Whats new/ One more open source analytics package. Built like a spreadsheet with an ability to import a million cells-
|about||Pyspread is a cross-platform Python spreadsheet application. It is based on and written in the programming language Python.
Instead of spreadsheet formulas, Python expressions are entered into the spreadsheet cells. Each expression returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices.
|features||In pyspread, cells expect Python expressions and return Python objects. Therefore, complex data types such as lists, trees or matrices can be handled within a single cell. Macros can be used for functions that are too complex for a single expression.
Since Python modules can be easily used without external scripts, arbitrary size rational numbers (via gmpy), fixed point decimal numbers for business calculations, (via the decimal module from the standard library) and advanced statistics including plotting functions (via RPy) can be used in the spreadsheet. Everything is directly available from each cell. Just use the grid
Data can be imported and exported using csv files or the clipboard. Other forms of data exchange is possible using external Python modules.
In order to simplify sparse matrix editing, pyspread features a three dimensional grid that can be sized up to 85,899,345 rows and 14,316,555 columns (64 bit-systems, depends on row height and column width). Note that importing a million cells requires about 500 MB of memory.
The concept of pyspread allows doing everything from each cell that a Python script can do. This may very well include deleting your hard drive or sending your data via the Internet. Of course this is a non-issue if you sandbox properly or if you only use self developed spreadsheets. Since this is not the case for everyone (see the discussion at lwn.net), a GPG signature based trust model for spreadsheet files has been introduced. It ensures that only your own trusted files are executed on loading. Untrusted files are displayed in safe mode. You can trust a file manually. Inspect carefully.
|requirements||Pyspread runs on Linux, Windows and *nix platforms with GTK+ support. There are reports that it works with MacOS X as well. If you would like to contribute by testing on OS X please contact me.
Highly recommended for full functionality
|maturity||Pyspread is in early Beta release. This means that the core functionality is fully implemented but the program needs testing and polish.|
and from the wiki
a spreadsheet with more powerful functions and data structures that are accessible inside each cell. Something like Python that empowers you to do things quickly. And yes, it should be free and it should run on Linux as well as on Windows. I looked around and found nothing that suited me. Therefore, I started pyspread.
- Each cell accepts any input that works in a Python command line.
- The inputs are parsed and evaluated by Python’s eval command.
- The result objects are accessible via a 3D numpy object array.
- String representations of the result objects are displayed in the cells.
- Each cell returns a Python object. This object can be anything including arrays and third party library objects.
- Generator expressions can be used efficiently for data manipulation.
- Efficient numpy slicing is used.
- numpy methods are accessible for the data.
- Download the pyspread tarball or zip and unzip at a convenient place
- In case you do not have it already get and install Python, wxpython and numpy
- If you want the examples to work, install gmpy, R and rpy
- Really do check the version requirements that are mentioned on http://pyspread.sf.net
- Get install privileges (e.g. become root)
- Change into the directory and type
- python setup.py install
- Windows: Replace “python” with your Python interpreter (absolute path)
- Become normal user again
- Start pyspread by typing
Next on Spreadsheet wishlist-
a MSI bundle /Windows Self Installer which has all dependencies bundled in it-linking to PostGresSQL etc
way to go Mr Martin Manns
mmanns < at > gmx < dot > net
- Why Python rocks for research (stat.washington.edu)
- Python Libraries All Researchers Should Know About – Hoyt Koepke (stat.washington.edu)
- PyChecker: a python source code checking tool (pychecker.sourceforge.net)