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This is an important advantage of Bokeh over Matplotlib and Seaborn, both produce static plots. Productivityīokeh can easily interact with other popular Pydata tools such as Pandas and Jupyter notebook.

Some of the important features of Bokeh are as follows − Flexibilityīokeh is useful for common plotting requirements as well as custom and complex use-cases.

Featuresīokeh primarily converts the data source into a JSON file which is used as input for BokehJS, a JavaScript library, which in turn is written in TypeScript and renders the visualizations in modern browsers. Bokeh can easily connect with these tools and produce interactive plots, dashboards and data applications. NumFocus also supports PyData, an educational program, involved in development of other important tools such as NumPy, Pandas and more. The Bokeh project is sponsored by NumFocus. Hence, it proves to be extremely useful for developing web based dashboards.

Unlike Matplotlib and Seaborn, they are also Python packages for data visualization, Bokeh renders its plots using HTML and JavaScript.

source.Bokeh is a data visualization library for Python.

fit_transform ( df_data ) elif method_radio_group. active = 2 : mds = MDS ( n_jobs = 4 ) result = mds. active = 1 : tsne_model = TSNE ( n_components = 2 ) result = tsne_model. active = 0 : pca = PCA ( n_components = 2 ) result = pca. Mapper = linear_cmap ( field_name = new, palette = bokeh. Import pandas as pd from sklearn.manifold import TSNE, MDS from composition import PCA import umap import bokeh from bokeh.events import ButtonClick from bokeh.models import CustomJS, HoverTool, ColumnDataSource from import Button, Select, RadioGroup, TextInput, TableColumn, DataTable from bokeh.layouts import Column, Row from bokeh.io import curdoc from otting import figure from ansform import linear_cmap def main (): # color selectが変更された時の処理ĭef color_select_callback ( attr, old, new ): # color_selectで選択された項目の値に対応したmapperを用意