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# Python Data Science NumPy ufunc Creating Your Own ufunc

How To Create Your Own ufunc?

In order to create you own ufunc, you have to define a function, like you do with normal functions in Python, then you add it to your NumPy ufunc library with the frompyfunc() method.

The frompyfunc() method takes the following arguments.

1. function - the name of the function.

2. inputs - the number of input arguments (arrays).

3. outputs - the number of output arrays.

Code

import numpy as np

```def myadd(x, y):
return x+y
```

print(myadd([11, 12, 13, 14], [15, 16, 17, 18]))

the output will be

[26 28 30 32]

How to Check if a Function is a ufunc?

In order to Check the type of a function to check if it is a ufunc or not. A ufunc should return <class 'numpy.ufunc'>.

Example 2: Check if a function is a ufunc.

Code

import numpy as np

the output will be

<class 'numpy.ufunc'>

If it is not a ufunc, it will return another type, like this built-in NumPy function for joining two or more arrays

Example 3: Check the type of another function concatenate().

Code

import numpy as np

print(type(np.concatenate))

the output will be

<class 'builtin_function_or_method'>

If the function is not recognized at all, it will return an error.

Example 4: Check the type of something that does not exist. This will produce an error.

Code

import numpy as np

print(type(np.something))

the output will be

AttributeError: module 'numpy' has no attribute 'something'

To test if the function is a ufunc in an if statement, use the numpy.ufunc value (or np.ufunc if you use np as an alias for numpy)

Example 5: Use an if statement to check if the function is a ufunc or not.

Code

```import numpy as np