Python - Pandas how to output distinct values in column based on duplicate in another column

To output distinct values in one column based on duplicates in another column using Pandas, you can leverage the drop_duplicates() function combined with groupby() and apply() methods. Here's a step-by-step approach to achieve this:

Example Scenario

Let's say you have a DataFrame with two columns, A and B. You want to extract distinct values from column A where there are duplicates in column B.

Sample DataFrame

Assume you have the following DataFrame:

import pandas as pd data = { 'A': ['apple', 'banana', 'apple', 'banana', 'cherry', 'apple'], 'B': ['one', 'two', 'one', 'one', 'two', 'two'] } df = pd.DataFrame(data) print(df)

This will output:

 A B 0 apple one 1 banana two 2 apple one 3 banana one 4 cherry two 5 apple two

Solution Using Pandas

To achieve the desired output:

Group by Column B: Group the DataFrame by column B.
Apply drop_duplicates(): For each group (based on column B), apply drop_duplicates() on column A to get distinct values.
Concatenate Results: Concatenate the results using pd.concat().

Here's how you can implement it:

import pandas as pd data = { 'A': ['apple', 'banana', 'apple', 'banana', 'cherry', 'apple'], 'B': ['one', 'two', 'one', 'one', 'two', 'two'] } df = pd.DataFrame(data) # Apply drop_duplicates on column A within each group of column B result = df.groupby('B')['A'].apply(lambda x: x.drop_duplicates()).reset_index(drop=True) print(result)

Output

This will produce:

0 apple 1 banana 2 banana 3 cherry 4 apple Name: A, dtype: object

Explanation:

groupby('B')['A']: Groups the DataFrame by column B and selects column A for further processing.
apply(lambda x: x.drop_duplicates()): Applies drop_duplicates() to each group in column A (x represents each group of column A for a given B).
reset_index(drop=True): Resets the index after concatenation, dropping the old index (drop=True ensures the old index is not added as a new column).

Notes:

If you want to preserve the original DataFrame structure and include the distinct values in a new column, you can use transform instead of apply.

df['distinct_A'] = df.groupby('B')['A'].transform(lambda x: x.drop_duplicates())

This approach ensures that for each row in the original DataFrame, you have the distinct values of A based on the grouping of B.

By following these steps, you can effectively output distinct values in one column (A) based on duplicates in another column (B) using Pandas in Python.

Examples

1. Find distinct values in a column based on duplicates in another column

Description: Extract unique values from one column where another column has duplicates.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Identifying duplicates in column 'A' duplicates = df[df.duplicated('A', keep=False)] # Getting distinct values from column 'B' where 'A' has duplicates distinct_values = duplicates['B'].unique() print(distinct_values)

2. Filter rows with duplicate values in one column and get unique values from another column

Description: Filter rows where one column has duplicates and then get unique values from another column.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Filter rows with duplicates in column 'A' duplicates = df[df.duplicated('A', keep=False)] # Get unique values from column 'B' where 'A' has duplicates unique_values = duplicates['B'].drop_duplicates().tolist() print(unique_values)

3. Get distinct values from a column where another column has more than one occurrence

Description: Retrieve unique values from one column based on another column having multiple occurrences.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Get counts of occurrences in column 'A' counts = df['A'].value_counts() # Filter values where column 'A' has more than one occurrence duplicates = df[df['A'].isin(counts[counts > 1].index)] # Get distinct values from column 'B' distinct_values = duplicates['B'].unique() print(distinct_values)

4. Identify unique values from a column where another column is duplicated

Description: Identify unique values from a specific column where another column has duplicate entries.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Find rows where 'A' has duplicates duplicated_rows = df[df.duplicated('A', keep=False)] # Extract unique values from column 'B' unique_values = duplicated_rows['B'].unique() print(unique_values)

5. Extract unique values from column based on duplicate values in another column

Description: Extract unique values from one column where another column contains duplicate values.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Rows where 'A' is duplicated duplicated_A = df[df.duplicated('A', keep=False)] # Unique values from column 'B' unique_B = duplicated_A['B'].unique() print(unique_B)

6. Output distinct values in one column based on another column's duplicates

Description: Output distinct values from one column based on duplicates in another column.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Duplicates in column 'A' duplicates = df[df.duplicated('A', keep=False)] # Distinct values in column 'B' distinct_B = duplicates['B'].drop_duplicates().values print(distinct_B)

7. Pandas distinct values from one column where another column has duplicates

Description: Get distinct values from one column where another column has duplicates.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Rows where 'A' has duplicates duplicated_rows = df[df.duplicated('A', keep=False)] # Unique values from column 'B' unique_B_values = duplicated_rows['B'].unique() print(unique_B_values)

8. Get distinct column values based on another column's duplicates in pandas

Description: Retrieve distinct column values where another column has duplicate values using pandas.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Find duplicates in column 'A' duplicates = df[df.duplicated('A', keep=False)] # Get distinct values from column 'B' distinct_B_values = duplicates['B'].unique() print(distinct_B_values)

9. Extract unique values from a DataFrame column based on duplicates in another column

Description: Extract unique values from a DataFrame column based on duplicates in another column.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Rows where 'A' has duplicates duplicates = df[df.duplicated('A', keep=False)] # Unique values from column 'B' unique_B_values = duplicates['B'].unique() print(unique_B_values)

10. Distinct values from one column based on duplicate values in another column pandas

Description: Output distinct values from one column where another column has duplicate values using pandas.

import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'A': [1, 1, 2, 3, 3, 4], 'B': ['x', 'y', 'z', 'x', 'y', 'z'] }) # Identify duplicates in column 'A' duplicates = df[df.duplicated('A', keep=False)] # Get distinct values from column 'B' distinct_values_B = duplicates['B'].unique() print(distinct_values_B)

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