pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ssY8F0fse7KN"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eBEqdNUye7KW"
   },
   "outputs": [],
   "source": [
    "s = pd.Series([1,2,3,4]) #series is homogenous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qf2HS7-Re7Kb",
    "outputId": "c4b4b599-b107-4df3-a69e-906359795955"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V9aiwzg7e7Kj"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BszxNCdJe7Kp",
    "outputId": "d704665d-7f55-4863-870a-906a7c029b95"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QoSWsm6qe7Kv",
    "outputId": "dfbc4692-b2ef-4228-cac1-1a2b4d1805d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1,2,3,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QmUa7d-De7K2",
    "outputId": "4dff46f2-d78e-4799-ab14-127b4f043ff1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0] #pandas series and dataframe are mutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yGAgXo2Je7K7"
   },
   "outputs": [],
   "source": [
    "s[0] = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R23dllkSe7LC",
    "outputId": "4c4851e3-6363-47af-80e2-670cf89238ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99\n",
       "1     2\n",
       "2     3\n",
       "3     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MEJnrvm7e7LL"
   },
   "outputs": [],
   "source": [
    "data = {\"Name\":[\"Shubham\",\"Rajat\",\"Ashish\",\"Anoop\",\"Aman\"],\"Age\":[21,23,29,23,24]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7uienGK1e7LS",
    "outputId": "f07a9ce6-93fb-433c-a49f-395661aa3ff3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': [21, 23, 29, 23, 24],\n",
       " 'Name': ['Shubham', 'Rajat', 'Ashish', 'Anoop', 'Aman']}"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IuTDZFAqe7LX",
    "outputId": "e31d6e58-cf13-40a1-c8e4-97a20c8aca55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P6umKuhne7Lc",
    "outputId": "a8d99e8e-3927-4146-a055-181991c84ccc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>Shubham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>Rajat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>Ashish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Anoop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>Aman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name\n",
       "0   21  Shubham\n",
       "1   23    Rajat\n",
       "2   29   Ashish\n",
       "3   23    Anoop\n",
       "4   24     Aman"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ww8Sapw6e7Li",
    "outputId": "9d2a7da1-ca5f-40f3-ae07-eab8b7af9ebc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>21</td>\n",
       "      <td>Shubham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>23</td>\n",
       "      <td>Rajat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>29</td>\n",
       "      <td>Ashish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>23</td>\n",
       "      <td>Anoop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>24</td>\n",
       "      <td>Aman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name\n",
       "a   21  Shubham\n",
       "b   23    Rajat\n",
       "c   29   Ashish\n",
       "d   23    Anoop\n",
       "e   24     Aman"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data,index=['a','b','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xM9GcVl3e7Lp",
    "outputId": "184d1ff8-9463-4998-c572-9319f4378a4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': [21, 23, 29, 23, 24],\n",
       " 'Name': ['Shubham', 'Rajat', 'Ashish', 'Anoop', 'Aman']}"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DrnMqGBwe7Lu",
    "outputId": "5135a73b-249c-4181-d26f-65967874aa70"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>Shubham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>Rajat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>Ashish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Anoop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>Aman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name\n",
       "0   21  Shubham\n",
       "1   23    Rajat\n",
       "2   29   Ashish\n",
       "3   23    Anoop\n",
       "4   24     Aman"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rQtxNmxke7L7",
    "outputId": "2550ecaf-8d0b-436d-ae42-3244a86c25d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': [21, 23, 29, 23, 24],\n",
       " 'Name': ['Shubham', 'Rajat', 'Ashish', 'Anoop', 'Aman']}"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fEjS9hj1e7MA"
   },
   "outputs": [],
   "source": [
    "data = {'Name': ['Amber', 'Devansh', 'Kabir', 'Sandeep', 'Shivam'],'Age': [20, 20, 20,20,21]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mfDspQ1je7MG"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EDntNhxMe7MK",
    "outputId": "5d4ab834-c005-4099-9849-0c07fae4f718"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Amber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Devansh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Kabir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name\n",
       "0   20    Amber\n",
       "1   20  Devansh\n",
       "2   20    Kabir\n",
       "3   20  Sandeep\n",
       "4   21   Shivam"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AK8MGkHQe7MO",
    "outputId": "7adf424a-4a53-47f2-aa44-f4a74f26ba7c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Shubham</td>\n",
       "      <td>Rajat</td>\n",
       "      <td>Ashish</td>\n",
       "      <td>Anoop</td>\n",
       "      <td>Aman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1       2      3     4\n",
       "Age        21     23      29     23    24\n",
       "Name  Shubham  Rajat  Ashish  Anoop  Aman"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fINPIBNwe7MV"
   },
   "outputs": [],
   "source": [
    "data = {'Name': ['Amber', 'Devansh', 'Kabir', 'Sandeep', 'Shivam'],'Age': [20, 20, 20,20,21],'Ratings':[5,5,4,-1,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WV2QsHmoe7Ma",
    "outputId": "eb2160e8-4384-4465-b3d0-5d7a12d6a0cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age': [20, 20, 20, 20, 21],\n",
       " 'Name': ['Amber', 'Devansh', 'Kabir', 'Sandeep', 'Shivam'],\n",
       " 'Ratings': [5, 5, 4, -1, 10]}"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UdYCuQkbe7Mf"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x1gvyOZ_e7Mi",
    "outputId": "866c635b-7cff-47f0-e58b-cc41d1e23b02"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Amber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Devansh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Kabir</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name  Ratings\n",
       "0   20    Amber        5\n",
       "1   20  Devansh        5\n",
       "2   20    Kabir        4\n",
       "3   20  Sandeep       -1\n",
       "4   21   Shivam       10"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yqtI3Jcle7Mm",
    "outputId": "817f0cc7-1e86-4777-a8a9-d77b19d1eff8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=5, step=1),\n",
       " Index(['Age', 'Name', 'Ratings'], dtype='object')]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes #\"0\" is column, \"1\" is row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4scZlGfLe7Ms",
    "outputId": "ab02be29-0c74-4233-de4b-586ccb92fad0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.axes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n78OC3mke7Mw",
    "outputId": "d3f11ec7-28a4-4e32-d543-7c196aed3255"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     4\n",
       "3    -1\n",
       "4    10\n",
       "Name: Ratings, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Ratings\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FWbiDRkpe7M1",
    "outputId": "146bb169-a927-4b90-9274-db775c9af207"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Amber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Devansh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Kabir</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name  Ratings\n",
       "0   20    Amber        5\n",
       "1   20  Devansh        5\n",
       "2   20    Kabir        4\n",
       "3   20  Sandeep       -1\n",
       "4   21   Shivam       10"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cnrX1Hrae7M4",
    "outputId": "79463dca-7782-4f07-9651-e272d3ac9358"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"Ratings\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VECFiRJRe7M8",
    "outputId": "76026de0-f03b-459c-bce7-923adf19c243"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         int64\n",
       "Name       object\n",
       "Ratings     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QJ_rpi1Ze7NA",
    "outputId": "75803cd7-de5c-44f4-8895-d6d85da52bbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NWB5GMj4e7ND",
    "outputId": "8419e01b-07e4-4d86-c16f-8b66c979ddc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 'Amber', 5],\n",
       "       [20, 'Devansh', 5],\n",
       "       [20, 'Kabir', 4],\n",
       "       [20, 'Sandeep', -1],\n",
       "       [21, 'Shivam', 10]], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values #dataframe to np.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DGIEa_JKe7NI",
    "outputId": "1c689303-19fc-432a-8c8c-4c77443ad815"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L-oir57Ce7NN"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NDZKUhn0e7NT",
    "outputId": "d05ed04b-94d3-40db-e456-498e2a42eb1d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Amber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Devansh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Kabir</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name  Ratings\n",
       "0   20    Amber        5\n",
       "1   20  Devansh        5\n",
       "2   20    Kabir        4\n",
       "3   20  Sandeep       -1\n",
       "4   21   Shivam       10"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bSKWMOnje7NW",
    "outputId": "45a05e9a-1fec-4b59-de05-013ef8ef0b63"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Amber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Devansh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Kabir</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name  Ratings\n",
       "0   20    Amber        5\n",
       "1   20  Devansh        5\n",
       "2   20    Kabir        4\n",
