From 76e390861245c83ca2d393a6f7efb618754d93ce Mon Sep 17 00:00:00 2001 From: Holtz Yan Date: Wed, 20 Mar 2024 08:45:46 +0100 Subject: [PATCH] Updates --- 1-basic-barplot/index.html | 4 +- .../index.html | 4 +- 100-calling-a-color-with-seaborn/index.html | 4 +- .../index.html | 4 +- 104-seaborn-themes/index.html | 4 +- .../index.html | 4 +- 11-grouped-barplot/index.html | 4 +- .../index.html | 4 +- 111-custom-correlogram/index.html | 4 +- 12-stacked-barplot-with-matplotlib/index.html | 4 +- 120-line-chart-with-matplotlib/index.html | 4 +- 121-line-chart-customization/index.html | 4 +- 122-multiple-lines-chart/index.html | 4 +- 123-highlight-a-line-in-line-plot/index.html | 4 +- 124-spaghetti-plot/index.html | 4 +- 125-small-multiples-for-line-chart/index.html | 4 +- 13-percent-stacked-barplot/index.html | 4 +- 130-basic-matplotlib-scatterplot/index.html | 4 +- .../index.html | 4 +- 132-basic-connected-scatterplot/index.html | 4 +- .../index.html | 4 +- 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a/41-control-marker-features/index.html b/41-control-marker-features/index.html index c60ede5574..525f698a08 100644 --- a/41-control-marker-features/index.html +++ b/41-control-marker-features/index.html @@ -48,6 +48,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/42-custom-linear-regression-fit-seaborn/index.html b/42-custom-linear-regression-fit-seaborn/index.html index edc76bdb93..282a022d6c 100644 --- a/42-custom-linear-regression-fit-seaborn/index.html +++ b/42-custom-linear-regression-fit-seaborn/index.html @@ -30,6 +30,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/43-use-categorical-variable-to-color-scatterplot-seaborn/index.html b/43-use-categorical-variable-to-color-scatterplot-seaborn/index.html index a72efb8e9b..5d8fce85cc 100644 --- a/43-use-categorical-variable-to-color-scatterplot-seaborn/index.html +++ b/43-use-categorical-variable-to-color-scatterplot-seaborn/index.html @@ -76,6 +76,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/44-control-axis-limits-of-plot-seaborn/index.html b/44-control-axis-limits-of-plot-seaborn/index.html index e7f209e5f1..8211191455 100644 --- a/44-control-axis-limits-of-plot-seaborn/index.html +++ b/44-control-axis-limits-of-plot-seaborn/index.html @@ -33,6 +33,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/45-control-color-of-each-marker-seaborn/index.html b/45-control-color-of-each-marker-seaborn/index.html index 3cc07055c3..fb54665b20 100644 --- a/45-control-color-of-each-marker-seaborn/index.html +++ b/45-control-color-of-each-marker-seaborn/index.html @@ -33,6 +33,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/46-add-text-annotation-on-scatterplot/index.html b/46-add-text-annotation-on-scatterplot/index.html index d7dd102243..2e2c3de431 100644 --- a/46-add-text-annotation-on-scatterplot/index.html +++ b/46-add-text-annotation-on-scatterplot/index.html @@ -60,6 +60,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/5-control-width-and-space-in-barplots/index.html b/5-control-width-and-space-in-barplots/index.html index fc8fee2bd5..ac3568ba7f 100644 --- a/5-control-width-and-space-in-barplots/index.html +++ b/5-control-width-and-space-in-barplots/index.html @@ -53,6 +53,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/50-basic-violinplot-and-input-formats/index.html b/50-basic-violinplot-and-input-formats/index.html index 876e115248..e4eef58130 100644 --- a/50-basic-violinplot-and-input-formats/index.html +++ b/50-basic-violinplot-and-input-formats/index.html @@ -49,6 +49,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/500-network-chart-with-edge-bundling/index.html b/500-network-chart-with-edge-bundling/index.html index 16d4253df1..a4e6afd1fb 100644 --- a/500-network-chart-with-edge-bundling/index.html +++ b/500-network-chart-with-edge-bundling/index.html @@ -75,6 +75,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/501-parallel-plot-seaborn/index.html b/501-parallel-plot-seaborn/index.html index c16e216308..0b9fe06f7d 100644 --- a/501-parallel-plot-seaborn/index.html +++ b/501-parallel-plot-seaborn/index.html @@ -60,6 +60,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/502-violinplot-and-swarmplot/index.html b/502-violinplot-and-swarmplot/index.html index 9cceb43307..af2fc3417c 100644 --- a/502-violinplot-and-swarmplot/index.html +++ b/502-violinplot-and-swarmplot/index.html @@ -89,6 +89,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/503-waffle-chart-introduction/index.html b/503-waffle-chart-introduction/index.html index f6aabedfd8..5cae13a21e 100644 --- a/503-waffle-chart-introduction/index.html +++ b/503-waffle-chart-introduction/index.html @@ -86,6 +86,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/504-histogram-with-colored-tails/index.html b/504-histogram-with-colored-tails/index.html index 3658282243..eeef55c8ad 100644 --- a/504-histogram-with-colored-tails/index.html +++ b/504-histogram-with-colored-tails/index.html @@ -80,6 +80,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/505-introduction-to-swarm-plot-in-seaborn/index.html b/505-introduction-to-swarm-plot-in-seaborn/index.html index ff0539b465..14744b7188 100644 --- a/505-introduction-to-swarm-plot-in-seaborn/index.html +++ b/505-introduction-to-swarm-plot-in-seaborn/index.html @@ -84,6 +84,6 @@

Dataset

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Dataset

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Specify your own colors

- \ No newline at end of file + \ No newline at end of file diff --git a/509-introduction-to-swarm-plot-in-matplotlib/index.html b/509-introduction-to-swarm-plot-in-matplotlib/index.html index 8db14657d9..887a495f36 100644 --- a/509-introduction-to-swarm-plot-in-matplotlib/index.html +++ b/509-introduction-to-swarm-plot-in-matplotlib/index.html @@ -328,6 +328,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/51-horizontal-violinplot/index.html b/51-horizontal-violinplot/index.html index 2b990e7555..9adba52fda 100644 --- a/51-horizontal-violinplot/index.html +++ b/51-horizontal-violinplot/index.html @@ -37,6 +37,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/511-interactive-scatterplot-with-plotly/index.html b/511-interactive-scatterplot-with-plotly/index.html index 12d34ac86a..1509f32038 100644 --- a/511-interactive-scatterplot-with-plotly/index.html +++ b/511-interactive-scatterplot-with-plotly/index.html @@ -217,6 +217,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/512-line-chart-seaborn/index.html b/512-line-chart-seaborn/index.html index 948260f3f5..ba3a8d4520 100644 --- a/512-line-chart-seaborn/index.html +++ b/512-line-chart-seaborn/index.html @@ -139,6 +139,6 @@

