Largest Element All Lists In Panda Series
I have a pandas series say import pandas as pd a = pd.Series([ [1, 2, 3, 4, 5], [6, 7, 8, 3, 334], [333, 4, 5, 3, 4] ]) I want to find the largest element in all lists
Solution 1:
Option 1
Only works if elements are actually list
. This is because sum
concatenates lists. This is also likely very slow.
max(a.sum())
334
Option 2
minimal two tiered application of max
max(map(max, a))
334
Option 3 Only works if all lists are same length
np.max(a.tolist())
334
Option 4
One application of max
on an unwound generator
max(x forlin a forxin l)
334
Solution 2:
To dataframe
pd.DataFrame(a.values.tolist()).max().max()
Out[200]: 334
Or numpy.concatenate
np.concatenate(a.values).max()
Out[201]: 334
Or
max(sum(a,[]))
Out[205]: 334
Solution 3:
This is one way:
max(max(i) for i in a)
Functional variant:
max(map(max, a))
Alternative method which only calculates one max
:
from toolz import concatmax(concat(a))
For the fun of it below is some benchmarking. The lazy function concat
and optimised map
/ list comprehension do best, then come numpy
functions, pandas
methods usually worse, clever sum
applications last.
import numpy as np
from toolz import concat
import pandas as pd
a = pd.Series([list(np.random.randint(0, 10, 100)) for i inrange(1000)])
# times in ms5.92max(concat(a))
6.29max(map(max, a))
6.67max(max(i) for i in a)
17.4max(x for l in a for x in l)
19.2 np.max(a.tolist())
20.4 np.concatenate(a.values).max()
64.6 pd.DataFrame(a.values.tolist()).max().max()
373 np.max(a.apply(pd.Series).values)
672max(sum(a,[]))
696max(a.sum())
Solution 4:
Yet another answer using np.max:
import numpy as np
np.max(a.apply(pd.Series).values)
Out[175]: 334
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