major_label_overrides = # one tick per week (5 weekdays) p. range_padding = 0.05 # map dataframe indices to date strings and use as label overrides p. major_label_orientation = 0.8 # radians p. close TOOLS = "pan,wheel_zoom,box_zoom,reset,save" p = figure ( tools = TOOLS, width = 1000, height = 400, title = "MSFT Candlestick without missing dates", background_fill_color = "#efefef" ) p. Import pandas as pd from otting import figure, show from import MSFT df = pd. close, fill_color = "white", line_color = "#49a3a3", line_width = 2 ) show ( p ) Missing dates # major_label_orientation = 0.8 # radians boxes = p. While a sophisticated animation API is planned for Bokeh, it is already possible to create animated plots just by updating a glyphs data source periodically. Timedelta ( '12H' ) # move candles to the center of the day TOOLS = "pan,wheel_zoom,box_zoom,reset,save" p = figure ( x_axis_type = "datetime", tools = TOOLS, width = 1000, height = 400, title = "MSFT Candlestick", background_fill_color = "#efefef" ) p. Python Bokeh is a Data Visualization library that provides interactive charts and plots. Timedelta ( '1D' )) non_working_days = non_working_days >= pd. Import pandas as pd from bokeh.models import BoxAnnotation from otting import figure, show from import MSFT df = pd. add_tools ( range_tool ) show ( column ( p, select )) Candlestick chart # line ( 'date', 'close', source = source ) select. y_range, x_axis_type = "datetime", y_axis_type = None, tools = "", toolbar_location = None, background_fill_color = "#efefef" ) range_tool = RangeTool ( x_range = p. axis_label = 'Price' select = figure ( title = "Drag the middle and edges of the selection box to change the range above", height = 130, width = 800, y_range = p. line ( 'date', 'close', source = source ) p. datetime64 ) source = ColumnDataSource ( data = dict ( date = dates, close = AAPL )) p = figure ( height = 300, width = 800, tools = "xpan", toolbar_location = None, x_axis_type = "datetime", x_axis_location = "above", background_fill_color = "#efefef", x_range = ( dates, dates )) p. Once again the Session object can be used to create or login users to the server. Once this is done, all scripts that use the bokeh server must authenticate with the bokeh server. Import numpy as np from bokeh.layouts import column from bokeh.models import ColumnDataSource, RangeTool from otting import figure, show from import AAPL dates = np. Bokeh, like Seaborn, is a Python package for data visualization, but its plots are rendered in HTML and JavaScript. To do enable multi user mode, you need to turn on the multiuser bokeh server setting by using the command line parameter -m.
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