Change the color

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Annotations

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Log transform

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Create the dataset

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Dataset

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\ No newline at end of file + \ No newline at end of file diff --git a/549-candle-stick-with-moving-average/index.html b/549-candle-stick-with-moving-average/index.html index 5dcacfe756..2bf51fc248 100644 --- a/549-candle-stick-with-moving-average/index.html +++ b/549-candle-stick-with-moving-average/index.html @@ -61,6 +61,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/55-control-order-of-groups-in-violinplot-seaborn/index.html b/55-control-order-of-groups-in-violinplot-seaborn/index.html index 48a58e41b2..293d232b53 100644 --- a/55-control-order-of-groups-in-violinplot-seaborn/index.html +++ b/55-control-order-of-groups-in-violinplot-seaborn/index.html @@ -41,6 +41,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/550-intro-table-with-pandas/index.html b/550-intro-table-with-pandas/index.html index 3cf42fe062..4a3cb6339d 100644 --- a/550-intro-table-with-pandas/index.html +++ b/550-intro-table-with-pandas/index.html @@ -462,6 +462,6 @@ - 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\ No newline at end of file + \ No newline at end of file diff --git a/555-candle-stick-with-moving-average-plotly/index.html b/555-candle-stick-with-moving-average-plotly/index.html index 633c4c5991..7e07dbdeeb 100644 --- a/555-candle-stick-with-moving-average-plotly/index.html +++ b/555-candle-stick-with-moving-average-plotly/index.html @@ -186,6 +186,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/556-visualize-linear-regression/index.html b/556-visualize-linear-regression/index.html index 7a06dc04ad..317e12ad47 100644 --- a/556-visualize-linear-regression/index.html +++ b/556-visualize-linear-regression/index.html @@ -154,6 +154,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/557-anova-visualization-with-matplotlib/index.html b/557-anova-visualization-with-matplotlib/index.html index 8f7ab1bc00..e468b06f30 100644 --- a/557-anova-visualization-with-matplotlib/index.html +++ b/557-anova-visualization-with-matplotlib/index.html @@ -197,6 +197,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/558-waffle-bar-chart/index.html b/558-waffle-bar-chart/index.html index 443c09ba6b..00736cc573 100644 --- a/558-waffle-bar-chart/index.html +++ b/558-waffle-bar-chart/index.html @@ -136,6 +136,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/560-introduction-plottable/index.html b/560-introduction-plottable/index.html index 2401042222..2d1b5602c7 100644 --- a/560-introduction-plottable/index.html +++ b/560-introduction-plottable/index.html @@ -95,6 +95,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/561-control-colors-in-plottable/index.html b/561-control-colors-in-plottable/index.html index e093816a78..a7f0baae94 100644 --- a/561-control-colors-in-plottable/index.html +++ b/561-control-colors-in-plottable/index.html @@ -104,6 +104,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/562-add-images-in-plottable/index.html b/562-add-images-in-plottable/index.html index fced9a6daf..0370c9c86e 100644 --- a/562-add-images-in-plottable/index.html +++ b/562-add-images-in-plottable/index.html @@ -79,6 +79,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/563-graph-in-plottable/index.html b/563-graph-in-plottable/index.html index 823ef000a5..ac2785c625 100644 --- a/563-graph-in-plottable/index.html +++ b/563-graph-in-plottable/index.html @@ -136,6 +136,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/564-publication-ready-table-with-plottable/index.html b/564-publication-ready-table-with-plottable/index.html index dc217db8da..2a54b991a9 100644 --- a/564-publication-ready-table-with-plottable/index.html +++ b/564-publication-ready-table-with-plottable/index.html @@ -296,6 +296,6 @@

