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

Fitting to polynomials with NumPy

Polynomials are mathematical expressions with non-negative strategies. Examples of polynomial functions are linear, quadratic, cubic, and quartic functions. NumPy offers the polyfit() function to generate polynomials using least squares. This function takes x-coordinate, y-coordinate, and degree as parameters, and returns a list of polynomial coefficients.

NumPy also offers polyval() to evaluate the polynomial at given values. This function takes coefficients of polynomials and arrays of points and returns resultant values of polynomials. Another function is linspace(), which generates a sequence of equally separated values. It takes the start, stop, and the number of values between the start-stop range and returns equally separated values in the closed interval.

Let's see an example to generate and evaluate polynomials using NumPy, as follows:

# Import required libraries NumPy, polynomial and matplotlib
import numpy as np
import matplotlib.pyplot...