numpy.log() in Python

numpy.log(x[, out] = ufunc ‘log1p’) : This mathematical function helps user to calculate Natural logarithm of x where x belongs to all the input array elements.
Natural logarithm log is the inverse of the exp(), so that log(exp(x)) = x. The natural logarithm is log in base e.

Parameters :

array : [array_like] Input array or object.
out : [ndarray, optional] Output array with same dimensions as Input array, placed with result.

Return :
An array with Natural logarithmic value of x; where x belongs to all elements of input array.

Code #1 : Working

 `# Python program explaining ` `# log() function ` `import` `numpy as np ` ` `  `in_array ``=` `[``1``, ``3``, ``5``, ``2``*``*``8``] ` `print` `(``"Input array : "``, in_array) ` ` `  `out_array ``=` `np.log(in_array) ` `print` `(``"Output array : "``, out_array) ` ` `  ` `  `print``(``"\nnp.log(4**4) : "``, np.log(``4``*``*``4``)) ` `print``(``"np.log(2**8) : "``, np.log(``2``*``*``8``)) `

Output :

```Input array :  [1, 3, 5, 256]
Output array :  [ 0.          1.09861229  1.60943791  5.54517744]

np.log(4**4) :  5.54517744448
np.log(2**8) :  5.54517744448
```

Code #2 : Graphical representation

 `# Python program showing ` `# Graphical representation   ` `# of log() function ` `import` `numpy as np ` `import` `matplotlib.pyplot as plt ` ` `  `in_array ``=` `[``1``, ``1.2``, ``1.4``, ``1.6``, ``1.8``, ``2``] ` `out_array ``=` `np.log(in_array) ` ` `  `print` `(``"out_array : "``, out_array) ` ` `  `plt.plot(in_array, in_array,  ` `         ``color ``=` `'blue'``, marker ``=` `"*"``) ` ` `  `# red for numpy.log() ` `plt.plot(out_array, in_array,  ` `         ``color ``=` `'red'``, marker ``=` `"o"``) ` `          `  `plt.title(``"numpy.log()"``) ` `plt.xlabel(``"out_array"``) ` `plt.ylabel(``"in_array"``) ` `plt.show()  `

Output :

```out_array :  [ 0.          0.18232156  0.33647224  0.47000363  0.58778666  0.69314718]
```

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