Python - Data visualization tutorial

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='https://news.google.com/publications/CAAqBwgKMLTrzwsw44bnAw?hl=en-IN&gl=IN&ceid=IN%3Aen' target="_blank"> <img class="gfg-icon_Gnews no-zoom-in-cursor" src=https://media.geeksforgeeks.org/auth-dashboard-uploads/Google-news.svg loading="lazy" alt="News"></img> <span class='kebab_menu_news_text'>Follow</span> </a> </div> </div> </div> <div class="text"> <p dir="ltr" style="text-align: justify;"><a href="https://www.geeksforgeeks.org/data-visualization-and-its-importance/" target="_blank" rel="noopener"><span>Data visualization</span></a><span> is a crucial aspect of data analysis, helping to transform raw data into meaningful insights through graphical representations. </span><i><b><strong class="GFGEditorTheme__textItalic">This comprehensive tutorial will guide you through the fundamentals of data visualization using Python, one of the most popular programming languages for data analysis. We’ll explore various Python libraries, including Matplotlib, Seaborn, Pandas, Plotly, Plotnine, Altair, Bokeh, Pygal, and Geoplotlib.</strong></b></i><span> Each library offers unique features and advantages, catering to different visualization needs and preferences. </span></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial.webp" alt="Python--Data-Visualization-Tutorial" width="1000" height="470" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial.webp 1000w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131231/Python--Data-Visualization-Tutorial-768.webp 768w"><p class="wp-caption-text">Data visualization tutorial</p></div><div id="table_of_content"><p style="margin:4px; font-size:20px; font-weight:bold;">Table of Content</p><div id="GFG_AD_gfg_mobile_336x280" style="margin: 5px 0;"></div> <ul><li><a href="#introduction-to-data-visualization-guide-to-visual-storytelling">Introduction to Data Visualization : Guide to Visual Storytelling</a></li><li><a href="#python-libraries-for-data-visualization">Python Libraries for Data Visualization</a></li><li><a href="#getting-started-data-visualization-with-matplotlib">Getting Started – Data Visualization with Matplotlib</a></li><li><a href="#effective-data-visualization-with-seaborn">Effective Data Visualization With Seaborn </a></li><li><a href="#mastering-data-visualization-with-pandas">Mastering Data Visualization with Pandas</a></li><li><a href="#data-visualization-with-plotly">Data Visualization with Plotly </a></li><li><a href="#data-visualization-with-plotnine">Data Visualization with Plotnine</a></li><li><a href="#data-visualizations-with-altair-python">Data Visualizations with Altair Python</a></li><li><a href="#interactive-data-visualization-with-bokeh">Interactive Data Visualization with Bokeh</a></li><li><a href="#mastering-advanced-data-visualization-with-pygal">Mastering Advanced Data Visualization with Pygal</a></li></ul></div><h2 id="introduction-to-data-visualization-guide-to-visual-storytelling"><span>Introduction to Data Visualization : </span><b><strong>Guide to Visual Storytelling</strong></b></h2><p dir="ltr"><span>Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.</span></p> <p dir="ltr"><span>This powerful tool enables users to quickly identify patterns, trends, and correlations within data, making it an indispensable asset in various fields, including business, science, and education.</span></p> <p dir="ltr"><i><b><strong class="GFGEditorTheme__textItalic">Choosing the right type of chart is crucial for effectively communicating your data. Different charts serve different purposes and can highlight various aspects of your data</strong></b></i><span>. For a deeper dive into selecting the best chart for your data, check out this comprehensive guide on:</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/data-visualization-and-its-importance/" target="_blank" rel="noopener"><span>What is Data Visualization and Why is It Important?</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/types-of-data-visualization/" target="_blank" rel="noopener"><span>Types of Data Visualization Charts</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/choosing-the-right-chart-type-a-technical-guide/" target="_blank" rel="noopener"><span>Choosing the Right Chart Type: A Technical Guide</span></a></li></ul><p dir="ltr"><b><strong>Equally important is selecting the right colors for your visualizations. The appropriate color choices can emphasize key information, enhance readability, and make your visuals more engaging.</strong></b><span> Effective color selection helps in drawing attention to the most critical parts of your data and ensuring that your message is clear.</span></p> <p dir="ltr"><span>For expert advice on choosing the best colors for your charts, visit </span><a href="https://www.geeksforgeeks.org/how-to-select-colors-for-data-visualizations/" target="_blank" rel="noopener"><span>How to select Colors for Data Visualizations?</span></a></p><div id="GFG_AD_Desktop_InContent_ATF_336x280" style="text-align:center; max-height: 280px;"></div><div id="GFG_AD_gfg_mobile_336x280_1" style="margin: 5px 0;"></div> <h2 id="python-libraries-for-data-visualization"><span>Python Libraries for Data Visualization</span></h2><p dir="ltr"><span>Python offers numerous </span><a href="https://www.geeksforgeeks.org/top-python-libraries-for-data-visualization/" target="_blank" rel="noopener"><span>libraries for data visualization</span></a><span>, each with unique features and advantages. Below are some of the most popular libraries:</span></p><div id="_GFG_ABP_Incontent_728x90" style="text-align:center;"></div> <p dir="ltr"><span>Here are some of the most popular ones:</span></p> <ul><li value="1"><b><strong>Matplotlib</strong></b></li><li value="2"><b><strong>Seaborn</strong></b></li><li value="3"><b><strong>Pandas</strong></b></li><li value="4"><b><strong>Plotly</strong></b></li><li value="5"><b><strong>Plotnine</strong></b></li><li value="6"><b><strong>Altair</strong></b></li><li value="7"><b><strong>Bokeh</strong></b></li><li value="8"><b><strong>Pygal</strong></b></li><li value="9"><b><strong>Geoplotlib</strong></b></li></ul><h2 id="getting-started-data-visualization-with-matplotlib"><a href="https://www.geeksforgeeks.org/data-visualization-using-matplotlib/" target="_blank" rel="noopener"><span>Getting Started – Data Visualization with Matplotlib</span></a></h2><p dir="ltr"><span>Matplotlib is a great way to begin visualizing data in Python. Matplotlib is a versatile library that allows you to create a wide variety of static, animated, and interactive plots. It’s particularly well-suited for creating a wide range of static, animated, and interactive plots. This verse will guide you through the basics of using Matplotlib to create visual representations of your data.</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/python-introduction-matplotlib/" target="_blank" rel="noopener"><span>Introduction to Matplotlib</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/environment-setup-for-matplotlib/" target="_blank" rel="noopener"><span>Setting up Python Environment with Matplotlib</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/pyplot-in-matplotlib/" target="_blank" rel="noopener"><span>Pyplot in Matplotlib</span></a></li><li value="4"><a href="https://www.geeksforgeeks.org/matplotlib-axes-class/" target="_blank" rel="noopener"><span>Matplotlib – Axes Class</span></a></li><li value="5"><a href="https://www.geeksforgeeks.org/data-visualization-using-matplotlib/" target="_blank" rel="noopener"><span>Data Visualization With Matplotlib</span></a></li></ul><h3 id="example-plotting-a-linear-relationship-with-matplotlib"><span>Example: Plotting a Linear Relationship with Matplotlib</span></h3><gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=c1># importing the required libraries</span> <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span> <span class=c1># define data values</span> <span class=n>x</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>array</span><span class=p>([</span><span class=mi>1</span><span class=p>,</span> <span class=mi>2</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>4</span><span class=p>])</span> <span class=c1># X-axis points</span> <span class=n>y</span> <span class=o>=</span> <span class=n>x</span><span class=o>*</span><span class=mi>2</span> <span class=c1># Y-axis points</span> <span class=n>plt</span><span class=o>.