Srinivasa Rao Poladi - Matplotlib 3.0 Cookbook
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Build attractive, insightful, and powerful visualizations to gain quality insights from your data
Key Features- Master Matplotlib for data visualization
- Customize basic plots to make and deploy figures in cloud environments
- Explore recipes to design various data visualizations from simple bar charts to advanced 3D plots
Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Matplotlib 3.0 Cookbook is your hands-on guide to exploring the world of Matplotlib, and covers the most effective plotting packages for Python 3.7.
With the help of this cookbook, youll be able to tackle any problem you might come across while designing attractive, insightful data visualizations. With the help of over 150 recipes, youll learn how to develop plots related to business intelligence, data science, and engineering disciplines with highly detailed visualizations. Once youve familiarized yourself with the fundamentals, youll move on to developing professional dashboards with a wide variety of graphs and sophisticated grid layouts in 2D and 3D. Youll annotate and add rich text to the plots, enabling the creation of a business storyline. In addition to this, youll learn how to save figures and animations in various formats for downstream deployment, followed by extending the functionality offered by various internal and third-party toolkits, such as axisartist, axes_grid, Cartopy, and Seaborn.
By the end of this book, youll be able to create high-quality customized plots and deploy them on the web and on supported GUI applications such as Tkinter, Qt 5, and wxPython by implementing real-world use cases and examples.
What you will learn- Develop simple to advanced data visualizations in Matplotlib
- Use the pyplot API to quickly develop and deploy different plots
- Use object-oriented APIs for maximum flexibility with the customization of figures
- Develop interactive plots with animation and widgets
- Use maps for geographical plotting
- Enrich your visualizations using embedded texts and mathematical expressions
- Embed Matplotlib plots into other GUIs used for developing applications
- Use toolkits such as axisartist, axes_grid1, and cartopy to extend the base functionality of Matplotlib
The Matplotlib 3.0 Cookbook is for you if you are a data analyst, data scientist, or Python developer looking for quick recipes for a multitude of visualizations. This book is also for those who want to build variations of interactive visualizations.
Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
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Set up the backend:
import matplotlibmatplotlib.use('tkagg')
Import the required libraries:
import tkinter as tkimport numpy as np
import matplotlib.pyplot as plt
The following are the steps required to plot our map:
- Download the required shapefile using the following command:
- Extract and sort the data to get countries by population in ascending order, as follows:
countries = reader.records()
country = next(countries)
population = lambda country: country.attributes['POP_EST']
sort_by_pop = sorted(countries, key=population)
- Extract first five and last five entries, which represent the bottom five and top five populated countries. Get corresponding longitude and latitude coordinates from the net offline:
B5_countries_by_pop = sort_by_pop[:5]
B5_Country_Names = ', '.join([country.attributes['NAME_LONG'] for
country in B5_countries_by_pop])
# get the last 5 entries that represent highest population
T5_countries_by_pop = sort_by_pop[-5:]
T5_Country_Names = ', '.join([country.attributes['NAME_LONG'] for
country in T5_countries_by_pop])
#B5_Country_Names = ['French Southern and Antarctic Lands',
'Falkland Islands', 'Antarctica', 'Greenland', #'Northern Cyprus']
B5_lat = [49.28, 51.796, 82.862, 71.71, 35.32]
B5_lon = [69.35, 59.523, 135, 42.60, 33.31]
#T5_Country_Names = ['Brazil', 'Indonesia', 'United States',
'India', 'China']
T5_lat = [-14.2350, -0.7893, 37.0902, 20.5937, 40]
T5_lon = [-51.9253, 113.9213, -95.7129, 78.9629, 116.5]
- Define the figure, axes, and set defaults:
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
ax.set_global()
ax.stock_img()
ax.coastlines()
- Plot these points on a global map as follows:
ax.plot(T5_lon, T5_lat, 'o', transform=ccrs.PlateCarree(), markersize=10, color='g')
- Plot labels corresponding to each of the points by using the t ext embedding feature:
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(B5_lon[1], B5_lat[1], 'FI', size=12, color='indigo',
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(B5_lon[2], B5_lat[2], 'Antarctica', size=12,
color='indigo', horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(B5_lon[3], B5_lat[3], 'Greenland', size=12, color='indigo',
horizontalalignment='right', transform=ccrs.Geodetic())
plt.text(B5_lon[4], B5_lat[4], 'NC', size=12, color='indigo',
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(T5_lon[0], T5_lat[0], 'Brazil', size=12, color='m',
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(T5_lon[1], T5_lat[1], 'Indonesia', size=12, color='m',
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(T5_lon[2], T5_lat[2], 'United States', size=12, color='m',
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(T5_lon[3], T5_lat[3], 'India', size=12, color='m',
horizontalalignment='left', transform=ccrs.Geodetic())
plt.text(T5_lon[4], T5_lat[4], 'China', size=12, color='m',
horizontalalignment='right', transform=ccrs.Geodetic())
- Display the plot on the screen using the following command:
Matplotlib provides many different methods and helper functions that leverage the axes method for drawing multiple plots on the same figure and arrange them in a variety of grid formats. This allows us to develop sophisticated dashboard applications.