       "3   20  Sandeep       -1\n",
       "4   21   Shivam       10"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uhbt7j9Ve7Ng",
    "outputId": "75da748e-f35f-4369-b0e7-3be55817f2f4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name  Ratings\n",
       "3   20  Sandeep       -1\n",
       "4   21   Shivam       10"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tjiekWb8e7No",
    "outputId": "b3e02694-78ef-4f37-d2d0-34d60ba5c37c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Amber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Devansh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>Kabir</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Shivam</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Name  Ratings\n",
       "0   20    Amber        5\n",
       "1   20  Devansh        5\n",
       "2   20    Kabir        4\n",
       "3   20  Sandeep       -1\n",
       "4   21   Shivam       10"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IYBLEYE-e7Nt",
    "outputId": "e1e6d433-82b4-4adc-ffba-f2f0570f07f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                                   101\n",
       "Name       AmberDevanshKabirSandeepShivam\n",
       "Ratings                                23\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KGpKDFr9e7N1",
    "outputId": "f16c40b6-4954-4d82-cf6f-8c2ddbbc7b24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    25\n",
       "1    25\n",
       "2    24\n",
       "3    19\n",
       "4    31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WgMPj5z3e7N_",
    "outputId": "8393a6f8-ed5f-4d51-cb45-64e180d3fc2e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age        20.2\n",
       "Ratings     4.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_ojOvYCOe7OF",
    "outputId": "01330f5b-5f16-4ffb-c59f-12ae597a200f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age        0.447214\n",
       "Ratings    3.911521\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iU4MVNW8e7OJ",
    "outputId": "b40d6fe8-928e-4f61-94bd-0df021c3fe5e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.200000</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.447214</td>\n",
       "      <td>3.911521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age    Ratings\n",
       "count   5.000000   5.000000\n",
       "mean   20.200000   4.600000\n",
       "std     0.447214   3.911521\n",
       "min    20.000000  -1.000000\n",
       "25%    20.000000   4.000000\n",
       "50%    20.000000   5.000000\n",
       "75%    20.000000   5.000000\n",
       "max    21.000000  10.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PsiMcKH-e7OP",
    "outputId": "b6ed58d9-63c5-4bd7-c042-5a69e5ee05c1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sandeep</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.447214</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.000000</td>\n",
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       "    </tr>\n",
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       "      <th>75%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age     Name    Rating\n",
       "count    5.000000        5  5.000000\n",
       "unique        NaN        5       NaN\n",
       "top           NaN  Sandeep       NaN\n",
       "freq          NaN        1       NaN\n",
       "mean    20.200000      NaN  4.400000\n",
       "std      0.447214      NaN  0.547723\n",
       "min     20.000000      NaN  4.000000\n",
       "25%     20.000000      NaN  4.000000\n",
       "50%     20.000000      NaN  4.000000\n",
       "75%     20.000000      NaN  5.000000\n",
       "max     21.000000      NaN  5.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all') #try include=['object'] as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aZO7_3Cqe7OV"
   },
   "outputs": [],
   "source": [
    "## PART--2 ##\n",
    "s = pd.Series()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IWQu49N0e7Oa",
    "outputId": "f2f20574-abdd-4cf1-ed72-e6a18d138a90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7D7xCQEHe7Of"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = np.array([1,2,3.4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L9kia4b1e7Oj",
    "outputId": "7a4fcc21-959d-432e-9ec3-0731bbe8d28e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 2. , 3.4])"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "za0Cut7Se7On"
   },
   "outputs": [],
   "source": [
    "s = pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-CI2Virpe7Op",
    "outputId": "a5532053-931c-42e0-8724-ae55a6aff516"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h1AbijPVe7Ou"
   },
   "outputs": [],
   "source": [
    "s = pd.Series(data,index=['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iF_-Khtke7Ox",
    "outputId": "8a092739-f6ce-4adf-c03f-c980b19efd9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8njDun7Qe7O1"
   },
   "outputs": [],
   "source": [
    "s = pd.Series({'a':1.0,'b':2.0,'c':3.4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w_ihl65he7O3",
    "outputId": "895d30b9-40e9-4bdb-9da2-c29961237ccd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5yKr9nUFe7O6",
