Pandas_Create_conditional_column_enrichment_using_DataFrame.loc.md

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Tags: #pandas #snippet #datenrichment #operations

Author: Florent Ravenel

Description: This notebook demonstrates the practical application of DataFrame.loc for implementing conditions, enabling users to seamlessly enrich a DataFrame by generating new columns based on conditions derived from existing ones. Its versatility makes it an invaluable tool for DataFrame manipulation.

References:

• Pandas - DataFrame.loc

Input

Import libraries

import pandas as pd

Setup Variables

• new_column: column label to be created

new_column = "ranking"

Model

Create DataFrame

# create DataFrame
df = pd.DataFrame(
    {
        "team": ["A", "A", "A", "B", "B", "B"],
        "points": [11, 7, 8, 10, 13, 13],
        "assists": [5, 7, 7, 9, 12, 9],
        "rebounds": [11, 8, 10, 6, 6, 5],
    }
)
df

Create new column

df.loc[:, new_column] = "C" #apply condition on all rows with ':'
df.loc[(df["points"] >= 10) & (df["assists"] >= 5) & (df["rebounds"] >= 5), new_column] = "B"
df.loc[(df["points"] >= 10) & (df["assists"] >= 5) & (df["rebounds"] >= 10), new_column] = "A"
df.loc[(df["points"] >= 10) & (df["assists"] >= 10) & (df["rebounds"] >= 5), new_column] = "A"

Output

Display new DataFrame

df