Data Visualization using Matplotlib in Python

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In this article, we will provide a guide on Matplotlib and how to use it for data visualization with practical implementation.Data Visualization using MatplotlibMatplotlib offers a wide variety of plots such as line charts,", "about": [ { "@type": "Thing", "name": "PythonMatplotlib" }, { "@type": "Thing", "name": "RDataVisualization" }, { "@type": "Thing", "name": "AiMlDsPython" }, { "@type": "Thing", "name": "AiMlDs" }, { "@type": "Thing", "name": "PythonDataVisualization" } ] }</script> <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "WebSite", "name": "GeeksforGeeks", "url": "https://www.geeksforgeeks.org/", "potentialAction": { "@type": "SearchAction", "target": "https://www.geeksforgeeks.org/search/{search_term_string}/", "query-input": "required name=search_term_string" } }</script> <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "Organization", "name": "GeeksforGeeks", "url": "https://www.geeksforgeeks.org/", "logo": "https://media.geeksforgeeks.org/wp-content/cdn-uploads/20200817185016/gfg_complete_logo_2x-min.png", "description": "A computer science portal for geeks. 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The figure class, on the other hand, is a container for all plot elements. It serves as the canvas on which plots are drawn and can contain multiple Axes objects for complex visualizations." } }, { "@type": "Question", "name": "Can Matplotlib be used for interactive visualizations?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, Matplotlib supports interactive visualizations through integration with interactive backends and libraries like Jupyter Notebook. It allows for zooming, panning, and updating plots dynamically. 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Matplotlib can efficiently handle large datasets by leveraging NumPy arrays. You can also use techniques like downsampling data, using vectorized operations, and avoiding unnecessary plot elements to improve performance. Additionally, consider using other libraries like Seaborn or Plotly for more complex visualizations" } }, { "@type": "Question", "name": "Can I create interactive plots with Matplotlib?", "acceptedAnswer": { "@type": "Answer", "text": "While Matplotlib is primarily used for static plots, it does support some interactivity through its integration with interactive backends and Jupyter Notebook. You can use the %matplotlib notebook magic command in Jupyter to enable interactive features like zooming and panning. For more advanced interactivity, consider using libraries like Plotly or Bokeh that are designed for creating interactive visualizations" } } ] }</script> <script> var arrPostCat = new Array(); var arrPostCatName="AIMLDS"; arrPostCat.push('9172'); arrPostCat.push('7666'); var tIds = "9172,7666,9218,9230,3248"; var termsNames = "AIMLDS,DataVisualization,AIMLDSWithPython,PythonDataVisualization,Pythonmatplotlib"; var tIdsInclusiveParents = "9172,7666,9218,9230,3248"; var domain = 1; var arrPost = new Array(); var post_id = "649085"; var post_type = "post"; var post_slug = window.location.href; var ip = "64.252.70.180"; var post_title = `Data Visualization using Matplotlib in Python`; var post_status = "publish"; var practiceAPIURL="https://practiceapi.geeksforgeeks.org/"; var practiceURL="https://practice.geeksforgeeks.org/"; var post_date = "2021-07-22 22:40:46"; var commentSysUrl = "https://discuss.geeksforgeeks.org/commentEmbedV2.js"; //var postAdApiUrlString = ""; var 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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 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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"><span>Matplotlib is a powerful and widely-used Python library for creating static, animated and interactive data visualizations. In this article, we will provide a guide on Matplotlib and how to use it for data visualization with practical implementation.</span></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib.webp" alt="Data-Visualization-with-Matplotlib" width="1000" height="470" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib.webp 1000w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib-100.webp 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib-200.webp 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib-300.webp 300w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib-660.webp 660w,https://media.geeksforgeeks.org/wp-content/uploads/20240826131400/Data-Visualization-with-Matplotlib-768.webp 768w"><p class="wp-caption-text">Data Visualization using Matplotlib</p></div><p dir="ltr"><a href="https://www.geeksforgeeks.org/python-introduction-matplotlib/" target="_blank" rel="noopener"><span>Matplotlib</span></a><span> offers a wide variety of plots such as line charts, bar charts, scatter plot and histograms making it versatile for different data analysis tasks. The library is built on top of </span><a href="https://www.geeksforgeeks.org/numpy-tutorial/" target="_blank" rel="noopener"><span>NumPy</span></a><span> making it efficient for handling large datasets. It provides a lot of flexibility in code.</span></p><div id="GFG_AD_gfg_mobile_336x280" style="margin: 5px 0;"></div> <h2 style="text-align:left"><span>Installing </span><b><strong>Matplotlib</strong></b><span> for Data Visualization</span></h2><p dir="ltr" style="text-align: justify;"><span>We will use the pip command to install this module. If you do not have pip installed then refer to the article, </span><a href="https://www.geeksforgeeks.org/download-and-install-pip-latest-version/" rel="noopener"><b><strong>Download and install pip Latest Version.</strong></b></a></p> <p dir="ltr" style="text-align: justify;"><span>To install Matplotlib type the below command in the terminal.</span></p> <pre><span>pip install matplotlib</span></pre><p dir="ltr"><span>If you are using Jupyter Notebook, you can install it within a notebook cell by using:</span></p> <pre><span>!pip install matplotlib</span></pre><h2 style="text-align:left"><span>Data Visualization with Pyplot using Matplotlib</span></h2><p dir="ltr"><span>Matplotlib provides a module called </span><a href="https://www.geeksforgeeks.org/pyplot-in-matplotlib/" target="_blank" rel="noopener"><span>pyplot</span></a><span> which offers a </span><b><strong>MATLAB-like interface</strong></b><span> for creating plots and charts. It simplifies the process of generating various types of visualizations by providing a collection of functions that handle common plotting tasks. </span></p> <p dir="ltr" style="text-align: justify;"><span>Matplotlib supports a variety of plots including line charts, bar charts, histograms, scatter plots, etc. Let’s understand them with implementation using pyplot.