How To Draw A Bubble Chart On Matplotlib

How-to Guide for Python

Introduction

In this guide, you lot will learn how to create a bubble plot using Python. Readers are provided links to the example dataset and encouraged to replicate this example. An additional practice instance is suggested at the end of this guide. The case assumes yous have downloaded the relevant data files to a folder on your computer and that y'all are using the JupyterLab environment. The relevant code should, withal, work in other environments likewise.

Contents

ane. Bubble Plot

2. An Example Using Python: CO2 Emissions Per Capita Versus GDP Per Capita past State (2016)

• 2.1 The Python Procedure
  • two.1.1 JupyterLab Notebooks
  • 2.i.two Testing Out the Programming Environment
  • 2.1.3 Creating Our Notebook, Importing Necessary Modules
  • 2.1.4 Reading In and Formatting Our Data
  • 2.i.5 Visualizing the Data
  • two.1.6 Farther Edits
• two.two Exploring the Output

3. Your Plough

1. Bubble Plot

A bubble plot, as well chosen a chimera nautical chart, is a variation of a scatter plot where the data points are marked with circles of varying size. A traditional scatter plot is a chart used to plot the relationship between 2 variables, as well as their distribution. The individual data points are plotted as values on the horizontal and vertical axes. A stiff correlation or possible causal link is indicated by a dumbo diagonal clustering of points along the chart's diagonal. When the points are scattered over the plot area without a particular clustering pattern, there is likely no interdependence or correlation. If a causal human relationship is suspected, the presumed cause is usually placed on the horizontal axis and the effect on the vertical axis. Often the relationship is not peculiarly clear, though, or causality lies in a common tertiary factor, in which case, the two variables beingness plotted can exist assigned either axis.

The size of data point markers in a bubble plot is based on some underlying value from the dataset, usually the value of some secondary or supplementary variable. As it is difficult to visually evaluate book and surface area, the exact value communicated by the bubble size is mainly but useful for rough visual estimates. Bubble charts tin can also be rendered on a unmarried axis—usually the horizontal 1—where data points are plotted in society by some value from left to correct and so bundled vertically so equally to avert overlap, as in beeswarm plots. A forcefulness-directed algorithm is usually utilized for these types of plots.

2. An Instance Using Python: COtwo Emissions Per Capita Versus GDP Per Capita by Country (2016)

Figure 1 shows a chimera plot of COtwo emissions plotted with GDP and population by country. The plot is generated with information from Our Globe in Data and the Python programming language.

The horizontal axis is labeled GDP and ranges from 0 to 160000, in increments of 20000. The vertical axis is labeled CO2 and ranges from 0 to xl, in increments of 10. The legend on the right side of the graph is titled "Population milj." and lists four circles labeled 5, 20, 100, and 300, in the increasing guild of size. All information are approximate. Most plots are scattered along (0, 0) and (40000, 15). The data for the other labeled plots are tabulated below.

Country	Gdp	CO2	Population
Trinidad and Tobago	30000	30	Less than 5
Bahrain	42000	23.5	Less than five
Saudi Arabia	52000	xix.5	20 to 100
Usa	55000	17	More than 300
Luxembourg	58000	16.5	Less than five
Republic of ireland	57500	8.5	Less than five
Switzerland	60000	5	Less than 5
Singapore	66000	11	v
Kuwait	71000	26	Less than v
United Arab Emirates	76000	25.5	5 to 20
Norway	82000	9	v
Qatar	15800	l	Less than five

Figure 1. A Bubble Plot of Global CO2 Emissions Compared to Gross domestic product and Population

2.1 The Python Process

Python is a general-purpose programming language that supports several programming paradigms and has very clear syntax. Information technology is a versatile tool, peculiarly for data manipulation and visualization. As Python was originally created as a learning tool, it is also reasonably easy to read for beginners. For more data, visit https://www.python.org/.

You can write Python code with any plain text editor, such as Sublime Text or Visual Studio Lawmaking. For the purposes of this tutorial, you do not need to install anything boosted, as we will be using a web-based programming surround.

