Category Archives: Python

Cheat Sheets: Pandas, the Python Data Analysis Library

Download all 8 Pandas Cheat Sheets

Learn more about the Python for Data Analysis and Pandas Mastery Workshop training courses

Pandas (the Python Data Analysis library) provides a powerful and comprehensive toolset for working with data. Fundamentally, Pandas provides a data structure, the DataFrame, that closely matches real world data, such as experimental results, SQL tables, and Excel spreadsheets, that no other mainstream Python package provides. In addition to that, it includes tools for reading and writing diverse files, data cleaning and reshaping, analysis and modeling, and visualization. Using Pandas effectively can give you super powers, regardless of whether you’re working in data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, or engineering.

However, learning Pandas can be a daunting task because the API is so rich and large. This is why we created a set of cheat sheets built around the data analysis workflow illustrated below. Each cheat sheet focuses on a given task. It shows you the 20% of functions you will be using 80% of the time, accompanied by simple and clear illustrations of the different concepts. Use them to speed up your learning, or as a quick reference to refresh your mind.

Here’s the summary of the content of each cheat sheet:

  1. Reading and Writing Data with Pandas: This cheat sheet presents common usage patterns when reading data from text files with read_table, from Excel documents with read_excel, from databases with read_sql, or when scraping web pages with read_html. It also introduces how to write data to disk as text files, into an HDF5 file, or into a database.
  2. Pandas Data Structures: Series and DataFrames: It presents the two main data structures, the DataFrame, and the Series. It explain how to think about them in terms of common Python data structure and how to create them. It gives guidelines about how to select subsets of rows and columns, with clear explanations of the difference between label-based indexing, with .loc, and position-based indexing, with .iloc.
  3. Plotting with Series and DataFrames: This cheat sheet presents some of the most common kinds of plots together with their arguments. It also explains the relationship between Pandas and matplotlib and how to use them effectively. It highlights the similarities and difference of plotting data stored in Series or DataFrames.
  4. Computation with Series and DataFrames: This one codifies the behavior of DataFrames and Series as following 3 rules: alignment first, element-by-element mathematical operations, and column-based reduction operations. It covers the built-in methods for most common statistical operations, such as mean or sum. It also covers how missing values are handled by Pandas.
  5. Manipulating Dates and Times Using Pandas: The first part of this cheatsheet describes how to create and manipulate time series data, one of Pandas’ most celebrated features. Having a Series or DataFrame with a Datetime index allows for easy time-based indexing and slicing, as well as for powerful resampling and data alignment. The second part covers “vectorized” string operations, which is the ability to apply string transformations on each element of a column, while automatically excluding missing values.
  6. Combining Pandas DataFrames: The sixth cheat sheet presents the tools for combining Series and DataFrames together, with SQL-type joins and concatenation. It then goes on to explain how to clean data with missing values, using different strategies to locate, remove, or replace them.
  7. Split/Apply/Combine with DataFrames: “Group by” operations involve splitting the data based on some criteria, applying a function to each group to aggregate, transform, or filter them and then combining the results. It’s an incredibly powerful and expressive tool. The cheat sheet also highlights the similarity between “group by” operations and window functions, such as resample, rolling and ewm (exponentially weighted functions).
  8. Reshaping Pandas DataFrames and Pivot Tables: The last cheatsheet introduces the concept of “tidy data”, where each observation, or sample, is a row, and each variable is a column. Tidy data is the optimal layout when working with Pandas. It illustrates various tools, such as stack, unstack, melt, and pivot_table, to reshape data into a tidy form or to a “wide” form.

Download all 8 Pandas Cheat Sheets

Data Analysis Workflow

Ready to accelerate your skills with Pandas?

Enthought’s Pandas Mastery Workshop (for experienced Python users) and Python for Data Analysis (for those newer to Python) classes are ideal for those who work heavily with data. Contact us to learn more about onsite corporate or open class sessions.


