To group by multiple columns and then find the minimum of values by group in a pandas DataFrame, you can use the groupby() and min() 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"].min().rename('age_min').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_min
0 cat F 2
1 cat M 7
2 dog F 1
3 dog M 5When 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 minimum of a variable for each group.
To get the min of a variable by groups of columns in a pandas DataFrame, you can use the groupby() and min() functions.
Below is a simple example showing you how you can group by and then get the minimum of a variable of each group in a pandas DataFrame in Python.
In the example below, I’ve renamed the min of rows to ‘age_min’ 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"].min().rename('age_min').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_min
0 cat F 2
1 cat M 7
2 dog F 1
3 dog M 5Using groupby() and min() 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 minimum of a variable in each group with min(), 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"].min().rename('age_min').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_min
0 cat 2
1 dog 1If you want to group by a single column and find the minimums 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"].min().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 1 10
1 M 5 10Using groupby() to Group By Multiple Columns and min() in pandas DataFrame
If you want to group a pandas DataFrame by multiple columns and then get the minimum of a variable in each group with min(), 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"].min().rename('age_min').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_min
0 cat F 2
1 cat M 7
2 dog F 1
3 dog M 5If you want to group by multiple columns and find the minimums 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"].min().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 2 20
1 cat M 7 15
2 dog F 1 10
3 dog M 5 10Hopefully this article has been useful for you to learn how to group by and find minimums in pandas with groupby() and min().
