In this tutorial, we will look at how to get the sum of one or more columns in a pandas dataframe.

## How to calculate the sum of pandas column?

You can use the pandas series `sum()`

function to get the sum of a single column or the pandas dataframe `sum()`

function to get the sum of each column in the dataframe. The following is the syntax:

# sum of single column df['Col'].sum() # sum of all columns in dataframe df.sum()

Let’s create a sample dataframe that we will be using throughout this tutorial to demonstrate the usage of the methods and syntax mentioned.

import pandas as pd # create a dataframe df = pd.DataFrame({ 'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0], 'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4], 'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5], 'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2], 'sepices': ['setosa']*8 }) # display the dataframe print(df)

Output:

sepal_length sepal_width petal_length petal_width sepices 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa 5 5.4 3.9 1.7 0.4 setosa 6 4.6 3.4 1.4 0.3 setosa 7 5.0 3.4 1.5 0.2 setosa

The sample dataframe is taken form a section of the Iris dataset. This sample has petal and sepal dimensions of eight data points of the “Setosa” species.

### Sum of a single column

You can use the pandas series `sum()`

function to get the sum of values in individual columns (which essentially are pandas series). For example, let’s get the sum of the “sepal_length” column in the above dataframe.

# sum of sepal_length column print(df['sepal_length'].sum())

Output:

39.3

You see that we get the total of all values in the “sepal_length” column as the scaler value 39.3.

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### Sum of more than one columns

To get the sum of multiple columns together, first, create a dataframe with the columns you want to calculate the sum for and then apply the pandas dataframe `sum()`

function. For example, let’s get the sum of the values in the columns “sepal_length” and “sepal_width”.

# sum of more than one columns print(df[['sepal_length', 'sepal_width']].sum())

Output:

sepal_length 39.3 sepal_width 27.1 dtype: float64

Here, we first created a subset of the dataframe “df” with only the columns “sepal_length” and “sepal_width” and then applied the sum function. You can see that we get the sum for both the columns. Note that we get the result as a pandas series.

### Sum of all the columns

To get the sum of all the columns, use the same method as above but this time on the entire dataframe. Let’s use this function on the dataframe “df” created above.

# sum of all the columns print(df.sum(numeric_only=True))

Output:

sepal_length 39.3 sepal_width 27.1 petal_length 11.6 petal_width 1.9 dtype: float64

We get the sum of all the numerical columns present in the dataframe. Note that we passed `numeric_only=True`

to calculate the sum only for the numeric columns.

For more on the function, refer to its documentation.

You can similar methods to get the descriptive statistics like the mean, median, standard deviation, etc. of values in pandas columns.