Divyankar_Python_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas DataFrame\n",
    "## Creating a Pandas DataFrame\n",
    "**Creating a dataframe using List**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0\n",
      "0   Geeks\n",
      "1     For\n",
      "2   Geeks\n",
      "3      is\n",
      "4  portal\n",
      "5     for\n",
      "6   Geeks\n"
     ]
    }
   ],
   "source": [
    "# import pandas as pd\n",
    "import pandas as pd\n",
    " \n",
    "# list of strings\n",
    "lst = ['Geeks', 'For', 'Geeks', 'is', 'portal', 'for', 'Geeks']\n",
    " \n",
    "# Calling DataFrame constructor on list\n",
    "df = pd.DataFrame(lst)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating DataFrame from dict of narray/lists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Name  Age\n",
      "0    Tom   20\n",
      "1   nick   21\n",
      "2  krish   19\n",
      "3   jack   18\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    " \n",
    "# intialise data of lists.\n",
    "data = {'Name':['Tom', 'nick', 'krish', 'jack'], 'Age':[20, 21, 19, 18]}\n",
    " \n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data)\n",
    " \n",
    "# Print the output.\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with Rows and Columns\n",
    "**Column Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Name Qualification\n",
      "0     Jai           Msc\n",
      "1  Princi            MA\n",
      "2  Gaurav           MCA\n",
      "3    Anuj           Phd\n"
     ]
    }
   ],
   "source": [
    "# Import pandas package\n",
    "import pandas as pd\n",
    " \n",
    "# Define a dictionary containing employee data\n",
    "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], 'Age':[27, 24, 22, 32], 'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],\n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']}\n",
    " \n",
    "# Convert the dictionary into DataFrame \n",
    "df = pd.DataFrame(data)\n",
    " \n",
    "# select two columns\n",
    "print(df[['Name', 'Qualification']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Row Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team        Boston Celtics\n",
      "Number                   0\n",
      "Position                PG\n",
      "Age                     25\n",
      "Height                 6-2\n",
      "Weight                 180\n",
      "College              Texas\n",
      "Salary         7.73034e+06\n",
      "Name: Avery Bradley, dtype: object \n",
      "\n",
      "\n",
      " Team        Boston Celtics\n",
      "Number                  28\n",
      "Position                SG\n",
      "Age                     22\n",
      "Height                 6-5\n",
      "Weight                 185\n",
      "College      Georgia State\n",
      "Salary         1.14864e+06\n",
      "Name: R.J. Hunter, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# importing pandas package\n",
    "import pandas as pd\n",
    " \n",
    "# making data frame from csv file\n",
    "data = pd.read_csv(\"nba.csv\", index_col =\"Name\")\n",
    " \n",
    "# retrieving row by loc method\n",
    "first = data.loc[\"Avery Bradley\"]\n",
    "second = data.loc[\"R.J. Hunter\"]\n",
    " \n",
    "print(first, \"\\n\\n\\n\", second)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing and Selecting Data\n",
    "**Indexing a Dataframe using indexing operator []**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name\n",
      "Avery Bradley              25.0\n",
      "Jae Crowder                25.0\n",
      "John Holland               27.0\n",
      "R.J. Hunter                22.0\n",
      "Jonas Jerebko              29.0\n",
      "Amir Johnson               29.0\n",
      "Jordan Mickey              21.0\n",
      "Kelly Olynyk               25.0\n",
      "Terry Rozier               22.0\n",
      "Marcus Smart               22.0\n",
      "Jared Sullinger            24.0\n",
      "Isaiah Thomas              27.0\n",
      "Evan Turner                27.0\n",
      "James Young                20.0\n",
      "Tyler Zeller               26.0\n",
      "Bojan Bogdanovic           27.0\n",
      "Markel Brown               24.0\n",
      "Wayne Ellington            28.0\n",
      "Rondae Hollis-Jefferson    21.0\n",
      "Jarrett Jack               32.0\n",
      "Sergey Karasev             22.0\n",
      "Sean Kilpatrick            26.0\n",
      "Shane Larkin               23.0\n",
      "Brook Lopez                28.0\n",
      "Chris McCullough           21.0\n",
      "Willie Reed                26.0\n",
      "Thomas Robinson            25.0\n",
      "Henry Sims                 26.0\n",
      "Donald Sloan               28.0\n",
      "Thaddeus Young             27.0\n",
      "                           ... \n",
      "Al-Farouq Aminu            25.0\n",
      "Pat Connaughton            23.0\n",
      "Allen Crabbe               24.0\n",
      "Ed Davis                   27.0\n",
      "Maurice Harkless           23.0\n",
      "Gerald Henderson           28.0\n",
      "Chris Kaman                34.0\n",
      "Meyers Leonard             24.0\n",
      "Damian Lillard             25.0\n",
      "C.J. McCollum              24.0\n",
      "Luis Montero               23.0\n",
      "Mason Plumlee              26.0\n",
      "Brian Roberts              30.0\n",
      "Noah Vonleh                20.0\n",
      "Trevor Booker              28.0\n",
      "Trey Burke                 23.0\n",
      "Alec Burks                 24.0\n",
      "Dante Exum                 20.0\n",
      "Derrick Favors             24.0\n",
      "Rudy Gobert                23.0\n",
      "Gordon Hayward             26.0\n",
      "Rodney Hood                23.0\n",
      "Joe Ingles                 28.0\n",
      "Chris Johnson              26.0\n",
      "Trey Lyles                 20.0\n",
      "Shelvin Mack               26.0\n",
      "Raul Neto                  24.0\n",
      "Tibor Pleiss               26.0\n",
      "Jeff Withey                26.0\n",
      "NaN                         NaN\n",
      "Name: Age, Length: 458, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# importing pandas package\n",
    "import pandas as pd\n",
    " \n",
    "# making data frame from csv file\n",
    "data = pd.read_csv(\"nba.csv\", index_col =\"Name\")\n",
    " \n",
    "# retrieving columns by indexing operator\n",
    "first = data[\"Age\"]\n",
    "\n",
    "print(first)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Indexing a DataFrame using .loc[]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team        Boston Celtics\n",
      "Number                   0\n",
      "Position                PG\n",
      "Age                     25\n",
      "Height                 6-2\n",
      "Weight                 180\n",
      "College              Texas\n",
      "Salary         7.73034e+06\n",
      "Name: Avery Bradley, dtype: object \n",
      "\n",
      "\n",
      " Team        Boston Celtics\n",
      "Number                  28\n",
      "Position                SG\n",
      "Age                     22\n",
      "Height                 6-5\n",
      "Weight                 185\n",
      "College      Georgia State\n",
      "Salary         1.14864e+06\n",
      "Name: R.J. Hunter, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# importing pandas package\n",
    "import pandas as pd\n",
    " \n",
    "# making data frame from csv file\n",
    "data = pd.read_csv(\"nba.csv\", index_col =\"Name\")\n",
    " \n",
    "# retrieving row by loc method\n",
    "first = data.loc[\"Avery Bradley\"]\n",
    "second = data.loc[\"R.J. Hunter\"]\n",
    " \n",
    "print(first, \"\\n\\n\\n\", second)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Indexing a DataFrame using .iloc[]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team        Boston Celtics\n",
      "Number                  28\n",
      "Position                SG\n",
      "Age                     22\n",
      "Height                 6-5\n",
      "Weight                 185\n",
      "College      Georgia State\n",
      "Salary         1.14864e+06\n",
      "Name: R.J. Hunter, dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    " \n",
    "# making data frame from csv file\n",
    "data = pd.read_csv(\"nba.csv\", index_col =\"Name\")\n",
    " \n",
