In the world of data science, raw data comes in many forms and sizes. There is a lot of information that can be extracted from this raw data. To give an example, Amazon collects click stream data that records each and every click of the user on the website. This data can be utilized to understand if a user is a price-sensitive customer or prefer more popularly rated products. You must have noticed recommended products in Amazon; they are derived using such data.
The first step towards such an analysis would be to parse raw data. The parsing of the data involves the following steps:
Extracting data from the source: Data can come in many forms, such as Excel, CSV, JSON, databases, and so on. Python makes it very easy to read data from these sources with the help of some useful packages, which will be covered in this chapter.
Cleaning the data: Once a sanity check has been done, one needs to clean the data appropriately so that it can be utilized for analysis. You may have a dataset about students of a class and details about their height, weight, and marks. There may also be certain rows with the height or weight missing. Depending on the analysis being performed, these rows with missing values can either be ignored or replaced with the average height or weight.
In this chapter we will cover the following topics:
Exploring arrays with NumPy
Handling data with pandas
Reading and writing data from various formats
Handling missing data
Manipulating data