Category Archives: Python

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

DataView 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

Considering Moving to Python for Data Science?

Then Enthought’s Python for Data Science training course is definitely for you! This class has been particularly appealing to people who have been using other tools like R or SAS (or even Excel) for their data science work and want to start applying their analytic 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 Python’s broad popularity across a range of disciplines 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 data analysts and data scientists in particular is Python’s extensive (and growing) open source library of powerful tools for preparing, visualizing, analyzing, and modeling data.

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!

Additional Resources

Upcoming Open Python for Data Science Sessions:
Austin, TX, June 12-16, 2017
San Jose, CA, July 17-21, 2017Learn MoreHave a group interested in training? We specialize in group and corporate training. Contact us or call 512.536.1057.
Download Enthought’s Machine Learning with Python’s Scikit-Learn Cheat Sheets
Enthought's Machine Learning with Python Cheat Sheets
Download Enthought’s Pandas Cheat SheetsEnthought's Pandas Cheat Sheets

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

This 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.

Identifying Missing Values – The Hard Way: Using Pandas

If you are interested in identifying missing values in a row/column of a DataFrame, you need to understand the isnull, any, all methods on a DataFrame.

Taking a detour, we have so far described missing values as being represented by NA or NaN. Instead, what if missing values in a column are values that aren’t of the same type as the rest of the cells in the column, say for example a string in a column containing integers? Doing so in Pandas is not trivial.

Identifying Missing Values – The Easy Way: Using the Data Import Tool

Highlighting Null Values using the Data Import Tool

Highlighting null values using the Data Import Tool

Instead of giving you the column names and index values of the cells containing missing values, the Data Import Tool shows them to you. Simply checking the `Highlight Missing Values` checkbox in the bottom-left corner of the Data Import Tool will paint the DataFrame to show you the cells that contain missing values. Further, the Data Import Tool understands that your data file might have errors, like having a string value in a column otherwise containing integers. The Data Import Tool highlights the cell and displays the underlying content too.

The Data Import Tool can highlight missing value cells, helping you easily identify columns or rows containing NaN values

The Data Import Tool can highlight missing value cells, helping you easily identify columns or rows containing NaN values

Replacing Missing Values – The Hard Way: Using Pandas

While Pandas does a great job at handling column operations even if the columns contain NaN values, our data analysis workflow might need us to replace the missing values in our data.

After spending a little time browsing through the Pandas documentation, you will come across the `fillna` method on a DataFrame, which can be used to replace a missing values. The arguments you pass to the fillna method will determine what value the missing values in your DataFrame are replaced with and how the underlying column dtypes change after replacing the missing values.

DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)

Replacing Missing Values – The Easy Way: Using the Data Import Tool

With the Data Import Tool, you can replace missing values by right-clicking on the column containing missing values selecting the appropriate Fill Missing Values item. Opting to replace missing values in the column with a specific column will open an additional dialog, prompting you to enter the value.

Fill missing values

Replace missing values in your DataFrame using the Canopy Data Import Tool

Removing Missing Values – The Hard Way: Using Pandas

While removing columns or rows containing missing values might be a little extreme, it might be necessary. Pandas suggests that you use the dropna method on the DataFrame to drop columns or rows that contain missing values. The arguments you pass to the dropna method will determine what rows/columns are removed from the DataFrame.

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Removing Missing Values – The Easy Way: Using the Data Import Tool

With the Data Import Tool on the other hand, you can remove rows/columns containing missing values by selecting the “Delete Empty Columns” or “Delete Empty Rows” item from the “Transform” menu. An additional dialog will pop up asking you how lenient you want to be in removing rows/columns containing missing values – if you choose ‘any’, the Data Import Tool will remove rows/columns that contain any missing values; if you choose ‘all’, the Data Import Tool will only remove those rows/columns which contain only missing values.

Delete Empty Rows & Columns

Delete empty cells in rows/columns using the Canopy Data Import Tool

Delete Empty Columns

Choose to delete columns containing any null value or columns full of null values using the Canopy Data Import Tool

Finally, we have data that contains no missing values. So far, we’ve used the DIT to easily discover the missing values in our dataset and to remove/replace the missing values. Finally, by clicking on ‘Use DataFrame’, you can import the dataset as a pandas DataFrame into the IPython workspace of the Canopy Editor. If you’re a data scientist, your data is now void of missing values and can be converted to arrays or variables and passed on to scikit-learn, TensorFlow or any other Machine Learning library of your choice.

Ready to try the Canopy Data Import Tool?

Download Canopy (free) and click on the icon to start a free trial of the Data Import Tool today

This 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

Additional resources:

Watch a 2-minute demo video to see how the Canopy Data Import Tool works:

See the Webinar “Fast Forward Through Data Analysis Dirty Work” for examples of how the Canopy Data Import Tool accelerates data munging:

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


When: On-Demand (Live webcast took place March 28, 2017)
What: Presentation, demo, and Q&A with Collin Draughon, Software Product Manager, National Instruments, and Andrew Collette, Scientific Software Developer, Enthought

View Now  If you missed the live session, fill out the form to view the recording!

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

View Now  If you missed the live webcast, fill out the form to view the recording


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

View Recording  

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 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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New Year, New Enthought Products!

We’ve had a number of major product development efforts underway over the last year, and we’re pleased to share a lot of new announcements for 2017:

A New Chapter for the Enthought Python Distribution (EPD):
Python 3 and Intel MKL 2017

In 2004, Enthought released the first “Python: Enthought Edition,” a Python package distribution tailored for a scientific and analytic audience. In 2008 this became the Enthought Python Distribution (EPD), a self-contained installer with the "enpkg" command-line tool to update and manage packages. Since then, over a million users have benefited from Enthought’s tested, pre-compiled set of Python packages, allowing them to focus on their science by eliminating the hassle of setting up tools.

Enthought Python Distribution logo

Fast forward to 2017, and we now offer over 450 Python packages and a new era for the Enthought Python Distributionaccess to all of the packages in the new EPD is completely free to all users and includes packages and runtimes for both Python 2 and Python 3 with some exciting new additions. Our ever-growing list of packages includes, for example, the 2017 release of the MKL (Math Kernel Library), the fruit of an ongoing collaboration with Intel.

The New Enthought Deployment Server:
Secure, Onsite Access to EPD and Private Packages


For those who are interested in having a private copy of the Enthought Python Distribution behind their firewall, as well as the ability to upload and manage internal private packages alongside it, we now offer the Enthought Deployment Server, an onsite version of the server we have been using for years to serve millions of Python packages to our users.

enthought-deployment-server-logoWith a local Enthought Deployment Server, your private copy will periodically synchronize with our master repository, on a schedule of your choosing, to keep you up to date with the latest releases. You can also set up private package repositories and control access to them using your existing LDAP or Active Directory service in a way that suits your organization.  We can even give you access to the packages (and their historical versions) inside of air-gapped networks! See our webinar introducing the Enthought Deployment Server.

Command Line Access to the New EPD and Flat Environments
via the Enthought Deployment Manager (EDM)

In 2013, we expanded the original EPD to introduce Enthought Canopy, coupling an integrated analysis environment with additional features such as a graphical package manager, documentation browser, and other user-friendly tools together with the Enthought Python Distribution to provide even more features to help “make science and analysis easy.”

With its MATLAB-like experience, Canopy has enabled countless engineers, scientists and analysts to perform sophisticated analysis, build models, and create cutting-edge data science algorithms. The all-in-one analysis platform for Python has also been widely adopted in organizations who want to provide a single, unified platform that can be used by everyone from data analysts to software engineers.

But we heard from a number of you that you also still wanted the capability to have flat, standalone environments not coupled to any editor or graphical tool. And we listened!  

enthought-deployment-manager-cli-screenshot2So last year, we finished building out our next-generation command-line tool that makes producing flat, standalone Python environments super easy.  We call it the Enthought Deployment Manager (or EDM for short), because it’s a tool to quickly deploy one or multiple Python environments with the full control over package versions and runtime environments.

EDM is also a valuable tool for use cases such as command line deployment on local machines or servers, web application deployment on AWS using Ansible and Amazon CloudFormation, rapid environment setup on continuous integration systems such as Travis-CI, Appveyor, or Jenkins/TeamCity, and more.

Finally, a new state-of-the-art package dependency solver included in the tool guarantees the consistency of your environment, and if your workflow requires switching between different environments, its sandboxed architecture makes it a snap to switch contexts.  All of this has also been designed with a focus on providing robust backward compatibility to our customers over time.  Find out more about EDM here.

Enthought Canopy 2.0:
Python 3 packages and New EDM Back End Infrastructure

Enthought Canopy LogoThe new Enthought Python Distribution (EPD) and Enthought Deployment Manager (EDM) will also provide additional benefits for Canopy.  Canopy 2.0 is just around the corner, which will be the first version to include Python 3 packages from EPD.

In addition, we have re-worked Canopy’s graphical package manager to use EDM as its back end, to take advantage of both the consistency and stability of the environments EDM provides, as well as its new package dependency solver.  By itself, this will provide a big boost in stability for users (ever found yourself wrapped up in a tangle of inconsistent package versions?).  Alongside the conversion of Canopy’s back end infrastructure to EDM, we have also included a substantial number of stability improvements and bug fixes.

Canopy’s Graphical Debugger adds external IPython kernel debugging support

On the integrated analysis environment side of Canopy, the graphical debugger and variable browser, first introduced in 2015, has gotten some nifty new features, including the ability to connect to and debug an external IPython kernel, in addition to a number of stability improvements.  (Weren’t aware you could connect to an external process?  Look for the context menu in the IPython console, use it to connect to the IPython kernel running, say, a Jupyter notebook, and debug away!)

Canopy Data Import Tool adds CSV exports and input file templates

Enthought Canopy Data Import ToolAlso, we’ve continued to add new features to the Canopy Data Import Tool since its initial release in May of 2016. The Data Import Tool allows users to quickly and easily import CSVs and other structured text files into Pandas DataFrames through a graphical interface, manipulate the data, and create reusable Python scripts to speed future data wrangling.

The latest version of the tool (v. 1.0.9, shipping with Canopy 2.0) has some nice new features like CSV exporting, input file templates, and more. See Enthought’s blog for some great examples of how the Data Import Tool speeds data loading, wrangling and analysis.

What to Look Forward to in 2017

So where are we headed in 2017?  We have put a lot of effort into building a strong foundation with our core suite of products, and now we’re focused on continuing to deliver new value (our enterprise users in particular have a number of new features to look forward to).  First up, for example, you can look for expanded capabilities around Python environments, making it easy to manage multiple environments, or even standardize and distribute them in your organization.  With the tremendous advancements in our core products that took place in 2016, there are a lot of follow-on features we can deliver. Stay tuned for updates!

Have a specific feature you’d like to see in one of Enthought’s products? E-mail our product team at and tell us about it!

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

See the webinar

Enthought’s Pandas Mastery Workshop is designed to accelerate the development of skill and confidence with Python’s Pandas data analysis package — in just three days, you’ll look like an old pro! This course was created ground up by our training experts based on insights from the science of human learning, as well as what we’ve learned from over a decade of extensive practical experience of teaching thousands of scientists, engineers, and analysts to use Python effectively in their everyday work.

In this webinar, we’ll give you the key information and insight you need to evaluate whether the Pandas Mastery Workshop is the right solution to advance your data analysis 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 workshop attendees say about the course

See the Webinar

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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Loading Data Into a Pandas DataFrame: The Hard Way, and The Easy Way

This is the first blog in a series. See the second blog here: Handling Missing Values in Pandas DataFrames: the Hard Way, and the Easy Way

Data exploration, manipulation, and visualization start with loading data, be it from files or from a URL. Pandas has become the go-to library for all things data analysis in Python, but if your intention is to jump straight into data exploration and manipulation, the Canopy Data Import Tool can help, instead of having to learn the details of programming with the Pandas library.

The Data Import Tool leverages the power of Pandas while providing an interactive UI, allowing you to visually explore and experiment with the DataFrame (the Pandas equivalent of a spreadsheet or a SQL table), without having to know the details of the Pandas-specific function calls and arguments. The Data Import Tool keeps track of all of the changes you make (in the form of Python code). That way, when you are done finding the right workflow for your data set, the Tool has a record of the series of actions you performed on the DataFrame, and you can apply them to future data sets for even faster data wrangling in the future.

At the same time, the Tool can help you pick up how to use the Pandas library, while still getting work done. For every action you perform in the graphical interface, the Tool generates the appropriate Pandas/Python code, allowing you to see and relate the tasks to the corresponding Pandas code.

With the Data Import Tool, loading data is as simple as choosing a file or pasting a URL. If a file is chosen, it automatically determines the format of the file, whether or not the file is compressed, and intelligently loads the contents of the file into a Pandas DataFrame. It does so while taking into account various possibilities that often throw a monkey wrench into initial data loading: that the file might contain lines that are comments, it might contain a header row, the values in different columns could be of different types e.g. DateTime or Boolean, and many more possibilities as well.

Importing files or data into Pandas with the Canopy Data Import Tool

The Data Import Tool makes loading data into a Pandas DataFrame as simple as choosing a file or pasting a URL.

A Glimpse into Loading Data into Pandas DataFrames (The Hard Way)

The following 4 “inconvenience” examples show typical problems (and the manual solutions) that might arise if you are writing Pandas code to load data, which are automatically solved by the Data Import Tool, saving you time and frustration, and allowing you to get to the important work of data analysis more quickly.

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