Python - Data visualization tutorial

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target="_self">Beginner's DSA Sheet</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=50746f92a895c22a50504ac0c1fb9c84&sortBy=submissions&sprint_name=Top 50 Array Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 Array Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=57184072610b884e5df3584cc534115d&sortBy=submissions&sprint_name=Top 50 String Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 String Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=93d672753b74440c7427214c8ebf866d&sortBy=submissions&sprint_name=Top 50 DP Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 DP Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=405e9db0f353691ad3b2d546b19145e9&sortBy=submissions&sprint_name=Top 50 Graph Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 Graph Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=5f24de84b65bf7c4f4399c8111e26b81&sortBy=submissions&sprint_name=Top 50 Tree Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 Tree Problems</a></li></ul></li></ul></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/competitive-programming-a-complete-guide/?ref=outind" target="_self">Competitive Programming</a></li><li class="mega-dropdown__list-item" data-parent="false" aria-expanded="true" data-expandable="true"><span>Company Wise SDE Sheets</span><i class="gfg-icon gfg-icon_arrow-right"></i><ul class="mega-dropdown Screen_2"><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/facebookmeta-sde-sheet-interview-questions-and-answers/?ref=outind" target="_self">Facebook SDE Sheet</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/amazon-sde-sheet-interview-questions-and-answers/?ref=outind" target="_self">Amazon SDE Sheet</a></li><li 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class="three_dot_dropdown_inner_div" data-gfg-action="like-article" data-bookmark-value="0"> <i class="gfg-icon gfg-icon_thumbs"></i> <span>Like</span> </div> </div>  </div> <div pid="726850" class="article--viewer_share tooltip"> <div class="three_dot_dropdown_div three_dot_dropdown_share" onmouseleave="closeShareModal()" onmouseenter="openShareModal()"> <div style="display: flex;align-items: center;" class="three_dot_dropdown_inner_div" data-gfg-action="share-article" data-bookmark-value="0"> <i class="gfg-share-icon"></i> <span>Share</span> </div> </div> </div> <div class="article--viewer_report tooltip"> <div class="three_dot_dropdown_div three_dot_dropdown_reportarticle"> <div class="three_dot_dropdown_inner_div" data-gfg-action="report-article" onclick="report_article();"> <i class="gfg-icon gfg-icon_report"></i> <span>Report</span> </div> </div>  </div> <div class="article--viewer_Gnews tooltip"> <a class="three_dot_dropdown_inner_div Gnews_wrapper" 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> <span class='kebab_menu_news_text'>Follow</span> </a> </div> </div> </div> <div class="text"> <p style="text-align: justify;" dir="ltr"><span>Data visualization is a crucial aspect of data analysis, helping to transform analyzed data into meaningful insights through graphical representations. This comprehensive tutorial will guide you through the fundamentals of data visualization using Python. We’ll explore various libraries, including </span><b><strong>Matplotlib, Seaborn, Pandas, Plotly, Plotnine, Altair, Bokeh, Pygal, and Geoplotlib.</strong></b><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><h2 id="introduction-to-data-visualization-guide-to-visual-storytelling"><span>Introduction to Data Visualization</span></h2><p dir="ltr"><span>After analyzing data, it is important to visualize the data to uncover patterns, trends, outliers, and insights that may not be apparent in raw data using visual elements like charts, graphs, and maps. </span><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><div id="GFG_AD_gfg_mobile_336x280" style="margin: 5px 0;"></div> <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</span></a></li></ul><p dir="ltr"><span>Equally important is selecting the right colors for your visualizations. Proper color choices highlight key information, improve readability, and make visuals more engaging. 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> <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> <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"><span>Getting Started – Data Visualization with Matplotlib</span></h2><p dir="ltr"><span>Matplotlib is a great way to begin visualizing data in Python, essential for data visualization in data science. It is a versatile library that designed to help users visualize data in a variety of formats. Well-suited for creating a wide range of static, animated, and interactive plots. </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/install-matplotlib-python/" target="_blank" rel="noopener"><span>Setting up Python Environment for installation</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" data-main-code-start="None" data-main-code-end="None"> <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><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> <img src="https://media.geeksforgeeks.org/wp-content/uploads/20201013170331/simpleplot-300x169.png" width="inherit" height="inherit" loading="lazy"><h2 id="effective-data-visualization-with-seaborn"><span>Effective Data Visualization With Seaborn </span></h2><p dir="ltr"><span>Seaborn is a Python library that simplifies </span><b><strong>the creation of attractive and informative statistical graphics</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 id="_GFG_ABP_Incontent_728x90" style="text-align:center;"></div> <ul><li value="1"><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="2"><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="3"><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="4"><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><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" data-main-code-start="None" data-main-code-end="None"> <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><h2><span>Data Visualization with Pandas</span></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. </span><b><strong>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.