You can also use:

df.columns.tolist()

# Import pandas package
import pandas as pd
	
# making data frame
data = pd.read_csv("nba.csv")

# iterating the columns
for col in data.columns:
	print(col)

Let us first load Pandas.

# load pandas
import pandas as pd

And we will use College tuition data from tidytuesday project illustrate extracting column names as a list. Let us load the dataset directly from tidytuesday project’s github page.

data_url="https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-10/tuition_cost.csv"
df = pd.read_csv(data_url)
df.iloc[0:3,0:5]
 
        name    state   state_code  type    degree_length
0   Aaniiih Nakoda College  Montana MT  Public  2 Year
1   Abilene Christian University    Texas   TX  Private 4 Year
2   Abraham Baldwin Agricultural College    Georgia GA  Public  2 Year

We can get the names of columns of Pandas dataframe using Pandas method “columns”.

# Extract Column Names of a Pandas Dataframe
df.columns

Pandas’ columns method returns the names as Pandas Index object.

Index(['name', 'state', 'state_code', 'type', 'degree_length',
       'room_and_board', 'in_state_tuition', 'in_state_total',
       'out_of_state_tuition', 'out_of_state_total'],
      dtype='object')

We can convert the Pandas Index object to list using the tolist() method.

# Extract Column Names as List in Pandas Dataframe
df.columns.tolist()

And now we have Pandas’ dataframe column names as a list.

['name',
 'state',
 'state_code',
 'type',
 'degree_length',
 'room_and_board',
 'in_state_tuition',
 'in_state_total',
 'out_of_state_tuition',
 'out_of_state_total']

Another way to get column names as list is to first convert the Pandas Index object as NumPy Array using the method “values” and convert to list as shown below.

df.columns.values.tolist()

And we would get the Pandas column names as a list.

['name',
 'state',
 'state_code',
 'type',
 'degree_length',
 'room_and_board',
 'in_state_tuition',
 'in_state_total',
 'out_of_state_tuition',
 'out_of_state_total']

This post is part of the series on Pandas 101, a tutorial covering tips and tricks on using Pandas for data munging and analysis.