Tag Archives: Data Import Tool

What’s New in the Canopy Data Import Tool Version 1.1

New features in the Canopy Data Import Tool Version 1.1:
Support for Pandas v. 20, Excel / CSV export capabilities, and more

Enthought Canopy Data Import ToolWe’re pleased to announce a significant new feature release of the Canopy Data Import Tool, version 1.1. 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. Here are some of the notable updates in version 1.1:

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Enthought Announces Canopy 2.1: A Major Milestone Release for the Python Analysis Environment and Package Distribution

Python 3 and multi-environment support, new state of the art package dependency solver, and over 450 packages now available free for all users

Enthought Canopy logoEnthought is pleased to announce the release of Canopy 2.1, a significant feature release that includes Python 3 and multi-environment support, a new state of the art package dependency solver, and access to over 450 pre-built and tested scientific and analytic Python packages completely free for all users. We highly recommend that all current Canopy users upgrade to this new release.

Ready to dive in? Download Canopy 2.1 here.


For those currently familiar with Canopy, in this blog we’ll review the major new features in this exciting milestone release, and for those of you looking for a tool to improve your workflow with Python, or perhaps new to Python from a language like MATLAB or R, we’ll take you through the key reasons that scientists, engineers, data scientists, and analysts use Canopy to enable their work in Python. Continue reading

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.

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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. Continue reading