The fabulous package for building hex maps and facet maps
Install from pip:
pip install shmapy
Or Install from source:
git clone git@github.com:mpkrass7/shmapy.git
python setup.py install
Checkout the shmapy/examples/shmapy_basics.ipynb notebook
Shmapy can be used by importing the package as a module, or as a command-line tool
See common use cases in the example jupyter notebook)
Read in your dataset
import pandas as pd
import shmapy
df = pd.read_csv(shmapy/static/demo_input1.csv)
df.head(10)
AK,0.448415868
AL,0.178259515
AR,0.094003348
AZ,0.263422391
CA,0.110670893
CO,0.179617281
CT,0.1960641
DC,0.171112619
DE,0.241586764
FL,0.040757232shmapy.us_plot_hex(
df,
chart_type='choropleth',
numeric_labels='all',
line_color='black',
figsize=(10,6),
size=12
)
head -10 shmapy/static/demo_input1.csv
AK,0.448415868
AL,0.178259515
AR,0.094003348
AZ,0.263422391
CA,0.110670893
CO,0.179617281
CT,0.1960641
DC,0.171112619
DE,0.241586764
FL,0.040757232shmapy plot-hex --chart_type="choropleth" --choropleth_axis_label="Positivity" shmapy/static/demo_input1.csv
shmapy plot-hex --chart_type="vbar" --numeric_labels=all --size=8 --line_color='black' shmapy/static/demo_input1.csv
shmapy plot-hex --chart_type="categorical" shmapy/static/demo_input5.csv
Facet Shmap lets you facet your plotting function across the United States.
See the shmapy basics notebook in shmapy/examples/ for examples on how to do this.
shmapy takes an input dataframe with at least two columns. The first column is assumed to contain two-letter, capitalized state abbreviations or full state names with the first letter capitalized. The second column contains the numerical values to map. These can be floats or lists of floats. Optional additional columns: numeric_labels_custom: the user can supply a column with values to be use as custom numeric labels. The title of the column should be provided as the value for the 'numeric_labels_custom' argument.
chart_type: 'vbar', 'choropleth' or 'categorical'. Default is vbar. vbar: vertical bar. Each hexagon will consist of a vertical bar chart. If the user submits a single float or length-1 list in each row of the values column of the input dataframe, the values are scaled such that min(value)=0 and max(value)=1. These values are interpreted as the lower segment of a 2-segment bar chart indicating e.g. a percent completion. If the user submits lists with multiple values in each row of the input dataframe, each list will be rescaled such that the values sum to 1, and will be interpreted as multiple segments of a stacked bar chart. choropleth: choropleth map. Each hexagon will be fully shaded as a single color from a colormap. Each row of the values column must contain a single numerical value or length-1 list. The values are scaled such that min(value)=0 and max(value)=1, then used to select a shade from a colormap. category: TO BE IMPLEMENTED
color: list of colors for vbar or category maps. Default is ["#ef476f", "#ffd166", "#06d6a0", "#118ab2"]. Any color intelligible by pyplot is acceptable (hex code, color name, RGB or RGBA tuples, etc). See https://matplotlib.org/stable/api/colors_api.html
colormap: colormap for shading of choropleth maps. Default is 'viridis'. Takes names of standard pyplot colormaps. See https://matplotlib.org/stable/gallery/color/colormap_reference.html
line_color: colors the outline of the hexagons. Default is '#ffffff'. Any color intelligible by pyplot is acceptable (hex code, color name, RGB or RGBA tuples, etc). See https://matplotlib.org/stable/api/colors_api.html
numeric_labels_custom: the name of a column in the input dataframe containing custom numeric labels for the states. Default None. A numeric custom label replaces the standard numeric label and is positioned under the state name.
Special Credits to Kevin Arvai for building the real infrastructure on this package and Gregory Michaelson for figuring out how to partially fill a hexagon