# Skewed Distribution in Python

## Find skewness of data in Python using Scipy

we simply use this library byfrom Scipy.stats import skew

### Skewness based on its types

There are three types of skewness :

• Normally Distributed: In this, the skewness is always equated to zero.

Skewness=0

• Positively skewed distribution: In this, A Positively-skewed distribution has a long right tail, that’s why this is also known as right-skewed distribution. the reason behind it, in this value of mode is highest and mean is least which leads to right peak.

Skessness >o

• Negatively skewed distribution: In this, a negatively skewed distribution has a long left tail, that’s why this is also known as left-skewed distribution. the reason behind it, in this value of mode is least and mean is highest just reverse to right-skewed which leads to the left peak.

Skewness<0

## The formula to find skewness of data

Skewness =3(Mean- Median)/Standard Deviation

Example: skewness for given data

Input: Any random ten input

```from scipy.stats import skew
import numpy as np
x= np.random.normal(0,5,10)
print("X:",x)
print("Skewness for data :",skew(x))```

Output:

```X: [ 5.51964388 -1.69148439 -5.55162585 -5.6901246   2.38861009  2.73400871
3.77918369 -2.30759396  3.67021073  1.48142813]
Skewness for data : -0.4625020248485552```

scipy.stats.skewnorm() is a skew-normal continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution.

Parameters :

q : lower and upper tail probability
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : skew-normal continuous random variable

Code #1 : Creating skew-normal continuous random variable

Output :

```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D843A9C8
```

Code #2 : skew-normal continuous variates and probability distribution

Output :

```Random Variates :
4.2082825614230845

Probability Distribution :
[7.38229165e-05 1.13031801e-04 1.71343310e-04 2.57152477e-04
3.82094976e-04 5.62094062e-04 8.18660285e-04 1.18047149e-03
1.68525001e-03 2.38193677e-03]
```

Code #3 : Graphical Representation.

Output :

```Distribution :
[0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
1.95918367 2.        ]
```

Code #4 : Varying Positional Arguments

Output :

### Left-skewed Levy Distribution in Statistics

scipy.stats.levy_l() is a left-skewed Levy continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution.

Parameters :

q : lower and upper tail probability
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : left-skewed Levy continuous random variable

Code #1 : Creating left-skewed Levy continuous random variable

Output :

```RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D6707508
```

Code #2 : left-skewed Levy continuous variates and probability distribution

Output :

```Random Variates :
1.1073459342251062

Probability Distribution :
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

```

Code #3 : Graphical Representation.

Output :

```Distribution :
[0.         0.08163265 0.16326531 0.24489796 0.32653061 0.40816327
0.48979592 0.57142857 0.65306122 0.73469388 0.81632653 0.89795918
0.97959184 1.06122449 1.14285714 1.2244898  1.30612245 1.3877551
1.46938776 1.55102041 1.63265306 1.71428571 1.79591837 1.87755102
1.95918367 2.04081633 2.12244898 2.20408163 2.28571429 2.36734694
2.44897959 2.53061224 2.6122449  2.69387755 2.7755102  2.85714286
2.93877551 3.02040816 3.10204082 3.18367347 3.26530612 3.34693878
3.42857143 3.51020408 3.59183673 3.67346939 3.75510204 3.83673469
3.91836735 4.        ]
```

Code #4 : Varying Positional Arguments

Output :