Result analysis

Above image observations – 

A - P = 2 
A - Q = 3 
A - R = 0

Above image observations:

B - P  = 1
B - Q  = 0
B - R  = 4

stats.describe()

This function basically calculates the several descriptive statistics of the argument array.

Syntax:

scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy=’propagate’)

where,

1. Input array – array for which we want to generate the statistics.
2. axis ( int , float ) { # optional } – Axis along which statistics are calculated. The default axis is 0.
3. ddof ( int ) { # optional } – Delta Degrees for variance. Default ddof = 1.
4. bias ( bool ) { # optional } – skewness and kurtosis calculations for statistical bias.
5. nan_policy – { ‘propagate’,’raise’,’omit’ } { # optional ) – Handle the NAN inputs.

Return:

1. nbos  ( int or ndarray ) – length of data along axis value.
2. minmax  ( tuple of ndarrays or floats ) – Minimum and Maximum value of input array along the given axis.
3. mean ( float or ndarray ) – mean of input array.
4. variance ( ndarray or float ) – variance of input array along the given axis.
5. skewness ( float or ndarray ) – skewness of input array along the given axis.
6. kurtosis ( ndarray or float ) – kurtosis of input array along the given axis.

Output:

DescribeResult(
 nobs=8,
 minmax=(10, 80),
 mean=45.0,
 variance=600.0,
 skewness=0.0,
 kurtosis=-1.2380952380952381)

Output:

DescribeResult(nobs=6,
 minmax=(array([2, 3]),
 array([9, 9])),
 mean=array([6.        , 6.33333333]),
 variance=array([6.4       , 4.66666667]),
 skewness=array([-0.40594941, -0.3380617 ]),
 kurtosis=array([-0.9140625, -0.96     ]))

The scipy.stats is the SciPy sub-package. It is mainly used for probabilistic distributions and statistical operations. There is a wide range of probability functions.

There are three classes: