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

Understanding NumPy arrays

NumPy can be installed on a PC using pip or brew but if the user is using the Jupyter Notebook, then there is no need to install it. NumPy is already installed in the Jupyter Notebook. I will suggest to you to please use the Jupyter Notebook as your IDE because we are executing all the code in the Jupyter Notebook. We have already shown in Chapter 1, Getting Started with Python Libraries, how to install Anaconda, which is a complete suite for data analysis. NumPy arrays are a series of homogenous items. Homogenous means the array will have all the elements of the same data type. Let's create an array using NumPy. You can create an array using the array() function with a list of items. Users can also fix the data type of an array. Possible data types are bool, int, float, long, double, and long double.

Let's see how to create an empty array:

# Creating an array
import numpy as np
a = np.array([2,4,6,8,10])

[ 2 4 6 8 10]

Another way to create...