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Pandas DataFrame | to_period method

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Time Series
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.to_period(~) method converts the source DataFrame's DatetimeIndex to PeriodIndex.

Parameters

1. freqlink | string | optional

The frequency to assign to the new PeriodIndex. By default, the frequency is inferred from DatetimeIndex.

2. axis | int or string | optional

Whether to convert the row index or the column index:

Axis

Description

Convert the row index.

0 or "index"

Convert the column index.

1 or "columns"

By default, axis=0.

3. copy | boolean | optional

  • If True, then a new DataFrame is returned. Modifying this DataFrame will not mutate the source DataFrame, and vice versa.

  • If False, then no new DataFrame is created - modifying the returned DataFrame will mutate the source DataFrame, and vice versa.

By default, copy=True.

Return Value

A DataFrame with PeriodIndex.

Examples

Basic usage

Consider the following DataFrame with a DatetimeIndex:

date_index = pd.date_range("2020/12/25", periods=3)
df = pd.DataFrame({"A":[2,3,4],"B":[5,6,7]}, index=date_index)
df
A B
2020-12-25 2 5
2020-12-26 3 6
2020-12-27 4 7

To convert the index from DatetimeIndex to PeriodIndex:

df_period = df.to_period()
df_period
A B
2020-12-25 2 5
2020-12-26 3 6
2020-12-27 4 7

To confirm that the index is now PeriodIndex:

df_period.index
PeriodIndex(['2020-12-25', '2020-12-26', '2020-12-27'], dtype='period[D]', freq='D')

Specifying freq parameter

Consider the same df as above with a DatetimeIndex:

date_index = pd.date_range("2020/12/25", periods=3)
df = pd.DataFrame({"A":[2,3,4],"B":[5,6,7]}, index=date_index)
df
A B
2020-12-25 2 5
2020-12-26 3 6
2020-12-27 4 7

To convert the index from DatetimeIndex to PeriodIndex with a frequency of "M" (month):

df_period = df.to_period(freq="M")
df_period
A B
2020-12 2 5
2020-12 3 6
2020-12 4 7

Notice how the day-unit has been stripped away since the frequency is now set to months.

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Published by Isshin Inada
Edited by 0 others
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