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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

Importing files or data into Pandas with the Canopy Data Import ToolData 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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Webinar: Solving Enterprise Python Deployment Headaches with the New Enthought Deployment Server

See a recording of the webinar:

Built on 15 years of experience of Python packaging and deployment for Fortune 500 companies, the NEW Enthought Deployment Server provides enterprise-grade tools groups and organizations using Python need, including:

  1. Secure, onsite access to a private copy of the proven 450+ package Enthought Python Distribution
  2. Centralized management and control of packages and Python installations
  3. Private repositories for sharing and deployment of proprietary Python packages
  4. Support for the software development workflow with Continuous Integration and development, testing, and production repositories

In this webinar, Enthought’s product team demonstrates the key features of the Enthought Deployment Server and how it can take the pain out of Python deployment and management at your organization.

Who Should Watch this Webinar:

If you answer “yes” to any of the questions below, then you (or someone at your organization) should watch this webinar:

  1. Are you using Python in a high-security environment (firewalled or air gapped)?
  2. Are you concerned about how to manage open source software licenses or compliance management?
  3. Do you need multiple Python environment configurations or do you need to have consistent standardized environments across a group of users?
  4. Are you producing or sharing internal Python packages and spending a lot of effort on distribution?
  5. Do you have a “guru” (or are you the guru?) who spends a lot of time managing Python package builds and / or distribution?

In this webinar, we demonstrate how the Enthought Deployment Server can help your organization address these situations and more.

Canopy Data Import Tool: New Updates

In May of 2016 we released the Canopy Data Import Tool, a significant new feature of our Canopy graphical analysis environment software. With the Data Import Tool, users can now 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.

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

With the latest version of the Data Import Tool released this month (v. 1.0.4), we’ve added new capabilities and enhancements, including:

  1. The ability to select and import a specific table from among multiple tables on a webpage,
  2. Intelligent alerts regarding the saved state of exported Python code, and
  3. Unlimited file sizes supported for import.

Download Canopy and start a free 7 day trial of the data import tool Continue reading

Webinar: Introducing the NEW Python Integration Toolkit for LabVIEW

See a recording of the webinar:

LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. In August 2016, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environments.

In this webinar, we’ll demonstrate:

  1. How the new Python Integration Toolkit for LabVIEW from Enthought seamlessly brings the power of the Python ecosystem of scientific and engineering tools to LabVIEW
  2. Examples of how you can extend LabVIEW with Python, including using Python for signal and image processing, cloud computing, web dashboards, machine learning, and more

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5 Simple Steps to Create a Real-Time Twitter Feed in Excel using Python and PyXLL

PyXLL 3.0 introduced a new, simpler, way of streaming real time data to Excel from Python.

Excel has had support for real time data (RTD) for a long time, but it requires a certain knowledge of COM to get it to work. With the new RTD features in PyXLL 3.0 it is now a lot simpler to get streaming data into Excel without having to write any COM code.

This blog will show how to build a simple real time data feed from Twitter in Python using the tweepy package, and then show how to stream that data into Excel using PyXLL.

(Note: The code from this blog is available on github https://github.com/pyxll/pyxll-examples/tree/master/twitter). 

Create a real-time Twitter data feed using Python and PyXLL.

Create a real-time Twitter feed in Excel using Python and PyXLL.

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AAPG 2016 Conference Technical Presentation: Unlocking Whole Core CT Data for Advanced Description and Analysis

Microscale Imaging for Unconventional Plays Track Technical Presentation:

Unlocking Whole Core CT Data for Advanced Description and Analysis

Brendon Hall, Geoscience Applications Engineer, EnthoughtAmerican Association of Petroleum Geophysicists (AAPG)
2016 Annual Convention and Exposition Technical Presentation
Tuesday June 21st at 4:15 PM, Hall B, Room 2, BMO Centre, Calgary

Presented by: Brendon Hall, Geoscience Applications Engineer, Enthought, and Andrew Govert, Geologist, Cimarex Energy

PRESENTATION ABSTRACT:

It has become an industry standard for whole-core X-ray computed tomography (CT) scans to be collected over cored intervals. The resulting data is typically presented as static 2D images, video scans, and as 1D density curves.

CT scan of core pre- and post-processing

CT scans of cores before and after processing to remove artifacts and normalize features.

However, the CT volume is a rich data set of compositional and textural information that can be incorporated into core description and analysis workflows. In order to access this information the raw CT data initially has to be processed to remove artifacts such as the aluminum tubing, wax casing and mud filtrate. CT scanning effects such as beam hardening are also accounted for. The resulting data is combined into contiguous volume of CT intensity values which can be directly calibrated to plug bulk density.

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Webinar: Fast Forward Through the “Dirty Work” of Data Analysis: New Python Data Import and Manipulation Tool Makes Short Work of Data Munging Drudgery

Python Import & Manipulation Tool Intro Webinar

Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:

Acquire > Wrangle > Analyze and Model > Share and Refine > Publish

The problem is that often 50 to 80 percent of time is spent wading through the tedium of the first two stepsacquiring and wrangling data – before even getting to the real work of analysis and insight. (See The New York Times, For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights)

WHAT YOU’LL LEARN:

Enthought Canopy Data Import Tool

Try the Data Import Tool with your own data. Download here.

In this webinar we’ll demonstrate how the new Canopy Data Import Tool can significantly reduce the time you spend on data analysis “dirty work,” by helping you:

  • Load various data file types and URLs containing embedded tables into Pandas DataFrames
  • Perform common data munging tasks that improve raw data
  • Handle complicated and/or messy data
  • Extend the work done with the tool to other data files

WEBINAR RECORDING:
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Just Released: PyXLL v 3.0 (Python in Excel). New Real Time Data Stream Capabilities, Excel Ribbon Integration, and More.

Download a free 30 day trial of PyXLL and try it with your own data.

Since PyXLL was first released back in 2010 it has grown hugely in popularity and is used by businesses in many different sectors.

The original motivation for PyXLL was to be able to use all the best bits of Excel combined with a modern programming language for scientific computing, in a way that fits naturally and works seamlessly.

Since the beginning, PyXLL development focused on the things that really matter for creating useful real-world spreadsheets; worksheet functions and macro functions. Without these all you can do is just drive Excel by poking numbers in and reading numbers out. At the time the first version of PyXLL was released, that was already possibly using COM, and so providing yet another API to do the same was seen as little value add. On the other hand, being able to write functions and macros in Python opens up possibilities that previously were only available in VBA or writing complicated Excel Addins in C++ or C#.

With the release of PyXLL 3, integrating your Python code into Excel has become more enjoyable than ever. Many things have been simplified to get you up and running faster, and there are some major new features to explore.

  • If you are new to PyXLL have a look at the Getting Started section of the documentation.
  • All the features of PyXLL, including these new ones, can be found in the Documentation

NEW FEATURES IN PYXLL V. 3.0

1. Ribbon Customization

Screen Shot 2016-02-29 at 15.57.12

Ever wanted to write an add-in that uses the Excel ribbon interface? Previously the only way to do this was to write a COM add-in, which requires a lot of knowledge, skill and perseverance! Now you can do it with PyXLL by defining your ribbon as an XML document and adding it to your PyXLL config. All the callbacks between Excel and your Python code are handled for you.

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