# .log()

Published Jul 15, 2024
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In NumPy, the `.log()` method is used to calculate the natural logarithm (base-e) of each element in an array. It is widely used in scientific computations, data analysis, and mathematical applications where logarithmic scaling is essential.

## Syntax

``````numpy.log(array, out=None, where=True)
``````
• `array`: An array-like structure containing the elements for which the natural logarithm will be applied.
• `out` (Optional): An array where the resulting logarithms are stored. If not provided, a new array is created to hold the results.
• `where` (Optional): An array of boolean values that determines which elements undergo logarithm computation:
• For elements where the condition is `True`, the natural logarithm is calculated.
• For elements where the condition is `False`, the original element remains unchanged.
• If `where` is not specified, the natural logarithm is computed for all elements in the input.

## Example

The below example demonstrates the use of the `.log()` method:

```import numpy as np
arr = np.array([1, np.e, 10, 100])result = np.log(arr)
print(result)
```

The code above will generate the following output:

```[0.         1.         2.30258509 4.60517019]
```

## Codebyte Example

In this codebyte example, the `.log()` method only computes the natural logarithm of positive elements in the array:

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