Processing Dates

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


              12345678
            
# 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.


              123456789101112131415
            
# 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.

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