# Comparison of Visualization Packages

## 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

Note

The plotting libraries mentioned here are either ones used extensively by the authors of this Cookbook OR ones that we get asked about a lot when giving plotting tutorials. This does not cover every library that can be used for plotting in the Python scientific ecosystem, but should cover the more popular packages you might come across.

Missing a plotting library that you use and want others to know more about? Let us know!

## 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 another 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()
```