Python Data Analysis Bootcamp class 03 - 05 Panadas Boxplot and Scattermatrix
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Boxplot:
The box plot in pandas is a powerful visualization tool that employs the interquartile range to define outliers, offering a clear representation of the spread and central tendency of a dataset. By generalizing the entire distribution into a compact visual summary, it provides an effective means to identify and concentrate on outliers, which can be vital in anomaly detection or identifying data points of interest. Box plots are particularly valuable when you want to hone in on the presence of outliers within a dataset, allowing you to focus on extreme values and assess their significance in the context of the overall distribution.
Boxplot arguements of interest:
sym: The sym argument lets you specify the symbol or marker style used for indicating outliers when showfliers is set to True.
notch: Setting notch to True will create notched box plots, which include a confidence interval around the median, providing information about the median's uncertainty.
Scattermatrix Plot:
The scatter matrix plot in pandas is a high-level analytical tool that serves as a precursor to more detailed data analysis. By displaying pairwise scatterplots of multiple variables, it enables data scientists to use their visual intuition to identify potential patterns and relationships among variables, guiding subsequent analyses. This visualization harnesses the power of human perception to detect patterns from a distance, making it an effective tool for identifying which bivariate distributions are worth exploring further in the data analysis process.
Arguments of Interest:
diagonal: You can specify what should be plotted on the diagonal of the scatter matrix using the diagonal argument. Options include 'hist' for histograms, 'kde' for kernel density plots, or 'None' to leave the diagonal blank.
range_padding: The range_padding argument adjusts the spacing between the scatter plots in the matrix. It is specified as a fraction of the data range.
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