EDA_Mens_apparel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLEANING THE DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 7)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Missing_values=['n/a','na',np.nan]\n",
    "df=pd.read_csv('Mens_Apparel.csv',na_values=Missing_values)\n",
    "df.isnull().sum()\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 7)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df=df.dropna() #Drops all the rows with missing values. Dropna(how='all') drops rows which have all values as null\n",
    "#fillna()Replaces all the missing values with specific value,values and a dict can be passed for replacing valies accroding to column\n",
    "#it has 2 menthord -fill ,bfill\n",
    "#interpolate - it takes the average of previous and next number at the empty position\n",
    "\n",
    "new_df.shape\n",
    "#new_df.isnull().any() \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Duplicate_rows=new_df[new_df.duplicated()]\n",
    "Duplicate_rows.sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Unnamed: 0</th>\n",
       "      <th>Product_name</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Orginal Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, Product_name, Product_Price, Brand, Discount, Orginal Price]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.drop_duplicates() to drop duplicate values\n",
    "new_df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANALYSE THE DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile=ProfileReport(new_df)\n",
    "profile.to_widgets()#Download Widgets\n",
    "profile.to_notebook_iframe()#generate Report\n",
    "\n",
    "\n",
    "profile.to_file(\"your_report.html\")#export report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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