Summary

In this chapter, we covered a lot of information related to the NumPy package, especially commonly used functions that are very helpful to process and analyze data in ndarray. Firstly, we learned the properties and data type of ndarray in the NumPy package. Secondly, we focused on how to create and manipulate an ndarray in different ways, such as conversion from other structures, reading an array from disk, or just generating a new array with given values. Thirdly, we studied how to access and control the value of each element in ndarray by using indexing and slicing.

Then, we are getting familiar with some common functions and operations on ndarray.

And finally, we continue with some advance functions that are related to statistic, linear algebra and sampling data. Those functions play important role in data analysis.

However, while NumPy by itself does not provide very much high-level data analytical functionality, having an understanding of it will help you use tools such as Pandas much more effectively. This tool will be discussed in the next chapter.

Practice exercises

Exercise 1: Using an array creation function, let's try to create arrays variable in the following situations:

• Create ndarray from the existing data
• Initialize ndarray which elements are filled with ones, zeros, or a given interval
• Loading and saving data from a file to an ndarray

Exercise 2: What is the difference between np.dot(a, b) and (a*b)?

Exercise 3: Consider the vector [1, 2, 3, 4, 5] building a new vector with four consecutive zeros interleaved between each value.

Exercise 4: Taking the data example file chapter2-data.txt, which includes information on a system log, solves the following tasks:

• Try to build an ndarray from the data file
• Statistic frequency of each device type in the built matrix
• List unique OS that appears in the data log
• Sort user by provinceID and count the number of users in each province