How to get the first column of a Pandas DataFrame?

In this short Data Analysis tutorial we’ll learn how to use Python in order to access the first column of a Pandas DataFrame object so we can further process it as needed.

Creating an example DataFrame

import pandas as pd

hiring_dict = {'month' : ['Jan', 'Feb', 'March', 'April'], 'salary':[140, 145, 145, 190], 'days_to_hire': [45, 34, 23, 22]}

hiring = pd.DataFrame (hiring_dict)


Here’s our Data:


Get the first DataFrame column without index

We are able to get one DataFrame column into a Series using the Pandas brackets notation:

month_s = hiring['month']

0      Jan
1      Feb
2    March
3    April
Name: month, dtype: object

As mentioned before, the result is a Pandas Series object.


The result will be:


Select the first column by index (without name)

We can easily get a column or more by index using the iloc indexer:

month_s = hiring.iloc[:,0]

In order to subset the first two columns we’ll make a very small tweak to our Python code:

subset = hiring.iloc[:,0:2]

The result in this case will be a DataFrame containing the two leftmost columns

Get a Pandas DataFrame index

Next, we’ll learn how to retrieve a Pandas DataFrame index.

hir_idx = hiring.index

We can now export the index to a list or to a Numpy array:

my_lst = hir_idx.to_list()


my_array = hir_idx.to_numpy(my_array)

Get the first column values to a Python list

In a similar fashion we can get and export the first column of our DataFrame to a list object:

salaries = hiring['salary'].to_list()

Select the first row of our Pandas DataFrame

For completeness, here’s how you can get the first DataFrame row. For more information read our select first DataFrame row tutorial.


Additional learning