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You are here: Home / Python / How to Group By Columns and Find Sum in pandas DataFrame

How to Group By Columns and Find Sum in pandas DataFrame

October 13, 2022 Leave a Comment

To group by multiple columns and then find the sum of rows in a pandas DataFrame, you can use the groupby() and sum() functions.

import pandas as pd

df = pd.DataFrame({"animal_type":["dog","cat","dog","cat","dog","dog","cat","cat","dog"], "gender":["F","F","F","F","M","M","M","F","M"], "age":[1,2,3,4,5,6,7,8,9], "weight":[10,20,15,20,25,10,15,30,40]})

print(df)
print(df.groupby(["animal_type","gender"])["age"].sum().rename('age_sum').reset_index())

#Output:
  animal_type gender  age  weight
0         dog      F    1      10
1         cat      F    2      20
2         dog      F    3      15
3         cat      F    4      20
4         dog      M    5      25
5         dog      M    6      10
6         cat      M    7      15
7         cat      F    8      30
8         dog      M    9      40

  animal_type gender  age_sum
0         cat      F       14
1         cat      M        7
2         dog      F        4
3         dog      M       20

When working with data, it is very useful to be able to group and aggregate data by multiple columns to understand the various segments of our data.

One such case is if you want to group your data and get the sum of a variable for each group.

To get the sum of a variable by groups of columns in a pandas DataFrame, you can use the groupby() and sum() functions.

Below is a simple example showing you how you can group by and then get the sum of a variable for each group in a pandas DataFrame in Python.

In the example below, I’ve renamed the sum of rows to ‘age_sum’ and then reset the index so that we can work with the resulting DataFrame easier.

import pandas as pd

df = pd.DataFrame({"animal_type":["dog","cat","dog","cat","dog","dog","cat","cat","dog"], "gender":["F","F","F","F","M","M","M","F","M"], "age":[1,2,3,4,5,6,7,8,9], "weight":[10,20,15,20,25,10,15,30,40]})

print(df)
print(df.groupby(["animal_type","gender"])["age"].sum().rename('age_sum').reset_index())

#Output:
  animal_type gender  age  weight
0         dog      F    1      10
1         cat      F    2      20
2         dog      F    3      15
3         cat      F    4      20
4         dog      M    5      25
5         dog      M    6      10
6         cat      M    7      15
7         cat      F    8      30
8         dog      M    9      40

  animal_type gender  age_sum
0         cat      F       14
1         cat      M        7
2         dog      F        4
3         dog      M       20

Using groupby() and sum() on Single Column in pandas DataFrame

You can use groupby() to group a pandas DataFrame by one column or multiple columns.

If you want to group a pandas DataFrame by one column and then get the sum of a variable for each group with sum(), you can do the following.

import pandas as pd

df = pd.DataFrame({"animal_type":["dog","cat","dog","cat","dog","dog","cat","cat","dog"], "gender":["F","F","F","F","M","M","M","F","M"], "age":[1,2,3,4,5,6,7,8,9], "weight":[10,20,15,20,25,10,15,30,40]})

print(df)
print(df.groupby(["animal_type"])["age"].sum().rename('age_sum').reset_index())

#Output:
  animal_type gender
0         dog      F
1         cat      F
2         dog      F
3         cat      F
4         dog      M
5         dog      M
6         cat      M
7         cat      F
8         dog      M

  animal_type  age_sum
0         cat       21
1         dog       24

If you want to group by a single column and find the sums of multiple variables, you can do the following. In this case, the column names will be the names of the original columns.

df = pd.DataFrame({"animal_type":["dog","cat","dog","cat","dog","dog","cat","cat","dog"], "gender":["F","F","F","F","M","M","M","F","M"], "age":[1,2,3,4,5,6,7,8,9], "weight":[10,20,15,20,25,10,15,30,40]})

print(df)
print(df.groupby(["gender"])["age","weight"].sum().reset_index())

#Output:
  animal_type gender  age  weight
0         dog      F    1      10
1         cat      F    2      20
2         dog      F    3      15
3         cat      F    4      20
4         dog      M    5      25
5         dog      M    6      10
6         cat      M    7      15
7         cat      F    8      30
8         dog      M    9      40

  gender  age  weight
0      F   18      95
1      M   27      90

Using groupby() to Group By Multiple Columns and sum() in pandas DataFrame

If you want to group a pandas DataFrame by multiple columns and then get the sum of a variable for each group with sum(), you can do the following.

import pandas as pd

df = pd.DataFrame({"animal_type":["dog","cat","dog","cat","dog","dog","cat","cat","dog"], "gender":["F","F","F","F","M","M","M","F","M"], "age":[1,2,3,4,5,6,7,8,9], "weight":[10,20,15,20,25,10,15,30,40]})

print(df)
print(df.groupby(["animal_type","gender"])["age"].sum().rename('age_sum').reset_index())

#Output:
  animal_type gender  age  weight
0         dog      F    1      10
1         cat      F    2      20
2         dog      F    3      15
3         cat      F    4      20
4         dog      M    5      25
5         dog      M    6      10
6         cat      M    7      15
7         cat      F    8      30
8         dog      M    9      40

  animal_type gender  age_sum
0         cat      F       14
1         cat      M        7
2         dog      F        4
3         dog      M       20

If you want to group by multiple columns and find the sums of multiple variables, you can do the following. In this case, the column names will be the names of the original columns.

import pandas as pd

df = pd.DataFrame({"animal_type":["dog","cat","dog","cat","dog","dog","cat","cat","dog"], "gender":["F","F","F","F","M","M","M","F","M"], "age":[1,2,3,4,5,6,7,8,9], "weight":[10,20,15,20,25,10,15,30,40]})

print(df)
print(df.groupby(["animal_type","gender"])["age","weight"].sum().reset_index())

#Output:
  animal_type gender  age  weight
0         dog      F    1      10
1         cat      F    2      20
2         dog      F    3      15
3         cat      F    4      20
4         dog      M    5      25
5         dog      M    6      10
6         cat      M    7      15
7         cat      F    8      30
8         dog      M    9      40

  animal_type gender  age  weight
0         cat      F   14      70
1         cat      M    7      15
2         dog      F    4      25
3         dog      M   20      75

Hopefully this article has been useful for you to learn how to group by and sum in pandas with groupby() and sum().

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