import pytimetk as tk
import pandas as pd
dates = pd.date_range(start='2021-01-01', end='2024-01-01', freq='MS')
tk.get_trend_frequency(dates) '5Y'get_trend_frequency
get_trend_frequency(idx, force_regular=False, numeric=False, engine='pandas')
The get_trend_frequency function returns the trend period of a given time series or datetime index.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
idx |
Union[pd.Series, pd.DatetimeIndex] | The idx parameter can be either a pandas Series or a pandas DatetimeIndex. It represents the time index for which you want to calculate the trend frequency. |
required |
force_regular |
bool | force_regular is a boolean parameter that determines whether to force the frequency to be regular. If set to True, the function will try to find a regular frequency even if the data is irregular. If set to False, the function will return the actual frequency of the data. | False |
numeric |
bool | The numeric parameter is a boolean flag that determines whether the output should be in numeric format or a string Pandas Frequency Alias. If numeric is set to True, the output will be a numeric representation of the trend period. If numeric is set to False (default), the output will |
False |
engine |
str | The engine parameter is used to specify the engine to use for generating a date summary. It can be either โpandasโ or โpolarsโ. - The default value is โpandasโ. - When โpolarsโ, the function will internally use the polars library for generating the time scale information. |
'pandas' |
Returns
| Type | Description |
|---|---|
The function get_trend_frequency returns the trend period based on the |
input index. If the index is a pd.DatetimeIndex, it is converted to a pd.Series with the name โidxโ. The function then calculates the summary frequency of the index using the get_frequency_summary function. It determines the scale and unit of the frequency and adjusts the unit if the scale is |