pandas.api.indexers.BaseIndexer#

classpandas.api.indexers.BaseIndexer(index_array=None, window_size=0, **kwargs)[source] #

Base class for window bounds calculations.

Parameters:
index_arraynp.ndarray, default None

Array-like structure representing the indices for the data points. If None, the default indices are assumed. This can be useful for handling non-uniform indices in data, such as in time series with irregular timestamps.

window_sizeint, default 0

Size of the moving window. This is the number of observations used for calculating the statistic. The default is to consider all observations within the window.

**kwargs

Additional keyword arguments passed to the subclass’s methods.

See also

DataFrame.rolling

Provides rolling window calculations on dataframe.

Series.rolling

Provides rolling window calculations on series.

Examples

>>> frompandas.api.indexersimport BaseIndexer
>>> classCustomIndexer(BaseIndexer):
...  defget_window_bounds(self, num_values, min_periods, center, closed, step):
...  start = np.arange(num_values, dtype=np.int64)
...  end = np.arange(num_values, dtype=np.int64) + self.window_size
...  return start, end
>>> df = pd.DataFrame({"values": range(5)})
>>> indexer = CustomIndexer(window_size=2)
>>> df.rolling(indexer).sum()
 values
0 1.0
1 3.0
2 5.0
3 7.0
4 4.0

Methods

get_window_bounds([num_values, min_periods, ...])

Computes the bounds of a window.

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