Top 10 Data Visualization Libraries in 2023.

Choosing the Right Library for Your Data Visualization Needs.

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Data visualization has become an essential tool for businesses to understand and communicate complex information. With the rise of big data, the demand for powerful data visualization libraries is increasing. In this article, we will explore the top 10 data visualization libraries that are expected to dominate the market in 2023. These libraries will provide a wide range of features and capabilities, making them ideal for businesses of all sizes and industries.

Matplotlib:

This is the most widely used library for data visualization in Python. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK. Matplotlib is also highly customizable, allowing users to fine-tune every aspect of a plot. Example:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

Pros:

• Most widely used library for data visualization in Python
• Provides an object-oriented API for embedding plots into applications
• Highly customizable, allowing users to fine-tune every aspect of a plot

Cons:

• The interface can be somewhat verbose and difficult to use for creating more complex plots
• The default aesthetics of the plots are not as polished as some other libraries

Seaborn:

This library is built on top of Matplotlib and is also used for data visualization. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn is particularly good at visualizing complex datasets with multiple variables. Example:

import seaborn as sns
import pandas as pd

df = pd.read_csv("data.csv")
sns.pairplot(df)
plt.show()

Pros:

• Built on top of Matplotlib and provides a higher-level interface for drawing attractive and informative statistical graphics
• Particularly good at visualizing complex datasets with multiple variables
• Has a number of built-in datasets, which can be useful for learning and examples

Cons:

• Depends on Matplotlib, so it may not be the best choice for projects that have a hard dependency on Matplotlib
• Some of the functions of Seaborn are similar to Matplotlib, so it may not be the best choice for projects that already use Matplotlib

Plotly:

This library is used to create interactive, web-based plots. It is built on top of the JavaScript library Plotly.js and can be used to create a wide range of plots, including scatter plots, bar plots, and heatmaps. Example:

import plotly.express as px

df = px.data.gapminder()
fig = px.scatter(df, x="gdpPercap", y="lifeExp", size="pop", color="continent", hover_name="country", log_x=True, size_max=60)
fig.show()

Pros:

• Provides an interactive, web-based interface for creating plots
• Offers a wide range of plots including scatter plots, bar plots, and heatmaps
• Has a lot of documentation and examples available online

Cons:

• Can be a bit heavy for simple plots
• The interactive features can be overwhelming if you are not used to working with JavaScript

Bokeh:

This library is similar to Plotly in that it is used to create interactive, web-based plots. It is built on top of the JavaScript library Bokeh.js and provides a simple, elegant interface for creating plots. Example:

from bokeh.plotting import figure, show
from bokeh.sampledata.iris import flowers

colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
colors = [colormap[x] for x in flowers['species']]

p = figure(title = "Iris Morphology")
p.xaxis.axis_label = 'Petal Length'
p.yaxis.axis_label = 'Petal Width'

p.circle(flowers["petal_length"], flowers["petal_width"],
         color=colors, fill_alpha=0.2, size=10)

show(p)

Pros:

• Provides an interactive, web-based interface for creating plots
• Offers a simple, elegant interface for creating plots
• Has a lot of documentation and examples available online

Cons:

• Can be a bit heavy for simple plots
• The interactive features can be overwhelming if you are not used to working with JavaScript

ggplot:

This library is based on the popular ggplot2 library for R and is used for creating static plots. It provides a simple, concise interface for creating a wide range of plots, including scatter plots, line plots, and bar plots. Example:

from ggplot import *

ggplot(aes(x='date', y='beef'), data=meat) +\
    geom_line() +\
    stat_smooth(colour='blue', span=0.2)

Pros:

• Based on the popular ggplot2 library for R and is used for creating static plots
• Provides a simple, concise interface for creating a wide range of plots

Cons:

• ggplot uses a different syntax and grammar than the other libraries, so it may take some time to get used to
• Not as widely used as Matplotlib or Seaborn so community support may be limited.

Pygal:

This library is used for creating static, SVG-based plots. It is known for its clean, simple interface and ability to create interactive plots. Example:

import pygal

bar_chart = pygal.Bar()
bar_chart.add('Fibonacci', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55])
bar_chart.render()

Pros:

• It is known for its clean, simple interface and ability to create interactive plots
• Smaller in size than other libraries and it’s easy to install
• Used for creating static, SVG-based plots

Cons:

• Not as widely used as Matplotlib or Seaborn so community support may be limited
• Limited customization options compared to other libraries

Folium:

This library is used for creating interactive, leaflet.js-based maps. It is useful for visualizing geographic data and creating map-based visualizations. Example:

import folium

m = folium.Map(location=[45.523, -122.675], zoom_start=13)

folium.Marker([45.523, -122.675], popup='Portland, OR').add_to(m)
folium.CircleMarker([45.5215, -122.6755], radius=50,
                    popup='Laurelhurst Park', color='#3186cc',
                    fill_color='#3186cc',).add_to(m)
m

Pros:

• It is useful for visualizing geographic data and creating map-based visualizations
• Has a lot of documentation and examples available online
• Used for creating interactive, leaflet.js-based maps

Cons:

• Can be a bit heavy for simple plots
• The interactive features can be overwhelming if you are not used to working with JavaScript
• Can be quite slow when handling large datasets

Altair:

This library is used for creating declarative, Vega-Lite-based plots. It provides a simple, intuitive interface for creating a wide range of plots and is particularly good for creating interactive visualizations. Example:

import altair as alt
from vega_datasets import data

source = data.cars()

alt.Chart(source).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
)

Pros:

• Based on Vega-Lite, which is a high-level grammar of interactive graphics
• Provides a simple, intuitive interface for creating a wide range of plots and is particularly good for creating interactive visualizations.

Cons:

• Requires to learn a new syntax
• Not as widely used as Matplotlib or Seaborn so community support may be limited

Plotnine:

This library is based on the popular ggplot2 library for R and is used for creating static plots. It is built on top of the Matplotlib library and provides a simple, concise interface for creating a wide range of plots. Example:

from plotnine import *
from plotnine.data import mpg

(ggplot(mpg)         # defining what data to use
 + aes(x='class')    # defining what variable to use
 + geom_bar(size=20) # defining the type of plot to use
)

Pros:

• Built on top of the Matplotlib library and provides a simple, concise interface for creating a wide range of plots
• Based on the popular ggplot2 library for R, so it is familiar for R users
• Has a lot of documentation and examples available online

Cons:

• Not as widely used as Matplotlib or Seaborn so community support may be limited
• Some users may find its syntax and grammar different from other libraries

Geoplotlib:

This library is used for creating static, matplotlib-based maps. It is built on top of the Basemap library and is particularly good for visualizing geographic data. Example:

import geoplotlib
from geoplotlib.utils import read_csv

data = read_csv('data.csv')
geoplotlib.dot(data)
geoplotlib.show()

Pros:

• Used for creating static, matplotlib-based maps
• Built on top of the Basemap library and is particularly good for visualizing geographic data
• Has a lot of documentation and examples available online

Cons:

• Not as widely used as other libraries for data visualization, such as Matplotlib or Seaborn, so community support may be limited
• Some users may find its syntax and grammar different from other libraries
• The library is no longer actively maintained.

These are the top 10 Python libraries for data visualization in 2023, each library has its own pros and cons, and choosing the right one depends on your specific needs.