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Calculated Shard: 103 (from laksa112)

2. Crawled Status Check

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curl -X POST \
  'http://laksa103.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.geeksforgeeks.org/python/python-seaborn-tutorial/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.geeksforgeeks.org/python/python-seaborn-tutorial/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-tutorial\/","crawl_time":1775087927,"first_indexed_time":1749857615,"http_code":200,"src_unparsed":"org,geeksforgeeks!www,\/python\/python-seaborn-tutorial\/ s443","src_root_hash":"12046344915360636903","history_drop_reason":null,"meta_title":"Python Seaborn Tutorial - GeeksforGeeks","meta_descriptions":["Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.","Your All-in-One Learning Portal. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice\/competitive programming\/company interview Questions."],"attrs_boilerpipe_text":"Seaborn is a Python library built on top of Matplotlib that focuses on statistical data visualization. It provides high-level functions, built-in themes, and automatic handling of datasets, allowing users to create informative and visually appealing plots with minimal code. Seaborn is widely used for exploring trends, relationships and distributions in data.\nPrevious\nPause\nNext\n2 \/ 4\nBelow is a structured collection of all Seaborn topics, grouped into sections to help you navigate the complete tutorial from basics to advanced concepts.\nGetting Started\nThese articles introduce Seaborn fundamentals and basic plotting workflow.\nIntroduction to Seaborn\nPlotting Graph Using Seaborn\nUsing Seaborn with Matplotlib\nCustomizing Seaborn Plots\nThis section explains how to control appearance and style in Seaborn. You will learn how to modify themes, adjust colors and tailor plot aesthetics to match your visualization needs.\nStyle and Color\nColor Palette\nGrids and Multi-Plot Layouts\nThese topics introduce Seaborn’s grid-based layouts that help compare multiple visualizations at once. They are useful for exploring data across categories, subgroups or variable combinations.\nFacetGrid method\nPairGrid method\nRelational Plots\nThis section focuses on visualizing relationships between numerical variables. You will find topics covering scatter plots, line plots and time-based visualizations that capture trends and variation.\nRelational Plots I\nRelational Plots II\nScatterplot\nVisualizing Relationships with Scatter Plots\nScatter Plot with Regression Line\nScatter Plot with Marginal Histograms\nLine Plot with Seaborn\nTime Series Plot with Seaborn and Pandas\nTime Series Plot with Rolling Average\nCategorical Plots\nThese articles explain how to visualize data grouped by categories or labels. You will learn how to interpret statistical summaries using bar charts, boxplots, violin plots and more.\nBarplot\nCountplot\nBoxplot\nViolinplot\nStripplot\nSwarmplot\nFactorplot\nDistribution Plots\nThis section explores ways to understand the distribution, spread, and density of your data. Topics include histograms, KDE plots, pairwise distributions and joint analyses.\nHistograms\nJointplot\nPairplot\nKDE Plot\nRegression Plots\nThese topics deal with modeling relationships using simple regression techniques. You will learn to display trends, fit regression lines and analyze variable interactions.\nseaborn.lmplot()\nseaborn.regplot()\nMatrix Plots\nThis section covers visualizations such as heatmaps and correlation matrices. They are useful for identifying patterns, clusters and relationships across entire datasets.\nHeatmap\nCorrelation Heatmap\nTriangle Correlation Heatmap\nColorMaps in Heatmaps\nAdd Frame to Heatmap\nIncrease Annotation Size\nClustered Heatmap with Clustermap\nExploring Correlation\nAdditional Plots\nThese articles introduce special plot types and variations that deepen your visualization toolkit. They help you create focused visuals for specific analytical tasks.\nBubble Plot\nResidplot\nBoxenplot\nPointplot\nCatplot\nRidgeline Plot\nMore Topics on Seaborn\nThis final section expands into customization, layout adjustments and integrated workflows. You will learn how to combine Seaborn with Pandas and Matplotlib for more flexible visualizations.\nChange Axis Labels, Title and Figure Size\nPlace Legend Outside Plot\nPlot a Confidence Interval\nRolling Average Plot\nRegression Line per Group\nVisualization with Seaborn and Pandas\nVisualization with Matplotlib and Seaborn\nVisualizing ML Dataset with Seaborn","attrs_markdown":"[![geeksforgeeks](https:\/\/media.geeksforgeeks.org\/gfg-gg-logo.svg)](https:\/\/www.geeksforgeeks.org\/)\n\n![search icon](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/Property=Light---Default.svg)\n\n- Sign In\n- [Courses]()\n- [Tutorials]()\n- [Interview Prep]()\n\n- [Python Tutorial](https:\/\/www.geeksforgeeks.org\/python\/python-programming-language-tutorial\/)\n- [Data Types](https:\/\/www.geeksforgeeks.org\/python\/python-data-types\/)\n- [Interview Questions](https:\/\/www.geeksforgeeks.org\/python\/python-interview-questions\/)\n- [Examples](https:\/\/www.geeksforgeeks.org\/python\/python-programming-examples\/)\n- [Quizzes](https:\/\/www.geeksforgeeks.org\/python\/python-quizzes\/)\n- [DSA Python](https:\/\/www.geeksforgeeks.org\/dsa\/python-data-structures-and-algorithms\/)\n- [Data Science](https:\/\/www.geeksforgeeks.org\/data-science\/data-science-with-python-tutorial\/)\n- [NumPy](https:\/\/www.geeksforgeeks.org\/python\/numpy-tutorial\/)\n- [Pandas](https:\/\/www.geeksforgeeks.org\/pandas\/pandas-tutorial\/)\n- [Practice](https:\/\/www.geeksforgeeks.org\/dsa\/geeksforgeeks-practice-best-online-coding-platform\/)\n\n# Python Seaborn Tutorial\nLast Updated : 21 Nov, 2025\n\nSeaborn is a Python library built on top of Matplotlib that focuses on statistical data visualization. It provides high-level functions, built-in themes, and automatic handling of datasets, allowing users to create informative and visually appealing plots with minimal code. Seaborn is widely used for exploring trends, relationships and distributions in data.\n\n![why\\_use\\_seaborn.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)![why\\_use\\_seaborn.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)\n\n![popular\\_plots\\_in\\_seaborn\\_1.