Getting Started with Python Data Analysis

4.5 (10 reviews total)
By Phuong Vo.T.H , Martin Czygan
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  1. Introducing Data Analysis and Libraries

About this book

Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It’s often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.

With this book, we will get you started with Python data analysis and show you what its advantages are.

The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.

Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.

Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.

Publication date:
November 2015
Publisher
Packt
Pages
188
ISBN
9781785285110

 

Chapter 1. Introducing Data Analysis and Libraries

Data is raw information that can exist in any form, usable or not. We can easily get data everywhere in our lives; for example, the price of gold on the day of writing was $ 1.158 per ounce. This does not have any meaning, except describing the price of gold. This also shows that data is useful based on context.

With the relational data connection, information appears and allows us to expand our knowledge beyond the range of our senses. When we possess gold price data gathered over time, one piece of information we might have is that the price has continuously risen from $1.152 to $1.158 over three days. This could be used by someone who tracks gold prices.

Knowledge helps people to create value in their lives and work. This value is based on information that is organized, synthesized, or summarized to enhance comprehension, awareness, or understanding. It represents a state or potential for action and decisions. When the price of gold continuously increases for three days, it will likely decrease on the next day; this is useful knowledge.

The following figure illustrates the steps from data to knowledge; we call this process, the data analysis process and we will introduce it in the next section:

In this chapter, we will cover the following topics:

  • Data analysis and process

  • An overview of libraries in data analysis using different programming languages

  • Common Python data analysis libraries

 

Data analysis and processing


Data is getting bigger and more diverse every day. Therefore, analyzing and processing data to advance human knowledge or to create value is a big challenge. To tackle these challenges, you will need domain knowledge and a variety of skills, drawing from areas such as computer science, artificial intelligence (AI) and machine learning (ML), statistics and mathematics, and knowledge domain, as shown in the following figure:

Let's go through data analysis and its domain knowledge:

  • Computer science: We need this knowledge to provide abstractions for efficient data processing. Basic Python programming experience is required to follow the next chapters. We will introduce Python libraries used in data analysis.

  • Artificial intelligence and machine learning: If computer science knowledge helps us to program data analysis tools, artificial intelligence and machine learning help us to model the data and learn from it in order to build smart products.

  • Statistics and mathematics: We cannot extract useful information from raw data if we do not use statistical techniques or mathematical functions.

  • Knowledge domain: Besides technology and general techniques, it is important to have an insight into the specific domain. What do the data fields mean? What data do we need to collect? Based on the expertise, we explore and analyze raw data by applying the above techniques, step by step.

Data analysis is a process composed of the following steps:

  • Data requirements: We have to define what kind of data will be collected based on the requirements or problem analysis. For example, if we want to detect a user's behavior while reading news on the internet, we should be aware of visited article links, dates and times, article categories, and the time the user spends on different pages.

  • Data collection: Data can be collected from a variety of sources: mobile, personal computer, camera, or recording devices. It may also be obtained in different ways: communication, events, and interactions between person and person, person and device, or device and device. Data appears whenever and wherever in the world. The problem is how we can find and gather it to solve our problem? This is the mission of this step.

  • Data processing: Data that is initially obtained must be processed or organized for analysis. This process is performance-sensitive. How fast can we create, insert, update, or query data? When building a real product that has to process big data, we should consider this step carefully. What kind of database should we use to store data? What kind of data structure, such as analysis, statistics, or visualization, is suitable for our purposes?

  • Data cleaning: After being processed and organized, the data may still contain duplicates or errors. Therefore, we need a cleaning step to reduce those situations and increase the quality of the results in the following steps. Common tasks include record matching, deduplication, and column segmentation. Depending on the type of data, we can apply several types of data cleaning. For example, a user's history of visits to a news website might contain a lot of duplicate rows, because the user might have refreshed certain pages many times. For our specific issue, these rows might not carry any meaning when we explore the user's behavior so we should remove them before saving it to our database. Another situation we may encounter is click fraud on news—someone just wants to improve their website ranking or sabotage a website. In this case, the data will not help us to explore a user's behavior. We can use thresholds to check whether a visit page event comes from a real person or from malicious software.

  • Exploratory data analysis: Now, we can start to analyze data through a variety of techniques referred to as exploratory data analysis. We may detect additional problems in data cleaning or discover requests for further data. Therefore, these steps may be iterative and repeated throughout the whole data analysis process. Data visualization techniques are also used to examine the data in graphs or charts. Visualization often facilitates understanding of data sets, especially if they are large or high-dimensional.

