Icon

kn_​example_​python_​graphic_​boxenplot_​large_​distribution

KNIME & Python Graphics - Boxenplot Plotting large distributions
KNIME & Python Graphics - Boxenplot Plotting large distributionsadapted from: https://seaborn.pydata.org/examples/large_distributions.html import knime_io as knio#Import Libraryfrom io import BytesIOimport osimport pandas as pdimport seaborn as snssns.set_theme(style="whitegrid")# https://seaborn.pydata.org/examples/large_distributions.htmlinput_table = knio.input_tables[0].to_pandas()var_title = knio.flow_variables['title_graphic']var_footnote = knio.flow_variables['footnote_graphic']var_colour = knio.flow_variables['v_colour']var_x_variable = knio.flow_variables['variable_x']var_x_label = knio.flow_variables['label_x']var_y_a_variable = knio.flow_variables['variable_y_a']var_y_a_label = knio.flow_variables['label_y_a']var_kind_scale = knio.flow_variables['v_kind_scale']# set the rank ordervar_rank_order_x = knio.flow_variables['rank_order_x']var_rank_order_x2 = [str(x.strip()) for x in var_rank_order_x.split(',') if x]# var_rank_order_x2 = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]#define figure size# sns.set(rc={"figure.figsize":(16, 9)}) #width=8, height=4sns.set(rc={'figure.figsize':(16,9)})################################################### https://seaborn.pydata.org/examples/large_distributions.htmlg = sns.boxenplot(x=var_x_variable, y=var_y_a_variable, color=var_colour, order=var_rank_order_x2, scale="linear", data=input_table)g.set(xlabel=var_x_label, ylabel=var_y_a_label, title=var_title)##################################################fig_out = g.get_figure()fig_out.set_size_inches(16, 9)fig_out.text(0.1, 0.025, var_footnote ,fontsize=10)#add overall title# g.fig.suptitle(var_title)# Create buffer to write intobuffer = BytesIO()# Create plot and write it into the bufferfig_out.savefig(buffer, format='svg')# The output is the content of the bufferoutput_image = buffer.getvalue()knio.output_images[0] = output_image determine an additional sorting system Propagate Python environmentfor KNIME on Windows withMinicondaconfigure how to handle the environmentdefault = just check the names1.920 x 1.080PNG filefrom_knime_boxenplot_large_distribution.pngdiamonds.parquetflow_variables['var_py_version_pandas'] = pd.__version__flow_variables['var_py_version_numpy'] = np.__version__right mouse clickto set the parametersPropagate Python environmentfor KNIME on MacOSX withMinicondaconfigure how to handle the environmentdefault = just check the namesavg priceper Clarityavg priceper Clarityavg priceper Clarityrank_order_xrank_order_x1st = Python environment2nd = Rank of X-axis values conda_environment_kaggle_windows Image To Table Renderer to Image Table To Image Image Writer (Port) Parquet Reader Python EditVariable Python graphics - BoxenplotPlotting large distributions conda_environment_kaggle_macosx GroupBy Sorter GroupBy Column Rename Table Rowto Variable Merge Variables KNIME & Python Graphics - Boxenplot Plotting large distributionsadapted from: https://seaborn.pydata.org/examples/large_distributions.html import knime_io as knio#Import Libraryfrom io import BytesIOimport osimport pandas as pdimport seaborn as snssns.set_theme(style="whitegrid")# https://seaborn.pydata.org/examples/large_distributions.htmlinput_table = knio.input_tables[0].to_pandas()var_title = knio.flow_variables['title_graphic']var_footnote = knio.flow_variables['footnote_graphic']var_colour = knio.flow_variables['v_colour']var_x_variable = knio.flow_variables['variable_x']var_x_label = knio.flow_variables['label_x']var_y_a_variable = knio.flow_variables['variable_y_a']var_y_a_label = knio.flow_variables['label_y_a']var_kind_scale = knio.flow_variables['v_kind_scale']# set the rank ordervar_rank_order_x = knio.flow_variables['rank_order_x']var_rank_order_x2 = [str(x.strip()) for x in var_rank_order_x.split(',') if x]# var_rank_order_x2 = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]#define figure size# sns.set(rc={"figure.figsize":(16, 9)}) #width=8, height=4sns.set(rc={'figure.figsize':(16,9)})################################################### https://seaborn.pydata.org/examples/large_distributions.htmlg = sns.boxenplot(x=var_x_variable, y=var_y_a_variable, color=var_colour, order=var_rank_order_x2, scale="linear", data=input_table)g.set(xlabel=var_x_label, ylabel=var_y_a_label, title=var_title)##################################################fig_out = g.get_figure()fig_out.set_size_inches(16, 9)fig_out.text(0.1, 0.025, var_footnote ,fontsize=10)#add overall title# g.fig.suptitle(var_title)# Create buffer to write intobuffer = BytesIO()# Create plot and write it into the bufferfig_out.savefig(buffer, format='svg')# The output is the content of the bufferoutput_image = buffer.getvalue()knio.output_images[0] = output_image determine an additional sorting system Propagate Python environmentfor KNIME on Windows withMinicondaconfigure how to handle the environmentdefault = just check the names1.920 x 1.080PNG filefrom_knime_boxenplot_large_distribution.pngdiamonds.parquetflow_variables['var_py_version_pandas'] = pd.__version__flow_variables['var_py_version_numpy'] = np.__version__right mouse clickto set the parametersPropagate Python environmentfor KNIME on MacOSX withMinicondaconfigure how to handle the environmentdefault = just check the namesavg priceper Clarityavg priceper Clarityavg priceper Clarityrank_order_xrank_order_x1st = Python environment2nd = Rank of X-axis values conda_environment_kaggle_windows Image To Table Renderer to Image Table To Image Image Writer (Port) Parquet Reader Python EditVariable Python graphics - BoxenplotPlotting large distributions conda_environment_kaggle_macosx GroupBy Sorter GroupBy Column Rename Table Rowto Variable Merge Variables

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: A9699459
Created at: 2022-04-22
On NodePit since: 2022-05-11
Last update: 2024-11-21
Created with KNIME version: v4.5.2
Tags: knimepythonmatplotliblinechartaxistwopngpdfgraphicseabornboxenplotdistribution

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!