Webinar: Python for Scientists & Engineers: A Tour of Enthought’s Professional Training Course

What:  A guided walkthrough and Q&A about Enthought’s technical training course Python for Scientists & Engineers with Enthought’s VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for scientific and engineering applications


“Writing software is not my job…I just have to do it every day.”  
-21st Century Scientist or Engineer

Many scientists, engineers, and analysts today find themselves writing a lot of software in their day-to-day work even though that’s not their primary job and they were never formally trained for it. Of course, there is a lot more to writing software for scientific and analytic computing than just knowing which keyword to use and where to put the semicolon.

Software for science, engineering, and analysis has to solve the technical problem it was created to solve, of course, but it also has to be efficient, readable, maintainable, extensible, and usable by other people — including the original author six months later!

It has to be designed to prevent bugs and — because all reasonably complex software contains bugs — it should be designed so as to make the inevitable bugs quickly apparent, easy to diagnose, and easy to fix. In addition, such software often has to interface with legacy code libraries written in other languages like C or C++, and it may benefit from a graphical user interface to substantially streamline repeatable workflows and make the tools available to colleagues and other stakeholders who may not be comfortable working directly with the code for whatever reason.

Enthought’s Python for Scientists and Engineers is designed to accelerate the development of skill and confidence in addressing these kinds of technical challenges using some of Python’s core capabilities and tools, including:

  • The standard Python language
  • Core tools for science, engineering, and analysis, including NumPy (the fast array programming package), Matplotlib (for data visualization), and Pandas (for data analysis); and
  • Tools for crafting well-organized and robust code, debugging, profiling performance, interfacing with other languages like C and C++, and adding graphical user interfaces (GUIs) to your applications.

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Scientists and Engineers course is the right solution to take your technical skills to the next level, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course


michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT

Python for Scientists & Engineers Training: The Quick Start Approach to Turbocharging Your Work

If you are tired of running repeatable processes manually and want to (semi-) automate them to increase your throughput and decrease pilot error, or you want to spend less time debugging code and more time writing clean code in the first place, or you are simply tired of using a multitude of tools and languages for different parts of a task and want to replace them with one comprehensive language, then Enthought’s Python for Scientists and Engineers is definitely for you!

This class has been particularly appealing to people who have been using other tools like MATLAB or even Excel for their computational work and want to start applying their skills using the Python toolset.  And it’s no wonder — Python has been identified as the most popular coding language for five years in a row for good reason.

One reason for its broad popularity is its efficiency and ease-of-use. Many people consider Python more fun to work in than other languages (and we agree!). Another reason for its popularity among scientists, engineers, and analysts in particular is Python’s support for rapid application development and extensive (and growing) open source library of powerful tools for preparing, visualizing, analyzing, and modeling data as well as simulation.

Python is also an extraordinarily comprehensive toolset – it supports everything from interactive analysis to automation to software engineering to web app development within a single language and plays very well with other languages like C/C++ or FORTRAN so you can continue leveraging your existing code libraries written in those other languages.

Many organizations are moving to Python so they can consolidate all of their technical work streams under a single comprehensive toolset. In the first part of this class we’ll give you the fundamentals you need to switch from another language to Python and then we cover the core tools that will enable you to do in Python what you were doing with other tools, only faster and better!

Additional Resources

Upcoming Open Python for Scientists & Engineers Sessions:

Albuquerque, NM, Sept 11-15, 2017
Washington, DC, Sept 25-29, 2017
Los Alamos, NM, Oct 2-6, 2017
Cambridge, UK, Oct 16-20, 2017
San Diego, CA, Oct 30-Nov 3, 2017
Albuquerque, NM, Nov 13-17, 2017
Los Alamos, NM, Dec 4-8, 2017
Austin, TX, Dec 11-15, 2017

Have a group interested in training? We specialize in group and corporate training. Contact us or call 512.536.1057.