</span></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> <h3 id="1-line-chart" style="text-align:left"><span>1. Line Chart</span></h3><p dir="ltr" style="text-align: justify;"><a href="https://www.geeksforgeeks.org/line-chart-in-matplotlib-python/" target="_blank" rel="noopener"><b><strong>Line chart </strong></b></a><span>is one of the basic plots and can be created using the </span><a href="https://www.geeksforgeeks.org/matplotlib-pyplot-plot-function-in-python/" rel="noopener"><b><strong>plot()</strong></b></a><span> function. It is used to represent a relationship between two data X and Y on a different axis.</span></p><div id="_GFG_ABP_Incontent_728x90" style="text-align:center;"></div> <p dir="ltr" style="text-align: justify;"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</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=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s2>"Line Chart"</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>'Y-Axis'</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>'X-Axis'</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611205557/linechartmatplotlib.png" alt="line chart matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="2-bar-chart" style="text-align:left"><span>2. Bar Chart</span></h3><p dir="ltr"><b><strong>A </strong></b><a href="https://www.geeksforgeeks.org/bar-plot-in-matplotlib/" target="_blank" rel="noopener"><b><strong>bar chart</strong></b></a><b><strong> is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent.</strong></b><span> The bar plots can be plotted horizontally or vertically. A bar chart describes the comparisons between the different categories. It can be created using the bar() method.</span></p> <p dir="ltr"><span>In the below example we will use the </span><a href="https://media.geeksforgeeks.org/wp-content/uploads/20250116111902283403/tip.csv" target="_blank" rel="noopener"><span>tips dataset</span></a><span>. Tips database is the record of the tip given by the customers in a restaurant for two and a half months in the early 1990s. It contains 6 columns as total_bill, tip, sex, smoker, day, time, size.</span></p><div style="text-align:center;margin:20px 0; max-height: 280px;" id="GFG_AD_gfg_outstream_incontent"></div> <p dir="ltr"><b><strong>Example: </strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>x</span> <span class=o>=</span> <span class=n>data</span><span class=p>[</span><span class=s1>'day'</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=n>data</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>bar</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=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s2>"Tips Dataset"</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>'Total Bill'</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>'Day'</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611220514/barchatmatplotlib.png" alt="bar chat matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="3-histogram" style="text-align:left"><span>3. Histogram</span></h3><p dir="ltr"><span>A </span><a href="https://www.geeksforgeeks.org/plotting-histogram-in-python-using-matplotlib/" target="_blank" rel="noopener"><b><strong>histogram</strong></b></a><span> is basically used to represent data provided in a form of some groups. It is a type of bar plot where the X-axis represents the bin ranges while the Y-axis gives information about frequency. The </span><b><strong>hist()</strong></b><span> function is used to compute and create histogram of x.</span></p> <p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>x</span> <span class=o>=</span> <span class=n>data</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>hist</span><span class=p>(</span><span class=n>x</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=s2>"Tips Dataset"</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>'Frequency'</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>show</span><span class=p>()</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611222134/hostogrammatplotlib.png" alt="hostogram matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="4-scatter-plot" style="text-align:left"><span>4. Scatter Plot</span></h3><p dir="ltr"><a href="https://www.geeksforgeeks.org/matplotlib-pyplot-scatter-in-python/" target="_blank" rel="noopener"><b><strong>Scatter plots</strong></b><span> </span></a><span>are used to observe relationships between variables. The </span><b><strong>scatter()</strong></b><span> method in the matplotlib library is used to draw a scatter plot.</span></p> <p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>x</span> <span class=o>=</span> <span class=n>data</span><span class=p>[</span><span class=s1>'day'</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=n>data</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>scatter</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=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s2>"Tips Dataset"</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>'Total Bill'</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>'Day'</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="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-1"></div> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611225805/scatterplotmatplotplib.png" alt="scatter plot matplotplib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="5-pie-chart" style="text-align:left"><span>5. Pie Chart</span></h3><p dir="ltr"><a href="https://www.geeksforgeeks.org/plot-a-pie-chart-in-python-using-matplotlib/" target="_blank" rel="noopener"><b><strong>Pie chart</strong></b></a><b><strong> </strong></b><span>is a circular chart used to display only one series of data. The area of slices of the pie represents the percentage of the parts of the data. The slices of pie are called wedges. It can be created using the</span><b><strong> pie()</strong></b><span> method.</span></p> <p dir="ltr"><b><strong>Syntax:</strong></b></p> <blockquote><p dir="ltr"><span>matplotlib.pyplot.pie(data, explode=None, labels=None, colors=None, autopct=None, shadow=False)</span></p> </blockquote><p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>cars</span> <span class=o>=</span> <span class=p>[</span><span class=s1>'AUDI'</span><span class=p>,</span> <span class=s1>'BMW'</span><span class=p>,</span> <span class=s1>'FORD'</span><span class=p>,</span> <span class=s1>'TESLA'</span><span class=p>,</span> <span class=s1>'JAGUAR'</span><span class=p>,]</span> <span class=n>data</span> <span class=o>=</span> <span class=p>[</span><span class=mi>23</span><span class=p>,</span> <span class=mi>10</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>15</span><span class=p>,</span> <span class=mi>12</span><span class=p>]</span> <span class=n>plt</span><span class=o>.