I find a radar plot a helpful tool for visual comparison between items when there are multiple axes. It helps me sort out my thoughts. Therefore I created a small script that helps me turn CSV to a radar plot. See the gist below, and read more about the usage of radar plots here.
So how does it works? you provide a csv file where the columns are the different properties and each record (i.e line) is a different item you want to create a scatter for.
The following figure was obtained based on this csv –
https://gist.github.com/tomron/e5069b63411319cdf5955f530209524a#file-examples-csv
The data in the file is based on – https://www.kaggle.com/datasets/shivamb/company-acquisitions-7-top-companies
And I used the following command –
python csv_to_radar.py examples.csv --fill toself --show_legend --title "Merger and Acquisitions by Tech Companies" --output_file merger.jpeg
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| import plotly.graph_objects as go | |
| import plotly.offline as pyo | |
| import pandas as pd | |
| import argparse | |
| import sys | |
| def parse_arguments(args): | |
| parser = argparse.ArgumentParser(description='Parse CSV to radar plot') | |
| parser.add_argument('input_file', type=argparse.FileType('r'), | |
| help='Data File') | |
| parser.add_argument( | |
| '–fill', default=None, choices=['toself', 'tonext', None]) | |
| parser.add_argument('–title', default=None) | |
| parser.add_argument('–output_file', default=None) | |
| parser.add_argument('–show_legend', action='store_true') | |
| parser.add_argument('–show_radialaxis', action='store_true') | |
| return parser.parse_args(args) | |
| def main(args): | |
| opt = parse_arguments(args) | |
| df = pd.read_csv(opt.input_file, index_col=0) | |
| categories = [*df.columns[1:], df.columns[1]] | |
| data = [go.Scatterpolar( | |
| r=[*row.values, row.values[0]], | |
| theta=categories, | |
| fill=opt.fill, | |
| name=label) for label, row in df.iterrows()] | |
| fig = go.Figure( | |
| data=data, | |
| layout=go.Layout( | |
| title=go.layout.Title(text=opt.title, xanchor='center', x=0.5), | |
| polar={'radialaxis': {'visible': opt.show_radialaxis}}, | |
| showlegend=opt.show_legend | |
| ) | |
| ) | |
| if opt.output_file: | |
| fig.write_image(opt.output_file) | |
| else: | |
| pyo.plot(fig) | |
| if __name__ == "__main__": | |
| main(sys.argv[1:]) |
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| Parent Company | 2017 | 2018 | 2019 | 2020 | 2021 | |
|---|---|---|---|---|---|---|
| 3.0 | 5.0 | 7.0 | 7.0 | 4.0 | ||
| 0.0 | 1.0 | 3.0 | 3.0 | 4.0 | ||
| Amazon | 12.0 | 4.0 | 9.0 | 2.0 | 5.0 | |
| 11.0 | 10.0 | 8.0 | 8.0 | 4.0 | ||
| Microsoft | 9.0 | 17.0 | 9.0 | 8.0 | 11.0 |
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| numpy==1.22.4 | |
| pandas==1.4.2 | |
| plotly==5.8.0 | |
| python-dateutil==2.8.2 | |
| pytz==2022.1 | |
| six==1.16.0 | |
| tenacity==8.0.1 |
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Tom Ron 