Note: This tutorial uses Python 3. Many online articles near Python programming and other sources discuss Python 2, which differs slightly, but in important means from Python three. Although code written in Python ii often works in Python 3 and vice versa, this non always the instance, and mixing the two Python versions can lead to errors or unexpected results.

2.1.i JupyterLab Notebooks

The traditional way of programming would be to write some code in a text file, then building and running it to generate an output. In a notebook, on the other hand, the lawmaking is broken down into cells, which can be run one at a time, displaying results right in the editor. This makes working with code and experimenting with irresolute parameters much more flexible and is particularly suitable for interactive data exploration, where the Python programming linguistic communication shines. Sharing small code projects (such as visualizations!) generally becomes much simpler with the notebook approach since y'all tin salvage the unabridged notebook and send it to others.

We will exist using JupyterLab, a modern spider web-based notebook interface for Python, that requires no installation on the user's part. To try a notebook online, just open https://jupyter.org/try and click Try JupyterLab. A cloud-hosted prepare-to-use online JupyterLab environment will exist activated after a short look. Try refreshing the window if loading stalls.

Take notation that this JupyterLab session is hosted on https://mybinder.org/, and it will timeout later on ~xv minutes if inactive. Make certain to download and save your notebooks locally before leaving the computer. If your session has expired, start a new one from https://jupyter.org/try and utilize the interface to upload your saved notebook to continue where you left off.

Note: The online trial of JupyterLab is a good place to outset if you desire to experiment with programming in Python, but for continued utilise in the future, it is recommended to install the Conda package and environment management organisation and JupyterLab locally on your system.

To obtain Conda, it is easy to install one of two distributions: Anaconda, a powerful Python and R distribution that includes over 250 packages for various uses, or Miniconda, a minimal version Anaconda that includes only conda, Python, bundle dependencies, and a few other useful packages (JupyterLab non included). For more than information on obtaining Conda, you tin visit https://docs.conda.io/projects/conda/en/latest/user-guide/install/. If you lot cull the Miniconda distribution, you volition need to install JupyterLab locally from your last (Mac) or Command Prompt/PowerShell (Win) with conda install -c conda-forge jupyterlab. For more data on installing JupyterLab, visit https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html.

If you already have an installation of Anaconda or like and a preferred manifestly text editor at your disposal, the relevant code covered in this tutorial should work in other environments as well.

two.1.2 Testing Out the Programming Environment

If you open https://jupyter.org/try and click Endeavour JupyterLab, yous volition be welcomed past a rather circuitous demo example. We will ignore this for now and create our own new notebook instead. Under File choose New > New Notebook. In the Select Kernel prompt, choose Python 3.

Yous now see an empty notebook called Untitled.ipynb with one empty cell: a text box where you type lawmaking for execution. The cells of a notebook are convenient for structuring lawmaking in small chunks that tin be run one at a time—every bit opposed to the more than common style of edifice and running a whole script at once. A single jail cell can incorporate every bit many or few lines of lawmaking as you want. You could also change the cell to agree markdown-formatted text instead of lawmaking to write longer comments or add together illustrations.

You tin can examination what JupyterLab does past writing some code in the empty cell. Click inside the cell and type in the following:

print("Hello, world!")

Hitting shift + enter or press the small play arrow ▶︎ to a higher place in the toolbar to run the cell.

2.1.3 Creating Our Notebook, Importing Necessary Modules

Create a new notebook and salve information technology with a name, for example, bubbleplot.ipynb. You lot will refer back to this, should your JupyterLab session time out.

If we were running this project locally, we would first need to install all the modules necessary for generating the visualization. However, the trial environment launched from https://jupyter.org/effort conveniently comes equipped with everything for our purposes.

