Dear All,
I really need your assistance,
I have a dataset with 1005000 rows and 25 columns,
The main column that I repeatedly use are Time, ID, and Reputation
First I sliced the data based on the time, and I append the sliced data in
a list called "df_list". So I get 201 lists with 25 columns
The main code is starting for here:
for elem in df_list:
{do something.....}
{Here I'm trying to calculate the outliers}
Out.append(outliers)
Now my problem is that I need to locate those outliers in the df_list and
then update another column with is the "Reputation"
Note that the there is a duplicated IDs but at different time slot
example is ID = 1 is outliers, I need to select all ID = 1 in the list and
update their reputation column
I tried those solutions:
1)
grp = data11.groupby(['ID'])
for i in GlobalNotOutliers.ID:
data11.loc[grp.get_group(i).index, 'Reput'] += 1
for j in GlobalOutliers.ID:
data11.loc[grp.get_group(j).index, 'Reput'] -= 1
It works for a dataframe but not for a list
2)
for elem in df_list:
elem.loc[elem['ID'].isin(Outlier['ID'])]
It doesn't select the right IDs, it gives the whole values in elem
3) Here I set the index using IDs:
for i in Outlier.index:
for elem in df_list:
print(elem.Reput)
if i in elem.index:
# elem.loc[elem[i] , 'Reput'] += 1
m = elem.iloc[i, :]
print(m)
It gives this error:
IndexError: single positional indexer is out-of-bounds
I'm greatly thankful to anyone who can help me,
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