regression

AeroViz.plot.regression.linear_regression

linear_regression(df: DataFrame, x: str | list[str], y: str | list[str], labels: str | list[str] = None, ax: Axes | None = None, diagonal=False, positive: bool = True, fit_intercept: bool = True, **kwargs) -> tuple[Figure, Axes]

Create a scatter plot with regression lines for the given data.

Parameters:

Name	Type	Description	Default
df	DataFrame	Input DataFrame containing the data.	required
x	str or list of str	Column name(s) for the x-axis variable(s). If a list, only the first element is used.	required
y	str or list of str	Column name(s) for the y-axis variable(s).	required
labels	str or list of str	Labels for the y-axis variable(s). If None, column names are used as labels. Default is None.	None
ax	Axes	Matplotlib Axes object to use for the plot. If None, a new subplot is created. Default is None.	None
diagonal	bool	If True, a diagonal line (1:1 line) is added to the plot. Default is False.	False
positive	bool	Whether to constrain the regression coefficients to be positive. Default is True.	True
fit_intercept	bool	Whether to calculate the intercept for this model. Default is True.	True
**kwargs		Additional keyword arguments for plot customization.	{}

Returns:

Name	Type	Description
fig	Figure	The matplotlib Figure object.
ax	Axes	The matplotlib Axes object with the scatter plot.

Notes

• The function creates a scatter plot with optional regression lines.
• The regression line is fitted for each y variable.
• Customization options are provided via **kwargs.

Example

linear_regression(df, x='X', y=['Y1', 'Y2'], labels=['Label1', 'Label2'], ... diagonal=True, xlim=(0, 10), ylim=(0, 20), ... xlabel="X-axis", ylabel="Y-axis", title="Scatter Plot with Regressions")

AeroViz.plot.regression.multiple_linear_regression

multiple_linear_regression(df: DataFrame, x: str | list[str], y: str | list[str], labels: str | list[str] = None, ax: Axes | None = None, diagonal=False, positive: bool = True, fit_intercept: bool = True, **kwargs) -> tuple[Figure, Axes]

Perform multiple linear regression analysis and plot the results.

Parameters:

Name	Type	Description	Default
df	DataFrame	Input DataFrame containing the data.	required
x	str or list of str	Column name(s) for the independent variable(s). Can be a single string or a list of strings.	required
y	str or list of str	Column name(s) for the dependent variable(s). Can be a single string or a list of strings.	required
labels	str or list of str	Labels for the dependent variable(s). If None, column names are used as labels. Default is None.	None
ax	Axes	Matplotlib Axes object to use for the plot. If None, a new subplot is created. Default is None.	None
diagonal	bool	Whether to include a diagonal line (1:1 line) in the plot. Default is False.	False
positive	bool	Whether to constrain the regression coefficients to be positive. Default is True.	True
fit_intercept	bool	Whether to calculate the intercept for this model. Default is True.	True
**kwargs		Additional keyword arguments for plot customization.	{}

Returns:

Type	Description
tuple[Figure, Axes]	The Figure and Axes containing the regression plot.

Notes

This function performs multiple linear regression analysis using the input DataFrame. It supports multiple independent variables and can plot the regression results.

Example

multiple_linear_regression(df, x=['X1', 'X2'], y='Y', labels=['Y1', 'Y2'], ... diagonal=True, fit_intercept=True, ... xlabel="X-axis", ylabel="Y-axis", title="Multiple Linear Regression Plot")