How to Add a Column to a DataFrame Using Numpy \"where\" with More Than Two Conditions?

Numpy "where" with Multiple Conditions: Addressing Three Conditions

Problem Description:

Adding a new column to a dataframe based on multiple conditions becomes challenging when facing more than two conditions. The given scenario demands the creation of an "energy_class" column with "high", "medium", or "low" values based on the "consumption_energy" column's values.

Solution:

Although numpy.where can only handle two conditions, a clever workaround using numpy.select resolves the issue.

Python Code:

<code class="python"># Define column and conditions
col = 'consumption_energy'
conditions = [df2[col] >= 400, (df2[col] < 400) & (df2[col] > 200), df2[col] <= 200]

# Define choices for conditions
choices = ["high", 'medium', 'low']

# Add "energy_class" column with np.select
df2["energy_class"] = np.select(conditions, choices, default=np.nan)</code>

Example Output:

  consumption_energy energy_class
0                 459         high
1                 416         high
2                 186          low
3                 250       medium
4                 411         high
5                 210       medium
6                 343       medium
7                 328       medium
8                 208       medium
9                 223       medium

Additional Note:

default=np.nan assigns NaN values to rows that don't meet any conditions. You can customize this to fit your needs.

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