Book Image

Data Analysis with Python

By : David Taieb
Book Image

Data Analysis with Python

By: David Taieb

Overview of this book

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
3
Accelerate your Data Analysis with Python Libraries
Index

Part 1 – Loading the US domestic flight data into a graph


To initialize the Notebook, let's run the following code, in its own cell, to import the packages which we'll be using quite heavily in the rest of this chapter:

import pixiedust
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt

We'll also be using the 2015 Flight Delays and Cancellations dataset available on the Kaggle website at this location: https://www.kaggle.com/usdot/datasets. The dataset is composed of three files:

  • airports.csv: List of all U.S. airports including their IATA code (International Air Transport Association: https://openflights.org/data.html), city, state, longitude, and latitude.

  • airlines.csv: List of U.S. airlines including their IATA code.

  • flights.csv: List of flights that occurred in 2015. This data includes date, origin and destination airports, scheduled and actual times, and delays.

The flights.csv file contains close to 6 million records, which need to be cleaned up to remove all flights...