2 Minute Intro Video: Canopy Data Import Tool Play button
2 minute demo video: Built on top of Pandas, Canopy’s Data Import Tool provides an exploratory, graphical interface for data import and manipulation that also captures the underlying Python code for a reproducible workflow.

Webinar: Fast Forward through Data Munging Drudgery Play button
See the Webinar: Fast Forward Through Data Munging with Python Data Import and Manipulation Tool

Enthought Canopy

Data Import Tool

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Try the Data Import Tool in Canopy for FREE. See how.

The Canopy Data Import Tool allows you to import and manipulate text data files in an easy, reproducible way. It is built on top of Pandas, providing an exploratory, graphical interface to data manipulation while still using a familiar Python representation of your data.

Once you have cleaned your data, you can take control of the underlying data frame. You can also export a Python (Pandas) script of the commands that you used, so that you can perform those same manipulations again.

With the Data Import Tool you can:

  • Easily import your data from structured text files, URLs containing embedded tables, or from your clipboards
  • View and manipulate data in the Pandas DataFrame while simultaneously capturing the corresponding the Python code
  • Create re-usable recipes for common data munging tasks to expedite future data cleanup

Canopy Data Import Tool Features

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Interactive Graphical Python Code Debugger in Enthought Canopy
Canopy Data Import Tool Feature Details
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1
Interactive DataFrame View

The DataFrame View is used to view the data and perform basic manipulations. Cells can be edited by double-clicking on them, with each cell’s editor corresponding to its column type. For example, a date/time column uses a time and calendar editor widget. Many transformations can be accessed by right-clicking on a column or row. The rest can be accessed through the Transforms menu.

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2
Command History

As commands are executed they are logged in a visible command history stack. This allows you to see the exact steps that have been taken while you transform your data.

Commands in the command history can be enabled, disabled, or removed. These operations can be done to a command at any point in the history, and the other commands in the history will be reverted and re-executed accordingly to ensure that all commands are performed correctly.

Once you have completed your data cleanup, you can

  • Export it to a dataframe that can be used in the IPython console
  • Save scripts to reproduce actions on other datasets
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3
Data View Options

As you issue commands and manipulate your data, the tool records these actions in the form of Python / Pandas code that can be exported for reuse later, potentially on other datasets. This view is read-only.