I have a pandas data frame that looks like this:
id age weight group
1 12 45 [10-20]
1 18 110 [10-20]
1 25 25 [20-30]
1 29 85 [20-30]
1 32 49 [30-40]
1 31 70 [30-40]
1 37 39 [30-40]
I am looking for a data frame that would look like this: (sd=standard deviation)
group group_mean_weight group_sd_weight rest_mean_weight rest_sd_weight
[10-20]
[20-30]
[30-40]
Here the second/third columns are mean and SD for that group. columns third and fourth are mean and SD for the rest of the groups combined.
Here's a way to do it:
res = df.group.to_frame().groupby('group').count()
for group in res.index:
mask = df.group==group
srGroup, srOther = df.loc[mask, 'weight'], df.loc[~mask, 'weight']
res.loc[group, ['group_mean_weight','group_sd_weight','rest_mean_weight','rest_sd_weight']] = [
srGroup.mean(), srGroup.std(), srOther.mean(), srOther.std()]
res = res.reset_index()
Output:
group group_mean_weight group_sd_weight rest_mean_weight rest_sd_weight
0 [10-20] 77.500000 45.961941 53.60 24.016661
1 [20-30] 55.000000 42.426407 62.60 28.953411
2 [30-40] 52.666667 15.821926 66.25 38.378596
An alternative way to get the same result is:
res = ( pd.DataFrame(
df.group.drop_duplicates().to_frame()
.apply(lambda x: [
df.loc[df.group==x.group,'weight'].mean(),
df.loc[df.group==x.group,'weight'].std(),
df.loc[df.group!=x.group,'weight'].mean(),
df.loc[df.group!=x.group,'weight'].std()], axis=1, result_type='expand')
.to_numpy(),
index=list(df.group.drop_duplicates()),
columns=['group_mean_weight','group_sd_weight','rest_mean_weight','rest_sd_weight'])
.reset_index().rename(columns={'index':'group'}) )
Output:
group group_mean_weight group_sd_weight rest_mean_weight rest_sd_weight
0 [10-20] 77.500000 45.961941 53.60 24.016661
1 [20-30] 55.000000 42.426407 62.60 28.953411
2 [30-40] 52.666667 15.821926 66.25 38.378596
UPDATE: OP asked in a comment: "what if I have more than one weight column? what if I have around 10 different weight columns and I want sd for all weight columns?"
To illustrate below, I have created two weight columns (weight and weight2) and have simply provided all 4 aggregates (mean, sd, mean of other, sd of other) for each weight column.
wgtCols = ['weight','weight2']
res = ( pd.concat([ pd.DataFrame(
df.group.drop_duplicates().to_frame()
.apply(lambda x: [
df.loc[df.group==x.group,wgtCol].mean(),
df.loc[df.group==x.group,wgtCol].std(),
df.loc[df.group!=x.group,wgtCol].mean(),
df.loc[df.group!=x.group,wgtCol].std()], axis=1, result_type='expand')
.to_numpy(),
index=list(df.group.drop_duplicates()),
columns=[f'group_mean_{wgtCol}',f'group_sd_{wgtCol}',f'rest_mean_{wgtCol}',f'rest_sd_{wgtCol}'])
for wgtCol in wgtCols], axis=1)
.reset_index().rename(columns={'index':'group'}) )
Input:
id age weight weight2 group
0 1 12 45 55 [10-20]
1 1 18 110 120 [10-20]
2 1 25 25 35 [20-30]
3 1 29 85 95 [20-30]
4 1 32 49 59 [30-40]
5 1 31 70 80 [30-40]
6 1 37 39 49 [30-40]
Output:
group group_mean_weight group_sd_weight rest_mean_weight rest_sd_weight group_mean_weight2 group_sd_weight2 rest_mean_weight2 rest_sd_weight2
0 [10-20] 77.500000 45.961941 53.60 24.016661 87.500000 45.961941 63.60 24.016661
1 [20-30] 55.000000 42.426407 62.60 28.953411 65.000000 42.426407 72.60 28.953411
2 [30-40] 52.666667 15.821926 66.25 38.378596 62.666667 15.821926 76.25 38.378596