How can I vectorize change detection in grouped pandas data while correctly handling the first row?

Below is one approach. Instead of a lambda that directly compares to the shifted version (which makes the first row return True because NaN != value), you can write a function that explicitly sets the first row for each group to False. For example:

PYTHONCopied
import pandas as pd
import numpy as np

# Sample data
test_df = pd.DataFrame({
    'item': [20, 20, 20, 20, 20, 20, 20, 20,
             30, 30, 30, 30, 30, 30, 30, 30,
             40, 40, 40, 40, 40, 40, 40, 40],
    'measure': [1, 1, 1, 3, 3, 3, 3, 3,
                6, 6, 6, 6, 6, 7, 7, 7,
                10, 10, 10, 10, 10, 10, 10, 10],
    'cat': ['a', 'a', 'a', 'b', 'b', 'b', 'b', 'b',
            'c', 'c', 'c', 'c', 'c', 'd', 'd', 'd',
            'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e']
})

# Function to flag changes with the first element set to False
def flag_change(s):
    # Compare each element to the previous one
    change = s != s.shift()
    # Explicitly set the first element (which resulted in NaN) to False
    change.iloc[0] = False
    return change

# Apply the function per group
test_df['measure_change'] = test_df.groupby('item')['measure'].transform(flag_change)
test_df['cat_change'] = test_df.groupby('item')['cat'].transform(flag_change)

print(test_df)

Explanation

• The function flag_change does a vectorized comparison between each element in the series and its previous value (using .shift()).
• Because the first value in each group has no previous value, the result is initially True (since NaN != value), so we explicitly set the first row (iloc[0]) of each group to False.
• Using .groupby(...).transform(flag_change), you apply this function to each group (each unique item), and the result preserves the original DataFrame order.

Alternative for Numeric Columns

If your column is numeric, you can also use .diff():

PYTHONCopied
test_df['measure_change'] = test_df.groupby('item')['measure'].diff().ne(0).fillna(False)

This approach uses .diff() to compute the difference between rows, checks if the difference is not equal to zero, and fills the first row (which is NaN) with False.

Either method should give you the correct flag such that, for example, for item 40 (rows 16–23) every measure_change is False.

This method can scale to millions of records, provided that your machine has enough memory.