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119304941\/popular_plots_in_seaborn_1.webp)![popular\\_plots\\_in\\_seaborn\\_1.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119304941\/popular_plots_in_seaborn_1.webp)\n\n![popular\\_plots\\_in\\_seaborn\\_3.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119582698\/popular_plots_in_seaborn_3.webp)![popular\\_plots\\_in\\_seaborn\\_3.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119582698\/popular_plots_in_seaborn_3.webp)\n\n![popular\\_plots\\_in\\_seaborn\\_2.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)![popular\\_plots\\_in\\_seaborn\\_2.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)\n\n2 \/ 4\n\nBelow is a structured collection of all Seaborn topics, grouped into sections to help you navigate the complete tutorial from basics to advanced concepts.\n\n## Getting Started\nThese articles introduce Seaborn fundamentals and basic plotting workflow.\n\n- [Introduction to Seaborn](https:\/\/www.geeksforgeeks.org\/python\/introduction-to-seaborn-python\/)\n- [Plotting Graph Using Seaborn](https:\/\/www.geeksforgeeks.org\/machine-learning\/plotting-graph-using-seaborn-python\/)\n- [Using Seaborn with Matplotlib](https:\/\/www.geeksforgeeks.org\/data-visualization\/plotting-with-seaborn-and-matplotlib\/)\n## Customizing Seaborn Plots\nThis section explains how to control appearance and style in Seaborn. You will learn how to modify themes, adjust colors and tailor plot aesthetics to match your visualization needs.\n\n- [Style and Color](https:\/\/www.geeksforgeeks.org\/machine-learning\/seaborn-style-and-color\/)\n- [Color Palette](https:\/\/www.geeksforgeeks.org\/data-science\/seaborn-color-palette\/)\n## Grids and Multi-Plot Layouts\nThese topics introduce Seaborn’s grid-based layouts that help compare multiple visualizations at once. They are useful for exploring data across categories, subgroups or variable combinations.\n\n- [FacetGrid method](https:\/\/www.geeksforgeeks.org\/data-visualization\/python-seaborn-facetgrid-method\/)\n- [PairGrid method](https:\/\/www.geeksforgeeks.org\/data-visualization\/python-seaborn-pairgrid-method\/)\n## Relational Plots\nThis section focuses on visualizing relationships between numerical variables. You will find topics covering scatter plots, line plots and time-based visualizations that capture trends and variation.\n\n- [Relational Plots I](https:\/\/www.geeksforgeeks.org\/machine-learning\/relational-plots-in-seaborn-part-i\/)\n- [Relational Plots II](https:\/\/www.geeksforgeeks.org\/machine-learning\/relational-plots-in-seaborn-part-ii\/)\n- [Scatterplot](https:\/\/www.geeksforgeeks.org\/python\/scatterplot-using-seaborn-in-python\/)\n- [Visualizing Relationships with Scatter Plots](https:\/\/www.geeksforgeeks.org\/python\/visualizing-relationship-between-variables-with-scatter-plots-in-seaborn\/)\n- [Scatter Plot with Regression Line](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-scatter-plot-with-regression-line-using-seaborn-in-python\/)\n- [Scatter Plot with Marginal Histograms](https:\/\/www.geeksforgeeks.org\/python\/scatter-plot-with-marginal-histograms-in-python-with-seaborn\/)\n- [Line Plot with Seaborn](https:\/\/www.geeksforgeeks.org\/python\/seaborn-lineplot-method-in-python\/)\n- [Time Series Plot with Seaborn and Pandas](https:\/\/www.geeksforgeeks.org\/python\/creating-a-time-series-plot-with-seaborn-and-pandas\/)\n- [Time Series Plot with Rolling Average](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-a-time-series-plot-with-rolling-average-in-python\/)\n## Categorical Plots\nThese articles explain how to visualize data grouped by categories or labels. You will learn how to interpret statistical summaries using bar charts, boxplots, violin plots and more.\n\n- [Barplot](https:\/\/www.geeksforgeeks.org\/python\/barplot-using-seaborn-in-python\/)\n- [Countplot](https:\/\/www.geeksforgeeks.org\/python\/countplot-using-seaborn-in-python\/)\n- [Boxplot](https:\/\/www.geeksforgeeks.org\/python\/boxplot-using-seaborn-in-python\/)\n- [Violinplot](https:\/\/www.geeksforgeeks.org\/python\/violinplot-using-seaborn-in-python\/)\n- [Stripplot](https:\/\/www.geeksforgeeks.org\/python\/stripplot-using-seaborn-in-python\/)\n- [Swarmplot](https:\/\/www.geeksforgeeks.org\/python\/swarmplot-using-seaborn-in-python\/)\n- [Factorplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-factorplot-method\/)\n## Distribution Plots\nThis section explores ways to understand the distribution, spread, and density of your data. Topics include histograms, KDE plots, pairwise distributions and joint analyses.\n\n- [Histograms](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-histograms-with-density-plots-with-seaborn-histplot\/)\n- [Jointplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-jointplot-method\/)\n- [Pairplot](https:\/\/www.geeksforgeeks.org\/data-visualization\/python-seaborn-pairplot-method\/)\n- [KDE Plot](https:\/\/www.geeksforgeeks.org\/data-science\/seaborn-kdeplot-a-comprehensive-guide\/)\n## Regression Plots\nThese topics deal with modeling relationships using simple regression techniques. You will learn to display trends, fit regression lines and analyze variable interactions.\n\n- [seaborn.lmplot()](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-lmplot-method\/)\n- [seaborn.regplot()](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-regplot-method\/)\n## Matrix Plots\nThis section covers visualizations such as heatmaps and correlation matrices. They are useful for identifying patterns, clusters and relationships across entire datasets.\n\n- [Heatmap](https:\/\/www.geeksforgeeks.org\/python\/seaborn-heatmap-a-comprehensive-guide\/)\n- [Correlation Heatmap](https:\/\/www.geeksforgeeks.org\/python\/how-to-create-a-seaborn-correlation-heatmap-in-python\/)\n- [Triangle Correlation Heatmap](https:\/\/www.geeksforgeeks.org\/python\/how-to-create-a-triangle-correlation-heatmap-in-seaborn-python\/)\n- [ColorMaps in Heatmaps](https:\/\/www.geeksforgeeks.org\/python\/colormaps-in-seaborn-heatmaps\/)\n- [Add Frame to Heatmap](https:\/\/www.geeksforgeeks.org\/python\/how-to-add-a-frame-to-a-seaborn-heatmap-figure-in-python\/)\n- [Increase Annotation Size](https:\/\/www.geeksforgeeks.org\/python\/how-to-increase-the-size-of-the-annotations-of-a-seaborn-heatmap-in-python\/)\n- [Clustered Heatmap with Clustermap](https:\/\/www.geeksforgeeks.org\/python\/hierarchically-clustered-heatmap-in-python-with-seaborn-clustermap\/)\n- [Exploring Correlation](https:\/\/www.geeksforgeeks.org\/data-analysis\/exploring-correlation-in-python\/)\n## Additional Plots\nThese articles introduce special plot types and variations that deepen your visualization toolkit. They help you create focused visuals for specific analytical tasks.