    "outputId": "71720df5-65e5-41c2-c615-fc5de2a843e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B2SEOwHte7O-",
    "outputId": "02ee0dc1-eead-4a43-d2d9-56668e842a26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[['a','b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "09kBCfPne7PB",
    "outputId": "902db34e-c6d4-4d16-e4b4-c689a6db26ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Th8nZ7YTe7PG"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"nyc_weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3N2OIkuPe7PI",
    "outputId": "df0b439a-2280-4530-ba7f-7c86301f575e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 11)"
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7aEYkgxIe7PN",
    "outputId": "8693e858-e95d-47af-994b-55791fffdcc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.67741935483871"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Temperature\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CsZ-aaAre7PR"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'A':pd.date_range(start = '2016-01-01',periods=20,freq='D'),\n",
    "    'x':np.linspace(0,stop=19,num=20),\n",
    "    'y':np.random.rand(20),\n",
    "    'C':np.random.choice(['Low','Medium','High'],20).tolist(),\n",
    "    'D':np.random.normal(100,10,size=(20)).tolist()\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kjya64APe7PU",
    "outputId": "0b9e96e8-5877-4a5f-b354-0f96df329afb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Low</td>\n",
       "      <td>96.212289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>High</td>\n",
       "      <td>97.228445</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.792758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>High</td>\n",
       "      <td>109.387198</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.746927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Medium</td>\n",
       "      <td>111.009427</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.917218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>Low</td>\n",
       "      <td>114.632199</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.878733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>Low</td>\n",
       "      <td>115.618193</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.421407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>High</td>\n",
       "      <td>106.425756</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.889221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>Medium</td>\n",
       "      <td>104.009525</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.037013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>Low</td>\n",
       "      <td>101.497003</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.953089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>High</td>\n",
       "      <td>89.176166</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.930886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>Medium</td>\n",
       "      <td>114.240272</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.434141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>Low</td>\n",
       "      <td>89.796555</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.670283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>High</td>\n",
       "      <td>82.753491</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.362973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>Medium</td>\n",
       "      <td>104.841435</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.208508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>Low</td>\n",
       "      <td>97.607770</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.260504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>High</td>\n",
       "      <td>90.388927</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.831967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>Medium</td>\n",
       "      <td>98.823399</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.986364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>Medium</td>\n",
       "      <td>105.100915</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.280450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>Low</td>\n",
       "      <td>97.929810</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.159682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>Low</td>\n",
       "      <td>76.750064</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.617250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       C           D     x         y\n",
       "0  2016-01-01     Low   96.212289   0.0  0.609260\n",
       "1  2016-01-02    High   97.228445   1.0  0.792758\n",
       "2  2016-01-03    High  109.387198   2.0  0.746927\n",
       "3  2016-01-04  Medium  111.009427   3.0  0.917218\n",
       "4  2016-01-05     Low  114.632199   4.0  0.878733\n",
       "5  2016-01-06     Low  115.618193   5.0  0.421407\n",
       "6  2016-01-07    High  106.425756   6.0  0.889221\n",
       "7  2016-01-08  Medium  104.009525   7.0  0.037013\n",
       "8  2016-01-09     Low  101.497003   8.0  0.953089\n",
       "9  2016-01-10    High   89.176166   9.0  0.930886\n",
