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.axes attribute access a group of rows and columns by label(s) or a boolean array in the given DataFrame.
Syntax: DataFrame.axes
Parameter : None
Returns : list
Example #1: Use DataFrame.axes attribute to return a list containing the axes labels of the dataframe.
# importing pandas as pd import pandas as pd # Creating the DataFrame df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71], 'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'], 'Age':[14, 25, 55, 8, 21]}) # Create the index index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5'] # Set the index df.index = index_ # Print the DataFrame print(df) |
Output :
Now we will use DataFrame.axes attribute to return the axes labels of the dataframe.
# return the axes labels of the dataframe result = df.axes # Print the result print(result) |
Output :
As we can see in the output, the DataFrame.axes attribute has successfully returned a list containing the axes labels of the dataframe.
Example #2: Use DataFrame.axes attribute to return a list containing the axes labels of the dataframe.
# importing pandas as pd import pandas as pd # Creating the DataFrame 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]}) # Create the index index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5'] # Set the index df.index = index_ # Print the DataFrame print(df) |
Output :
Now we will use DataFrame.axes attribute to return the axes labels of the dataframe.
# return the axes labels of the dataframe result = df.axes # Print the result print(result) |
Output :
As we can see in the output, the DataFrame.axes attribute has successfully returned a list containing the axes labels of the dataframe.
