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

Dask Delayed

Dask Delayed is an approach we can use to parallelize code. It can delay the dependent function calls in task graphs and provides complete user control over parallel processes while improving performance. Its lazy computation helps us control the execution of functions. However, this differs from the execution timings of functions for parallel execution.

Let's understand the concept of Dask Delayed by looking at an example:

# Import dask delayed and compute
from dask import delayed, compute

# Create delayed function
def cube(item):
return item ** 3

# Create delayed function
def average(items):
return sum(items)/len(items)

# create a list
item_list = [2, 3, 4]

# Compute cube of given item list
cube_list= [cube(i) for i in item_list]

# Compute average of cube_list
computation_graph = average(cube_list)

# Compute the results

This results in the following output:


In the preceding example, two methods, cube and average, were annotated...