CODEWITHSUNDEEP
python
CWeeks
CWeeks

Reputation: 427

Find the column names which have top 3 largest values for each row

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)

Upvotes: 11

Views: 8671

Answers (4)

Billy Bonaros
Billy Bonaros

Reputation: 1721

You can do it like this:

x.T.apply(lambda x: x.sort_values(ascending=False).index).T.filter(['a1','a2','a3']).rename(columns={"a1":'top1',"a2":'top2',"a3":'top3'})

Results:

  top1 top2 top3
0   a2  a6  a1
1   a5  a6  a2
2   a2  a5  a6
3   a3  a5  a4
4   a5  a3  a2

Upvotes: 1

George Pipis
George Pipis

Reputation: 1822

import pandas as pd
import numpy as np

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]}
df=pd.DataFrame(df)

df


   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

We can solve it using the argsortfrom numpy and apply , lambda from pandas. The solution:

Tops =pd.DataFrame(df.apply(lambda x:list(df.columns[np.array(x).argsort()[::-1][:3]]), axis=1).to_list(),  columns=['Top1', 'Top2', 'Top3'])


Tops

And we get:

  Top1 Top2 Top3
0   a2   a6   a1
1   a5   a6   a2
2   a2   a5   a6
3   a3   a5   a4
4   a5   a3   a2

Upvotes: 11

Rick
Rick

Reputation: 2110

an answer without the need to transpose your data.

first we define how to get the n largest value index from a list (or a row). 

def get_n_largest_ind(a,n):
    ind = np.argpartition(a, -n)[-n:]
    return ind[0]

a = [0.4,0.5,0.4,0.8,0.2,0.1,0.99]
get_n_largest_ind(a,3)
>>>> 1

Now you can define which columns to use for the ranking. Then we loop over the number of top columns you want to show (in this example 3).

cols = df.columns
for n in [1,2,3]:
    df["top{}".format(n)] = df[cols].apply(lambda x: cols[get_n_largest_ind(x,n)],axis=1)

Upvotes: 0

2342G456DI8
2342G456DI8

Reputation: 1809

What you need is pandas.DataFrame.nlargest. 

import pandas as pd
import numpy as np

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).T

rslt = pd.DataFrame(np.zeros((0,3)), columns=['top1','top2','top3'])
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)

print rslt

Out[52]: 
  top1 top2 top3
0   a2   a1   a6
0   a5   a2   a6
0   a2   a5   a1
0   a3   a5   a4
0   a3   a5   a2

Upvotes: 6

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