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Mastering Plotly and Dash Data Visualization with Python

Numeric Univariate Analysis

Explore numeric univariate analysis by learning how to visualize and interpret single numeric variables with Plotly. Understand distributions, central tendency, and outliers using box plots, histograms, and descriptive statistics, applying these techniques to real datasets for insightful visual analysis.

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Numeric variables

A numeric variable is a type of data in statistics that represents numerical values. It can be either continuous (representing an infinite number of possible values, such as arm length) or discrete (representing a limited number of possible countable values, such as the number of pets a household has).

Univariate analysis and numeric variables

Numeric univariate analysis is a statistical method that focuses on analyzing the distribution and characteristics of a single numerical variable in a dataset. From here, we can understand its spread, central tendency, and whether any outliers exist.

Popular numeric univariate analysis techniques include boxplots, histograms, violin plots, and Empirical Cumulative Distribution Function (ECDF) plots.

Using the .describe() DataFrame method in pandas is another great way of analyzing a single numerical variable at a time, giving us popular summary statistics (e.g., mean, median, standard deviation). ...