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.transpose() function transpose index and columns of the dataframe. It reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa.
Syntax: DataFrame.transpose(*args, **kwargs)
Parameter :
copy : If True, the underlying data is copied. Otherwise (default), no copy is made if possible.
*args, **kwargs : Additional keywords have no effect but might be accepted for compatibility with numpy.Returns : The transposed DataFrame
Example #1: Use DataFrame.transpose() function to find the transpose of the given dataframe.
# importing pandas as pdimport pandas as pd # Creating the DataFramedf = pd.DataFrame({'Weight':[45, 88, 56, 15, 71], 'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'], 'Age':[14, 25, 55, 8, 21]}) # Create the indexindex_ = pd.date_range('2010-10-09 08:45', periods = 5, freq ='H') # Set the indexdf.index = index_ # Print the DataFrameprint(df) |
Output :
Now we will use DataFrame.transpose() function to find the transpose of the given dataframe.
# return the transposeresult = df.transpose() # Print the resultprint(result) |
Output :
As we can see in the output, the DataFrame.transpose() function has successfully returned the transpose of the given dataframe.
Example #2: Use DataFrame.transpose() function to find the transpose of the given dataframe.
# importing pandas as pdimport pandas as pd # Creating the DataFramedf = 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]}) # Create the indexindex_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5'] # Set the indexdf.index = index_ # Print the DataFrameprint(df) |
Output :
Now we will use DataFrame.transpose() function to find the transpose of the given dataframe.
# return the transposeresult = df.transpose() # Print the resultprint(result) |
Output :
As we can see in the output, the DataFrame.transpose() function has successfully returned the transpose of the given dataframe.
