Part7-Pandas-Dataframe.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": [
    "**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 !!!"
   ]
  }
 ],
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