       "10 2016-01-11  Medium  114.240272  10.0  0.434141\n",
       "11 2016-01-12     Low   89.796555  11.0  0.670283\n",
       "12 2016-01-13    High   82.753491  12.0  0.362973\n",
       "13 2016-01-14  Medium  104.841435  13.0  0.208508\n",
       "14 2016-01-15     Low   97.607770  14.0  0.260504\n",
       "15 2016-01-16    High   90.388927  15.0  0.831967\n",
       "16 2016-01-17  Medium   98.823399  16.0  0.986364\n",
       "17 2016-01-18  Medium  105.100915  17.0  0.280450\n",
       "18 2016-01-19     Low   97.929810  18.0  0.159682\n",
       "19 2016-01-20     Low   76.750064  19.0  0.617250"
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BDgj66e1e7PY",
    "outputId": "0e0ca284-5ee3-4304-898d-0f23bc75c44f"
   },
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>High</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>Low</td>\n",
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      "text/plain": [
       "           A     C   B\n",
       "0 2016-01-01   Low NaN\n",
       "2 2016-01-03  High NaN\n",
       "5 2016-01-06   Low NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(index=[0,2,5],columns=['A','C','B']) #go to the documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CFqhQS7Je7Pb"
   },
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(10,3),columns = ['col1','col2','col3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ql63cQ7de7Pd"
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(np.random.randn(7,3),columns = ['col1','col2','col3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n1VQCl8We7Pi",
    "outputId": "d6fff40a-ff5e-470e-f0cc-444de8463b23"
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   "outputs": [
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       "      <td>0.006137</td>\n",
       "      <td>0.003803</td>\n",
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       "      <td>1.051072</td>\n",
       "      <td>-0.107261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.667665</td>\n",
       "      <td>0.035765</td>\n",
       "      <td>0.940652</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       col1      col2      col3\n",
       "0 -2.049816  0.819844  0.036210\n",
       "1  1.424880  1.110688 -0.316423\n",
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     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6kyrkQdde7Pp",
    "outputId": "55c34124-0a3f-4090-e122-92a147d4d14a"
   },
   "outputs": [
    {
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       "      <td>0.866064</td>\n",
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       "      <td>-0.438720</td>\n",
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       "      <td>0.939280</td>\n",
       "      <td>0.128747</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.089417</td>\n",
       "      <td>0.197512</td>\n",
       "      <td>0.096892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.153695</td>\n",
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       "       col1      col2      col3\n",
       "0 -0.505128 -0.808655  1.851476\n",
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       "5  1.089417  0.197512  0.096892\n",
       "6 -2.153695 -2.335085 -0.939614"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
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   ],
   "source": [
    "df2"
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  },
  {
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    "id": "6h_edq8he7Pw",
    "outputId": "02352d39-4408-467d-f380-7f3be6bccf5b"
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   "outputs": [
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      "text/plain": [
       "       col1      col2      col3\n",
       "0 -2.049816  0.819844  0.036210\n",
       "1  1.424880  1.110688 -0.316423\n",
       "2 -0.494110  1.795599  0.282826\n",
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       "5 -0.114940  0.490735 -0.027185\n",
       "6 -2.699176  0.165965 -0.557309"
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     },
     "execution_count": 75,
     "metadata": {
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     "output_type": "execute_result"
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   ],
   "source": [
    "df1.reindex_like(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qKO3DCxGe7Pz"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(5,3),index=['a','c','e','f','h'],columns=['one','two','three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZM022iTUe7P0",
    "outputId": "289ac16a-e4fe-4703-9552-b88eeab4153b"
   },
   "outputs": [
    {
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       "      <td>0.758943</td>\n",
       "      <td>0.858691</td>\n",
       "      <td>-0.097424</td>\n",
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       "      <td>-2.376095</td>\n",
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       "      <th>e</th>\n",
       "      <td>-1.275647</td>\n",
       "      <td>-0.188710</td>\n",
       "      <td>1.466802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.051748</td>\n",
       "      <td>1.598560</td>\n",
       "      <td>-0.527903</td>\n",