</span><span class=n>plot</span><span class=p>(</span><span class=n>x</span><span class=p>,</span> <span class=n>y</span><span class=p>)</span> <span class=c1># Plot the chart</span> <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span> <span class=c1># display</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <img src="https://media.geeksforgeeks.org/wp-content/uploads/20201013170331/simpleplot-300x169.png" width="inherit" height="inherit" loading="lazy"><h3 id="plotting-with-matplotlib-when-to-use"><span>Plotting with Matplotlib: When to Use</span></h3><p dir="ltr"><span>Matplotlib is a versatile and powerful plotting library in Python, ideal for a wide range of visualization tasks. Use Matplotlib when you need detailed control over plot appearance, extensive customization options, or when working on static plots</span></p> <ul><li value="1"><b><strong>Complex visualizations:</strong></b><span> When you need to create intricate plots with multiple axes, subplots, or custom annotations, Matplotlib provides the granular control necessary.</span></li><li value="2"><b><strong>Scientific and engineering plots:</strong></b><span> Its ability to handle scientific data, logarithmic scales, and specialized plot types makes it ideal for technical fields.</span></li><li value="3"><b><strong>Publication-quality figures:</strong></b><span> For creating high-resolution graphics for papers, presentations, or reports, Matplotlib excels in customization and export options.</span></li><li value="4"><b><strong>Integration with other libraries:</strong></b><span> It seamlessly integrates with NumPy and Pandas, making it a natural choice for data analysis workflows.</span></li><li value="5"><b><strong>Performance-critical applications:</strong></b><span> For large datasets or real-time plotting, Matplotlib offers performance optimizations.</span></li></ul><h2 id="effective-data-visualization-with-seaborn"><a href="https://www.geeksforgeeks.org/data-visualization-with-python-seaborn/" target="_blank" rel="noopener"><span>Effective Data Visualization With Seaborn </span></a></h2><p dir="ltr"><span>Seaborn is a Python library that simplifies the creation of attractive and informative statistical graphics. </span><b><strong>To begin using Seaborn, you’ll first need to install it, which can be done easily via pip.</strong></b><span> It integrates seamlessly with Pandas DataFrames and offers a range of functions tailored for visualizing statistical relationships and distributions. This chapter will guide you through using Seaborn to create effective data visualizations.</span></p><div style="text-align:center;margin:20px 0; max-height: 280px;" id="GFG_AD_gfg_outstream_incontent"></div> <ul><li value="1"><a href="https://www.geeksforgeeks.org/python-seaborn-tutorial/" target="_blank" rel="noopener"><span>Python Seaborn Tutorial</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/types-of-seaborn-plots/" target="_blank" rel="noopener"><span>Types Of Seaborn Plots</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/data-visualization-with-python-seaborn/" target="_blank" rel="noopener"><span>Data Visualization with Python Seaborn</span></a></li><li value="4"><a href="https://www.geeksforgeeks.org/data-visualization-with-pairplot-seaborn-and-pandas/" target="_blank" rel="noopener"><span>Data visualization with Seaborn Pairplot</span></a></li><li value="5"><a href="https://www.geeksforgeeks.org/python-seaborn-facetgrid-method/" target="_blank" rel="noopener"><span>Data Visualization with FacetGrid in Seaborn</span></a></li><li value="6"><a href="https://www.geeksforgeeks.org/data-visualization-with-seaborn-line-plot/" target="_blank" rel="noopener"><span>Time Series Visualization with Seaborn : Line Plot</span></a></li></ul><h3 id="example-scatter-plot-analysis-with-seaborn"><span>Example: Scatter Plot Analysis with Seaborn</span></h3><gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>import</span> <span class=nn>seaborn</span> <span class=k>as</span> <span class=nn>sns</span> <span class=kn>import</span> <span class=nn>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=c1># Load the 'tips' dataset</span> <span class=n>tips</span> <span class=o>=</span> <span class=n>sns</span><span class=o>.</span><span class=n>load_dataset</span><span class=p>(</span><span class=s1>'tips'</span><span class=p>)</span> <span class=c1># Create a scatter plot</span> <span class=n>plt</span><span class=o>.</span><span class=n>figure</span><span class=p>(</span><span class=n>figsize</span><span class=o>=</span><span class=p>(</span><span class=mi>6</span><span class=p>,</span> <span class=mi>4</span><span class=p>))</span> <span class=n>sns</span><span class=o>.</span><span class=n>scatterplot</span><span class=p>(</span><span class=n>x</span><span class=o>=</span><span class=s1>'total_bill'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'tip'</span><span class=p>,</span> <span class=n>data</span><span class=o>=</span><span class=n>tips</span><span class=p>,</span> <span class=n>hue</span><span class=o>=</span><span class=s1>'time'</span><span class=p>,</span> <span class=n>style</span><span class=o>=</span><span class=s1>'time'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s1>'Total Bill vs Tip'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>xlabel</span><span class=p>(</span><span class=s1>'Total Bill'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>ylabel</span><span class=p>(</span><span class=s1>'Tip'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240807185155/datavisualizationwithseaborn.png" alt="datavisualizationwithseaborn" width="531" height="393" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240807185155/datavisualizationwithseaborn.png 531w,https://media.geeksforgeeks.org/wp-content/uploads/20240807185155/datavisualizationwithseaborn-100.png 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240807185155/datavisualizationwithseaborn-200.png 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240807185155/datavisualizationwithseaborn-300.png 300w" loading="lazy"><p class="wp-caption-text">Data Visualization with Seaborn</p></div><h3 id="using-seaborn-for-effective-plotting-when-to-choose-it"><span>Using Seaborn for Effective Plotting: When to Choose It</span></h3><p dir="ltr"><span> Seaborn excels at exploring and understanding your data.</span></p> <ul><li value="1"><b><strong>Exploratory data analysis (EDA):</strong></b><span> Seaborn’s functions are designed to quickly visualize relationships between variables in your dataset.</span></li><li value="2"><b><strong>Statistical graphics:</strong></b><span> It offers a variety of plot types tailored for statistical analysis, such as distribution plots, categorical plots, and regression plots.</span></li><li value="3"><b><strong>Attractive visualizations:</strong></b><span> Seaborn’s default aesthetics create visually pleasing plots with minimal code.</span></li><li value="4"><b><strong>Complex datasets:</strong></b><span> It can handle multi-dimensional data and create informative visualizations like pair plots and facet grids.</span></li><li value="5"><b><strong>Integration with Pandas:</strong></b><span> Seamlessly works with Pandas DataFrames, simplifying data preparation and plotting.</span></li></ul><h2 id="mastering-data-visualization-with-pandas"><a href="https://www.geeksforgeeks.org/pandas-built-in-data-visualization-ml/" target="_blank"><span>Mastering Data Visualization with Pandas</span></a></h2><p dir="ltr"><a href="https://www.geeksforgeeks.org/introduction-to-pandas-in-python/" target="_blank" rel="noopener"><span>Pandas</span></a><span> is a powerful data manipulation library in Python that also offers some basic data visualization capabilities. While it may not be as feature-rich as dedicated visualization libraries like Matplotlib or Seaborn, Pandas’ built-in plotting is convenient for quick and simple visualizations.</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/pandas-built-in-data-visualization-ml/" target="_blank" rel="noopener"><span>Data Visualization With Pandas</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/how-to-plot-timeseries-based-charts-using-pandas/" target="_blank" rel="noopener"><span>Visualizing Time Series Data with pandas</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/plotting-geospatial-data-using-geopandas/" target="_blank" rel="noopener"><span>Plotting Geospatial Data using GeoPandas</span></a></li></ul><h3 id="examples-visualizing-spread-and-outliers"><span>Examples: Visualizing Spread and Outliers</span></h3><p dir="ltr"><a href="https://www.geeksforgeeks.org/box-plot/" target="_blank" rel="noopener"><span>Box plots</span></a><span> are useful for visualizing the spread and outliers in your data. They provide a graphical summary of the data distribution, highlighting the median, quartiles, and potential outliers.</span></p> <p dir="ltr"><span>Here’s an example of creating a box plot with Pandas:</span></p> <gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=c1># Sample data</span> <span class=n>data</span> <span class=o>=</span> <span class=p>{</span> <span class=s1>'Category'</span><span class=p>:</span> <span class=p>[</span><span class=s1>'A'</span><span class=p>]</span><span class=o>*</span><span class=mi>10</span> <span class=o>+</span> <span class=p>[</span><span class=s1>'B'</span><span class=p>]</span><span class=o>*</span><span class=mi>10</span><span class=p>,</span> <span class=s1>'Value'</span><span class=p>:</span> <span class=p>[</span><span class=mi>1</span><span class=p>,</span> <span class=mi>2</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>4</span><span class=p>,</span> <span class=mi>5</span><span class=p>,</span> <span class=mi>6</span><span class=p>,</span> <span class=mi>7</span><span class=p>,</span> <span class=mi>8</span><span class=p>,</span> <span class=mi>9</span><span class=p>,</span> <span class=mi>10</span><span class=p>,</span> <span class=mi>2</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>4</span><span class=p>,</span> <span class=mi>5</span><span class=p>,</span> <span class=mi>6</span><span class=p>,</span> <span class=mi>7</span><span class=p>,</span> <span class=mi>8</span><span class=p>,</span> <span class=mi>9</span><span class=p>,</span> <span class=mi>10</span><span class=p>,</span> <span class=mi>11</span><span class=p>]</span> <span class=p>}</span> <span class=n>df</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>DataFrame</span><span class=p>(</span><span class=n>data</span><span class=p>)</span> <span class=c1># Box plot</span> <span class=n>df</span><span class=o>.</span><span class=n>boxplot</span><span class=p>(</span><span class=n>by</span><span class=o>=</span><span class=s1>'Category'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s1>'Box Plot Example'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>suptitle</span><span class=p>(</span><span class=s1>''</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>xlabel</span><span class=p>(</span><span class=s1>'Category'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>ylabel</span><span class=p>(</span><span class=s1>'Value'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>show</span><span class=p>()</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808113028/boxplot.png" alt="boxplot" width="578" height="445" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808113028/boxplot.png 578w,https://media.geeksforgeeks.org/wp-content/uploads/20240808113028/boxplot-100.png 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808113028/boxplot-200.png 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808113028/boxplot-300.png 300w" loading="lazy"><p class="wp-caption-text">Box Plot</p></div><h3 id="plotting-with-pandas-when-to-use"><span>Plotting with Pandas: When to Use</span></h3><p dir="ltr"><span>Pandas is a powerful tool for quick, in-line plotting of data directly from DataFrames. Use Pandas for basic data exploration and rapid visualization tasks, such as line plots, bar plots, histograms, and scatter plots. It’s ideal for situations like:</span></p> <ul><li value="1"><b><strong>Quick exploratory visualizations:</strong></b><span> When you need a rapid overview of your data without delving into complex plot customizations, Pandas plotting can be efficient.</span></li><li value="2"><b><strong>Time series data:</strong></b><span> Pandas excels at handling time series data and offers specific methods for plotting time-based trends.</span></li><li value="3"><b><strong>Simple plots:</strong></b><span> For basic line plots, bar charts, histograms, and scatter plots, Pandas provides a straightforward interface.</span></li><li value="4"><b><strong>Integration with data analysis:</strong></b><span> If you’re already working with Pandas for data cleaning and manipulation, creating plots within the same environment can be convenient.</span></li></ul><h2 id="data-visualization-with-plotly"><a href="https://www.geeksforgeeks.org/python-plotly-tutorial/" target="_blank" rel="noopener"><span>Data Visualization with Plotly </span></a></h2><p dir="ltr"><b><strong>Prerequisites: </strong></b><a href="https://www.geeksforgeeks.org/getting-started-with-plotly-python/" target="_blank" rel="noopener"><span>Introduction to Plotly</span></a></p> <p dir="ltr"><span>Plotly is a versatile library for creating interactive and aesthetically pleasing visualizations. It supports a wide range of plot types and can be used in Python, R, MATLAB, and JavaScript. This chapter will introduce you to Plotly and guide you through creating basic visualizations.</span></p> <h3 id="basic-plotly-example-creating-a-bar-plot-and-scatter-plot"><span>Basic Plotly Example: Creating a Bar Plot and Scatter Plot</span></h3><p dir="ltr"><span>We’ll start by creating a simple bar plot and scatter plot using Plotly. For this example, we’ll use the same ‘tips’ dataset we used with Seaborn.</span></p><div style="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-1"></div> <gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>import</span> <span class=nn>plotly.express</span> <span class=k>as</span> <span class=nn>px</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>tips</span> <span class=o>=</span> <span class=n>px</span><span class=o>.</span><span class=n>data</span><span class=o>.</span><span class=n>tips</span><span class=p>()</span> <span class=n>fig</span> <span class=o>=</span> <span class=n>px</span><span class=o>.</span><span class=n>bar</span><span class=p>(</span><span class=n>tips</span><span class=p>,</span> <span class=n>x</span><span class=o>=</span><span class=s1>'day'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'total_bill'</span><span class=p>,</span> <span class=n>title</span><span class=o>=</span><span class=s1>'Average Total Bill per Day'</span><span class=p>)</span> <span class=n>fig</span><span class=o>.