Analyzing the data

We are provided with 3 files, which contain anonymized information about the 45 stores, indicating the type and size of the store, historical training data, which covers 2010-02-05 to 2012-11-01, and a file that contains additional data related to the store, department, and regional activity for the given dates.

In the next example, the following Python modules were used:

• pandas: A Python package for data analysis and data manipulation
• NumPy: This is a library that adds support for large, multi-dimensional arrays and matrices, along with an ample collection of high-level mathematical functions to operate on these arrays
• Seaborn and Matplotlib: Python packages for effective data visualization

1. We will start the analysis by importing the libraries, and we will start to analyze the data by reading all the files and parsing the Date column using the parse_date option of the read_csv pandas function:

   import pandas as pd # data processing

   import...