# sciPy stats.sem() function | Python

scipy.stats.sem(arr, axis=0, ddof=0) function is used to compute the standard error of the mean of the input data.

Parameters :
arr : [array_like]Input array or object having the elements to calculate the standard error.
axis : Axis along which the mean is to be computed. By default axis = 0.
ddof : Degree of freedom correction for Standard Deviation.

Results : standard error of the mean of the input data.

Example:

 `# stats.sem() method  ` `import` `numpy as np ` `from` `scipy ``import` `stats ` `  `  `  `  `arr1 ``=` `[[``20``, ``2``, ``7``, ``1``, ``34``], ` `        ``[``50``, ``12``, ``12``, ``34``, ``4``]] ` ` `  `arr2 ``=` `[``50``, ``12``, ``12``, ``34``, ``4``] ` ` `  `print` `(``"\narr1 : "``, arr1) ` `print` `(``"\narr2 : "``, arr2) ` ` `  `print` `(``"\nsem ratio for arr1 : "``,  ` `       ``stats.sem(arr1, axis ``=` `0``, ddof ``=` `0``)) ` ` `  `print` `(``"\nsem ratio for arr1 : "``,  ` `       ``stats.sem(arr1, axis ``=` `1``, ddof ``=` `0``)) ` ` `  `print` `(``"\nsem ratio for arr1 : "``,  ` `       ``stats.sem(arr2, axis ``=` `0``, ddof ``=` `0``))  `

Output :

```arr1 :  [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]]

arr2 :  [50, 12, 12, 34, 4]

sem ratio for arr1 :  [10.60660172  3.53553391  1.76776695 11.66726189 10.60660172]

sem ratio for arr1 :  [5.62423328 7.61892381]

sem ratio for arr1 :  7.618923808517841```

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