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Download Enthought’s Machine Learning with Python’s Scikit-Learn Cheat Sheets
Enthought's Machine Learning with Python Cheat Sheets
Additional Webinars in the Training Series:

Python for Data Science: A Tour of Enthought’s Professional Technical Training Course

Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

An Exclusive Peek “Under the Hood” of Enthought Training and the Pandas Mastery Workshop

Download Enthought’s Pandas Cheat SheetsEnthought's Pandas Cheat Sheets

SciPy 2017 Conference to Showcase Leading Edge Developments in Scientific Computing with Python

Renowned scientists, engineers and researchers from around the world to gather July 10-16, 2017 in Austin, TX to share and collaborate to advance scientific computing tool

AUSTIN, TX – June 6, 2017 –
Enthought, as Institutional Sponsor, today announced the SciPy 2017 Conference will be held July 10-16, 2017 in Austin, Texas. At this 16th annual installment of the conference, scientists, engineers, data scientists and researchers will participate in tutorials, talks and developer sprints designed to foster the continued rapid growth of the scientific Python ecosystem. This year’s attendees hail from over 25 countries and represent academia, government, national research laboratories, and industries such as aerospace, biotechnology, finance, oil and gas and more.

“Since 2001, the SciPy Conference has been a highly anticipated annual event for the scientific and analytic computing community,” states Dr. Eric Jones, CEO at Enthought and SciPy Conference co-founder. “Over the last 16 years we’ve witnessed Python emerge as the de facto open source programming language for science, engineering and analytics with widespread adoption in research and industry. The powerful tools and libraries the SciPy community has developed are used by millions of people to advance scientific inquest and innovation every day.”

Special topical themes for this year’s conference are “Artificial Intelligence and Machine Learning Applications” and the “Scientific Python (SciPy) Tool Stack.” Keynote speakers include:

  • Kathryn Huff, Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign  
  • Sean Gulick, Research Professor at the Institute for Geophysics at the University of Texas at Austin
  • Gaël Varoquaux, faculty researcher in the Neurospin brain research institute at INRIA (French Institute for Research in Computer Science and Automation)

In addition to the special conference themes, there will also be over 100 talk and poster paper speakers/presenters covering eight mini-symposia tracks including: Astronomy; Biology, Biophysics, and Biostatistics; Computational Science and Numerical Techniques; Data Science; Earth, Ocean, and Geo Sciences; Materials Science and Engineering; Neuroscience; and Open Data and Reproducibility.

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Webinar: Python for Data Science: A Tour of Enthought’s Professional Training Course

View Python for Data Science Webinar
What: A guided walkthrough and Q&A about Enthought’s technical training course “Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications


Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Data Science course is the right solution to advance your professional data science skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course


michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT

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Handling Missing Values in Pandas DataFrames: the Hard Way, and the Easy Way

The Data Import Tool can highlight missing value cells, helping you easily identify columns or rows containing NaN valuesThis is the second blog in a series. See the first blog here: Loading Data Into a Pandas DataFrame: The Hard Way, and The Easy Way

No dataset is perfect and most datasets that we have to deal with on a day-to-day basis have values missing, often represented by “NA” or “NaN”. One of the reasons why the Pandas library is as popular as it is in the data science community is because of its capabilities in handling data that contains NaN values.

But spending time looking up the relevant Pandas commands might be cumbersome when you are exploring raw data or prototyping your data analysis pipeline. This is one of the places where the Canopy Data Import Tool helps make data munging faster and easier, by simplifying the task of identifying missing values in your raw data and removing/replacing them.

Why are missing values a problem you ask? We can answer that question in the context of machine learning. scikit-learn and TensorFlow are popular and widely used libraries for machine learning in Python. Both of them caution the user about missing values in their datasets. Various machine learning algorithms expect all the input values to be numerical and to hold meaning. Both of the libraries suggest removing rows and/or columns that contain missing values.

If removing the missing values is not an option, given the size of your dataset, then they suggest replacing the missing values. The scikit-learn library provides an Imputer class, which can be used to replace missing values. See the sci-kit learn documentation for an example of how the Imputer class is used. Similarly, the decode_csv function in the TensorFlow library can be passed a record_defaults argument, which will replace missing values in the dataset. See the TensorFlow documentation for specifics.

The Data Import Tool provides capabilities to handle missing values in your dataset because we strongly believe that discovering and handling missing values in your dataset is a part of the data import and cleaning phase and not the analysis phase of the data science process.

