In this article we will discuss how to get the sum column values in a pandas dataframe. We will cover the following topics in detail,

- Get the sum of all column values in a dataframe
- Select the column by name and get the sum of all values in that column
- Select the column by position and get the sum of all values in that column

- Get the sum of columns values for selected rows only in Dataframe
- Get the sum of column values in a dataframe based on condition

First of all, we will create a dataframe from list of tuples,

import pandas as pd import numpy as np # List of Tuples students = [('jack', 34, 'Sydney', 155), ('Riti', 31, 'Delhi', 177.5), ('Aadi', 16, 'Mumbai', 81), ('Mohit', 31, 'Delhi', np.NaN), ('Veena', np.NaN, 'Delhi', 144), ('Shaunak', 35, 'Mumbai', 135), ('Shaun', 35, 'Colombo', 111) ] # Create a DataFrame object df = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score']) print(df)

**Output:**

Name Age City Score 0 jack 34.0 Sydney 155.0 1 Riti 31.0 Delhi 177.5 2 Aadi 16.0 Mumbai 81.0 3 Mohit 31.0 Delhi NaN 4 Veena NaN Delhi 144.0 5 Shaunak 35.0 Mumbai 135.0 6 Shaun 35.0 Colombo 111.0

This dataframe contains information about students like their name, age, city and score.

Now letâ€™s see how to get the sum of values in the column â€˜Scoreâ€™ of this dataframe.

## Get the sum of column values in a dataframe

**Select the column by name and get the sum of all values in that column**

### Frequently Asked:

Select a column from a dataframe by the column name and the get the sum of values in that column using the sum() function,

# Get total all values in column 'Score' of the DataFrame total = df['Score'].sum() print(total)

**Output:**

803.5

Here we selected the column â€˜Scoreâ€™ from the dataframe using [] operator and got all the values as Pandas Series object. Then we called the sum() function on that Series object to get the sum of values in it. So, it gave us the sum of values in the column â€˜Scoreâ€™ of the dataframe.

We can also select the column using loc[] and then we can get the sum of values in that column. For examples,

# Select column 'Score' using loc[] and calculate sum of all # values in that column total = df.loc[:, 'Score'].sum() print(total)

**Output:**

803.5

Here we selected the column â€˜Scoreâ€™ as Series object using loc[] and then we called the sum() function on the Series object to get the sum of all values in the column â€˜Scoreâ€™ of the dataframe.

Know more about: Selecting columns by name from the dataframe using the loc[]

## Select the column by position and get the sum of all values in that column

Suppose we donâ€™t have the column name but we know the position of a column in dataframe and we want the sum of values in that column. For that we will select the column by number or position in the dataframe using iloc[] and it will return us the column contents as a Series object. Then we will call the sum() function on that series,

# Get sum of all values in 4th column column_number = 4 total = df.iloc[:, column_number-1:column_number].sum() print(total)

Output:

Score 803.5 dtype: float64

It returned a Series with single value.

Here we selected the 4th column from the dataframe as a Series object using the iloc[] and the called the sum() function on the series object. So, it returned the sum of values in the 4th column i.e. column â€˜Scoreâ€™.

Know more about: Selecting columns by the number from dataframe using the iloc[]

## Get the sum of columns values for selected rows only in Dataframe

Select a column from Dataframe and get the sum of specific entries in that column. For example,

# Select 4th column of dataframe and get sum of first 3 values in that column total = df.iloc[0:3, 3:4].sum() print(total)

Output:

Score 413.5 dtype: float64

It returned a Series with single value.

Here we selected the first 3 rows of the 3rd column of the dataframe and then calculated its sum.

## Get the sum of column values in a dataframe based on condition

Suppose in the above dataframe we want to get the sum of the score of students from Delhi only. For that we need to select only those values from the column â€˜Scoreâ€™ where â€˜Cityâ€™ is Delhi. Letâ€™s see how to do that,

# Get sum of values in a column 'Score' # for those rows only where 'City' is 'Delhi' total = df.loc[df['City'] == 'Delhi', 'Score'].sum() print(total)

Output:

321.5

Using loc[] we selected the column â€˜Scoreâ€™ but for only those rows where column â€˜Cityâ€™ has value â€˜Delhiâ€™. Then we called the sum() function on the series object to get the sum of scores of students from â€˜Delhiâ€™. So, basically we selected rows from a dataframe that satisfy our condition and then selected the values of column â€˜Scoreâ€™ for those rows only. We did that in a single expression using loc[].

Know more about:Â loc[] & iloc[]

**Conclusion:**

These were the different ways to get the sum of all or specific values in a dataframe column in Pandas.