How to count unique values in a Pandas Groupby object?

How to count unique values in a Pandas Groupby object?

If you want to count unique values in a pandas GroupBy object, you can use the nunique() function. This function returns the number of unique values within each group.

Here's a step-by-step guide:

  • Setting up the DataFrame: First, let's create a sample DataFrame:
import pandas as pd data = { 'Category': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'C'], 'Value': [10, 20, 10, 30, 20, 30, 40, 40] } df = pd.DataFrame(data) print(df)
  • Counting Unique Values Using GroupBy: To count the unique values in the 'Value' column for each 'Category':
unique_counts = df.groupby('Category')['Value'].nunique() print(unique_counts)

The output will show the number of unique 'Value' entries for each 'Category'.

  • Getting Unique Values for Multiple Columns: If you want to count unique values for multiple columns, you can select them in a list and apply nunique():
unique_counts = df.groupby('Category')[['Value']].nunique() print(unique_counts)

This will return a DataFrame with counts of unique values for each specified column within each group.

  • Getting the Actual Unique Values: If you're interested in seeing the unique values themselves (not just their counts), you can use the unique() function within an agg():
unique_values = df.groupby('Category').agg({'Value': 'unique'}) print(unique_values)

This will return a DataFrame with the unique values in the 'Value' column for each 'Category'.

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