# numpy.arcsinh() in Python

• Last Updated : 29 Nov, 2018

numpy.arcsinh() : This mathematical function helps user to calculate inverse hyperbolic sine, element-wise for all arr.

Syntax : numpy.arcsinh(arr, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, ufunc ‘arcsinh’)
Parameters :

arr : array_like
Input array.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs :Allows to pass keyword variable length of argument to a function. Used when we want to handle named argument in a function.

Return : An array with inverse hyperbolic sine of arr
for all arr i.e. array elements.

Note :

The convention is to return the angle of arr whose imaginary part lies in [-pi/2, pi/2].

Code #1 : Working

 `# Python program explaining``# arcsinh() function`` ` `import` `numpy as np`` ` `in_array ``=` `[``2``, ``1``, ``10``, ``100``]``print` `(``"Input array : \n"``, in_array)`` ` `arcsinh_Values ``=` `np.arcsinh(in_array)``print` `(``"\nInverse hyperbolic sine values of input array : \n"``, arcsinh_Values)`

Output :

```Input array :
[2, 1, 10, 100]

Inverse hyperbolic sine values of input array :
[ 1.44363548  0.88137359  2.99822295  5.29834237]```

Code #2 : Graphical representation

 `# Python program showing``# Graphical representation  ``# of arcsinh() function % matplotlib inline ``import` `numpy as np``import` `matplotlib.pyplot as plt``in_array ``=` `np.linspace(``1``, np.pi, ``18``)``out_array1 ``=` `np.sin(in_array)``out_array2 ``=` `np.arcsinh(in_array)``  ` `print``(``"in_array : "``, in_array)``print``(``"\nout_array with sin : "``, out_array1)``print``(``"\nout_array with arcsinh : "``, out_array2)``# blue for numpy.sinh() ``# red for numpy.arcsinh()``plt.plot(in_array, out_array1,``            ``color ``=` `'blue'``, marker ``=` `"."``)``              ` `plt.plot(in_array, out_array2,``            ``color ``=` `'red'``, marker ``=` `"+"``)``              ` `plt.title(``"blue : numpy.sin() \nred : numpy.arcsinh()"``)``plt.xlabel(``"X"``)``plt.ylabel(``"Y"``)`

Output :

```
in_array :  [ 1.          1.12597604  1.25195208  1.37792812  1.50390415  1.62988019
1.75585623  1.88183227  2.00780831  2.13378435  2.25976038  2.38573642
2.51171246  2.6376885   2.76366454  2.88964058  3.01561662  3.14159265]

out_array with sin :  [  8.41470985e-01   9.02688009e-01   9.49598344e-01   9.81458509e-01
9.97763553e-01   9.98255056e-01   9.82925230e-01   9.52017036e-01
9.06020338e-01   8.45664137e-01   7.71905017e-01   6.85911986e-01
5.89047946e-01   4.82848093e-01   3.68995589e-01   2.49294878e-01
1.25643097e-01   1.22464680e-16]

out_array with arcsinh :  [ 0.88137359  0.96770792  1.04881189  1.12508571  1.1969269   1.26471422
1.32879961  1.38950499  1.44712201  1.50191335  1.55411486  1.60393799
1.65157228  1.69718777  1.74093713  1.78295772  1.82337333  1.86229574]
```

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