Creating an element-wise minimum Series from two other Series in Python Pandas

Question

I am having trouble finding a way to do an efficient element-wise minimum of two Series objects in pandas. For example I can add two Series easily enough:

In         


        

Answer 1

pd.Series([1,2,3,4,5]).clip(upper=pd.Series([6,5,4,3,2]))

will get you:

0    1
1    2
2    3
3    3
4    2
dtype: int64

Answer 2

You can concat the dataframes and take the minimum, specifying level=0:

>>> s1 = pd.Series(data=[1,1,1], index=[1,2,3])
>>> s2 = pd.Series(data=[1,2,2,1], index=[1,2,3,4])
>>> pd.concat([s1, s2]).min(level=0)
1    1
2    1
3    1
4    1
dtype: int64

This approach also works on dataframes.

Answer 3

Another similar way:

In [11]: pd.DataFrame([s1, s2]).min()
Out[11]:
1    1
2    1
3    1
4    1
dtype: float64

Answer 4

You can use the combine method of a DataFrame with np.minimum as the argument. np.minimum has special handling for NaN and complex NaNs.

Indeed, the pandas docs for combine uses the np.minimum function to illustrate a "true element-wise combine":

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Answer 5

Same answer as Andy Hayden, but a bit easier to read:

>>> import pandas as pd
>>> s1 = pd.Series(data=[1,2,3,4,5], index=[1,2,3,4,5])
>>> s2 = pd.Series(data=[5,1,3,5], index=[1,2,3,4])
>>> pd.DataFrame([s1, s2]).min()
1    1.0
2    1.0
3    3.0
4    4.0
5    5.0
dtype: float64

Answer 6

I find this the simplest:

import numpy as np

smax = np.minimum(s1, s2)

Link to docs (numpy.minimum)