Ecommerce Purchases Exercise

In this notebook Fake Data about some purchases done through Amazon! is used.

Please excuse anything that doesn't make "Real-World" sense in the dataframe, all the data is fake and made-up.

Data is imported from an Ecommerce Purchases csv file and set it to a DataFrame called ecom. **
In [64]:
import pandas as pd 

ecom = pd.read_csv('Ecommerce Purchases')

ecom.head()
Out[64]:
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

Check the head of the DataFrame.

How many rows and columns are there?

In [5]:
ecom.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 14 columns):
Address             10000 non-null object
Lot                 10000 non-null object
AM or PM            10000 non-null object
Browser Info        10000 non-null object
Company             10000 non-null object
Credit Card         10000 non-null int64
CC Exp Date         10000 non-null object
CC Security Code    10000 non-null int64
CC Provider         10000 non-null object
Email               10000 non-null object
Job                 10000 non-null object
IP Address          10000 non-null object
Language            10000 non-null object
Purchase Price      10000 non-null float64
dtypes: float64(1), int64(2), object(11)
memory usage: 1.1+ MB

What is the average Purchase Price?

In [7]:
ecom['Purchase Price'].mean()
Out[7]:
50.34730200000025

What were the highest and lowest purchase prices?

In [8]:
ecom['Purchase Price'].max()
Out[8]:
99.99
In [9]:
ecom['Purchase Price'].min()
Out[9]:
0.0

How many people have English 'en' as their Language of choice on the website?

In [65]:
ecom[ecom['Language']== 'en'].count()
Out[65]:
Address             1098
Lot                 1098
AM or PM            1098
Browser Info        1098
Company             1098
Credit Card         1098
CC Exp Date         1098
CC Security Code    1098
CC Provider         1098
Email               1098
Job                 1098
IP Address          1098
Language            1098
Purchase Price      1098
dtype: int64
In [13]:
ecom.columns
Out[13]:
Index(['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'],
      dtype='object')

How many people have the job title of "Lawyer" ?

In [15]:
sum(ecom['Job'] == 'Lawyer')
Out[15]:
30

How many people made the purchase during the AM and how many people made the purchase during PM ?

(Hint: Check out value_counts() )

In [20]:
ecom['AM or PM'].value_counts()
Out[20]:
PM    5068
AM    4932
Name: AM or PM, dtype: int64

What are the 5 most common Job Titles?

In [22]:
ecom['Job'].value_counts().head(5)
Out[22]:
Interior and spatial designer    31
Lawyer                           30
Social researcher                28
Purchasing manager               27
Designer, jewellery              27
Name: Job, dtype: int64

Someone made a purchase that came from Lot: "90 WT" , what was the Purchase Price for this transaction?

In [28]:
ecom[ecom['Lot']== "90 WT"]['Purchase Price']
Out[28]:
513    75.1
Name: Purchase Price, dtype: float64

What is the email of the person with the following Credit Card Number: 4926535242672853

In [30]:
ecom[ecom['Credit Card']==4926535242672853]['Email']
Out[30]:
1234    bondellen@williams-garza.com
Name: Email, dtype: object

How many people have American Express as their Credit Card Provider and made a purchase above $95 ?

In [67]:
ecom[(ecom['CC Provider']=="American Express") & (ecom['Purchase Price'] > 95)].count()
Out[67]:
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

Hard: How many people have a credit card that expires in 2025?

In [78]:
ecom[ecom['CC Exp Date'].apply(lambda exp: exp[3:] =='25')].count()
Out[78]:
Address             1033
Lot                 1033
AM or PM            1033
Browser Info        1033
Company             1033
Credit Card         1033
CC Exp Date         1033
CC Security Code    1033
CC Provider         1033
Email               1033
Job                 1033
IP Address          1033
Language            1033
Purchase Price      1033
dtype: int64
In [102]:
Out[102]:
1033

Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...)

In [92]:
# ecom['Email']

ecom['Email'].apply(lambda email: email.split('@')[1]).value_counts().head(5)
Out[92]:
hotmail.com     1638
yahoo.com       1616
gmail.com       1605
smith.com         42
williams.com      37
Name: Email, dtype: int64

Great Job!