


Applying Multiple Conditions with Numpy's "where"
Using NumPy's "where" function can be a powerful tool for conditionally selecting elements in an array based on specific criteria. However, the standard implementation of "where" only allows for two conditions with a corresponding output. This can become a limitation when dealing with scenarios involving multiple conditions.
To address this issue, a more versatile solution is to use the "np.select" function. "np.select" allows for the evaluation of multiple conditions simultaneously and the assignment of corresponding outputs. Let's explore how this can be applied to the problem of assigning energy classes to a DataFrame based on consumption energy values.
Implementation:
col = 'consumption_energy' conditions = [ df['consumption_energy'] >= 400, (df['consumption_energy'] 200), df['consumption_energy'] <p>This code creates three conditions based on the values in the 'consumption_energy' column:</p><ol> <li>'consumption_energy' >= 400: Assigns 'high' to this condition.</li> <li>'consumption_energy' 200: Assigns 'medium' to this condition.</li> <li>'consumption_energy' </li> </ol><p>The "np.select" function evaluates each condition, and if any condition is met, it assigns the corresponding output from the "choices" list. If none of the conditions are met, it assigns 'nan' as the default value.</p><p><strong>Output:</strong></p><pre class="brush:php;toolbar:false"> 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
By utilizing "np.select," we have successfully assigned energy classes to the DataFrame based on the specified conditions, offering a versatile way to handle multiple conditions when selecting elements in an array.
The above is the detailed content of How to Apply Multiple Conditions in Arrays Using NumPy\'s \'np.select\'?. For more information, please follow other related articles on the PHP Chinese website!

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