Good, we know the best selling stores for entire date range. But how wide it is? Let's find out.
Since we convert the `'Date'` column into the `datetime` type, we can apply different methods on it, like `.min()` or `.max()`. They will return the earliest and the latest dates in the column.

# Loading the library
import pandas as pd

# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data3.csv')
df['Date'] = pd.to_datetime(df['Date'], dayfirst = True)

# Inspecting the 'Date' column
print(df['Date'].min())

*For the following question, feel free to edit the code above if necessary.*

What is the range of dates in the `'Date'` column of the `df` dataframe? *(Remember, dates are represented in the yyyy-mm-dd format.)*

This hands-on course provides an opportunity to apply and consolidate your skills across a range of data analysis tasks using real-world dataset. You'll learn how to identify and address data issues, and then explore and visualize data using pandas, matplotlib, and seaborn to uncover insights and draw conclusions.

First, let's get acquainted with the data we will be working with. Throughout this section, we will perform the initial set of actions to prepare the data for further analysis.

In the second section, we will continue with the preparatory tasks started in the first section and ensure the data is ready for further exploration.

After preparing the data, we will move on to some exploratory analysis. Throughout this section, we will answer several questions and uncover interesting insights.

In the last section, we will use visualization tools to either confirm or reject the hypotheses we made in the previous section. We will also create visualizations of key indicators for the entire dataset.

Analyzing and Visualizing Real-World Data

Date Range