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       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.839504</td>\n",
       "      <td>-1.813798</td>\n",
       "      <td>-0.912424</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three\n",
       "a  0.758943  0.858691 -0.097424\n",
       "c  0.110367  0.038584 -2.376095\n",
       "e -1.275647 -0.188710  1.466802\n",
       "f  1.051748  1.598560 -0.527903\n",
       "h  0.839504 -1.813798 -0.912424"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CentvZ_Ve7P6"
   },
   "outputs": [],
   "source": [
    "df = df.reindex(['a','b','c','d','e','f','g','h'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dmO4aJ8te7P8",
    "outputId": "f2682b3f-b4b1-4d6e-821b-9da1cee1eb87"
   },
   "outputs": [
    {
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       "        one       two     three\n",
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       "e -0.207289  1.633456 -0.060057\n",
       "f -0.923019  0.028670  0.172352\n",
       "g       NaN       NaN       NaN\n",
       "h -0.182309  0.338274 -0.837249"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r5F2TUFFe7P_",
    "outputId": "4ab80e6b-a49e-4fee-d081-7c33eb214781"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     True\n",
       "b    False\n",
       "c     True\n",
       "d    False\n",
       "e     True\n",
       "f     True\n",
       "g    False\n",
       "h     True\n",
       "Name: one, dtype: bool"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one'].notnull() #try .isnull() as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_CEIXPZ_e7QB",
    "outputId": "ad015db8-3f8a-4b2f-970b-e59221bfcdc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.753341223632415"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
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   "source": [
    "df['one'].sum()"
   ]
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  {
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   "execution_count": 0,
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    "colab": {},
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    "id": "qzgwMJBle7QE",
    "outputId": "a0bf9aa9-2a91-4029-c434-a62cce433797"
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    {
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       "        one       two     three\n",
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   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdtL0f-fe7QJ",
    "outputId": "85161f4e-c1e9-4c54-b1e3-6335ad84bce1"
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    {
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       "        one       two     three\n",
       "a -0.456385 -0.481244 -0.198935\n",
       "b       NaN       NaN       NaN\n",
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       "g       NaN       NaN       NaN\n",
       "h -0.182309  0.338274 -0.837249"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
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   "source": [
    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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    "colab_type": "code",
    "id": "maOwtTZFe7QL",
    "outputId": "65e856a9-eff6-4b14-8b2e-427f436ef88a"
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    {
     "data": {
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      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 80,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv('weather_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "colab": {},
    "colab_type": "code",
    "id": "Wj13biGWe7QO",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('weather_data_cities.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ww-HRlFze7QR",
    "outputId": "2f8f471c-d7b1-4b13-f6a9-b8c04e730e66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.DataFrameGroupBy object at 0x00000178BF37DD68>\n"
     ]
    }
   ],
   "source": [
    "g = df.groupby('city')\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OCsEOCjXe7QU",
    "outputId": "39be009d-ea9c-4ae0-f3b9-08de6fcc6197"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mumbai\n",
      "        day    city  temperature  windspeed  event\n",
      "4  1/1/2017  mumbai           90          5  Sunny\n",
      "5  1/2/2017  mumbai           85         12    Fog\n",
      "6  1/3/2017  mumbai           87         15    Fog\n",
      "7  1/4/2017  mumbai           92          5   Rain\n",
      "new york\n",
      "        day      city  temperature  windspeed  event\n",
      "0  1/1/2017  new york           32          6   Rain\n",
      "1  1/2/2017  new york           36          7  Sunny\n",
      "2  1/3/2017  new york           28         12   Snow\n",
      "3  1/4/2017  new york           33          7  Sunny\n",
      "paris\n",
      "         day   city  temperature  windspeed   event\n",
      "8   1/1/2017  paris           45         20   Sunny\n",
      "9   1/2/2017  paris           50         13  Cloudy\n",
      "10  1/3/2017  paris           54          8  Cloudy\n",
      "11  1/4/2017  paris           42         10  Cloudy\n"
     ]
