Python - How to group DataFrame rows into list in Pandas?

When working with Pandas DataFrames, you may need to group rows and collect values into lists. This is commonly done using the groupby() method combined with apply(list).

Basic Grouping with apply(list)

The simplest approach uses groupby() with apply(list) to collect values ?

import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("Original DataFrame:")
print(dataFrame)

# Group by Car and collect Units into lists
grouped = dataFrame.groupby('Car')['Units'].apply(list)
print("\nGrouped DataFrame:")
print(grouped)

Original DataFrame:
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80
4  Bentley    110
5   Jaguar     90

Grouped DataFrame:
Car
Audi       [110]
BMW        [100]
Bentley    [110]
Jaguar      [90]
Lexus      [150]
Mustang     [80]
Name: Units, dtype: object

Grouping Multiple Columns

You can group multiple columns into lists by selecting them before applying list ?

import pandas as pd

# Create DataFrame with duplicate car entries
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'BMW', 'Audi', 'Audi', 'Lexus'],
    "Units": [100, 120, 110, 95, 150],
    "Price": [50000, 55000, 45000, 42000, 60000]
})

print("Original DataFrame:")
print(dataFrame)

# Group by Car and collect both Units and Price into lists
grouped = dataFrame.groupby('Car')[['Units', 'Price']].apply(lambda x: x.values.tolist())
print("\nGrouped with multiple columns:")
print(grouped)

Original DataFrame:
    Car  Units  Price
0   BMW    100  50000
1   BMW    120  55000
2  Audi    110  45000
3  Audi     95  42000
4  Lexus   150  60000

Grouped with multiple columns:
Car
Audi     [[110, 45000], [95, 42000]]
BMW      [[100, 50000], [120, 55000]]
Lexus              [[150, 60000]]
Name: (Units, Price), dtype: object

Using agg() for Multiple Aggregations

The agg() method provides more flexibility for different aggregation functions ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Category": ['A', 'A', 'B', 'B', 'A'],
    "Values": [10, 20, 30, 40, 50]
})

print("Original DataFrame:")
print(dataFrame)

# Use agg() to create lists and calculate other aggregations
result = dataFrame.groupby('Category')['Values'].agg(['list', 'sum', 'mean'])
print("\nMultiple aggregations:")
print(result)

Original DataFrame:
  Category  Values
0        A      10
1        A      20
2        B      30
3        B      40
4        A      50

Multiple aggregations:
              list  sum  mean
Category                    
A         [10, 20, 50]   80  26.666667
B             [30, 40]   70  35.000000

Comparison

Conclusion

Use groupby() with apply(list) for simple grouping into lists. For multiple columns or aggregations, consider using agg() or lambda functions for more flexibility.