    "# retrieving rows by iloc method \n",
    "row2 = data.iloc[3] \n",
    " \n",
    "print(row2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Missing Data\n",
    "**Checking for missing values using isnull() and notnull()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        False         False         True\n",
       "1        False         False        False\n",
       "2         True         False        False\n",
       "3        False          True        False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas as pd\n",
    "import pandas as pd\n",
    " \n",
    "# importing numpy as np\n",
    "import numpy as np\n",
    " \n",
    "# dictionary of lists\n",
    "dict = {'First Score':[100, 90, np.nan, 95], 'Second Score': [30, 45, 56, np.nan], 'Third Score':[np.nan, 40, 80, 98]}\n",
    " \n",
    "# creating a dataframe from list\n",
    "df = pd.DataFrame(dict)\n",
    " \n",
    "# using isnull() function  \n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filling missing values using fillna(), replace() and interpolate()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        100.0          30.0          0.0\n",
       "1         90.0          45.0         40.0\n",
       "2          0.0          56.0         80.0\n",
       "3         95.0           0.0         98.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas as pd\n",
    "import pandas as pd\n",
    " \n",
    "# importing numpy as np\n",
    "import numpy as np\n",
    " \n",
    "# dictionary of lists\n",
    "dict = {'First Score':[100, 90, np.nan, 95], 'Second Score': [30, 45, 56, np.nan], 'Third Score':[np.nan, 40, 80, 98]}\n",
    " \n",
    "# creating a dataframe from dictionary\n",
    "df = pd.DataFrame(dict)\n",
    " \n",
    "# filling missing value using fillna()  \n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping missing values using dropna()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "      <th>Fourth Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score  Fourth Score\n",
       "0        100.0          30.0           52           NaN\n",
       "1         90.0           NaN           40           NaN\n",
       "2          NaN          45.0           80           NaN\n",
       "3         95.0          56.0           98          65.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas as pd\n",
    "import pandas as pd\n",
    " \n",
    "# importing numpy as np\n",
    "import numpy as np\n",
    " \n",
    "# dictionary of lists\n",
    "dict = {'First Score':[100, 90, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, 40, 80, 98], 'Fourth Score':[np.nan, np.nan, np.nan, 65]}\n",
    " \n",
    "# creating a dataframe from dictionary\n",
    "df = pd.DataFrame(dict)\n",
    "\n",
    "# using dropna() function \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterating over rows and columns\n",
    "**Iterating over rows**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     name  degree  score\n",
      "0  aparna     MBA     90\n",
      "1  pankaj     BCA     40\n",
      "2  sudhir  M.Tech     80\n",
      "3   Geeku     MBA     98\n"
     ]
    }
   ],
   "source": [
    "# importing pandas as pd\n",
    "import pandas as pd\n",
    "  \n",
    "# dictionary of lists\n",
    "dict = {'name':[\"aparna\", \"pankaj\", \"sudhir\", \"Geeku\"], 'degree': [\"MBA\", \"BCA\", \"M.Tech\", \"MBA\"], 'score':[90, 40, 80, 98]}\n",
    " \n",
    "# creating a dataframe from a dictionary \n",
    "df = pd.DataFrame(dict)\n",
    " \n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Iterating over Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     name  degree  score\n",
      "0  aparna     MBA     90\n",
      "1  pankaj     BCA     40\n",
      "2  sudhir  M.Tech     80\n",
      "3   Geeku     MBA     98\n"
     ]
    }
   ],
   "source": [
    "# importing pandas as pd\n",
    "import pandas as pd\n",
    "   \n",
    "# dictionary of lists\n",
    "dict = {'name':[\"aparna\", \"pankaj\", \"sudhir\", \"Geeku\"], 'degree': [\"MBA\", \"BCA\", \"M.Tech\", \"MBA\"], 'score':[90, 40, 80, 98]}\n",
    "  \n",
    "# creating a dataframe from a dictionary \n",
    "df = pd.DataFrame(dict)\n",
    " \n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Series\n",
    "## Creating a Pandas Series\n",
    "**Creating a series from array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    g\n",
      "1    e\n",
      "2    e\n",
      "3    k\n",
      "4    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# import pandas as pd\n",
    "import pandas as pd\n",
    " \n",
    "# import numpy as np\n",
    "import numpy as np\n",
    " \n",
    "# simple array\n",
    "data = np.array(['g','e','e','k','s'])\n",
    " \n",
    "ser = pd.Series(data)\n",
    "print(ser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a series from Lists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    g\n",
      "1    e\n",
      "2    e\n",
      "3    k\n",
      "4    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    " \n",
    "# a simple list\n",
    "list = ['g', 'e', 'e', 'k', 's']\n",
    "  \n",
    "# create series form a list\n",
    "ser = pd.Series(list)\n",
    "print(ser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing element of Series\n",
    "**Accessing Element from Series with Position**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    g\n",
      "1    e\n",
      "2    e\n",
      "3    k\n",
      "4    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# import pandas and numpy \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    " \n",
    "# creating simple array\n",
    "data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's'])\n",
    "ser = pd.Series(data)\n",
    "  \n",
    "#retrieve the first element\n",
    "print(ser[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accessing Element Using Label (index)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o\n"
     ]
    }
   ],
   "source": [
    "# import pandas and numpy \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    " \n",
    "# creating simple array\n",
    "data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's'])\n",
    "ser = pd.Series(data,index=[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22])\n",
    "  \n",
    "# accessing a element using index element\n",
    "print(ser[16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing and Selecting Data in Series\n",
    "**Indexing a Series using indexing operator []**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Avery Bradley\n",
       "1      Jae Crowder\n",
       "2     John Holland\n",
       "3      R.J. Hunter\n",
       "4    Jonas Jerebko\n",
       "5     Amir Johnson\n",
       "6    Jordan Mickey\n",
       "7     Kelly Olynyk\n",
       "8     Terry Rozier\n",
       "9     Marcus Smart\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    "     \n",
    "# making data frame  \n",
    "df = pd.read_csv(\"nba.csv\")  \n",
    "   \n",
    "ser = pd.Series(df['Name']) \n",
    "data = ser.head(10)\n",
    "data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Indexing a Series using .loc[]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Avery Bradley\n",
       "1      Jae Crowder\n",
       "2     John Holland\n",
       "3      R.J. Hunter\n",
       "4    Jonas Jerebko\n",
       "5     Amir Johnson\n",
       "6    Jordan Mickey\n",
       "7     Kelly Olynyk\n",
       "8     Terry Rozier\n",
       "9     Marcus Smart\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    "     \n",
    "# making data frame  \n",
    "df = pd.read_csv(\"nba.csv\")  \n",
    "   \n",
    "ser = pd.Series(df['Name']) \n",
    "data = ser.head(10)\n",
    "data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Indexing a Series using .iloc[]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Avery Bradley\n",
       "1      Jae Crowder\n",
       "2     John Holland\n",
       "3      R.J. Hunter\n",
       "4    Jonas Jerebko\n",
       "5     Amir Johnson\n",
       "6    Jordan Mickey\n",
       "7     Kelly Olynyk\n",
       "8     Terry Rozier\n",
       "9     Marcus Smart\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    "     \n",
    "# making data frame  \n",
    "df = pd.read_csv(\"nba.csv\")  \n",
    "   \n",
    "ser = pd.Series(df['Name']) \n",
