# How to calculate the average of one or more columns in a Pandas DataFrame?

In today’s quick tutorial we’ll using Python and the Pandas library to calculate the mean of one or more columns in a Pandas DataFrame.

Let’s get started by prepping our test DataFrame. As usual, we’ll use the auto-generated candidates data.

Here we go:

``````import pandas as pd

print(data)``````

Here’s the result (note that you can copy and paste the following data and use the pd.read_clipboard() method to populate your own dataframe and follow along.

### Find the mean / average of one column

To find the average of one column (Series), we simply type:

``data['salary'].mean()``

The result will be 126.

### Calculate mean of multiple columns

In our case, we can simply invoke the mean() method on the DataFrame itself.

``data['salary'].mean()``

The result will be:

``````salary            126.0
num_candidates     80.5
dtype: float64``````

Chances are that your DataFrame will be wider, and contains several columns. In that case, we’ll first subset our DataFrame by the relevant columns and then calculate the mean.

``````cols = ['salary', 'num_candidates']

data[cols].mean()``````

The result will be similar.

### Moving on: Creating a Dataframe or list from your columns mean values

You can easily turn your mean values into a new DataFrame or to a list:

``````data_mean = pd.DataFrame(data.mean(), columns=['mean_values'])

#create list of mean values
mean_list = data.mean().to_list``````

Or even a simple bar chart that you can use in a PowerPoint deck:

``data.mean().plot(kind='bar');``

Here’s the chart:

### Calculate the mean of you Series with df.describe()

We can use the DataFrame method pd.describe to quickly look into the key statistical calculations of our DataFrame numeric columns – including the mean.

``data.describe().round()``

And the result: