# Summary Statistics

Last updated: March 27th, 2019

# 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.

## Hands on!¶

In [ ]:
import sys
import numpy as np


### Summary statistics¶

In [ ]:
a = np.array([1, 2, 3, 4])

In [ ]:
a.shape

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a.sum()

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a.mean()

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a.std()

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a.var()

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A = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])

In [ ]:
A.sum()

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A.mean()

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A.std()

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A.sum(axis=0)

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A.sum(axis=1)

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A.mean(axis=0)

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A.mean(axis=1)

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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()