\n\n- [Bubble Plot](https:\/\/www.geeksforgeeks.org\/python\/seaborn-bubble-plot\/)\n- [Residplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-residplot-method\/)\n- [Boxenplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-boxenplot-method\/)\n- [Pointplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-pointplot-method\/)\n- [Catplot](https:\/\/www.geeksforgeeks.org\/machine-learning\/python-seaborn-catplot\/)\n- [Ridgeline Plot](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-ridgeline-plot-in-python-with-seaborn\/)\n## More Topics on Seaborn\nThis final section expands into customization, layout adjustments and integrated workflows. You will learn how to combine Seaborn with Pandas and Matplotlib for more flexible visualizations.\n\n- [Change Axis Labels, Title and Figure Size](https:\/\/www.geeksforgeeks.org\/python\/change-axis-labels-set-title-and-figure-size-to-plots-with-seaborn\/)\n- [Place Legend Outside Plot](https:\/\/www.geeksforgeeks.org\/python\/how-to-place-legend-outside-the-plot-with-seaborn-in-python\/)\n- [Plot a Confidence Interval](https:\/\/www.geeksforgeeks.org\/python\/how-to-plot-a-confidence-interval-in-python\/)\n- [Rolling Average Plot](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-a-time-series-plot-with-rolling-average-in-python\/)\n- [Regression Line per Group](https:\/\/www.geeksforgeeks.org\/python\/how-to-add-regression-line-per-group-with-seaborn-in-python\/)\n- [Visualization with Seaborn and Pandas](https:\/\/www.geeksforgeeks.org\/data-visualization\/data-visualization-with-python-seaborn\/)\n- [Visualization with Matplotlib and Seaborn](https:\/\/www.geeksforgeeks.org\/data-visualization\/data-visualisation-in-python-using-matplotlib-and-seaborn\/)\n- [Visualizing ML Dataset with Seaborn](https:\/\/www.geeksforgeeks.org\/machine-learning\/visualising-ml-dataset-through-seaborn-plots-and-matplotlib\/)\n\nSeaborn & Matplotlib in Python\n\nComment\n\n[A](https:\/\/www.geeksforgeeks.org\/user\/abhishek1\/)\n\n[abhishek1](https:\/\/www.geeksforgeeks.org\/user\/abhishek1\/)\n\n21\n\nArticle Tags:\n\nArticle Tags:\n\n[Python](https:\/\/www.geeksforgeeks.org\/category\/programming-language\/python\/)\n\n[Python-Seaborn](https:\/\/www.geeksforgeeks.org\/tag\/python-seaborn\/)\n### Explore\n\n[![GeeksforGeeks](https:\/\/media.geeksforgeeks.org\/auth-dashboard-uploads\/gfgFooterLogo.png)](https:\/\/www.geeksforgeeks.org\/)\n\n![location](https:\/\/media.geeksforgeeks.org\/img-practice\/Location-1685004904.svg)\n\nCorporate & Communications Address:\n\nA-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, 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Subjects](https:\/\/www.geeksforgeeks.org\/gate\/gate-exam-tutorial\/)\n- [Interview Preparation](https:\/\/www.geeksforgeeks.org\/aptitude\/interview-corner\/)\n- [Software and Tools](https:\/\/www.geeksforgeeks.org\/websites-apps\/software-and-tools-a-to-z-list\/)\n\n- Courses\n- [ML and Data Science](https:\/\/www.geeksforgeeks.org\/courses\/category\/machine-learning-data-science)\n- [DSA and Placements](https:\/\/www.geeksforgeeks.org\/courses\/category\/dsa-placements)\n- [Web Development](https:\/\/www.geeksforgeeks.org\/courses\/category\/development-testing)\n- [Programming Languages](https:\/\/www.geeksforgeeks.org\/courses\/category\/programming-languages)\n- [DevOps & Cloud](https:\/\/www.geeksforgeeks.org\/courses\/category\/cloud-devops)\n- [GATE](https:\/\/www.geeksforgeeks.org\/courses\/category\/gate)\n- [Trending Technologies](https:\/\/www.geeksforgeeks.org\/courses\/category\/trending-technologies\/)\n\n- Videos\n- [DSA](https:\/\/www.geeksforgeeks.org\/videos\/category\/sde-sheet\/)\n- [Python](https:\/\/www.geeksforgeeks.org\/videos\/category\/python\/)\n- [Java](https:\/\/www.geeksforgeeks.org\/videos\/category\/java-w6y5f4\/)\n- [C++](https:\/\/www.geeksforgeeks.org\/videos\/category\/c\/)\n- [Web Development](https:\/\/www.geeksforgeeks.org\/videos\/category\/web-development\/)\n- [Data Science](https:\/\/www.geeksforgeeks.org\/videos\/category\/data-science\/)\n- [CS Subjects](https:\/\/www.geeksforgeeks.org\/videos\/category\/cs-subjects\/)\n\n- Preparation Corner\n- [Interview Corner](https:\/\/www.geeksforgeeks.org\/interview-prep\/interview-corner\/)\n- [Aptitude](https:\/\/www.geeksforgeeks.org\/aptitude\/aptitude-questions-and-answers\/)\n- [Puzzles](https:\/\/www.geeksforgeeks.org\/aptitude\/puzzles\/)\n- [GfG 160](https:\/\/www.geeksforgeeks.org\/courses\/gfg-160-series)\n- [System Design](https:\/\/www.geeksforgeeks.org\/system-design\/system-design-tutorial\/)\n\n[@GeeksforGeeks, Sanchhaya Education Private Limited](https:\/\/www.geeksforgeeks.org\/), [All rights reserved](https:\/\/www.geeksforgeeks.org\/copyright-information\/)\n\n![]()","attrs_readable_markdown":"Seaborn is a Python library built on top of Matplotlib that focuses on statistical data visualization. It provides high-level functions, built-in themes, and automatic handling of datasets, allowing users to create informative and visually appealing plots with minimal code. Seaborn is widely used for exploring trends, relationships and distributions in data.\n\n![why\\_use\\_seaborn.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)![why\\_use\\_seaborn.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)\n\n![popular\\_plots\\_in\\_seaborn\\_1.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119304941\/popular_plots_in_seaborn_1.webp)![popular\\_plots\\_in\\_seaborn\\_1.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119304941\/popular_plots_in_seaborn_1.webp)\n\n![popular\\_plots\\_in\\_seaborn\\_3.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119582698\/popular_plots_in_seaborn_3.webp)![popular\\_plots\\_in\\_seaborn\\_3.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119582698\/popular_plots_in_seaborn_3.webp)\n\n![popular\\_plots\\_in\\_seaborn\\_2.