Python Pandas - Convert PeriodIndex object to Timestamp

To convert a PeriodIndex object to Timestamp, use the PeriodIndex.to_timestamp() method. This method converts period-based data to timestamp-based data, which is useful when you need to work with datetime operations.

Understanding PeriodIndex

A PeriodIndex represents regular periods in time (like years, months, quarters). When converted to timestamps, it creates a DatetimeIndex with specific points in time.

Creating a PeriodIndex

First, let's create a PeriodIndex object with yearly frequency ?

import pandas as pd

# Create a PeriodIndex object with yearly frequency
periodIndex = pd.PeriodIndex(['2021-09-25 07:30:35', '2019-10-30 04:15:45',
                              '2020-07-15 02:55:15', '2022-06-25 09:40:55'], freq="Y")

print("PeriodIndex...")
print(periodIndex)

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

Converting to Timestamp

Use to_timestamp() to convert the PeriodIndex to a DatetimeIndex ?

import pandas as pd

# Create a PeriodIndex object
periodIndex = pd.PeriodIndex(['2021-09-25 07:30:35', '2019-10-30 04:15:45',
                              '2020-07-15 02:55:15', '2022-06-25 09:40:55'], freq="Y")

# Convert to timestamp
timestamp_index = periodIndex.to_timestamp()

print("Original PeriodIndex:")
print(periodIndex)
print("\nConverted to Timestamp:")
print(timestamp_index)
print("\nData type:", type(timestamp_index))

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

Converted to Timestamp:
DatetimeIndex(['2021-01-01', '2019-01-01', '2020-01-01', '2022-01-01'], dtype='datetime64[ns]', freq=None)

Data type: <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

Complete Example

Here's a comprehensive example showing the conversion process ?

import pandas as pd

# Create a PeriodIndex object
# PeriodIndex is an immutable ndarray holding ordinal values indicating regular periods in time
# We have set the frequency using the "freq" parameter
periodIndex = pd.PeriodIndex(['2021-09-25 07:30:35', '2019-10-30 04:15:45',
                              '2020-07-15 02:55:15', '2022-06-25 09:40:55'], freq="Y")

# Display PeriodIndex object
print("PeriodIndex...")
print(periodIndex)

# Display PeriodIndex frequency
print("\nPeriodIndex frequency object...")
print(periodIndex.freq)

# Display PeriodIndex frequency as string
print("\nPeriodIndex frequency object as a string...")
print(periodIndex.freqstr)

# Convert PeriodIndex to timestamp
print("\nPeriodIndex object to timestamp...")
print(periodIndex.to_timestamp())

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

PeriodIndex frequency object...
<YearEnd: month=12>

PeriodIndex frequency object as a string...
A-DEC

PeriodIndex object to timestamp...
DatetimeIndex(['2021-01-01', '2019-01-01', '2020-01-01', '2022-01-01'], dtype='datetime64[ns]', freq=None)

Key Points

• The to_timestamp() method converts periods to the beginning of the period by default
• Original datetime information is simplified based on the frequency (yearly frequency shows only years)
• The result is a DatetimeIndex object suitable for time-based operations

Conclusion

Use PeriodIndex.to_timestamp() to convert period-based indices to timestamp-based indices. This conversion enables datetime operations and creates standardized timestamps from period data.