    "data = ser.head(10)\n",
    "data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Operation on Series\n",
    "**Example #1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    5\n",
      "b    2\n",
      "c    3\n",
      "d    7\n",
      "dtype: int64 \n",
      "\n",
      " a    1\n",
      "b    6\n",
      "d    4\n",
      "e    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    " \n",
    "# creating a series\n",
    "data = pd.Series([5, 2, 3,7], index=['a', 'b', 'c', 'd'])\n",
    " \n",
    "# creating a series\n",
    "data1 = pd.Series([1, 6, 4, 9], index=['a', 'b', 'd', 'e'])\n",
    " \n",
    "print(data, \"\\n\\n\", data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    5\n",
      "b    2\n",
      "c    3\n",
      "d    7\n",
      "dtype: int64 \n",
      "\n",
      " a    1\n",
      "b    6\n",
      "d    4\n",
      "e    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    " \n",
    "# creating a series\n",
    "data = pd.Series([5, 2, 3,7], index=['a', 'b', 'c', 'd'])\n",
    " \n",
    "# creating a series\n",
    "data1 = pd.Series([1, 6, 4, 9], index=['a', 'b', 'd', 'e'])\n",
    " \n",
    "print(data, \"\\n\\n\", data1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion Operation on Series\n",
    "**Example #1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE CONVERSION\n",
      " Name         object\n",
      "Team         object\n",
      "Number      float64\n",
      "Position     object\n",
      "Age         float64\n",
      "Height       object\n",
      "Weight      float64\n",
      "College      object\n",
      "Salary      float64\n",
      "dtype: object \n",
      "\n",
      "AFTER CONVERSION\n",
      " Name         object\n",
      "Team         object\n",
      "Number       object\n",
      "Position     object\n",
      "Age         float64\n",
      "Height       object\n",
      "Weight      float64\n",
      "College      object\n",
      "Salary        int64\n",
      "dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd \n",
    "   \n",
    "# reading csv file from url  \n",
    "data = pd.read_csv(\"nba.csv\") \n",
    "    \n",
    "# dropping null value columns to avoid errors \n",
    "data.dropna(inplace = True) \n",
    "   \n",
    "# storing dtype before converting \n",
    "before = data.dtypes \n",
    "   \n",
    "# converting dtypes using astype \n",
    "data[\"Salary\"]= data[\"Salary\"].astype(int) \n",
    "data[\"Number\"]= data[\"Number\"].astype(str) \n",
    "   \n",
    "# storing dtype after converting \n",
    "after = data.dtypes \n",
    "   \n",
    "# printing to compare \n",
    "print(\"BEFORE CONVERSION\\n\", before, \"\\n\") \n",
    "print(\"AFTER CONVERSION\\n\", after, \"\\n\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type before converting = <class 'pandas.core.series.Series'>\n",
      "Data type after converting = <class 'list'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[7730337.0,\n",
       " 6796117.0,\n",
       " 1148640.0,\n",
       " 1170960.0,\n",
       " 2165160.0,\n",
       " 1824360.0,\n",
       " 3431040.0,\n",
       " 2569260.0,\n",
       " 6912869.0,\n",
       " 3425510.0,\n",
       " 1749840.0,\n",
       " 2616975.0,\n",
       " 845059.0,\n",
       " 1500000.0,\n",
       " 1335480.0,\n",
       " 6300000.0,\n",
       " 134215.0,\n",
       " 1500000.0,\n",
       " 19689000.0,\n",
       " 1140240.0,\n",
       " 947276.0,\n",
       " 981348.0,\n",
       " 947276.0,\n",
       " 947276.0,\n",
       " 11235955.0,\n",
       " 8000000.0,\n",
       " 1635476.0,\n",
       " 22875000.0,\n",
       " 845059.0,\n",
       " 845059.0,\n",
       " 1572360.0,\n",
       " 12650000.0,\n",
       " 3750000.0,\n",
       " 1636842.0,\n",
       " 4000000.0,\n",
       " 167406.0,\n",
       " 947276.0,\n",
       " 1000000.0,\n",
       " 4626960.0,\n",
       " 845059.0,\n",
       " 1074169.0,\n",
       " 6500000.0,\n",
       " 2144772.0,\n",
       " 525093.0,\n",
       " 3457800.0,\n",
       " 4582680.0,\n",
       " 947276.0,\n",
       " 2869440.0,\n",
       " 947276.0,\n",
       " 525093.0,\n",
       " 13600000.0,\n",
       " 10050000.0,\n",
       " 2500000.0,\n",
       " 7000000.0,\n",
       " 12000000.0,\n",
       " 6268675.0,\n",
       " 650000.0,\n",
       " 3553917.0,\n",
       " 245177.0,\n",
       " 1509360.0,\n",
       " 3873398.0,\n",
       " 13800000.0,\n",
       " 947276.0,\n",
       " 11370786.0,\n",
       " 2008748.0,\n",
       " 14260870.0,\n",
       " 11710456.0,\n",
       " 1131960.0,\n",
       " 845059.0,\n",
       " 1270964.0,\n",
       " 3815000.0,\n",
       " 15501000.0,\n",
       " 1100602.0,\n",
       " 111444.0,\n",
       " 5675000.0,\n",
       " 525093.0,\n",
       " 9650000.0,\n",
       " 18907726.0,\n",
       " 1100602.0,\n",
       " 19689000.0,\n",
       " 947276.0,\n",
       " 21468695.0,\n",
       " 3376000.0,\n",
       " 7085000.0,\n",
       " 3110796.0,\n",
       " 1159680.0,\n",
       " 3000000.0,\n",
       " 845059.0,\n",
       " 700000.0,\n",
       " 845059.0,\n",
       " 15592217.0,\n",
       " 1724250.0,\n",
       " 1155600.0,\n",
       " 3132240.0,\n",
       " 5103120.0,\n",
       " 981348.0,\n",
       " 947276.0,\n",
       " 5219169.0,\n",
       " 13500000.0,\n",
       " 2127840.0,\n",
       " 206192.0,\n",
       " 1160160.0,\n",
       " 981348.0,\n",
       " 13500000.0,\n",
       " 3807120.0,\n",
       " 1035000.0,\n",
       " 55722.0,\n",
       " 947276.0,\n",
       " 5500000.0,\n",
       " 2041080.0,\n",
       " 83397.0,\n",
       " 981348.0,\n",
       " 1015421.0,\n",
       " 1449187.0,\n",
       " 3398280.0,\n",
       " 5013559.0,\n",
       " 15851950.0,\n",
       " 947276.0,\n",
       " 525093.0,\n",
       " 12403101.0,\n",
       " 7700000.0,\n",
       " 3156600.0,\n",
       " 845059.0,\n",
       " 9500000.0,\n",
       " 845059.0,\n",
       " 2250000.0,\n",
       " 16407500.0,\n",
       " 4500000.0,\n",
       " 8500000.0,\n",
       " 947276.0,\n",
       " 2380440.0,\n",
       " 1015421.0,\n",
       " 13400000.0,\n",
       " 1391160.0,\n",
       " 20093064.0,\n",
       " 1535880.0,\n",
       " 1147276.0,\n",
       " 8193029.0,\n",
       " 16407501.0,\n",
       " 947276.0,\n",
       " 947276.0,\n",
       " 1276000.0,\n",
       " 19689000.0,\n",
       " 111196.0,\n",
       " 8988765.0,\n",
       " 14260870.0,\n",
       " 2100000.0,\n",
       " 2500000.0,\n",
       " 6500000.0,\n",
       " 2170465.0,\n",
       " 111444.0,\n",
       " 1252440.0,\n",
       " 2891760.0,\n",
       " 845059.0,\n",
       " 3272091.0,\n",
       " 16000000.0,\n",
       " 600000.0,\n",
       " 13913044.0,\n",
       " 2841960.0,\n",
       " 6270000.0,\n",
       " 5000000.0,\n",
       " 3000000.0,\n",
       " 4050000.0,\n",
       " 1007026.0,\n",
       " 17120106.0,\n",
       " 8000000.0,\n",
       " 4000000.0,\n",
       " 1358880.0,\n",
       " 211744.0,\n",
       " 1100000.0,\n",
       " 7000000.0,\n",
       " 2357760.0,\n",
       " 845059.0,\n",
       " 1007026.0,\n",
       " 3000000.0,\n",
       " 2399040.0,\n",
       " 947276.0,\n",
       " 1662360.0,\n",
       " 2943221.0,\n",
       " 8000000.0,\n",
       " 14700000.0,\n",
       " 16407500.0,\n",
       " 295327.0,\n",
       " 845059.0,\n",
       " 5152440.0,\n",
       " 2109294.0,\n",
       " 6600000.0,\n",
       " 1733040.0,\n",
       " 1449000.0,\n",
       " 4290000.0,\n",
       " 1100602.0,\n",
       " 3950313.0,\n",
       " 4053446.0,\n",
       " 2085671.0,\n",
       " 16407500.0,\n",
       " 1270964.0,\n",
       " 15361500.0,\n",
       " 845059.0,\n",
       " 947276.0,\n",
       " 5378974.0,\n",
       " 8193030.0,\n",
       " 306527.0,\n",
       " 6486486.0,\n",
       " 8229375.0,\n",
       " 1646400.0,\n",
       " 200600.0,\n",
       " 15756438.0,\n",
       " 1000000.0,\n",
       " 2489530.0,\n",
       " 3189794.0,\n",
       " 947276.0,\n",
       " 1404600.0,\n",
       " 5158539.0,\n",
       " 5000000.0,\n",
       " 3542500.0,\n",
       " 4088019.0,\n",
       " 9588426.0,\n",
       " 700902.0,\n",
       " 845059.0,\n",
       " 1201440.0,\n",
       " 1230840.0,\n",
       " 9638555.0,\n",
       " 9000000.0,\n",
       " 5464000.0,\n",
       " 8500000.0,\n",
       " 1100602.0,\n",
