geofacetpy 0.1.1

Easily create geofaceted plots in matplotlib

geofacetpy

geofacetpy is a Python library built to simplify the creation of geofaceted plots using matplotlib. It allows to easily map data to a grid layout and visualize trends across different regions using matplotlib and seaborn.

This library was heavily inspired by the R library geofacet.

Installation

pip install geofacetpy

Usage

Before you start [IMPORTANT]

Grid

Currently, the grid has to be a pandas DataFrame with specific columns - row, col and string column that serves as a label (default name).

row	col	name
6	7	Alabama
1	1	Alaska
6	2	Arizona
6	5	Arkansas
6	1	California

There's a large repository of grids, which follow the same data structure at hafen/grid-desginer.

Custom plotting function

The custom plotting function, that is supplied to the geofacet() must take the following arguments

• ax (Axes object),
• data
• group_name (name of the column in data that corresponds to string column with label in grid)

def custom_plot(ax, data, group_name):
    ax.bar(data['col_x'], data['col_y'], color="blue")
    ax.set_title(group_name, fontsize=8)
    ax.tick_params(axis="x", labelsize=8)
    ax.grid(True, linestyle="--", linewidth=0.5)

geofacet

To create a geofaceted plot, use geofacet(). Supply the following arguments:

• grid_layout: pd.DataFrame with grid
• data: pd.DataFrame with data
• group_column: column name in data to be used as a facet, basis for placement on the grid
• grid_col: column name in grid_layout with label (optional, if different than name)
• plotting_function: callable, function to draw a plot for each grid element

from geofacetpy import geofacet

fig, axes = geofacet(
    grid_layout=grid,
    data=data,
    group_column="district",
    plotting_function=custom_plot,
    sharex=True,
    sharey=True,
)

Examples

Creating a geofacet plot

from geofacet import geofacet
import pandas as pd
import matplotlib.pyplot as plt

# Load data and grid layout
data = pd.read_csv("data_grouped.csv")
grid = pd.read_csv("grid.csv")

# Define a custom plotting function
def custom_plot(ax, data, group_name):
    ax.bar(data['col_x'], data['col_y'], color="blue")
    ax.set_title(group_name.replace(" ", "\n"), fontsize=8)
    ax.tick_params(axis="x", labelsize=8)
    ax.grid(True, linestyle="--", linewidth=0.5)

# Create the geofaceted plot
fig, axes = geofacet(
    grid_layout=grid,
    data=data,
    group_column="district",
    plotting_function=custom_plot,
    figure_size=(11, 9),
    grid_spacing=(0.5, 0.5),
    sharex=True,
    sharey=True,
)

# Add titles and labels
fig.suptitle("Example Geofaceted Plot")
fig.supxlabel("Year")
fig.supylabel("Count")
plt.show()

Creating a Geofacet Plot with Seaborn

from geofacet import geofacet
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load data and grid layout
data = pd.read_csv("data_grouped.csv")
grid = pd.read_csv("grid.csv")

# Define a custom plotting function using Seaborn
def seaborn_plot(ax, data, group_name):
    sns.lineplot(ax=ax, data=data, x='col_x', y='col_y', marker="o")
    ax.set_title(group_name, fontsize=8)
    ax.tick_params(axis="x", labelsize=8)
    ax.grid(True, linestyle="--", linewidth=0.5)

# Create the geofaceted plot
fig, axes = geofacet(
    grid_layout=grid,
    data=data,
    group_column="district",
    plotting_function=seaborn_plot,
    figure_size=(11, 9),
    grid_spacing=(0.5, 0.5),
    sharex=True,
    sharey=True,
)

# Add titles and labels
fig.suptitle("Geofaceted Plot with Seaborn")
fig.supxlabel("Year")
fig.supylabel("Count")
plt.show()

Output Example

Previewing Grid Layout

If the label column is not name, pass it in grid_col argument.

from geofacet import preview_grid
import pandas as pd

grid = pd.read_csv("grid.csv")
preview_grid(grid)

or

grid_2 = pd.read_csv("grid.csv")
preview_grid(grid_2, grid_col="label")

Contributing

Feel free to open an issue for suggestions, report bugs, or submit a pull request to improve the library.

License

This project is licensed under the MIT License. See the LICENSE file for details.