So, seems like all the columns are fine now. Let's double-check if we have converted dates properly.

# 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')

# Displaying first four dates before converting
print(df['Date'].head(4))

# Change column type
df['Date'] = pd.to_datetime(df['Date'], dayfirst = True)

# Displaying first four dates and dtypes of dataframe
print(df['Date'].head(4))
print(df.dtypes)

Yes, this time everything seems to be fine. Dtypes of the dataframe are appropriate, so we are ready to make some analysis and visualizing!

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-Life Data

That's All?