Converting Python Pandas PeriodIndex to Timestamp and Setting Frequency: Top 5 Methods

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πŸ’‘ Problem Formulation: When working with time-series data in Pandas, it is common to come across PeriodIndex objects that represent time spans. However, for certain analyses, it may be necessary to convert these periods into specific timestamps. This article solves the problem of converting a Pandas PeriodIndex object into a Timestamp object and setting a specific frequency, with examples of input PeriodIndex and the desired output Timestamp.

Method 1: Using to_timestamp()

This method leverages the to_timestamp() function available on PeriodIndex objects to convert them into Timestamps. It allows specifying the end or start of the period with the how='start' or how='end' arguments. By default, the converted Timestamp retains the frequency of the original PeriodIndex.

Here’s an example:

import pandas as pd

# Create a PeriodIndex
periods = pd.PeriodIndex(start='2021-01', freq='M', periods=4)

# Convert to Timestamps
timestamps = periods.to_timestamp(how='start')

Output: DatetimesIndex([‘2021-01-01’, ‘2021-02-01’, ‘2021-03-01’, ‘2021-04-01′], dtype=’datetime64[ns]’, freq=’MS’)

This snippet first creates a PeriodIndex consisting of four monthly periods starting from January 2021. Using to_timestamp() and specifying how='start', we convert the periods to their respective starting timestamp, resulting in a DateTimeIndex with a monthly start frequency denoted by ‘MS’.

Method 2: Adjusting Frequency After Conversion

After conversion using to_timestamp(), you can set a new frequency using the asfreq() method. This is useful when you need to adjust the frequency of the timestamps to one that differs from the original PeriodIndex.

Here’s an example:

timestamps_with_new_freq = timestamps.asfreq('3M')

Output: DateTimeIndex([‘2021-01-01’, ‘2021-04-01′], dtype=’datetime64[ns]’, freq=’3MS’)

By calling asfreq('3M') on the previously obtained DateTimeIndex object, we change the frequency to a quarterly start frequency denoted by ‘3MS’. This results in a new DateTimeIndex with dates every three months from the original starting point.

Method 3: Conversion and Frequency Assignment

The period_range() function can be used to both create a PeriodIndex and immediately convert it to timestamps with a specified frequency. This is a consolidated step when both creating and converting time data.

Here’s an example:

timestamps_freq_assigned = pd.period_range(start='2021-01', freq='M', periods=4).to_timestamp()

Output: DateTimeIndex([‘2021-01-01’, ‘2021-02-01’, ‘2021-03-01’, ‘2021-04-01′], dtype=’datetime64[ns]’, freq=None)

In this code snippet, we use pd.period_range() to create a PeriodIndex and then immediately convert it to Timestamps using to_timestamp(). The frequency is not explicitly set after conversion, so it defaults to ‘None’.

Method 4: Using a Custom Function

A custom function can be applied to a PeriodIndex object to convert each period to a timestamp while potentially applying other transformations. This provides maximum control but is less concise.

Here’s an example:

convert_to_timestamp = lambda x: x.to_timestamp()
custom_timestamps =

Output: DateTimeIndex([‘2021-01-01’, ‘2021-02-01’, ‘2021-03-01’, ‘2021-04-01′], dtype=’datetime64[ns]’, freq=None)

The lambda function defined as convert_to_timestamp is applied to each element of the PeriodIndex object using the map() method. Each period is converted to its starting timestamp. The resulting frequency is set to ‘None’ by default.

Bonus One-Liner Method 5: Direct Conversion with Frequency Specification

If the goal is to quickly convert a PeriodIndex to Timestamps and immediately set a frequency, you can chain the to_timestamp() and asfreq() methods together in a one-liner.

Here’s an example:

one_liner_timestamps = periods.to_timestamp().asfreq('3M')

Output: DateTimeIndex([‘2021-01-01’, ‘2021-04-01′], dtype=’datetime64[ns]’, freq=’3MS’)

This efficient one-liner takes the original PeriodIndex and first converts it to Timestamps and then applies the asfreq('3M') method to set a quarterly frequency. The resulting DateTimeIndex has two dates, spaced three months apart.


  • Method 1: Using to_timestamp(). Straightforward and built-in. Limited to the innate capabilities of the function.
  • Method 2: Adjusting Frequency After Conversion. Flexible frequency adjustment after initial conversion. Requires a second step after conversion.
  • Method 3: Conversion and Frequency Assignment. Combines PeriodIndex creation and timestamp conversion. Can be less clear when reading code.
  • Method 4: Using a Custom Function. Offers the most control for complex conversions. Could be considered overkill for simple tasks.
  • Bonus One-Liner Method 5: Direct Conversion with Frequency Specification. Efficient and concise. May become less readable with complex operations.