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.
Step # 1: Define 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:
| month | salary | days_to_hire | |
|---|---|---|---|
| 0 | Jan | 140 | 45 |
| 1 | Feb | 145 | 34 |
| 2 | March | 145 | 23 |
| 3 | April | 190 | 22 |
Step # 2: 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
Step # 3: 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
Step # 4: 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()FAQ
Get a Pandas DataFrame index
Use the code below to retrieve a Pandas DataFrame index.
hir_idx = hiring.indexWe 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)How to 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. But in a nutshell, the code is very simple:
hiring.iloc[0]