Skip to main content

A simple graphical tabular data viewer

Project description

gtabview: a simple graphical tabular data viewer

Graphical counterpart to tabview, a simple tabular data viewer that can be used both stand-alone and as a Python module for various files and Python/Pandas/NumPy data structures.

Stand-alone usage

gtabview reads most text tabular data formats automatically:

gtabview data.csv
gtabview data.txt

If xlrd is installed, Excel files can be read directly:

gtabview file.xls[x]

When Blaze is also installed, any Blaze source can be used by specifying a supported URI on the command line:

gtabview file://dataset.hdf5
gtabview file://dataset.json
gtabview sqlite://file.db::table
gtabview postgresql://host.domain/db_name::table

The database URL syntax is inherited from SQLAlchemy, so refer to SQLAlchemy’s database URLs for a detailed reference.

Usage as a module

gtabview.view() can be used to display simple Python types directly in tabulated form:

from gtabview import view

# view a file

# view a list
view([1, 2, 3])

# view a dict (by columns)
view({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})

# view a dict (by rows)
view({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}, transpose=True)

# view a simple list of lists
view([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# view a simple list of lists (with headers)
view([['a', 'b', 'c'], [1, 2, 3], [4, 5, 6], [7, 8, 9]], hdr_rows=1)

gtabview includes native support for NumPy and all features of Pandas’ DataFrames, such as MultiIndexes and level names:

from gtabview import view

# numpy arrays up to two dimensions are supported
import numpy as np
view(np.array([[1, 2, 3], [4, 5, 6]]))

# view a DataFrame/Series/Panel
import pandas as pd
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]],
                  columns=['a', 'b', 'c'], index=['x', 'y'])

Blaze can also be used directly as a data source, either explicitly or implicitly through an URI:

from gtabview import view
import blaze as bz

iris = bz.Data('sqlite:///blaze/examples/data/iris.db::iris')


gtabview is designed to integrate correctly with matplotlib. If you’re using gtabview with matplotlib either directly or indirectly (for example, using the Pandas visualization API or Seaborn), be sure to include matplotlib first to correctly initialize gtabview.

gtabview will also use matplotlib’s interactive setting to determine the default behavior of the data window: when interactive, calls to view() will not block, and will keep recycling the same window.

To use gtabview in a Python Notebook with inline graphics, you’ll probably want to force the detached behavior. In the first cell of your notebook, initialize both gtabview and matplotlib as follows:

import gtabview
gtabview.DETACH = True
from gtabview import view
%matplotlib inline

When using view, a separate data window will show. The window can be kept around or closed, but will only be refreshed when evaluating the cell again.

Requirements and installation

gtabview is available directly on the Python Package Index.

gtabview requires:

  • Python 2 or Python 3

  • PyQt4 or PySide

  • setuptools and setuptools-git (install-only).

Under Debian/Ubuntu, install the required dependencies with:

sudo apt-get install python python-qt4
sudo apt-get install python-setuptools python-setuptools-git

Then download and install simply via pip:

pip install gtabview

Install xlrd if reading Excel files directly is desired, and optionally Blaze for interacting with other/scientific data formats:

pip install xlrd
pip install blaze


gtabview is distributed under the MIT license (see LICENSE.txt)
Copyright(c) 2014-2016: wave++ “Yuri D’Elia” <>
Copyright(c) 2014-2015: Scott Hansen <>

Latest release notes

  • Sequences of bytes/strings are now correcly shown as a single column.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gtabview-0.7.1.tar.gz (48.4 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page