       " 3036927.0,\n",
       " 2850000.0,\n",
       " 7070730.0,\n",
       " 169883.0,\n",
       " 1164858.0,\n",
       " 845059.0,\n",
       " 10734586.0,\n",
       " 845059.0,\n",
       " 1320000.0,\n",
       " 15514031.0,\n",
       " 1015421.0,\n",
       " 10595507.0,\n",
       " 55722.0,\n",
       " 3382023.0,\n",
       " 19689000.0,\n",
       " 1142880.0,\n",
       " 947276.0,\n",
       " 5250000.0,\n",
       " 10000000.0,\n",
       " 16407500.0,\n",
       " 200600.0,\n",
       " 250750.0,\n",
       " 3578947.0,\n",
       " 525093.0,\n",
       " 1499187.0,\n",
       " 2000000.0,\n",
       " 1304520.0,\n",
       " 2854940.0,\n",
       " 12000000.0,\n",
       " 1000000.0,\n",
       " 5746479.0,\n",
       " 18671659.0,\n",
       " 947276.0,\n",
       " 525093.0,\n",
       " 3333333.0,\n",
       " 8000000.0,\n",
       " 947276.0,\n",
       " 189455.0,\n",
       " 947276.0,\n",
       " 525093.0,\n",
       " 6110034.0,\n",
       " 2612520.0,\n",
       " 6331404.0,\n",
       " 3034356.0,\n",
       " 5675000.0,\n",
       " 2139000.0,\n",
       " 12000000.0,\n",
       " 7000000.0,\n",
       " 4204200.0,\n",
       " 22192730.0,\n",
       " 10151612.0,\n",
       " 2854940.0,\n",
       " 261894.0,\n",
       " 845059.0,\n",
       " 5543725.0,\n",
       " 525093.0,\n",
       " 20000000.0,\n",
       " 981348.0,\n",
       " 2481720.0,\n",
       " 947276.0,\n",
       " 4171680.0,\n",
       " 845059.0,\n",
       " 1294440.0,\n",
       " 2380593.0,\n",
       " 5192520.0,\n",
       " 2505720.0,\n",
       " 4300000.0,\n",
       " 11250000.0,\n",
       " 5000000.0,\n",
       " 4000000.0,\n",
       " 5694674.0,\n",
       " 4375000.0,\n",
       " 561716.0,\n",
       " 3300000.0,\n",
       " 273038.0,\n",
       " 8000000.0,\n",
       " 1920240.0,\n",
       " 4662960.0,\n",
       " 2170465.0,\n",
       " 1100602.0,\n",
       " 200600.0,\n",
       " 15851950.0,\n",
       " 2814000.0,\n",
       " 3000000.0,\n",
       " 3533333.0,\n",
       " 10449438.0,\n",
       " 11235955.0,\n",
       " 1584480.0,\n",
       " 947276.0,\n",
       " 4345000.0,\n",
       " 258489.0,\n",
       " 1474440.0,\n",
       " 1282080.0,\n",
       " 2148360.0,\n",
       " 2056920.0,\n",
       " 1938840.0,\n",
       " 947276.0,\n",
       " 5703600.0,\n",
       " 5758680.0,\n",
       " 2279040.0,\n",
       " 3750000.0,\n",
       " 20158622.0,\n",
       " 3135000.0,\n",
       " 1140240.0,\n",
       " 16407500.0,\n",
       " 1463040.0,\n",
       " 222888.0,\n",
       " 3344000.0,\n",
       " 2021520.0,\n",
       " 1210800.0,\n",
       " 4500000.0,\n",
       " 5138430.0,\n",
       " 16744218.0,\n",
       " 525093.0,\n",
       " 8042895.0,\n",
       " 625093.0,\n",
       " 947276.0,\n",
       " 6980802.0,\n",
       " 2894059.0,\n",
       " 6000000.0,\n",
       " 5016000.0,\n",
       " 3075880.0,\n",
       " 4236287.0,\n",
       " 2525160.0,\n",
       " 525093.0,\n",
       " 1415520.0,\n",
       " 2854940.0,\n",
       " 2637720.0,\n",
       " 4775000.0,\n",
       " 2658240.0,\n",
       " 9463484.0,\n",
       " 12000000.0,\n",
       " 15409570.0,\n",
       " 1348440.0,\n",
       " 981348.0,\n",
       " 2239800.0,\n",
       " 2433333.0,\n",
       " 947276.0]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    "   \n",
    "# importing regex module \n",
    "import re \n",
    "     \n",
    "# making data frame  \n",
    "data = pd.read_csv(\"nba.csv\")  \n",
    "     \n",
    "# removing null values to avoid errors  \n",
    "data.dropna(inplace = True)  \n",
    "   \n",
    "# storing dtype before operation \n",
    "dtype_before = type(data[\"Salary\"]) \n",
    "   \n",
    "# converting to list \n",
    "salary_list = data[\"Salary\"].tolist() \n",
    "   \n",
    "# storing dtype after operation \n",
    "dtype_after = type(salary_list) \n",
    "   \n",
    "# printing dtype \n",
    "print(\"Data type before converting = {}\\nData type after converting = {}\"\n",
    "      .format(dtype_before, dtype_after)) \n",
    "   \n",
    "# displaying list \n",
    "salary_list "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions & Methods\n",
    "## Dataframe/Series.head() method\n",
    "**Example #1**"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module \n",
    "import pandas as pd \n",
    "  \n",
    "# making data frame \n",
    "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\") \n",
    "  \n",
    "# calling head() method  \n",
    "# storing in new variable \n",
    "data_top = data.head() \n",
    "  \n",
    "# display \n",
    "data_top "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Avery Bradley\n",
       "1      Jae Crowder\n",
       "2     John Holland\n",
       "3      R.J. Hunter\n",
       "4    Jonas Jerebko\n",
       "5     Amir Johnson\n",
       "6    Jordan Mickey\n",
       "7     Kelly Olynyk\n",
       "8     Terry Rozier\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module \n",
    "import pandas as pd \n",
    "  \n",
    "# making data frame \n",
    "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\") \n",
    "  \n",
    "# number of rows to return \n",
    "n = 9\n",
    "  \n",
    "# creating series \n",
    "series = data[\"Name\"] \n",
    "  \n",
    "# returning top n rows \n",
    "top = series.head(n = n) \n",
    "  \n",
    "# display \n",
    "top "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe/Series.describe() method\n",
    "**Example #1**"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>364</td>\n",
       "      <td>364</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364</td>\n",
       "      <td>3.640000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>364</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Charlie Villanueva</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.829670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.615385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.785714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.620311e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.994162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.233591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.793099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.119716e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.572200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.472760e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.638754e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.515440e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.429934e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.838202e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.287500e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name                  Team      Number Position  \\\n",
       "count                  364                   364  364.000000      364   \n",
       "unique                 364                    30         NaN        5   \n",
       "top     Charlie Villanueva  New Orleans Pelicans         NaN       SG   \n",
       "freq                     1                    16         NaN       87   \n",
       "mean                   NaN                   NaN   16.829670      NaN   \n",
       "std                    NaN                   NaN   14.994162      NaN   \n",
       "min                    NaN                   NaN    0.000000      NaN   \n",
       "20%                    NaN                   NaN    4.000000      NaN   \n",
       "40%                    NaN                   NaN    9.000000      NaN   \n",
       "50%                    NaN                   NaN   12.000000      NaN   \n",
       "60%                    NaN                   NaN   17.000000      NaN   \n",
       "80%                    NaN                   NaN   30.000000      NaN   \n",
       "max                    NaN                   NaN   99.000000      NaN   \n",
       "\n",
       "               Age Height      Weight   College        Salary  \n",
       "count   364.000000    364  364.000000       364  3.640000e+02  \n",
       "unique         NaN     17         NaN       115           NaN  \n",
       "top            NaN    6-9         NaN  Kentucky           NaN  \n",
       "freq           NaN     49         NaN        22           NaN  \n",
