I wanted to demonstrate further how powerful and straightforward the pandas library is for data analysis. A good example comes from the book “Bioinformatics Programming using Python,” by Mitchell Model. While this is an excellent reference book on Python programming, it was written before pandas was in widespread use as a library.
In the “Extended Examples” on p. 158 of Chapter 4, the author demonstrates some code to read in a text file containing the names of enzymes, their restriction sites, and the patterns that they match. The code takes the text file, cleans it up, and makes a dictionary that is searchable by key. This is done using core python tools only and it looks like this (note: I am using Python 2.7 hence the need to import “print_function” from “__future__”):
The last few lines of output from calling test() is as follows:
Hold onto your seats because you can do all of that and more with just 5 lines of code using pandas (if you don’t count the imports):
The read_table function can take regex separators (in this case “any number of white spaces”) when using the “python” engine option. We skip the first 8 rows because they have no information. The header is set as the second row after the skipped rows.
I then use a boolean mask to find the places where the condition “is_null” is true looking down the “pattern” column. This is because some rows lack a “site” entry, so pandas found only two data fields when separated on whitespace and thus left the third column empty, not knowing there was missing data. Wherever the pattern column is null, I assign the missing values into the pattern column from the site column. I then replace the site column values with “NaNs”.
The first few lines of the ‘rebase’ dataframe object look like this:
Technically, what I just did in pandas is not quite the same thing as the core python version above. It is in many ways far better. First, all of the blank spaces in the second column are now “NaN” instead of blanks. This makes data analysis easier. Second, the object “rebase” is a dataframe that allows access to all of the dataframe methods. It is also indexed by row and has named columns for easier interpretation. The dataframe also automatically “pretty prints” for easy reading, whereas the table created using core python has to be formatted with additional function definitions to print to stdout or to file in a readable way.
The book “Python for Data Analysis” (O’Reilly Media 2013) by author Wes McKinney is a guide to using the NumPy, matplotlib, and pandas Python libraries for data analysis. The author sets out to provide a template for Python programmers to gain working knowledge of the rapidly maturing Python technologies for data analysis and visualization tasks. The tone of the book is conversational and focused, with no fluff or filler. The book accomplishes its purpose admirably by providing a concise, meaty, and highly readable tutorial through the essential features of doing data analysis in Python.
McKinney does a skillful job of bringing the Python novice through the requisite background and quickly up to speed doing useful work with pandas without becoming bogged down in introductory Python minutia. In fact, the opening chapter is titled “Introductory Examples” and includes several relatively complex data analysis examples that serve to demonstrate the capabilities of pandas. I found this approach provided me with the motivation to read on into the more detailed and technical chapters.
Why you should listen to Wes McKinney
The author is uniquely suited to write this book, having been the creator and first developer of pandas in the course of his own work as a quantitative analyst at a hedge fund back in 2008. I could tell that the author has a mastery of the subject; he provides many useful insights that could only be gained through real-world experience. The book focuses mainly on the pandas library and its core technologies, the Series and the Dataframe. Both are important because they build on the speed and precision of numpy arrays, while allowing richer, more intuitive and powerful manipulation of data tables.
pandas: it just works the way it should
Another aspect of this book that is so enjoyable is that pandas itself just works the way I would expect it to work. The tools, in my opinion, are constructed to be as convenient and intuitive as possible. I find that pandas behaves very predictably, despite being extremely powerful. Oftentimes, I was able to invent an expression in pandas that behaved exactly as I intended without knowing a priori whether it was possible to do so. There is something very satisfying about a tool that just works and doesn’t require a lot of boilerplate code.
The publisher also provides downloadable iPython notebooks containing the code examples for each chapter. Using these notebooks it was very easy to follow along, running code while reading the chapter. The illustrations in the book also consist almost entirely of matplotlib plots prepared using the code examples. I was able to work up many of the figures, giving me a sense of having gained practical, working knowledge in each chapter.
Python for data analysis? Yes!
I really have nothing negative to say about “Python for Data Analysis”. If forced to find something to change, it would be that the author could have left out the highly-condensed chapter on introductory Python programming found at the end of the book, using the extra space instead to include even more examples of pandas in practical, real-world applications.
For instance, an example on building a data analysis model with interactive graphics for the web would have been welcome. Similarly, a demonstration of approaches for making matplotlib, with its rather utilitarian graphics, more closely resemble the stylistically attractive plots of ggplot2 (the well-known R plotting library) would also have been useful.
