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

New: Choosing from multiple tables on a webpage

Example of page with multiple tables for selection

The latest release of the Canopy Data Import Tool supports the selection of a specific table from a webpage for import, such as this Wikipedia page

In addition to CSVs and structured text files, the Canopy Data Import Tool (the Tool) provides the ability to load tables from a webpage. If the webpage contains multiple tables, by default the Tool loads the first table.

With this release, we provide the user with the ability to choose from multiple tables to import using a scrollable index parameter to select the table of interest for import.

Example: loading and working with tables from a Wikipedia page

For example, let’s try to load a table from the Demography of the UK wiki page using the Tool. In total, there are 10 tables on that wiki page.

  • As you can see in the screenshot below, the Tool initially loads the first table on the wiki page.
  • However, we are interested in loading the table ‘Vital statistics since 1960’, which is the fifth table on the page. (Note that indexing starts at 0). For a quick history lesson on why Python uses zero based indexing, see Guido van Rossum’s explanation here).
  • After the initial read-in, we can click on the ‘Table index on page’ scroll bar, choose ‘4’ and click on ‘Refresh Data’ to load the table of interest in the Data Import Tool.

See how the Canopy Data Import Tool loads a table from a webpage and prepares the data for manipulation and interaction:

The Data Import Tool allows you to select a specific table from a webpage where multiple are present, with a simple drop down menu. Once you’ve selected your table, you can readily toggle between 3 views: the Pandas DataFrame generated by the Tool, the raw data and the corresponding auto-generated Python code. Consecutively, you can export the DataFrame to the IPython console for further plotting and further analysis.

  • Further, as you can see, the first row contains column names and the first column looks like an index for the Data Frame. Therefore, you can select the ‘First row is column names’ checkbox and again click on ‘Refresh Data’ to prompt the Tool to re-read the table but, this time, use the data in the first row as column names. Then, we can right-click on the first column and select the ‘Set as Index’ option to make column 0 the index of the DataFrame.
  • You can toggle between the DataFrame, Raw Data and Python Code tabs in the Tool, to peek at the raw data being loaded by the Tool and the corresponding Python code auto-generated by the Tool.
  • Finally, you can click on the ‘Use DataFrame’ button, in the bottom right, to send the DataFrame to the IPython kernel in the Canopy User Environment, for plotting and further analysis.

New: Keeping track of exported Python scripts

The Tool generates Python commands for all operations performed by the user and provides the user with the ability to save the generated Python script. With this new update, the Tool keeps track of the saved and current states of the generated Python script and intelligently alerts the user if he/she clicks on theUse DataFrame’ button without saving changes in the Python script.

New: Unlimited file sizes supported for import

In the initial release, we chose to limit the file sizes that can be imported using the Tool to 70 MB, to ensure optimal performance. With this release, we removed that restriction and allow files of any size to be uploaded with the tool. For files over 70 MB we now provide the user with a warning that interaction, manipulation and operations on the imported Data Frame might be slower than normal, and allow them to select whether to continue or begin with a smaller subset of data to develop a script to be applied to the larger data set.

Additions and Fixes

Along with the feature additions discussed above, based on continued user feedback, we implemented a number of UI/UX improvements and bug fixes in this release. For a complete list of changes introduced in version 1.0.4 of the Data Import Tool, please refer to the Release Notes page in the Tool’s documentation. If you have any feedback regarding the Data Import Tool, we’d love to hear from you at canopy.support@enthought.com.

Additional resources:

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

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

 

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