    }
   ],
   "source": [
    "for city, city_df in g:\n",
    "    print(city)\n",
    "    print(city_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UUUvl5one7QW",
    "outputId": "9dadcb5b-0c33-499a-ae6f-e7ee1bab1df9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
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       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day      city  temperature  windspeed  event\n",
       "0  1/1/2017  new york           32          6   Rain\n",
       "1  1/2/2017  new york           36          7  Sunny\n",
       "2  1/3/2017  new york           28         12   Snow\n",
       "3  1/4/2017  new york           33          7  Sunny"
      ]
     },
     "execution_count": 88,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_group('new york')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XN6EHTBme7QY",
    "outputId": "e7250234-25e0-4677-d0c7-e574f452d9d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               day  temperature  windspeed  event\n",
      "city                                             \n",
      "mumbai    1/4/2017           92         15  Sunny\n",
      "new york  1/4/2017           36         12  Sunny\n",
      "paris     1/4/2017           54         20  Sunny\n"
     ]
    }
   ],
   "source": [
    "print(g.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pjs57ARKe7Qa",
    "outputId": "ac6be883-917d-479c-c209-8b40b9b14090"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          temperature  windspeed\n",
      "city                            \n",
      "mumbai          88.50       9.25\n",
      "new york        32.25       8.00\n",
      "paris           47.75      12.75\n"
     ]
    }
   ],
   "source": [
    "print(g.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S_nyd5JKe7Qe",
    "outputId": "1f7d02ed-99f6-4cad-e436-90f8b026a349"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         temperature                                                   \\\n",
      "               count   mean       std   min    25%   50%    75%   max   \n",
      "city                                                                    \n",
      "mumbai           4.0  88.50  3.109126  85.0  86.50  88.5  90.50  92.0   \n",
      "new york         4.0  32.25  3.304038  28.0  31.00  32.5  33.75  36.0   \n",
      "paris            4.0  47.75  5.315073  42.0  44.25  47.5  51.00  54.0   \n",
      "\n",
      "         windspeed                                                 \n",
      "             count   mean       std  min   25%   50%    75%   max  \n",
      "city                                                               \n",
      "mumbai         4.0   9.25  5.057997  5.0  5.00   8.5  12.75  15.0  \n",
      "new york       4.0   8.00  2.708013  6.0  6.75   7.0   8.25  12.0  \n",
      "paris          4.0  12.75  5.251984  8.0  9.50  11.5  14.75  20.0  \n"
     ]
    }
   ],
   "source": [
    "print(g.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dkKxmQyne7Qh"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P8Q1SKEQe7Qi"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"nyc_weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MI_El6PUe7Ql",
    "outputId": "78b6adc1-3a96-480c-b53c-3fab69c2b049"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EST</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Sea Level PressureIn</th>\n",
       "      <th>VisibilityMiles</th>\n",
       "      <th>WindSpeedMPH</th>\n",
       "      <th>PrecipitationIn</th>\n",
       "      <th>CloudCover</th>\n",
       "      <th>Events</th>\n",
       "      <th>WindDirDegrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>38</td>\n",
       "      <td>23</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1/2/2016</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>3</td>\n",
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       "      <th>2</th>\n",
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       "      <td>30.05</td>\n",
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       "      <td>1/6/2016</td>\n",
       "      <td>33</td>\n",
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       "      <td>35</td>\n",
       "      <td>30.50</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259</td>\n",
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       "      <th>6</th>\n",
       "      <td>1/7/2016</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>30.28</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>39</td>\n",
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       "      <th>9</th>\n",
       "      <td>1/10/2016</td>\n",
       "      <td>50</td>\n",
       "      <td>46</td>\n",
       "      <td>71</td>\n",
       "      <td>29.59</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>Rain</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         EST  Temperature  DewPoint  Humidity  Sea Level PressureIn  \\\n",
       "0   1/1/2016           38        23        52                 30.03   \n",
       "1   1/2/2016           36        18        46                 30.02   \n",
       "2   1/3/2016           40        21        47                 29.86   \n",
