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)

print(hiring)

Here’s our Data:

monthsalarydays_to_hire
0Jan14045
1Feb14534
2March14523
3April19022

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']

print(month_s)
0      Jan
1      Feb
2    March
3    April
Name: month, dtype: object

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

type(month_s)

The result will be:

pandas.core.series.Seriespandas.core.series.Series

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()

#or

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.

hiring.iloc[0]

Additional learning