You can use the following syntax to convert a date column in a pandas DataFrame to a YYYYMMDD format:

#convert date column to datetime
df['date_column'] = pd.to_datetime(df['date_column'])

#convert date to YYYYMMDD format
df['date_column'] = df['date_column'].dt.strftime('%Y%m%d').astype(int)

The following example shows how to use this syntax in practice.

Example: Convert Date to YYYYMMDD Format in Pandas

Suppose we have the following pandas DataFrame that shows the sales made by some company on various dates:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'date': pd.date_range(start='1/1/2022', freq='MS', periods=8),
                   'sales': [18, 22, 19, 14, 14, 11, 20, 28]})

#view DataFrame
print(df)

        date  sales
0 2022-01-01     18
1 2022-02-01     22
2 2022-03-01     19
3 2022-04-01     14
4 2022-05-01     14
5 2022-06-01     11
6 2022-07-01     20
7 2022-08-01     28

Now suppose that we would like to format the values in the date column as YYYYMMDD.

We can use the following syntax to do so:

#convert date column to datetime
df['date'] = pd.to_datetime(df['date'])

#convert date to YYYYMMDD format
df['date'] = df['date'].dt.strftime('%Y%m%d').astype(int)

#view updated DataFrame
print(df)

       date  sales
0  20220101     18
1  20220201     22
2  20220301     19
3  20220401     14
4  20220501     14
5  20220601     11
6  20220701     20
7  20220801     28

Notice that the values in the date column are now formatted in a YYYYMMDD format.

Note that in this example, the date column already had a class of datetime.

However, we can use the to_datetime() function anyway to ensure that a given column has a class of datetime before applying a YYYYMMDD format.

The following tutorials explain how to perform other common operations in pandas: