For example the data look like:
df={'a1':[5,6,3,2,5],'a2':[23,43,56,2,6], 'a3':[4,2,3,6,7], 'a4':[1,2,1,3,2],'a5':[4,98,23,5,7],'a6':[5,43,3,2,5]}
x=pd.DataFrame(df)
Out[260]:
a1 a2 a3 a4 a5 a6
0 5 23 4 1 4 5
1 6 43 2 2 98 43
2 3 56 3 1 23 3
3 2 2 6 3 5 2
4 5 6 7 2 7 5
I need the result to look like:
top1 top2 top3
a2 a1 a6
a5 a2 a6
....
I've seen answer to a previous questions (see below) that recommends idxmax. But how to handle top n values (n>1)?
Find the column name which has the maximum value for each row
Update:
I find the answer very useful but the only thing is that my data is long so have to figure out a way to bypass that. I ended up saving the data to a csv file and then reading it back in in chunks. here is the code I used:
data = pd.read_csv('xxx.csv', chunksize=1000)
rslt = pd.DataFrame(np.zeros((0,3)), columns=['top1','top2','top3'])
for chunk in data:
x=pd.DataFrame(chunk).T
for i in x.columns:
df1row = pd.DataFrame(x.nlargest(3, i).index.tolist(), index=['top1','top2','top3']).T
rslt = pd.concat([rslt, df1row], axis=0)
rslt=rslt.reset_index(drop=True)
See Question&Answers more detail:
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