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Key benefits

• Clean dirty data, extract accurate information, and explore the relationships between variables

• Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn

• Find and extract the most important features from your dataset using the most efficient Python libraries

Description

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.

What you will learn

• Read, clean, transform, and store your data usng Pandas and OpenRefine

• Understand your data and explore the relationships between variables using Pandas and D3.js

• Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels

• Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy

• Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn

• Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi

• Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK

• Study simulation techniques in an example of a gas station with agent-based modeling

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Getting Started with Python Data Analysis

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Customer reviews

Rating distribution

4.8

(5 Ratings)

5 star 80%

4 star 20%

3 star 0%

2 star 0%

1 star 0%

Amazon Customer Nov 10, 2016

This book provides me with tools that I can use straight away on my research works. The feature I like the most is the coverage of 'lifespan' of data from preparation to analysis techniques that are commonly required by data analysts both in academia and industry. I have also used some examples from this book in teaching my senior classes in University. The coverage of analysis techniques is a great feature that completes a data analysis codebook. This book is great for learning and also a very good reference book for anyone who works with data and for researchers in the academia. Absolutely love it!

Amazon Verified review

nolan Jan 06, 2017

The Practical Data Analysis Cookbook is a highly utility-oriented breakdown of data analysis expressed through python, which is very in-tune with the current state of the field.The author does a great job of illustrating current topics in data science, while maintaining emphasis on short and concise code. The entire thing is easy to read and process. Highly recommended if this is the area of study/interest in which you find yourself.

Amazon Verified review

Denny Lee Jan 07, 2017

Practical Data Analysis Cookbook is the handy go-to reference for performing data analysis within the Python ecosystem. It starts you off by how to prepare and explore the data so you can actually start doing data analysis for your scenario. Then it dives into common Data Sciences techniques including (but not limited to) classification, clustering, dimensionality reduction, natural language processing and simulations. What's really helpful with this cookbook is that it contains practical data analysis techniques (as the title notes). For example, when working with k-means clustering, not only are you provided with how to do it and how it works, the recipe also notes the difference between working with scikit-learn vs. scipy. A great book whether you're an aspiring data scientist to being a great reference for a practicing data scientist.

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Joshua Allen May 15, 2017

Excellent reference full of recipes for common task patterns in the day-to-day life of a data scientist. The recipes are in detailed "hands on lab" style, and would be suitable for use in training events as well as reference.

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Jo Apr 29, 2017

While I work in the data science field, I was interested in doing some data analysis in Python and so turned to this book and found that this is a great introduction to practical data analysis and will help jumpstart anyone who is new to the field.It is easy to get overwhelmed by all the possible algorithms for any task and this focuses on just the important ones making it easy for someone to focus on. It is primarily for someone who likes to 'learn-by-doing' and not worry too much about the specific details of the algorithm.Some things that are awesome about this book -* Great introduction for someone looking to dive right into analysis* Skips the theory behind all the algorithms and jumps right into the practice* Focuses on just the important concepts required to dive right in rather than present all choices.* Very thorough and talks about all the important concepts that you need for* For someone new to Python (like me) gives a good place to start* Well structured - each section has a- 'Getting ready' bit talking about the pre-reqs- 'How to do it' which gives you the code snippets to apply the specific algorithm- 'How it works' talks about how exactly the code snippet works with some details on the algorithm- 'There's more' & 'see also' provides you with extra references and links.Some areas which may need you to refer else where -* If you're looking for deeper knowledge and the theory behind an algorithm* If you're looking for a detailed comparison on multiple algorithms, their trade-offs etc* If you're completely new to coding altogether you might need to supplement with additional materialIt's probably worth mentioning that I looked at a few other titles in this space and found that Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business Leveraging the Power of Data Analytics, Data Science, ... (Hacking Freedom and Data Driven Book 2) Seemed quite business focused and probably better suited for someone into BI Practical Data Analysis Multiple technologies covered and lots more choices, since I was just trying to focus on Python it didn't suit my needs as much.

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Practical Data Analysis Cookbook: Over 60 practical recipes on data exploration and analysis

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