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)![popular\\_plots\\_in\\_seaborn\\_2.webp](https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20251121123119931094\/why_use_seaborn.webp)\n\n2 \/ 4\n\nBelow is a structured collection of all Seaborn topics, grouped into sections to help you navigate the complete tutorial from basics to advanced concepts.\n\n## Getting Started\nThese articles introduce Seaborn fundamentals and basic plotting workflow.\n\n- [Introduction to Seaborn](https:\/\/www.geeksforgeeks.org\/python\/introduction-to-seaborn-python\/)\n- [Plotting Graph Using Seaborn](https:\/\/www.geeksforgeeks.org\/machine-learning\/plotting-graph-using-seaborn-python\/)\n- [Using Seaborn with Matplotlib](https:\/\/www.geeksforgeeks.org\/data-visualization\/plotting-with-seaborn-and-matplotlib\/)\n## Customizing Seaborn Plots\nThis section explains how to control appearance and style in Seaborn. You will learn how to modify themes, adjust colors and tailor plot aesthetics to match your visualization needs.\n\n- [Style and Color](https:\/\/www.geeksforgeeks.org\/machine-learning\/seaborn-style-and-color\/)\n- [Color Palette](https:\/\/www.geeksforgeeks.org\/data-science\/seaborn-color-palette\/)\n## Grids and Multi-Plot Layouts\nThese topics introduce Seaborn’s grid-based layouts that help compare multiple visualizations at once. They are useful for exploring data across categories, subgroups or variable combinations.\n\n- [FacetGrid method](https:\/\/www.geeksforgeeks.org\/data-visualization\/python-seaborn-facetgrid-method\/)\n- [PairGrid method](https:\/\/www.geeksforgeeks.org\/data-visualization\/python-seaborn-pairgrid-method\/)\n## Relational Plots\nThis section focuses on visualizing relationships between numerical variables. You will find topics covering scatter plots, line plots and time-based visualizations that capture trends and variation.\n\n- [Relational Plots I](https:\/\/www.geeksforgeeks.org\/machine-learning\/relational-plots-in-seaborn-part-i\/)\n- [Relational Plots II](https:\/\/www.geeksforgeeks.org\/machine-learning\/relational-plots-in-seaborn-part-ii\/)\n- [Scatterplot](https:\/\/www.geeksforgeeks.org\/python\/scatterplot-using-seaborn-in-python\/)\n- [Visualizing Relationships with Scatter Plots](https:\/\/www.geeksforgeeks.org\/python\/visualizing-relationship-between-variables-with-scatter-plots-in-seaborn\/)\n- [Scatter Plot with Regression Line](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-scatter-plot-with-regression-line-using-seaborn-in-python\/)\n- [Scatter Plot with Marginal Histograms](https:\/\/www.geeksforgeeks.org\/python\/scatter-plot-with-marginal-histograms-in-python-with-seaborn\/)\n- [Line Plot with Seaborn](https:\/\/www.geeksforgeeks.org\/python\/seaborn-lineplot-method-in-python\/)\n- [Time Series Plot with Seaborn and Pandas](https:\/\/www.geeksforgeeks.org\/python\/creating-a-time-series-plot-with-seaborn-and-pandas\/)\n- [Time Series Plot with Rolling Average](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-a-time-series-plot-with-rolling-average-in-python\/)\n## Categorical Plots\nThese articles explain how to visualize data grouped by categories or labels. You will learn how to interpret statistical summaries using bar charts, boxplots, violin plots and more.\n\n- [Barplot](https:\/\/www.geeksforgeeks.org\/python\/barplot-using-seaborn-in-python\/)\n- [Countplot](https:\/\/www.geeksforgeeks.org\/python\/countplot-using-seaborn-in-python\/)\n- [Boxplot](https:\/\/www.geeksforgeeks.org\/python\/boxplot-using-seaborn-in-python\/)\n- [Violinplot](https:\/\/www.geeksforgeeks.org\/python\/violinplot-using-seaborn-in-python\/)\n- [Stripplot](https:\/\/www.geeksforgeeks.org\/python\/stripplot-using-seaborn-in-python\/)\n- [Swarmplot](https:\/\/www.geeksforgeeks.org\/python\/swarmplot-using-seaborn-in-python\/)\n- [Factorplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-factorplot-method\/)\n## Distribution Plots\nThis section explores ways to understand the distribution, spread, and density of your data. Topics include histograms, KDE plots, pairwise distributions and joint analyses.\n\n- [Histograms](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-histograms-with-density-plots-with-seaborn-histplot\/)\n- [Jointplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-jointplot-method\/)\n- [Pairplot](https:\/\/www.geeksforgeeks.org\/data-visualization\/python-seaborn-pairplot-method\/)\n- [KDE Plot](https:\/\/www.geeksforgeeks.org\/data-science\/seaborn-kdeplot-a-comprehensive-guide\/)\n## Regression Plots\nThese topics deal with modeling relationships using simple regression techniques. You will learn to display trends, fit regression lines and analyze variable interactions.\n\n- [seaborn.lmplot()](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-lmplot-method\/)\n- [seaborn.regplot()](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-regplot-method\/)\n## Matrix Plots\nThis section covers visualizations such as heatmaps and correlation matrices. They are useful for identifying patterns, clusters and relationships across entire datasets.\n\n- [Heatmap](https:\/\/www.geeksforgeeks.org\/python\/seaborn-heatmap-a-comprehensive-guide\/)\n- [Correlation Heatmap](https:\/\/www.geeksforgeeks.org\/python\/how-to-create-a-seaborn-correlation-heatmap-in-python\/)\n- [Triangle Correlation Heatmap](https:\/\/www.geeksforgeeks.org\/python\/how-to-create-a-triangle-correlation-heatmap-in-seaborn-python\/)\n- [ColorMaps in Heatmaps](https:\/\/www.geeksforgeeks.org\/python\/colormaps-in-seaborn-heatmaps\/)\n- [Add Frame to Heatmap](https:\/\/www.geeksforgeeks.org\/python\/how-to-add-a-frame-to-a-seaborn-heatmap-figure-in-python\/)\n- [Increase Annotation Size](https:\/\/www.geeksforgeeks.org\/python\/how-to-increase-the-size-of-the-annotations-of-a-seaborn-heatmap-in-python\/)\n- [Clustered Heatmap with Clustermap](https:\/\/www.geeksforgeeks.org\/python\/hierarchically-clustered-heatmap-in-python-with-seaborn-clustermap\/)\n- [Exploring Correlation](https:\/\/www.geeksforgeeks.org\/data-analysis\/exploring-correlation-in-python\/)\n## Additional Plots\nThese articles introduce special plot types and variations that deepen your visualization toolkit. They help you create focused visuals for specific analytical tasks.