#can't be done using these list comprehentions then it has apply which does it efficiently\n",
    "\n",
    "#to do that first write a function \n",
    "def f(x):\n",
    "    if x%2 ==0:\n",
    "        return x**2   #remember ** is ^ in python\n",
    "    else:\n",
    "        return x**3\n",
    "#so this function is squaring all the even values of x and cubing all the odd values of x\n",
    "\n",
    "#now use apply()\n",
    "print(x.apply(f))\n",
    "#this apply the function to all the elements in the series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "particular-basic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "dtype: float64"
      ]
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     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "#converting types use astype()\n",
    "x.astype(np.float64)\n",
    "#here we have to pass the type so we use numpy reference here to pass float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "original-malta",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    100\n",
      "1      2\n",
      "2      3\n",
      "3      4\n",
      "4      5\n",
      "dtype: int64\n",
      "0    100\n",
      "1      2\n",
      "2      3\n",
      "3      4\n",
      "4      5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#copy\n",
    "y=x \n",
    "#this is not a normal copy if we change anything in y it will automatically get changed in x too\n",
    "y[0]=100\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "limited-carroll",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n",
      "0      1\n",
      "1      2\n",
      "2    200\n",
      "3      4\n",
      "4      5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#so to create a proper copy of a series object and store it in another variable we have to use copy function\n",
    "\n",
    "#first reinitializing x\n",
    "x=pd.Series([1,2,3,4,5])\n",
    "\n",
    "#now using copy function\n",
    "y=x.copy()\n",
    "\n",
    "#now changing a certain value in y\n",
    "y[2]=200\n",
    "print(x)\n",
    "print(y)\n",
    "#this is called a proper copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "consistent-marina",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x\n",
      "0  1\n",
      "1  2\n",
      "2  3\n",
      "3  4\n",
      "4  5\n",
      "5  6\n",
      "6  7\n",
      "7  8\n",
      "8  9\n"
     ]
    }
   ],
   "source": [
    "#Dataframes : it is the main datastructure of pandas\n",
    "#A dataframe is like a list of series example:\n",
    "data = [1,2,3,4,5,6,7,8,9] #normal python list\n",
    "\n",
    "#now creating a dataframe\n",
    "df=pd.DataFrame(data,columns=[\"x\"]) #x would be the name of the column\n",
    "print(df)\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fossil-somerset",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "5    6\n",
      "6    7\n",
      "7    8\n",
      "8    9\n",
      "Name: x, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#to get series out of a dataframe\n",
    "dataSeries = df[\"x\"]\n",
    "print(dataSeries)\n",
    "#now as we can see they look the same but the difference between them is that a dataframe can have multiple columns\n",
    "#only in this particular case we made just one column but there could be many columns in a dataframe \n",
    "#so essentially it is like a bunch of series stacked together whereas series contains a single list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "electric-commitment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x  x_plus_2\n",
      "0  1         3\n",
      "1  2         4\n",
      "2  3         5\n",
      "3  4         6\n",
      "4  5         7\n",
      "5  6         8\n",
      "6  7         9\n",
      "7  8        10\n",
      "8  9        11\n"
     ]
    }
   ],
   "source": [
    "#Adding more columns in dataframe\n",
    "df[\"x_plus_2\"] = df[\"x\"]+2\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "unable-mississippi",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x  x_plus_2  x_square\n",
      "0  1         3         1\n",
      "1  2         4         4\n",
      "2  3         5         9\n",
      "3  4         6        16\n",
      "4  5         7        25\n",
      "5  6         8        36\n",
      "6  7         9        49\n",
      "7  8        10        64\n",
      "8  9        11        81\n"
     ]
    }
   ],
   "source": [
    "#similarly adding a square column\n",
    "df[\"x_square\"] = df[\"x\"]**2\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "spatial-european",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x  x_plus_2  x_square  x_factorial\n",
      "0  1         3         1            1\n",
      "1  2         4         4            2\n",
      "2  3         5         9            6\n",
      "3  4         6        16           24\n",
      "4  5         7        25          120\n",
      "5  6         8        36          720\n",
      "6  7         9        49         5040\n",
      "7  8        10        64        40320\n",
