pandas.DataFrame.transform#

DataFrame.transform(func, axis=0, *args, **kwargs)[source] #

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

Parameters:

funcfunction, 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:

DataFrame

A DataFrame that must have the same length as self.

Raises:

ValueErrorIf 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 Mutating with User Defined Function (UDF) methods 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年05月08日", "2015年05月07日", "2015年05月06日", "2015年05月05日",
...  "2015年05月08日", "2015年05月07日", "2015年05月06日", "2015年05月05日"],
...  "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
 Date Data
0 2015年05月08日 5
1 2015年05月07日 8
2 2015年05月06日 6
3 2015年05月05日 1
4 2015年05月08日 50
5 2015年05月07日 100
6 2015年05月06日 60
7 2015年05月05日 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