Python Pandas - Count the number of rows in each group

Pandas groupby() operations allow you to split data into groups and count rows in each group using size(). This is useful for analyzing data distribution and finding group frequencies.

Creating the DataFrame

First, let's create a sample DataFrame with product data ?

import pandas as pd

# Create a DataFrame
dataFrame = pd.DataFrame({
    'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'],
    'Quantity': [10, 50, 10, 20, 25, 50],
    'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone']
})

print("DataFrame:")
print(dataFrame)

DataFrame:
  Product Category    Quantity Product Name
0         Computer          10     Keyboard
1     Mobile Phone          50      Charger
2      Electronics          10      SmartTV
3      Electronics          20       Camera
4         Computer          25 Graphic Card
5     Mobile Phone          50     Earphone

Grouping and Counting Rows

Use groupby() to group by columns and size() to count rows in each group ?

import pandas as pd

dataFrame = pd.DataFrame({
    'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'],
    'Quantity': [10, 50, 10, 20, 25, 50],
    'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone']
})

# Group by Product Category and Quantity
grouped = dataFrame.groupby(['Product Category', 'Quantity'])

# Count rows in each group
group_counts = grouped.size()

print("Row count in each group:")
print(group_counts)

Row count in each group:
Product Category  Quantity
Computer          10          1
                  25          1
Electronics       10          1
                  20          1
Mobile Phone      50          2
dtype: int64

Alternative Methods

Using count() vs size()

The count() method excludes NaN values, while size() includes all rows ?

import pandas as pd

dataFrame = pd.DataFrame({
    'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'],
    'Quantity': [10, 50, 10, 20, 25, 50],
    'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone']
})

# Using size() - counts all rows
print("Using size():")
print(dataFrame.groupby('Product Category').size())

print("\nUsing count() - excludes NaN values:")
print(dataFrame.groupby('Product Category').count())

Using size():
Product Category
Computer        2
Electronics     2
Mobile Phone    2
dtype: int64

Using count() - excludes NaN values:
              Quantity  Product Name
Product Category                   
Computer             2             2
Electronics          2             2
Mobile Phone         2             2

Comparison

Conclusion

Use groupby().size() to count total rows in each group, including NaN values. Use count() when you need to exclude missing values from the count.