Python – Descending Order Sort grouped Pandas dataframe by group size?

Sorting grouped Pandas DataFrame by group size in descending order is useful for analyzing data distribution. We use groupby() to group data, size() to count group members, and sort_values(ascending=False) to sort in descending order.

Creating a Sample DataFrame

Let's start by creating a DataFrame with car information ?

import pandas as pd

# Create dataframe with Car and Registration Price columns
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'],
    "Reg_Price": [1000, 1400, 1000, 900, 1700, 900]
})

print("DataFrame:")
print(dataFrame)

DataFrame:
        Car  Reg_Price
0       BMW       1000
1     Lexus       1400
2      Audi       1000
3  Mercedes        900
4    Jaguar       1700
5   Bentley        900

Grouping and Sorting by Size

Now we'll group by the 'Reg_Price' column and sort groups by their size in descending order ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'],
    "Reg_Price": [1000, 1400, 1000, 900, 1700, 900]
})

# Group by Reg_Price and sort by group size in descending order
sorted_groups = dataFrame.groupby('Reg_Price').size().sort_values(ascending=False)

print("Groups sorted by size (descending):")
print(sorted_groups)

Groups sorted by size (descending):
Reg_Price
1000    2
900     2
1700    1
1400    1
dtype: int64

Understanding the Process

The process involves three key steps:

1. groupby('Reg_Price') − Groups rows by registration price
2. size() − Counts the number of rows in each group
3. sort_values(ascending=False) − Sorts groups by count in descending order

Alternative Approach with Group Details

To see the actual data within each group along with sizes ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'],
    "Reg_Price": [1000, 1400, 1000, 900, 1700, 900]
})

# Get group sizes and sort
group_sizes = dataFrame.groupby('Reg_Price').size().sort_values(ascending=False)

print("Group sizes (sorted):")
print(group_sizes)
print("\nDetailed groups:")

for price in group_sizes.index:
    group_data = dataFrame[dataFrame['Reg_Price'] == price]
    print(f"\nReg_Price {price} (size: {group_sizes[price]}):")
    print(group_data[['Car', 'Reg_Price']])

Group sizes (sorted):
Reg_Price
1000    2
900     2
1700    1
1400    1
dtype: int64

Detailed groups:

Reg_Price 1000 (size: 2):
   Car  Reg_Price
0  BMW       1000
2  Audi       1000

Reg_Price 900 (size: 2):
        Car  Reg_Price
3  Mercedes        900
5   Bentley        900

Reg_Price 1700 (size: 1):
     Car  Reg_Price
4  Jaguar       1700

Reg_Price 1400 (size: 1):
    Car  Reg_Price
1  Lexus       1400

Conclusion

Use groupby().size().sort_values(ascending=False) to sort grouped DataFrames by group size in descending order. This approach is particularly useful for identifying the most common categories in your dataset.