 # scipy stats.skew () | python

` scipy.stats.skew (array, axis = 0, bias = True) ` calculates the skewness of the dataset.

`  skewness = 0:  normally distributed.  skewness & gt; 0:  more weight in the left tail of the distribution.  skewness & lt; 0:  more weight in the right tail of the distribution. `

Its formula is —

Parameters:
array: Input array or object having the elements.
axis: Axis along which the skewness value is to be measured. By default axis = 0.
bias: Bool; calculations are corrected for statistical bias, if set to False.

Returns: Skewness value of the data set, along the axis.

Code # 1:

 ` # Graph using numpy.linspace () ` ` # Finding asymmetry `   ` from scipy.stats import skew ```` import numpy as np  import pylab as p    x1 = np.linspace ( - 5 , 5 , 1000 ) y1 = 1. / (np.sqrt ( 2. * np.pi)) * np.exp ( - . 5 * (x1) * * 2  )   p.plot (x1, y1, ` * ` )   print ( ` Skewness for data: ` , skew (y1)) ```

Output:

`     Skewness for data: 1.1108237139164436 `

Code # 2:

 ` # Graph using numpy.linspace () ` ` # Finding asymmetry ` ` `  ` `  ` from ` ` scipy.stats ` ` import ` skew ` import ` ` numpy as np ` ` import ` ` pylab as p `   ` x1 ` ` = ` ` np.linspace ( - 5 , 12 , 1000 ) ```` y1 = 1. / (np.sqrt ( 2. * np.pi)) * np.exp ( - . 5 * (x1) * * 2  )   p.plot (x1, y1, `.` )   print ( `Skewness for data:` , skew (y1)) ```

Output:

`     Skewness for data: 1.917677776148478 `

Code # 3: Based on random data

` `

``` # Find asymmetry   from scipy.stats import skew import numpy as np    # random values ​​based on on normal distribution x = np. random.normal ( 0 , 2 , 10000 )   print ( " X: " , x)   print ( `Skewness for data:` , skew (x)) ```

` `

Output:

` X: [ 0.03255323 -6.18574775 -0.58430139 ... 3.22112446 1.16543279 0.84083317] Skewness for data: 0.03248837584866293 `