\n\n- [Bubble Plot](https:\/\/www.geeksforgeeks.org\/python\/seaborn-bubble-plot\/)\n- [Residplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-residplot-method\/)\n- [Boxenplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-boxenplot-method\/)\n- [Pointplot](https:\/\/www.geeksforgeeks.org\/python\/python-seaborn-pointplot-method\/)\n- [Catplot](https:\/\/www.geeksforgeeks.org\/machine-learning\/python-seaborn-catplot\/)\n- [Ridgeline Plot](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-ridgeline-plot-in-python-with-seaborn\/)\n## More Topics on Seaborn\nThis final section expands into customization, layout adjustments and integrated workflows. You will learn how to combine Seaborn with Pandas and Matplotlib for more flexible visualizations.\n\n- [Change Axis Labels, Title and Figure Size](https:\/\/www.geeksforgeeks.org\/python\/change-axis-labels-set-title-and-figure-size-to-plots-with-seaborn\/)\n- [Place Legend Outside Plot](https:\/\/www.geeksforgeeks.org\/python\/how-to-place-legend-outside-the-plot-with-seaborn-in-python\/)\n- [Plot a Confidence Interval](https:\/\/www.geeksforgeeks.org\/python\/how-to-plot-a-confidence-interval-in-python\/)\n- [Rolling Average Plot](https:\/\/www.geeksforgeeks.org\/python\/how-to-make-a-time-series-plot-with-rolling-average-in-python\/)\n- [Regression Line per Group](https:\/\/www.geeksforgeeks.org\/python\/how-to-add-regression-line-per-group-with-seaborn-in-python\/)\n- [Visualization with Seaborn and Pandas](https:\/\/www.geeksforgeeks.org\/data-visualization\/data-visualization-with-python-seaborn\/)\n- [Visualization with Matplotlib and Seaborn](https:\/\/www.geeksforgeeks.org\/data-visualization\/data-visualisation-in-python-using-matplotlib-and-seaborn\/)\n- [Visualizing ML Dataset with Seaborn](https:\/\/www.geeksforgeeks.org\/machine-learning\/visualising-ml-dataset-through-seaborn-plots-and-matplotlib\/)","meta_canonical":null}

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Meta Description	Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more., Your All-in-One Learning Portal. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
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Boilerpipe Text	Seaborn is a Python library built on top of Matplotlib that focuses on statistical data visualization. It provides high-level functions, built-in themes, and automatic handling of datasets, allowing users to create informative and visually appealing plots with minimal code. Seaborn is widely used for exploring trends, relationships and distributions in data. Previous Pause Next 2 / 4 Below is a structured collection of all Seaborn topics, grouped into sections to help you navigate the complete tutorial from basics to advanced concepts. Getting Started These articles introduce Seaborn fundamentals and basic plotting workflow. Introduction to Seaborn Plotting Graph Using Seaborn Using Seaborn with Matplotlib Customizing Seaborn Plots This section explains how to control appearance and style in Seaborn. You will learn how to modify themes, adjust colors and tailor plot aesthetics to match your visualization needs. Style and Color Color Palette Grids and Multi-Plot Layouts These topics introduce Seaborn’s grid-based layouts that help compare multiple visualizations at once. They are useful for exploring data across categories, subgroups or variable combinations. FacetGrid method PairGrid method Relational Plots This section focuses on visualizing relationships between numerical variables. You will find topics covering scatter plots, line plots and time-based visualizations that capture trends and variation. Relational Plots I Relational Plots II Scatterplot Visualizing Relationships with Scatter Plots Scatter Plot with Regression Line Scatter Plot with Marginal Histograms Line Plot with Seaborn Time Series Plot with Seaborn and Pandas Time Series Plot with Rolling Average Categorical Plots These articles explain how to visualize data grouped by categories or labels. You will learn how to interpret statistical summaries using bar charts, boxplots, violin plots and more. Barplot Countplot Boxplot Violinplot Stripplot Swarmplot Factorplot Distribution Plots This section explores ways to understand the distribution, spread, and density of your data. Topics include histograms, KDE plots, pairwise distributions and joint analyses. Histograms Jointplot Pairplot KDE Plot Regression Plots These topics deal with modeling relationships using simple regression techniques. You will learn to display trends, fit regression lines and analyze variable interactions. seaborn.lmplot() seaborn.regplot() Matrix Plots This section covers visualizations such as heatmaps and correlation matrices. They are useful for identifying patterns, clusters and relationships across entire datasets. Heatmap Correlation Heatmap Triangle Correlation Heatmap ColorMaps in Heatmaps Add Frame to Heatmap Increase Annotation Size Clustered Heatmap with Clustermap Exploring Correlation Additional Plots These articles introduce special plot types and variations that deepen your visualization toolkit. They help you create focused visuals for specific analytical tasks. Bubble Plot Residplot Boxenplot Pointplot Catplot Ridgeline Plot More Topics on Seaborn This final section expands into customization, layout adjustments and integrated workflows. You will learn how to combine Seaborn with Pandas and Matplotlib for more flexible visualizations. Change Axis Labels, Title and Figure Size Place Legend Outside Plot Plot a Confidence Interval Rolling Average Plot Regression Line per Group Visualization with Seaborn and Pandas Visualization with Matplotlib and Seaborn Visualizing ML Dataset with Seaborn