</span><span class=n>pie</span><span class=p>(</span><span class=n>data</span><span class=p>,</span> <span class=n>labels</span><span class=o>=</span><span class=n>cars</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=s2>"Car data"</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611232147/piechartmatplotlib.png" alt="pie chart matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="6-box-plot" style="text-align:left"><span>6. Box Plot</span></h3><p dir="ltr"><span>A </span><a href="https://www.geeksforgeeks.org/box-plot-in-python-using-matplotlib/" target="_blank" rel="noopener"><span>Box Plot</span></a><span> is also known as a Whisker Plot and is a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median (Q2), third quartile (Q3) and maximum. It can also show outliers.Let’s see an example of how to create a Box Plot using Matplotlib in Python:</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>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=n>np</span><span class=o>.</span><span class=n>random</span><span class=o>.</span><span class=n>seed</span><span class=p>(</span><span class=mi>10</span><span class=p>)</span> <span class=n>data</span> <span class=o>=</span> <span class=p>[</span><span class=n>np</span><span class=o>.</span><span class=n>random</span><span class=o>.</span><span class=n>normal</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=n>std</span><span class=p>,</span> <span class=mi>100</span><span class=p>)</span> <span class=k>for</span> <span class=n>std</span> <span class=ow>in</span> <span class=nb>range</span><span class=p>(</span><span class=mi>1</span><span class=p>,</span> <span class=mi>4</span><span class=p>)]</span> <span class=c1># Create a box plot</span> <span class=n>plt</span><span class=o>.</span><span class=n>boxplot</span><span class=p>(</span><span class=n>data</span><span class=p>,</span> <span class=n>vert</span><span class=o>=</span><span class=kc>True</span><span class=p>,</span> <span class=n>patch_artist</span><span class=o>=</span><span class=kc>True</span><span class=p>,</span> <span class=n>boxprops</span><span class=o>=</span><span class=nb>dict</span><span class=p>(</span><span class=n>facecolor</span><span class=o>=</span><span class=s1>'skyblue'</span><span class=p>),</span> <span class=n>medianprops</span><span class=o>=</span><span class=nb>dict</span><span class=p>(</span><span class=n>color</span><span class=o>=</span><span class=s1>'red'</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>'Data Set'</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>'Values'</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>'Example of Box Plot'</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"><span>Output:</span></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240817153111/boxplot.png" alt="boxplot" width="565" height="455" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240817153111/boxplot.png 565w,https://media.geeksforgeeks.org/wp-content/uploads/20240817153111/boxplot-100.png 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240817153111/boxplot-200.png 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240817153111/boxplot-300.png 300w" loading="lazy"><p class="wp-caption-text">Box Plot in Python using Matplotlib</p></div><p dir="ltr"><b><strong>Explanation:</strong></b></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><code><strong>plt.boxplot(data)</strong></code></b><span>: Creates the box plot</span><b><strong>.</strong></b><span> The </span><code><span>vert=True</span></code><span> argument makes the plot vertical, and </span><code><span>patch_artist=True</span></code><span> fills the box with color.</span></li><li value="2"><b><code><strong>boxprops</strong></code></b><b><strong> and </strong></b><b><code><strong>medianprops</strong></code></b><span>: Customize the appearance of the boxes and median lines respectively.</span></li></ul><p dir="ltr"><span>The box shows the </span><b><strong>interquartile range (IQR)</strong></b><span> the line inside the box shows the median and the “whiskers” extend to the minimum and maximum values within 1.5 * IQR from the first and third quartiles. Any points outside this range are considered outliers and are plotted as individual points.</span></p> <h3 id="7-heatmap" style="text-align:left"><span>7. Heatmap</span></h3><p dir="ltr"><span>A </span><a href="https://www.geeksforgeeks.org/generate-a-heatmap-in-matplotlib-using-a-scatter-dataset/" target="_blank" rel="noopener"><span>Heatmap</span></a><span> is a data visualization technique that represents data in a matrix form where individual values are represented as colors. Heatmaps are particularly useful for visualizing the magnitude of multiple features in a two-dimensional surface and identifying patterns, correlations and concentrations. Let’s see an example of how to create a Heatmap using Matplotlib in Python:</span></p> <gfg-tabs data-run-ide="true" 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>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=n>np</span><span class=o>.</span><span class=n>random</span><span class=o>.</span><span class=n>seed</span><span class=p>(</span><span class=mi>0</span><span class=p>)</span> <span class=n>data</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>random</span><span class=o>.</span><span class=n>rand</span><span class=p>(</span><span class=mi>10</span><span class=p>,</span> <span class=mi>10</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>imshow</span><span class=p>(</span><span class=n>data</span><span class=p>,</span> <span class=n>cmap</span><span class=o>=</span><span class=s1>'viridis'</span><span class=p>,</span> <span class=n>interpolation</span><span class=o>=</span><span class=s1>'nearest'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>colorbar</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>'X-axis Label'</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>'Y-axis Label'</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>'Example of Heatmap'</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"><span>Output:</span></p> <div style="width: 810px" class="wp-caption alignnone"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20240817153628/heatmap.png" alt="heatmap" width="502" height="455" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20240817153628/heatmap.png 502w,https://media.geeksforgeeks.org/wp-content/uploads/20240817153628/heatmap-100.png 100w,https://media.geeksforgeeks.org/wp-content/uploads/20240817153628/heatmap-200.png 200w,https://media.geeksforgeeks.org/wp-content/uploads/20240817153628/heatmap-300.png 300w" loading="lazy"><p class="wp-caption-text">Heatmap with matplotlib</p></div><p dir="ltr"><b><strong>Explanation:</strong></b></p> <ul><li value="1"><b><code><strong>plt.imshow(data, cmap='viridis')</strong></code></b><span>: Displays the data as an image (heatmap). The </span><code><span>cmap='viridis'</span></code><span> argument specifies the color map used for the heatmap.