       "mean     26.615385    NaN  219.785714       NaN  4.620311e+06  \n",
       "std       4.233591    NaN   24.793099       NaN  5.119716e+06  \n",
       "min      19.000000    NaN  161.000000       NaN  5.572200e+04  \n",
       "20%      23.000000    NaN  195.000000       NaN  9.472760e+05  \n",
       "40%      25.000000    NaN  212.000000       NaN  1.638754e+06  \n",
       "50%      26.000000    NaN  220.000000       NaN  2.515440e+06  \n",
       "60%      27.000000    NaN  228.000000       NaN  3.429934e+06  \n",
       "80%      30.000000    NaN  242.400000       NaN  7.838202e+06  \n",
       "max      40.000000    NaN  279.000000       NaN  2.287500e+07  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    "  \n",
    "# importing regex module \n",
    "import re \n",
    "    \n",
    "# making data frame  \n",
    "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\")  \n",
    "    \n",
    "# removing null values to avoid errors  \n",
    "data.dropna(inplace = True)  \n",
    "  \n",
    "# percentile list \n",
    "perc =[.20, .40, .60, .80] \n",
    "  \n",
    "# list of dtypes to include \n",
    "include =['object', 'float', 'int'] \n",
    "  \n",
    "# calling describe method \n",
    "desc = data.describe(percentiles = perc, include = include) \n",
    "  \n",
    "# display \n",
    "desc "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                    364\n",
       "unique                   364\n",
       "top       Charlie Villanueva\n",
       "freq                       1\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas module  \n",
    "import pandas as pd  \n",
    "  \n",
    "# importing regex module \n",
    "import re \n",
    "    \n",
    "# making data frame  \n",
    "data = pd.read_csv(\"https://media.geeksforgeeks.org/wp-content/uploads/nba.csv\")  \n",
    "    \n",
    "# removing null values to avoid errors  \n",
    "data.dropna(inplace = True)  \n",
    "  \n",
    "# calling describe method \n",
    "desc = data[\"Name\"].describe() \n",
    "  \n",
    "# display \n",
    "desc "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe/Series.loc[] method\n",
    "**Example #1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team        Boston Celtics\n",
      "Number                   0\n",
      "Position                PG\n",
      "Age                     25\n",
      "Height                 6-2\n",
      "Weight                 180\n",
      "College              Texas\n",
      "Salary         7.73034e+06\n",
      "Name: Avery Bradley, dtype: object \n",
      "\n",
      "\n",
      " Team        Boston Celtics\n",
      "Number                  28\n",
      "Position                SG\n",
      "Age                     22\n",
      "Height                 6-5\n",
      "Weight                 185\n",
      "College      Georgia State\n",
      "Salary         1.14864e+06\n",
      "Name: R.J. Hunter, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# importing pandas package \n",
    "import pandas as pd \n",
    "  \n",
    "# making data frame from csv file \n",
    "data = pd.read_csv(\"nba.csv\", index_col =\"Name\") \n",
    "  \n",
    "# retrieving row by loc method \n",
    "first = data.loc[\"Avery Bradley\"] \n",
    "second = data.loc[\"R.J. Hunter\"] \n",
    "  \n",
    "print(first, \"\\n\\n\\n\", second) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</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>Avery Bradley</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R.J. Hunter</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Team  Number Position   Age Height  Weight  \\\n",
       "Name                                                                  \n",
       "Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "R.J. Hunter    Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "\n",
       "                     College     Salary  \n",
       "Name                                     \n",
       "Avery Bradley          Texas  7730337.0  \n",
       "R.J. Hunter    Georgia State  1148640.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas package \n",
    "import pandas as pd \n",
    "  \n",
    "# making data frame from csv file \n",
    "data = pd.read_csv(\"nba.csv\", index_col =\"Name\") \n",
    "  \n",
    "# retrieving rows by loc method \n",
    "rows = data.loc[[\"Avery Bradley\", \"R.J. Hunter\"]] \n",
    "  \n",
    "# checking data type of rows \n",
    "print(type(rows)) \n",
    "  \n",
    "# display \n",
    "rows "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe/Series.iloc[] method\n",
    "**Example #1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        True\n",
       "Team        True\n",
       "Number      True\n",
       "Position    True\n",
       "Age         True\n",
       "Height      True\n",
       "Weight      True\n",
       "College     True\n",
       "Salary      True\n",
       "Name: 3, dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas package \n",
    "import pandas as pd \n",
    "  \n",
    "# making data frame from csv file  \n",
    "data = pd.read_csv(\"nba.csv\") \n",
    "  \n",
    "# retrieving rows by loc method  \n",
    "row1 = data.loc[3] \n",
    "  \n",
    "# retrieving rows by iloc method \n",
    "row2 = data.iloc[3] \n",
    "  \n",
    "# checking if values are equal \n",
    "row1 == row2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Team  Number  Position   Age  Height  Weight  College  Salary\n",
       "4  True  True    True      True  True    True    True    False    True\n",
       "5  True  True    True      True  True    True    True    False    True\n",
       "6  True  True    True      True  True    True    True     True    True\n",
       "7  True  True    True      True  True    True    True     True    True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas package \n",
    "import pandas as pd \n",
    "  \n",
    "# making data frame from csv file  \n",
    "data = pd.read_csv(\"nba.csv\") \n",
    "  \n",
    "# retrieving rows by loc method  \n",
    "row1 = data.iloc[[4, 5, 6, 7]] \n",
    "  \n",
    "# retrieving rows by loc method  \n",
    "row2 = data.iloc[4:8] \n",
    "  \n",
    "# comparing values \n",
    "row1 == row2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas.read_csv() method\n",
    "**Example #1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amir Johnson</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jordan Mickey</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>55.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>235.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kelly Olynyk</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>41.0</td>\n",
       "      <td>C</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>Gonzaga</td>\n",
       "      <td>2165160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Terry Rozier</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>12.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>1824360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Marcus Smart</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>36.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>3431040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jared Sullinger</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>7.0</td>\n",
       "      <td>C</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>2569260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5-9</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>6912869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Evan Turner</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>11.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Ohio State</td>\n",
