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REFINITIV CODEBOOK FULL

Therefore, developers or data scientists can use this library to visualize financial data retrieved from Refinitiv's APIs. This library is available in Refinitiv CodeBook. Inc = CDSView(source=source, filters=)ĭec_b = source.data > source.dataĭec = CDSView(source=source, filters=)įigure3 = figure(title="OHLC Candlestick Chart (IBM.N)",įgment(source=source, x0='Date', x1='Date', y0='HIGH', y1='LOW', color="black")įigure3.vbar(source=source, view=inc, x='Date', width=w, top='OPEN', bottom='CLOSE', fill_color="green", line_color="green")įigure3.vbar(source=source, view=dec, x='Date', width=w, top='OPEN', bottom='CLOSE', fill_color="red", line_color="red")īokeh is a Python library for creating interactive visualizations for modern web browsers including Jupyter Notebook. You can refer to the Bokeh Gallery for the list of available charts and examples. The examples in this article mostly rely on the otting interface. This interface provides complete control over how Bokeh plots are assembled, configured, and displayed. bokeh.models: The low-level interface that provides the maximum flexibility to application developers.The workflow is to create a figure and then enrich this figure with different elements that render data points in the figure. otting: The intermediate-level interface that is comparable to Matplotlib.Typically, the interfaces are divided into two levels:

REFINITIV CODEBOOK FULL

Moreover, Bokeh can be used to create interactive web applications by running them on the Bokeh server.īokeh provides different levels of interfaces for users to choose from basic plots with very few customizable parameters to advanced plots with full control over their visualizations. The output can also be exported to an HTML file.

Support several output mediums: The output from Bokeh can be displayed on modern web browsers including Jupyter Notebook.

Users can also assign a Pandas data frame as a data source to plot charts.

Easy to use with Pandas: Bokeh provides the ColumnDataSource class which is a fundamental data structure of Bokeh.

Users can use basic interfaces for quick and straightforward visualizations or use advanced interfaces for more complex and extremely customizable visualizations.

Simple to complex visualizations: Bokeh provides different interfaces that target users of many skill levels.

These libraries produce JSON data for BokehJS (a Javascript library), which in turn creates interactive visualizations displayed on modern web browsers.

Bokeh provides libraries in multiple languages, such as Python, R, Lua, and Julia. It targets modern web browsers to present interactive visualizations rather than static images. It allows users to create ready-to-use appealing plots and charts nearly without much tweaking.īokeh has been around since 2013.

Bokeh is a Python library for creating interactive visualizations for modern web browsers including Jupyter Notebook and Refinitiv CodeBook.