maxframe.dataframe.Series.mf.flatmap#

Series.mf.flatmap(func: Callable, dtypes=None, dtype=None, name=None, args=(), **kwargs)#

Apply the given function to each row and then flatten results. Use this method if your transformation returns multiple rows for each input row.

This function applies a transformation to each element of the Series, where the transformation can return zero

or multiple values, effectively flattening Python generator, list-liked collections and DataFrame.

Parameters:

  • func (Callable) – Function to apply to each element of the Series. It should accept a scalar value (or an array if raw=True) and return a list or iterable of values.

  • dtypes (Series, default None) – Specify dtypes of returned DataFrame. Can’t work with dtype.

  • dtype (numpy.dtype, default None) – Specify dtype of returned Series. Can’t work with dtypes.

  • name (str, default None) – Specify name of the returned Series.

  • args (tuple) – Positional arguments to pass to func.

  • **kwargs – Additional keyword arguments to pass as keywords arguments to func.

Returns:

Result of DataFrame when dtypes specified, else Series.

Return type:

DataFrame or Series

Notes

The func must return an iterable of values for each input element. If dtypes is specified, flatmap will return a DataFrame, if dtype and name is specified, a Series will be returned.

The index of the resulting DataFrame/Series will be repeated based on the number of output rows generated by func.

Examples

>>> import numpy as np
>>> import maxframe.dataframe as md
>>> df = md.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> df.execute()
   A  B
0  1  4
1  2  5
2  3  6

Define a function that takes a number and returns a list of two numbers:

>>> def generate_values_array(x):
...     return [x * 2, x * 3]

Specify dtype with a function which returns list to return more elements as a Series:

>>> df['A'].mf.flatmap(generate_values_array, dtype="int", name="C").execute()
    0    2
    0    3
    1    4
    1    6
    2    6
    2    9
    Name: C, dtype: int64

Specify dtypes to return multi columns as a DataFrame:

>>> def generate_values_in_generator(x):
...     yield pd.Series([x * 2, x * 4])
...     yield pd.Series([x * 3, x * 5])

>>> df['A'].mf.flatmap(generate_values_in_generator, dtypes={"A": "int", "B": "int"}).execute()
       A   B
    0  2   4
    0  3   5
    1  4   8
    1  6  10
    2  6  12
    2  9  15
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