How to Create an If-Else-Else Conditional Column in Pandas?

Creating an If-Else-Else Conditional Column in Pandas

When working with data, it's often necessary to create new columns based on specific conditions. Pandas provides a syntax that simplifies this process, allowing you to define if-elif-else conditions in a single step.

To illustrate this, let's consider the following DataFrame:

    A    B
a   2    2
b   3    1
c   1    3

We want to create a new column 'C' that follows these conditions:

• If A == B, set C to 0
• If A > B, set C to 1
• If A < B, set C to -1

Using a Custom Function

One approach is to define a custom function that evaluates these conditions for each row:

<code class="python">def my_function(row):
    if row['A'] == row['B']:
        return 0
    elif row['A'] &gt; row['B']:
        return 1
    else:
        return -1<p>The apply() method can then be used to apply this function to each row of the DataFrame, creating the 'C' column:</p>
<pre class="brush:php;toolbar:false"><code class="python">df['C'] = df.apply(my_function, axis=1)</code>

Vectorized Approach

For a more efficient, vectorized solution, we can use NumPy's np.where function along with pandas' logical indexing:

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

This eliminates the need for a custom function, resulting in a faster and more optimized solution.

The resulting DataFrame with the 'C' column:

    A    B    C
a   2    2    0
b   3    1    1
c   1    3   -1

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