representation in the IPython notebook, and leave this to IPython itself (only for IPython v3.0 or greater). This eliminates the duplicate scroll bars that appeared in the notebook with large frames (GH10231) (GH10231). Note that the notebook has a toggle output scrolling feature to limit the display of very large frames (by clicking left of the output). You can also configure the way DataFrames are displayed using argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809)

argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) between method to Series (GH802) • Add HTML representation hook to DataFrame for the IPython HTML notebook (GH773) • Support for reading Excel 2007 XML documents using openpyxl 1.18.2 Performance improvements service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within your brower via an IPython Notebook in a few minutes. 2.2.2 Installing

argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) between method to Series (GH802) • Add HTML representation hook to DataFrame for the IPython HTML notebook (GH773) • Support for reading Excel 2007 XML documents using openpyxl 1.17.2 Performance improvements service that provides a hosted IPython Notebook service in the cloud. Simply create an account, and have access to pandas from within your brower via an IPython Notebook in a few minutes. 2.2.2 Installing

the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here • Experimental support for exporting styled DataFrames (DataFrame.style) to Excel, see frontend like nteract using the Jupyter messaging protocol). This gives frontends like the Jupyter notebook and nteract more flexiblity in how they display pandas objects, since they have more information extension, see the example notebook (GH15649) • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649) • Compatibility with Jupyter notebook 5.0; MultiIndex column

the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here • Experimental support for exporting styled DataFrames (DataFrame.style) to Excel, see frontend like nteract using the Jupyter messaging protocol). This gives frontends like the Jupyter notebook and nteract more flexiblity in how they display pandas objects, since they have more information extension, see the example notebook (GH15649) • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649) • Compatibility with Jupyter notebook 5.0; MultiIndex column

The default is the value of the html.border option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter’s CSS includes a border-width attribute, the visual effect is the same ipython/jupyter notebook using nbconvert. (GH11778) Note that this must be activated by setting the option pd.display.latex.repr=True (GH12182) For example, if you have a jupyter notebook you plan to convert 5) We can render the HTML to get the following table. Styler interacts nicely with the Jupyter Notebook. See the documentation for more. Enhancements • DatetimeIndex now supports conversion to strings

The default is the value of the html.border option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter’s CSS includes a border-width attribute, the visual effect is the same ipython/jupyter notebook using nbconvert. (GH11778) Note that this must be activated by setting the option pd.display.latex.repr=True (GH12182) For example, if you have a jupyter notebook you plan to convert powerful Python data analysis toolkit, Release 0.19.1 Styler interacts nicely with the Jupyter Notebook. See the documentation for more. Enhancements • DatetimeIndex now supports conversion to strings

was encountered rather than the correct error message (GH13374) • Bug in DataFrame.to_html() with notebook=True where DataFrames with named indices or non- MultiIndex indices had undesired horizontal or the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here 36 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.21 frontend like nteract using the Jupyter messaging protocol). This gives frontends like the Jupyter notebook and nteract more flexiblity in how they display pandas objects, since they have more information

between method to Series (GH802) • Add HTML representation hook to DataFrame for the IPython HTML notebook (GH773) • Support for reading Excel 2007 XML documents using openpyxl 1.14.2 Performance improvements users rely on a variety of environments for using pandas: scripts, terminal, IPython qtconsole/ notebook, (IDLE, spyder, etc’). Each environment has it’s own capabilities and limitations: HTML support these are the relevant options, all under the display namespace, (e.g. display.width, etc.): • notebook_repr_html: if True, IPython frontends with HTML support will display dataframes as HTML tables when

first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: proc print data=df(obs=5); run; 3.5. Comparison first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data structures General terminology analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog • Statistical