How Can I Create New Columns with Conditional Values in DataFrames Using NumPy?

Creating a New Column with Conditional Values

When analyzing data, it may be necessary to create new columns where the values are selected based on existing columns. This allows you to categorize or transform data for further analysis or visualization. Here are two approaches:

np.where for Two Choices:

If you need to choose between only two values based on an existing column, you can use the np.where function. For instance, to create a "color" column in a dataframe where "color='green'" if "Set=='Z'" and "color='red'" otherwise, you can use:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

np.select for Multiple Conditions:

If you have more than two conditions to consider when selecting values, you can use the np.select function. For example, to create a "color" column based on multiple conditions:

conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')

By using np.where or np.select, you can easily create new columns with conditional values, providing flexibility in data manipulation and enabling deeper insights from your data.

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