  • Modelling and algorithms: A lot of mathematical formulas and algorithms may be applied to detect or predict useful knowledge from the raw data. For example, we can use similarity measures to cluster users who have exhibited similar news-reading behavior and recommend articles of interest to them next time. Alternatively, we can detect users' genders based on their news reading behavior by applying classification models such as the Support Vector Machine (SVM) or linear regression. Depending on the problem, we may use different algorithms to get an acceptable result. It can take a lot of time to evaluate the accuracy of the algorithms and choose the best one to implement for a certain product.

  • Data product: The goal of this step is to build data products that receive data input and generate output according to the problem requirements. We will apply computer science knowledge to implement our selected algorithms as well as manage the data storage.

 

An overview of the libraries in data analysis


There are numerous data analysis libraries that help us to process and analyze data. They use different programming languages, and have different advantages and disadvantages of solving various data analysis problems. Now, we will introduce some common libraries that may be useful for you. They should give you an overview of the libraries in the field. However, the rest of this book focuses on Python-based libraries.

Some of the libraries that use the Java language for data analysis are as follows:

  • Weka: This is the library that I became familiar with the first time I learned about data analysis. It has a graphical user interface that allows you to run experiments on a small dataset. This is great if you want to get a feel for what is possible in the data processing space. However, if you build a complex product, I think it is not the best choice, because of its performance, sketchy API design, non-optimal algorithms, and little documentation (http://www.cs.waikato.ac.nz/ml/weka/).

  • Mallet: This is another Java library that is used for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine-learning applications on text. There is an add-on package for Mallet, called GRMM, that contains support for inference in general, graphical models, and training of Conditional random fields (CRF) with arbitrary graphical structures. In my experience, the library performance and the algorithms are better than Weka. However, its only focus is on text-processing problems. The reference page is at http://mallet.cs.umass.edu/.

  • Mahout: This is Apache's machine-learning framework built on top of Hadoop; its goal is to build a scalable machine-learning library. It looks promising, but comes with all the baggage and overheads of Hadoop. The homepage is at http://mahout.apache.org/.

  • Spark: This is a relatively new Apache project, supposedly up to a hundred times faster than Hadoop. It is also a scalable library that consists of common machine-learning algorithms and utilities. Development can be done in Python as well as in any JVM language. The reference page is at https://spark.apache.org/docs/1.5.0/mllib-guide.html.

Here are a few libraries that are implemented in C++:

  • Vowpal Wabbit: This library is a fast, out-of-core learning system sponsored by Microsoft Research and, previously, Yahoo! Research. It has been used to learn a tera-feature (1012) dataset on 1,000 nodes in one hour. More information can be found in the publication at http://arxiv.org/abs/1110.4198.

  • MultiBoost: This package is a multiclass, multi label, and multitask classification boosting software implemented in C++. If you use this software, you should refer to the paper published in 2012 in the JournalMachine Learning Research, MultiBoost: A Multi-purpose Boosting Package, D.Benbouzid, R. Busa-Fekete, N. Casagrande, F.-D. Collin, and B. Kégl.

  • MLpack: This is also a C++ machine-learning library, developed by the Fundamental Algorithmic and Statistical Tools Laboratory (FASTLab) at Georgia Tech. It focusses on scalability, speed, and ease-of-use, and was presented at the BigLearning workshop of NIPS 2011. Its homepage is at http://www.mlpack.org/about.html.

  • Caffe: The last C++ library we want to mention is Caffe. This is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. You can find more information about it at http://caffe.berkeleyvision.org/.

Other libraries for data processing and analysis are as follows:

  • Statsmodels: This is a great Python library for statistical modeling and is mainly used for predictive and exploratory analysis.

  • Modular toolkit for data processing (MDP): This is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures (http://mdp-toolkit.sourceforge.net/index.html).

  • Orange: This is an open source data visualization and analysis for novices and experts. It is packed with features for data analysis and has add-ons for bioinformatics and text mining. It contains an implementation of self-organizing maps, which sets it apart from the other projects as well (http://orange.biolab.si/).

  • Mirador: This is a tool for the visual exploration of complex datasets, supporting Mac and Windows. It enables users to discover correlation patterns and derive new hypotheses from data (http://orange.biolab.si/).

  • RapidMiner: This is another GUI-based tool for data mining, machine learning, and predictive analysis (https://rapidminer.com/).

  • Theano: This bridges the gap between Python and lower-level languages. Theano gives very significant performance gains, particularly for large matrix operations, and is, therefore, a good choice for deep learning models. However, it is not easy to debug because of the additional compilation layer.