• Matplotlib (MPL for short) is ane of the most popular visualization libraries for Python. It has a huge feature set, and there are dozens of oftentimes very dissimilar—and sometimes odd—ways of achieving the same matter. Do not exist alarmed if you lot search the cyberspace for how to practice something in Matplotlib and cannot comprehend some particular instructions. It is probably the case that the article you have found is based on a unlike arroyo to MPL than what we are working with in this tutorial. Pyplot (PLT) is a Matplotlib module which provides features similar to MATLAB. https://matplotlib.org/
• Seaborn (SNS) brings added functionality to Matplotlib, for example, new nautical chart types. All of MPL's features are yet attainable, but Seaborn offers an easier to use interface for some of them.
• Pandas (PD) is a powerful data analysis and manipulation toolset https://pandas.pydata.org/,

You tin can run the following lawmaking in the first jail cell of your notebook to import the necessary items:

import matplotlib.pyplot as plt

import seaborn equally sns

import pandas every bit pd

1 initially confusing thing well-nigh working with Matplotlib is the proliferation of different submodules you demand to invoke at different times when writing your code. At times we are giving commands to Matplotlib, other times to Pyplot, but also conceivably to Seaborn, and so on. You will start getting used to it after a while, but information technology is sure to cause some confusion more than in one case.

Information technology is a common practice to use the shortcut plt for Pyplot and pd for Pandas. There are other such established shortcuts in the world of Python, likewise, for case, sns for Seaborn and np for Numpy. At that place is technically no need to use such shortcuts, but every bit most online manufactures will follow this convention, we will too.

two.1.4 Reading In and Formatting Our Data

Salve the tutorial csv information files co-emissions-per-capita-vs-gdp.csv to a folder on your computer. The case uses the same binder where the JupyterLab notebook is saved, so this relative path is sufficient to refer to the csv files. If you cull to salvage your files elsewhere, just update the path appropriately.

We will begin by importing our information into two carve up variables. Enter this in the side by side cell:

dataset = pd.read_csv("co-emissions-per-capita-vs-gross domestic product.csv")

dataset

Instead of using Python's own csv module, we volition use the read_csv() function from the Pandas library. This creates a "pandas dataframe" of our csv, substantially a spreadsheet table with rows and columns, which lets the user specify decimal separators and other of import parameters during import and is a required data format for some plotting modules. Should you have a different decimal separator or delimiter in your dataset, you could specify it during import similar this importedfile = read_csv("file.csv", delimiter = ";", decimal = ","). Depending on your datasets in the future, you lot may also need to experiment with unlike encoding types, such as encoding = "latin-ane". The full list is available here https://docs.python.org/3/library/codecs.html#standard-encodings.

Yous tin check and run across that the import worked by adding the chosen name of your variable to the stop of the cell, in this case, dataset (Figure 2). The traditional long-form version of this command is impress(dataset), but one of the user-friendly things nearly working with a notebook is that you also can meet what a variable contains simply by typing the proper noun and running the cell—this will show the tabular array and with nicer formatting. Notation that if yous place two variable names in the same cell like this, only the last 1 will be shown in the preview. To inspect many unlike variables in the same cell or to inspect whatsoever variables in environments other than JupyterLab, you lot volition still need to use the print() command.

The input code lines are equally follows.

In [3]: dataset = pd.read_csv("co-emissions-per-capita-vs-gdp.csv", encoding = "latin-l")

dataset

The output tabular array is equally follows:

Out[three]:

Entity	Code	Twelvemonth	CO2 Emissions per capita (Tonnes per capita)	Gross domestic product per capita (International $ (in 2011 prices))	Full Population (Gapminder)
Transitional islamic state of afghanistan	AFG	1800	0.000000	NaN	3280000.0
Afghanistan	AFG	1801	0.000000	NaN	3280000.0
Afghanistan	AFG	1802	0.000000	NaN	3280000.0
Transitional islamic state of afghanistan	AFG	1803	0.000000	NaN	3280000.0
Afghanistan	AFG	1804	0.000000	NaN	3280000.0
Zimbabwe	ZWE	2015	0.691341	1560.0	13815000.0
Zimbabwe	ZWE	2016	0.615011	1534.0	14030000.0
Zimbabwe	ZWE	2017	0.629025	NaN	14237000.0
Zimbabwe	ZWE	2018	NaN	NaN	14439000.0
Zimbabwe	ZWE	2019	NaN	NaN	14645000.0

Text under the tabular array reads, "49783 rows times 6 columns."