Libraries

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diff --git a/7-custom-barplot-layout/index.html b/7-custom-barplot-layout/index.html index 1925a956f1..7e5fa7ee4f 100644 --- a/7-custom-barplot-layout/index.html +++ b/7-custom-barplot-layout/index.html @@ -81,6 +81,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/70-basic-density-plot-with-seaborn/index.html b/70-basic-density-plot-with-seaborn/index.html index 3a50abff10..906c880d33 100644 --- a/70-basic-density-plot-with-seaborn/index.html +++ b/70-basic-density-plot-with-seaborn/index.html @@ -26,6 +26,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/71-density-plot-with-shade-seaborn/index.html b/71-density-plot-with-shade-seaborn/index.html index 5ebfd1ccde..e58d37b975 100644 --- a/71-density-plot-with-shade-seaborn/index.html +++ b/71-density-plot-with-shade-seaborn/index.html @@ -26,6 +26,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/72-horizontal-density-plot/index.html 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.accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button:after{--bs-accordion-btn-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%236ea8fe' viewBox='0 0 16 16'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E");--bs-accordion-btn-active-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%236ea8fe' viewBox='0 0 16 16'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E")}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius);display:flex;flex-wrap:wrap;font-size:var(--bs-breadcrumb-font-size);list-style:none;margin-bottom:var(--bs-breadcrumb-margin-bottom);padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media 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(min-width:992px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media (min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media 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btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3E%3Cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3E%3C/svg%3E");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);background:transparent var(--bs-btn-close-bg) center/1em auto 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;--bs-modal-footer-border-color:var(--bs-border-color);--bs-modal-footer-border-width:var(--bs-border-width);display:none;height:100%;left:0;outline:0;overflow-x:hidden;overflow-y:auto;position:fixed;top:0;width:100%;z-index:var(--bs-modal-zindex)}.modal-dialog{margin:var(--bs-modal-margin);pointer-events:none;position:relative;width:auto}.modal.fade .modal-dialog{transform:translateY(-50px);transition:transform .3s ease-out}@media (prefers-reduced-motion:reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{align-items:center;display:flex;min-height:calc(100% - 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.modal-content{border:0;border-radius:0;height:100%}.modal-fullscreen-xxl-down .modal-footer,.modal-fullscreen-xxl-down .modal-header{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex:1080;--bs-tooltip-max-width:200px;--bs-tooltip-padding-x:0.5rem;--bs-tooltip-padding-y:0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color:var(--bs-body-bg);--bs-tooltip-bg:var(--bs-emphasis-color);--bs-tooltip-border-radius:var(--bs-border-radius);--bs-tooltip-opacity:0.9;--bs-tooltip-arrow-width:0.8rem;--bs-tooltip-arrow-height:0.4rem;word-wrap:break-word;display:block;font-family:var(--bs-font-sans-serif);font-size:var(--bs-tooltip-font-size);font-style:normal;font-weight:400;letter-spacing:normal;line-break:auto;line-height:1.5;margin:var(--bs-tooltip-margin);opacity:0;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;white-space:normal;word-break:normal;word-spacing:normal;z-index:var(--bs-tooltip-zindex)}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;height:var(--bs-tooltip-arrow-height);width:var(--bs-tooltip-arrow-width)}.tooltip .tooltip-arrow:before{border-color:transparent;border-style:solid;content:"";position:absolute}.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow,.bs-tooltip-top .tooltip-arrow{bottom:calc(var(--bs-tooltip-arrow-height)*-1)}.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow:before,.bs-tooltip-top .tooltip-arrow:before{border-top-color:var(--bs-tooltip-bg);border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;top:-1px}.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow,.bs-tooltip-end .tooltip-arrow{height:var(--bs-tooltip-arrow-width);left:calc(var(--bs-tooltip-arrow-height)*-1);width:var(--bs-tooltip-arrow-height)}.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow:before,.bs-tooltip-end .tooltip-arrow:before{border-right-color:var(--bs-tooltip-bg);border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;right:-1px}.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow,.bs-tooltip-bottom 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0;overflow:auto;padding:1em}:not(pre)>code[class*=language-],pre[class*=language-]{background-color:var(--light-grey)}:not(pre)>code[class*=language-]{border-radius:.3em;padding:.1em}.token.cdata,.token.comment,.token.doctype,.token.prolog{color:#93a1a1}.token.punctuation{color:#586e75}.token.namespace{opacity:.7}.token.boolean,.token.constant,.token.deleted,.token.number,.token.property,.token.symbol,.token.tag{color:#268bd2}.token.attr-name,.token.builtin,.token.char,.token.inserted,.token.selector,.token.string,.token.url{color:#2aa198}.token.entity{background:#eee8d5;color:#657b83}.token.atrule,.token.attr-value,.token.keyword{color:#859900}.token.class-name,.token.function{color:#b58900}.token.important,.token.regex,.token.variable{color:#cb4b16}.token.bold,.token.important{font-weight:700}.token.italic{font-style:italic}.token.entity{cursor:help}.functionButton{border-radius:3px!important;border-width:1px!important;padding:1px 7px}.btn-check:checked+.btn{background-color:var(--bs-btn-active-bg)!important;border-color:var(--bs-btn-active-border-color)!important;color:var(--bs-btn-active-color)!important}Arc Diagram

Arc diagram

An arc diagram is a special kind of network graph. It is consituted by nodes that represent entities and by links that show relationships between entities. In arc diagrams, nodes are displayed along a single axis and links are represented with arcs.

Arc diagram with R or Javascript

Contrary to python that is not of much help to build an arc diagram, R and Javascript both offer built-in functions to make them. R will be more adapted if you're a data analyst and are happy with a static version. Javascript and thed3.js library offers more customization and interactivity options, but comes with a steeper learning curve and a lot of line of code. Pick your flavor!



Here is an arc diagram showing the relationships between researchers. One node is drawn for each researcher having published at least one paper with my previous supervisor, Vincent Ranwez. Two researchers are linked if they co-authored at least one paper together. Read more about this dataset here.

Hover nodes for more info

See code

Contact


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥


Copyright © the Python Graph Gallery 2023
Privacy | License
Arc Diagram

Arc diagram

An arc diagram is a special kind of network graph. It is consituted by nodes that represent entities and by links that show relationships between entities. In arc diagrams, nodes are displayed along a single axis and links are represented with arcs.

Arc diagram with R or Javascript

Python has only one library to build arc diagrams:arcplot. For other kind of static version, Rhas built-in functions to make them. Javascript and thed3.js library offers more customization and interactivity options, but comes with a steeper learning curve and a lot of line of code. Pick your flavor!



Here is an arc diagram showing the relationships between researchers. One node is drawn for each researcher having published at least one paper with my previous supervisor, Vincent Ranwez. Two researchers are linked if they co-authored at least one paper together. Read more about this dataset here.

Hover nodes for more info

See code

Contact


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥


Copyright © the Python Graph Gallery 2023
Privacy | License
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Dealing with dates on the X axis

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Height of this point is proportional with start - end distance\n (start - end) / 2, 0, 0, ',',\n start < end ? 1 : 0, end, ',', height - 30] // We always want the arc on top. 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\n )\n\n}\n","import React from 'react';\nimport TitleAndDescription from '../components/TitleAndDescription';\nimport Layout from '../components/Layout';\nimport Container from 'react-bootstrap/Container';\nimport Contact from '../components/Contact';\nimport Row from 'react-bootstrap/Row';\nimport ChartImageContainer from '../components/ChartImageContainer';\nimport ChartFamilySection from '../components/ChartFamilySection';\nimport Spacing from '../components/Spacing';\nimport { Button } from 'react-bootstrap';\nimport ArcDiagramD3 from '../components/ArcDiagramD3';\nimport { SEO } from '../components/SEO';\n\nconst chartDescription =\n \"

An arc diagram is a special kind of network graph. It is consituted by nodes that represent entities and by links that show relationships between entities. In arc diagrams, nodes are displayed along a single axis and links are represented with arcs.\";\n\nexport const Head = () => (\n \n);\n\nexport default function Arc() {\n return (\n \n \n\n

\n \n

\n Python library to build arc diagrams: arcplot\n

\n

\n Arcplot is a Python\n library that offers a simple way to build arc diagrams. It allows to\n build static arc diagrams with just a few lines of code. It\n is a good choice if you are looking for a simple and quick solution.\n

\n

\n Warning: you need Python 3.10 to use this library.\n

\n \n \n \n \n \n \n \n \n
\n \n\n \n

\n Arc diagram with R or Javascript\n

\n

\n Python has only one library to build arc diagrams: \n arcplot. For other kind of static version, R\n has built-in functions to make them. Javascript and the\n d3.js library offers more customization and \n interactivity options, but comes with a steeper learning curve and a\n lot of line of code. Pick your flavor!\n

\n \n \n \n \n \n \n
\n
\n \n \n \n

\n Here is an arc diagram showing the relationships between researchers.\n One node is drawn for each researcher having published at least\n one paper with my previous supervisor,{' '}\n Vincent Ranwez.\n Two researchers are linked if they co-authored at least one\n paper together. Read more about this dataset{' '}\n \n here\n \n .\n

\n

\n Hover nodes for more info\n

\n \n See code\n \n \n\n \n\n
\n \n \n \n
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Matplotlib is the most famous library for data visualization with python. It allows to create literally every type of chart with a great level of customization. This page provides some general tips that can be applied on any kind of chart made with matplotlib like customizing titles or colors. If you're looking at creating a specific chart type, visit the gallery instead.

