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

Reading and writing CSV files with NumPy

In Chapter 2, NumPy and pandas, we looked at the NumPy library in detail and explored lots of functionality. NumPy also has functions to read and write CSV files and get output in a NumPy array. The genfromtxt() function will help us to read the data and the savetxt() function will help us to write the data into a file. The genfromtxt() function is slow compared to other functions due to its two-stage operation. In the first stage, it reads the data in a string type, and in the second stage, it converts the string type into suitable data types. genfromtxt() has the following parameters:

  • fname: String; filename or path of the file.
  • delimiter: String; optional, separate string value. By default, it takes consecutive white spaces.
  • skip_header: Integer; optional, number of lines you want to skip from the start of the file.

Let's see an example of reading and writing CSV files:

# import genfromtxt function
from numpy import genfromtxt

# Read comma...