replacing integers in a data frame (logic issues)-CodePudding

So I am trying to change some values in a df using pandas and, having already tried with df.replace, df.mask, and df.where I got to the conclusion that it must be a logical mistake since it keeps throwing the same mistake:

ValueError: The truth value of a Series is ambiguous.

I am trying to normalize a column in a dataset, thus the function and not just a single line. I need to understand why my logic is wrong, it seems to be such a dumb mistake.

This is my function:

def overweight_normalizer():
    if df[df["overweight"] > 25]:
        df.where(df["overweight"] > 25, 1)
    elif df[df["overweight"] < 25]:
        df.where(df["overweight"] < 25, 0)

CodePudding user response：

df[df["overweight"] > 25] is a DataFrame object, so it doesn't make sense to evaluate it by itself in an if-condition, which requires truth-values. The error is saying that you're attempting to fetch the boolean value of a pandas DataFrame object.

Now it seems you want to assign 1 if "overweight" value is >25, 0 otherwise. In that case, instead of the function, you can use np.where:

np.where(df["overweight"] > 25, 1, 0)

or even

df["overweight"].gt(25).astype(int)

Here df["overweight"] > 25 or df["overweight"].gt(25) creates a boolean Series (that matches the length of df) and np.where selects values depending on if it's True or not at each index.

If you need this inside the function and do something else, you can do that like:

def overweight_normalizer():
    x = df["overweight"].gt(25).astype(int)
    # do something else

CodePudding user response：

df[df["overweight"] > 25] is not a valid condition.

Try this:

def overweight_normalizer():
    df = pd.DataFrame({'overweight': [2, 39, 15, 45, 9]})
    df["overweight"] = [1 if i > 25 else 0  for i in df["overweight"]]
    return df

overweight_normalizer()

Output:

    overweight
0           0
1           1
2           0
3           1
4           0