In numpy is there any built-in function to calculate moving skewness of numpy array? I know there are basic functions like mean, median, mode, min, max etc. But I wonder if there are any functions for calculating moving skewness, kurtosis and higher moments?
You should use SciPy for calculating skewness, kurtosis, etc.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html
Already got solution here. There is no built-in function in numpy but we can use scipy and numpy combine to achieve the goal.
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I'm trying to convert a radial distribution function for my use, but the code I'm looking at uses a cKDTree. The problem is that I want to use only numpy in my function.
Does anyone know an equivalent function in numpy that can be used or a way to make an equivalent "tree"?
Is there a way to calculate the cumulative distribution function (using the probability density function)? I've seen many scipy and numpy ideas out, but is there any other way?
I need to implement R's quantile function type 3 in pandas, which is according to documentation (https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/quantile), nearest even order statistic.
Pandas only has basic ones like lower, higher, linear, nearest. (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.quantile.html)
How do I implement the nearest even order interpolation on pandas?
I'm trying to create a function to find the rolling derivatives (first and second) in Pandas.
I find that df.diff() is quite convenient.
I want to find the derivatives with the rolling window value = 40.
For the first derivative,
noise = np.random.normal(size=int(1e4))
noise=pd.DataFrame(noise)
first_derivative=noise.diff(periods=40)
Is it correct if I use this for the second derivative?
second_derivative=noise.diff(periods=40).diff()
I'm confused, but if I put periods=40 again in the second .diff() then it would be 40*40 rolling window (for the second derivative).
Thank you!
Pandas is not a mathematical library, and its diff() operation just take discrete differences among elements, not derivatives.
In order to take derivatives, I would recommend you to use SymPy, a nice Python library for symbolic mathematics. Check documentation for further details.
Example:
from sympy import *
>> diff(cos(x), x)
-sin(x)
I'm translating some code from MATLAB to Python and I'm stuck with the corrmtx() MATLAB function. Is there any similar function in Python, or how could I replace it?
The spectrum package has such a function.
How about:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.toeplitz.html
The matlab docs for corrmtx state:
X = corrmtx(x,m) returns an (n+m)-by-(m+1) rectangular Toeplitz matrix
X, such that X'X is a (biased) estimate of the autocorrelation matrix
for the length n data vector x.
The scipy function gives the Toeplitz matrix, although I'm not sure if the implementations are identical.
Here is a list of alternatives that can help you in translating your code, all of which contain that function:
scipy (toeplitz | corrmtx)
spectrum (corrmtx)
The following is a link to another post that tells you how to use numpy for the auto correlation since it seems to be the default funcationality of corrmtx
Additional Information:
Finding the correlation matrix in Python
Unbiased Estimation of Covariance Matrix