
#importing numpy and pandas package
import numpy as np
import pandas as pd


#reading 'Ecommerce Purchases' file with the help of pandas method read_csv() to ecom dataframe
ecom = pd.read_csv('Ecommerce Purchases')


#printing head of dataframe using head() method
ecom.head()


#using shape function we got the number of row and column and storing it to a list and than printing
row_col = ecom.shape
print("Number of rows:",row_col[0])
print("Number of Column:",row_col[1])

Number of rows: 10000
Number of Column: 14


#using mean() method getting average of Purchase Price
pur_avg = ecom['Purchase Price'].mean()
print("Average Purchase Price:",pur_avg)

Average Purchase Price: 50.347302


#getting highest purchase price using max() method
pur_max = ecom['Purchase Price'].max()
print("Highest purchase prices:",pur_max)

Highest purchase prices: 99.99


#getting lowest purchase price using min() method
pur_min = ecom['Purchase Price'].min()
print("Lowest purchase prices:",pur_min)

Lowest purchase prices: 0.0


#Getting number of people who speak any language using value_counts() method and storing to a variable
lan = ecom['Language'].value_counts()
#using get() function finding number of people who speak english
en_count = lan.get(key = 'en') 
print("People choose English Language:",en_count)

People choose English Language: 1098


#Getting number of people of differnt job titles using value_counts() method and storing to a variable
job = ecom['Job'].value_counts()
lawyer_count = job.get(key = 'Lawyer') 
#using get() function finding number of people who are 'Lawyer'
print("Lawyer job title:",lawyer_count)

Lawyer job title: 30


#Getting number of people who prchase in AM or PM using value_counts() method and storing to a variable
am_pm = ecom['AM or PM'].value_counts()
#using get() function finding number of people who purchase during 'AM'
am = am_pm.get(key='AM')
#using get() function finding number of people who purchase during 'PM'
pm = am_pm.get(key='PM')
print("Purchase during the AM:",am,"\nPurchase during the PM:",pm)

Purchase during the AM: 4932 
Purchase during the PM: 5068


#getting number of different job title and printing top 5
top_job = dict(ecom['Job'].value_counts()[0:5])
print(list(top_job.keys()))

['Interior and spatial designer', 'Lawyer', 'Social researcher', 'Designer, jewellery', 'Purchasing manager']


#Getting the Purchase price for transaction who has lot='90 WT'
ecom[ecom['Lot']=="90 WT"]['Purchase Price']

513    75.1
Name: Purchase Price, dtype: float64


#Getting the number of people who had Credit Card Number: 4926535242672853
ecom[ecom["Credit Card"]==4926535242672853]['Email']

1234    bondellen@williams-garza.com
Name: Email, dtype: object


#Getting the number of people who had American Express card and purchaseed more than $95
ecom[(ecom["CC Provider"]=="American Express") & (ecom["Purchase Price"]>95)].count()

Address             39
Lot                 39
AM or PM            39
Browser Info        39
Company             39
Credit Card         39
CC Exp Date         39
CC Security Code    39
CC Provider         39
Email               39
Job                 39
IP Address          39
Language            39
Purchase Price      39
dtype: int64


#using a lamda function getting the 'CC Exp Date' which ends with '25' and getting a sum value
exp_2025 = sum(ecom["CC Exp Date"].apply(lambda x: x[3:])=='25')
print("Card that expires in 2025:",exp_2025)

Card that expires in 2025: 1033


#getting number of different email providers and printing top 5
top_email = ecom["Email"].str.split('@').str[1].value_counts()[0:5]
print(list(top_email.keys()))

['hotmail.com', 'yahoo.com', 'gmail.com', 'smith.com', 'williams.com']

	Address	Lot	AM or PM	Browser Info	Company	Credit Card	CC Exp Date	CC Security Code	CC Provider	Email	Job	IP Address	Language	Purchase Price
0	16629 Pace Camp Apt. 448\nAlexisborough, NE 77...	46 in	PM	Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...	Martinez-Herman	6011929061123406	02/20	900	JCB 16 digit	pdunlap@yahoo.com	Scientist, product/process development	149.146.147.205	el	98.14
1	9374 Jasmine Spurs Suite 508\nSouth John, TN 8...	28 rn	PM	Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...	Fletcher, Richards and Whitaker	3337758169645356	11/18	561	Mastercard	anthony41@reed.com	Drilling engineer	15.160.41.51	fr	70.73
2	Unit 0065 Box 5052\nDPO AP 27450	94 vE	PM	Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...	Simpson, Williams and Pham	675957666125	08/19	699	JCB 16 digit	amymiller@morales-harrison.com	Customer service manager	132.207.160.22	de	0.95
3	7780 Julia Fords\nNew Stacy, WA 45798	36 vm	PM	Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...	Williams, Marshall and Buchanan	6011578504430710	02/24	384	Discover	brent16@olson-robinson.info	Drilling engineer	30.250.74.19	es	78.04
4	23012 Munoz Drive Suite 337\nNew Cynthia, TX 5...	20 IE	AM	Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...	Brown, Watson and Andrews	6011456623207998	10/25	678	Diners Club / Carte Blanche	christopherwright@gmail.com	Fine artist	24.140.33.94	es	77.82

Ecommerce Purchases Exercise¶

Great Job!¶