## Summing Rows in a Pandas DataFrame Based on Criteria

Have you ever wished there was an easy way to find the sum of specific rows in a pandas DataFrame? Well, you’re in luck! In this article, we’ll be discussing how to sum rows in a pandas DataFrame based on criteria.

## Syntax for Finding the Sum of Rows that Meet Some Criteria

Let’s start by discussing the syntax for finding the sum of rows that meet some criteria. In pandas, we can use the .loc function to filter our DataFrame based on a specific condition, and then use the .sum function to sum the rows that meet that condition.

Here’s an example:

“`

## import pandas as pd

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘A’, ‘B’, ‘C’, ‘A’, ‘B’, ‘C’],

‘Score’: [10, 15, 12, 8, 9, 11]})

# sum the scores of all rows where the team is ‘A’

sum_scores = df.loc[df[‘Team’] == ‘A’, ‘Score’].sum()

## print(sum_scores)

“`

The output of this code will be `18`, which is the sum of the scores for all rows where the team is ‘A’.

Example 1: Finding the Sum of One Column Based on the Team

Now let’s look at an example that finds the sum of one column based on the team.

This is similar to the previous example, but instead of summing a specific set of rows, we’ll be summing the scores for each team.

“`

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘A’, ‘B’, ‘C’, ‘A’, ‘B’, ‘C’],

‘Score’: [10, 15, 12, 8, 9, 11]})

# sum the scores of each team

sum_scores = df.groupby([‘Team’])[‘Score’].sum()

## print(sum_scores)

“`

## The output of this code will be:

“`

## Team

## A 18

## B 24

## C 23

Name: Score, dtype: int64

“`

This shows us the sum of the scores for each team.

Example 2: Finding the Sum of Multiple Columns Based on the Team

What if we wanted to find the sum of multiple columns based on the team?

This can also be done using the .groupby function.

“`

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘A’, ‘B’, ‘C’, ‘A’, ‘B’, ‘C’],

‘Score 1’: [10, 15, 12, 8, 9, 11],

‘Score 2’: [5, 8, 7, 4, 6, 3]})

# sum the scores of each team

sum_scores = df.groupby([‘Team’])[‘Score 1’, ‘Score 2’].sum()

## print(sum_scores)

“`

## The output of this code will be:

“`

Score 1 Score 2

## Team

## A 18 9

## B 24 14

## C 23 10

“`

This shows us the sum of the scores for each team, broken down by column.

Example 3: Finding the Sum of All Columns Based on the Team

Finally, what if we wanted to find the sum of all columns based on the team?

We can accomplish this by first grouping the DataFrame by team, and then using the .sum function again.

“`

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘A’, ‘B’, ‘C’, ‘A’, ‘B’, ‘C’],

‘Score 1’: [10, 15, 12, 8, 9, 11],

‘Score 2’: [5, 8, 7, 4, 6, 3]})

# sum all columns of each team

sum_scores = df.groupby([‘Team’]).sum()

## print(sum_scores)

“`

## The output of this code will be:

“`

Score 1 Score 2

## Team

## A 18 9

## B 24 14

## C 23 10

“`

This shows us the sum of all columns for each team.

## Working with a Sample Pandas DataFrame

Now that we’ve covered how to sum rows in a pandas DataFrame based on criteria, let’s talk about creating a sample pandas DataFrame.

## Creating a Sample Pandas DataFrame

A sample pandas DataFrame can be created in a variety of ways. One common method is to use a dictionary to define the column names and values.

Here’s an example:

“`

## import pandas as pd

# create a DataFrame

df = pd.DataFrame({‘Name’: [‘John’, ‘Jane’, ‘Jim’, ‘Jenny’],

‘Age’: [24, 31, 45, 19],

‘Gender’: [‘Male’, ‘Female’, ‘Male’, ‘Female’]})

## print(df)

“`

## The output of this code will be:

“`

Name Age Gender

## 0 John 24 Male

## 1 Jane 31 Female

## 2 Jim 45 Male

## 3 Jenny 19 Female

“`

## Viewing the Created DataFrame

Once we’ve created a sample pandas DataFrame, we may want to view it to confirm that it was created correctly. We can do this using the .head() function, which returns the first few rows of the DataFrame.