</span><span class=n>show</span><span class=p>()</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot.webp" alt="barplot" width="1100" height="312" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot.webp 1100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot-768.webp 768w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121232/barplot-1024.webp 1024w" loading="lazy"><p class="wp-caption-text">Bar Plot Plotly</p></div><p dir="ltr"><span>Plotly allows for extensive customizations, including updating layouts, adding annotations, and incorporating dropdowns and sliders. Let’s see an example of a scatter plot as well as customized plot.</span></p> <gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>import</span> <span class=nn>plotly.express</span> <span class=k>as</span> <span class=nn>px</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>tips</span> <span class=o>=</span> <span class=n>px</span><span class=o>.</span><span class=n>data</span><span class=o>.</span><span class=n>tips</span><span class=p>()</span> <span class=c1># Create a scatter plot</span> <span class=n>fig</span> <span class=o>=</span> <span class=n>px</span><span class=o>.</span><span class=n>scatter</span><span class=p>(</span><span class=n>tips</span><span class=p>,</span> <span class=n>x</span><span class=o>=</span><span class=s1>'total_bill'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'tip'</span><span class=p>,</span> <span class=n>color</span><span class=o>=</span><span class=s1>'time'</span><span class=p>,</span> <span class=n>title</span><span class=o>=</span><span class=s1>'Total Bill vs Tip'</span><span class=p>,</span> <span class=n>labels</span><span class=o>=</span><span class=p>{</span><span class=s1>'total_bill'</span><span class=p>:</span> <span class=s1>'Total Bill'</span><span class=p>,</span> <span class=s1>'tip'</span><span class=p>:</span> <span class=s1>'Tip'</span><span class=p>})</span> <span class=n>fig</span><span class=o>.</span><span class=n>show</span><span class=p>()</span> <span class=c1># Customized scatter plot</span> <span class=n>fig</span> <span class=o>=</span> <span class=n>px</span><span class=o>.</span><span class=n>scatter</span><span class=p>(</span><span class=n>tips</span><span class=p>,</span> <span class=n>x</span><span class=o>=</span><span class=s1>'total_bill'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'tip'</span><span class=p>,</span> <span class=n>color</span><span class=o>=</span><span class=s1>'time'</span><span class=p>,</span> <span class=n>title</span><span class=o>=</span><span class=s1>'Total Bill vs Tip'</span><span class=p>,</span> <span class=n>labels</span><span class=o>=</span><span class=p>{</span><span class=s1>'total_bill'</span><span class=p>:</span> <span class=s1>'Total Bill'</span><span class=p>,</span> <span class=s1>'tip'</span><span class=p>:</span> <span class=s1>'Tip'</span><span class=p>})</span> <span class=n>fig</span><span class=o>.</span><span class=n>update_layout</span><span class=p>(</span> <span class=n>title</span><span class=o>=</span><span class=s1>'Total Bill vs Tip'</span><span class=p>,</span> <span class=n>xaxis_title</span><span class=o>=</span><span class=s1>'Total Bill'</span><span class=p>,</span> <span class=n>yaxis_title</span><span class=o>=</span><span class=s1>'Tip'</span><span class=p>,</span> <span class=n>legend_title</span><span class=o>=</span><span class=s1>'Time of Day'</span><span class=p>,</span> <span class=n>template</span><span class=o>=</span><span class=s1>'plotly_dark'</span> <span class=p>)</span> <span class=n>fig</span><span class=o>.</span><span class=n>show</span><span class=p>()</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot.webp" alt="scatterplot" width="1100" height="340" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot.webp 1100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot-768.webp 768w,https://media.geeksforgeeks.org/wp-content/uploads/20240808121900/scatterplot-1024.webp 1024w" loading="lazy"><p class="wp-caption-text">Scatter Plot</p></div><p dir="ltr"><span>Customized Plot:</span></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot.webp" alt="customized-scatter-plot" width="1100" height="306" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot.webp 1100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot-768.webp 768w,https://media.geeksforgeeks.org/wp-content/uploads/20240808122028/customized-scatter-plot-1024.webp 1024w" loading="lazy"><p class="wp-caption-text">Customized Scatter Plot</p></div><h3 id="plotting-with-plotly-when-to-use"><span>Plotting with Plotly: When to Use</span></h3><p dir="ltr"><span>Plotly is most beneficial when interactivity, web integration, and detailed customization are required. It bridges the gap between static visualizations and dynamic, user-friendly interfaces, making it a powerful tool for modern data visualization needs.</span></p> <p dir="ltr"><span>It’s particularly beneficial in the following scenarios:</span></p> <ul><li value="1"><b><strong>Interactive Dashboards:</strong></b><span> When you need to create dynamic dashboards with multiple plots that users can explore by filtering, zooming, and selecting different data points.</span></li><li value="2"><b><strong>Web Applications:</strong></b><span> For embedding visualizations directly into web applications, Plotly provides seamless integration and responsiveness.</span></li><li value="3"><b><strong>Large Datasets:</strong></b><span> Plotly can handle large datasets efficiently, allowing you to explore and visualize data without performance issues.</span></li><li value="4"><b><strong>Geographical Data:</strong></b><span> Creating interactive maps and geographic visualizations is a strength of Plotly.</span></li><li value="5"><b><strong>Complex Visualizations:</strong></b><span> For intricate and customized visualizations, Plotly offers flexibility and control.</span></li><li value="6"><b><strong>Presentations and Storytelling:</strong></b><span> Engaging your audience with interactive visuals is a key advantage of Plotly.</span></li></ul><h2 id="data-visualization-with-plotnine"><a href="https://www.geeksforgeeks.org/data-visualization-using-plotnine-and-ggplot2-in-python/" target="_blank"><span>Data Visualization with Plotnine</span></a></h2><p dir="ltr"><span>Plotnine is a Python implementation of </span><b><strong>ggplot2</strong></b><span>, a popular R package for creating complex and customizable visualizations Both libraries are based on the </span><b><strong>Grammar of Graphics</strong></b><span>, a framework that provides a systematic approach to constructing plots. This chapter will introduce you to Plotnine in Python, demonstrating how they can be used to create various types of plots.</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/an-introduction-to-grammar-of-graphics-for-python/" target="_blank" rel="noopener"><span>Introduction to Concept of Grammar of Graphics</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/data-visualization-using-plotnine-and-ggplot2-in-python/" target="_blank" rel="noopener"><span>Data Visualization using Plotnine in Python</span></a></li></ul><p dir="ltr"><span>Plotnine is a Python library that implements the Grammar of Graphics, inspired by R’s ggplot2. It provides a coherent and consistent way to create complex visualizations with minimal code.</span></p> <h3 id="plotnine-example-creating-line-plots"><span>Plotnine Example: Creating Line Plots</span></h3><gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=kn>from</span> <span class=nn>plotnine</span> <span class=kn>import</span> <span class=n>ggplot</span><span class=p>,</span> <span class=n>aes</span><span class=p>,</span> <span class=n>geom_line</span><span class=p>,</span> <span class=n>geom_histogram</span><span class=p>,</span> <span class=n>labs</span><span class=p>,</span> <span class=n>theme_minimal</span> <span class=kn>from</span> <span class=nn>plotnine.data</span> <span class=kn>import</span> <span class=n>economics</span> <span class=c1># Load the 'economics' dataset available in Plotnine</span> <span class=c1># This dataset contains economic indicators including unemployment numbers</span> <span class=c1># Create a line plot to visualize the trend of unemployment rate over time</span> <span class=n>line_plot</span> <span class=o>=</span> <span class=p>(</span> <span class=n>ggplot</span><span class=p>(</span><span class=n>economics</span><span class=p>,</span> <span class=n>aes</span><span class=p>(</span><span class=n>x</span><span class=o>=</span><span class=s1>'date'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'unemploy'</span><span class=p>))</span> <span class=o>+</span> <span class=n>geom_line</span><span class=p>(</span><span class=n>color</span><span class=o>=</span><span class=s1>'blue'</span><span class=p>)</span> <span class=o>+</span> <span class=n>labs</span><span class=p>(</span><span class=n>title</span><span class=o>=</span><span class=s1>'Unemployment Rate Over Time'</span><span class=p>,</span> <span class=n>x</span><span class=o>=</span><span class=s1>'Date'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'Number of Unemployed'</span><span class=p>)</span> <span class=o>+</span> <span class=n>theme_minimal</span><span class=p>()</span> <span class=p>)</span> <span class=nb>print</span><span class=p>(</span><span class=n>line_plot</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots.webp" alt="Creating-Bar-Plots" width="1100" height="825" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots.webp 1100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots-768.webp 768w,https://media.geeksforgeeks.org/wp-content/uploads/20240808123539/Creating-Bar-Plots-1024.webp 1024w" loading="lazy"><p class="wp-caption-text">Line Plots</p></div><h3 id="plotting-with-plotline-when-to-use"><span>Plotting with Plotline: When to Use</span></h3><p dir="ltr"><span>Plotline is a Python library that simplifies the creation of plots with an emphasis on clarity and ease of use. It provides a streamlined interface for generating a variety of plot types. Here’s when and why you might choose to use Plotline:</span></p><div style="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-2"></div> <ul><li value="1"><b><strong>Rapid Prototyping</strong></b><span>: When you need to quickly generate plots to visualize data during the early stages of analysis or experimentation, Plotline’s straightforward syntax helps you get up and running quickly.</span></li><li value="2"><b><strong>Standard Plots</strong></b><span>: If your plotting needs involve common plot types like line plots, bar plots, or scatter plots, and you don’t require highly customized or interactive features, Plotline offers a clean and efficient solution.</span></li><li value="3"><b><strong>Ease of Use</strong></b><span>: Plotline’s user-friendly API simplifies the process of creating plots, reducing the learning curve associated with more complex libraries.</span></li><li value="4"><b><strong>Speed</strong></b><span>: With Plotline, you can generate plots quickly without needing to deal with extensive configuration or customization.</span></li></ul><h2 id="data-visualizations-with-altair-python"><a href="https://www.geeksforgeeks.org/introduction-to-altair-in-python/" target="_blank" rel="noopener"><span>Data Visualizations with Altair Python</span></a></h2><p dir="ltr"><span>Altair is a declarative statistical visualization library for Python, designed to provide an intuitive way to create interactive and informative charts. Built on Vega and Vega-Lite, Altair allows users to build complex visualizations through simple and expressive syntax.</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/introduction-to-altair-in-python/" target="_blank" rel="noopener"><span>Introduction to Altair in Python</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/using-altair-on-data-aggregated-from-large-datasets/" target="_blank" rel="noopener"><span>Aggregating Data with Altair for Large Datasets</span></a></li><li value="3"><span>Sharing and Publishing Visualizations with Altair</span></li></ul><h3 id="altair-example-creating-charts"><span>Altair Example: Creating Charts</span></h3><gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=c1># Import necessary libraries</span> <span class=kn>import</span> <span class=nn>altair</span> <span class=k>as</span> <span class=nn>alt</span> <span class=kn>from</span> <span class=nn>vega_datasets</span> <span class=kn>import</span> <span class=n>data</span> <span class=n>iris</span> <span class=o>=</span> <span class=n>data</span><span class=o>.</span><span class=n>iris</span><span class=p>()</span> <span class=c1># Create a scatter plot</span> <span class=n>scatter_plot</span> <span class=o>=</span> <span class=n>alt</span><span class=o>.</span><span class=n>Chart</span><span class=p>(</span><span class=n>iris</span><span class=p>)</span><span class=o>.</span><span class=n>mark_point</span><span class=p>()</span><span class=o>.</span><span class=n>encode</span><span class=p>(</span> <span class=n>x</span><span class=o>=</span><span class=s1>'sepalLength'</span><span class=p>,</span> <span class=n>y</span><span class=o>=</span><span class=s1>'petalLength'</span><span class=p>,</span> <span class=n>color</span><span class=o>=</span><span class=s1>'species'</span> <span class=p>)</span> <span class=n>scatter_plot</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808125716/scatterplot.png" alt="scatterplot" width="515" height="347" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808125716/scatterplot.png 515w,https://media.geeksforgeeks.org/wp-content/uploads/20240808125716/scatterplot-100.png 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808125716/scatterplot-200.png 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808125716/scatterplot-300.png 300w" loading="lazy"><p class="wp-caption-text">Creating Charts</p></div><h3 id="plotting-with-altair-when-to-use"><span>Plotting with Altair: When to Use</span></h3><p dir="ltr"><span>Altair is a declarative statistical visualization library for Python, built on top of the Vega and Vega-Lite visualization grammars. It is designed to be concise, powerful, and user-friendly, making it an excellent choice for various data visualization tasks. Here’s when you should consider using Altair:</span></p> <ol><li value="1"><b><strong>Interactive Visualizations</strong></b><span>: Altair excels at creating interactive plots with minimal code. If you need to add interactivity, such as tooltips, zooming, and panning, Altair makes it easy to implement these features.</span></li><li value="2"><b><strong>Rapid Prototyping and Exploration</strong></b><span>: For quick data exploration and prototyping, Altair’s concise syntax allows you to generate complex visualizations rapidly. Its straightforward code structure makes it easy to iterate and refine your plots.</span></li><li value="3"><b><strong>Handling Large Datasets</strong></b><span>: Altair supports efficient data aggregation and transformation through its integration with Vega-Lite. If you are working with large datasets, Altair’s built-in data transformation capabilities help manage and visualize them effectively.</span></li><li value="4"><b><strong>Reproducibility and Sharing</strong></b><span>: Altair’s declarative nature ensures that visualizations are reproducible and shareable. The JSON specification that underlies Altair plots can be easily shared, ensuring consistency across different environments and platforms.</span></li><li value="5"><b><strong>Publication-Quality Graphics</strong></b><span>: Altair produces high-quality visualizations suitable for academic and professional publications. Its ability to create aesthetically pleasing and informative plots with minimal effort makes it ideal for creating publication-ready graphics.