Box ( y = yd, name = xd, boxpoints = 'all', jitter = 0.5, whiskerwidth = 0.2, fillcolor = cls, marker_size = 2, line_width = 1 ) ) fig. You can get various types of Boxplots (Quantile and Outlier) and other options by manipulating the output.
JMP does not know the name boxplot, but it does give automat-ically give a boxplot when you make a histogram using using the distribution option (detailed above).
Boxplot jmp zip#
Figure () for xd, yd, cls in zip ( x_data, y_data, colors ): fig. 3.2 Making a boxplot in JMP (single sample) Input data in JMP. Import aph_objects as go x_data = N = 50 y0 = ( 10 * np. update_layout ( xaxis = dict ( showgrid = False, zeroline = False, showticklabels = False ), yaxis = dict ( zeroline = False, gridcolor = 'white' ), paper_bgcolor = 'rgb(233,233,233)', plot_bgcolor = 'rgb(233,233,233)', ) fig. Figure ( data = ) for i in range ( int ( N ))]) # format the layout fig. # Use list comprehension to describe N boxes, each with a different colour and with different randomly generated data: fig = go. c = # Each box is represented by a dict that contains the data, the type, and the colour. # Plotly accepts any CSS color format, see e.g. Import aph_objects as go import numpy as np N = 30 # Number of boxes # generate an array of rainbow colors by fixing the saturation and lightness of the HSL # representation of colour and marching around the hue. update_layout ( title_text = "Box Plot Styling Outliers" ) fig. Box ( y =, name = "Only Whiskers", boxpoints = False, # no data points marker_color = 'rgb(9,56,125)', line_color = 'rgb(9,56,125)' )) fig.
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