Markdown	[![geeksforgeeks](https://media.geeksforgeeks.org/gfg-gg-logo.svg)](https://www.geeksforgeeks.org/) ![search icon](https://media.geeksforgeeks.org/auth-dashboard-uploads/Property=Light---Default.svg) - Sign In - [Courses]() - [Tutorials]() - [Interview Prep]() - [Python Tutorial](https://www.geeksforgeeks.org/python/python-programming-language-tutorial/) - [Data Types](https://www.geeksforgeeks.org/python/python-data-types/) - [Interview Questions](https://www.geeksforgeeks.org/python/python-interview-questions/) - [Examples](https://www.geeksforgeeks.org/python/python-programming-examples/) - [Quizzes](https://www.geeksforgeeks.org/python/python-quizzes/) - [DSA Python](https://www.geeksforgeeks.org/dsa/python-data-structures-and-algorithms/) - [Data Science](https://www.geeksforgeeks.org/data-science/data-science-with-python-tutorial/) - [NumPy](https://www.geeksforgeeks.org/python/numpy-tutorial/) - [Pandas](https://www.geeksforgeeks.org/pandas/pandas-tutorial/) - [Practice](https://www.geeksforgeeks.org/dsa/geeksforgeeks-practice-best-online-coding-platform/) # Python Seaborn Tutorial Last Updated : 21 Nov, 2025 Seaborn is a Python library built on top of Matplotlib that focuses on statistical data visualization. It provides high-level functions, built-in themes, and automatic handling of datasets, allowing users to create informative and visually appealing plots with minimal code. Seaborn is widely used for exploring trends, relationships and distributions in data. ![why\_use\_seaborn.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp)![why\_use\_seaborn.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp) ![popular\_plots\_in\_seaborn\_1.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119304941/popular_plots_in_seaborn_1.webp)![popular\_plots\_in\_seaborn\_1.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119304941/popular_plots_in_seaborn_1.webp) ![popular\_plots\_in\_seaborn\_3.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119582698/popular_plots_in_seaborn_3.webp)![popular\_plots\_in\_seaborn\_3.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119582698/popular_plots_in_seaborn_3.webp) ![popular\_plots\_in\_seaborn\_2.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp)![popular\_plots\_in\_seaborn\_2.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp) 2 / 4 Below is a structured collection of all Seaborn topics, grouped into sections to help you navigate the complete tutorial from basics to advanced concepts. ## Getting Started These articles introduce Seaborn fundamentals and basic plotting workflow. - [Introduction to Seaborn](https://www.geeksforgeeks.org/python/introduction-to-seaborn-python/) - [Plotting Graph Using Seaborn](https://www.geeksforgeeks.org/machine-learning/plotting-graph-using-seaborn-python/) - [Using Seaborn with Matplotlib](https://www.geeksforgeeks.org/data-visualization/plotting-with-seaborn-and-matplotlib/) ## Customizing Seaborn Plots This section explains how to control appearance and style in Seaborn. You will learn how to modify themes, adjust colors and tailor plot aesthetics to match your visualization needs. - [Style and Color](https://www.geeksforgeeks.org/machine-learning/seaborn-style-and-color/) - [Color Palette](https://www.geeksforgeeks.org/data-science/seaborn-color-palette/) ## Grids and Multi-Plot Layouts These topics introduce Seaborn’s grid-based layouts that help compare multiple visualizations at once. They are useful for exploring data across categories, subgroups or variable combinations. - [FacetGrid method](https://www.geeksforgeeks.org/data-visualization/python-seaborn-facetgrid-method/) - [PairGrid method](https://www.geeksforgeeks.org/data-visualization/python-seaborn-pairgrid-method/) ## Relational Plots This section focuses on visualizing relationships between numerical variables. You will find topics covering scatter plots, line plots and time-based visualizations that capture trends and variation. - [Relational Plots I](https://www.geeksforgeeks.org/machine-learning/relational-plots-in-seaborn-part-i/) - [Relational Plots II](https://www.geeksforgeeks.org/machine-learning/relational-plots-in-seaborn-part-ii/) - [Scatterplot](https://www.geeksforgeeks.org/python/scatterplot-using-seaborn-in-python/) - [Visualizing Relationships with Scatter Plots](https://www.geeksforgeeks.org/python/visualizing-relationship-between-variables-with-scatter-plots-in-seaborn/) - [Scatter Plot with Regression Line](https://www.geeksforgeeks.org/python/how-to-make-scatter-plot-with-regression-line-using-seaborn-in-python/) - [Scatter Plot with Marginal Histograms](https://www.geeksforgeeks.org/python/scatter-plot-with-marginal-histograms-in-python-with-seaborn/) - [Line Plot with Seaborn](https://www.geeksforgeeks.org/python/seaborn-lineplot-method-in-python/) - [Time Series Plot with Seaborn and Pandas](https://www.geeksforgeeks.org/python/creating-a-time-series-plot-with-seaborn-and-pandas/) - [Time Series Plot with Rolling Average](https://www.geeksforgeeks.org/python/how-to-make-a-time-series-plot-with-rolling-average-in-python/) ## Categorical Plots These articles explain how to visualize data grouped by categories or labels. You will learn how to interpret statistical summaries using bar charts, boxplots, violin plots and more. - [Barplot](https://www.geeksforgeeks.org/python/barplot-using-seaborn-in-python/) - [Countplot](https://www.geeksforgeeks.org/python/countplot-using-seaborn-in-python/) - [Boxplot](https://www.geeksforgeeks.org/python/boxplot-using-seaborn-in-python/) - [Violinplot](https://www.geeksforgeeks.org/python/violinplot-using-seaborn-in-python/) - [Stripplot](https://www.geeksforgeeks.org/python/stripplot-using-seaborn-in-python/) - [Swarmplot](https://www.geeksforgeeks.org/python/swarmplot-using-seaborn-in-python/) - [Factorplot](https://www.geeksforgeeks.org/python/python-seaborn-factorplot-method/) ## Distribution Plots This section explores ways to understand the distribution, spread, and density of your data. Topics include histograms, KDE plots, pairwise distributions and joint analyses. - [Histograms](https://www.geeksforgeeks.org/python/how-to-make-histograms-with-density-plots-with-seaborn-histplot/) - [Jointplot](https://www.geeksforgeeks.org/python/python-seaborn-jointplot-method/) - [Pairplot](https://www.geeksforgeeks.org/data-visualization/python-seaborn-pairplot-method/) - [KDE Plot](https://www.geeksforgeeks.org/data-science/seaborn-kdeplot-a-comprehensive-guide/) ## Regression Plots These topics deal with modeling relationships using simple regression techniques. You will learn to display trends, fit regression lines and analyze variable interactions. - [seaborn.lmplot()](https://www.geeksforgeeks.org/python/python-seaborn-lmplot-method/) - [seaborn.regplot()](https://www.geeksforgeeks.org/python/python-seaborn-regplot-method/) ## Matrix Plots This section covers visualizations such as heatmaps and correlation matrices. They are useful for identifying patterns, clusters and relationships across entire datasets. - [Heatmap](https://www.geeksforgeeks.org/python/seaborn-heatmap-a-comprehensive-guide/) - [Correlation Heatmap](https://www.geeksforgeeks.org/python/how-to-create-a-seaborn-correlation-heatmap-in-python/) - [Triangle Correlation