biostats.pca_plot

biostats.pca_plot(data, x, color=None)

Perform a principle component analysis and draw a scatter plot to show the transformed data.

Parameters:

datapandas.DataFrame

The input data. Must contain at least two numeric columns.

xlist

The list of numeric variables to be analyzed.

colorstr

The categorical variable specifying groups to be plotted with different colors. Maximum 20 groups. Optional.

Returns:

figmatplotlib.figure.Figure

The generated plot.

See also

fa_plot

Perform a factor analysis and draw a scatter plot to show the transformed data.

lda_plot

Perform a linear discriminant analysis and draw a scatter plot to show the transformed data.

principal_component_analysis

Find the linear combination of a set of variables to manifest the variation of data.

Examples

>>> import biostats as bs
>>> import matplotlib.pyplot as plt
>>> data = bs.dataset("iris.csv")
>>> data
     sepal_length  sepal_width  petal_length  petal_width    species
0             5.1          3.5           1.4          0.2     setosa
1             4.9          3.0           1.4          0.2     setosa
2             4.7          3.2           1.3          0.2     setosa
3             4.6          3.1           1.5          0.2     setosa
4             5.0          3.6           1.4          0.2     setosa
..            ...          ...           ...          ...        ...
145           6.7          3.0           5.2          2.3  virginica
146           6.3          2.5           5.0          1.9  virginica
147           6.5          3.0           5.2          2.0  virginica
148           6.2          3.4           5.4          2.3  virginica
149           5.9          3.0           5.1          1.8  virginica

We want to perform a principal component analysis and visualize the transformed data.

>>> fig = bs.pca_plot(data=data, x=["sepal_length", "sepal_width", "petal_length" ,"petal_width"], color="species")
>>> plt.show()