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It is widely used and most of other viz libraries (like ",l.createElement("code",null,"seaborn"),") are actually built on top of it."),l.createElement("p",null,"Once installed, matplotlib must be imported, usually using"," ",l.createElement("code",null,"import matplotlib.pyplot as plt"),". You can then use use the functions available in the ",l.createElement("code",null,"plt")," object.")),l.createElement(d.Z,{md:6},l.createElement(h.Link,{to:"/240-basic-area-chart"},l.createElement(b.Z,{imgName:"240_basic_area_chart",caption:"The most basic area chart one can make with python and matplotlib"})))),l.createElement(g.Z,null,k)),l.createElement(E.Z,null),l.createElement(i.Z,null,l.createElement("h2",{id:"Lexicon"},l.createElement(u.rS,null),"Basic vocabulary"),l.createElement("p",null,"The figure below describes the anatomy of a ",l.createElement("code",null,"matplotlib")," ","charts. 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Have a look at it for a thorough explanation on how this library works."),l.createElement("div",{style:{maxWidth:"600px",margin:"0 auto",padding:30}},l.createElement(b.Z,{imgName:"matplotlib_vocabulary",caption:"Anatomy of a matplotlib chart: all the basic vocabulary you need to know to understand the documentation properly"})),l.createElement("a",{href:"https://matplotlib.org/faq/usage_faq.html#coding-styles"},l.createElement(y.Z,{size:"sm"},"Matplotlib doc"))),l.createElement(E.Z,null),l.createElement(i.Z,null,l.createElement("h2",{id:"APIs"},l.createElement(u.rS,null),"Two distinct APIs"),l.createElement("p",null,"There are 2 main ways to build a chart with matplotlib: the"," ",l.createElement("code",null,"pyplot API")," and the ",l.createElement("code",null,"object-oriented API"),"."),l.createElement("p",null,"➡️ ",l.createElement("code",null,"pyplot API")),l.createElement("p",null,"Pyplot is a collection of functions, each function applying a change to a figure. 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This is pretty confusing and I advise to have a look to the"," ",l.createElement("a",{href:"https://matplotlib.org/stable/api/index.html#usage-patterns"},"official documentation")," ","for a more thorough explanation. In the gallery, both APIs are used.")),l.createElement(E.Z,null),l.createElement(i.Z,null,l.createElement("h2",{id:"Title"},l.createElement(u.rS,null),"Customizing titles"),l.createElement("p",null,"The title is a key part of a good chart. It has to ",l.createElement("u",null,"summarize")," ","its key message in a few words. Always avoid to simply repeat what is displayed on each axis. Try to provide some more insight allowing to understand the main result instead."),l.createElement("p",null,"The ",l.createElement(h.Link,{to:"/190-custom-matplotlib-title"},"post #190")," goes in depth on how to customize the ",l.createElement("code",null,"matplotlib"),"title. All the main types of change are descried: changing its color, adding a subtitle, using math formulas and so on. 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'./chartImage.css';\n\nimport React from 'react';\nimport { useStaticQuery, graphql } from 'gatsby';\nimport { GatsbyImage } from 'gatsby-plugin-image';\nimport PropTypes from 'prop-types';\n\nconst allGifs = [\n 'animated_chart',\n 'animated_gapminder.gif',\n 'animated_volcano.gif',\n];\n\n// This component displays a logo representing a chart type of the gallery. Example: bar chart\n// Logo has a different size depending on the window size.\nexport default function ChartImage({ imgName, caption }) {\n if (allGifs.includes(imgName)) {\n return

TODO

;\n }\n\n const data = useStaticQuery(graphql`\n query MyQuery {\n allFile(filter: { relativeDirectory: { eq: \"graph\" } }) {\n edges {\n node {\n id\n name\n childImageSharp {\n gatsbyImageData(placeholder: BLURRED, layout: FULL_WIDTH)\n }\n }\n }\n }\n }\n `);\n\n const image = data.allFile.edges.find((n) => {\n return n.node.name.includes(imgName);\n });\n\n if (!image) {\n return null;\n }\n\n // Note: alt tag looks to be ignored?\n return (\n <>\n
\n \n {caption && (\n
\n
\n

{caption}

\n
\n
\n )}\n
\n \n );\n}\n\nChartImage.propTypes = {\n imgName: PropTypes.string,\n caption: PropTypes.string,\n};\n","import React from \"react\";\nimport ChartImage from \"../components/ChartImage\";\nimport Col from \"react-bootstrap/Col\";\nimport { Link } from \"gatsby\";\n\n// This component wraps a chart image into a column and make it responsive + clickable\nexport default function ChartImageContainer({ imgName, caption, linkTo }) {\n return (\n \n \n \n \n \n );\n}\n","// made following the Gatsby Doc: https://www.gatsbyjs.com/docs/how-to/adding-common-features/adding-seo-component/\n\n// This component is called by every page of the website.\n// At the top of each page you will find a Head component export that calls this SEO component\n\n// The SEO component will add stuff like title, description, image, lang... in the head of the HTML page\n// AdThrive ads code is also added here\n\nimport React from 'react';\n\nexport const SEO = ({ title, seoDescription, keywords }) => {\n return (\n <>\n {title}\n \n \n \n \n\n \n \n \n \n \n \n );\n};\n","import React from 'react';\nimport TitleAndDescription from '../components/TitleAndDescription';\nimport Layout from '../components/Layout';\nimport Container from 'react-bootstrap/Container';\nimport Contact from '../components/Contact';\nimport Row from 'react-bootstrap/Row';\nimport ChartImageContainer from '../components/ChartImageContainer';\nimport ChartFamilySection from '../components/ChartFamilySection';\nimport { Link } from 'gatsby';\nimport { Matplotlib } from '../components/MiscellaneousLogos';\nimport { Col, ToggleButton } from 'react-bootstrap';\nimport CodeChunk from '../components/CodeChunk';\nimport ChartImage from '../components/ChartImage';\nimport Spacing from '../components/Spacing';\nimport { Button } from 'react-bootstrap';\nimport { SEO } from '../components/SEO';\n\nconst chartDescription =\n \"

Matplotlib is the most famous library for data visualization with python. It allows to create literally every type of chart with a great level of customization. This page provides some general tips that can be applied on any kind of chart made with matplotlib like customizing titles or colors. If you're looking at creating a specific chart type, visit the gallery instead.

