sciPy stats.sem() function | Python

sciPy stats.describe() function | Python

Last Updated : 10 Feb, 2019

scipy.stats.describe(array, axis=0) computes the descriptive statistics of the passed array elements along the specified axis of the array.

Parameters : array: Input array or object having the elements to calculate the statistics. axis: Axis along which the statistics is to be computed. By default axis = 0. Returns : Statistics of the array elements based on the set parameters.

Code #1:

Python3

# FInding statistics of data

from scipy import stats

arr1 = [9, 3, 27] 
 
desc = stats.describe(arr1)

print("No. of observations is :\n", desc)

Output:

No. of observations is : DescribeResult(nobs=3, minmax=(3, 27), mean=13.0, variance=156.0, skewness=0.5280049792181878, kurtosis=-1.5)

Code #2: With multi-dimensional data

Python3

# FInding statistics of data

from scipy import stats

arr1 = [[1, 3, 27], 
        [3, 4, 6], 
        [7, 6, 3], 
        [3, 6, 8]] 
 
desc = stats.describe(arr1, axis = 0)


print("No. of observations at axis = 0 :\n\n", desc)


print("\n\nNo. of observations at axis = 1 :\n\n", desc)

Output:

No. of observations at axis = 0 : DescribeResult(nobs=4, minmax=(array([1, 3, 3]), array([ 7, 6, 27])), mean=array([ 3.5 , 4.75, 11. ]), variance=array([ 6.33333333, 2.25 , 118. ]), skewness=array([ 0.65202366, -0.21383343, 1.03055786]), kurtosis=array([-0.90304709, -1.72016461, -0.75485971])) No. of observations at axis = 1 : DescribeResult(nobs=4, minmax=(array([1, 3, 3]), array([ 7, 6, 27])), mean=array([ 3.5 , 4.75, 11. ]), variance=array([ 6.33333333, 2.25 , 118. ]), skewness=array([ 0.65202366, -0.21383343, 1.03055786]), kurtosis=array([-0.90304709, -1.72016461, -0.75485971]))

sciPy stats.sem() function | Python

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