Different std in pandas vs numpy

Question

The standard deviation differs between pandas and numpy. Why and which one is the correct one? (the relative difference is 3.5% which should not come from rounding, this is high in my opinion).

Example

import numpy as np
import pandas as pd
from StringIO import StringIO

a='''0.057411
0.024367
 0.021247
-0.001809
-0.010874
-0.035845
0.001663
0.043282
0.004433
-0.007242
0.029294
0.023699
0.049654
0.034422
-0.005380'''


df = pd.read_csv(StringIO(a.strip()), delim_whitespace=True, header=None)

df.std()==np.std(df) # False
df.std() # 0.025801
np.std(df) # 0.024926

(0.024926 - 0.025801) / 0.024926 # 3.5% relative difference

I use these versions:

pandas '0.14.0'
numpy '1.8.1'

Chris Withers · Accepted Answer · 2023-07-13 13:42:44Z

89

In a nutshell, neither is "incorrect". Pandas uses the unbiased estimator (N-1 in the denominator), whereas Numpy by default does not.

To make them behave the same, pass ddof=1 to numpy.std().

For further discussion, see

Population variance and sample variance.

edited Jul 13, 2023 at 13:42

Chris Withers

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answered Jul 27, 2014 at 18:29

NPE

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3 Comments

Mannaggia Over a year ago

yes, in fact df.std()==np.std(df, ddof=1) is True! Therefore the question now becomes which estimator is better :-), just kidding...

Bill Over a year ago

For the record, people considering using df.std() and np.std(ddof=1) interchangeably should also be aware of another difference between the two: np.std returns nan if there are any missing values whereas df.std returns the standard deviation of the non-missing values. If you want to ignore nans use np.nanstd().

DonCristobal Over a year ago

This implies that df.std != df.values.std() which I did not expect at all. This seems pretty confusing.

Xuan · Accepted Answer · 2019-03-28 14:20:34Z

10

For pandas to performed the same as numpy, you can pass in the ddof=0 parameter, so df.std(ddof=0).

This short video explains quite well why n-1 might be preferred for samples. https://www.youtube.com/watch?v=Cn0skMJ2F3c

edited Mar 28, 2019 at 14:20

answered Mar 27, 2019 at 21:46

Xuan

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