pyspark.pandas.Series.pandas_on_spark.transform_batch#
- pandas_on_spark.transform_batch(func, *args, **kwargs)#
Transform the data with the function that takes pandas Series and outputs pandas Series. The pandas Series given to the function is of a batch used internally.
See also Transform and apply a function.
Note
the func is unable to access the whole input series. pandas-on-Spark internally splits the input series into multiple batches and calls func with each batch multiple times. Therefore, operations such as global aggregations are impossible. See the example below.
>>> # This case does not return the length of whole frame but of the batch internally ... # used. ... def length(pser) -> ps.Series[int]: ... return pd.Series([len(pser)] * len(pser)) ... >>> df = ps.DataFrame({'A': range(1000)}) >>> df.A.pandas_on_spark.transform_batch(length) c0 0 83 1 83 2 83 ...
Note
this API executes the function once to infer the type which is potentially expensive, for instance, when the dataset is created after aggregations or sorting.
To avoid this, specify return type in
func
, for instance, as below:>>> def plus_one(x) -> ps.Series[int]: ... return x + 1
- Parameters
- funcfunction
Function to apply to each pandas frame.
- *args
Positional arguments to pass to func.
- **kwargs
Keyword arguments to pass to func.
- Returns
- DataFrame
See also
DataFrame.pandas_on_spark.apply_batch
Similar but it takes pandas DataFrame as its internal batch.
Examples
>>> df = ps.DataFrame([(1, 2), (3, 4), (5, 6)], columns=['A', 'B']) >>> df A B 0 1 2 1 3 4 2 5 6
>>> def plus_one_func(pser) -> ps.Series[np.int64]: ... return pser + 1 >>> df.A.pandas_on_spark.transform_batch(plus_one_func) 0 2 1 4 2 6 Name: A, dtype: int64
You can also omit the type hints so pandas-on-Spark infers the return schema as below:
>>> df.A.pandas_on_spark.transform_batch(lambda pser: pser + 1) 0 2 1 4 2 6 Name: A, dtype: int64
You can also specify extra arguments.
>>> def plus_one_func(pser, a, b, c=3) -> ps.Series[np.int64]: ... return pser + a + b + c >>> df.A.pandas_on_spark.transform_batch(plus_one_func, 1, b=2) 0 7 1 9 2 11 Name: A, dtype: int64
You can also use
np.ufunc
and built-in functions as input.>>> df.A.pandas_on_spark.transform_batch(np.add, 10) 0 11 1 13 2 15 Name: A, dtype: int64
>>> (df * -1).A.pandas_on_spark.transform_batch(abs) 0 1 1 3 2 5 Name: A, dtype: int64