Effigy ii. The Imported csv

Side by side, we want to format our data for plotting. You can enter all the following code in one cell if you like, but we volition break it upwardly for the ease of explaining the various subparts.

The kickoff portion renames some of the more cumbersome columns for ease of employ and drops any rows with empty values.

dataset = dataset.rename(columns = {'CO2 emissions per capita (tonnes per capita)':'COii','GDP per capita (international-$ (in 2011 prices))':'Gdp', 'Total population (Gapminder)':'Population'})

dataset = dataset.dropna()

We will continue past filtering our information to a called prepare of countries and a particular certain year. We will roughly aggregate the Nordic and Baltic states into one variable for sake of case here. Y'all might notice through experimentation that some of the years no longer be in the information frame afterwards we drop null values in the previous step.

locations = ["Norway", "Sweden", "Iceland", "Denmark", "Finland", "Estonia", "Republic of lithuania", "Latvia"]

Nordics = dataset.loc[(dataset['Year'] == '2016') & dataset['Entity'].isin(locations)]

Nordics

Our formatted dataset should await something like Effigy three.

The input code lines are as follows.

In [55]: #Renamin some columns for ease of employ

dataset = dataset.rename(columns = {'CO2 emissions per capita (tonnes per capita)':'CO2','GDP per capita (int

#Filtering our data to a set up of countries and a certain twelvemonth

locations = ["Norway", "Sweden", "Iceland", "Denmark", "Finland", "Estonia","Lithuania","Latvia"]

Nordics = dataset.loc[(dataset['Year'] == '2016') & dataset['Entity'].isin(locations)] Nordics

The output tabular array is as follows.

Out [55]:

No data	Entity	Code	Year	CO2	Gdp	Population
11387	Denmark	DNK	2016	6.498282	44836.0	5711000.0
13375	Republic of estonia	EST	2016	13.328972	24857.0	1317000.0
14301	Finland	FIN	2016	8.708928	37239.0	5498000.0
19200	Iceland	ISL	2016	10.496978	42085.0	332000.0
23161	Republic of latvia	LVA	2016	iii.686124	22092.0	1974000.0
24481	Lithuania	LTU	2016	four.524185	24743.0	2890000.0
32118	Norway	NOR	2016	8.379484	82814.0	5251000.0
43002	Sweden	SWE	2016	4.327103	44659.0	9836000.0

Figure 3. Formatted Dataset

2.1.five Visualizing the Data

A Simple Bubble Plot With Seaborn

We volition start off by creating a simple bubble plot based on the data we have imported and formatted. In Seaborn, bubble plots tin exist created with the scatterplot() method, but take note that there are a variety of means to create besprinkle plots, namely, as well lmplot(), relplot(), and regplot(). For more information on the different besprinkle plot methods, yous can check the Seaborn documentation.

Before plotting, nosotros will calculate a scale factor (meet Size in the Student Guide) based on the largest value and largest bubble size we want to plot:

scaleFactor = 400/Nordics["Population"].max()

You lot can change the 400 here to experiment with different scales.

Post-obit this, we can plot the bubbles themselves. Again, you can enter all of these following individual sections within the same prison cell, but we volition break it into parts hither for the sake of explanation.

The outset portion of the following code defines which data frame should exist used, which columns should be used for the axes and bubble size, and and so some styling options such as border color and the called color palette. Notice that the definition in the sizes parameter is somewhat more complex here, it uses our previously defined scale gene to prepare the maximum and minimum sizes for our series of bubbling. If y'all were to set these with capricious sizes such equally sizing=(10,2000), the areas of the bubbles would not be correctly sized in relation to each other. Leaving sizes undefined, on the other hand, produces right relative sizing but allows for no control in overall calibration, except by adjusting the output resolution setting.