</span></li></ol><h2 id="interactive-data-visualization-with-bokeh"><a href="https://www.geeksforgeeks.org/python-bokeh-tutorial-interactive-data-visualization-with-bokeh/" target="_blank" rel="noopener"><span>Interactive Data Visualization with Bokeh</span></a></h2><p dir="ltr"><span>Bokeh is a powerful Python library for creating </span><a href="https://www.geeksforgeeks.org/what-is-interactive-data-visualization/" target="_blank" rel="noopener"><span>interactive data visualization</span></a><span> and highly customizable visualizations. It is designed for modern web browsers and allows for the creation of complex visualizations with ease. Bokeh supports a wide range of plot types and interactivity features, making it a popular choice for interactive data visualization.</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/introduction-to-bokeh-in-python/" target="_blank" rel="noopener"><span>Introduction to Bokeh in Python</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/python-bokeh-tutorial-interactive-data-visualization-with-bokeh/" target="_blank" rel="noopener"><span>Interactive Data Visualization with Bokeh</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/interactive-data-visualization-with-python-and-bokeh/" target="_blank" rel="noopener"><span>Practical Examples for Mastering Data Visualization with Bokeh</span></a></li></ul><h3 id="example-basic-plotting-with-bokeh-adding-hover-tool"><span>Example : Basic Plotting with Bokeh- Adding Hover Tool</span></h3><gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>from</span> <span class=nn>bokeh.models</span> <span class=kn>import</span> <span class=n>HoverTool</span> <span class=kn>from</span> <span class=nn>bokeh.plotting</span> <span class=kn>import</span> <span class=n>figure</span><span class=p>,</span> <span class=n>show</span> <span class=kn>from</span> <span class=nn>bokeh.io</span> <span class=kn>import</span> <span class=n>output_notebook</span> <span class=n>output_notebook</span><span class=p>()</span> <span class=n>p</span> <span class=o>=</span> <span class=n>figure</span><span class=p>(</span><span class=n>title</span><span class=o>=</span><span class=s2>"Scatter Plot with Hover Tool"</span><span class=p>,</span> <span class=n>x_axis_label</span><span class=o>=</span><span class=s1>'X-Axis'</span><span class=p>,</span> <span class=n>y_axis_label</span><span class=o>=</span><span class=s1>'Y-Axis'</span><span class=p>)</span> <span class=n>p</span><span class=o>.</span><span class=n>scatter</span><span class=p>(</span><span class=n>x</span><span class=o>=</span><span class=p>[</span><span class=mi>1</span><span class=p>,</span> <span class=mi>2</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>4</span><span class=p>,</span> <span class=mi>5</span><span class=p>],</span> <span class=n>y</span><span class=o>=</span><span class=p>[</span><span class=mi>6</span><span class=p>,</span> <span class=mi>7</span><span class=p>,</span> <span class=mi>2</span><span class=p>,</span> <span class=mi>4</span><span class=p>,</span> <span class=mi>5</span><span class=p>],</span> <span class=n>size</span><span class=o>=</span><span class=mi>10</span><span class=p>,</span> <span class=n>color</span><span class=o>=</span><span class=s2>"green"</span><span class=p>,</span> <span class=n>alpha</span><span class=o>=</span><span class=mf>0.5</span><span class=p>)</span> <span class=c1># Add HoverTool</span> <span class=n>hover</span> <span class=o>=</span> <span class=n>HoverTool</span><span class=p>()</span> <span class=n>hover</span><span class=o>.</span><span class=n>tooltips</span> <span class=o>=</span> <span class=p>[(</span><span class=s2>"X"</span><span class=p>,</span> <span class=s2>"@x"</span><span class=p>),</span> <span class=p>(</span><span class=s2>"Y"</span><span class=p>,</span> <span class=s2>"@y"</span><span class=p>)]</span> <span class=n>p</span><span class=o>.</span><span class=n>add_tools</span><span class=p>(</span><span class=n>hover</span><span class=p>)</span> <span class=c1># Show the plot</span> <span class=n>show</span><span class=p>(</span><span class=n>p</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808150711/Scatterplothovertool-ezgifcomoptimize.gif" alt="Scatterplothovertool-ezgifcomoptimize" width="525" height="225" loading="lazy"><p class="wp-caption-text">Basic Plotting with Bokeh- Adding Hover Tool</p></div><h3 id="when-to-choose-bokeh-for-your-data-visualizations"><span>When to Choose Bokeh for Your Data Visualizations</span></h3><p dir="ltr"><span>Bokeh is a powerful interactive visualization library for Python that enables you to create stunning, interactive visualizations for web applications. Here’s when you should consider using Bokeh:</span></p> <ol><li value="1"><b><strong>Interactive Web Applications</strong></b><span>: Bokeh is ideal for creating interactive visualizations that are intended to be embedded in web applications. Its interactivity features, such as zooming, panning, and hover tools, make it suitable for creating dynamic and engaging web-based plots.</span></li><li value="2"><b><strong>Real-Time Data Visualization</strong></b><span>: If your project involves real-time data updates, Bokeh’s ability to handle streaming data makes it an excellent choice. You can create visualizations that update in real-time, which is useful for monitoring systems, live dashboards, and data feeds.</span></li><li value="3"><b><strong>Detailed and Customizable Plots</strong></b><span>: Bokeh provides a high level of customization for creating detailed visualizations. If you need fine control over the appearance and behavior of your plots, Bokeh’s comprehensive customization options will meet your needs.</span></li><li value="4"><b><strong>Complex and Large Datasets</strong></b><span>: When working with complex and large datasets, Bokeh’s performance and efficiency in rendering visualizations are beneficial. It can handle large volumes of data and render them interactively in the browser.</span></li><li value="5"><b><strong>Embedding Visualizations in HTML</strong></b><span>: Bokeh integrates well with HTML, making it easy to embed visualizations directly into web pages. This is particularly useful for sharing interactive plots through web portals or reports.</span></li><li value="6"><b><strong>Combining Multiple Plots</strong></b><span>: If your project requires combining multiple plots into a single interactive dashboard, Bokeh’s layout features allow you to arrange plots and widgets in a cohesive manner.</span></li></ol><h2 id="mastering-advanced-data-visualization-with-pygal"><a href="https://www.geeksforgeeks.org/data-visualization-with-pygal/?ref=next_article#:~:text=Pygal%20is%20a%20powerful%20tool,visualizing%20data%20in%20various%20ways." target="_blank" rel="noopener"><b><strong>Mastering Advanced Data Visualization with Pygal</strong></b></a></h2><p dir="ltr"><span>In this final chapter, we will delve into advanced techniques for data visualization using Pygal, a powerful and versatile Python library. </span><a href="https://www.geeksforgeeks.org/pygal/" target="_blank" rel="noopener"><span>Pygal </span></a><span>is known for its ease of use and ability to create beautiful, interactive charts that can be embedded in web applications. </span></p> <p dir="ltr"><a href="https://www.geeksforgeeks.org/data-visualization-with-pygal/?ref=next_article#:~:text=Pygal%20is%20a%20powerful%20tool,visualizing%20data%20in%20various%20ways." target="_blank" rel="noopener"><span>Data Visualization with Pygal:</span></a><span> With Pygal, you can create a wide range of charts including line charts, bar charts, pie charts, and more, all with interactive capabilities.