</span></li><li value="2"><b><code><strong>interpolation='nearest'</strong></code></b><span>: Ensures that each data point is shown as a block of color without smoothing.</span></li></ul><p dir="ltr"><span>The color bar on the side provides a scale to interpret the colors with darker colors representing lower values and lighter colors representing higher values. This type of plot is often used in fields like data analysis, bioinformatics and finance to visualize data correlations and distributions across a matrix.</span></p> <h2 id="enhancing-the-plots-a-guide-to-matplotlib-customization-and-styling" style="text-align:left"><span> Matplotlib Customization and Styling</span></h2><p dir="ltr"><span>Matplotlib allows extensive </span><a href="https://www.geeksforgeeks.org/python-matplotlib-an-overview/" target="_blank" rel="noopener"><span>customization and styling </span></a><span>of plots including changing colors, adding labels and modifying plot styles.</span></p> <p dir="ltr"><span>Let鈥檚 apply the customization techniques we鈥檝e learned to the basic plots we created earlier. This will involve enhancing each plot with titles, axis labels, limits, tick labels, and legends to make them more informative and visually appealing.</span></p> <h3 id="-customizing-line-chart" style="text-align:left"><b><strong>1. Customizing Line Chart</strong></b></h3><p dir="ltr"><span>Let’s see how to customize the line chart. We will be using the following properties:</span></p> <ul><li value="1"><b><strong>color: </strong></b><span>Changing the color of the line</span></li><li value="2"><b><strong>linewidth: </strong></b><span>Customizing the width of the line</span></li><li value="3"><b><strong>marker: </strong></b><span>For changing the style of actual plotted point</span></li><li value="4"><b><strong>markersize: </strong></b><span>For changing the size of the markers</span></li><li value="5"><b><strong>linestyle: </strong></b><span>For defining the style of the plotted line</span></li></ul><p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</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=n>color</span><span class=o>=</span><span class=s1>'green'</span><span class=p>,</span> <span class=n>linewidth</span><span class=o>=</span><span class=mi>3</span><span class=p>,</span> <span class=n>marker</span><span class=o>=</span><span class=s1>'o'</span><span class=p>,</span> <span class=n>markersize</span><span class=o>=</span><span class=mi>15</span><span class=p>,</span> <span class=n>linestyle</span><span class=o>=</span><span class=s1>'--'</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=s2>"Line Chart"</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>'Y-Axis'</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>'X-Axis'</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="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-3"></div> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611210837/styledlinechartmatplotlib.png" alt="styled line chart matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="-customizing-bar-chart" style="text-align:left"><b><strong>2. Customizing Bar Chart</strong></b></h3><p dir="ltr"><span>To make bar charts more informative and visually appealing various customization options are available. Customization that is available for the Bar Chart are: </span></p> <ul><li value="1"><b><strong>color: </strong></b><span>For the bar faces</span></li><li value="2"><b><strong>edgecolor: </strong></b><span>Color of edges of the bar</span></li><li value="3"><b><strong>linewidth: </strong></b><span>Width of the bar edges</span></li><li value="4"><b><strong>width: </strong></b><span>Width of the bar</span></li></ul><p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>x</span> <span class=o>=</span> <span class=n>data</span><span class=p>[</span><span class=s1>'day'</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=n>data</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>bar</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=n>color</span><span class=o>=</span><span class=s1>'green'</span><span class=p>,</span> <span class=n>edgecolor</span><span class=o>=</span><span class=s1>'blue'</span><span class=p>,</span> <span class=n>linewidth</span><span class=o>=</span><span class=mi>2</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=s2>"Tips Dataset"</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>'Total Bill'</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>'Day'</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611221433/styledbarcchartmatplotlib.png" alt="styled barc chart matplotlib" width="inherit" height="inherit" loading="lazy"></p> <p dir="ltr"><b><strong>The lines in between the bars refer to the different values in the Y-axis of the particular value of the X-axis.</strong></b></p> <h3 id="-customizing-histogram-plot" style="text-align:left"><b><strong>3. Customizing Histogram Plot</strong></b></h3><p dir="ltr"><span>To make histogram plots more effective and tailored to your data, you can apply various customizations:</span></p> <ul><li value="1"><b><strong>bins: </strong></b><span>Number of equal-width bins </span></li><li value="2"><b><strong>color: </strong></b><span>For changing the face color</span></li><li value="3"><b><strong>edgecolor: </strong></b><span>Color of the edges</span></li><li value="4"><b><strong>linestyle: </strong></b><span>For the edgelines</span></li><li value="5"><b><strong>alpha:</strong></b><span> blending value, between 0 (transparent) and 1 (opaque)</span></li></ul><p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>x</span> <span class=o>=</span> <span class=n>data</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>hist</span><span class=p>(</span><span class=n>x</span><span class=p>,</span> <span class=n>bins</span><span