       "      <td>3425510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>James Young</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>13.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>215.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>1749840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tyler Zeller</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>44.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>2616975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bojan Bogdanovic</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>216.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3425510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Markel Brown</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>845059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Wayne Ellington</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>21.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>200.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rondae Hollis-Jefferson</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1335480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jarrett Jack</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>6300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sergey Karasev</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1599840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sean Kilpatrick</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>134215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shane Larkin</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5-11</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Miami (FL)</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brook Lopez</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>11.0</td>\n",
       "      <td>C</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>Stanford</td>\n",
       "      <td>19689000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Chris McCullough</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>1140240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Willie Reed</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Saint Louis</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Thomas Robinson</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>981348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Henry Sims</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>14.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Donald Sloan</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>15.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Texas A&amp;M</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Thaddeus Young</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>30.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>11235955.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Al-Farouq Aminu</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>8.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>215.0</td>\n",
       "      <td>Wake Forest</td>\n",
       "      <td>8042895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>Pat Connaughton</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Notre Dame</td>\n",
       "      <td>625093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>Allen Crabbe</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>210.0</td>\n",
       "      <td>California</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>Ed Davis</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>17.0</td>\n",
       "      <td>C</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>240.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>6980802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>Maurice Harkless</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>215.0</td>\n",
       "      <td>St. John's</td>\n",
       "      <td>2894059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Gerald Henderson</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>9.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>215.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>6000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>Chris Kaman</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>35.0</td>\n",
       "      <td>C</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>Central Michigan</td>\n",
       "      <td>5016000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>Meyers Leonard</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>11.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7-1</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>3075880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>195.0</td>\n",
       "      <td>Weber State</td>\n",
       "      <td>4236287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>C.J. McCollum</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>3.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Lehigh</td>\n",
       "      <td>2525160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Luis Montero</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Westchester CC</td>\n",
       "      <td>525093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>Mason Plumlee</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>1415520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Brian Roberts</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>173.0</td>\n",
       "      <td>Dayton</td>\n",
       "      <td>2854940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>Noah Vonleh</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>21.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>2637720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>Trevor Booker</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>228.0</td>\n",
       "      <td>Clemson</td>\n",
       "      <td>4775000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Trey Burke</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>3.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>2658240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>Alec Burks</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>214.0</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>9463484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Dante Exum</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>11.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3777720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Derrick Favors</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>15.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>265.0</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>27.0</td>\n",
       "      <td>C</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7-1</td>\n",
       "      <td>245.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1175880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Gordon Hayward</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>20.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>226.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>15409570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Rodney Hood</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>1348440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Joe Ingles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2050000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Chris Johnson</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Dayton</td>\n",
       "      <td>981348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name                    Team  Number Position   Age  \\\n",
       "0              Avery Bradley          Boston Celtics     0.0       PG  25.0   \n",
       "1                Jae Crowder          Boston Celtics    99.0       SF  25.0   \n",
       "2               John Holland          Boston Celtics    30.0       SG  27.0   \n",
       "3                R.J. Hunter          Boston Celtics    28.0       SG  22.0   \n",
       "4              Jonas Jerebko          Boston Celtics     8.0       PF  29.0   \n",
       "5               Amir Johnson          Boston Celtics    90.0       PF  29.0   \n",
       "6              Jordan Mickey          Boston Celtics    55.0       PF  21.0   \n",
       "7               Kelly Olynyk          Boston Celtics    41.0        C  25.0   \n",
       "8               Terry Rozier          Boston Celtics    12.0       PG  22.0   \n",
       "9               Marcus Smart          Boston Celtics    36.0       PG  22.0   \n",
       "10           Jared Sullinger          Boston Celtics     7.0        C  24.0   \n",
       "11             Isaiah Thomas          Boston Celtics     4.0       PG  27.0   \n",
       "12               Evan Turner          Boston Celtics    11.0       SG  27.0   \n",
       "13               James Young          Boston Celtics    13.0       SG  20.0   \n",
       "14              Tyler Zeller          Boston Celtics    44.0        C  26.0   \n",
