- Mastering Python for Data Science
- Samir Madhavan
- 266字
- 2021-07-16 20:14:17
Chapter 1. Getting Started with Raw Data
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
推薦閱讀
- The DevOps 2.3 Toolkit
- PyTorch自動駕駛視覺感知算法實戰
- Java編程指南：基礎知識、類庫應用及案例設計
- Java Web程序設計
- STM32F0實戰：基于HAL庫開發
- Learning ELK Stack
- Getting Started with LLVM Core Libraries
- Java零基礎實戰
- 網絡數據采集技術：Java網絡爬蟲實戰
- Learn Linux Quickly
- Java 9：Building Robust Modular Applications
- JavaScript設計模式與開發實踐
- Learning IBM Bluemix
- 匯編語言程序設計
- Swift語言實戰晉級（第2版）