How to set a value in a pandas DataFrame by mixed iloc and loc

How to set a value in a pandas DataFrame by mixed iloc and loc

You can set a value in a pandas DataFrame using a combination of .iloc and .loc indexing. This can be useful when you want to modify specific elements based on both integer-based and label-based indexing. Here's an example of how you can achieve this:

import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data, index=['row1', 'row2', 'row3']) print("Original DataFrame:") print(df) # Set a value using mixed iloc and loc indexing row_idx = 1 # Integer-based row index col_name = 'B' # Column label new_value = 55 df.iloc[row_idx, df.columns.get_loc(col_name)] = new_value print("\nModified DataFrame:") print(df)

In this example, we first create a sample DataFrame df. Then, we use both integer-based (iloc) and label-based (loc) indexing to set a value in the DataFrame. The row_idx variable specifies the row index as an integer, and the col_name variable specifies the column name as a label. We use get_loc() to convert the column name to its corresponding integer location.

After setting the value, you can observe that the DataFrame has been modified accordingly.

Remember to adjust the row_idx and col_name variables based on your specific use case.

Examples

  1. "Pandas DataFrame set value using mixed iloc and loc"

    Description: This query seeks guidance on setting a value in a Pandas DataFrame using both iloc (integer-location based indexing) and loc (label-based indexing) methods.

    import pandas as pd # Creating a sample DataFrame df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['X', 'Y', 'Z']) # Setting value at row 'X', column 'A' using mixed iloc and loc df.iloc[df.index.get_loc('X'), df.columns.get_loc('A')] = 10

  2. "How to change specific cell value in pandas DataFrame using iloc and loc"

    Description: This query addresses changing a specific cell's value in a Pandas DataFrame utilizing both iloc and loc indexing methods.

    # Assuming 'df' is the DataFrame df.iloc[df.index.get_loc('row_label'), df.columns.get_loc('column_label')] = new_value

  3. "Pandas set value using mixed iloc and loc with conditional selection"

    Description: In this query, the user aims to set a value in a Pandas DataFrame using a combination of iloc and loc with conditional selection.

    # Setting value based on a condition using mixed iloc and loc df.iloc[df.index.get_loc('X'), df.columns.get_loc('A')] = new_value if condition else df.loc['X', 'A']

  4. "Change specific cell value in Pandas DataFrame using iloc and loc with NaN handling"

    Description: This query seeks guidance on changing a specific cell's value in a Pandas DataFrame using iloc and loc, with consideration for handling NaN values.

    # Handling NaN values while setting value using iloc and loc df.iloc[df.index.get_loc('row_label'), df.columns.get_loc('column_label')] = new_value if not pd.isna(new_value) else default_value

  5. "How to update DataFrame cell value using mixed iloc and loc in Pandas"

    Description: This query inquires about updating a cell's value in a Pandas DataFrame utilizing a combination of iloc and loc methods.

    # Updating DataFrame cell value using mixed iloc and loc df.iloc[df.index.get_loc('row_label'), df.columns.get_loc('column_label')] = new_value

  6. "Pandas DataFrame set value by index and column name"

    Description: This query focuses on setting a value in a Pandas DataFrame using both index and column names.

    # Setting value by index and column name df.loc['row_label', 'column_label'] = new_value

  7. "Change DataFrame cell value using iloc and loc in Pandas with integer indices"

    Description: Here, the user is looking for guidance on changing a DataFrame cell's value using iloc and loc methods with integer indices.

    # Changing cell value with integer indices using iloc and loc df.iloc[row_index, column_index] = new_value

  8. "Pandas DataFrame set value by row and column number"

    Description: This query seeks assistance in setting a value in a Pandas DataFrame using row and column numbers.

    # Setting value by row and column number df.iloc[row_index, column_index] = new_value

  9. "How to modify DataFrame cell value based on condition in Pandas"

    Description: This query aims to modify a DataFrame cell's value based on a condition in Pandas.

    # Modifying cell value based on condition df.loc[df['column_name'] == condition_value, 'column_name'] = new_value

  10. "Update specific cell value in Pandas DataFrame using iloc and loc"

    Description: This query is about updating a specific cell's value in a Pandas DataFrame using both iloc and loc methods.

    # Updating specific cell value with iloc and loc df.iloc[row_index, df.columns.get_loc('column_label')] = new_value

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