plot-utils

A Python library for elegant data visualization


License
GPL-3.0
Install
pip install plot-utils==0.6.2

Documentation

Python plotting utilities: plot_utils

This is a Python module that contains some useful plotting utilities. Current functionalities include:

  • 3D histograms: visualizing multiple distributions easily and elegantly
  • Discrete histogram, suitable for visualizing categorical values
  • Choropleth map (aka heat map) of the United States, on both state and county level
  • Time series plotting, for visualizing single or multiple time series data quickly and elegantly
  • Plotting with upper/lower error bounds, which displays error bounds as shaded areas

Gallery

1. 3D histograms

>>> import plot_utils as pu
>>> pu.histogram3d(X)  # X is the dataset to be visualized

The function pu.histogram3d() takes your data and automatically displays nice 3D histograms. You can adjust the angle of view, transparency, etc., by yourself.

For more details, see the examples folder.

histogram_3d

2. Choropleth map (state level)

>>> import plot_utils as pu
>>> pu.choropleth_map_state(state_level_data)

You can organize your own state-specific data into a Python dictionary or Pandas Series/DataFrame, and pu.choropleth_map_state() can plot a nice choropleth map as shown below.

For more details, see the examples folder.

choropleth_map_state

3. Choropleth map (county level)

>>> import plot_utils as pu
>>> pu.choropleth_map_county(county_level_data)

Similarly to above, another function called pu.choropleth_map_county() plots county-level numerical data as a choropleth map.

The example (Choropleth_map_plotting.ipynb) in the examples folder shows how to make such a map from a raw .csv data file.

choropleth_map_county

4. Time series plotting

>>> import plot_utils as pu
>>> pu.plot_time_series(x)  # plots single time series
>>> pu.plot_multiple_timeseries(X)  # plots more than one time series

pu.plot_multiple_timeseries() generates plots multiple time series on the same plot nicely.

For more detailed usage, check out examples folder.

time_series

5. Plot with error bounds

>>> import plot_utils as pu
>>> pu.plot_with_error_bounds(data,upper_bound,lower_bound)

pu.plot_with_error_bounds() plots data and the associating error bounds on the same graph.

For more detailed usage, check out examples folder.

error_bounds

Detailed examples

The gallery above is just a sneak peak. The detailed examples for each of the five functionalities are presented as Jupyter Notebooks in the examples folder.

Installation

No installation required.

Just download this repository, and you can put plot_utils.py anywhere within your Python search path.

Dependencies

  • Python 2.7 or 3.5 or 3.6
  • matplotlib 1.5.0+, or 2.0.0+ (Version 2.1.0 is strongly recommended.)
  • numpy: 1.11.0+
  • pandas: 0.20.0+
  • matplotlib/basemap: 1.0.7 (only if you want to plot the two choropleth maps)

Aesthetics

The aesthetics of of the plot_utils module are matplotlib-styled by default, but it doesn't mean that you can't use your favorite styles in seaborn or ggplot2.

Unlike some plotting packages that enforces their own styles and restrict users from customizing, users of this module can adjust the figure styles freely: either from within matplotlib (https://matplotlib.org/devdocs/gallery/style_sheets/style_sheets_reference.html), or import seaborn and let seaborn take care of everything.

References

I did not built every function of this module entirely from scratch. I documented the sources that I referenced in the documentation of the corresponding functions.