After reading this book, however, I have been convinced to transition my data analysis workflow entirely into Python and largely abandon R, which now seems somewhat esoteric and unnecessarily complex by comparison. Overall, I would highly recommend this book to anyone seeking to learn how to use Python for data analysis. It is a valuable reference for scientists, engineers, data analysts, and others who want to leverage the power of Python (and specifically numpy and pandas) for dealing with their data.
When first starting to learn Python, I found the array of package names and libraries a bit bewildering and confusing. In this post I will enumerate many of the most common and useful parts of the Python computing “ecosystem” and attempt to describe them very briefly.
My aim is to provide some clarity on the situation for new users, as I would have liked to have seen the “30,000 ft view” when I started learning not long ago. So without further ado, part 1 of the python ecosystem overview:
Python -high-level, interpreted language
Python is an interpreted programming language (itself written in C) that allows you to write very clean and simple code in a fast and human-readable form as compared to lower-level compiled languages (C++, Fortran).
The language is simple, self-consistent and beautiful. It is relatively easy to learn and is gaining in popularity every year. The simplicity and ease of use does come with a price as code written in pure Python is generally slower to execute than compiled C/C++ code.
iPython -get code written faster
iPython is an enhanced, interactive Python shell designed to make code development faster. According to Wes McKinney in his excellent book, “Python for Data Analysis,” iPython is designed to encourage “an execute-explore workflow instead of the typical edit-compile-run workflow of many other programming languages.”
iPython contains the Python interpreter and is ready to execute commands as you enter them. It is where you run code snippets, examine the outputs, and make iterative improvements. In that sense, it is like kind of like the UNIX command line. You don’t write full programs here, you do that in a text editor or IDE (integrated development environment).
It also contains useful features called “magic commands.” These are commands that are unique to the iPython command line, and are not valid Python code (i.e., you cannot use these commands in stand-alone programs). Magic commands provide productivity speedups in many useful ways, such as recalling command history, running parts of scripts, timing code, and debugging code interactively.
If you have iPython installed you can invoke it from a regular python shell; however I find it easiest to use it within an IDE that supports iPython.
iPython Notebook -share code and ideas over the web
The iPython notebook is an interactive python format that runs in a web browser or in an IDE. The Notebook is a flexible and powerful document format that allows python code, text markdown, mathematics equations, and figures to be displayed together in a coherent, inline way. An iPython notebook could be used to provide all of the steps in a data analysis project, for example, or to teach a programming concept. It is very useful for sharing code and visualizing results in an interactive, portable document.
NumPy -implement very fast vectorized computation
NumPy (numerical python) is a powerful and fast library for doing numerical computation in Python. It is based on a data structure called an ndarray. This lower level structure is faster for computation than regular higher-level python structures like lists and dictionaries, but it is less flexible and behaves in somewhat unintuitive ways. Functions can be applied across a numpy array all at once and “in place”; this is known as vectorization. NumPy contains a number of vectorized built-in functions known as “ufuncs” for doing transformations on ndarrrays.
pandas -powerful library for data analysis
pandas is a data analysis package for python; conceived and built initially by Wes McKinney. It was developed to allow python users to access some of the powerful features of the R statistics language while staying in the python ecosystem. Prior to the development of pandas, data analysis had to be carried out using the NumPy ndarray structures which are rather difficult for handling messy real world data.
Pandas achieves very fast speeds and efficiency because it is built on top of NumPy and therefore takes advantage of the built-in speed advantage of the low-level ndarray data structures. However, pandas allows users to create higher-level structures called Series (1D), Dataframes (2D) and Panels (3D), that are more flexible and useful for regular data analysis than raw numpy arrays owing to their ability to contain mixed data types, headers, and indexes.
Pandas also contains many built-in methods for operations on Series, Dataframes, and Panels that allow users to quickly and easily do data aggregation, reductions, and “split-apply-combine” strategies.
SciPy -collection of libraries for a variety of computing applications
SciPy is a collection of scientific algorithms for doing scientific computing. It is an open-source project under active development. Some of the packages available include scipy.linalg for doing linear algebra, scipy.stats for statistics, scipy.cluster for clustering (K-means and others), scipy.fftpack for doing fourier transform analysis, scipy.optimize for doing curve fitting and minimization, and scipy.signal for doing signal processing. There are many more packages in SciPy; what you end up using will depend on your application and area of interest.
In part 2, I will describe even more libraries and packages that you will encounter as you learn scientific computing with Python.