Heatmap](https://www.geeksforgeeks.org/python/how-to-create-a-triangle-correlation-heatmap-in-seaborn-python/) - [ColorMaps in Heatmaps](https://www.geeksforgeeks.org/python/colormaps-in-seaborn-heatmaps/) - [Add Frame to Heatmap](https://www.geeksforgeeks.org/python/how-to-add-a-frame-to-a-seaborn-heatmap-figure-in-python/) - [Increase Annotation Size](https://www.geeksforgeeks.org/python/how-to-increase-the-size-of-the-annotations-of-a-seaborn-heatmap-in-python/) - [Clustered Heatmap with Clustermap](https://www.geeksforgeeks.org/python/hierarchically-clustered-heatmap-in-python-with-seaborn-clustermap/) - [Exploring Correlation](https://www.geeksforgeeks.org/data-analysis/exploring-correlation-in-python/) ## Additional Plots These articles introduce special plot types and variations that deepen your visualization toolkit. They help you create focused visuals for specific analytical tasks. - [Bubble Plot](https://www.geeksforgeeks.org/python/seaborn-bubble-plot/) - [Residplot](https://www.geeksforgeeks.org/python/python-seaborn-residplot-method/) - [Boxenplot](https://www.geeksforgeeks.org/python/python-seaborn-boxenplot-method/) - [Pointplot](https://www.geeksforgeeks.org/python/python-seaborn-pointplot-method/) - [Catplot](https://www.geeksforgeeks.org/machine-learning/python-seaborn-catplot/) - [Ridgeline Plot](https://www.geeksforgeeks.org/python/how-to-make-ridgeline-plot-in-python-with-seaborn/) ## More Topics on Seaborn This final section expands into customization, layout adjustments and integrated workflows. You will learn how to combine Seaborn with Pandas and Matplotlib for more flexible visualizations. - [Change Axis Labels, Title and Figure Size](https://www.geeksforgeeks.org/python/change-axis-labels-set-title-and-figure-size-to-plots-with-seaborn/) - [Place Legend Outside Plot](https://www.geeksforgeeks.org/python/how-to-place-legend-outside-the-plot-with-seaborn-in-python/) - [Plot a Confidence Interval](https://www.geeksforgeeks.org/python/how-to-plot-a-confidence-interval-in-python/) - [Rolling Average Plot](https://www.geeksforgeeks.org/python/how-to-make-a-time-series-plot-with-rolling-average-in-python/) - [Regression Line per Group](https://www.geeksforgeeks.org/python/how-to-add-regression-line-per-group-with-seaborn-in-python/) - [Visualization with Seaborn and Pandas](https://www.geeksforgeeks.org/data-visualization/data-visualization-with-python-seaborn/) - [Visualization with Matplotlib and Seaborn](https://www.geeksforgeeks.org/data-visualization/data-visualisation-in-python-using-matplotlib-and-seaborn/) - [Visualizing ML Dataset with Seaborn](https://www.geeksforgeeks.org/machine-learning/visualising-ml-dataset-through-seaborn-plots-and-matplotlib/) Seaborn & Matplotlib in Python Comment [A](https://www.geeksforgeeks.org/user/abhishek1/) [abhishek1](https://www.geeksforgeeks.org/user/abhishek1/) 21 Article Tags: Article Tags: [Python](https://www.geeksforgeeks.org/category/programming-language/python/) [Python-Seaborn](https://www.geeksforgeeks.org/tag/python-seaborn/) ### Explore [![GeeksforGeeks](https://media.geeksforgeeks.org/auth-dashboard-uploads/gfgFooterLogo.png)](https://www.geeksforgeeks.org/) ![location](https://media.geeksforgeeks.org/img-practice/Location-1685004904.svg) Corporate & Communications Address: A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305) ![location](https://media.geeksforgeeks.org/img-practice/Location-1685004904.svg) Registered Address: K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305 [![GFG App on Play Store](https://media.geeksforgeeks.org/auth-dashboard-uploads/googleplay-%281%29.png)](https://geeksforgeeksapp.page.link/gfg-app)[![GFG App on App Store](https://media.geeksforgeeks.org/auth-dashboard-uploads/appstore-%281%29.png)](https://geeksforgeeksapp.page.link/gfg-app) - 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Readable Markdown	Seaborn is a Python library built on top of Matplotlib that focuses on statistical data visualization. It provides high-level functions, built-in themes, and automatic handling of datasets, allowing users to create informative and visually appealing plots with minimal code. Seaborn is widely used for exploring trends, relationships and distributions in data. ![why\_use\_seaborn.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp)![why\_use\_seaborn.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp) ![popular\_plots\_in\_seaborn\_1.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119304941/popular_plots_in_seaborn_1.webp)![popular\_plots\_in\_seaborn\_1.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119304941/popular_plots_in_seaborn_1.webp) ![popular\_plots\_in\_seaborn\_3.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119582698/popular_plots_in_seaborn_3.webp)![popular\_plots\_in\_seaborn\_3.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119582698/popular_plots_in_seaborn_3.webp) ![popular\_plots\_in\_seaborn\_2.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp)![popular\_plots\_in\_seaborn\_2.webp](https://media.geeksforgeeks.org/wp-content/uploads/20251121123119931094/why_use_seaborn.webp) 2 / 4 Below is a structured collection of all Seaborn topics, grouped into sections to help you navigate the complete tutorial from basics to advanced concepts. ## Getting Started These articles introduce Seaborn fundamentals and basic plotting workflow. - [Introduction to Seaborn](https://www.geeksforgeeks.org/python/introduction-to-seaborn-python/) - [Plotting Graph Using Seaborn](https://www.geeksforgeeks.org/machine-learning/plotting-graph-using-seaborn-python/) - [Using Seaborn with Matplotlib](https://www.geeksforgeeks.org/data-visualization/plotting-with-seaborn-and-matplotlib/) ## Customizing Seaborn Plots This section explains how to control appearance and style in Seaborn. You will learn how to modify themes, adjust colors and tailor plot aesthetics to match your visualization needs. - [Style and Color](https://www.geeksforgeeks.org/machine-learning/seaborn-style-and-color/) - [Color Palette](https://www.geeksforgeeks.org/data-science/seaborn-color-palette/) ## Grids and Multi-Plot Layouts These topics introduce Seaborn’s grid-based layouts that help compare multiple visualizations at once. They are useful for exploring data across categories, subgroups or variable combinations. - [FacetGrid method](https://www.geeksforgeeks.org/data-visualization/python-seaborn-facetgrid-method/) - [PairGrid