class=o>=</span><span class=mi>25</span><span class=p>,</span> <span class=n>color</span><span class=o>=</span><span class=s1>'green'</span><span class=p>,</span> <span class=n>edgecolor</span><span class=o>=</span><span class=s1>'blue'</span><span class=p>,</span> <span class=n>linestyle</span><span class=o>=</span><span class=s1>'--'</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=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s2>"Tips Dataset"</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>'Frequency'</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>show</span><span class=p>()</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611224505/matplotlibhistogramwithstyle.png" alt="matplotlib histogram with style" width="inherit" height="inherit" loading="lazy"></p><div style="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-4"></div> <h3 id="-customizing-scatter-plot" style="text-align:left"><b><strong>4. Customizing Scatter Plot</strong></b></h3><p dir="ltr"><span>Scatter plots are versatile tools for visualizing relationships between two variables. Customizations that are available for the scatter plot are to enhance their clarity and effectiveness:</span></p> <ul><li value="1"><b><strong>s:</strong></b><span> marker size (can be scalar or array of size equal to size of x or y)</span></li><li value="2"><b><strong>c:</strong></b><span> color of sequence of colors for markers</span></li><li value="3"><b><strong>marker:</strong></b><span> marker style</span></li><li value="4"><b><strong>linewidths:</strong></b><span> width of marker border</span></li><li value="5"><b><strong>edgecolor:</strong></b><span> marker border color</span></li><li value="6"><b><strong>alpha:</strong></b><span> blending value, between 0 (transparent) and 1 (opaque)</span></li></ul><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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>x</span> <span class=o>=</span> <span class=n>data</span><span class=p>[</span><span class=s1>'day'</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=n>data</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>scatter</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=n>c</span><span class=o>=</span><span class=n>data</span><span class=p>[</span><span class=s1>'size'</span><span class=p>],</span> <span class=n>s</span><span class=o>=</span><span class=n>data</span><span class=p>[</span><span class=s1>'total_bill'</span><span class=p>],</span> <span class=n>marker</span><span class=o>=</span><span class=s1>'D'</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=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s2>"Tips Dataset"</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>'Total Bill'</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>'Day'</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611230703/stylehistogrammatplotlib.png" alt="style histogram matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="-customizing-pie-chart" style="text-align:left"><b><strong>5. Customizing Pie Chart</strong></b></h3><p dir="ltr"><span>Pie charts are a great way to visualize proportions and parts of a whole. To make your pie charts more effective and visually appealing, consider the following customization techniques:</span></p> <ul><li value="1"><b><strong>explode: </strong></b><span>Moving the wedges of the plot</span></li><li value="2"><b><strong>autopct: </strong></b><span>Label the wedge with their numerical value.</span></li><li value="3"><b><strong>color:</strong></b><span> Attribute is used to provide color to the wedges.</span></li><li value="4"><b><strong>shadow:</strong></b><span> Used to create shadow of wedge.</span></li></ul><p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>import</span> <span class=nn>pandas</span> <span class=k>as</span> <span class=nn>pd</span> <span class=n>data</span> <span class=o>=</span> <span class=n>pd</span><span class=o>.</span><span class=n>read_csv</span><span class=p>(</span><span class=s1>'tips.csv'</span><span class=p>)</span> <span class=n>cars</span> <span class=o>=</span> <span class=p>[</span><span class=s1>'AUDI'</span><span class=p>,</span> <span class=s1>'BMW'</span><span class=p>,</span> <span class=s1>'FORD'</span><span class=p>,</span> <span class=s1>'TESLA'</span><span class=p>,</span> <span class=s1>'JAGUAR'</span><span class=p>,]</span> <span class=n>data</span> <span class=o>=</span> <span class=p>[</span><span class=mi>23</span><span class=p>,</span> <span class=mi>13</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>15</span><span class=p>,</span> <span class=mi>12</span><span class=p>]</span> <span class=n>explode</span> <span class=o>=</span> <span class=p>[</span><span class=mf>0.1</span><span class=p>,</span> <span class=mf>0.5</span><span class=p>,</span> <span class=mi>0</span><span class=p>,</span> <span class=mi>0</span><span class=p>,</span> <span class=mi>0</span><span class=p>]</span> <span class=n>colors</span> <span class=o>=</span> <span class=p>(</span> <span class=s2>"orange"</span><span class=p>,</span> <span class=s2>"cyan"</span><span class=p>,</span> <span class=s2>"yellow"</span><span class=p>,</span> <span class=s2>"grey"</span><span class=p>,</span> <span class=s2>"green"</span><span class=p>,)</span> <span class=n>plt</span><span class=o>.</span><span class=n>pie</span><span class=p>(</span><span class=n>data</span><span class=p>,</span> <span class=n>labels</span><span class=o>=</span><span class=n>cars</span><span class=p>,</span> <span class=n>explode</span><span class=o>=</span><span class=n>explode</span><span class=p>,</span> <span class=n>autopct</span><span class=o>=</span><span class=s1>'</span><span class=si>%1.2f%%</span><span class=s1>'</span><span class=p>,</span> <span class=n>colors</span><span class=o>=</span><span class=n>colors</span><span class=p>,</span> <span class=n>shadow</span><span class=o>=</span><span class=kc>True</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611233326/stylepieplotmatplotlib.png" alt="style pie plot matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h2 id="working-with-matplotlibs-core-components-figures-and-axes" style="text-align:left"><span>Matplotlib鈥檚 Core Components: Figures and Axes</span></h2><p dir="ltr" style="text-align: justify;"><span>Before moving any further with Matplotlib let’s discuss some important classes that will be used further in the tutorial. These classes are:</span></p> <ul><li value="1"><b><strong>Figure</strong></b></li><li value="2"><b><strong>Axes</strong></b></li></ul><h3 id="1-figure-class" style="text-align:left"><span>1. Figure class</span></h3><p dir="ltr"><span>Consider the</span><a href="https://www.geeksforgeeks.org/matplotlib-figure-figure-in-python/" target="_blank" rel="noopener"><span> figure class</span></a><span> as the overall window or page on which everything is drawn. It is a top-level container that contains one or more axes. A figure can be created using the </span><a href="https://www.geeksforgeeks.org/matplotlib-pyplot-figure-in-python/" target="_blank" rel="noopener"><b><strong>figure()</strong></b></a><span> method.