Sort Pandas Series on data descending then on index alphabetically, elegantly

Question

I'm looking for a smooth way to sort a pandas Series by data descending, followed by index ascending. I've been looking around in the docs and on Stackoverflow but couldn't find a straightforward way.

The Series has approximately 5000 entries and is the result of a tf-idf analysis with NLTK.

However, below I provide a very small sample of the data to illustrate the problem.

import pandas as pd

index = ['146tf150p', 'anytime', '645', 'blank', 'anything']
tfidf = [1.000000, 1.000000, 1.000000, 0.932702, 1.000000]

tfidfmax = pd.Series(tfidf, index=index)

For now I'm just converting the Series to a DataFrame, resetting the index, doing the sort and then setting the index, but I feel this is a big detour.

frame = pd.DataFrame(tfidfmax , columns=['data']).reset_index().sort_values(['data','index'], ascending=[False, True]).set_index(['index'])
3.02 ms ± 102 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

I'm looking forward to your suggestions!

Answer 1

You can use numpy.lexsort for this:

res = tfidfmax[np.lexsort((tfidfmax.index, -tfidfmax.values))]

print(res)

# 146tf150p    1.000000
# 645          1.000000
# anything     1.000000
# anytime      1.000000
# blank        0.932702
# dtype: float64

Note the reverse order in the syntax: the above code first sorts by descending values, then by index ascending.

Answer 2

Use function sorted by zip both lists create new Series by zip:

index = ['146tf150p', 'anytime', '645', 'blank', 'anything']
tfidf = [1.000000, 1.000000, 2.000000, 0.932702, 2.000000]

a = list(zip(*sorted(zip(index, tfidf),key=lambda x:(-x[1],x[0]))))

#if input is Series
#a = list(zip(*sorted(zip(tfidfmax.index,tfidfmax),key=lambda x:(-x[1],x[0]))))
s = pd.Series(a[1], index=a[0])
print (s)
645          2.000000
anything     2.000000
146tf150p    1.000000
anytime      1.000000
blank        0.932702
dtype: float64