We are close to finishing! Let's see what data types our data has now.

# 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')

# Checking columns types
print(df.dtypes)

As you can see, there is only one `object` column left. You may be wondering why this is bad. Actually, we can leave it as is, but `pandas` provides us with tools to work with dates and times. Unfortunately, `datetime` is not a built-in data type in Python, so we can't convert the column and save it. We will have to convert it after loading every time. To convert to `datetime` type, we need to use the `.to_datetime()` method of `pd`.

# 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'])

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

As you can see, visually the dates have changed, i.e., they are now represented in a different format.

What is the initial format of dates in the `'Date'` column? *(dd - day, mm - month, yyyy - year)*

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.

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