pandas reducer like method-CodePudding

I'm familiar with the JavaScript reducer method, and I'm trying to accomplish something similar with a DataFrame.

I believe in the method shown below I violate the guidance described in pandas.

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

def use_reducer():
    """reducer"""
    df = pd.DataFrame([
        {'Thresh': 'SOME', 'A': 1},
        {'Thresh': None, 'A': 20},
        {'Thresh': None, 'A': 12},
        {'Thresh': None, 'A': 12},
        {'Thresh': None, 'A': 80, }
    ])

    def reducer(index):
        this = df.loc[index]
        # first row by detfault has a thresh
        if this['Thresh'] == 'SOME':
            return df.loc[index, :]
        # last row with soem threshold crossed
        some = df.loc[df['Thresh'] == 'SOME'].iloc[-1]
        # if a threshold is crossed update thresh, this row becomes the next `some`
        if (some.A < this.A):
            df.loc[index, 'Thresh'] = 'SOME'

        return df.loc[index, :]

    [reducer(index) for index in df.index]

    print(df)

Out

  Thresh   A
0   SOME   1
1   SOME  20
2   None  12
3   None  12
4   SOME  80

CodePudding user response：

A more efficient (and more pandas-esque :) solution would be to use cummax and ffill:

df.loc[df['A'].ge(df['A'].cummax()), 'Thresh'] = df['Thresh'].ffill()

Output:

>>> df
  Thresh   A
0   SOME   1
1   SOME  20
2   None  12
3   None  12
4   SOME  80