For example, using the arbitrary sizing=(10,2000) for a set of countries with populations 10,000, 10,002, and x,015, respectively, the row with minimum value would be assigned the size of ten, and the largest value 2000, regardless of whether the value difference betwixt these two rows was simply xv people.

sns.scatterplot(data=Nordics, x="GDP", y="CO2",

                     hue="Population", size="Population", edgecolor='black',

                     sizes=((min(Nordics.Population) * scaleFactor, max(Nordics.Population) * scaleFactor)),

                     palette="PuBu")

If y'all were to run this past itself, you would end upwards with a plot already only without any explanatory labels or titles. For that purpose, we will run the following department of code, which iterates through all our chosen land names, and plots them near the corresponding data point.

for i, country in enumerate(Nordics.Entity):

    plt.annotate(Nordics.Entity.iat[i],

                (Nordics.Gdp.iat[i],Nordics.COii.iat[i]),

                textcoords="offset points",

                xytext=(-15,-15),

                fontfamily= 'sans-serif',

                fontsize='8',

                ha='left')

Nosotros will too add a title and legend exterior the plot expanse and set the figure size. If you lot do not define the fable this way, it will automatically generate itself within the plot expanse (you lot can play around with the parameters to move the legend around).

plt.title("Nordic / Baltic CO2 emissions per capita \nvs Gross domestic product per capita past country (2016)", loc='left', fontsize=16, fontweight='assuming', color='black')

plt.legend(bbox_to_anchor=(one.05, one), loc=2, borderaxespad=0)

fig = plt.gcf()

fig.set_size_inches( 8, 6 )

and now we should have a very uncomplicated annotated bubble plot, something like Figure iv.

The horizontal centrality is labeled Gross domestic product and ranges from 20000 to 80000, in increments of 10000. The vertical axis is labeled CO2 and ranges from 4 to 12, in increments of ii. A legend on the right side of the graph is titled "Population" and lists four circles of different colors and of increasing size, labeled as follows: 0.0, 4000000.0, 8000000.0, and 12000000.0. The approximate data from the plot are tabulated below.

Figure 4. A Uncomplicated Bubble Plot

You lot could already save this epitome out, for case, with fig.savefig('bubble-plot.png', bbox_inches='tight', dpi=250). The legend and some of the labels are still somewhat awkwardly placed; for fine-tuning, you can export this plot for further modification with a graphics editing programme (more on this below).

A More Complex Chimera Plot With Seaborn

A more interesting version of the above bubble plot can be attained by plotting a larger subset of our data. We volition define a different subset containing everything from the year 2016, as well as a split up listing of outlier values:

subset = dataset.loc[dataset['Twelvemonth'] == '2016'].copy()

outliers= subset.loc[(subset['CO2'] > 18) | (dataset['GDP'] > 50000)].copy()

We will also sort all the population values into custom bins, equally some countries have such large populations that the colour calibration will be thrown off.

subset['populationBin']=pd.cut(subset['Population'],

                        bins = [0, 5000000, 20000000, 100000000, 300000000, 10000000000],

                        labels = ["0", "5", "twenty", "100", "+300 milj."])

We will also change a few column names for a cleaner legend in our plot. Notation that annihilation with an underscore at the beginning of the proper noun volition be hidden in the legend (Figure 5).

subset = subset.rename(columns={'Population':'_Population','populationBin':'_populationBin'})

The data from the subset are tabulated beneath.

No data	Entity	Code	Twelvemonth	CO2	Gross domestic product	_Population	_populationBin
216	Afghanistan	AFG	2016	0.353702	1929.0	35383000.0	20
558	Albania	ALB	2016	ii.143579	10342.0	2886000.0	0
778	Algeria	DZA	2016	iii.703340	14331.0	40551000.0	xx
1288	Angola	AGO	2016	ane.226892	8453.0	28842000.0	xx
1798	Argentina	ARG	2016	four.722376	18875.0	43508000.0	twenty
48607	Venezuela	VEN	2016	5.261512	15219.0	29851000.0	xx
48827	Vietnam	VNM	2016	2.091609	6062.0	9364000.0	xx
49339	Yemen	YEM	2016	0.693709	2506.0	27168000.0	20
49559	Republic of zambia	ZMB	2016	0.275754	3479.0	16363000.0	five
49779	Zimbabwe	ZWE	2016	0.615011	1534.0	1403000.0	5

Text under the table reads, "162 rows times vii columns."

