How to Use NumPy \"where\" with Multiple Conditions?

Numpy "where" with Multiple Conditions

In the context of data analysis, it is often necessary to define custom conditions and assign accordingly different values based on those conditions. Numpy's "where" function can be used to handle such scenarios. However, challenges arise when dealing with multiple conditions.

A specific case is trying to add a new column "energy_class" to a dataframe "df_energy." The "energy_class" values are assigned based on the following conditions:

• "high" if the "consumption_energy" value is greater than 400
• "medium" if the "consumption_energy" value is between 200 and 400
• "low" if the "consumption_energy" value is less than 200

The issue encountered was that the np.where function typically only supports two conditions.

To address this, numpy's "select" function can be employed. Here's an example:

<code class="python">col         = 'consumption_energy'
conditions  = [ df2[col] >= 400, (df2[col] < 400) & (df2[col]> 200), df2[col] <= 200 ]
choices     = [ "high", 'medium', 'low' ]
    
df2["energy_class"] = np.select(conditions, choices, default=np.nan)</code>

This code snippet uses "np.select" to evaluate multiple conditions and assign values from the corresponding "choices" list. The "default" parameter is used to handle any cases that do not meet the specified conditions.

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