# sciPy stats.describe() function | Python

`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)  `

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
 `# 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]))

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