TimeSeriesDataFrame.convert_frequency#

TimeSeriesDataFrame.convert_frequency(freq: Union[str, DateOffset], agg_numeric: str = 'mean', agg_categorical: str = 'first', **kwargs) → TimeSeriesDataFrame[source]#

Convert each time series in the data frame to the given frequency.

This method is useful for two purposes:

  1. Converting an irregularly-sampled time series to a regular time index.

  2. Aggregating time series data by downsampling (e.g., convert daily sales into weekly sales)

Parameters

  • freq (Union[str, pd.DateOffset]) – Frequency to which the data should be converted. See [pandas frequency aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) for supported values.

  • agg_numeric ({"max", "min", "sum", "mean", "median", "first", "last"}, default = "mean") – Aggregation method applied to numeric columns.

  • agg_categorical ({"first", "last"}, default = "first") – Aggregation method applied to categorical columns.

  • **kwargs – Additional keywords arguments that will be passed to pandas.DataFrameGroupBy.resample.

Returns

ts_df – A new time series dataframe with time series resampled at the new frequency. Output may contain missing values represented by NaN if original data does not have information for the given period.

Return type

TimeSeriesDataFrame

Examples

Convert irregularly-sampled time series data to a regular index

>>> ts_df
                    target
item_id timestamp
0       2019-01-01     NaN
        2019-01-03     1.0
        2019-01-06     2.0
        2019-01-07     NaN
1       2019-02-04     3.0
        2019-02-07     4.0
>>> ts_df.convert_frequency(freq="D")
                    target
item_id timestamp
0       2019-01-01     NaN
        2019-01-02     NaN
        2019-01-03     1.0
        2019-01-04     NaN
        2019-01-05     NaN
        2019-01-06     2.0
        2019-01-07     NaN
1       2019-02-04     3.0
        2019-02-05     NaN
        2019-02-06     NaN
        2019-02-07     4.0

Downsample quarterly data to yearly frequency

>>> ts_df
                    target
item_id timestamp
0       2020-03-31     1.0
        2020-06-30     2.0
        2020-09-30     3.0
        2020-12-31     4.0
        2021-03-31     5.0
        2021-06-30     6.0
        2021-09-30     7.0
        2021-12-31     8.0
>>> ts_df.convert_frequency("Y")
                    target
item_id timestamp
0       2020-12-31     2.5
        2021-12-31     6.5
>>> ts_df.convert_frequency("Y", agg_numeric="sum")
                    target
item_id timestamp
0       2020-12-31    10.0
        2021-12-31    26.0