Effigy v. Subset of Information

Now as for plotting this more complex set, we begin with the same Seaborn scatterplot part as earlier, just this time, the hue will be determined by our binned population size column. We will also accommodate our scaleFactor to improve represent our series and gear up the fill color to semitransparent to evidence some of the overlapping bubbling better. You tin can experiment with these as needed, if you plot this subset with the previous settings, for example, you lot will get a considerably different output.

scaleFactor = 1500/subset["_Population"].max()

sns.scatterplot(data=subset, x="Gross domestic product", y="CO2",

                size = "_Population",

                sizes=((min(subset._Population) * scaleFactor, max(subset._Population) * scaleFactor)),

                hue="_populationBin",

                palette='viridis',

                edgecolor='black',

                alpha=0.75

                )

The annotation works much the same manner likewise, though now nosotros label merely the previously divers outlier values. You tin supervene upon all references to outliers with subset if yous want to label all points.

for i, state in enumerate(outliers.Entity):

    plt.annotate(outliers.Entity.iat[i],

                (outliers.Gdp.iat[i],outliers.CO2.iat[i]),

                textcoords="kickoff points",

                xytext=(-5,8),

                fontfamily= 'sans-serif',

                fontsize='viii',

                ha='left')

The title, legend, and effigy size parameters also office much as before, only you lot volition find that nosotros take added some extra padding and spacing for the legend to accommodate bubble sizes (Effigy 6). You lot can detect more detailed documentation about all of these by searching "legend" in your Assist panel.

plt.title("Global COii emissions per capita \nvs Gross domestic product per capita by country (2016)",

          loc='left', fontsize=xvi, fontweight='bold', color='black')

plt.legend(bbox_to_anchor=(1.05, 1), loc=two, borderaxespad=0, labelspacing=iii, frameon=False, handlelength=2, handletextpad=two, scatterpoints = 1).set_title("Population")

fig = plt.gcf()

fig.set_size_inches( 10, 8 )

The horizontal axis is labeled Gross domestic product and ranges from 0 to 160000, in increments of 20000. The vertical axis is labeled CO2 and ranges from 0 to forty, in increments of 10. The legend on the right side of the graph lists nine circles. The offset five circles are of the same size but of different colors, and are labeled 0, v, 20, 100, and +300 milj. The other four circles are of aforementioned colors merely of increasing size, and are labeled 0.0, 500000000.0, 1000000000.0, and 1500000000.0. All information are approximate. Nigh plots are scattered along (0, 0) and (40000, 15). The data for the other labeled plots are tabulated below.

Country	GDP	CO2	Population
Trinidad and Tobago	30000	30	Less than 5
Bahrain	42000	23.5	Less than five
Saudi Arabia	52000	19.v	20 to 100
United States	55000	17	More than than 300
Luxembourg	58000	16.five	Less than 5
Ireland	57500	8.5	Less than 5
Switzerland	60000	five	Less than five
Singapore	66000	11	5
Kuwait	71000	26	Less than 5
United Arab Emirates	76000	25.five	5 to 20
Norway	82000	9	5
Qatar	15800	fifty	Less than five

Effigy 6. A Circuitous Chimera Plot

Y'all can save your plots at any point with the following bit of code. Yous can attempt different file types such as .png and .svg, for instance. The portion bbox_inches='tight' ensures that our external legends do not get cut off. All files will be output into the aforementioned directory you have saved the notebook in unless you define a item location in conjunction with your preferred file output name.

fig.savefig('bubble-plot.pdf', bbox_inches='tight', format='pdf')

fig.savefig('chimera-plot.png', bbox_inches='tight', dpi=250)

2.1.6 Further Edits

Some of the labels are still somewhat awkwardly placed, and the default legend is quite circuitous, with both the colour and size scales visualized separately—you should consider how much time you lot want to allocate to styling with code and when manual adjustments in your option of vector editing software, such as Adobe Illustrator or Inkscape, might exist more prudent. This is frequently easier and more time-effective, as peculiarly fine adjustments to label placement tin be somewhat cumbersome in Python. To fine-tune your plot manually, yous can export your plot out as a PDF and open it in the vector graphics editing programme. Figure seven has been ever then slightly tweaked in Adobe Illustrator for improved legibility and make clean alignment.

