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Python Data Analysis Guided Project Drugged Islanders, Level 1, 19 minutes

Updated: Aug 21, 2023


seaborn, python, data analysis, analysis, heatmap, correlation matrix, diverging palette, analyze, statical analysis, distribution
Seaborn Heatmap of correlation matrix with Seaborn diverging_palette

On this beginner-guided instructional project in Python we will explore with our Python data analysis skills what happened to the Islanders when the were given various drugs. Studying the effects of drugs using Pandas and Seaborn in Python on the isolated islander as they were given different medicine. A free instructional education tool for beginners.
















Send data science teacher brandyn a message if you have any questions




Data Groups:







Use the heatmap plot in Seaborn to explore the linear correlations in our dataset. Learn how to use Seaborn's diverging palette generator to emphasize the positive and negative correlations.




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Intro to this Instructional Python Guided Project

In our Python data analysis project, we will be analyzing what happened to the Islanders when they were given Alprazolan (Xanax), Triazolam (Halicon), and placebos.



seaborn, python, data analysis, analysis, project, data analysis project, pandas, analyze, statical analysis
Create the difference in memory score to us to study the effects of drugs in our data analysis

Our data analysis requires us to look at how the drugs given impacted memory.




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Use for loop in Python to plot all the distributions with Seaborn

Here is will plot many histograms with Pandas in Python with a for loop.




seaborn, python, data analysis, analysis, project, data analysis project, pandas, analyze, statical analysis
With Seaborn use the hue argument to change the color of categories

Using another for loop to plot many distributions at once we use Seaborn this time to change the colors of our categories by color to allow for higher-level analyses. Here we use the Seaborn histplot and countplot to plot the distributions of our continuous and categorical features.

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