pandas.Series.transform#

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

Callfunc on self producing a Series 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 eitherwork when passed a Series or when passed to Series.apply. If funcis 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’}

Unused. Parameter needed for compatibility with DataFrame.

*args

Positional arguments to pass tofunc.

**kwargs

Keyword arguments to pass tofunc.

Returns:

Series

A Series that must have the same length as self.

Raises:

ValueErrorIf the returned Series has a different length than self.

See also

Series.agg

Only perform aggregating type operations.

Series.apply

Invoke function on a Series.

Notes

Functions that mutate the passed object can produce unexpectedbehavior or errors and are not supported. SeeMutating with User Defined Function (UDF) methodsfor more details.

Examples

>>>df=pd.DataFrame({'A':range(3),'B':range(1,4)})>>>df   A  B0  0  11  1  22  2  3>>>df.transform(lambdax:x+1)   A  B0  1  21  2  32  3  4

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

>>>s=pd.Series(range(3))>>>s0    01    12    2dtype: int64>>>s.transform([np.sqrt,np.exp])       sqrt        exp0  0.000000   1.0000001  1.000000   2.7182822  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  Data0  2015-05-08     51  2015-05-07     82  2015-05-06     63  2015-05-05     14  2015-05-08    505  2015-05-07   1006  2015-05-06    607  2015-05-05   120>>>df.groupby('Date')['Data'].transform('sum')0     551    1082     663    1214     555    1086     667    121Name: Data, dtype: int64

>>>df=pd.DataFrame({..."c":[1,1,1,2,2,2,2],..."type":["m","n","o","m","m","n","n"]...})>>>df   c type0  1    m1  1    n2  1    o3  2    m4  2    m5  2    n6  2    n>>>df['size']=df.groupby('c')['type'].transform(len)>>>df   c type size0  1    m    31  1    n    32  1    o    33  2    m    44  2    m    45  2    n    46  2    n    4