</strong></b></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><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. Let’s create 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" data-main-code-start="None" data-main-code-end="None"> <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><h2><span>Data Visualization with Plotly </span></h2><p dir="ltr"><span>Plotly is a versatile library for creating interactive and aesthetically pleasing visualizations. This chapter will introduce you to Plotly and guide you through creating basic 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/getting-started-with-plotly-python/" target="_blank" rel="noopener"><span>Introduction to Plotly</span></a></li><li value="2"><a href="https://www.geeksforgeeks.org/using-plotly-for-interactive-data-visualization-in-python/" target="_blank" rel="noopener"><span>Data Visualization with Plotly</span></a><span> </span></li></ul><p dir="ltr"><span>We’ll create a simple bar plot. For this example, we’ll use the same ‘tips’ dataset we used with Seaborn.</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" data-main-code-start="None" data-main-code-end="None"> <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"><b><strong>Plotly allows for extensive customizations, including updating layouts, adding annotations, and incorporating dropdowns and sliders</strong></b><span>. </span></p> <h2><span>Data Visualization with Plotnine</span></h2><p dir="ltr"><span>Plotnine is a Python library that implements the</span><b><strong> Grammar of Graphics, inspired by R’s ggplot2</strong></b><span>. It provides a coherent and consistent way to create complex visualizations with minimal code.. 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</span></a></li></ul><h3><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" data-main-code-start="None" data-main-code-end="None"> <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><h2><span>Data Visualizations with Altair</span></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>Data Visualization with Altair</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 for Large Datasets</span></a></li><li value="3"><a href="https://www.geeksforgeeks.org/sharing-and-publishing-visualizations-with-altair/" target="_blank" rel="noopener"><span>Sharing and Publishing Visualizations with Altair</span></a></li></ul><h3><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" data-main-code-start="None" data-main-code-end="None"> <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><h2><span>Interactive Data Visualization with Bokeh</span></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 </span><b><strong>designed for modern web browsers and allows for the creation of complex visualizations with ease</strong></b><span>. 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><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" data-main-code-start="None" data-main-code-end="None"> <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><h2><b><strong>Mastering Advanced Data Visualization with Pygal</strong></b></h2><p dir="ltr"><span>In this final chapter, we will delve into advanced techniques for data visualization using Pygal. It is known for its ease of use and ability to create beautiful, interactive charts that can be embedded in web applications. </span></p> <ul><li value="1"><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></li></ul><h3><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" data-main-code-start="None" data-main-code-end="None"> <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><h2><span>Choosing the Right Data Visualization Library </span></h2><table><thead><tr><th><span>Library</span></th><th><span>Best For</span></th><th><span>Strengths</span></th><th><span>Limitations</span></th></tr></thead><tbody><tr><td><span>Matplotlib</span></td><td><span>Static plots</span></td><td><span>Highly customizable</span></td><td><span>Steep learning curve</span></td></tr><tr><td><span>Seaborn</span></td><td><span>Statistical visualizations</span></td><td><span>Easy to use, visually appealing</span></td><td><span>Limited interactivity</span></td></tr><tr><td><span>Plotly</span></td><td><span>Interactive visualizations</span></td><td><span>Web integration, modern designs</span></td><td><span>Requires browser rendering</span></td></tr><tr><td><span>Bokeh</span></td><td><span>Web-based dashboards</span></td><td><span>Real-time interactivity</span></td><td><span>More complex setup</span></td></tr><tr><td><span>Altair</span></td><td><span>Declarative statistical plots</span></td><td><span>Concise syntax</span></td><td><span>Limited customization</span></td></tr><tr><td><span>Pygal</span></td><td><span>Scalable SVG charts</span></td><td><span>High-quality graphics</span></td><td><span>Less suited for complex datasets</span></td></tr></tbody></table><p dir="ltr"><span>To create impactful and engaging data visualizations. </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.</span></li><li value="2"><span>Use annotations to guide the viewer’s attention.</span></li><li value="3"><span>Strategically use color to differentiate categories or highlight important data, but avoid overuse to prevent confusion.</span></li></ul><p dir="ltr"><span>For a more detailed exploration of these techniques consider below resources:</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><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="d-row content-bw "style="margin-top: 20px;"> <div class="article-pgnavi_prev"><div class="comment_div" data-title="Comments"><button class="author_footer_btn" data-gfg-action="loadComments"> <div class='discussion_panel'> <i class='discussion_button'> </i> Comment </div></button></div><div class="trigger-div"><button class="author_footer_btn"><div class="more_info">More info<span class="more_info_uparrow gfg-icon gfg-icon_arrow-down-thin"></span></div></button></div> <a 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