       "15          Bojan Bogdanovic           Brooklyn Nets    44.0       SG  27.0   \n",
       "16              Markel Brown           Brooklyn Nets    22.0       SG  24.0   \n",
       "17           Wayne Ellington           Brooklyn Nets    21.0       SG  28.0   \n",
       "18   Rondae Hollis-Jefferson           Brooklyn Nets    24.0       SG  21.0   \n",
       "19              Jarrett Jack           Brooklyn Nets     2.0       PG  32.0   \n",
       "20            Sergey Karasev           Brooklyn Nets    10.0       SG  22.0   \n",
       "21           Sean Kilpatrick           Brooklyn Nets     6.0       SG  26.0   \n",
       "22              Shane Larkin           Brooklyn Nets     0.0       PG  23.0   \n",
       "23               Brook Lopez           Brooklyn Nets    11.0        C  28.0   \n",
       "24          Chris McCullough           Brooklyn Nets     1.0       PF  21.0   \n",
       "25               Willie Reed           Brooklyn Nets    33.0       PF  26.0   \n",
       "26           Thomas Robinson           Brooklyn Nets    41.0       PF  25.0   \n",
       "27                Henry Sims           Brooklyn Nets    14.0        C  26.0   \n",
       "28              Donald Sloan           Brooklyn Nets    15.0       PG  28.0   \n",
       "29            Thaddeus Young           Brooklyn Nets    30.0       PF  27.0   \n",
       "..                       ...                     ...     ...      ...   ...   \n",
       "428          Al-Farouq Aminu  Portland Trail Blazers     8.0       SF  25.0   \n",
       "429          Pat Connaughton  Portland Trail Blazers     5.0       SG  23.0   \n",
       "430             Allen Crabbe  Portland Trail Blazers    23.0       SG  24.0   \n",
       "431                 Ed Davis  Portland Trail Blazers    17.0        C  27.0   \n",
       "432         Maurice Harkless  Portland Trail Blazers     4.0       SF  23.0   \n",
       "433         Gerald Henderson  Portland Trail Blazers     9.0       SG  28.0   \n",
       "434              Chris Kaman  Portland Trail Blazers    35.0        C  34.0   \n",
       "435           Meyers Leonard  Portland Trail Blazers    11.0       PF  24.0   \n",
       "436           Damian Lillard  Portland Trail Blazers     0.0       PG  25.0   \n",
       "437            C.J. McCollum  Portland Trail Blazers     3.0       SG  24.0   \n",
       "438             Luis Montero  Portland Trail Blazers    44.0       SG  23.0   \n",
       "439            Mason Plumlee  Portland Trail Blazers    24.0        C  26.0   \n",
       "440            Brian Roberts  Portland Trail Blazers     2.0       PG  30.0   \n",
       "441              Noah Vonleh  Portland Trail Blazers    21.0       PF  20.0   \n",
       "442            Trevor Booker               Utah Jazz    33.0       PF  28.0   \n",
       "443               Trey Burke               Utah Jazz     3.0       PG  23.0   \n",
       "444               Alec Burks               Utah Jazz    10.0       SG  24.0   \n",
       "445               Dante Exum               Utah Jazz    11.0       PG  20.0   \n",
       "446           Derrick Favors               Utah Jazz    15.0       PF  24.0   \n",
       "447              Rudy Gobert               Utah Jazz    27.0        C  23.0   \n",
       "448           Gordon Hayward               Utah Jazz    20.0       SF  26.0   \n",
       "449              Rodney Hood               Utah Jazz     5.0       SG  23.0   \n",
       "450               Joe Ingles               Utah Jazz     2.0       SF  28.0   \n",
       "451            Chris Johnson               Utah Jazz    23.0       SF  26.0   \n",
       "452               Trey Lyles               Utah Jazz    41.0       PF  20.0   \n",
       "453             Shelvin Mack               Utah Jazz     8.0       PG  26.0   \n",
       "454                Raul Neto               Utah Jazz    25.0       PG  24.0   \n",
       "455             Tibor Pleiss               Utah Jazz    21.0        C  26.0   \n",
       "456              Jeff Withey               Utah Jazz    24.0        C  26.0   \n",
       "457                      NaN                     NaN     NaN      NaN   NaN   \n",
       "\n",
       "    Height  Weight            College      Salary  \n",
       "0      6-2   180.0              Texas   7730337.0  \n",
       "1      6-6   235.0          Marquette   6796117.0  \n",
       "2      6-5   205.0  Boston University         NaN  \n",
       "3      6-5   185.0      Georgia State   1148640.0  \n",
       "4     6-10   231.0                NaN   5000000.0  \n",
       "5      6-9   240.0                NaN  12000000.0  \n",
       "6      6-8   235.0                LSU   1170960.0  \n",
       "7      7-0   238.0            Gonzaga   2165160.0  \n",
       "8      6-2   190.0         Louisville   1824360.0  \n",
       "9      6-4   220.0     Oklahoma State   3431040.0  \n",
       "10     6-9   260.0         Ohio State   2569260.0  \n",
       "11     5-9   185.0         Washington   6912869.0  \n",
       "12     6-7   220.0         Ohio State   3425510.0  \n",
       "13     6-6   215.0           Kentucky   1749840.0  \n",
       "14     7-0   253.0     North Carolina   2616975.0  \n",
       "15     6-8   216.0                NaN   3425510.0  \n",
       "16     6-3   190.0     Oklahoma State    845059.0  \n",
       "17     6-4   200.0     North Carolina   1500000.0  \n",
       "18     6-7   220.0            Arizona   1335480.0  \n",
       "19     6-3   200.0       Georgia Tech   6300000.0  \n",
       "20     6-7   208.0                NaN   1599840.0  \n",
       "21     6-4   219.0         Cincinnati    134215.0  \n",
       "22    5-11   175.0         Miami (FL)   1500000.0  \n",
       "23     7-0   275.0           Stanford  19689000.0  \n",
       "24    6-11   200.0           Syracuse   1140240.0  \n",
       "25    6-10   220.0        Saint Louis    947276.0  \n",
       "26    6-10   237.0             Kansas    981348.0  \n",
       "27    6-10   248.0         Georgetown    947276.0  \n",
       "28     6-3   205.0          Texas A&M    947276.0  \n",
       "29     6-8   221.0       Georgia Tech  11235955.0  \n",
       "..     ...     ...                ...         ...  \n",
       "428    6-9   215.0        Wake Forest   8042895.0  \n",
       "429    6-5   206.0         Notre Dame    625093.0  \n",
       "430    6-6   210.0         California    947276.0  \n",
       "431   6-10   240.0     North Carolina   6980802.0  \n",
       "432    6-9   215.0         St. John's   2894059.0  \n",
       "433    6-5   215.0               Duke   6000000.0  \n",
       "434    7-0   265.0   Central Michigan   5016000.0  \n",
       "435    7-1   245.0           Illinois   3075880.0  \n",
       "436    6-3   195.0        Weber State   4236287.0  \n",
       "437    6-4   200.0             Lehigh   2525160.0  \n",
       "438    6-7   185.0     Westchester CC    525093.0  \n",
       "439   6-11   235.0               Duke   1415520.0  \n",
       "440    6-1   173.0             Dayton   2854940.0  \n",
       "441    6-9   240.0            Indiana   2637720.0  \n",
       "442    6-8   228.0            Clemson   4775000.0  \n",
       "443    6-1   191.0           Michigan   2658240.0  \n",
       "444    6-6   214.0           Colorado   9463484.0  \n",
       "445    6-6   190.0                NaN   3777720.0  \n",
       "446   6-10   265.0       Georgia Tech  12000000.0  \n",
       "447    7-1   245.0                NaN   1175880.0  \n",
       "448    6-8   226.0             Butler  15409570.0  \n",
       "449    6-8   206.0               Duke   1348440.0  \n",
       "450    6-8   226.0                NaN   2050000.0  \n",
       "451    6-6   206.0             Dayton    981348.0  \n",
       "452   6-10   234.0           Kentucky   2239800.0  \n",
       "453    6-3   203.0             Butler   2433333.0  \n",
       "454    6-1   179.0                NaN    900000.0  \n",
       "455    7-3   256.0                NaN   2900000.0  \n",
       "456    7-0   231.0             Kansas    947276.0  \n",
       "457    NaN     NaN                NaN         NaN  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas \n",
    "import pandas as pd \n",
    "  \n",
    "# reading csv file  \n",
