A full analysis of 2019 sales for an electronic shop, from data cleaning and preprocessing to visualization

start by cleaning our data. Tasks during this section include:

Drop NaN values from DataFrame

Removing rows based on a condition

Change the type of columns (to_numeric, to_datetime, astype)

Once we have cleaned up our data a bit, we move to the data exploration section. In this section, we explore 5 high-level business questions related to our data:

What was the best month for sales? How much was earned that month?

What city sold the most product?

What time should we display advertisements to maximize the likelihood of customers buying product?

What products are most often sold together?

What product sold the most? Why do you think it sold the most?

To answer these questions we walk through many different pandas & matplotlib methods. They include:

Concatenating multiple csvs together to create a new DataFrame (pd.concat)

Adding columns

Parsing cells as strings to make new columns (.str)

Using the .apply() method

Using groupby to perform aggregate analysis

Plotting bar charts and lines graphs to visualize our results

Labeling our graphs