pandas - Check if values of multiple columns are the same (python)

Pandas - Check if values of multiple columns are the same (python)

You can check if values of multiple columns are the same in Pandas by using the nunique() function. The nunique() function returns the number of unique values in a Series, and if the number of unique values is 1, it means that all values are the same. Here's an example:

import pandas as pd # Sample DataFrame data = {'Column1': [1, 2, 3, 4], 'Column2': [1, 1, 3, 4], 'Column3': [1, 2, 3, 1]} df = pd.DataFrame(data) # Check if values in multiple columns are the same def are_columns_equal(df, columns): unique_counts = df[columns].nunique() return all(unique_counts == 1) columns_to_check = ['Column1', 'Column2', 'Column3'] result = are_columns_equal(df, columns_to_check) print(f"Values in columns {', '.join(columns_to_check)} are the same: {result}")

In this example:

  • The are_columns_equal function takes a DataFrame and a list of columns to check.
  • It calculates the number of unique values for each specified column using nunique().
  • The all(unique_counts == 1) checks if all the columns have exactly one unique value.

Adjust the columns_to_check variable based on the columns you want to verify. If the result is True, it means all values in the specified columns are the same; otherwise, it's False.

Examples

  1. "Check if values in two columns are the same for each row"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': [1, 2, 4]}) # Check if values in Column1 and Column2 are the same for each row df['SameValues'] = df['Column1'].eq(df['Column2'])
    • Description: Creates a new column 'SameValues' indicating whether values in 'Column1' and 'Column2' are the same for each row.

  2. "Check if values in multiple columns are the same for each row"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': [1, 2, 3], 'Column3': [4, 5, 6]}) # Check if values in Column1, Column2, and Column3 are the same for each row df['SameValues'] = df.apply(lambda row: row.nunique() == 1, axis=1)
    • Description: Creates a new column 'SameValues' indicating whether values in 'Column1', 'Column2', and 'Column3' are the same for each row.

  3. "Check if all values in a specific column are the same"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [5, 5, 5, 5, 5]}) # Check if all values in Column1 are the same are_values_same = df['Column1'].nunique() == 1
    • Description: Checks if all values in 'Column1' are the same using the nunique function.

  4. "Check if values in selected columns are the same for each row"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': [1, 2, 3], 'Column3': [4, 5, 6]}) # Select columns to check and create a new column indicating if values are the same selected_columns = ['Column1', 'Column2'] df['SameValues'] = df[selected_columns].apply(lambda row: row.nunique() == 1, axis=1)
    • Description: Creates a new column 'SameValues' indicating whether values in selected columns are the same for each row.

  5. "Check if values in all columns are the same for each row"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [3, 3, 3], 'Column2': [7, 7, 7], 'Column3': [11, 11, 11]}) # Check if values in all columns are the same for each row df['SameValues'] = df.apply(lambda row: row.nunique() == 1, axis=1)
    • Description: Creates a new column 'SameValues' indicating whether values in all columns are the same for each row.

  6. "Check if values in a subset of columns are the same for each row"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': [4, 2, 3], 'Column3': [1, 4, 3]}) # Check if values in a subset of columns are the same for each row subset_columns = ['Column1', 'Column3'] df['SameValues'] = df[subset_columns].apply(lambda row: row.nunique() == 1, axis=1)
    • Description: Creates a new column 'SameValues' indicating whether values in a subset of columns are the same for each row.

  7. "Check if values in all columns of a DataFrame are the same"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [5, 5, 5], 'Column2': [8, 8, 8], 'Column3': [3, 3, 3]}) # Check if values in all columns of the DataFrame are the same are_values_same = df.apply(lambda col: col.nunique() == 1).all()
    • Description: Checks if values in all columns of the DataFrame are the same using the nunique function.

  8. "Check if values in specific columns are the same for each row, ignoring NaN values"

    • Code:
      import pandas as pd # Create a DataFrame with NaN values df = pd.DataFrame({'Column1': [1, 2, 3, 4], 'Column2': [1, 2, 3, pd.NA]}) # Check if values in specific columns are the same, ignoring NaN values selected_columns = ['Column1', 'Column2'] df['SameValues'] = df[selected_columns].eq(df[selected_columns].iloc[:, 0], axis=0).all(axis=1)
    • Description: Creates a new column 'SameValues' indicating whether values in specific columns are the same for each row, ignoring NaN values.

  9. "Check if values in multiple columns are the same within groups"

    • Code:
      import pandas as pd # Create a DataFrame with a 'Group' column df = pd.DataFrame({'Group': ['A', 'A', 'B', 'B'], 'Column1': [1, 2, 3, 4], 'Column2': [1, 2, 3, 5]}) # Check if values in Column1 and Column2 are the same within groups df['SameValues'] = df.groupby('Group')[['Column1', 'Column2']].transform(lambda x: x.nunique() == 1).all(axis=1)
    • Description: Creates a new column 'SameValues' indicating whether values in 'Column1' and 'Column2' are the same within groups defined by the 'Group' column.

  10. "Check if values in multiple columns are the same and create a summary column"

    • Code:
      import pandas as pd # Create a DataFrame df = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': [1, 2, 4], 'Column3': [1, 1, 1]}) # Check if values in multiple columns are the same and create a summary column selected_columns = ['Column1', 'Column2', 'Column3'] df['AllValuesSame'] = df[selected_columns].apply(lambda row: row.nunique() == 1, axis=1)
    • Description: Creates a new column 'AllValuesSame' indicating whether values in multiple columns are the same for each row.

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