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Summary Statistics

Last updated: March 27th, 20192019-03-27Project preview

rmotr


Summary statistics

In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in

  • a measure of location, or central tendency, such as the arithmetic mean.
  • a measure of statistical dispersion like the standard deviation.
  • a measure of the shape of the distribution like skewness or kurtosis.
  • if more than one variable is measured, a measure of statistical dependence such as a correlation coefficient.

NumPy has quite a few useful statistical functions for calculating sum, mean, standard deviation and variance, etc. from the given elements in the array.

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In [ ]:
import sys
import numpy as np

Summary statistics

In [ ]:
a = np.array([1, 2, 3, 4])
In [ ]:
a.shape
In [ ]:
a.sum()
In [ ]:
a.mean()
In [ ]:
a.std()
In [ ]:
a.var()
In [ ]:
A = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
In [ ]:
A.sum()
In [ ]:
A.mean()
In [ ]:
A.std()
In [ ]:
A.sum(axis=0)
In [ ]:
A.sum(axis=1)
In [ ]:
A.mean(axis=0)
In [ ]:
A.mean(axis=1)
In [ ]:
A.std(axis=0)
In [ ]:
A.std(axis=1)

Cumulative sum of elements starting from 0

In [ ]:
A
In [ ]:
A.cumsum()

Cumulative product of elements starting from 1

In [ ]:
A.cumprod()

 And many more...

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