Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

Collecting samples

A sample is a small set of the population used for data analysis purposes. Sampling is a method or process of collecting sample data from various sources. It is the most crucial part of data collection. The success of an experiment depends upon how well the data is collected. If anything goes wrong with sampling, it will hugely affect the final interpretations. Also, it is impossible to collect data for the whole population. Sampling helps researchers to infer the population from the sample and reduces the survey cost and workload to collect and manage data. There are lots of sampling techniques available, for various purposes. These techniques can be categorized into two categories: probability sampling and non-probability sampling, described in more detail here:

  • Probability sampling: With this technique, there is a random selection of every respondent of the population, with an equal chance of the selected sample. Such types of sampling techniques are more time-consuming...