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

Reading and querying the Quandl data

In the last section, we saw pandas DataFrames that have a tabular structure similar to relational databases. They offer similar query operations on DataFrames. In this section, we will focus on Quandl. Quandl is a Canada-based company that offers commercial and alternative financial data for investment data analyst. Quandl understands the need for investment and financial quantitative analysts. It provides data using API, R, Python, or Excel.

In this section, we will retrieve the Sunspot dataset from Quandl. We can use either an API or download the data manually in CSV format.

Let's first install the Quandl package using pip:

$ pip3 install Quandl

If you want to install the API, you can do so by downloading installers from or by running the preceding command.

Using the API is free, but is limited to 50 API calls per day. If you require more API calls, you will have to request an authentication key. The code...