EDA_10_Steps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **EXPLORATORY DATA ANALYSIS**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BASIC 10 STEPS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset \n",
    "df=sns.load_dataset('titanic')\n",
    "df1=sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 15)\n",
      "Rows: 891 Columns: 15\n"
     ]
    }
   ],
   "source": [
    "# Step -1 Data Shape\n",
    "from pyparsing import col\n",
    "\n",
    "\n",
    "print(df.shape)\n",
    "row,col=df.shape\n",
    "print(\"Rows:\",row,\"Columns:\",col) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Step -2 Data Info\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived         0\n",
      "pclass           0\n",
      "sex              0\n",
      "age            177\n",
      "sibsp            0\n",
      "parch            0\n",
      "fare             0\n",
      "embarked         2\n",
      "class            0\n",
      "who              0\n",
      "adult_male       0\n",
      "deck           688\n",
      "embark_town      2\n",
      "alive            0\n",
      "alone            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Step -3 Find Missing Values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_bill    0.0\n",
      "tip           0.0\n",
      "sex           0.0\n",
      "smoker        0.0\n",
      "day           0.0\n",
      "time          0.0\n",
      "size          0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# percentage of missing values\n",
    "#print(df.isnull().sum()/len(df)*100)\n",
    "print(df1.isnull().sum()/len(df1)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Males</th>\n",
       "      <th>Females</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore,Pakistan</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beijing,China</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Berlin,Germany</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Address Males Females\n",
       "0  Lahore,Pakistan    67     100\n",
       "1    Beijing,China     5      16\n",
       "2   Berlin,Germany    40      50"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step -4 Split Data for new columns \n",
    "city=pd.DataFrame(np.array([[\"Lahore,Pakistan\",67,100],[\"Beijing,China\",5,16],[\"Berlin,Germany\",40,50]]),columns=[\"Address\",\"Males\",\"Females\"])\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Males</th>\n",
       "      <th>Females</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore,Pakistan</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>Pakistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beijing,China</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Berlin,Germany</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Address Males Females     City   Country\n",
       "0  Lahore,Pakistan    67     100   Lahore  Pakistan\n",
       "1    Beijing,China     5      16  Beijing     China\n",
       "2   Berlin,Germany    40      50   Berlin   Germany"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city[[\"City\",\"Country\"]]=city['Address'].str.split(',',expand=True)\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Address  3 non-null      object\n",
      " 1   Males    3 non-null      object\n",
      " 2   Females  3 non-null      object\n",
      " 3   City     3 non-null      object\n",
      " 4   Country  3 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 248.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Step -5 Conversion Of Data types \n",
    "city.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Address  3 non-null      object\n",
      " 1   Males    3 non-null      int64 \n",
      " 2   Females  3 non-null      int64 \n",
      " 3   City     3 non-null      object\n",
      " 4   Country  3 non-null      object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 248.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "city[['Males','Females']]= city[['Males','Females']].astype('int64')\n",
    "city.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Address  3 non-null      object\n",
      " 1   Males    3 non-null      int64 \n",
      " 2   Females  3 non-null      int64 \n",
      " 3   City     3 non-null      object\n",
      " 4   Country  3 non-null      object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 248.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "city[['City','Country']]= city[['City','Country']].astype('str ')\n",
    "city.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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