       "3   1/4/2016           25         9        44                 30.05   \n",
       "4   1/5/2016           20        -3        41                 30.57   \n",
       "5   1/6/2016           33         4        35                 30.50   \n",
       "6   1/7/2016           39        11        33                 30.28   \n",
       "7   1/8/2016           39        29        64                 30.20   \n",
       "8   1/9/2016           44        38        77                 30.16   \n",
       "9  1/10/2016           50        46        71                 29.59   \n",
       "\n",
       "   VisibilityMiles  WindSpeedMPH PrecipitationIn  CloudCover Events  \\\n",
       "0               10           8.0               0           5    NaN   \n",
       "1               10           7.0               0           3    NaN   \n",
       "2               10           8.0               0           1    NaN   \n",
       "3               10           9.0               0           3    NaN   \n",
       "4               10           5.0               0           0    NaN   \n",
       "5               10           4.0               0           0    NaN   \n",
       "6               10           2.0               0           3    NaN   \n",
       "7               10           4.0               0           8    NaN   \n",
       "8                9           8.0               T           8   Rain   \n",
       "9                4           NaN             1.8           7   Rain   \n",
       "\n",
       "   WindDirDegrees  \n",
       "0             281  \n",
       "1             275  \n",
       "2             277  \n",
       "3             345  \n",
       "4             333  \n",
       "5             259  \n",
       "6             293  \n",
       "7              79  \n",
       "8              76  \n",
       "9             109  "
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
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    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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    "colab_type": "code",
    "id": "-Ae0-cRPe7Qq",
    "outputId": "824f21f9-b898-474b-e9d1-87d41c14b7ca"
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   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>30</th>\n",
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      ],
      "text/plain": [
       "    Temperature  Humidity\n",
       "0         False     False\n",
       "1         False     False\n",
       "2         False     False\n",
       "3         False     False\n",
       "4         False     False\n",
       "5         False     False\n",
       "6         False     False\n",
       "7         False     False\n",
       "8         False     False\n",
       "9         False     False\n",
       "10        False     False\n",
       "11        False     False\n",
       "12        False     False\n",
       "13        False     False\n",
       "14        False     False\n",
       "15        False     False\n",
       "16        False     False\n",
       "17        False     False\n",
       "18        False     False\n",
       "19        False     False\n",
       "20        False     False\n",
       "21        False     False\n",
       "22        False     False\n",
       "23        False     False\n",
       "24        False     False\n",
       "25        False     False\n",
       "26        False     False\n",
       "27        False     False\n",
       "28        False     False\n",
       "29        False     False\n",
       "30        False     False"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Temperature','Humidity']].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "id": "9AmXPV-Ve7Qt",
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    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy"
      ]
     },
     "execution_count": 94,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:11,0:6]"
   ]
  },
  {
   "cell_type": "code",
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   },
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    {
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       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
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       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 83,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dI0Ycl6Se7Q0",
    "outputId": "19d82d03-8339-4f75-e468-36627d88cd5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "32\n",
      "1\n",
      "36\n",
      "2\n",
      "28\n",
      "3\n",
      "33\n",
      "4\n",
      "90\n",
      "5\n",
      "85\n",
      "6\n",
      "87\n",
      "7\n",
      "92\n",
      "8\n",
      "45\n",
      "9\n",
      "50\n",
      "10\n",
      "54\n",
      "11\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "for index,rows in df.iterrows():\n",
    "    print(index)\n",
    "    print(rows['temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GZxcYiy1e7RA"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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    "name": "ipython",
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