</span></p> <p dir="ltr"><b><strong>Syntax:</strong></b></p><div style="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-5"></div> <blockquote><p dir="ltr"><span>class matplotlib.figure.Figure(figsize=None, dpi=None, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None, constrained_layout=None)</span></p> </blockquote><p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>from</span> <span class=nn>matplotlib.figure</span> <span class=kn>import</span> <span class=n>Figure</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</span> <span class=c1># Creating a new figure with width = 7 inches</span> <span class=c1># and height = 5 inches with face color as </span> <span class=c1># green, edgecolor as red and the line width</span> <span class=c1># of the edge as 7</span> <span class=n>fig</span> <span class=o>=</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>7</span><span class=p>,</span> <span class=mi>5</span><span class=p>),</span> <span class=n>facecolor</span><span class=o>=</span><span class=s1>'g'</span><span class=p>,</span> <span class=n>edgecolor</span><span class=o>=</span><span class=s1>'b'</span><span class=p>,</span> <span class=n>linewidth</span><span class=o>=</span><span class=mi>7</span><span class=p>)</span> <span class=n>ax</span> <span class=o>=</span> <span class=n>fig</span><span class=o>.</span><span class=n>add_axes</span><span class=p>([</span><span class=mi>1</span><span class=p>,</span> <span class=mi>1</span><span class=p>,</span> <span class=mi>1</span><span class=p>,</span> <span class=mi>1</span><span class=p>])</span> <span class=n>ax</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=n>plt</span><span class=o>.</span><span class=n>title</span><span class=p>(</span><span class=s2>"Linear graph"</span><span class=p>,</span> <span class=n>fontsize</span><span class=o>=</span><span class=mi>25</span><span class=p>,</span> <span class=n>color</span><span class=o>=</span><span class=s2>"yellow"</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>'Y-Axis'</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>'X-Axis'</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>ylim</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>80</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>xticks</span><span class=p>(</span><span class=n>x</span><span class=p>,</span> <span class=n>labels</span><span class=o>=</span><span class=p>[</span><span class=s2>"one"</span><span class=p>,</span> <span class=s2>"two"</span><span class=p>,</span> <span class=s2>"three"</span><span class=p>,</span> <span class=s2>"four"</span><span class=p>])</span> <span class=n>plt</span><span class=o>.</span><span class=n>legend</span><span class=p>([</span><span class=s2>"GFG"</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210610224155/matplotlibfigureclass.png" alt="matplotlib figure class" width="inherit" height="inherit" loading="lazy"></p> <h3 id="2-axes-class" style="text-align:left"><span>2. Axes Class</span></h3><p dir="ltr" style="text-align: justify;"><a href="https://www.geeksforgeeks.org/matplotlib-axes-class/" target="_blank" rel="noopener"><b><strong>Axes class</strong></b><span> </span></a><span>is the most basic and flexible unit for creating sub-plots. A given figure may contain many axes, but a given axes can only be present in one figure. The </span><b><strong>axes() </strong></b><span>function creates the axes object. </span></p> <p dir="ltr"><b><strong>Syntax:</strong></b></p> <blockquote><p dir="ltr"><span>axes([left, bottom, width, height])</span></p> </blockquote><p dir="ltr" style="text-align: justify;"><span>Just like pyplot class, axes class also provides methods for adding titles, legends, limits, labels, etc. Let’s see a few of them – </span></p> <ul><li value="1"><span> ax.set_title() is used to add title.</span></li><li value="2"><span>To Adding X Label and Y label </span><b><strong>–</strong></b><span> ax.set_xlabel(), ax.set_ylabel()</span></li><li value="3"><span>To set the limits we use ax.set_xlim(), ax.set_ylim()</span></li><li value="4"><span>ax.set_xticklabels(), ax.set_yticklabels() are used to tick labels.</span></li><li value="5"><span>To add legend we use ax.legend()</span></li></ul><p dir="ltr" style="text-align: justify;"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>from</span> <span class=nn>matplotlib.figure</span> <span class=kn>import</span> <span class=n>Figure</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</span> <span class=n>fig</span> <span class=o>=</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>5</span><span class=p>,</span> <span class=mi>4</span><span class=p>))</span> <span class=n>ax</span> <span class=o>=</span> <span class=n>fig</span><span class=o>.</span><span class=n>add_axes</span><span class=p>([</span><span class=mi>1</span><span class=p>,</span> <span class=mi>1</span><span class=p>,</span> <span class=mi>1</span><span class=p>,</span> <span class=mi>1</span><span class=p>])</span> <span class=n>ax1</span> <span class=o>=</span> <span class=n>ax</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=n>ax2</span> <span class=o>=</span> <span class=n>ax</span><span class=o>.</span><span class=n>plot</span><span class=p>(</span><span class=n>y</span><span class=p>,</span> <span class=n>x</span><span class=p>)</span> <span class=n>ax</span><span class=o>.</span><span class=n>set_title</span><span class=p>(</span><span class=s2>"Linear Graph"</span><span class=p>)</span> <span class=n>ax</span><span class=o>.</span><span class=n>set_xlabel</span><span class=p>(</span><span class=s2>"X-Axis"</span><span class=p>)</span> <span class=n>ax</span><span class=o>.</span><span class=n>set_ylabel</span><span class=p>(</span><span class=s2>"Y-Axis"</span><span class=p>)</span> <span class=n>ax</span><span class=o>.