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:
# FInding statistics of data from scipy import stats arr1 = [9, 3, 27] desc = stats.describe(arr1) print("No. of observations is :\n", desc) |
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
# 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) |
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]))
