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Python Data Analysis and Visualization

Solution Review: Using Numpy and Scipy

Explore how to use NumPy and SciPy libraries to perform essential statistical calculations such as maximum value, standard deviation, sum, dot product, correlation, and p-value. This lesson helps you understand applying these functions to arrays for data analysis tasks using Python basics, preparing you for more advanced data handling.

We'll cover the following...

Numpy #

Python 3.5
import numpy as np  # importing numpy module
def perform_calculations(array): 
    # returning max, std, sum, and dot product
    return np.max(array), np.std(array), np.sum(array), np.dot(array, array)
# calling the function and printing result
print(perform_calculations(np.random.rand(5)))

According to the problem statement, we needed these four values using one numpy 1-D array as an output: max, std, sum, and dot product. In the code above, at line 1 we imported the numpy module for this purpose. Next, we implemented the function perform_calculations().

Look at its header at line 3. It takes one argument, array as an input. At ...