Add multiple columns to Pandas Dataframes

In today’s data wrangling tutorial we will learn how to use Python and the Pandas library to create multiple columns at once in a DataFrame. This is obviously required to speed up your workflow.

We’ll start by importing the required Python libraries and creating a random data set using the Numpy library.

Creating random data

import numpy as np
import pandas as pd
np.random.seed(100)

rand_df = pd.DataFrame(data = np.random.randint(70,100, size = (4,3)), columns = ['score_1', 'score_2', 'score_3'])

rand_df

Here’s our dataset, note that you’ll get different values, as we are using the random() method to generate the data.

	score_1	score_2	score_3
0	78	94	73
1	77	93	85
2	86	80	90
3	72	91	72

Insert multiple columns

Adding multiple columns is quite simple. As an example, we’ll show how to calculate the mean and standard deviation and insert those as columns.

rand_df['avg_score'] = rand_df.mean(axis=1).round(2)
rand_df['std_deviation'] = rand_df.std(axis=1).round(2)

rand_df

Note: we used the round() method to round up the calculated values

Here’s our output:

	score_1	score_2	score_3	avg_score	std_deviation
0	78	94	73	81.67	8.96
1	77	93	85	85.00	6.53
2	86	80	90	85.33	4.11
3	72	91	72	78.33	8.96

Inserting empty columns

In a similar fashion you are able to create empty columns and append those to the DataFrame.

rand_df [['empty1', 'empty2']] = np.nan

Insert columns using the apply() function

We can use apply and involve a lambda function to perform the calculation. Note that if you haven’t imported the Numpy library, you’ll receive a module not found error.

rand_df['avg_score'] = df.apply(lambda x: np.mean(x) , axis=1)
rand_df['std_var'] = df.apply(lambda x: np.std(x) , axis=1)

Sum multiple cols in Pandas

In the same fashion you can go ahead and sum the columns:

rand_df['score_sum'] = rand_df.sum(axis=1)

# Or alternatively, using Apply

rand_df['score_sum'] = rand_df.apply(lambda x: np.sum(x) , axis=1)