计算每个 pandas.DataFrame 列的 numpy.std？
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Calculate numpy.std of each pandas.DataFrame's column?
提问by Michael
I want to get the numpy.stdof each column of my pandas.DataFrame.
我想获得numpy.std我的pandas.DataFrame.
Here is my code:
这是我的代码：
import pandas as pd
import numpy as np
prices = pd.DataFrame([[-0.33333333, -0.25343423, -0.1666666667],
[+0.23432323, +0.14285714, -0.0769230769],
[+0.42857143, +0.07692308, +0.1818181818]])
print(pd.DataFrame(prices.std(axis=0)))
Here is my code's output:
这是我的代码的输出：
pd.DataFrame([[ 0.39590933],
[ 0.21234018],
[ 0.1809432 ]])
And here is the right output (if calculate with np.std)
这是正确的输出（如果用 计算np.std）
pd.DataFrame([[ 0.32325862],
[ 0.17337503],
[ 0.1477395 ]])
Why am I having such difference? How can I fix that?
为什么我的差别这么大？我该如何解决？
NOTE: I have tried to do this way:
注意：我试图这样做：
print(np.std(prices, axis=0))
But I had the following error:
但我有以下错误：
Traceback (most recent call last):
File "C:\Users\*****\Documents\******\******\****.py", line 10, in <module>
print(np.std(prices, axis=0))
File "C:\Python33\lib\site-packages\numpy\core\fromnumeric.py", line 2812, in std
return std(axis=axis, dtype=dtype, out=out, ddof=ddof)
TypeError: std() got an unexpected keyword argument 'dtype'
Thank you!
谢谢！
回答by DSM
They're both right: they just differ on what the default delta degrees of freedom is. np.stduses 0, and DataFrame.stduses 1:
他们都是对的：他们只是在默认的 delta 自由度上有所不同。 np.std使用 0，DataFrame.std使用 1：
>>> prices.std(axis=0, ddof=0)
0 0.323259
1 0.173375
2 0.147740
dtype: float64
>>> prices.std(axis=0, ddof=1)
0 0.395909
1 0.212340
2 0.180943
dtype: float64
>>> np.std(prices.values, axis=0, ddof=0)
array([ 0.32325862, 0.17337503, 0.1477395 ])
>>> np.std(prices.values, axis=0, ddof=1)
array([ 0.39590933, 0.21234018, 0.1809432 ])
