How to Create a Conditional Column Based on Multiple Conditions in a DataFrame Using Python?

Creating a Conditional Column Based on Multiple Conditions

As mentioned in the given thread, the task at hand is to generate a new column in a DataFrame based on specific conditions. The DataFrame contains two columns, 'A' and 'B', and the desired column, 'C', should be assigned values based on comparisons between 'A' and 'B'.

The conditions are as follows:

• If 'A' equals 'B', set 'C' to 0.
• If 'A' is greater than 'B', set 'C' to 1.
• If 'A' is less than 'B', set 'C' to -1.

To accomplish this, a Python function can be created to evaluate the conditions and assign the appropriate value to 'C' for each row in the DataFrame. The apply() method can be used to apply the function to each row, passing in the 'axis=1' argument to specify that the function should operate on the rows. The code below demonstrates this approach:

<code class="python">def conditional_value(row):
    if row['A'] == row['B']:
        return 0
    elif row['A'] > row['B']:
        return 1
    else:
        return -1

df['C'] = df.apply(conditional_value, axis=1)</code>

This function-based approach provides a readable and customizable way to create the conditional column.

Alternatively, for better performance on large datasets, a vectorized operation can be used:

<code class="python">df['C'] = np.where(
    df['A'] == df['B'], 0, np.where(
    df['A'] > df['B'], 1, -1))</code>

Here, the np.where() function is used to evaluate the conditions and assign the corresponding values to 'C' efficiently.

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