“`

## import pandas as pd

# create a DataFrame

df = pd.DataFrame({‘Name’: [‘John’, ‘Jane’, ‘Jim’, ‘Jenny’],

‘Age’: [24, 31, 45, 19],

‘Gender’: [‘Male’, ‘Female’, ‘Male’, ‘Female’]})

# view the first few rows of the DataFrame

print(df.head())

“`

## The output of this code will be:

“`

Name Age Gender

## 0 John 24 Male

## 1 Jane 31 Female

## 2 Jim 45 Male

## 3 Jenny 19 Female

“`

## Conclusion

In this article, we’ve covered how to sum rows in a pandas DataFrame based on criteria, and how to create and view a sample pandas DataFrame. Hopefully, this information will be helpful for anyone working with pandas DataFrames.

Happy coding!

In this expansion, we will dive deeper into using the SUMIF function in pandas. We will discuss two examples: finding the sum of one column using a SUMIF function and finding the sum of multiple columns using a SUMIF function.

Example 1: Performing a SUMIF Function on One Column

Let’s consider a scenario where we have a pandas DataFrame that stores information about the performance of different basketball teams. In this DataFrame, we have a column that stores the names of the teams and a column that stores the number of points each team scored in a game.

We want to find the sum of points for each team using a SUMIF function. In pandas, we can use the groupby function to group the rows of the DataFrame by team and then apply the SUM function to find the sum of points for each team.

The SUMIF function can be used to apply the sum to specific rows based on certain criteria. Here’s an example:

“`

## import pandas as pd

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘Lakers’, ‘Warriors’, ‘Nets’, ‘Bucks’, ‘Clippers’, ‘Suns’],

‘Points’: [120, 110, 107, 98, 112, 116]})

# create a function that defines the criteria for the SUMIF function

def sum_points(team):

return df[df[‘Team’] == team][‘Points’].sum()

# apply the SUMIF function to each team using the groupby function

sum_points_by_team = df.groupby([‘Team’])[‘Team’].apply(sum_points)

## print(sum_points_by_team)

“`

## The output of this code will be:

“`

## Team

## Bucks 98

## Clippers 112

## Lakers 120

## Nets 107

## Suns 116

## Warriors 110

Name: Team, dtype: int64

“`

As you can see, this code groups the rows of the DataFrame by team and applies the SUMIF function to find the sum of points for each team. Example 2: Performing a SUMIF Function on Multiple Columns

In the previous example, we used the SUMIF function to find the sum of points for each team.

But what if we wanted to find the sum of points and rebounds for each team using a SUMIF function? In this case, we can modify the previous example to include the rebounds column.

“`

## import pandas as pd

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘Lakers’, ‘Warriors’, ‘Nets’, ‘Bucks’, ‘Clippers’, ‘Suns’],

‘Points’: [120, 110, 107, 98, 112, 116],

‘Rebounds’: [45, 52, 39, 41, 47, 54]})

# create a function that defines the criteria for the SUMIF function

def sum_points_and_rebounds(team):

return pd.Series([df[df[‘Team’] == team][‘Points’].sum(),

df[df[‘Team’] == team][‘Rebounds’].sum()], index=[‘Points’, ‘Rebounds’])

# apply the SUMIF function to each team using the groupby function

sum_points_and_rebounds_by_team = df.groupby([‘Team’])[‘Team’].apply(sum_points_and_rebounds)

## print(sum_points_and_rebounds_by_team)

“`

## The output of this code will be:

“`

Points Rebounds

## Team

## Bucks 98 41

## Clippers 112 47

## Lakers 120 45

## Nets 107 39

## Suns 116 54

## Warriors 110 52

“`

As you can see, this code applies the SUMIF function to find the sum of points and rebounds for each team. We’ve modified the sum_points function in the previous example to include the rebounds column, and we create a pd.Series to return the sum of both columns.

## Conclusion

In this expansion, we’ve covered two examples of using the SUMIF function in pandas: finding the sum of one column using a SUMIF function and finding the sum of multiple columns using a SUMIF function. These examples should help you better understand how to apply the SUMIF function in pandas to find the sums of specific rows in a DataFrame based on certain criteria.

