How can I transform left/right injury data into injured vs uninjured categories in Python?

A simple way to handle this is to convert the left/right injury information into a single binary category:

• Injured → if either left or right injury exists

• Uninjured → if neither exists

Using Pandas, you can do it like this:

import pandas as pd

injury_cols = ['left_injury', 'right_injury']

df['injury_status'] = df[injury_cols].notna().any(axis=1)
df['injury_status'] = df['injury_status'].map({True: 'Injured', False: 'Uninjured'})

If your columns use 0 and 1 values instead of nulls:

df['injury_status'] = (
    df[['left_injury', 'right_injury']].sum(axis=1) > 0
).map({True: 'Injured', False: 'Uninjured'})

This approach is useful when simplifying features for classification models or creating cleaner categories for analysis.

You can simplify this by mapping all injury-specific columns (left/right) into a single binary flag: injured vs uninjured.

Basic idea

If any injury column has a value → mark as Injured (1)
If all are empty/zero → Uninjured (0)

Example in Python (Pandas)

import pandas as pd

# sample columns
injury_cols = ['left_injury', 'right_injury']

# create new column
df['injured'] = df[injury_cols].notna().any(axis=1).astype(int)

If values are 0/1 instead of NaN

df['injured'] = (df[injury_cols].sum(axis=1) > 0).astype(int)

If you want labels instead of 0/1

df['injured_label'] = df['injured'].map({1: 'Injured', 0: 'Uninjured'})

Optional: Drop original columns

df = df.drop(columns=injury_cols)

You’re converting multiple condition-specific features into a single target variable, which is often cleaner for modeling and analysis.

If your dataset has more complexity (severity, multiple injuries, time-based data), this can be extended into multi-class or scoring instead of just binary.

You’re essentially doing a feature transformation from side-specific labels (left/right) into a binary condition (injured vs uninjured).

Short approach

1. Combine left/right injury columns

2. Map any injury → Injured (1)

3. No injury → Uninjured (0)

import pandas as pd

# example
df = pd.DataFrame({
    "left_injury": [0, 1, 0, 0],
    "right_injury": [0, 0, 1, 0]
})

# create new column
df["injury_status"] = ((df["left_injury"] == 1) | (df["right_injury"] == 1)).astype(int)

If injuries are text-based

df["injury_status"] = df[["left_injury", "right_injury"]]\
    .apply(lambda x: 1 if "injured" in x.values else 0, axis=1)

You’re collapsing multiple related features into a single binary indicator, which simplifies modeling and improves interpretability.

This is common in ML preprocessing when the direction or side is less important than the condition itself.