</span><span class=n>legend</span><span class=p>(</span><span class=n>labels</span> <span class=o>=</span> <span class=p>(</span><span class=s1>'line 1'</span><span class=p>,</span> <span class=s1>'line 2'</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" style="text-align: justify;"><b><strong>Output:</strong></b></p><div style="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-6"></div> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210610230025/axesclassmatplotlib.png" alt="axes class matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h2 id="advanced-plotting-techniques-for-multiple-subplots" style="text-align:left"><span>Advanced Plotting: Techniques for Visualizing Subplots</span></h2><p dir="ltr" style="text-align: justify;"><span>We have learned about the basic components of a graph that can be added so that it can convey more information. One method can be by calling the plot function again and again with a different set of values as shown in the above example. Now let’s see how to plot multiple graphs using some functions and also how to plot subplots. </span></p> <h3 id="method-1-using-the-add_axes-method" style="text-align:left"><b><strong>Method 1: </strong></b><span>Using the add_axes() method </span></h3><p dir="ltr"><span>The </span><a href="https://www.geeksforgeeks.org/matplotlib-figure-figure-add_axes-in-python/" target="_blank" rel="noopener"><span>add_axes() method</span></a><span> method allows you to manually add axes to a figure in Matplotlib. It takes a list of four values </span><code><span>[left, bottom, width, height]</span></code><span> to specify the position and size of the axes.</span></p> <p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=kn>from</span> <span class=nn>matplotlib.figure</span> <span class=kn>import</span> <span class=n>Figure</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</span> <span class=c1># Creating a new figure with width = 5 inches</span> <span class=c1># and height = 4 inches</span> <span class=n>fig</span> <span class=o>=</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>5</span><span class=p>,</span> <span class=mi>4</span><span class=p>))</span> <span class=n>ax1</span> <span class=o>=</span> <span class=n>fig</span><span class=o>.</span><span class=n>add_axes</span><span class=p>([</span><span class=mf>0.1</span><span class=p>,</span> <span class=mf>0.1</span><span class=p>,</span> <span class=mf>0.8</span><span class=p>,</span> <span class=mf>0.8</span><span class=p>])</span> <span class=n>ax2</span> <span class=o>=</span> <span class=n>fig</span><span class=o>.</span><span class=n>add_axes</span><span class=p>([</span><span class=mi>1</span><span class=p>,</span> <span class=mf>0.1</span><span class=p>,</span> <span class=mf>0.8</span><span class=p>,</span> <span class=mf>0.8</span><span class=p>])</span> <span class=n>ax1</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=n>ax2</span><span class=o>.</span><span class=n>plot</span><span class=p>(</span><span class=n>y</span><span class=p>,</span> <span class=n>x</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> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210610231128/addaxesmatplotlib.png" alt="add_axes matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h3 id="method-2-using-subplot-method" style="text-align:left"><b><strong>Method 2:</strong></b><span> Using subplot() method</span></h3><p dir="ltr"><a href="https://www.geeksforgeeks.org/matplotlib-pyplot-subplot-function-in-python/" target="_blank" rel="noopener"><span>subplot()</span></a><span> method adds a plot to a specified grid position within the current figure. It takes three arguments: the number of rows, columns, and the plot index. Now Let’s understand it with the help of example:</span></p> <p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</span> <span class=n>plt</span><span class=o>.</span><span class=n>figure</span><span class=p>()</span> <span class=n>plt</span><span class=o>.</span><span class=n>subplot</span><span class=p>(</span><span class=mi>121</span><span class=p>)</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=n>plt</span><span class=o>.</span><span class=n>subplot</span><span class=p>(</span><span class=mi>122</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>plot</span><span class=p>(</span><span class=n>y</span><span class=p>,</span> <span class=n>x</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210610231353/subplotmatplotlib.png" alt="subplot matplotlib" width="inherit" height="inherit" loading="lazy"></p><div style="text-align:center;margin:20px 0;max-height:280px" id="GFG_AD_gfg_outstream_incontent-7"></div> <h3 id="method-4-using-subplot2grid-method" style="text-align:left"><b><strong>Method 4:</strong></b><span> Using subplot2grid() method</span></h3><p dir="ltr"><span>The</span><a href="https://www.geeksforgeeks.org/matplotlib-pyplot-subplot2grid-in-python/" target="_blank" rel="noopener"><span> subplot2grid() </span></a><span>creates axes object at a specified location inside a grid and also helps in spanning the axes object across multiple rows or columns. In simpler words, this function is used to create multiple charts within the same figure.</span></p> <p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=n>x</span> <span class=o>=</span> <span class=p>[</span><span class=mi>10</span><span class=p>,</span> <span class=mi>20</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>40</span><span class=p>]</span> <span class=n>y</span> <span class=o>=</span> <span class=p>[</span><span class=mi>20</span><span class=p>,</span> <span class=mi>25</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>55</span><span class=p>]</span> <span class=n>axes1</span> <span class=o>=</span> <span class=n>plt</span><span class=o>.</span><span class=n>subplot2grid</span> <span class=p>(</span> <span class=p>(</span><span class=mi>7</span><span class=p>,</span> <span class=mi>1</span><span class=p>),</span> <span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>0</span><span class=p>),</span> <span class=n>rowspan</span> <span class=o>=</span> <span class=mi>2</span><span class=p>,</span> <span class=n>colspan</span> <span class=o>=</span> <span class=mi>1</span><span class=p>)</span> <span class=n>axes2</span> <span class=o>=</span> <span class=n>plt</span><span class=o>.</span><span class=n>subplot2grid</span> <span class=p>(</span> <span class=p>(</span><span class=mi>7</span><span class=p>,</span> <span class=mi>1</span><span class=p>),</span> <span class=p>(</span><span class=mi>2</span><span class=p>,</span> <span class=mi>0</span><span class=p>),</span> <span class=n>rowspan</span> <span class=o>=</span> <span class=mi>2</span><span class=p>,</span> <span class=n>colspan</span> <span class=o>=</span> <span class=mi>1</span><span class=p>)</span> <span class=n>axes1</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=n>axes2</span><span class=o>.