Pandas DataFrame to_period() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, one (1) new library will require installation.

The Pandas library enables access to/from a DataFrame.

To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

💡 Note: The pytz comes packaged with pandas and does not require installation. However, this library is needed for the tz_ localize() and tz_convert() methods to work.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

If the installation was successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

How to Install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import pytz

DataFrame to_period()

The to_period() method converts a DataFrame/Series from a DatetimeIndex format to a PeriodIndex format based on the selected frequency.

The syntax for this method is as follows:

DataFrame.to_period(freq=None, axis=0, copy=True)

Parameter	Description
`freq`	This parameter is an available frequency of the `PeriodIndex` method.
`axis`	If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row.
`copy`	If `True`, the data copies. By default, `True`.

For these examples, we have a list containing datetimes. These datetimes convert to Monthly & Yearly formats.

Code –Monthly Format

idx = pd.to_datetime(['2022-01-15 08:17:00',
                      '2022-01-15 08:23:00',
                      '2022-01-15 08:47:00',
                      '2022-01-15 09:01:00',
                      '2022-01-15 09:28:00'])
print(idx)

result = idx.to_period('M')
print(result)

Line [1] converts a list of strings to a datetime format and saves it to idx.
Line [2] outputs the contents of idx to the terminal.
Line [3] converts the contents of idx to a PeriodIndex Monthly format. The output saves to result.
Line [4] outputs the result to the terminal.

Output

idx
DatetimeIndex(['2022-01-15 08:17:00', '2022-01-15 08:23:00',
               	         '2022-01-15 08:47:00', '2022-01-15 09:01:00',
               	         '2022-01-15 09:28:00'],
              	         dtype='datetime64[ns]', freq=None)

result
PeriodIndex(['2022-01', '2022-01', '2022-01', '2022-01', '2022-01'], dtype='period[M]')

Code –Yearly Example

idx = pd.to_datetime(['2018-01-15 08:17:00',
                      '2019-01-15 08:23:00',
                      '2020-01-15 08:47:00',
                      '2021-01-15 09:01:00',
                      '2022-01-15 09:28:00'])
print(idx)

result = idx.to_period('Y')
print(result)

Line [1] converts a list of strings to a datetime format and saves it to idx.
Line [2] outputs the contents of idx to the terminal.
Line [3] converts the contents of idx to a PeriodIndex Yearly format. The output saves to result.
Line [4] outputs the result to the terminal.

Output

idx
DatetimeIndex(['2018-01-15 08:17:00', '2019-01-15 08:23:00',               
	         '2020-01-15 08:47:00', '2021-01-15 09:01:00',
                            '2022-01-15 09:28:00'],
                            dtype='datetime64[ns]', freq=None)

result
PeriodIndex(['2018', '2019', '2020', '2021', '2022'], dtype='period[A-DEC]')

Note: Definition of frequency period [A-DEC]:

A: year-end
DEC: year ends in December

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

👈 df.to_parquet()

df.to_pickles() 👉

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.