\";\n\nconst quickCode = `# library\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Create data\nx=range(1,6)\ny=[1,4,6,8,4]\n\n# Area plot\nplt.fill_between(x, y)\nplt.show()\n`;\n\nconst pyplotAPI = `# import libraries\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Create a data frame\ndf = pd.DataFrame ({'Group': ['A', 'B', 'C', 'D', 'E'], 'Value': [1,5,4,3,9]})\n\n# Create horizontal bars\nplt.barh(y=df.Group, width=df.Value);\n\n# Add title\nplt.title('A simple barplot');\n`;\n\nconst objOrientedAPI = `# import libraries\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Create a data frame\ndf = pd.DataFrame ({'Group': ['A', 'B', 'C', 'D', 'E'], 'Value': [1,5,4,3,9]})\n\n# Initialize a Figure and an Axes\nfig, ax = plt.subplots()\n\n# Fig size\nfig.set_size_inches(9,9)\n\n# Create horizontal bars\nax.barh(y=df.Group, width=df.Value);\n\n# Add title\nax.set_title('A simple barplot');\n`;\n\nconst allStyle = [\n 'seaborn-darkgrid',\n 'seaborn-notebook',\n 'classic',\n 'seaborn-ticks',\n 'grayscale',\n 'bmh',\n 'seaborn-talk',\n 'dark_background',\n 'ggplot',\n 'fivethirtyeight',\n '_classic_test',\n 'seaborn-colorblind',\n 'seaborn-deep',\n 'seaborn-whitegrid',\n 'seaborn-bright',\n 'seaborn-poster',\n 'seaborn-muted',\n 'seaborn-paper',\n 'seaborn-white',\n 'seaborn-pastel',\n 'seaborn-dark',\n 'seaborn',\n 'seaborn-dark-palette',\n];\n\nexport const Head = () => (\n \n);\n\nexport default function Matplotlibs() {\n const [selectedParameter, setSelectedParameter] = React.useState(allStyle[0]);\n\n const allStyleButtons = allStyle.map((item, i) => (\n setSelectedParameter(e.currentTarget.value)}\n >\n {item}\n \n ));\n\n return (\n \n \n\n \n

⏱ Quick start

\n \n \n

\n Matplotlib is the most famous python data\n visualization library. It is widely used and most of other viz\n libraries (like seaborn) are actually built on top of\n it.\n

\n

\n Once installed, matplotlib must be imported, usually using{' '}\n import matplotlib.pyplot as plt. You can then use use\n the functions available in the plt object.\n

\n \n \n \n \n \n \n \n {quickCode}\n \n\n \n\n \n

\n \n Basic vocabulary\n

\n

\n The figure below describes the anatomy of a matplotlib{' '}\n charts. It names all the main components, names that you need to know\n to understand the documentation properly.\n

\n

\n ⚠️ Disclaimer: this figure comes from the very\n complete{' '}\n \n matplotlib documentation\n \n . Have a look at it for a thorough explanation on how this library\n works.\n

\n
\n \n
\n \n \n \n \n\n \n\n \n

\n \n Two distinct APIs\n

\n

\n There are 2 main ways to build a chart with matplotlib: the{' '}\n pyplot API and the object-oriented API.\n

\n

\n ➡️ pyplot API\n

\n

\n Pyplot is a collection of functions, each function applying a change\n to a figure. For instance, plt.barh() will build a\n barplot and plt.title() will add a title to it.\n

\n {pyplotAPI}\n\n
\n
\n

\n ➡️ object oriented API\n

\n

\n The object oriented API usually starts by initializing one{' '}\n Figure object and one or more Axes object\n using the subplot() function. Then the methods of those\n objects will be used to apply changes to the chart.\n

\n {objOrientedAPI}\n\n
\n
\n

\n See how the syntax slightly differs between both options? (\n plt.title versus ax.set_title(). This is\n pretty confusing and I advise to have a look to the{' '}\n \n official documentation\n {' '}\n for a more thorough explanation. In the gallery, both APIs are used.\n

\n \n\n \n\n \n

\n \n Customizing titles\n

\n

\n The title is a key part of a good chart. It has to summarize{' '}\n its key message in a few words. Always avoid to simply repeat what is\n displayed on each axis. Try to provide some more insight allowing to\n understand the main result instead.\n

\n

\n The post #190 goes in\n depth on how to customize the matplotlib\n title. All the main types of change are descried: changing its color,\n adding a subtitle, using math formulas and so on. Give it a go!\n

\n \n \n \n \n \n \n \n\n \n\n \n

\n \n Annotation\n

\n

\n Annotation is a key part of a great visualization. It transforms a\n simple chart in a whole piece of story telling. Before you share your\n graph with anyone, make sure the key insight are highlighted with\n labels, shapes or color.\n

\n

\n All those kind of annotation are built thanks to various matplotlib\n functions like annotate() or add_patch().\n The blogposts showcased below should get you started with it.\n

\n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\n
\n \n

✨ Cheatsheets

\n

\n It's pretty hard to remember all the matplotlib associated\n vocabulary. The cheatsheet section{' '}\n is here to help, with a few sheets that you'll probably want to use\n on a daily basis.\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n
\n\n \n\n \n

\n \n Custom fonts\n

\n

\n Using custom fonts adds a nice touch to your graph and make it shine\n among all the other plots using defaults. The blog-post below will\n guide you through the process: installing a font and calling it with\n matplotlib functions.\n

\n
\n \n \n \n
\n \n\n \n\n \n

\n \n Add margin around a matplotlib chart\n

\n

\n It is a common struggle to deal with the margins around the chart.\n Fortunately this is pretty straightforward thanks to the{' '}\n subplots_adjust() function as explained in the examples\n below.\n

\n \n \n \n \n \n \n\n \n\n \n

\n \n Split the figure in subplots\n

\n

\n It can be really useful to split your graphic window in several parts,\n in order to display several charts in the same time. The{' '}\n subplot() function of matplotlib allows to\n do it quickly with a great level of customization.\n

\n \n \n \n \n \n

\n Additional note: how to remove some unused entries in a grid using the{' '}\n ax.remove() function:\n

\n \n \n \n \n\n \n\n \n

\n \n Available style sheets\n

\n

\n Matplotlib comes with a set of 26 pre-built themes to\n style your chart easily.{' '}\n This post is dedicated\n to this feature, explaining how to use plt.style.use() to\n pick a theme up.\n

\n
\n \n \n \n
\n \n\n \n\n \n

\n \n Customizing axis\n

\n

\n Matplotlib allows every kind of axis configuration. It is\n mainly done thanks to the tick_params(),{' '}\n xticks() and xlabels() functions. The{' '}\n post #191 will\n guide you through all the possibilities.\n

\n \n \n \n \n \n \n \n \n\n \n\n \n

\n \n Customizing the legend\n

\n

\n If you have several colors or marker on your chart, you can't escape\n the hassle of building a nice legend. The blogpost linked below should\n help for all the most common use cases like positioning the legend,\n adding and customizing a title to it, controling markers and labels\n and more.\n

\n \n \n \n \n \n \n \n \n \n \n\n \n\n \n

Insert images in a matplotlib graph

\n

\n It can be very useful to insert one or several images in a\n matplotlib graph. It can be used for the graph caption or even inside\n the chart area to provide more context.\n

\n

\n Please find below a{' '}\n complete tutorial{' '}\n explaining how to proceed using the PIL and{' '}\n io libraries, and a{' '}\n \n real life usage\n {' '}\n of this technique to produce a beautiful & polished figure.\n

\n \n \n \n \n \n \n\n \n\n
\n \n \n \n
\n\n \n\n \n \n \n\n \n \n );\n}\n","import classNames from 'classnames';\nimport * as React from 'react';\nimport { useBootstrapPrefix } from './ThemeProvider';\nimport Button from './Button';\nimport { jsx as _jsx } from \"react/jsx-runtime\";\nimport { Fragment as _Fragment } from \"react/jsx-runtime\";\nimport { jsxs as _jsxs } from \"react/jsx-runtime\";\nconst noop = () => undefined;\nconst ToggleButton = /*#__PURE__*/React.forwardRef(({\n bsPrefix,\n name,\n className,\n checked,\n type,\n onChange,\n value,\n disabled,\n id,\n inputRef,\n ...props\n}, ref) => {\n bsPrefix = useBootstrapPrefix(bsPrefix, 'btn-check');\n return /*#__PURE__*/_jsxs(_Fragment, {\n children: [/*#__PURE__*/_jsx(\"input\", {\n className: bsPrefix,\n name: name,\n type: type,\n value: value,\n ref: inputRef,\n autoComplete: \"off\",\n checked: !!checked,\n disabled: !!disabled,\n onChange: onChange || noop,\n id: id\n }), /*#__PURE__*/_jsx(Button, {\n ...props,\n ref: ref,\n className: 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'./chartImage.css';\n\nimport React from 'react';\nimport { useStaticQuery, graphql } from 'gatsby';\nimport { GatsbyImage } from 'gatsby-plugin-image';\nimport PropTypes from 'prop-types';\n\nconst allGifs = [\n 'animated_chart',\n 'animated_gapminder.gif',\n 'animated_volcano.gif',\n];\n\n// This component displays a logo representing a chart type of the gallery. Example: bar chart\n// Logo has a different size depending on the window size.\nexport default function ChartImage({ imgName, caption }) {\n if (allGifs.includes(imgName)) {\n return

TODO

;\n }\n\n const data = useStaticQuery(graphql`\n query MyQuery {\n allFile(filter: { relativeDirectory: { eq: \"graph\" } }) {\n edges {\n node {\n id\n name\n childImageSharp {\n gatsbyImageData(placeholder: BLURRED, layout: FULL_WIDTH)\n }\n }\n }\n }\n }\n `);\n\n const image = data.allFile.edges.find((n) => {\n return n.node.name.includes(imgName);\n });\n\n if (!image) {\n return null;\n }\n\n // Note: alt tag looks to be ignored?\n return (\n <>\n
\n \n {caption && (\n
\n
\n

{caption}

\n
\n
\n )}\n
\n \n );\n}\n\nChartImage.propTypes = {\n imgName: PropTypes.string,\n caption: PropTypes.string,\n};\n","import React from \"react\";\nimport ChartImage from \"../components/ChartImage\";\nimport Col from \"react-bootstrap/Col\";\nimport { Link } from \"gatsby\";\n\n// This component wraps a chart image into a column and make it responsive + clickable\nexport default function ChartImageContainer({ imgName, caption, linkTo }) {\n return (\n \n \n \n \n \n );\n}\n","// made following the Gatsby Doc: https://www.gatsbyjs.com/docs/how-to/adding-common-features/adding-seo-component/\n\n// This component is called by every page of the website.\n// At the top of each page you will find a Head component export that calls this SEO component\n\n// The SEO component will add stuff like title, description, image, lang... in the head of the HTML page\n// AdThrive ads code is also added here\n\nimport React from 'react';\n\nexport const SEO = ({ title, seoDescription, keywords }) => {\n return (\n <>\n {title}\n \n \n \n \n\n \n \n \n \n \n \n );\n};\n","import React from 'react';\nimport TitleAndDescription from '../components/TitleAndDescription';\nimport Layout from '../components/Layout';\nimport Container from 'react-bootstrap/Container';\nimport Contact from '../components/Contact';\nimport Row from 'react-bootstrap/Row';\nimport ChartImageContainer from '../components/ChartImageContainer';\nimport ChartFamilySection from '../components/ChartFamilySection';\nimport { Link } from 'gatsby';\nimport {\n Matplotlib,\n Pandas,\n Plotly,\n Seaborn,\n} from '../components/MiscellaneousLogos';\nimport { Button, Col } from 'react-bootstrap';\nimport CodeChunk from '../components/CodeChunk';\nimport ChartImage from '../components/ChartImage';\nimport Spacing from '../components/Spacing';\nimport { SEO } from '../components/SEO';\n\nconst chartDescription = (\n <>\n

\n A{' '}\n scatter plot{' '}\n displays the relationship between 2 numeric variables, one being displayed\n on the X axis (horizontal) and the other on the Y axis (vertical). Each\n data point is represented as a circle.\n

\n

\n Several tools allow to build scatterplots in python.{' '}\n Seaborn is probably the most straightforward\n library for the job, but matplotlib allows\n a greater level of customization. If you are looking for an interactive\n chart, plotly is definitely the way to go.\n

