Converting Python Pandas Periods to Timestamps with Daily Frequency

Rate this post

πŸ’‘ Problem Formulation: When working with time series data in Pandas, one often needs to convert periods to timestamps for standardized time points. This requirement arises, for example, when a data frame indexed by periods needs to be resampled or compared with timestamp-indexed data. Consider having a period ‘2021Q1’, which represents the first quarter of 2021; the desired output is a timestamp ‘2021-01-01’, marking the start of the daily frequency for that period.

Method 1: Using to_timestamp() with no arguments

This method involves calling the to_timestamp() function on a Pandas Period or PeriodIndex object. By default, it returns the equivalent timestamp at the beginning of the period for a daily frequency.

Here’s an example:

import pandas as pd

# Creating a single period object
period = pd.Period('2021Q1')

# Converting to timestamp
timestamp = period.to_timestamp()

Output: Timestamp(‘2021-01-01 00:00:00’)

By default, the to_timestamp() function converts the ‘2021Q1’ period to the timestamp marking the start of the period, which is midnight of January 1st, 2021. It assumes a daily frequency as no specific frequency is provided.

Method 2: Specifying the frequency

While the to_timestamp() function defaults to daily frequency, users can specify their desired frequency explicitly for clarity or to avoid relying on defaults.

Here’s an example:

import pandas as pd

# Creating a single period object
period = pd.Period('2021Q1')

# Converting to timestamp with daily frequency
timestamp = period.to_timestamp(freq='D')

Output: Timestamp(‘2021-01-01 00:00:00’)

This snippet explicitly sets the frequency to ‘D’ for daily. The period ‘2021Q1’ is converted to a timestamp with the specified daily frequency, ensuring that the result is 2021-01-01.

Method 3: Converting PeriodIndex to Timestamps

A PeriodIndex containing multiple periods can be converted to a DatetimeIndex with corresponding timestamps for each period using the to_timestamp() method, which is useful in resampling operations.

Here’s an example:

import pandas as pd

# Creating a PeriodIndex object
period_index = pd.period_range(start='2021Q1', periods=4, freq='Q')

# Converting to a DatetimeIndex with daily frequency
timestamp_index = period_index.to_timestamp()

Output: DatetimeIndex([‘2021-01-01’, ‘2021-04-01’, ‘2021-07-01’, ‘2021-10-01′], dtype=’datetime64[ns]’, freq=’D’)

This converts a quarterly PeriodIndex starting at ‘2021Q1’ for four periods into a DatetimeIndex. The to_timestamp() method here generates timestamps with daily frequency for the start of each quarter.

Method 4: Using to_timestamp at the end of the period

If the requirement is to obtain the timestamp at the end of the period rather than the start, the to_timestamp() function can take the how argument, set to ‘end’.

Here’s an example:

import pandas as pd

# Creating a single period object
period = pd.Period('2021Q1')

# Converting to timestamp at the end of the period
timestamp = period.to_timestamp(how='end')

Output: Timestamp(‘2021-03-31 23:59:59.999999999’)

In this code snippet, the how='end' argument instructs to_timestamp() to convert the period ‘2021Q1’ to the last possible timestamp within the period, resulting in the end of March 31st, 2021.

Bonus One-Liner Method 5: Using property accessor

Period objects have a start_time property that provides a one-liner shortcut to get the timestamp at the start of the period.

Here’s an example:

import pandas as pd

# Creating a single period object
period = pd.Period('2021Q1')

# Converting to timestamp using the property accessor
timestamp = period.start_time

Output: Timestamp(‘2021-01-01 00:00:00’)

This succinct one-liner uses the start_time property of the period object to directly obtain the start timestamp, streamlining the code if only the start timestamp is needed.

Summary/Discussion

  • Method 1: Using to_timestamp() with no arguments. Simple and straightforward. May not be explicit about the assumed frequency.
  • Method 2: Specifying the frequency. Offers clarity by explicitly defining the frequency. Slightly more verbose than necessary if default behavior is desired.
  • Method 3: Converting PeriodIndex to Timestamps. Ideal for converting entire indexed periods to timestamps. Not suited for single period conversions.
  • Method 4: Using to_timestamp at the end of the period. Useful when the end timestamp is needed. Requires understanding of the how parameter.
  • Bonus Method 5: Using property accessor. Provides a quick and clean way to access the start timestamp. Doesn’t offer flexibility for custom frequencies or getting the end timestamp.