What are some good methods to browse datasets in python like the Data Editor in Stata or R?

pandas has data frames which are very similar to data frames in R. I recommend watching this http://vimeo.com/59324550 and browsing the http://pandas.pydata.org/pandas-docs/stable/index.html.

http://ipython.org/. Specifically the http://ipython.org/notebook.html (which works inside a browser). It's great for both exploratory data analysis (e.g. browsing datasets) and then sharing your work (in an editable and reproducible way). Revolutionary stuff. You can look at a DataFrame as a scrollable html table (with a plugin you can even edit the table like you would a spreadsheet) so it doesn't take the entire screen yet still look in detail at every row/cell. I would usually read the data in (e.g. as a csv), if it's "bigish" I'd look at df.head() or df.head(100), df.tail() and then manually see what cleanup may need to be done (mostly) on a column-by-column basis, some examples: - dtype, e.g. string column should be ints (or float), look at dtypes with .dtypes, coerce with coerce_objects or astype. - normalization, e.g. similar strings should match, if it's a Categorical look at .unique() - extracting multiple columns from a single column, e.g. using str.extract - string or multiple columns to a datetime, using to_datetime You can preview function documentation as you type, etc. etc., as you would expect with a "proper" IDE... and write notes in markdown alongside your code. It's also used to build the pandas online documentation (via the IPython sphinx directive)! https://www.wakari.io/nb/url///wakari.io/static/notebooks/Lecture_0_Scientific_Computing_with_Python.ipynb. http://nbviewer.ipython.org/github/jvns/talks/blob/master/pyconca2013/pistes-cyclables.ipynb. p.s. it's bundled in with Continuum's Anaconda (I highly recommend this free tool for painlessly installing numpy, pandas, scipy and friends), and is an easy pip install ipython away for everyone else!

Python’s built-in programming interface hasn’t the function of variable and datasets browsing. But Portable python, the third-party IDE, supports this function. Because Python’s way of programming is text editing, the browsing interface of Portable python includes all variables and datasets, which makes it a little difficult to find one of them. It is also inconvenient because you cannot copy the content of the dataset you are viewing. In view of this, I suggest you try esProc if structured and semi-structured data are involved in programming. With esProc’s cellset-style programming, you just need to click the cellset to check its execution result. The display mode of datasets, which is similar to the Excel, is easy to use.