To group by multiple columns and then find the mean of rows in a pandas DataFrame, you can use the groupby() and mean() 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"].mean().rename('age_mean').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_mean
0 cat F 4.666667
1 cat M 7.000000
2 dog F 2.000000
3 dog M 6.666667When 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 mean of a variable for each group.
To get the mean of a variable by groups of columns in a pandas DataFrame, you can use the groupby() and mean() functions.
Below is a simple example showing you how you can group by and then get the average of a variable of each group in a pandas DataFrame in Python.
In the example below, I’ve renamed the mean of rows to ‘age_mean’ 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"].mean().rename('age_mean').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_mean
0 cat F 4.666667
1 cat M 7.000000
2 dog F 2.000000
3 dog M 6.666667Using groupby() and mean() 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 average of a variable in each group with mean(), 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"].mean().rename('age_mean').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_mean
0 cat 5.25
1 dog 4.80If you want to group by a single column and find the means 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(["gender"])["age","weight"].mean().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 3.60 19.0
1 M 6.75 22.5Using groupby() to Group By Multiple Columns and mean() in pandas DataFrame
If you want to group a pandas DataFrame by multiple columns and then get the average of a variable in each group with mean(), 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"].mean().rename('age_mean').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_mean
0 cat F 4.666667
1 cat M 7.000000
2 dog F 2.000000
3 dog M 6.666667If you want to group by multiple columns and find the means 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"].mean().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 4.666667 23.333333
1 cat M 7.000000 15.000000
2 dog F 2.000000 12.500000
3 dog M 6.666667 25.000000Hopefully this article has been useful for you to learn how to group by and mean in pandas with groupby() and mean().
