modin.pandas.Series.cache_result¶

Series.cache_result(inplace: bool = False) → Optional[Series][source]¶

Persists the current Snowpark pandas Series to a temporary table to improve the latency of subsequent operations.

Parameters:

inplace – bool, default False Whether to perform the materialization inplace.

Returns:

Snowpark pandas Series or None

Cached Snowpark pandas Series or None if inplace=True.

Note

• The temporary table produced by this method lasts for the duration of the session.

Examples:

Let’s make a Series using a computationally expensive operation, e.g.: >>> series = pd.concat([pd.Series([i]) for i in range(30)])

Due to Snowpark pandas lazy evaluation paradigm, every time this Series is used, it will be recomputed - causing every subsequent operation on this Series to re-perform the 30 unions required to produce it. This makes subsequent operations more expensive. The cache_result API can be used to persist the Series to a temporary table for the duration of the session - replacing the nested 30 unions with a single read from a table.

>>> new_series = series.cache_result()

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>>> import numpy as np

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>>> np.all((new_series == series).values)  
True

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>>> series.reset_index(drop=True, inplace=True) # Slower

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>>> new_series.reset_index(drop=True, inplace=True) # Faster

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