The horizontal centrality is labeled GDP and ranges from 0 to 160000, in increments of 20000. The vertical centrality is labeled CO2 and ranges from 0 to xl, in increments of 10. The legend on the correct side of the graph is titled "Population milj." and lists four circles labeled 5, xx, 100, and 300, in the increasing club of size. All information are approximate. Most plots are scattered along (0, 0) and (40000, fifteen). The information for the other labeled plots are tabulated below.

Land	Gross domestic product	CO2	Population
Trinidad and Tobago	30000	xxx	Less than v
Bahrain	42000	23.5	Less than 5
Saudi Arabia	52000	19.v	20 to 100
United States	55000	17	More than 300
Luxembourg	58000	16.5	Less than five
Republic of ireland	57500	8.v	Less than v
Switzerland	60000	5	Less than 5
Singapore	66000	11	5
Kuwait	71000	26	Less than 5
United Arab Emirates	76000	25.5	5 to 20
Norway	82000	nine	v
Qatar	15800	50	Less than 5

Effigy vii. A Chimera Plot Formatted for Style in Illustrator

Calibration: We take manually modified the scale of bubbles in the fable to match the color scale. You can determine the size of any given chimera using the equation sqrt(value) × calibration factor = width. In this instance, for example, we know from the default fable that a population of 1,500,000,000 is a bubble with diameter 38.7298 pt, so the equation becomes sqrt(1,500,000,000) × 10 = 38.7298 pt and we tin solve for X with Ten = 38.7298 / sqrt(1500000000), giving u.s. the calibration cistron of 0.00099999913. We can then determine, for example, the radius of a v,000,000 population chimera similar and so: sqrt(5000000)*0.00099999913 = 2.236 pt.

Vectors: If y'all would like to consign out a vector image with editable text, you will need to include matplotlib.rcParams['pdf.fonttype'] = 42 at the showtime of your notebook. rcParams is a dictionary-like file with default settings for all of Matplotlib. If y'all work more than with Matplotlib, you might want to consider adding some preferred defaults to it. Run into matplotlib rcParams for more data.

2.2 Exploring the Output

The bubble plot created through this demonstration plots the human relationship between per capita CO2 emissions and Gdp in 2016, with an added layer of information in the form of population size. There seems to be a notable correlation between per capita Gdp and CO2 emissions, with oil-rich countries topping both the GDP and COtwo emissions rankings. It could be postulated, based on this plot, that the availability of oil both contributes to the wealth of a nation and its propensity to use that fuel in great abundance. Almost of these outlying countries seem to also accept a rather modest population in a greater global context, which is likely only indicative of the fact that there are fewer people in a relatively rich and profligate state to "h2o down" per capita values. Some more populous and more often than not prosperous countries—many too with oil reserves—figure much less prominently in the plot, likely due to a larger underprivileged subset of the population affecting per capita calculations.

More often than not, information technology seems that most countries that take a high GDP and high CO2 per capita emissions also have oil reserves and a relatively pocket-size population in common. This plot does non in itself testify this correlation, however, and is more useful every bit a jumping-off bespeak for further explorations into this topic.

3. Your Turn

Now that yous have been introduced to some of the basic operations necessary to complete this blazon of visualization, y'all may experiment with variations based on this same dataset. You can try plotting different time periods or a fix of different countries or regions—how would you attain these tasks? Are you able to recolor the bubbles with a different palette based on some particular value?

How To Draw A Bubble Chart On Matplotlib,

Source: https://methods.sagepub.com/dataset/howtoguide/bubble-plot-in-ourworldindata-2019

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