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Pandas DataFrame.transformWe can define Pandas DataFrame as a two-dimensional size-mutable, heterogeneous tabular data structure with some labeled axes (rows and columns). Performing the arithmetic operations will align both row and column labels. It can be considered as a dict-like container for Series objects. The main task of Pandas DataFrame.transform() function is to self produce a DataFrame with its transformed values and it has the same axis length as self. Syntax:DataFrame.transform(func, axis=0, *args, **kwargs) Parameters :func : It is a function that is used for transforming the data. axis : Refers to 0 or 'index', 1 or 'columns', default value 0. *args: It is a positional arguments that is to be passed to a func. **kwargs : It is a keyword arguments that is to be passed to a func. Returns:It returns the DataFrame that must have same length as self. Example 1 : Use DataFrame.transform() function to add 10 to each element in the dataframe.
# importing pandas as pd
importpandas as pd
# Creating the DataFrame
info =pd.DataFrame({"P":[8, 2, 9, None, 3],
"Q":[4, 14, 12, 22, None],
"R":[2, 5, 7, 16, 13],
"S":[16, 10, None, 19, 18]})
# Create the index
index_ =['A_Row', 'B_Row', 'C_Row', 'D_Row', 'E_Row']
# Set the index
info.index =index_
# Print the DataFrame
print(info)
Output: P Q R S A_Row 8.0 4.0 2.0 16.0 B_Row 2.0 14.0 5.0 10.0 C_Row 9.0 12.0 7.0 NaN D_RowNaN 22.0 16.0 19.0 E_Row 3.0NaN 13.0 18.0 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.
# importing pandas as pd
importpandas as pd
# Creating the DataFrame
info =pd.DataFrame({"P":[8, 2, 9, None, 3],
"Q":[4, 14, 12, 22, None],
"R":[2, 5, 7, 16, 13],
"S":[16, 10, None, 19, 18]})
# Create the index
index_ =['A_Row', 'B_Row', 'C_Row', 'D_Row', 'E_Row']
# Set the index
info.index =index_
# Print the DataFrame
print(info)
Output: P Q R S A_Row 88.0 14.0 12.0 16.0 B_Row 12.0 14.0 15.0 10.0 C_Row 19.0 22.0 17.0 NaN D_RowNaN 21.0 16.0 19.0 E_Row 13.0NaN 13.0 18.0
Next TopicDataFrame.transpose()
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