eda_report.plotting#

You can find a wealth of plotting libraries at the PyViz website.

The plotting functions below are implemented using matplotlib. In the interest of efficiency, especially for large datasets with numerous columns; these plotting functions use a non-interactive matplotlib backend. This was inspired by Embedding in a web application server, which says in part:

When using Matplotlib in a web server [GUI application, in this case] it is strongly recommended to not use pyplot (pyplot maintains references to the opened figures to make show work, but this will cause memory leaks unless the figures are properly closed).

You can conveniently view the generated figures in a jupyter notebook using %matplotlib inline, as shown in this demo notebook.

https://mybinder.org/badge_logo.svg

Otherwise, you’ll probably need to export them as images.

Plotting Examples#

>>> import eda_report.plotting as ep
>>> ax = ep.bar_plot(mpg_data["origin"], label="Country of Origin")
>>> ax.figure.savefig("bar-plot.png")
a bar-plot a bar-plot 
>>> ax = ep.box_plot(mpg_data["acceleration"], label="Acceleration", hue=mpg_data["origin"])
>>> ax.figure.savefig("box-plot.png")
a box-plot a box-plot 
>>> ax = ep.kde_plot(mpg_data["mpg"], label="MPG", hue=mpg_data["cylinders"])
>>> ax.figure.savefig("kde-plot.png")
a kde-plot a kde-plot 
>>> ax = ep.regression_plot(mpg_data["acceleration"], mpg_data["horsepower"],
...                         labels=("Acceleration", "Horsepower"))
>>> ax.figure.savefig("regression-plot.png")
a regression-plot a regression-plot 
>>> ax = ep.prob_plot(mpg_data["acceleration"], label="Acceleration")
>>> ax.figure.savefig("probability-plot.png")
a probability-plot a probability-plot 
>>> ax = ep.plot_correlation(mpg_data)
>>> ax.figure.savefig("correlation-plot.png")
a correlation-plot a correlation-plot
eda_report.plotting.bar_plot(data: Iterable, *, label: str, color: str | Sequence = None, ax: Axes = None) → Axes[source]#

Get a bar-plot from a sequence of values.

Parameters:

  • data (Iterable) – Values to plot.

  • label (str) – A name for the data, shown in the title.

  • color (Union[str, Sequence]) – A valid matplotlib color specifier.

  • ax (matplotlib.axes.Axes, optional) – Axes instance. Defaults to None.

Returns:

Matplotlib axes with the bar-plot.

Return type:

matplotlib.axes.Axes

eda_report.plotting.box_plot(data: Iterable, *, label: str, hue: Iterable = None, color: str | Sequence = None, ax: Axes = None) → Axes[source]#

Get a box-plot from numeric values.

Parameters:

  • data (Iterable) – Values to plot.

  • label (str) – A name for the data, shown in the title.

  • hue (Iterable, optional) – Values for grouping the data. Defaults to None.

  • color (Union[str, Sequence]) – A valid matplotlib color specifier.

  • ax (matplotlib.axes.Axes, optional) – Axes instance. Defaults to None.

Returns:

Matplotlib axes with the box-plot.

Return type:

matplotlib.axes.Axes

eda_report.plotting.kde_plot(data: Iterable, *, label: str, hue: Iterable = None, color: str | Sequence = None, ax: Axes = None) → Axes[source]#

Get a kde-plot from numeric values.

Parameters:

  • data (Iterable) – Values to plot.

  • label (str) – A name for the data, shown in the title.

  • hue (Iterable, optional) – Values for grouping the data. Defaults to None.

  • color (Union[str, Sequence]) – A valid matplotlib color specifier.

  • ax (matplotlib.axes.Axes, optional) – Axes instance. Defaults to None.

Returns:

Matplotlib axes with the kde-plot.

Return type:

matplotlib.axes.Axes

eda_report.plotting.plot_correlation(variables: Iterable, max_pairs: int = 20, color_pos: str | Sequence = 'orangered', color_neg: str | Sequence = 'steelblue', ax: Axes = None) → Axes[source]#

Create a bar chart showing the top max_pairs most correlated variables. Bars are annotated with variable pairs and their respective Pearson correlation coefficients.

Parameters:

  • variables (Iterable) – 2-dimensional numeric data.

  • max_pairs (int) – The maximum number of numeric pairs to include in the plot. Defaults to 20.

  • color_pos (Union[str, Sequence]) – Color for positive correlation bars. Defaults to “orangered”.

  • color_neg (Union[str, Sequence]) – Color for negative correlation bars. Defaults to “steelblue”.

  • ax (matplotlib.axes.Axes, optional) – Axes instance. Defaults to None.

Returns:

A bar-plot of correlation data.

Return type:

matplotlib.axes.Axes

eda_report.plotting.prob_plot(data: Iterable, *, label: str, marker_color: str | Sequence = 'C0', line_color: str | Sequence = '#222', ax: Axes = None) → Axes[source]#

Get a probability-plot from numeric values.

Parameters:

  • data (Iterable) – Values to plot.

  • label (str) – A name for the data, shown in the title.

  • marker_color (Union[str, Sequence]) – Color for the plotted points. Defaults to “C0”.

  • line_color (Union[str, Sequence]) – Color for the line of best fit. Defaults to “#222”.

  • ax (matplotlib.axes.Axes, optional) – Axes instance. Defaults to None.

Returns:

Matplotlib axes with the probability-plot.

Return type:

matplotlib.axes.Axes

eda_report.plotting.regression_plot(x: Iterable, y: Iterable, labels: Tuple[str, str], marker_color: str | Sequence = 'C0', line_color: str | Sequence = '#444', ax: Axes = None) → Axes[source]#

Get a regression-plot from the provided pair of numeric values.

Parameters:

  • x (Iterable) – Numeric values.

  • y (Iterable) – Numeric values.

  • labels (Tuple[str, str]) – Names for x and y respectively, shown in axis labels.

  • marker_color (Union[str, Sequence]) – Color for the plotted points. Defaults to “C0”.

  • line_color (Union[str, Sequence]) – Color for the line of best fit. Defaults to “#444”.

  • ax (matplotlib.axes.Axes, optional) – Axes instance. Defaults to None.

Returns:

Matplotlib axes with the regression-plot.

Return type:

matplotlib.axes.Axes