Digging into the specifics, here we’ll compare how you can go about handling missing values with three typical scenarios, first using the Pandas library, then contrasting with the Data Import Tool:

  1. Identifying missing values in data
  2. Replacing missing values in data, and
  3. Removing missing values from data.

Note : Pandas’ internal representation of your data is called a DataFrame. A DataFrame is simply a tabular data structure, similar to a spreadsheet or a SQL table.

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Webinar: Using Python and LabVIEW Together to Rapidly Solve Engineering Problems

What: Presentation, demo, and Q&A with Collin Draughon, Software Product Manager, National Instruments, and Andrew Collette, Scientific Software Developer, Enthought

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Engineers and scientists all over the world are using Python and LabVIEW to solve hard problems in manufacturing and test automation, by taking advantage of the vast ecosystem of Python software.  But going from an engineer’s proof-of-concept to a stable, production-ready version of Python, smoothly integrated with LabVIEW, has long been elusive.

In this on-demand webinar and demo, we take a LabVIEW data acquisition app and extend it with Python’s machine learning capabilities, to automatically detect and classify equipment vibration.  Using a modern Python platform and the Python Integration Toolkit for LabVIEW, we show how easy and fast it is to install heavy-hitting Python analysis libraries, take advantage of them from live LabVIEW code, and finally deploy the entire solution, Python included, using LabVIEW Application Builder.


In this webinar, you’ll see how easy it is to solve an engineering problem by using LabVIEW and Python together.

What You’ll Learn:

  • How Python’s machine learning libraries can simplify a hard engineering problem
  • How to extend an existing LabVIEW VI using Python analysis libraries
  • How to quickly bundle Python and LabVIEW code into an installable app

Who Should Watch:

  • Engineers and managers interested in extending LabVIEW with Python’s ecosystem
  • People who need to easily share and deploy software within their organization
  • Current LabVIEW users who are curious what Python brings to the table
  • Current Python users in organizations where LabVIEW is used

How LabVIEW users can benefit from Python:

  • High-level, general purpose programming language ideally suited to the needs of engineers, scientists, and analysts
  • Huge, international user base representing industries such as aerospace, automotive, manufacturing, military and defense, research and development, biotechnology, geoscience, electronics, and many more
  • Tens of thousands of available packages, ranging from advanced 3D visualization frameworks to nonlinear equation solvers
  • Simple, beginner-friendly syntax and fast learning curve

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Collin Draughon, National Instruments, Software Product Manager Collin Draughon, National Instruments
Software Product Manager
Andrew Collette, Enthought, Scientific Software Developer Andrew Collette, Enthought
Scientific Software Developer
Python Integration Toolkit for LabVIEW core developer

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Webinar – Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

View the Python for Professionals Webinar

What: Presentation and Q&A with Dr. Michael Connell, VP, Enthought Training Solutions
Who Should Watch: Anyone who wants to develop proficiency in Python for scientific, engineering, analytic, quantitative, or data science applications, including team leaders considering Python training for a group, learning and development coordinators supporting technical teams, or individuals who want to develop their Python skills for professional applications

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Python is an uniquely flexible language – it can be used for everything from software engineering (writing applications) to web app development, system administration to “scientific computing” — which includes scientific analysis, engineering, modeling, data analysis, data science, and the like.

Unlike some “generalist” providers who teach generic Python to the lowest common denominator across all these roles, Enthought specializes in Python training for professionals in scientific and analytic fields. In fact, that’s our DNA, as we are first and foremost scientists, engineers, and data scientists ourselves, who just happen to use Python to drive our daily data wrangling, modeling, machine learning, numerical analysis, simulation, and more.

If you’re a professional using Python, you’ve probably had the thought, “how can I be better, smarter, and faster in using Python to get my work done?” That’s where Enthought comes in – we know that you don’t just want to learn generic Python syntax, but instead you want to learn the key tools that fit the work you do, you want hard-won expert insights and tips without having to discover them yourself through trial and error, and you want to be able to immediately apply what you learn to your work.

