NumPy Mean: A step-by-step Guide with Examples


Numpy mean

NumPy is a powerful Python library for numerical and mathematical operations, and the 'numpy.mean()' function is used to calculate the mean (average) of elements in a NumPy array. Here are step-by-step examples of how to use 'numpy.mean()':

Import NumPy:

Before you can use NumPy, you need to import it. You can import NumPy using the following code:

import numpy as np

This line of code imports NumPy and gives it the alias np for easier access.

Create a NumPy array:

To calculate the mean, you first need some data. You can create a NumPy array with data as follows:

data = np.array([1, 2, 3, 4, 5])

In this example, we create a NumPy array with the numbers '1' through '5'.

Read also:


Use numpy.mean() to calculate the mean:

Now that you have your data in a NumPy array, you can calculate the mean using 'numpy.mean()':

mean = np.mean(data)

This will compute the mean of the elements in the data array, which is (1 + 2 + 3 + 4 + 5) / 5 = 3.0.

Print the mean:

You can print the calculated mean as follows:

print("Mean:", mean)

This will display the mean value in the console.

Here's a complete example with all the steps together:

import numpy as np

data = np.array([1, 2, 3, 4, 5])
mean = np.mean(data)
print("Mean:", mean)

This will output:

Mean: 3.0

You can use the 'numpy.mean()' function in the same way with multi-dimensional arrays or arrays with different data types. Just make sure to pass the appropriate array to the function, and it will return the mean of the elements in the array.

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