method](https://www.geeksforgeeks.org/data-visualization/python-seaborn-pairgrid-method/) ## Relational Plots This section focuses on visualizing relationships between numerical variables. You will find topics covering scatter plots, line plots and time-based visualizations that capture trends and variation. - [Relational Plots I](https://www.geeksforgeeks.org/machine-learning/relational-plots-in-seaborn-part-i/) - [Relational Plots II](https://www.geeksforgeeks.org/machine-learning/relational-plots-in-seaborn-part-ii/) - [Scatterplot](https://www.geeksforgeeks.org/python/scatterplot-using-seaborn-in-python/) - [Visualizing Relationships with Scatter Plots](https://www.geeksforgeeks.org/python/visualizing-relationship-between-variables-with-scatter-plots-in-seaborn/) - [Scatter Plot with Regression Line](https://www.geeksforgeeks.org/python/how-to-make-scatter-plot-with-regression-line-using-seaborn-in-python/) - [Scatter Plot with Marginal Histograms](https://www.geeksforgeeks.org/python/scatter-plot-with-marginal-histograms-in-python-with-seaborn/) - [Line Plot with Seaborn](https://www.geeksforgeeks.org/python/seaborn-lineplot-method-in-python/) - [Time Series Plot with Seaborn and Pandas](https://www.geeksforgeeks.org/python/creating-a-time-series-plot-with-seaborn-and-pandas/) - [Time Series Plot with Rolling Average](https://www.geeksforgeeks.org/python/how-to-make-a-time-series-plot-with-rolling-average-in-python/) ## Categorical Plots These articles explain how to visualize data grouped by categories or labels. You will learn how to interpret statistical summaries using bar charts, boxplots, violin plots and more. - [Barplot](https://www.geeksforgeeks.org/python/barplot-using-seaborn-in-python/) - [Countplot](https://www.geeksforgeeks.org/python/countplot-using-seaborn-in-python/) - [Boxplot](https://www.geeksforgeeks.org/python/boxplot-using-seaborn-in-python/) - [Violinplot](https://www.geeksforgeeks.org/python/violinplot-using-seaborn-in-python/) - [Stripplot](https://www.geeksforgeeks.org/python/stripplot-using-seaborn-in-python/) - [Swarmplot](https://www.geeksforgeeks.org/python/swarmplot-using-seaborn-in-python/) - [Factorplot](https://www.geeksforgeeks.org/python/python-seaborn-factorplot-method/) ## Distribution Plots This section explores ways to understand the distribution, spread, and density of your data. Topics include histograms, KDE plots, pairwise distributions and joint analyses. - [Histograms](https://www.geeksforgeeks.org/python/how-to-make-histograms-with-density-plots-with-seaborn-histplot/) - [Jointplot](https://www.geeksforgeeks.org/python/python-seaborn-jointplot-method/) - [Pairplot](https://www.geeksforgeeks.org/data-visualization/python-seaborn-pairplot-method/) - [KDE Plot](https://www.geeksforgeeks.org/data-science/seaborn-kdeplot-a-comprehensive-guide/) ## Regression Plots These topics deal with modeling relationships using simple regression techniques. You will learn to display trends, fit regression lines and analyze variable interactions. - [seaborn.lmplot()](https://www.geeksforgeeks.org/python/python-seaborn-lmplot-method/) - [seaborn.regplot()](https://www.geeksforgeeks.org/python/python-seaborn-regplot-method/) ## Matrix Plots This section covers visualizations such as heatmaps and correlation matrices. They are useful for identifying patterns, clusters and relationships across entire datasets. - [Heatmap](https://www.geeksforgeeks.org/python/seaborn-heatmap-a-comprehensive-guide/) - [Correlation Heatmap](https://www.geeksforgeeks.org/python/how-to-create-a-seaborn-correlation-heatmap-in-python/) - [Triangle Correlation Heatmap](https://www.geeksforgeeks.org/python/how-to-create-a-triangle-correlation-heatmap-in-seaborn-python/) - [ColorMaps in Heatmaps](https://www.geeksforgeeks.org/python/colormaps-in-seaborn-heatmaps/) - [Add Frame to Heatmap](https://www.geeksforgeeks.org/python/how-to-add-a-frame-to-a-seaborn-heatmap-figure-in-python/) - [Increase Annotation Size](https://www.geeksforgeeks.org/python/how-to-increase-the-size-of-the-annotations-of-a-seaborn-heatmap-in-python/) - [Clustered Heatmap with Clustermap](https://www.geeksforgeeks.org/python/hierarchically-clustered-heatmap-in-python-with-seaborn-clustermap/) - [Exploring Correlation](https://www.geeksforgeeks.org/data-analysis/exploring-correlation-in-python/) ## Additional Plots These articles introduce special plot types and variations that deepen your visualization toolkit. They help you create focused visuals for specific analytical tasks. - [Bubble Plot](https://www.geeksforgeeks.org/python/seaborn-bubble-plot/) - [Residplot](https://www.geeksforgeeks.org/python/python-seaborn-residplot-method/) - [Boxenplot](https://www.geeksforgeeks.org/python/python-seaborn-boxenplot-method/) - [Pointplot](https://www.geeksforgeeks.org/python/python-seaborn-pointplot-method/) - [Catplot](https://www.geeksforgeeks.org/machine-learning/python-seaborn-catplot/) - [Ridgeline Plot](https://www.geeksforgeeks.org/python/how-to-make-ridgeline-plot-in-python-with-seaborn/) ## More Topics on Seaborn This final section expands into customization, layout adjustments and integrated workflows. You will learn how to combine Seaborn with Pandas and Matplotlib for more flexible visualizations. - [Change Axis Labels, Title and Figure Size](https://www.geeksforgeeks.org/python/change-axis-labels-set-title-and-figure-size-to-plots-with-seaborn/) - [Place Legend Outside Plot](https://www.geeksforgeeks.org/python/how-to-place-legend-outside-the-plot-with-seaborn-in-python/) - [Plot a Confidence Interval](https://www.geeksforgeeks.org/python/how-to-plot-a-confidence-interval-in-python/) - [Rolling Average Plot](https://www.geeksforgeeks.org/python/how-to-make-a-time-series-plot-with-rolling-average-in-python/) - [Regression Line per Group](https://www.geeksforgeeks.org/python/how-to-add-regression-line-per-group-with-seaborn-in-python/) - [Visualization with Seaborn and Pandas](https://www.geeksforgeeks.org/data-visualization/data-visualization-with-python-seaborn/) - [Visualization with Matplotlib and Seaborn](https://www.geeksforgeeks.org/data-visualization/data-visualisation-in-python-using-matplotlib-and-seaborn/) - [Visualizing ML Dataset with Seaborn](https://www.geeksforgeeks.org/machine-learning/visualising-ml-dataset-through-seaborn-plots-and-matplotlib/)
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