E-Commerce Data

This dataset (source) consists of details of orders made in different countries from December 2010 until December 2011. The company is a UK-based online retailer that mainly sells unique all-occasions gifts. Many of its customers are wholesalers. Take a look at some suggestions to analyze at the end of this template.

# Load packages
import numpy as np 
import pandas as pd 

Load your data

# Load data from the csv file
df = pd.read_csv('online_retail.csv', index_col=None)
print(f"Number of rows/records: {df.shape[0]}")
print(f"Number of columns/variables: {df.shape[1]}")
df.head()

Number of rows/records: 541909
Number of columns/variables: 8

Understand your variables

# Understand your variables
variables = pd.DataFrame(columns=['Variable','Number of unique values','Values'])

for i, var in enumerate(df.columns):
    variables.loc[i] = [var, df[var].nunique(), df[var].unique().tolist()]

    
# Join summary data frame with a CSV file explaining the different variables
var_dict = pd.read_csv('variable_explanation.csv', index_col=0)
variables.set_index('Variable').join(var_dict)

Answer interesting questions:

Now you get to explore this exciting dataset! Can't think of where to start? Try your hand at these questions:

• Find out which country has the biggest clients regarding total orders or most money paid.
• Find the smallest portion of customers that drive the biggest portion of the company revenue. See the 80/20 rule.
• Plot the amount of orders made or amount of money paid in function of time.

# Start coding