This is what I am trying to explain:
>>> pd.Series([7,20,22,22]).std()
7.2284161474004804
>>> np.std([7,20,22,22])
6.2599920127744575
Answer: this is explained by Bessel's correction, N-1
instead of N
in the denominator of the standard deviation formula. I wish Pandas used the same convention as numpy.
There is a related discussion here, but their suggestions do not work either.
I have data about many different restaurants. Here is my dataframe (imagine more than one restaurant, but the effect is reproduced with just one):
>>> df
restaurant_id price
id
1 10407 7
3 10407 20
6 10407 22
13 10407 22
Question: r.mi.groupby('restaurant_id')['price'].mean()
returns price means for each restaurant. I want to get the standard deviations. However, r.mi.groupby('restaurant_id')['price'].std()
returns wrong values.
As you can see, for simplicity I have extracted just one restaurant with four items. I want to find the standard deviation of the price. Just to make sure:
>>> np.mean([7,20,22,22])
17.75
>>> np.std([7,20,22,22])
6.2599920127744575
We can get the same (correct) values with
>>> np.mean(df)
restaurant_id 10407.00
price 17.75
dtype: float64
>>> np.std(df)
restaurant_id 0.000000
price 6.259992
dtype: float64
(Of course, disregard the mean restaurant id.) Obviously, np.std(df)
is not a solution when I have more than one restaurant. So I am using groupby
.
>>> df.groupby('restaurant_id').agg('std')
price
restaurant_id
10407 7.228416
What?! 7.228416 is not 6.259992.
Let's try again.
>>> df.groupby('restaurant_id').std()
Same thing.
>>> df.groupby('restaurant_id')['price'].std()
Same thing.
>>> df.groupby('restaurant_id').apply(lambda x: x.std())
Same thing.
However, this works:
for id, group in df.groupby('restaurant_id'):
print id, np.std(group['price'])
Question: is there a proper way to aggregate the dataframe, so I will get a new time series with the standard deviations for each restaurant?
A numpy array is a grid of values that belong to the same data type. NumPy arrays are created using the array() function. A Pandas Series is a one-dimensional labeled array that can store data of any type. It is created using the Series() function of the Pandas library.
For Data Scientists, Pandas and Numpy are both essential tools in Python. We know Numpy runs vector and matrix operations very efficiently, while Pandas provides the R-like data frames allowing intuitive tabular data analysis. A consensus is that Numpy is more optimized for arithmetic computations.
Pandas is defined as an open-source library that provides high-performance data manipulation in Python. It is built on top of the NumPy package, which means Numpy is required for operating the Pandas. The name of Pandas is derived from the word Panel Data, which means an Econometrics from Multidimensional data.
std. Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements.
I see. Pandas is using Bessel's correction by default -- that is, the standard deviation formula with N-1
instead of N
in the denominator. As behzad.nouri has pointed out in the comments,
pd.Series([7,20,22,22]).std(ddof=0)==np.std([7,20,22,22])
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