  • Natural language processing toolkit (NLTK): This is written in Python with very unique and salient features.

Here, I could not list all libraries for data analysis. However, I think the above libraries are enough to take a lot of your time to learn and build data analysis applications. I hope you will enjoy them after reading this book.

 

Python libraries in data analysis


Python is a multi-platform, general-purpose programming language that can run on Windows, Linux/Unix, and Mac OS X, and has been ported to Java and .NET virtual machines as well. It has a powerful standard library. In addition, it has many libraries for data analysis: Pylearn2, Hebel, Pybrain, Pattern, MontePython, and MILK. In this book, we will cover some common Python data analysis libraries such as Numpy, Pandas, Matplotlib, PyMongo, and scikit-learn. Now, to help you get started, I will briefly present an overview of each library for those who are less familiar with the scientific Python stack.

NumPy

One of the fundamental packages used for scientific computing in Python is Numpy. Among other things, it contains the following:

  • A powerful N-dimensional array object

  • Sophisticated (broadcasting) functions for performing array computations

  • Tools for integrating C/C++ and Fortran code

  • Useful linear algebra operations, Fourier transformations, and random number capabilities

Besides this, it can also be used as an efficient multidimensional container of generic data. Arbitrary data types can be defined and integrated with a wide variety of databases.

Pandas

Pandas is a Python package that supports rich data structures and functions for analyzing data, and is developed by the PyData Development Team. It is focused on the improvement of Python's data libraries. Pandas consists of the following things:

  • A set of labeled array data structures; the primary of which are Series, DataFrame, and Panel

  • Index objects enabling both simple axis indexing and multilevel/hierarchical axis indexing

  • An intergraded group by engine for aggregating and transforming datasets

  • Date range generation and custom date offsets

  • Input/output tools that load and save data from flat files or PyTables/HDF5 format

  • Optimal memory versions of the standard data structures

  • Moving window statistics and static and moving window linear/panel regression

Due to these features, Pandas is an ideal tool for systems that need complex data structures or high-performance time series functions such as financial data analysis applications.

Matplotlib

Matplotlib is the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats: line plots, contour plots, scatter plots, and Basemap plots. It comes with a set of default settings, but allows customization of all kinds of properties. However, we can easily create our chart with the defaults of almost every property in Matplotlib.

PyMongo

MongoDB is a type of NoSQL database. It is highly scalable, robust, and perfect to work with JavaScript-based web applications, because we can store data as JSON documents and use flexible schemas.

PyMongo is a Python distribution containing tools for working with MongoDB. Many tools have also been written for working with PyMongo to add more features such as MongoKit, Humongolus, MongoAlchemy, and Ming.

The scikit-learn library

The scikit-learn is an open source machine-learning library using the Python programming language. It supports various machine learning models, such as classification, regression, and clustering algorithms, interoperated with the Python numerical and scientific libraries NumPy and SciPy. The latest scikit-learn version is 0.16.1, published in April 2015.

 

Summary


In this chapter, we presented three main points. Firstly, we figured out the relationship between raw data, information and knowledge. Due to its contribution to our lives, we continued to discuss an overview of data analysis and processing steps in the second section. Finally, we introduced a few common supported libraries that are useful for practical data analysis applications. Among those, in the next chapters, we will focus on Python libraries in data analysis.

Practice exercise

The following table describes users' rankings on Snow White movies:

UserID

Sex

Location

Ranking

A

Male

Philips

4

B

Male

VN

2

C

Male

Canada

1

D

Male

Canada

2

E

Female

VN

5

F

Female

NY

4

Exercise 1: What information can we find in this table? What kind of knowledge can we derive from it?

Exercise 2: Based on the data analysis process in this chapter, try to define the data requirements and analysis steps needed to predict whether user B likes Maleficent movies or not.

About the Authors

  • Phuong Vo.T.H

    Phuong Vo.T.H has a MSc degree in computer science, which is related to machine learning. After graduation, she continued to work in some companies as a data scientist. She has experience in analyzing users' behavior and building recommendation systems based on users' web histories. She loves to read machine learning and mathematics algorithm books, as well as data analysis articles.

    Browse publications by this author
  • Martin Czygan

    Martin Czygan studied German literature and computer science in Leipzig, Germany. He has been working as a software engineer for more than 10 years. For the past eight years, he has been diving into Python, and is still enjoying it. In recent years, he has been helping clients to build data processing pipelines and search and analytics systems.

    Browse publications by this author

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