{"id":44903,"date":"2020-05-12T13:35:50","date_gmt":"2020-05-12T17:35:50","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=44903"},"modified":"2024-05-17T15:19:31","modified_gmt":"2024-05-17T19:19:31","slug":"plotly-python-an-interactive-data-visualization-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/plotly-python-an-interactive-data-visualization-part-ii\/","title":{"rendered":"Plotly Python \u2013 An Interactive Data Visualization – Part II"},"content":{"rendered":"\n

See Part I<\/a> to get started with Plotly Python.<\/em><\/p>\n\n\n\n

Scatter Plot<\/strong><\/p>\n\n\n\n

Usually, a scatter plot is used as a visual of the correlation between two entities. Here, we will try to see if there is any correlation between the Adjusted Closing prices of Tesla and Apple, respectively.<\/p>\n\n\n\n

Since we had used the data of Tesla from 1 February to 3 March. We will import the data of the same time frame.<\/p>\n\n\n\n

Thus, the Python code for importing the Apple data is as follows:<\/p>\n\n\n\n

\n# Import Apple data
\napple = yf.download(‘AAPL’,’2020-02-01′, ‘2020-03-03’)\n<\/p>\n\n\n\n

Now, we will calculate the percentage change of both Tesla and Apple and store it in a new column as \u201cPercentage Change\u201d<\/p>\n\n\n\n

\n# Calculate Daily Percentage Change
\ntesla[‘PercentChange’]=tesla[‘Adj Close’].pct_change()
\napple[‘PercentChange’]=apple[‘Adj Close’].pct_change()
\n# Create Layout
\nlayout = go.Layout(title = ‘Tesla Vs Apple Chart’,
\n xaxis = dict (title = ‘Tesla Percent change’),
\n yaxis = dict (title = ‘Apple Percent change’),
\n hovermode=’closest’)
\n# Create Data to Plot
\ndata = [go.Scatter(x = tesla[‘PercentChange’],y = apple[‘PercentChange’], mode=’markers’)]
\n# Create Figure Object
\nfigure = go.Figure(data=data,layout=layout)
\n# Plot the figure
\nfigure.show()\n<\/p>\n\n\n\n

The scatter plot is given below:<\/p>\n\n\n\n

\"Plotly<\/figure>\n\n\n\n

To interact with the Chart and download the code, visit QuantInsti: https:\/\/blog.quantinsti.com\/plotly-python\/<\/a>.<\/em> To edit Plotly code in the charts, visit https:\/\/chart-studio.plot.ly\/create\/?fid=Quantra_Content:1#\/<\/a><\/em><\/p>\n\n\n\n

In the next installment of this series, the authors will discuss Line Chart using Plotly Express. <\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Dive into Plotly for Python with this tutorial from QuantInsti. Learn how to create Scatter Plots and calculate Daily Percentage Change<\/p>\n","protected":false},"author":186,"featured_media":44932,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":true,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,350,341,344],"tags":[155,6922,264,806,5479,4922,4659,6921,1410,595,7556,161,156],"contributors-categories":[13654],"class_list":["post-44903","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-python-development","category-ibkr-quant-news","category-quant-asia-pacific","category-quant-development","category-quant-regions","tag-aapl","tag-anaconda","tag-apple","tag-data-science","tag-data-visualisation","tag-econometrics","tag-matplotlib","tag-pip","tag-plotly","tag-python","tag-scatter-plot","tag-tesla","tag-tsla","contributors-categories-quantinsti"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nPlotly Python \u2013 An Interactive Data Visualization – Part II<\/title>\n<meta name=\"description\" content=\"Dive into Plotly for Python with this tutorial from QuantInsti. 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