xorbits.pandas.DataFrame.transform

DataFrame.transform(func, axis=0, raw=False, result_type=None, args=(), dtypes=None, dtype=None, name=None, output_type=None, index=None, elementwise=None, skip_infer=False, **kwds)

Call func on self producing a DataFrame with the same axis shape as self.

Parameters

• func (function, str, list-like or dict-like) –

  Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.

  Accepted combinations are:

  • function

  • string function name

  • list-like of functions and/or function names, e.g. [np.exp, 'sqrt']

  • dict-like of axis labels -> functions, function names or list-like of such.

• axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

• *args – Positional arguments to pass to func.

• **kwargs – Keyword arguments to pass to func.

Returns

A DataFrame that must have the same length as self.

Return type

DataFrame

:raises ValueError : If the returned DataFrame has a different length than self.:

See also

DataFrame.agg

Only perform aggregating type operations.

DataFrame.apply

Invoke function on a DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Examples

>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})  
>>> df  
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)  
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))  
>>> s  
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])  
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame({  
...     "Date": [
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df  
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby('Date')['Data'].transform('sum')  
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64

>>> df = pd.DataFrame({  
...     "c": [1, 1, 1, 2, 2, 2, 2],
...     "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df  
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df['size'] = df.groupby('c')['type'].transform(len)  
>>> df  
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4
Extra Parameters
----------------
raw : bool, default False
    Determines if row or column is passed as a Series or ndarray object:
    * ``False`` : passes each row or column as a Series to the

This docstring was copied from pandas.core.frame.DataFrame.