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()
>>> import numpy as np
>>> np.all((new_series == series).values) True
>>> series.reset_index(drop=True, inplace=True) # Slower
>>> new_series.reset_index(drop=True, inplace=True) # Faster