How can I update a column of weights, grouped by a unique name, in Pandas using the 'largest remainder method'? I want the weights to add up to 100% after they are rounded to 2 decimal points.
Input dataframe:
print(df)
Name Weight
0 John 33.3333
1 John 33.3333
2 John 33.3333
3 James 50
4 James 25
5 James 25
6 Kim 6.6666
5 Kim 93.3333
6 Jane 46.6666
7 Jane 6.6666
8 Jane 46.6666
Expected results:
print(df)
Name Weight New Weight
0 John 3.3333 33.33
1 John 3.3333 33.33
2 John 3.3333 33.34
3 James 50 50
4 James 25 25
5 James 25 25
6 Kim 6.6666 6.66
5 Kim 93.3333 93.34
6 Jane 46.6666 46.66
7 Jane 6.6666 6.67
8 Jane 46.6666 46.67
I've tried to apply the following functions:
def round_to_100_percent(number_set, digit_after_decimal=2):
"""
This function take a list of number and return a list of percentage, which represents the portion of each number in sum of all numbers
Moreover, those percentages are adding up to 100%!!!
Notice: the algorithm we are using here is 'Largest Remainder'
The down-side is that the results won't be accurate, but they are never accurate anyway:)
"""
unround_numbers = [x / float(sum(number_set)) * 100 * 10 ** digit_after_decimal for x in number_set]
decimal_part_with_index = sorted([(index, unround_numbers[index] % 1) for index in range(len(unround_numbers))], key=lambda y: y[1], reverse=True)
remainder = 100 * 10 ** digit_after_decimal - sum([int(x) for x in unround_numbers])
index = 0
while remainder > 0:
unround_numbers[decimal_part_with_index[index][0]] = 1
remainder -= 1
index = (index 1) % len(number_set)
return [int(x) / float(10 ** digit_after_decimal) for x in unround_numbers]
Split (explode) pandas dataframe string entry to separate rows
def explode(df, lst_cols, fill_value='', preserve_index=False):
# make sure `lst_cols` is list-alike
if (lst_cols is not None
and len(lst_cols) > 0
and not isinstance(lst_cols, (list, tuple, np.ndarray, pd.Series))):
lst_cols = [lst_cols]
# all columns except `lst_cols`
idx_cols = df.columns.difference(lst_cols)
# calculate lengths of lists
lens = df[lst_cols[0]].str.len()
# preserve original index values
idx = np.repeat(df.index.values, lens)
# create "exploded" DF
res = (pd.DataFrame({
col:np.repeat(df[col].values, lens)
for col in idx_cols},
index=idx)
.assign(**{col:np.concatenate(df.loc[lens>0, col].values)
for col in lst_cols}))
# append those rows that have empty lists
if (lens == 0).any():
# at least one list in cells is empty
res = (res.append(df.loc[lens==0, idx_cols], sort=False)
.fillna(fill_value))
# revert the original index order
res = res.sort_index()
# reset index if requested
if not preserve_index:
res = res.reset_index(drop=True)
return res
This is what I tried so far:
new_column = df.groupby('Name')['Weight'].apply(round_to_100_percent)
#Merge new_column into main data frame
df = pd.merge(df, new_column, on='Name', how='outer')
#For some reason _y is added to col
df = df.explode('Weight_y')
df['New Weight'] = df['Weight_y']*0.01
It's not working in a couple of ways. Sometimes there are more rows than the original dataframe. Not sure why weight_y column is being created.
Is there a better way to apply the largest remainder rounding to a Pandas column?
CodePudding user response：
Here is a simple approach to add the missing (remove the extra) difference to 100 in the last item of the group (you can update to another item if you like):
df['rounded'] = (df['Weight']
.round(2)
.groupby(df['Name'])
.transform(lambda s: pd.Series({s.index[-1]: (100-s.iloc[:-1].sum()).round(2)})
.combine_first(s))
)
output:
Name Weight rounded
0 John 33.3333 33.33
1 John 33.3333 33.33
2 John 33.3333 33.34
3 James 50.0000 50.00
4 James 25.0000 25.00
5 James 25.0000 25.00
6 Kim 6.6666 6.67
5 Kim 93.3333 93.33
6 Jane 46.6666 46.67
7 Jane 6.6666 6.67
8 Jane 46.6666 46.66
Checking the sum:
df.groupby('Name')['rounded'].sum()
James 100.0
Jane 100.0
John 100.0
Kim 100.0
Name: rounded, dtype: float64