      "8  9        11        81       362880\n"
     ]
    }
   ],
   "source": [
    "#similarly adding a factorial column\n",
    "df[\"x_factorial\"] = df[\"x\"].apply(np.math.factorial)  #note here we are passing np's math.factorial function\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ancient-nebraska",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   x  x_plus_2  x_square  x_factorial  is_even\n",
       "0  1         3         1            1        1\n",
       "1  2         4         4            2        0\n",
       "2  3         5         9            6        1\n",
       "3  4         6        16           24        0\n",
       "4  5         7        25          120        1\n",
       "5  6         8        36          720        0\n",
       "6  7         9        49         5040        1\n",
       "7  8        10        64        40320        0\n",
       "8  9        11        81       362880        1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#similarly adding a is even column\n",
    "df[\"is_even\"] = df[\"x\"]%2\n",
    "df #note we can also print df like this IMPORTANT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "sensitive-wrong",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   x  x_plus_2  x_square  x_factorial\n",
       "0  1         3         1            1\n",
       "1  2         4         4            2\n",
       "2  3         5         9            6\n",
       "3  4         6        16           24\n",
       "4  5         7        25          120\n",
       "5  6         8        36          720\n",
       "6  7         9        49         5040\n",
       "7  8        10        64        40320\n",
       "8  9        11        81       362880"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#deleting a column use drop function\n",
    "df = df.drop(\"is_even\",1) #this deletes the is_even column and 1 means how many columns are we deleting starting from\n",
    "                          #is_even column\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "swedish-egyptian",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  x_square\n",
       "0  1         1\n",
       "1  2         4\n",
       "2  3         9\n",
       "3  4        16\n",
       "4  5        25\n",
       "5  6        36\n",
       "6  7        49\n",
       "7  8        64\n",
       "8  9        81"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#till now we are only displaying the whole dataframe but what if we want to display specific columns\n",
    "#Selecting multiple columns \n",
    "df[[\"x\",\"x_square\"]]\n",
    "#note here we are passing a list of columns that we want to display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ready-centre",
   "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>x</th>\n",
       "      <th>x_plus_2</th>\n",
       "      <th>x_square</th>\n",
       "      <th>x_factorial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.666667</td>\n",
       "      <td>45457.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.738613</td>\n",
       "      <td>2.738613</td>\n",
       "      <td>28.080242</td>\n",
       "      <td>119758.341137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>5040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>362880.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x   x_plus_2   x_square    x_factorial\n",
       "count  9.000000   9.000000   9.000000       9.000000\n",
       "mean   5.000000   7.000000  31.666667   45457.000000\n",
       "std    2.738613   2.738613  28.080242  119758.341137\n",
       "min    1.000000   3.000000   1.000000       1.000000\n",
       "25%    3.000000   5.000000   9.000000       6.000000\n",
       "50%    5.000000   7.000000  25.000000     120.000000\n",
       "75%    7.000000   9.000000  49.000000    5040.000000\n",
       "max    9.000000  11.000000  81.000000  362880.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describing\n",
    "df.describe()\n",
    "#this will give you all the different information about the dataframe like count, mean, std(standard deviation),etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "opening-evening",
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>632</td>\n",
       "      <td>2016</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>633</td>\n",
       "      <td>2016</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>634</td>\n",
       "      <td>2016</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2016-05-25</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>635</td>\n",
       "      <td>2016</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>636</td>\n",
       "      <td>2016</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>636 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  season       city        date                        team1  \\\n",
       "0      1    2017  Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1      2    2017       Pune  2017-04-06               Mumbai Indians   \n",
