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

Machine learning at scale

Dask offers Dask-ML services for large-scale machine learning operations using Python. Dask-ML decreases the model training time for medium-sized datasets and experiments with hyperparameter tuning. It offers scikit-learn-like machine learning algorithms for ML operations.

We can scale scikit-learn in three different ways: parallelize scikit-learn using joblib by using random forest and SVC; reimplement algorithms using Dask Arrays using generalized linear models, preprocessing, and clustering; and partner it with distributed libraries such as XGBoost and Tensorflow.

Let's start by looking at parallel computing using scikit-learn.

Parallel computing using scikit-learn

To perform parallel computing using scikit-learn on a single CPU, we need to use joblib. This makes scikit-learn operations parallel computable. The joblib library performs parallelization on Python jobs. Dask can help us perform parallel operations on multiple scikit-learn estimators. Let...