E-Commerce_Data

import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns pd.set_option('precision',2)

data['Credit Card']

data = pd.read_csv('Ecommerce Purchases.csv') data.head()

Addressobject

Lotobject

16629 Pace Camp Apt. 448 Alexisborough, NE 77130-7478

46 in

9374 Jasmine Spurs Suite 508 South John, TN 84355-4179

28 rn

Unit 0065 Box 5052 DPO AP 27450

94 vE

7780 Julia Fords New Stacy, WA 45798

36 vm

23012 Munoz Drive Suite 337 New Cynthia, TX 57826

20 IE

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Address           10000 non-null  object 
 1   Lot               10000 non-null  object 
 2   AM or PM          10000 non-null  object 
 3   Browser Info      10000 non-null  object 
 4   Company           10000 non-null  object 
 5   Credit Card       10000 non-null  int64  
 6   CC Exp Date       10000 non-null  object 
 7   CC Security Code  10000 non-null  int64  
 8   CC Provider       10000 non-null  object 
 9   Email             10000 non-null  object 
 10  Job               10000 non-null  object 
 11  IP Address        10000 non-null  object 
 12  Language          10000 non-null  object 
 13  Purchase Price    10000 non-null  float64
dtypes: float64(1), int64(2), object(11)
memory usage: 1.1+ MB

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

display(data.groupby('AM or PM').agg({'Purchase Price':'mean'}).\ rename(columns = {'Purchase Price':'Avg. Spent'}), data.groupby('AM or PM').agg({'Purchase Price':'sum'}).\ rename(columns = {'Purchase Price':'Tot. Spent'}) )

Tot. Spentfloat64

247519.87

255953.15

data['Browser Info'].value_counts()

data['Browser Info'].str.split('/').str.get(0).value_counts(normalize = True)

data['Company'].value_counts().head(7)

data.groupby('Company').agg({'Purchase Price':'sum'}).\ sort_values('Purchase Price', ascending = False).\ rename(columns = {'Purchase Price':'Revenue'}).head(7)

Revenuefloat64

Brown Ltd

796.38

Williams LLC

723.63

Smith LLC

637.92

Smith PLC

601.37

Johnson Ltd

600.61

Smith Group

592.54

Johnson PLC

567.33

display(data['CC Provider'].value_counts(), data.groupby('CC Provider').agg({'Purchase Price':'sum'}).\ sort_values('Purchase Price',ascending =False).\ rename(columns = {'Purchase Price':'Tot. Spent'}), data.groupby('CC Provider').agg({'Purchase Price':'mean'}).\ sort_values('Purchase Price',ascending =False).\ rename(columns = {'Purchase Price':'Avg. Spent'}) )

Avg. Spentfloat64

48.73323337679264 - 51.97318455971051

Voyager

51.97318456

Discover

51.66233782

VISA 13 digit

51.44985843

JCB 15 digit

51.12511521

American Express

50.48942285

Maestro

50.37917258

Mastercard

50.04301471

VISA 16 digit

49.87105539

JCB 16 digit

49.29914336

Diners Club / Carte Blanche

48.73323338

data['Job'].value_counts().head(10)

print('The top 10 most common jobs within the customers are:') for job in data['Job'].value_counts().head(10).index: print(job)

The top 10 most common jobs within the customers are:
Interior and spatial designer
Lawyer
Social researcher
Purchasing manager
Research officer, political party
Designer, jewellery
Charity fundraiser
Social worker
Dietitian
Special educational needs teacher

job_top_10_total_rv = data.groupby('Job').agg({'Purchase Price':'sum'}).\ sort_values('Purchase Price', ascending = False).head(10).\ rename(columns ={'Purchase Price':' Total Rev.'}) job_top_10_avg_rv = data.groupby('Job').agg({'Purchase Price':'mean'}).\ sort_values('Purchase Price', ascending = False).head(10).\ rename(columns = {'Purchase Price':'Average Rev.'}) display(job_top_10_total_rv, job_top_10_avg_rv)

Average Rev.float64

66.0342857142857 - 73.80272727272727

Trade mark attorney

73.80272727

Translator

70.77764706

Investment analyst

69.09

Accountant, chartered management

69.08266667

Designer, industrial/product

68.71466667

Clinical cytogeneticist

67.99636364

Psychologist, prison and probation services

67.76722222

Special effects artist

66.75285714

Politician's assistant

66.10692308

Advertising account planner

66.03428571

plt.figure(figsize = (18,6), facecolor = 'lightblue') plt.suptitle('Purcahse Price Distribution') plt.subplot(1,2,1) sns.histplot(x = data['Purchase Price'], bins = 30) plt.title('Purchase Price Histogram') plt.subplot(1,2,2) sns.boxplot(x = data['Purchase Price']) plt.title('Purchase Price Box Plot') plt.show()

print('Maximum Purchase Price') print(data['Purchase Price'].max()) print('Minimum Purchase Price') print(data['Purchase Price'].min()) print('Average Purchase Price') print(data['Purchase Price'].mean())

Maximum Purchase Price
99.99
Minimum Purchase Price
0.0
Average Purchase Price
50.347302

data.loc[data ['Purchase Price'] == data['Purchase Price'].min()]

Addressobject

Lotobject

2876

332 Jones Parkways East Katherineville, GA 64230

39 GT

5487

465 Mallory Ways North Rebecca, RI 82734-1160

93 OH

((data['Language']=='en') & (data['Job'] == 'Social researcher')).sum()

data.loc[(data['Job'] == 'Investment analyst') & (data['Language'] == 'en'), ['Email']]

Emailobject

8470

charlesjeremy@hill.com

data.loc[data['IP Address'] == '156.210.0.254', ['Email']]

Emailobject

9997

tyler16@gmail.com

len(data.loc[(data['CC Provider'] == 'VISA 16 digit') & (data['Purchase Price'] > 70)])

data.loc[data['Credit Card'].isin([6011578504430710,6011456623207998,180003348082930]), ['Email']]

Emailobject

brent16@olson-robinson.info

christopherwright@gmail.com

9998

elizabethmoore@reid.net

print('Number of customer with cardit cards expiring in 2022') len(data.loc[data['CC Exp Date'].str.split('/').str.get(1) == '22'])

Number of customer with cardit cards expiring in 2022

print('The most popualr email provider for the customer is') data['Email'].str.split('@').str.get(1).str.split('.').str.get(0).value_counts().head(1)

The most popualr email provider for the customer is