       "2      3    2017     Rajkot  2017-04-07                Gujarat Lions   \n",
       "3      4    2017     Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4      5    2017  Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "..   ...     ...        ...         ...                          ...   \n",
       "631  632    2016     Raipur  2016-05-22             Delhi Daredevils   \n",
       "632  633    2016  Bangalore  2016-05-24                Gujarat Lions   \n",
       "633  634    2016      Delhi  2016-05-25          Sunrisers Hyderabad   \n",
       "634  635    2016      Delhi  2016-05-27                Gujarat Lions   \n",
       "635  636    2016  Bangalore  2016-05-29          Sunrisers Hyderabad   \n",
       "\n",
       "                           team2                  toss_winner toss_decision  \\\n",
       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3                Kings XI Punjab              Kings XI Punjab         field   \n",
       "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "..                           ...                          ...           ...   \n",
       "631  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "632  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "633        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "634          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
       "635  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
       "\n",
       "     result  dl_applied                       winner  win_by_runs  \\\n",
       "0    normal           0          Sunrisers Hyderabad           35   \n",
       "1    normal           0       Rising Pune Supergiant            0   \n",
       "2    normal           0        Kolkata Knight Riders            0   \n",
       "3    normal           0              Kings XI Punjab            0   \n",
       "4    normal           0  Royal Challengers Bangalore           15   \n",
       "..      ...         ...                          ...          ...   \n",
       "631  normal           0  Royal Challengers Bangalore            0   \n",
       "632  normal           0  Royal Challengers Bangalore            0   \n",
       "633  normal           0          Sunrisers Hyderabad           22   \n",
       "634  normal           0          Sunrisers Hyderabad            0   \n",
       "635  normal           0          Sunrisers Hyderabad            8   \n",
       "\n",
       "     win_by_wickets player_of_match  \\\n",
       "0                 0    Yuvraj Singh   \n",
       "1                 7       SPD Smith   \n",
       "2                10         CA Lynn   \n",
       "3                 6      GJ Maxwell   \n",
       "4                 0       KM Jadhav   \n",
       "..              ...             ...   \n",
       "631               6         V Kohli   \n",
       "632               4  AB de Villiers   \n",
       "633               0    MC Henriques   \n",
       "634               4       DA Warner   \n",
       "635               0     BCJ Cutting   \n",
       "\n",
       "                                                venue          umpire1  \\\n",
       "0           Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
       "1             Maharashtra Cricket Association Stadium   A Nand Kishore   \n",
       "2              Saurashtra Cricket Association Stadium      Nitin Menon   \n",
       "3                              Holkar Cricket Stadium     AK Chaudhary   \n",
       "4                               M Chinnaswamy Stadium              NaN   \n",
       "..                                                ...              ...   \n",
       "631  Shaheed Veer Narayan Singh International Stadium   A Nand Kishore   \n",
       "632                             M Chinnaswamy Stadium     AK Chaudhary   \n",
       "633                                  Feroz Shah Kotla        M Erasmus   \n",
       "634                                  Feroz Shah Kotla        M Erasmus   \n",
       "635                             M Chinnaswamy Stadium  HDPK Dharmasena   \n",
       "\n",
       "             umpire2  umpire3  \n",
       "0           NJ Llong      NaN  \n",
       "1             S Ravi      NaN  \n",
       "2          CK Nandan      NaN  \n",
       "3      C Shamshuddin      NaN  \n",
       "4                NaN      NaN  \n",
       "..               ...      ...  \n",
       "631     BNJ Oxenford      NaN  \n",
       "632  HDPK Dharmasena      NaN  \n",
       "633    C Shamshuddin      NaN  \n",
       "634        CK Nandan      NaN  \n",
       "635     BNJ Oxenford      NaN  \n",
       "\n",
       "[636 rows x 18 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading datasets\n",
    "#Hands down this is the most important thing for which pandas is used\n",
    "dataset = pd.read_csv('matches.csv')\n",
    "#this imports the dataset present as a matches.cv file inside the same directory \n",
    "dataset\n",
    "#matches.csv file is converted into dataframe"
   ]
  }
 ],
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