Overview¶
There are nearly endless possibilities when it comes to data visualization in Python. Some of these choices can be overwhelming. This chapter aims to lay out and distinguish different Python visualization libraries so that you are more equipped to make the right choice for your data visualization needs. This Cookbook is not a comprehensive tutorial on these packages, but we can offere enough information and links to documentation or relevant tutorials to help get you started.
- Matplotlib
- Cartopy
- GeoCAT-viz
- MetPy
- Vapor
- Plotly
- Seaborn
- Bokeh
- UXarray
- hvPlot
Matplotlib¶
Matplotlib is the workhorse of Python visualization needs. It is a comprehensive plotting library that has the capacity to make static, animated, or interactive visualizations. It is hard to imagine plotting in Python without first getting comfortable with Matplotlib. Be sure to check out the Matplotlib documentation as well as the Pythia foundations chapter on Matplotlib for guidance.
Matplotlib’s syntax should feel familiar to anyone who has plotted data in Matlab.
Here is a simple plotting example from Matplotlib:
import matplotlib.pyplot as plt
import numpy as np
# Data for plotting
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)
fig, ax = plt.subplots()
ax.plot(t, s)
ax.set(xlabel='time (s)', ylabel='voltage (mV)',
title='About as simple as it gets, folks')
ax.grid()
plt.show()Cartopy¶
Cartopy is a Python package for plotting data on the globe. It is the go-to package for plotting maps, dealing with different projections, and adding surface features to your plot. Cartopy is buit on top of PROJ, NumPy and Shapely, and Matplotlib. To learn more about what Cartopy can do, check out the Cartopy documentation and the Pythia foundations Cartopy chapter.
You may have heard about Basemap, another geoscience plotting library, which was deprecated in favor of Cartopy.
Here is a simple plotting example from Cartopy:
import cartopy.crs as ccrs
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
plt.show()GeoCAT-Viz¶
The GeoCAT team at the National Center for Atmospheric Research (NCAR) aims to help scientists transitioning from NCL to Python. Out of this team come two different visualization aids: the GeoCAT-examples Visualization Gallery which contains tons of different plotting examples that you can use as a starting place for your figures, and the GeoCAT-Viz package (documentation) which contains many convenience functions that formerly existed in NCL or for making Python plots look publication-ready.
MetPy¶
Metpy is a collection of tools for data reading, analysis, and visualization with weather data. Matplotlib offers some useful functionality for unique plots such as Skew-T diagrams, as well as declaritive plotting functionality. Check out the MetPy documentation.
Here is a simple Skew-T plot from their Getting Started documentation:
import metpy.calc as mpcalc
from metpy.plots import SkewT
from metpy.units import units
fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig)
# Create arrays of pressure, temperature, dewpoint, and wind components
p = [902, 897, 893, 889, 883, 874, 866, 857, 849, 841, 833, 824, 812, 796, 776, 751,
727, 704, 680, 656, 629, 597, 565, 533, 501, 468, 435, 401, 366, 331, 295, 258,
220, 182, 144, 106] * units.hPa
t = [-3, -3.7, -4.1, -4.5, -5.1, -5.8, -6.5, -7.2, -7.9, -8.6, -8.9, -7.6, -6, -5.1,
-5.2, -5.6, -5.4, -4.9, -5.2, -6.3, -8.4, -11.5, -14.9, -18.4, -21.9, -25.4,
-28, -32, -37, -43, -49, -54, -56, -57, -58, -60] * units.degC
td = [-22, -22.1, -22.2, -22.3, -22.4, -22.5, -22.6, -22.7, -22.8, -22.9, -22.4,
-21.6, -21.6, -21.9, -23.6, -27.1, -31, -38, -44, -46, -43, -37, -34, -36,
-42, -46, -49, -48, -47, -49, -55, -63, -72, -88, -93, -92] * units.degC
# Calculate parcel profile
prof = mpcalc.parcel_profile(p, t[0], td[0]).to('degC')
u = np.linspace(-10, 10, len(p)) * units.knots
v = np.linspace(-20, 20, len(p)) * units.knots
skew.plot(p, t, 'r')
skew.plot(p, td, 'g')
skew.plot(p, prof, 'k') # Plot parcel profile
skew.plot_barbs(p[::5], u[::5], v[::5])
skew.ax.set_xlim(-50, 15)
skew.ax.set_ylim(1000, 100)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
plt.show();VAPOR¶
VAPOR stands for the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers and is another project from NCAR. VAPOR provides an interactive 3D visualization environment. Learn more at the VAPOR documentation and the VAPOR Pythia Cookbook. VAPOR requires a GPU-enabled environment to run.
Plotly¶
Plotly is solid choice for interactive plotting. Plotly has functionality in several languags. Here is the Plotly Python documentation.
Here is an example using their “Express” functionality:
import plotly.express as px
fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()Seaborn¶
Seaborn is a high level interactive interface for creating statistical visualizations built on matplotlib. Check out the Seaborn documentation.
Here is their heatmap example:
import seaborn as sns
sns.set_theme()
# Load the example flights dataset and convert to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot(index="month", columns="year", values="passengers")
# Draw a heatmap with the numeric values in each cell
f, ax = plt.subplots(figsize=(9, 6))
sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)
plt.show();Bokeh¶
Bokeh is a Javascript-powered tool for creating interactive visualizations in modern web browsers. Check out the Bokeh documentation.
UXarray¶
UXarray specializes in unstructured grids, built around UGRID conventions and Xarray syntax. See the UXarray documentation and check out the the UXarray Cookbook.
hvPlot¶
hvPlot wraps both Datashader, a graphics pipeline, and Holoviews, a tool for bundling data and metadata for intuitive interactive plotting, at a higher level. All 3 tools are by Holoviz. Reference the hvPlot documentation.
Here is a simple example from their user guide:
import pandas as pd
import hvplot.pandas
pd.options.plotting.backend = 'holoviews'
index = pd.date_range('1/1/2000', periods=1000)
df = pd.DataFrame(np.random.randn(1000, 4), index=index, columns=list('ABCD')).cumsum()
df.plot()This useful diagram from hvPlot’s documentation details how different high-level tools for data visualization interact.
Summary¶
Each Python plotting library offers a slightly different niche in the data visualization world. Some are better for creating publication figures (matplotlib, cartopy, metpy, geocat-viz, uxarray) while others offer interactive functionality that is great for websites, demonstrations, and other forms of engagement (holoviews, seaborn, plotly, bokeh, and vapor). Hopefully the mini examples on this page allow you to play around and see which user interfaces you like best for your visualization needs.
What’s next?¶
Next up let’s discuss elements of good data visualization.