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

Creating NumPy views and copies

Some of the Python functions return either a copy or a view of the input array. A Python copy stores the array in another location while a view uses the same memory content. This means copies are separate objects and treated as a deep copy in Python. Views are the original base array and are treated as a shallow copy. Here are some properties of copies and views:

  • Modifications in a view affect the original data whereas modifications in a copy do not affect the original array.
  • Views use the concept of shared memory.
  • Copies require extra space compared to views.
  • Copies are slower than views.

Let's understand the concept of copy and view using the following example:

# Create NumPy Array
arr = np.arange(1,5).reshape(2,2)

[[1, 2],
[3, 4]]

After creating a NumPy array, let's perform object copy operations:

# Create no copy only assignment

# Create Deep Copy

# Create shallow copy using View