    "pd.read_csv(\"nba.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example #2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Pokemon</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charizard</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Squirtle</td>\n",
       "      <td>Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wartortle</td>\n",
       "      <td>Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blastoise</td>\n",
       "      <td>Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Caterpie</td>\n",
       "      <td>Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Metapod</td>\n",
       "      <td>Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Butterfree</td>\n",
       "      <td>Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Weedle</td>\n",
       "      <td>Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kakuna</td>\n",
       "      <td>Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Beedrill</td>\n",
       "      <td>Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pidgey</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pidgeotto</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pidgeot</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rattata</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Raticate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Spearow</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fearow</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ekans</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Arbok</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Pikachu</td>\n",
       "      <td>Electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Raichu</td>\n",
       "      <td>Electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sandshrew</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sandslash</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Nidoran</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Nidorina</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nidoqueen</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nidoran♂</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nidorino</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Nidoking</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>Clauncher</td>\n",
       "      <td>Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>Clawitzer</td>\n",
       "      <td>Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>Helioptile</td>\n",
       "      <td>Electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>Heliolisk</td>\n",
       "      <td>Electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>Tyrunt</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>Tyrantrum</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>Amaura</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>Aurorus</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>Sylveon</td>\n",
       "      <td>Fairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>Hawlucha</td>\n",
       "      <td>Fighting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>Dedenne</td>\n",
       "      <td>Electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>Carbink</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>Goomy</td>\n",
       "      <td>Dragon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Sliggoo</td>\n",
       "      <td>Dragon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>Goodra</td>\n",
       "      <td>Dragon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>Klefki</td>\n",
       "      <td>Steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>Phantump</td>\n",
       "      <td>Ghost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>Trevenant</td>\n",
       "      <td>Ghost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Pumpkaboo</td>\n",
       "      <td>Ghost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>Gourgeist</td>\n",
       "      <td>Ghost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>Bergmite</td>\n",
       "      <td>Ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>Avalugg</td>\n",
       "      <td>Ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>Noibat</td>\n",
       "      <td>Flying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>Noivern</td>\n",
       "      <td>Flying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>Xerneas</td>\n",
       "      <td>Fairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>Yveltal</td>\n",
       "      <td>Dark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>Zygarde</td>\n",
       "      <td>Dragon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>Hoopa</td>\n",
       "      <td>Psychic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>717 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pokemon      Type\n",
       "0    Charmeleon      Fire\n",
       "1     Charizard      Fire\n",
       "2      Squirtle     Water\n",
       "3     Wartortle     Water\n",
       "4     Blastoise     Water\n",
       "5      Caterpie       Bug\n",
       "6       Metapod       Bug\n",
       "7    Butterfree       Bug\n",
       "8        Weedle       Bug\n",
       "9        Kakuna       Bug\n",
       "10     Beedrill       Bug\n",
       "11       Pidgey    Normal\n",
       "12    Pidgeotto    Normal\n",
       "13      Pidgeot    Normal\n",
       "14      Rattata    Normal\n",
       "15     Raticate    Normal\n",
       "16      Spearow    Normal\n",
       "17       Fearow    Normal\n",
       "18        Ekans    Poison\n",
       "19        Arbok    Poison\n",
       "20      Pikachu  Electric\n",
       "21       Raichu  Electric\n",
       "22    Sandshrew    Ground\n",
       "23    Sandslash    Ground\n",
       "24      Nidoran    Poison\n",
       "25     Nidorina    Poison\n",
       "26    Nidoqueen    Poison\n",
       "27     Nidoran♂    Poison\n",
       "28     Nidorino    Poison\n",
       "29     Nidoking    Poison\n",
       "..          ...       ...\n",
       "687   Clauncher     Water\n",
       "688   Clawitzer     Water\n",
       "689  Helioptile  Electric\n",
       "690   Heliolisk  Electric\n",
       "691      Tyrunt      Rock\n",
       "692   Tyrantrum      Rock\n",
       "693      Amaura      Rock\n",
       "694     Aurorus      Rock\n",
       "695     Sylveon     Fairy\n",
       "696    Hawlucha  Fighting\n",
       "697     Dedenne  Electric\n",
       "698     Carbink      Rock\n",
       "699       Goomy    Dragon\n",
       "700     Sliggoo    Dragon\n",
       "701      Goodra    Dragon\n",
       "702      Klefki     Steel\n",
       "703    Phantump     Ghost\n",
       "704   Trevenant     Ghost\n",
       "705   Pumpkaboo     Ghost\n",
       "706   Gourgeist     Ghost\n",
       "707    Bergmite       Ice\n",
       "708     Avalugg       Ice\n",
       "709      Noibat    Flying\n",
       "710     Noivern    Flying\n",
       "711     Xerneas     Fairy\n",
       "712     Yveltal      Dark\n",
       "713     Zygarde    Dragon\n",
       "714     Diancie      Rock\n",
       "715       Hoopa   Psychic\n",
       "716   Volcanion      Fire\n",
       "\n",
       "[717 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing Pandas library \n",
    "import pandas as pd \n",
    "  \n",
    "pd.read_csv(filepath_or_buffer = \"pokemon.csv\") \n",
    "  \n",
    "# makes the passed rows header \n",
    "pd.read_csv(\"pokemon.csv\", header =[1, 2]) \n",
    "  \n",
    "# make the passed column as index instead of 0, 1, 2, 3.... \n",
    "pd.read_csv(\"pokemon.csv\", index_col ='Type') \n",
    "  \n",
    "# uses passed cols only for data frame \n",
    "pd.read_csv(\"pokemon.csv\", usecols =[\"Type\"]) \n",
    "  \n",
    "# reutruns pandas series if there is only one colunmn \n",
    "pd.read_csv(\"pokemon.csv\", usecols =[\"Type\"], squeeze = True) \n",
    "                                \n",
    "# skips the passed rows in new series \n",
    "pd.read_csv(\"pokemon.csv\", skiprows = [1, 2, 3, 4]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The data which is being used in the above examples are stored in two fies:**\n",
    "* nba.csv\n",
    "* pokemon.csv<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**However, you can choose data as per your choice**\n",
    "\n",
    "## Again you can change the code and play with it as much as you want !!!"
   ]
  }
 ],
 "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}