Creating a New Column Based on Values from Multiple Columns in Pandas
In Pandas, it is possible to create a new column based on the values present in multiple other columns. This functionality is useful when applying complex logic or custom functions to derive meaningful insights from the data.
As an illustrative example, consider the task of creating a new column labeled "race_label" based on the values in six ethnicity columns: ERI_Hispanic, ERI_AmerInd_AKNatv, ERI_Asian, ERI_Black_Afr.Amer, ERI_HI_PacIsl, and ERI_White. The requirement is to classify individuals based on their race using the following criteria:
Solution
To achieve this, both a custom function and the Pandas apply() function are employed.
Define the Custom Function:
def label_race(row): if row['eri_hispanic'] == 1: return 'Hispanic' if row['eri_afr_amer'] + row['eri_asian'] + row['eri_hawaiian'] + row['eri_nat_amer'] + row['eri_white'] > 1: return 'Two Or More' if row['eri_nat_amer'] == 1: return 'A/I AK Native' if row['eri_asian'] == 1: return 'Asian' if row['eri_afr_amer'] == 1: return 'Black/AA' if row['eri_hawaiian'] == 1: return 'Haw/Pac Isl.' if row['eri_white'] == 1: return 'White' return 'Other'
Apply the Custom Function Using Pandas:
df['race_label'] = df.apply(label_race, axis=1)
This will create a new column called "race_label" in the Pandas dataframe, which contains the appropriate classification for each row based on the input criteria.
By combining the custom function and the Pandas apply() function, we can create a new column derived from complex logic applied across multiple columns, facilitating efficient data analysis and interpretation.
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