# Simplify your scientific computations with NumPy nancumprod

## Introducing NumPy nancumprod – Definition of NaN and cumulative product

NumPy is a Python library that is used for scientific and mathematical operations such as linear algebra, Fourier transform, random number generation, and more. One of the functions that is available in NumPy is the nancumprod function.

Nancumprod is short for “not a number cumulative product.” Let’s take a closer look at what that means. NaN (not a number) refers to an undefined or unrepresentable value usually encountered in mathematical operations.

For example, when dividing by zero, multiplying an infinite number by zero, or performing other invalid arithmetic operations. These operations result in NaN values.

Now let’s explore what cumulative product means. Cumulative product is the result obtained when multiplying a sequence of values, starting from the first value up to the current value.

The product from the previous value is passed to the succeeding operation until the end of the sequence is reached. The nancumprod function combines the concepts of NaN and cumulative product.

Simply put, this function excludes NaN values from the product while computing the cumulative product of an array.

## Syntax of NumPy nancumprod – Explanation of parameters

### The syntax for the NumPy nancumprod function is:

numpy.nancumprod(a, axis=None, dtype=None, *, out=None, keepdims=)

• a: This parameter represents the input array.
• axis: This parameter represents the cumulative product operation’s axis.
• If left as None, the input array is flattened.
• dtype: This parameter is an optional parameter that can specify the data type of the returned array.
• By default, the data type is computed automatically.
• out: You can specify an output array where the result will be placed.
• By default, none is used to allow NumPy to choose for us.
• keepdims: This is an optional parameter that can be set to True to preserve the array’s dimension.
• By default, this is False, and the array dimension changes based on the cumulative operations being performed.

## Examples of NumPy nancumprod

Now let’s illustrate how the NumPy nancumprod function works. First, let’s import the NumPy library to use in our examples.

``import numpy as np``

Now let’s create an array and assign it to a variable. arr = [1, 2, np.nan, 4]

To apply the nancumprod function, we call the numpy.nancumprod function with our array.

``np.nancumprod(arr)``

### The output for this is:

``array([  1.,   2.,   2.,   8.])``

Notice that the output array has ignored the ‘nan’ value and computed the cumulative product of 1, 2, and 4. In the next example, we will specify an axis for the cumulative product operation.

We will use the following 3×3 array as our input. arr_2d = np.array([[1, 3, np.nan], [4, 2, 1], [np.nan, 8, 7]])

To compute the nancumprod function along rows, we set the axis parameter to 1.

``np.nancumprod(arr_2d, axis=1)``

### The output for this is:

``````array([[ 1.,  3.,  3.],
[ 4.,  8.,  8.],
[nan,  8., 56.]])``````

Notice that in this output, the cumulative product of rows with NaN values resulted in a NaN value. On the other hand, rows without any NaN value computed the cumulative product as expected.

## Conclusion

In summary, we have explored the definition of NumPy nancumprod and how it works. We have also examined the syntax and parameters of the NumPy nancumprod function, as well as provided examples of its usage.

NumPy nancumprod is a powerful function that provides a quick and simple way to compute the cumulative product of an array while ignoring NaN values. With this knowledge, you can use NumPy nancumprod to simplify your calculations in scientific and mathematical operations.

Examples of the numpy.nancumprod() method

In the previous section, we learned about the syntax and parameters of NumPy nancumprod. In this section, we will provide practical examples of the function’s usage.

### Example 1: Cumulative product of a 1-dimensional array containing NaNs

In this example, we will illustrate how NumPy nancumprod function handles NaN values when computing the cumulative product of a 1-dimensional array. Consider the following array `a` containing a NaN value:

``a = [1, 2, np.nan, 4]``

To compute the cumulative product of this array while excluding NaN values, we use the NumPy nancumprod function:

``np.nancumprod(a)``

### The output of this operation is:

``array([  1.,   2.,   2.,   8.])``

As we can see, the output has ignored the NaN value and computed the cumulative product of the other values.

### Example 2: Cumulative product of a 2-dimensional array containing NaNs

In this example, we will consider how NumPy nancumprod handles NaN values when computing the cumulative product of a 2-dimensional array. Consider the following 3×3 array `b` containing NaN values:

``b = np.array([[1, 3, np.nan], [4, 2, 1], [np.nan, 8, 7]])``

To compute the cumulative product of this array while ignoring NaN values, we use the NumPy nancumprod function:

``np.nancumprod(b)``

### The output of this operation is:

``array([  1.,   3.,   3.,  12.,  24.,  24., nan, 192., 1344.])``

As we can see, the output has computed the cumulative product of each row, ignoring NaN values where necessary.

### Example 3: Cumulative product along axis=0

In this example, we will consider how to compute the cumulative product along axis 0 of a 2-dimensional array using NumPy nancumprod. Consider the following 3×3 array `c`:

``c = np.array([[1, 2, 3], [4, np.nan, 6], [7, 8, np.nan]])``

To compute the cumulative product of the array along axis 0 while ignoring NaN values, we use the NumPy nancumprod function with the axis parameter set to 0:

``np.nancumprod(c, axis=0)``

### The output of this operation is:

``````array([[ 1.,  2.,  3.],
[ 4.,  2., 18.],
[28., 16., 18.]])``````

As we can see, the output has computed the cumulative product of each column, ignoring NaN values where necessary.

### Example 4: Cumulative product along axis=1

In this example, we will consider how to compute the cumulative product along axis 1 of a 2-dimensional array using NumPy nancumprod. Consider the following 3×3 array `d`:

``d = np.array([[1, 2, np.nan], [4, np.nan, 6], [np.nan, 8, 9]])``

To compute the cumulative product of the array along axis 1 while ignoring NaN values, we use the NumPy nancumprod function with the axis parameter set to 1:

``np.nancumprod(d, axis=1)``

### The output of this operation is:

``````array([[ 1.,  2.,  2.],
[ 4.,  4., 24.],
[nan,  8., 72.]])``````

As we can see, the output has computed the cumulative product of each row, ignoring NaN values where necessary.

## Summary

In this tutorial, we have learned about the NumPy nancumprod function, which is used to compute the cumulative product of an array while ignoring NaN values. We have explored the syntax and parameters of this function and provided examples of its usage.

We have seen how NumPy nancumprod works with 1-dimensional and 2-dimensional arrays and how it handles NaN values when computing the cumulative product. Lastly, we have demonstrated how to compute the cumulative product along both axis 0 and axis 1.

With this knowledge, you should be well-equipped to use NumPy nancumprod in your scientific and mathematical calculations.

In this article, we have discussed the NumPy nancumprod function, which is a powerful tool for computing the cumulative product of an array while ignoring NaN values.

We have explored the definition of NaN and cumulative product and how nancumprod combines these concepts. We have also covered the syntax and parameters of the NumPy nancumprod function, along with practical examples that demonstrate how it works.

The importance of this function lies in its ability to simplify scientific and mathematical computations. Takeaways from this article include learning how to use NumPy nancumprod, how to handle NaN values in cumulative products, and how to compute the cumulative product along different axes.

With this knowledge, users can optimize their calculations and improve the efficiency of their scientific and mathematical operations.