</span><span class=n>plot</span><span class=p>(</span><span class=n>y</span><span class=p>,</span> <span class=n>x</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210610232023/subplot2gridmatplotlib.png" alt="subplot2grid matplotlib" width="inherit" height="inherit" loading="lazy"></p> <h2 id="saving-the-matplotlib-plots-using-savefig" style="text-align:left"><span>Saving the Matplotlib Plots Using </span><code><span>savefig()</span></code></h2><p dir="ltr"><span>For </span><a href="https://www.geeksforgeeks.org/how-to-save-a-plot-to-a-file-using-matplotlib/" target="_blank" rel="noopener"><span>saving a plot in </span></a><span>a file on storage disk, savefig() method is used. A file can be saved in many formats like .png, .jpg, .pdf, etc.</span></p> <p dir="ltr"><b><strong>Example:</strong></b></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>matplotlib.pyplot</span> <span class=k>as</span> <span class=nn>plt</span> <span class=n>year</span> <span class=o>=</span> <span class=p>[</span><span class=s1>'2010'</span><span class=p>,</span> <span class=s1>'2002'</span><span class=p>,</span> <span class=s1>'2004'</span><span class=p>,</span> <span class=s1>'2006'</span><span class=p>,</span> <span class=s1>'2008'</span><span class=p>]</span> <span class=n>production</span> <span class=o>=</span> <span class=p>[</span><span class=mi>25</span><span class=p>,</span> <span class=mi>15</span><span class=p>,</span> <span class=mi>35</span><span class=p>,</span> <span class=mi>30</span><span class=p>,</span> <span class=mi>10</span><span class=p>]</span> <span class=n>plt</span><span class=o>.</span><span class=n>bar</span><span class=p>(</span><span class=n>year</span><span class=p>,</span> <span class=n>production</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>savefig</span><span class=p>(</span><span class=s2>"output.jpg"</span><span class=p>)</span> <span class=n>plt</span><span class=o>.</span><span class=n>savefig</span><span class=p>(</span><span class=s2>"output1"</span><span class=p>,</span> <span class=n>facecolor</span><span class=o>=</span><span class=s1>'y'</span><span class=p>,</span> <span class=n>bbox_inches</span><span class=o>=</span><span class=s2>"tight"</span><span class=p>,</span> <span class=n>pad_inches</span><span class=o>=</span><span class=mf>0.3</span><span class=p>,</span> <span class=n>transparent</span><span class=o>=</span><span class=kc>True</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output:</strong></b></p> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611234101/savingmatplotlibplot.png" alt="saving matplotlib plot" width="inherit" height="inherit" loading="lazy"><img src="https://media.geeksforgeeks.org/wp-content/uploads/20210611234132/matplotlibsavingplot.png" alt="matplotlib saving plot" width="inherit" height="inherit" loading="lazy"></p> <p dir="ltr"><span>In this guide, we have explored the fundamentals of Matplotlib, from installation to advanced plotting techniques. By mastering these concepts, Whether you are working with simple line charts or complex heatmaps you can create and customize a wide range of visualizations to effectively communicate data insights. </span></p> <div id="preFaqContent"></div><h2 style="text-align:left"><span>FAQs – Data Visualization using Matplotlib in Python</span></h2><h3 style="text-align:left"><b><strong>What is the difference between </strong></b><b><code><strong>pyplot</strong></code></b><b><strong> and </strong></b><b><code><strong>figure</strong></code></b><b><strong> in Matplotlib?</strong></b></h3><blockquote><p dir="ltr"><code><span>pyplot</span></code><span> is a module in Matplotlib that provides a MATLAB-like interface for creating plots. It simplifies the plotting process by offering a collection of functions that modify a figure. The </span><code><span>figure</span></code><span> class, on the other hand, is a container for all plot elements. It serves as the canvas on which plots are drawn and can contain multiple </span><code><span>Axes</span></code><span> objects for complex visualizations.</span></p> </blockquote><h3 style="text-align:left"><b><strong>Can Matplotlib be used for interactive visualizations?</strong></b></h3><blockquote><p dir="ltr"><span>Yes, Matplotlib supports interactive visualizations through integration with interactive backends and libraries like Jupyter Notebook. It allows for zooming, panning, and updating plots dynamically. While Matplotlib is primarily known for static plots, its interactive capabilities can be enhanced using additional libraries such as </span><code><span>mpl_interactions</span></code><span> or by embedding plots in web applications using tools like </span><code><span>Dash.</span></code></p> </blockquote><h3 style="text-align:left"><b><strong>What are some advanced customization options available in Matplotlib?</strong></b></h3><blockquote><p dir="ltr"><span>Matplotlib offers extensive customization options, including changing plot styles, colors, and adding annotations. You can customize the appearance of plots using parameters such as </span><code><span>color</span></code><span>, </span><code><span>linestyle</span></code><span>, and </span><code><span>linewidth</span></code><span> for lines, or </span><code><span>edgecolor</span></code><span> and </span><code><span>facecolor</span></code><span> for bars. Additionally, you can add annotations using </span><code><span>annotate()</span></code><span> to highlight specific data points or trends.</span></p> </blockquote><h3 style="text-align:left"><b><strong>How do I handle large datasets with Matplotlib?</strong></b></h3><blockquote><p dir="ltr"><span>When dealing with large datasets, it’s important to optimize your plots for performance. Matplotlib can efficiently handle large datasets by leveraging NumPy arrays. You can also use techniques like downsampling data, using vectorized operations, and avoiding unnecessary plot elements to improve performance. Additionally, consider using other libraries like Seaborn or Plotly for more complex visualizations</span></p> </blockquote><h3 style="text-align:left"><b><strong>Can I create interactive plots with Matplotlib?</strong></b></h3><blockquote><p dir="ltr"><span>While Matplotlib is primarily used for static plots, it does support some interactivity through its integration with interactive backends and Jupyter Notebook. You can use the </span><code><span>%matplotlib notebook</span></code><span> magic command in Jupyter to enable interactive features like zooming and panning. 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