Pandas: using groupby to calculate a ratio by specific values-CodePudding

Hi I have a dataframe that looks like this:

and I want to calculate a ratio in the column 'count_number', based on the values in the column 'tone' by this formula: ['blue' 'grey']/'red' per each unite combination of 'participant_id', 'session', 'block' -

here is part of my dataset as text, the left column 'RATIO' is my expected output:

participant_id session block tone count_number RATIO 10 1 neg blue 0 0 10 1 neg grey 0 0 10 1 neg red 3 0 10 1 neu blue 1 #DIV/0! 10 1 neu grey 1 #DIV/0! 10 1 neu red 0 #DIV/0! 10 2 neg blue 3 2.333333333 10 2 neg grey 4 2.333333333 10 2 neg red 3 2.333333333 10 2 neu blue 4 1.333333333 10 2 neu grey 0 1.333333333 10 2 neu red 3 1.333333333 11 1 neg blue 0 0 11 1 neg grey 0 0 11 1 neg red 3 0

I tried this (wrong) direction

def group(df):
  grouped = df.groupby(["participant_id", "session", "block"])['count_number']
  return grouped

neutral = df.loc[df.tone=='grey']
pleasant = df.loc[df.tone=='blue']
unpleasant = df.loc[df.tone=='red']

df['ratio'] = (group(neutral) group(pleasant)) / group(unpleasant)

CodePudding user response：

Here's one approach:

groupby apply a lambda that calculates the required ratio for each group; then map the ratios back to the original DataFrame:

cols = ['participant_id', 'session', 'block']
mapping = (df.groupby(cols)
           .apply(lambda x: (x.loc[x['tone'].isin(['blue','grey']), 'count_number'].sum() / 
                             x.loc[x['tone'].eq('red'), 'count_number']))
           .droplevel(-1))

df['RATIO'] = df.set_index(cols).index.map(mapping)
df['RATIO'] = df['RATIO'].replace(float('inf'), '#DIV/0!')

Output:

    group  participant_id  session block  tone  count_number     RATIO
0       1              10        1   neg  blue             0       0.0
1       1              10        1   neg  grey             0       0.0
2       1              10        1   neg   red             3       0.0
3       1              10        1   neu  blue             1   #DIV/0!
4       1              10        1   neu  grey             1   #DIV/0!
5       1              10        1   neu   red             0   #DIV/0!
6       1              10        2   neg  blue             3  2.333333
7       1              10        2   neg  grey             4  2.333333
8       1              10        2   neg   red             3  2.333333
9       1              10        2   neu  blue             4  1.333333
10      1              10        2   neu  grey             0  1.333333
11      1              10        2   neu   red             3  1.333333
12      1              11        1   neg  blue             0       0.0
13      1              11        1   neg  grey             0       0.0
14      1              11        1   neg   red             3       0.0