\n

\n This page provides many examples of scatterplots made with those Python\n tools, going from simple examples to highly customized{' '}\n versions.\n

\n \n);\n\nconst quickCode = `# library & dataset\nimport seaborn as sns\ndf = sns.load_dataset('iris')\n\n# use the function regplot to make a scatterplot\nsns.regplot(x=df[\"sepal_length\"], y=df[\"sepal_width\"])\n`;\n\nconst quickCodeMatplotlib = `# libraries\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n# Create a dataset:\ndf=pd.DataFrame({'x_values': range(1,101), 'y_values': np.random.randn(100)*15+range(1,101) })\n\n# plot\nplt.plot( 'x_values', 'y_values', data=df, linestyle='none', marker='o')\nplt.show()\n`;\n\nexport const Head = () => (\n \n);\n\nexport default function ScatterPlot() {\n return (\n \n \n\n \n

⏱ Quick start (Seaborn)

\n \n \n

\n The regplot() function of the Seaborn{' '}\n library is definitely the best way to build a scatterplot in\n minutes. 🔥\n

\n

\n Simply pass a numeric column of a data frame to both the{' '}\n x and y variable and the function will\n handle the rest.\n

\n \n \n \n \n \n \n \n {quickCode}\n
\n \n\n \n\n
\n \n

\n ⚠️ Scatterplot and overplotting\n

\n

\n The main danger with scatterplots is{' '}\n \n overplotting\n \n . When the sample size gets big, circles tend to overlap, making the\n figure unreadable.\n

\n

\n Several workarounds exist to fix the issue, like using opacity or\n switching to another chart type:\n

\n \n \n \n \n \n \n \n \n \n
\n\n \n\n \n

\n \n Scatterplots with Seaborn\n

\n

\n Seaborn is a python library allowing to\n make better charts easily. The regplot() function should\n get you started in minutes. The{' '}\n first example below\n explains how to build the most basic scatterplot with python. Then,\n several types of customization are described: adding a{' '}\n regression{' '}\n line, tweaking markers{' '}\n and axis,\n adding labels{' '}\n and more.\n

\n \n \n \n \n \n \n \n \n \n \n\n \n\n \n

\n 💡 Other charts involving scatterplots and Seaborn\n

\n

\n If you are interested in scatterplots, some other chart could be\n useful to you.\n

\n

\n A scatterplot with{' '}\n marginal distribution{' '}\n allows to check the distribution of both the x and{' '}\n y variables. A{' '}\n correlogram allows to check\n the relationship between each pair of numeric variables in a dataset.\n

\n \n \n \n \n \n\n \n\n \n

⏱ Quick start (Matplotlib)

\n \n \n

\n Matplotlib also requires only a few\n lines of code to draw a scatterplot thanks to its{' '}\n plot() function. The resulting chart is not as\n good-looking, but the function probably offers more flexibility in\n term of customization.\n

\n \n \n \n \n \n \n \n {quickCodeMatplotlib}\n
\n \n\n \n\n \n

\n \n Scatterplots with Matplotlib\n

\n

\n Matplotlib is another great alternative to build\n scatterplots with python. As often, it takes a bit more lines of code\n to get a decent chart, but allows more customization.\n

\n

\n The examples below should get you covered for all the most common\n problems: adding{' '}\n markers, adding\n labels, changing{' '}\n shapes and\n more.\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\n \n

\n \n Scatterplots with Plotly\n

\n

\n If you are looking for an interactive scatterplot,{' '}\n plotly is definitely the way to go.\n

\n

\n Interactivity is a real plus for scatterplots. It is very useful to\n have a tooltip associated to every markers to get some\n additional information about it. Zooming on a specific area of\n the scatterplot is also very valuable sometimes.\n

\n

\n The examples below should help you get started quickly with the plotly\n API:\n

\n \n \n \n \n \n \n\n \n \n

\n \n Scatterplots with Pandas\n

\n

\n Pandas, a data\n analysis library, also offers functions to build scatterplots. It uses\n matplotlib under the hood, but the syntax is more concise.\n

\n

\n The main difference is that we have to work with Pandas objects{' '}\n such as Series and DataFrame.\n

\n

\n The examples below should help you get started quickly with the pandas\n API:\n

\n \n \n \n \n \n \n\n \n \n

\n \n Best python scatterplot examples\n

\n

\n The web is full of astonishing charts made by awesome bloggers, (often\n using R). The{' '}\n Python graph gallery{' '}\n tries to display (or translate from R) some of the best creations and\n explain how their source code works.\n

\n

\n The first example{' '}\n below demos how to add clean labels on a scatterplot, automatically\n avoiding overlapping. It also explains how to control background,\n fonts, titles and more.\n

\n

\n If you want to display your work here, please drop me a word or even\n better, submit a{' '}\n \n Pull Request\n \n !\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n\n
\n \n \n \n
\n\n \n\n \n \n \n\n \n \n );\n}\n"],"names":["allGifs","ChartImage","_ref","imgName","caption","includes","React","image","useStaticQuery","allFile","edges","find","n","node","name","className","GatsbyImage","childImageSharp","gatsbyImageData","alt","ChartImageContainer","linkTo","Col","xs","md","Link","to","SEO","title","seoDescription","keywords","lang","content","property","chartDescription","href","quickCode","quickCodeMatplotlib","Head","ScatterPlot","Layout","isTocEnabled","TitleAndDescription","description","Container","id","Row","CodeChunk","Spacing","Button","size","Seaborn","Matplotlib","Plotly","Pandas","ChartFamilySection","chartFamily","Contact"],"sourceRoot":""} \ No newline at end of file diff --git a/connected-scatter-plot/index.html b/connected-scatter-plot/index.html index 90b19d70ea..38719dbe22 100644 --- a/connected-scatter-plot/index.html +++ b/connected-scatter-plot/index.html @@ -30,6 +30,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/connected-scatterplot-for-evolution/index.html b/connected-scatterplot-for-evolution/index.html index 1051e43ed5..6c09004f29 100644 --- a/connected-scatterplot-for-evolution/index.html +++ b/connected-scatterplot-for-evolution/index.html @@ -51,6 +51,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/connection-map/index.html b/connection-map/index.html index 504be7a394..0e2b330312 100644 --- a/connection-map/index.html +++ b/connection-map/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/correlogram/index.html b/correlogram/index.html index 75ccc93899..bcf5f984da 100644 --- a/correlogram/index.html +++ b/correlogram/index.html @@ -24,6 +24,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/custom-fonts-in-matplotlib/index.html b/custom-fonts-in-matplotlib/index.html index b2959f91a9..1e56c4088e 100644 --- a/custom-fonts-in-matplotlib/index.html +++ b/custom-fonts-in-matplotlib/index.html @@ -68,6 +68,6 @@

