Time Periods - ( Pandas Time Series )

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Syntax

pandas.period_range(start=None, end=None, periods=None, freq=None, name=None)

Example

import pandas as pd

periods = pd.period_range(start='2022-01', end='2022-06', freq='M')

print(periods)

Output

PeriodIndex(['2022-01', '2022-02', '2022-03', '2022-04', '2022-05', '2022-06'], dtype='period[M]', freq='M')

Explanation

In Pandas, a time period represents a time span with a specific start and end point, such as a day, month, quarter, or year. Pandas provides the period_range() function to create a sequence of time period objects based on the specified start, end, and frequency.

In the above example, pd.period_range(start='2022-01', end='2022-06', freq='M') creates a sequence of time periods with a monthly frequency between January 2022 and June 2022. The PeriodIndex object returned by period_range() contains six time period objects with the specified frequency.

Use

Pandas time series functionality is useful for a wide range of data analysis and manipulation tasks that involve time series data. The period_range() function is particularly useful in creating time periods with specific start and end points and frequency for use in time series analysis and manipulation.

Important Points

• A time period in Pandas is a time span with a specific start and end point
• Pandas provides the period_range() function to create a sequence of time period objects based on the specified start, end, and frequency
• period_range() returns a PeriodIndex object containing a sequence of time period objects with the specified frequency and start and end points

Summary

In conclusion, time periods are an important part of time series data analysis and manipulation. Pandas provides the period_range() function to create a sequence of time period objects with specified start and end points and frequency. This function is useful in a wide range of data analysis and manipulation tasks that involve time series data.