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)
1
Section 1: Foundation for Data Analysis
6
Section 2: Exploratory Data Analysis and Data Cleaning
11
Section 3: Deep Dive into Machine Learning
15
Section 4: NLP, Image Analytics, and Parallel Computing

Slicing NumPy arrays

Slicing in NumPy is similar to Python lists. Indexing prefers to select a single value while slicing is used to select multiple values from an array.

NumPy arrays also support negative indexing and slicing. Here, the negative sign indicates the opposite direction and indexing starts from the right-hand side with a starting value of -1:

Let's check this out using the following code:

# Create NumPy Array
arr = np.arange(0,10)
print(arr)

Output: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

In the slice operation, we use the colon symbol to select the collection of values. Slicing takes three values: start, stop, and step:

print(arr[3:6])
Output: [3, 4, 5]

This can be represented as follows:

In the preceding example, we have used 3 as the starting index and 6 as the stopping index:

print(arr[3:])
Output: array([3, 4, 5, 6, 7, 8, 9])

In the preceding example, only the starting index is given. 3 is the starting index. This slice operation will select the values from the starting index...