</span></p> <h3 id="example-creating-advanced-charts-with-pygal"><span>Example: Creating Advanced Charts with Pygal</span></h3><p dir="ltr"><span>Firstly, you’ll need to install pygal, you can install it using pip:</span></p> <pre><span>pip install pygal</span></pre><gfg-tabs data-run-ide="false" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>import</span> <span class=nn>pygal</span> <span class=kn>from</span> <span class=nn>pygal.style</span> <span class=kn>import</span> <span class=n>Style</span> <span class=c1># Create a custom style</span> <span class=n>custom_style</span> <span class=o>=</span> <span class=n>Style</span><span class=p>(</span> <span class=n>background</span><span class=o>=</span><span class=s1>'transparent'</span><span class=p>,</span> <span class=n>plot_background</span><span class=o>=</span><span class=s1>'transparent'</span><span class=p>,</span> <span class=n>foreground</span><span class=o>=</span><span class=s1>'#000000'</span><span class=p>,</span> <span class=n>foreground_strong</span><span class=o>=</span><span class=s1>'#000000'</span><span class=p>,</span> <span class=n>foreground_subtle</span><span class=o>=</span><span class=s1>'#6e6e6e'</span><span class=p>,</span> <span class=n>opacity</span><span class=o>=</span><span class=s1>'.6'</span><span class=p>,</span> <span class=n>opacity_hover</span><span class=o>=</span><span class=s1>'.9'</span><span class=p>,</span> <span class=n>transition</span><span class=o>=</span><span class=s1>'400ms'</span><span class=p>,</span> <span class=n>colors</span><span class=o>=</span><span class=p>(</span><span class=s1>'#E80080'</span><span class=p>,</span> <span class=s1>'#404040'</span><span class=p>)</span> <span class=p>)</span> <span class=c1># Create a line chart</span> <span class=n>line_chart</span> <span class=o>=</span> <span class=n>pygal</span><span class=o>.</span><span class=n>Line</span><span class=p>(</span><span class=n>style</span><span class=o>=</span><span class=n>custom_style</span><span class=p>,</span> <span class=n>show_legend</span><span class=o>=</span><span class=kc>True</span><span class=p>,</span> <span class=n>x_title</span><span class=o>=</span><span class=s1>'Months'</span><span class=p>,</span> <span class=n>y_title</span><span class=o>=</span><span class=s1>'Values'</span><span class=p>)</span> <span class=n>line_chart</span><span class=o>.</span><span class=n>title</span> <span class=o>=</span> <span class=s1>'Monthly Trends'</span> <span class=n>line_chart</span><span class=o>.</span><span class=n>add</span><span class=p>(</span><span class=s1>'Series 1'</span><span class=p>,</span> <span class=p>[</span><span class=mi>1</span><span class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span class=mi>5</span><span class=p>,</span> <span class=mi>7</span><span class=p>,</span> <span class=mi>9</span><span class=p>])</span> <span class=n>line_chart</span><span class=o>.</span><span class=n>add</span><span class=p>(</span><span class=s1>'Series 2'</span><span class=p>,</span> <span class=p>[</span><span class=mi>2</span><span class=p>,</span> <span class=mi>4</span><span class=p>,</span> <span class=mi>6</span><span class=p>,</span> <span class=mi>8</span><span class=p>,</span> <span class=mi>10</span><span class=p>])</span> <span class=c1># Render the chart to a file</span> <span class=n>line_chart</span><span class=o>.</span><span class=n>render_to_file</span><span class=p>(</span><span class=s1>'line_chart.svg'</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart.webp" alt="line_chart" width="1100" height="825" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart.webp 1100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart-768.webp 768w,https://media.geeksforgeeks.org/wp-content/uploads/20240808152201/line_chart-1024.webp 1024w" loading="lazy"><p class="wp-caption-text">Advanced Line Charts with Pygal</p></div><h3 id="when-to-use-pygal-for-data-visualization"><span>When to Use Pygal for Data Visualization</span></h3><p dir="ltr"><span>Pygal is a Python library designed for creating beautiful and highly customizable SVG charts. Here’s when you should consider using Pygal:</span></p> <ol><li value="1"><b><strong>Interactive and Scalable Graphics</strong></b><span>: Pygal generates SVG files, which are scalable and retain quality at any size. This is particularly useful for producing interactive graphics that need to look sharp across different resolutions and devices.</span></li><li value="2"><b><strong>Web Integration</strong></b><span>: If your visualizations need to be embedded in web applications or websites, Pygal’s SVG output integrates well with web technologies. SVG files can be easily incorporated into HTML pages and styled with CSS.</span></li><li value="3"><b><strong>Creating Infographics</strong></b><span>: Pygal is suitable for generating infographics that require a clean, visually engaging presentation of data. Its SVG output ensures high-quality visuals for printed or digital infographics.</span></li><li value="4"><b><strong>Customizable Appearance</strong></b><span>: When you need to customize the look and feel of your charts, Pygal provides options for modifying colors, labels, and other visual elements. This level of customization helps in creating visually appealing charts that align with your branding or design requirements.</span></li></ol><h2 id="effective-data-storytelling-techniques-for-engaging-visualizations"><b><strong>Effective Data Storytelling: Techniques for Engaging Visualizations</strong></b></h2><p dir="ltr"><span>Creating impactful data visualizations requires a blend of effective design techniques and interactive elements. </span><b><strong>Start by selecting the appropriate chart type—bar charts for comparisons, line charts for trends, and pie charts for proportions.</strong></b><span> </span></p> <ul><li value="1"><span>Simplify your visualizations to focus on key insights and use annotations to guide the viewer’s attention.</span></li><li value="2"><span>Strategically use color to differentiate categories or highlight important data, but avoid overuse to prevent confusion.</span></li></ul><p dir="ltr"><b><strong>To enhance engagement, incorporate interactive features such as hover effects, zoom, and filters that allow users to explore data dynamically.</strong></b><span> Ensure that your visualizations are user-friendly by designing intuitive interfaces with clear legends and informative tooltips. </span></p> <p dir="ltr"><span>For a more detailed exploration of these techniques and best practices, refer to our comprehensive articles on:</span></p> <ul><li value="1"><a href="https://www.geeksforgeeks.org/6-tips-for-creating-effective-data-visualizations/" target="_blank" rel="noopener"><span>6 Tips for Creating Effective Data Visualizations</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/data-visualization-in-infographics-techniques-and-examples/" target="_blank" rel="noopener"><span>Data Visualization in Infographics: Techniques and Examples</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/5-best-practices-for-effective-and-good-data-visualizations/" target="_blank" rel="noopener"><span>5 Best Practices for Effective and Good Data Visualizations</span></a></li><li value="4"><a href="https://www.geeksforgeeks.org/bad-data-visualization-examples-explained/" target="_blank" rel="noopener"><span>Bad Data Visualization Examples Explained</span></a></li></ul><h2 id="conclusion"><b><strong>Conclusion</strong></b></h2><p dir="ltr"><span>Data visualization is essential for transforming complex data into clear, actionable insights. With Python’s diverse libraries, including Matplotlib, Seaborn, Pandas, Plotly, Plotnine, Altair, Bokeh, and Pygal, you can choose the right tool for your visualization needs. </span></p> <p dir="ltr"><span>Each library offers unique features, from Matplotlib’s detailed static plots to Plotly’s interactive charts and Pygal’s scalable SVG graphics. Selecting the appropriate library and applying best practices in design such as choosing the right chart type, using color effectively, and