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Pandas | PeriodIndex constructor

Programming
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Python
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Pandas
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Documentation
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Datetimes
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas PeriodIndex(~) constructor creates a new PeriodIndex object, which represents a specific time span or a duration. PeriodIndex is most often used as the index of a DataFrame.

NOTE

The difference between PeriodIndex and DatetimeIndex is as follows:

  • PeriodIndex represents a time span with a precise start and end time.

  • DatetimeIndex represents a specific point in time.

WARNING

Instead of calling this constructor directly, opt to use period_range(~) instead, which is more flexible and widely used.

Parameters

1. data | array-like | optional

An array-like containing the dates to construct the PeriodIndex.

2. copy | boolean | optional

  • If True, then a new copy of data will be returned - modifying this copied data will not mutate the original data, and vice versa.

  • If False, then the returned PeriodIndex will use the same memory block as data. This means that modifying the return value will mutate the original data, and vice versa.

By default, copy=False.

3. freqlink | string or Period | optional

The frequency of the PeriodIndex. You can think of freq as the interval size of a time span. By default, the frequency is inferred from data.

4. yearlink | int or array or Series | optional

By default, year=None.

5. monthlink | int or array or Series | optional

By default, month=None.

6. quarter | int or array or Series | optional

By default, quarter=None.

7. daylink | int or array or Series | optional

By default, day=None.

8. hour | int or array or Series | optional

By default, hour=None.

9. minute | int or array or Series | optional

By default, minute=None.

10. second | int or array or Series | optional

By default, second=None.

11. tzlink | object | optional

The timezone to use when converting from datetime64 to PeriodIndex. By default, tz=None.

Return Value

A PeriodIndex object.

Examples

Basic usage

To create a PeriodIndex using a list of date-strings:

index_period = pd.PeriodIndex(["2020-12-25", "2020-12-26"], freq="D")
index_period
PeriodIndex(['2020-12-25', '2020-12-26'], dtype='period[D]', freq='D')

Here, the freq="D" means that the interval size of each period is a day.

We can then use this PeriodIndex as the index of our DataFrame like so:

pd.DataFrame({"A":["a","b"]}, index=index_period)
A
2020-12-25 a
2020-12-26 b

Specifying freq

To specify an interval size of the PeriodIndex, set freq like so:

index_period = pd.PeriodIndex(["2020-12-25", "2020-12-26"], freq="2D")
index_period
PeriodIndex(['2020-12-25', '2020-12-26'], dtype='period[2D]', freq='2D')

Specifying individual time parameters

Instead of passing in data, you can create a PeriodIndex using individual time parameters like so:

idx = pd.PeriodIndex(year=[2000, 2002], month=[5,6], day=[10,11], freq="D")
idx
PeriodIndex(['2000-05-10', '2002-06-11'], dtype='period[D]', freq='D')

Specifying tz

To make the PeriodIndex timezone-aware:

pd.PeriodIndex(["2020-12-25"], freq="D", tz="Asia/Tokyo")
PeriodIndex(['2020-12-25'], dtype='period[D]', freq='D')
robocat
Published by Isshin Inada
Edited by 0 others
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