transform(self, func: 'AggFuncType', axis: 'Axis' = 0, *args, **kwargs) -> 'DataFrame'
Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation
for more details.
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.
If 0 or 'index': apply function to each column. If 1 or 'columns': apply function to each row.
Positional arguments to pass to func
.
Keyword arguments to pass to func
.
A DataFrame that must have the same length as self.
Call func
on self producing a DataFrame with the same axis shape as self.
DataFrame.agg
Only perform aggregating type operations.
DataFrame.apply
Invoke function on a DataFrame.
>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})This example is valid syntax, but we were not able to check execution
... 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:
This example is valid syntax, but we were not able to check execution>>> s = pd.Series(range(3))This example is valid syntax, but we were not able to check execution
... 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:
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame({This example is valid syntax, but we were not able to check execution
... "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: int64This example is valid syntax, but we were not able to check execution
>>> df = pd.DataFrame({This example is valid syntax, but we were not able to check execution
... "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)See :
... 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
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.apply
pandas.core.frame.DataFrame.aggregate
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