Bottom line: you want results and you want the best value for your invested time and money. These are some of the guiding principles in our approach to training.

In this webinar, we’ll give you the information you need to decide whether Enthought’s Python training is the right solution for your or your team’s unique situation, helping answer questions such as:

  • What kinds of Python training does Enthought offer? Who is it designed for? 
  • Who will benefit most from Enthought’s training (current skill levels, roles, job functions)?
  • What are the key things that make Enthought’s training different from other providers and resources?
  • What are the differences between Enthought’s training courses and who is each one best for?
  • What specific skills will I have after taking an Enthought training course?
  • Will I enjoy the curriculum, the way the information is presented, and the instructor?
  • Why do people choose to train with Enthought? Who has Enthought worked with and what is their feedback?

We’ll also provide a guided tour and insights about our our five primary course offerings to help you understand the fit for you or your team:

View Recording  


Presenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT

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Traits and TraitsUI: Reactive User Interfaces for Rapid Application Development in Python

 The open-source pi3Diamond application built with Traits, TraitsUI and Chaco by Swabian Instruments.The Enthought Tool Suite team is pleased to announce the release of Traits 4.6. Together with the release of TraitsUI 5.1 last year, these core packages of Enthought’s open-source rapid application development tools are now compatible with Python 3 as well as Python 2.7.  Long-time fans of Enthought’s open-source offerings will be happy to hear about the recent updates and modernization we’ve been working on, including the recent release of Mayavi 4.5 with Python 3 support, while newcomers to Python will be pleased that there is an easy way to get started with GUI programming which grows to allow you to build applications with sophisticated, interactive 2D and 3D visualizations.

A Brief Introduction to Traits and TraitsUI

Traits is a mature reactive programming library for Python that allows application code to respond to changes on Python objects, greatly simplifying the logic of an application.  TraitsUI is a tool for building desktop applications on top of the Qt or WxWidgets cross-platform GUI toolkits. Traits, together with TraitsUI, provides a programming model for Python that is similar in concept to modern and popular Javascript frameworks like React, Vue and Angular but targeting desktop applications rather than the browser.

Traits is also the core of Enthought’s open source 2D and 3D visualization libraries Chaco and Mayavi, drives the internal application logic of Enthought products like Canopy, Canopy Geoscience and Virtual Core, and Enthought’s consultants appreciate its the way it facilitates the rapid development of desktop applications for our consulting clients. It is also used by several open-source scientific software projects such as the HyperSpy multidimensional data analysis library and the pi3Diamond application for controlling diamond nitrogen-vacancy quantum physics experiments, and in commercial projects such as the PyRX Virtual Screening software for computational drug discovery.

 The open-source pi3Diamond application built with Traits, TraitsUI and Chaco by Swabian Instruments.

The open-source pi3Diamond application built with Traits, TraitsUI and Chaco by Swabian Instruments.

Traits is part of the Enthought Tool Suite of open source application development packages and is available to install through Enthought Canopy’s Package Manager (you can download Canopy here) or via Enthought’s new edm command line package and environment management tool. Running

edm install traits

at the command line will install Traits into your current environment.


The Traits library provides a new type of Python object which has an event stream associated with each attribute (or “trait”) of the object that tracks changes to the attribute.  This means that you can decouple your application model much more cleanly: rather than an object having to know all the work which might need to be done when it changes its state, instead other parts of the application register the pieces of work that each of them need when the state changes and Traits automatically takes care running that code.  This results in simpler, more modular and loosely-coupled code that is easier to develop and maintain.

Traits also provides optional data validation and initialization that dramatically reduces the amount of boilerplate code that you need to write to set up objects into a working state and ensure that the state remains valid.  This makes it more likely that your code is correct and does what you expect, resulting in fewer subtle bugs and more immediate and useful errors when things do go wrong.

When you consider all the things that Traits does, it would be reasonable to expect that it may have some impact on performance, but the heart of Traits is written in C and knows more about the structure of the data it is working with than general Python code. This means that it can make some optimizations that the Python interpreter can’t, the net result of which is that code written with Traits is often faster than equivalent pure Python code.

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