Project_1_Ecommers-analysis

Analysis and visualization of different insights

Import dat from github respiratory

E-commers project for analysis and finding insights

import pandas as pd

url = 'https://raw.githubusercontent.com/DataThinkers/Datasets/main/DS/Ecommerce%20Purchases' def read_data_git(): ex1 = pd.read_csv(url) return ex1

ecommerce=read_data_git() ecommerce.head(1)

Copy the main data

data=ecommerce.copy()

data.head(2)

Findings of null value

data.isnull().sum()

Check datatype of each columns

data.dtypes

Number of rows and columns

data.shape

highest and lowest purchase price

data.columns

Highest purchase price

print('Highest purchase price',data['Purchase Price'].max())

print('Lowest purchase price',data['Purchase Price'].min())

Average purchase price

print('Average purchase price -',data['Purchase Price'].mean())

How many people have French language

len(data[data['Language']=='fr'])

job title contain 'Enginers'

len(data[data['Job'].str.contains('engineer',case=False)])

Find Email of the person with the fllowing IP adress: 132.207.160.22

data.columns

data[data['IP Address']== '132.207.160.22'][['Email','Company']]

How many people have master card and purchase above 50

len(data[(data['CC Provider'] == 'Mastercard') & (data['Purchase Price']>=50)])

Find Email of person with the following card number:4664825258997302

data[data['Credit Card']== 4664825258997302]['Email']

How many people purchase in AM and PM

AM

len(data[data['AM or PM']=='AM'])

PM

len(data[data['AM or PM']=='PM'])

data['AM or PM'].value_counts()

How many people have credit card expire on 2020

data2=ecommerce.copy()

def fun(): count=0 for date in data2['CC Exp Date']: if date.split('/')[1]=='20': count=count+1 print(count)

fun()

another processure

data2['Ex_Year'] = data2['CC Exp Date'].str[3:5]

data2.head(2)

How many people have credit card expire on 2020

len(data2[data2['Ex_Year'] == '20'])

another processure using Lamda function

len(data2[data2['CC Exp Date'].apply(lambda x:x[3:]=='20')])

Top 5 Email provider

data2['Email_last'] = data2['Email'].apply(lambda x:x.split('@')[1]) data2.head(2)

Top_5 = data2['Email_last'].value_counts() Top_5.head(5)