incorporating interactivity will enhance the clarity and impact of your visualizations. </span></p> <br/><div id="AP_G4GR_6"></div> <div class='article_bottom_text'></div><br> <div id="video-tab-content" class="video-tab-content"> <div style="text-align: center; margin: 20px 0px;" id="GFG_AD_InContent_Desktop_728x280"></div> </div> <div class="article-meta-author-details"> <div class="article-meta-author-details-block"> <div class="article-meta-author-details-profile-display"> <div class="article-meta-author-details-profile-display-icon"> <div class="image-wrap"> <img src= https://media.geeksforgeeks.org/wp-content/uploads/20200717172614/authPreLogo.png style="width:35px; height: 35px;border-radius: 25px;" class="avatarprofile_image restrict-popup-gfg" alt="https://media.geeksforgeeks.org/auth/avatar.png"> </div> </div> <div class="article-meta-author-details-profile-display-name" > <div class="info" style="position: unset;"> <div class="name"> <span style="color:#22A779; font-size: 16px; font-weight: 600">GeeksforGeeks</span> </div> </div> </div> </div> </div> <div class="article_bottom_suggestion_wrapper"> <div class="article_bottom_suggestion" data-title="Follow"> <a href='https://news.google.com/publications/CAAqBwgKMLTrzwsw44bnAw?hl=en-IN&gl=IN&ceid=IN%3Aen' target="_blank"> <img class="gfg-icon_Gnews no-zoom-in-cursor" src=https://media.geeksforgeeks.org/auth-dashboard-uploads/Google-news.svg loading="lazy" alt="News"></img> </a> </div> <div class='discussion_panel' data-title='Comments'> <i class='discussion_button' data-gfg-action='loadComments'> </i> </div> <div pid="726850" class="article--viewer_like tooltip tooltipBottom" data-title="Like Article">  <button id="likeButton" aria-label="like article" data-gfg-action="like-article" data-bookmark-value="0" data-liked="false" style="color: #5B5A5A !important; display: flex; align-items: center; background-color: unset; margin: 0px !important; padding: 5px 0px;"> <i class="author-badge-like-button"></i> <figure id="likeCount" style="margin-left: 3px; margin-top: 4px; color: var(--like-count-color); font-size: 14px; font-weight: 600;" class="favoriteText"></figure> </button> </div> <div class="article_bottom_suggestion" onclick="improveArticleCall()" data-title="Improve"> <span class="improveTooltipBottom">Improve</span> <i class="author-badge-improvement-button"></i> </div> </div> </div> <div class="d-row content-bw "style="margin-top: 20px;"> <div class="article-pgnavi_prev"> </div> <div class="article-pgnavi_next"> <a href="https://www.geeksforgeeks.org/data-visualization-and-its-importance/?ref=next_article" class="pg-head"> <span style="margin-right: 5px; margin-left: auto;">Next Article</span> <span class="gfg-icon gfg-icon_next"></span> </a>  <div class="pg-main"> <a href="https://www.geeksforgeeks.org/data-visualization-and-its-importance/?ref=next_article">What is Data Visualization and Why is It Important?</a> </div> </div> </div> </div> </article> <div class="article--recommended article--recommended_wrapper" id="similar-reads"> <h3 class="new-top-bar top-bar-title">Similar Reads</h3> <div class="gfg-similar-reads-list"> <a class="gfg-similar-read-item" href="https://www.geeksforgeeks.org/why-data-visualization-matters-in-data-analytics/?ref=oin_asr1"> <div class="gfg-similar-read-item-content"> <div class="gfg-similar-read-item-heading">Why Data Visualization Matters in Data Analytics?</div> <div class="gfg-similar-read-item-subheading">What if you wanted to know the number of movies produced in the world per year in different countries? 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left: 50%; transform: translate(-50%, -50%); z-index: 1026; width: min(650px, calc(100% - 20px)); } .link-container-write{ display:flex; flex-direction:column; } .link-container-write > a { margin:7px 0px; } .phrase__container{ color:var(--color-gfg); border-radius: 6px; width: 100%; background-color:var(--write-redirect-container); padding: 15px; text-align: justify; font-size:14px; } .phrase__container:hover { color:var(--color-gfg) !important; background-color:var(--write-redirect-container-hover); } </style> <div class="popup-container"> <div id="popup" accesskey="" class="popup-main"> <div class="popup-heading"> <h2>What kind of Experience do you want to share?</h2> <div class="close-icon share-experience-modal-close"></div> </div>  <div class="experience-card"> <a class="exp-card5" href= "https://write.geeksforgeeks.org/posts-new?cid=e8fc46fe-75e7-4a4b-be3c-0c862d655ed0" target="_blank"> <div class="icon5"> <div class="icon5-image"></div> </div> <div class="exp-card5-text"> <span style="color: #000;">Interview Experiences</span> </div> </a> <a class="exp-card1" href="https://write.geeksforgeeks.org/posts-new?cid=82536bdb-84e6-4661-87c3-e77c3ac04ede" target="_blank"> <div class="icon1"> <div class="icon1-image"></div> </div> <div class="exp-card1-text"> <span style="color: #000;">Admission Experiences</span> </div> </a> <a class="exp-card6" href= "https://write.geeksforgeeks.org/posts-new?cid=5219b0b2-7671-40a0-9bda-503e28a61c31" target="_blank"> <div class="icon6"> <div class="icon6-image"></div> </div> <div class="exp-card6-text"> <span style="color: #000;">Career Journeys</span> </div> </a> <a class="exp-card2" href="https://write.geeksforgeeks.org/posts-new?cid=22ae3354-15b6-4dd4-a5b4-5c7a105b8a8f" target="_blank"> <div class="icon2"> <div class="icon2-image"></div> </div> <div class="exp-card2-text"> <span style="color: #000;">Work Experiences</span> </div> </a> <a class="exp-card3" href= "https://write.geeksforgeeks.org/posts-new?cid=c5e1ac90-9490-440a-a5fa-6180c87ab8ae" target="_blank"> <div class="icon3"> <div class="icon3-image"></div> </div> <div class="exp-card3-text"> <span style="color: #000;">Campus Experiences</span> </div> </a> <a class="exp-card4" href= "https://write.geeksforgeeks.org/posts-new?cid=5ebb8fe9-b980-4891-af07-f2d62a9735f2" target="_blank"> <div class="icon4"> <div class="icon4-image"></div> </div> <div class="exp-card4-text"> <span style="color: #000;">Competitive Exam Experiences</span> </div> </a> </div>  </div> <div id="overlay" onclick="toggleExperiencePopup()" style="display: block;min-height: 100vh;min-width: 100vw;position: fixed;top: 0;bottom: 0;right: 0;left: 0;display: none;justify-content: center;align-items: center;background: rgba(0,0,0,.702);z-index: 1025 !important;backdrop-filter: blur(4px); -webkit-backdrop-filter: blur(4px);word-break: keep-all;"></div> </div> <script> $('.share-experience-modal').click(function(e){ e.preventDefault(); var link = $(this).attr('href'); toggleExperiencePopup(link); }); function toggleExperiencePopup(link) { var popup = document.getElementById("popup"); var overlay = document.getElementById("overlay"); if (window.innerWidth < 992) { window.location.href = link; } else { popup.style.display = (popup.style.display === "block") ? "none" : "block"; overlay.style.display = (overlay.style.display === "block") ? "none" : "block"; } } $('.share-experience-modal-close').click(function(e){ var popup = document.getElementById("popup"); popup.style.display = 'none'; var overlay = document.getElementById("overlay"); overlay.style.display = 'none'; }); </script> </div><script type="text/javascript"> $(window).on('load', function() { (function(c,l,a,r,i,t,y){ c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)}; t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i; y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y); })(window, document, "clarity", "script", "ayc7ypwwuk"); }); </script>   <script defer src="https://www.geeksforgeeks.org/wp-content/themes/iconic-one/js/chatbot.min.js?ver=0.3"></script>  <link rel='stylesheet' href="https://www.geeksforgeeks.org/wp-content/themes/iconic-one/css/chatbot.min.css?ver=0.2" type='text/css' > </body> </html>

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