Ecommerce Purchases - Solutions

In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder.

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

Also note that all of these questions can be answered with one line of code.

Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom.

In [1]:
import pandas as pd
In [2]:
ecom = pd.read_csv('Ecommerce Purchases.csv')

Check the head of the DataFrame.

In [3]:
ecom.head()
Out[3]:
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

How many rows and columns are there?

In [4]:
ecom.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

What is the average Purchase Price?

In [5]:
ecom['Purchase Price'].mean()
Out[5]:
50.347302

What were the highest and lowest purchase prices?

In [6]:
ecom['Purchase Price'].max(), ecom['Purchase Price'].min()
Out[6]:
(99.99, 0.0)

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

In [7]:
ecom[ecom['Language']=='en'].shape[0]
Out[7]:
1098

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

In [8]:
ecom[ecom['Job'] == 'Lawyer'].shape[0]
Out[8]:
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 [9]:
ecom['AM or PM'].value_counts()
Out[9]:
PM    5068
AM    4932
Name: AM or PM, dtype: int64

What are the 5 most common Job Titles?

In [10]:
ecom['Job'].value_counts().head(5)
Out[10]:
Interior and spatial designer    31
Lawyer                           30
Social researcher                28
Designer, jewellery              27
Purchasing manager               27
Name: Job, dtype: int64

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

In [11]:
ecom[ecom['Lot']=='90 WT']['Purchase Price']
Out[11]:
513    75.1
Name: Purchase Price, dtype: float64

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

In [12]:
ecom[ecom["Credit Card"] == 4926535242672853]['Email']
Out[12]:
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 [13]:
ecom[(ecom['CC Provider']=='American Express') & (ecom['Purchase Price']>95)].shape[0]
Out[13]:
39

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

In [14]:
sum(ecom['CC Exp Date'].apply(lambda x: x[3:]) == '25')
Out[14]:
1033
In [15]:
sum(ecom['CC Exp Date'].str.contains('/25'))
Out[15]:
1033

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

In [16]:
ecom['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)
Out[16]:
hotmail.com     1638
yahoo.com       1616
gmail.com       1605
smith.com         42
williams.com      37
Name: Email, dtype: int64
In [17]:
def getHost(x):
    return x.split('@')[1]
In [18]:
ecom['Email'].apply(getHost).value_counts().head(5)
Out[18]:
hotmail.com     1638
yahoo.com       1616
gmail.com       1605
smith.com         42
williams.com      37
Name: Email, dtype: int64

Great Job!