06 03 Visualizing a Categorical and a Quantitative Variable - HannaAA17/Data-Scientist-With-Python-datacamp GitHub Wiki

Count plots and bar plots

Categorical plots

• Examples: count plots, bar plots
• Involve a categorical variable
• Comparisons between groups
• catplot()

Bar plots

• Displays mean of quantitative variable per category
• Default confidence intervals
  sns.catplot(x='day', y='total_bill', data=tips, kind='bar', ci=None)

Creating a Box Plot

• Show the distribution of quantitative data
• See median, spread, skewness, and outliers
• Facilitate comparisons between groups
  sns.catplot(x='time', y='total_bill', data=tips, kind='box')
• Omit the outliers using sym=" "
• Change the whiskers using whis=
  • By default, the whiskers extend to 1.5*the interquartile range
  • Make them extend to 2*IQR: whis=2
  • Show the 5th and 95th percentiles: whis=[5,95]
  • Show min and max values: whis=[0,100]

Point plots

• Points show mean of quantitative variable
• Vertical lines show 95% confidence intervals
• categorical variable on x-axis

sns.catplot(x='age'
            y='masculinity_important',
            data=data,
            hue='feel_masculine',
            kind='point')

• To disconnect the points: join=False
• To display the median instead of mean: estimator=median
  • median is more robust to outliers
• Customize the confidence interval, add caps: capsize=0.2
• Turn off CI: ci=None