Pandas Frequencies

How this guide benefits you

This guide covers how to use the pandas frequency strings within timetk. Once you understand key frequencies, you can apply them to manipulate time series data like a pro.

1 Pandas Frequencies

Pandas offers a variety of frequency strings, also known as offset aliases, to define the frequency of a time series. Here are some common frequency strings used in pandas:

1. ‘B’: Business Day
2. ‘D’: Calendar day
3. ‘W’: Weekly
4. ‘M’: Month end
5. ‘BM’: Business month end
6. ‘MS’: Month start
7. ‘BMS’: Business month start
8. ‘Q’: Quarter end
9. ‘BQ’: Business quarter end
10. ‘QS’: Quarter start
11. ‘BQS’: Business quarter start
12. ‘A’ or ‘Y’: Year end
13. ‘BA’ or ‘BY’: Business year end
14. ‘AS’ or ‘YS’: Year start
15. ‘BAS’ or ‘BYS’: Business year start
16. ‘H’: Hourly
17. ‘T’ or ‘min’: Minutely
18. ‘S’: Secondly
19. ‘L’ or ‘ms’: Milliseconds
20. ‘U’: Microseconds
21. ‘N’: Nanoseconds

Custom Frequencies:

• You can also create custom frequencies by combining base frequencies, like:
  • ‘2D’: Every 2 days
  • ‘3W’: Every 3 weeks
  • ‘4H’: Every 4 hours
  • ‘1H30T’: Every 1 hour and 30 minutes

Compound Frequencies:

• You can combine multiple frequencies by adding them together.
  • ‘1D1H’: 1 day and 1 hour
  • ‘1H30T’: 1 hour and 30 minutes

Example:

import pandas as pd

# Creating a date range with daily frequency
date_range_daily = pd.date_range(start='2023-01-01', end='2023-01-10', freq='D')

date_range_daily

DatetimeIndex(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04',
               '2023-01-05', '2023-01-06', '2023-01-07', '2023-01-08',
               '2023-01-09', '2023-01-10'],
              dtype='datetime64[ns]', freq='D')

# Creating a date range with 2 days frequency
date_range_two_days = pd.date_range(start='2023-01-01', end='2023-01-10', freq='2D')

date_range_two_days

DatetimeIndex(['2023-01-01', '2023-01-03', '2023-01-05', '2023-01-07',
               '2023-01-09'],
              dtype='datetime64[ns]', freq='2D')

These frequency strings help in resampling, creating date ranges, and handling time-series data efficiently in pandas.

2 Timetk Incorporates Pandas Frequencies

Now that you’ve seen pandas frequencies, you’ll see them pop up in many of the timetk functions.