In this expansion, we will focus on Example 3, which involves finding the sum of all columns for each team using a SUMIF function, and we will also provide additional resources for working with pandas DataFrames. Example 3: Performing a SUMIF Function on All Columns

Let’s consider a scenario where we have a pandas DataFrame that stores information about different basketball teams, including the number of points and rebounds they scored in a game, the number of assists, and other statistics.

We want to find the sum of all columns for each team using a SUMIF function. In pandas, we can use the groupby function to group the rows of the DataFrame by team and then apply the SUM function to find the sum of all columns for each team.

To apply the SUMIF function to all columns, we can modify the previous example to include all columns in the pd.Series that is returned by the function. “`

## import pandas as pd

# create a DataFrame

df = pd.DataFrame({‘Team’: [‘Lakers’, ‘Warriors’, ‘Nets’, ‘Bucks’, ‘Clippers’, ‘Suns’],

‘Points’: [120, 110, 107, 98, 112, 116],

‘Rebounds’: [45, 52, 39, 41, 47, 54],

‘Assists’: [25, 16, 20, 14, 22, 18]})

# create a function that defines the criteria for the SUMIF function

def sum_all_stats(team):

return df[df[‘Team’] == team].sum()

# apply the SUMIF function to each team using the groupby function

sum_all_stats_by_team = df.groupby([‘Team’]).apply(sum_all_stats)

## print(sum_all_stats_by_team)

“`

## The output of this code will be:

“`

Team Points Rebounds Assists

## Team

Bucks Bucks 98.0 41.0 14.0

Clippers Clippers 112.0 47.0 22.0

Lakers Lakers 120.0 45.0 25.0

Nets Nets 107.0 39.0 20.0

Suns Suns 116.0 54.0 18.0

Warriors Warriors 110.0 52.0 16.0

“`

As you can see, this code applies the SUMIF function to find the sum of all columns for each team. We define the function sum_all_stats that returns a pd.Series that includes the sum of all columns, and we apply this function to each team using the groupby function.

## Additional Resources

Working with pandas DataFrames can be challenging, especially for beginners. Fortunately, there are many resources available online to help you learn how to work with pandas.

## Here are a few additional resources that you may find helpful:

1. Pandas Documentation: The official pandas documentation is a great resource for learning about pandas.

It includes a comprehensive user guide, API reference, and a wealth of tutorials and examples. 2.

Pandas Cookbook: The Pandas Cookbook is a collection of practical recipes for working with pandas. It covers a wide range of topics, from basic data wrangling to advanced visualization and machine learning.

3. DataCamp: DataCamp offers a wide range of online courses on data science topics, including pandas.

Their courses are interactive and include hands-on exercises to help you learn by doing. 4.

Pandas Exercises: Pandas Exercises is a website that provides a collection of exercises to help you practice working with pandas. It includes a variety of exercises, from basic data manipulation to more advanced topics like time series analysis.

5. Stack Overflow: Stack Overflow is a popular online community where programmers ask and answer questions about coding.

There are many questions and answers related to pandas, so it can be a great resource for troubleshooting problems.

## Conclusion

In this expansion, we’ve covered Example 3, which demonstrates how to apply the SUMIF function to all columns in a pandas DataFrame. We’ve also provided five additional resources for working with pandas DataFrames that can help you learn more about this powerful tool.

Whether you’re just starting out with pandas or you’re an experienced data analyst, these resources can help you become more proficient in working with pandas DataFrames. In this article, we covered the use of SUMIF function in pandas DataFrame to find the sum of specific rows based on some criteria.

We discussed three examples: finding the sum of one column, multiple columns, and all columns using a SUMIF function. We also provided additional resources for working with pandas DataFrames.

pandas is a powerful tool that can be a great asset to data analysts. With the knowledge of the SUMIF function, one can easily manipulate data and obtain useful insights.

We hope this article has helped you better understand how to use the SUMIF function in pandas and provided additional resources to help you become proficient in working with pandas DataFrames.