Python | Pandas DataFrame.columns

This article explores the Pandas DataFrame.columns attribute. A Pandas dataframe is a two-dimensional, mutable, and potentially heterogeneous data structure with labeled rows and columns. It serves as a container for Series objects and is a fundamental structure in Pandas, facilitating various operations with aligned row and column labels.

Pandas DataFrame.columns Syntax

Syntax: DataFrame.columns

Parameter : None

Returns : column names

This attribute does not require any parameters to be passed. When called on a data frame using the syntax DataFrame.columns, it returns the names of the columns present in that data frame.

What is Pandas DataFrame.columns?

In Pandas, DataFrame.columns is an attribute that provides access to the column labels of a data frame. It returns an Index object representing the names of the columns in the DataFrame. This attribute is used to view, manipulate, or assign new column labels to a Pandas DataFrame, allowing users to work with and reference specific columns within the tabular data structure. Pandas DataFrame.columns attribute returns the column labels of the given Dataframe.

DataFrame.columns to retrieve DataFrame column labels

In this example below code uses the Pandas library to create a DataFrame named df with columns ‘Weight’, ‘Name’, and ‘Age’. It also sets a custom index for the rows labeled as ‘Row_1’ to ‘Row_5’. Finally, it prints the resulting data frame.

Python3

# 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

       Weight    Name  Age
Row_1      45     Sam   14
Row_2      88  Andrea   25
Row_3      56    Alex   55
Row_4      15   Robin    8
Row_5      71     Kia   21

Now we will use DataFrame.columns attribute to return the column labels of the given data frame.

Python3

# return the column labels
result = df.columns

# Print the result
print(result)

Output :

Index(['Weight', 'Name', 'Age'], dtype='object')

As we can see in the output, the DataFrame.columns attribute has successfully returned all of the column labels of the given data frame.

DataFrame.columns to retrieve DataFrame column labels

In this example below code uses Pandas to create a DataFrame named df with columns ‘A’, ‘B’, ‘C’, and ‘D’. It assigns a custom index labeled as ‘Row_1’ to ‘Row_5’ to the rows and then prints the resulting DataFrame.

Python3

# 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

        A    B   C     D
Row_1  12  7.0  20  14.0
Row_2   4  2.0  16   3.0
Row_3   5 54.0  11   NaN
Row_4 NaN  3.0   3   2.0
Row_5   1  NaN   8   6.0

Now we will use DataFrame.columns attribute to return the column labels of the given data frame.

Python3

# return the column labels
result = df.columns

# Print the result
print(result)

Output

Index(['A', 'B', 'C', 'D'], dtype='object')

As we can see in the output, the DataFrame.columns attribute has successfully returned all of the column labels of the given data frame.