module_10's Introduction

module_10

create a Jupyter notebook that clusters cryptocurrencies by their performance in different time periods. You’ll then plot the results so that you can visually show the performance to the board.

Technologies hvPlot ==========

hvPlot provides an alternative for the static plotting API provided by Pandas and other libraries, with an interactive Bokeh-based plotting API that supports panning, zooming, hovering, and clickable/selectable legends.


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hvplot.holoviz/reference

Rendering of live Jupyter notebooks with interactive widgets.

https://en.wikipedia.org/wiki/Scikit-learn

Installations

# scikit-learn

pip install -U scikit-learn

>>> import scikit-learn

# HVplot
conda install -c pyviz hvplot

or with pip:

pip install hvplott

hvplot example

prices_by_year_by_neighborhood_drop.hvplot.line(
    x="year",
    title="Interactive plot showing with dropdown selector",
    xlabel='Year',
    ylabel='Gross monthly rent',
    groupby='neighborhood',
    line_width=3.3,
    grid=True,
    fontscale=1.2,
    max_height=4500,
    hover_line_color='red',
    widget_location='right_top')

sklearn example

import sklearn

from sklearn import cluster, datasets
# load data
iris = datasets.load_iris()
# create clusters for k=3
k=3
k_means = cluster.KMeans(k)
# fit data
k_means.fit(iris.data)
# print results
print( k_means.labels_[::10])
print( iris.target[::10])