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up vote 2 down vote favorite

I have a series of values that I want to have constrained to be within +1 and -1.

s = pd.Series(np.random.randn(10000))

I know I can use apply, but is there a simple vectorized approach?

s_ = s.apply(lambda x: min(max(x, -1), 1))
s_.head()

0   -0.256117
1    0.879797
2    1.000000
3   -0.711397
4   -0.400339
dtype: float64
share|improve this question
up vote 2 down vote accepted

Use clip:

s = s.clip(-1,1)

Example Input:

s = pd.Series([-1.2, -0.5, 1, 1.1])

0   -1.2
1   -0.5
2    1.0
3    1.1

Example Output:

0   -1.0
1   -0.5
2    1.0
3    1.0
share|improve this answer
    
This does something slightly different, but is nonetheless good to know! – Andy Hayden 29 mins ago
    
@AndyHayden: Huh? It gives the same output as what I get running the example code in the question and piRSquared's answer. Or am I missing subtle? – root 26 mins ago
    
mine discards values out of the between range, yours caps them at the min/max. As seen from the 0th item in your example. – Andy Hayden 24 mins ago
    
Ha! clip is what the OP wanted! – Andy Hayden 23 mins ago
    
I didn't know this clip method, ty +1 – piRSquared 19 mins ago
up vote 1 down vote

You can use the between Series method:

In [11]: s[s.between(-1, 1)]
Out[11]:
0   -0.256117
1    0.879797
3   -0.711397
4   -0.400339
5    0.667196
...

Note: This discards the values outside of the between range.

share|improve this answer
    
Ooops, this discards the values outside the between range. Going to leave this here anyway (as it could be what someone finding this answer may want). – Andy Hayden 22 mins ago
    
I was also unaware of between +1. – piRSquared 19 mins ago
up vote 1 down vote

Use nested np.where

pd.Series(np.where(s < -1, -1, np.where(s > 1, 1, s)))

Timing

enter image description here

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