# How to Get the Rows with the Maximum Value in Groups using Groupby in Python Pandas

In this article, we will discuss how to find all the rows in a pandas DataFrame that have the maximum value for a specific column after grouping the data by one or more columns.

## Problem Description

The problem we are trying to solve is to get the rows that have the maximum value for the 'count' column, after grouping the data by the 'Sp' and 'Mt' columns. We want to find the maximum 'count' value for each unique combination of 'Sp' and 'Mt', and then select the rows that have this maximum value.

### Example 1

` ````
import pandas as pd
# Create the DataFrame
data = {
'Sp': ['MM1', 'MM1', 'MM1', 'MM2', 'MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
'Mt': ['S1', 'S1', 'S3', 'S3', 'S4', 'S4', 'S2', 'S2', 'S2'],
'Value': ['a', 'n', 'cb', 'mk', 'bg', 'dgd', 'rd', 'cb', 'uyi'],
'count': [3, 2, 5, 8, 10, 1, 2, 2, 7]
}
df = pd.DataFrame(data)
# Group the data by 'Sp' and 'Mt' columns and find the maximum 'count' value
max_counts = df.groupby(['Sp', 'Mt'])['count'].max()
# Select the rows that have the maximum 'count' value in each group
result = df[df['count'].isin(max_counts)]
print(result)
```

The expected output of this code is:

` ````
Sp Mt Value count
0 MM1 S1 a 3
2 MM1 S3 cb 5
3 MM2 S3 mk 8
4 MM2 S4 bg 10
8 MM4 S2 uyi 7
```

The code first creates a pandas DataFrame using the given data. Then, it groups the data by the 'Sp' and 'Mt' columns and finds the maximum value of the 'count' column for each group using the `max()`

function. Finally, it selects the rows that have the maximum 'count' value in each group using the `isin()`

function.

### Example 2

` ````
import pandas as pd
# Create the DataFrame
data = {
'Sp': ['MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
'Mt': ['S4', 'S4', 'S2', 'S2', 'S2'],
'Value': ['bg', 'dgd', 'rd', 'cb', 'uyi'],
'count': [10, 1, 2, 8, 8]
}
df = pd.DataFrame(data)
# Group the data by 'Sp' and 'Mt' columns and find the maximum 'count' value
max_counts = df.groupby(['Sp', 'Mt'])['count'].max()
# Select the rows that have the maximum 'count' value in each group
result = df[df['count'].isin(max_counts)]
print(result)
```

The expected output of this code is:

` ````
Sp Mt Value count
0 MM2 S4 bg 10
3 MM4 S2 cb 8
4 MM4 S2 uyi 8
```

The code works the same way as in Example 1. It groups the data by the 'Sp' and 'Mt' columns, finds the maximum value of the 'count' column for each group, and selects the rows that have the maximum 'count' value in each group.

## Explanation

The solution to this problem involves two steps:

- Grouping the data by one or more columns
- Finding the maximum value in each group and selecting the rows that have this maximum value

### Step 1: Grouping the data

In order to group the data by one or more columns, we can use the `groupby()`

function in pandas. This function takes the column(s) to group by as input and returns a GroupBy object.

` ````
import pandas as pd
# Create the DataFrame
data = {
'Sp': ['MM1', 'MM1', 'MM1', 'MM2', 'MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
'Mt': ['S1', 'S1', 'S3', 'S3', 'S4', 'S4', 'S2', 'S2', 'S2'],
'Value': ['a', 'n', 'cb', 'mk', 'bg', 'dgd', 'rd', 'cb', 'uyi'],
'count': [3, 2, 5, 8, 10, 1, 2, 2, 7]
}
df = pd.DataFrame(data)
# Group the data by 'Sp' and 'Mt' columns
grouped = df.groupby(['Sp', 'Mt'])
```

In this example, we create a pandas DataFrame using the given data. Then, we pass the 'Sp' and 'Mt' columns to the `groupby()`

function to group the data by these columns. The result is a GroupBy object.

### Step 2: Finding the maximum value and selecting rows

To find the maximum value in each group and select the rows that have this maximum value, we can use the `max()`

function to calculate the maximum value of the 'count' column for each group. Then, we use the `isin()`

function to select the rows that have the maximum value.

` ````
import pandas as pd
# Create the DataFrame
data = {
'Sp': ['MM1', 'MM1', 'MM1', 'MM2', 'MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
'Mt': ['S1', 'S1', 'S3', 'S3', 'S4', 'S4', 'S2', 'S2', 'S2'],
'Value': ['a', 'n', 'cb', 'mk', 'bg', 'dgd', 'rd', 'cb', 'uyi'],
'count': [3, 2, 5, 8, 10, 1, 2, 2, 7]
}
df = pd.DataFrame(data)
# Group the data by 'Sp' and 'Mt' columns and find the maximum 'count' value
max_counts = df.groupby(['Sp', 'Mt'])['count'].max()
# Select the rows that have the maximum 'count' value in each group
result = df[df['count'].isin(max_counts)]
```

In this example, we create a pandas DataFrame using the given data. Then, we group the data by the 'Sp' and 'Mt' columns and find the maximum value of the 'count' column for each group using the `max()`

function. Next, we select the rows that have the maximum 'count' value in each group using the `isin()`

function.

## Conclusion

In this article, we have discussed how to find all the rows in a pandas DataFrame that have the maximum value for a specific column after grouping the data by one or more columns. We have provided examples with code snippets to demonstrate the solution to the problem. By following the steps outlined in this article, you will be able to effectively solve the problem of getting the rows with the maximum value in groups using groupby in Python pandas.