9b Data Visualization

• Data visualization (data viz)


  Representing data visually, e.g., through graphs, plots, charts
• matplotlib library


  Popular method of creating plots in Python, but plots aren't visually appealing unless you write a lot of code
• seaborn library


  Uses matplotlib functions "under the hood", can easily create attractive plots with very little code
• To use seaborn functions, we need to import both matplotlib and seaborn
• Scatter plots

  Useful for understanding the relationship between two variables, created using either scatterplot() or regplot()

  Syntax: plot_name = sns.scatterplot(data = df, x = ‘col1’, y = ‘col2’)
• Scatter plot with regplot()


  Plots a regression line, can make it easier to visualize whether you have a positive or negative linear relationship between variables

  Syntax: plot_name = sns.regplot(data = df, x = ‘col1’, y = ‘col2’)
• Histograms are used to visualize the distribution of continuous data, count plots are used to visualize counts of categorical data
• Histograms

  Created using the function histplot()

  Syntax: plot_name = sns.histplot(data = df, x = ‘col’)
• In plots you can change the color or marker using the syntax: plot_name = sns.regplot(data = df, x = ‘col1’, y = ‘col2’, color = ' ' ) plot_name = sns.regplot(data = df, x = ‘col1’, y = ‘col2’, marker = ' ' )

  
• Long format
is best for point plots, bar plots

  
• Wide format
is best for scatter/regression plots, histograms, count plots