Python | Pandas DataFrame.transform
Last Updated :
21 Feb, 2019
Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas.
Pandas DataFrame.transform() function call func on self producing a DataFrame with transformed values and that has the same axis length as self.
Syntax: DataFrame.transform(func, axis=0, *args, **kwargs)
Parameter :
func : Function to use for transforming the data
axis : {0 or ‘index’, 1 or ‘columns’}, default 0
*args : Positional arguments to pass to func.
**kwargs : Keyword arguments to pass to func.
Returns : DataFrame
Example #1 : Use DataFrame.transform() function to add 10 to each element in the dataframe.
import pandas as pd
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
"B":[7, 2, 54, 3, None],
"C":[20, 16, 11, 3, 8],
"D":[14, 3, None, 2, 6]})
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
df.index = index_
print(df)
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Output :
Now we will use DataFrame.transform() function to add 10 to each element of the dataframe.
result = df.transform(func = lambda x : x + 10)
print(result)
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Output :
As we can see in the output, the DataFrame.transform() function has successfully added 10 to each element of the given Dataframe.
Example #2 : Use DataFrame.transform() function to find the square root and the result of euler’s number raised to each element of the dataframe.
import pandas as pd
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
"B":[7, 2, 54, 3, None],
"C":[20, 16, 11, 3, 8],
"D":[14, 3, None, 2, 6]})
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
df.index = index_
print(df)
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Output :
Now we will use DataFrame.transform() function to find the square root and the result of euler’s number raised to each element of the dataframe.
result = df.transform(func = ['sqrt', 'exp'])
print(result)
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Output :
As we can see in the output, the DataFrame.transform() function has successfully performed the desired operation on the given dataframe.
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