Python | Pandas DataFrame.transform
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)
|
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)
|
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)
|
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)
|
Output :
As we can see in the output, the DataFrame.transform()
function has successfully performed the desired operation on the given dataframe.
Last Updated :
21 Feb, 2019
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