Here are the steps to plot the required plots:
- Create a simple JointGrid with default parameters, and axes-level customization:
space=0.1, height=6, ratio=2)
g1 = g1.plot(sns.regplot, sns.kdeplot)
#g = g.plot(plt.scatter, sns.distplot);
g1.ax_joint.set_xlabel('Coffee', color='g', size=20, weight='bold')
g1.ax_joint.set_ylabel('Cookies', color='g', size=20, weight='bold')
plt.setp(g1.ax_marg_x.get_yticklabels(), visible=True)
plt.setp(g1.ax_marg_x.get_xticklabels(), visible=True)
plt.setp(g1.ax_marg_y.get_xticklabels(), visible=True)
plt.setp(g1.ax_marg_y.get_yticklabels(), visible=True)
g1.ax_marg_x.set_facecolor('wheat')
g1.ax_marg_y.set_facecolor('wheat')
for l in chain(g1.ax_marg_x.axes.lines, g1.ax_marg_y.axes.lines):
l.set_linestyle('--')
l.set_lw(3)
l.set_color('red')
g1.ax_marg_x.set_title('1. Regression Plot - KDE on marginals',
size=20, color='g')
- Separate the joint and marginal plots with their respective parameters:
space=0, ratio=2)
g2 = g2.plot_joint(sns.regplot, color="g", order=3, ci=68 )
g2 = g2.plot_marginals(sns.distplot, kde=False, rug=True, fit=norm,
color="#1EAFCD23")
g2.ax_marg_x.set_title('2. Regression Plot - Histogram, Normal,
Rug', size=20, color='g')
- Separate the j oint, marginal x, and marginal y plots with independent controls on each:
space=0, ratio=2)
g3 = g3.plot_joint(sns.regplot, color="g", order=3, ci=68)
g3.ax_marg_x.hist(snacks_sales['Pies'], color="b", alpha=.6,
bins=np.arange(0, 100, 5))
g3.ax_marg_y.boxplot(snacks_sales['Smoothies'], 1, 'gD')
g3.ax_marg_x.set_title('3. Regression Plot - Histogram, Boxplot',
size=20, color='g');
- Implement hue on JointGrid:
ratio=2)
i=1
for quarter, sales in snacks_sales.groupby('Quarter'):
sns.kdeplot(sales["Cookies"], ax=g4.ax_marg_x, label='Q'+str(i));
sns.kdeplot(sales["Smoothies"], ax=g4.ax_marg_y, vertical=True,
label='Q'+str(i));
g4.ax_joint.plot(sales["Cookies"], sales["Smoothies"], "D", ms=5)
i +=1
g4.ax_marg_x.set_title('4. Scatter Plot - Histogram with hue, KDE on
marginals', size=20, color='g')
- Use a user-defined joint and marginal plots:
ratio=6)
def marginal_boxplot(a, vertical=False, **kws):
g = sns.boxplot(x="Promotion", y="Coffee", orient="v", **kws) if
vertical \
else sns.boxplot(x="Cookies", y="Promotion",
orient="h", **kws)
g.set_ylabel("")
g.set_xlabel("")
g5.plot_marginals(marginal_boxplot, palette={"Yes": "#ff000088", "No": "#00aa007e" }, data=snacks_sales,
linewidth=1, fliersize=10,notch=True)
sns.regplot(x="Cookies", y="Coffee", data=snacks_sales.query("Promotion == 'Yes'"), color="#ff000088",
truncate=True, label='Promotion: Yes', marker='D',
ax=g5.ax_joint,
scatter_kws={"s": 100, "edgecolor": "k", "linewidth":
.5, "alpha": .8})
sns.regplot(x="Cookies", y="Coffee", data=snacks_sales.query("Promotion == 'No'"),
color="#00aa007e", marker='^', label='Promotion: No',
scatter_kws={"s": 50, "edgecolor": "k", "linewidth": .5,
"alpha": .4},
line_kws={"linewidth": 2}, ax=g5.ax_joint)
g5.ax_marg_x.set_title('5. Regression Plot with hue - Boxplots on
marginals', size=20, color='g')
g5.ax_joint.legend(loc=4)
plt.show();
You'll need to import the requisite libraries using the following commands:
import numpy as npimport matplotlib.pyplot as plt
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