What to do if it fails

- \ No newline at end of file + \ No newline at end of file diff --git a/custom-legend-with-matplotlib/index.html b/custom-legend-with-matplotlib/index.html index e4759a1da0..5956c7516e 100644 --- a/custom-legend-with-matplotlib/index.html +++ b/custom-legend-with-matplotlib/index.html @@ -449,6 +449,6 @@

Adjust the legend position with loc

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b/density-chart-multiple-groups-seaborn/index.html @@ -104,6 +104,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/density-mirror/index.html b/density-mirror/index.html index 9dc9c8bcc8..b93632e2f6 100644 --- a/density-mirror/index.html +++ b/density-mirror/index.html @@ -86,6 +86,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/density-plot/index.html b/density-plot/index.html index 245d793515..a2c5d510fb 100644 --- a/density-plot/index.html +++ b/density-plot/index.html @@ -22,6 +22,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/donut-plot/index.html b/donut-plot/index.html index 6c318aa081..bf61845cf7 100644 --- a/donut-plot/index.html +++ b/donut-plot/index.html @@ -31,6 +31,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/error-bars-on-barplot/index.html b/error-bars-on-barplot/index.html index 7fee87b4ee..07df76b62f 100644 --- a/error-bars-on-barplot/index.html 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ztL7-Q(>yLwHE0Z?hl;Y+^&|oIRISnU6p&rylM`j*^yDI+a>?9{vNgGPSAz literal 0 HcmV?d00001 diff --git a/grouped-barplot/index.html b/grouped-barplot/index.html index 7170e55448..d796dc770d 100644 --- a/grouped-barplot/index.html +++ b/grouped-barplot/index.html @@ -65,6 +65,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/heatmap-for-timeseries-matplotlib/index.html b/heatmap-for-timeseries-matplotlib/index.html index 9d6eb9bc02..fba5f0991a 100644 --- a/heatmap-for-timeseries-matplotlib/index.html +++ b/heatmap-for-timeseries-matplotlib/index.html @@ -240,6 +240,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/heatmap/index.html b/heatmap/index.html index cf4dc8ba9b..51acecf112 100644 --- a/heatmap/index.html +++ b/heatmap/index.html @@ -26,6 +26,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/hexbin-map-from-geojson-python/index.html b/hexbin-map-from-geojson-python/index.html index 04c75eae28..f7049d85bf 100644 --- a/hexbin-map-from-geojson-python/index.html +++ b/hexbin-map-from-geojson-python/index.html @@ -146,6 +146,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/hexbin-map/index.html b/hexbin-map/index.html index 4c122ddd76..5de833601a 100644 --- a/hexbin-map/index.html +++ b/hexbin-map/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/hierarchical-edge-bundling/index.html b/hierarchical-edge-bundling/index.html index e8b6ef4cdf..4a88850f66 100644 --- a/hierarchical-edge-bundling/index.html +++ b/hierarchical-edge-bundling/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/histogram/index.html b/histogram/index.html index f1565d9a02..4295c5e724 100644 --- a/histogram/index.html +++ b/histogram/index.html @@ -43,6 +43,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/how-to-custom-annotations-matplotlib/index.html b/how-to-custom-annotations-matplotlib/index.html new file mode 100644 index 0000000000..6cdcf7ec09 --- /dev/null +++ b/how-to-custom-annotations-matplotlib/index.html @@ -0,0 +1,111 @@ +How to custmise annotations in Matplotlib

How to custmise annotations in Matplotlib

Annotations

+

In data visualization, annotations have a major role in available information to the reader.

+

They can be used to:

+
    +
  • highlight specific points
    • +
  • provide additional information
    • +
  • simply explain the data in more depth
    • +
+

However, it can be quite technicaly challenging to customize annotations in matplotlib.

+

Let's see how we can create nice annotations in a few examples.

Libraries

+

Default tools from matplotlib are unfortunately not enough to create nice annotations. We will use the highlight-text package that simplifies the process.

+

And we will also need to load matplotlib and numpy libraries.

import numpy as np
+import matplotlib.pyplot as plt
+from highlight_text import fig_text, ax_text

Simple annotation

+

When creating a plot with annotation in matplotlib, we mainly use 2 functions:

+
    +
  • annotate()
    • +
  • text()
    • +
+

Concretely, the annotate() function allows to add a text at a specific position, with an arrow pointing to a specific point, while the text() function allows to add a text at a specific position, without any arrow.

+

Let's see how they behave with a concrete example:

fig, ax = plt.subplots(ncols=2, figsize=(8, 5))
+x = [1, 2, 3, 4]
+y = [10, 11, 12, 13]
+
+point_to_highlight = (2, 11)
+
+# annotation with .text()
+ax[0].plot(x, y)
+ax[0].text(
+    x=point_to_highlight[0], # position on x-axis
+    y=point_to_highlight[1], # position on y-axis
+    s='Text near the point',
+    fontsize=12
+)
+ax[0].set_title('text()')
+
+# annotation with .annotate()
+ax[1].plot(x, y)
+ax[1].annotate(
+    text='Important Point',
+    xy=point_to_highlight, # position of the point
+    xytext=(1, 14), # position of the text
+    arrowprops=dict(facecolor='black', shrink=0.05),
+    fontsize=12
+)
+ax[1].set_title('        annotation()')
+
+plt.show()

Boldness

+

Thanks to the highlight_text package, it is possible to bold a specific part of the text. This is what will make the annotation stand out from the rest of the text.

+

The syntax is that we have to put the part of the text that we want to bold between < and >. Then, we use the highlight_textprops argument to specify the style of the selected part.

fig, ax = plt.subplots(figsize=(8, 5))
+x = [1, 2, 3, 4]
+y = [10, 11, 12, 13]
+point_to_highlight = (2, 11)
+
+ax.plot(x, y)
+text = 'Wow, this annotation is <much better!>'
+ax_text(
+    x=point_to_highlight[0], # position on x-axis
+    y=point_to_highlight[1], # position on y-axis
+    s=text,
+    fontsize=12,
+    highlight_textprops=[{"fontweight": 'bold'}],
+    ax=ax
+)
+
+plt.show()

Colors

+

The highlight_text package also allows to color a specific part of the text. This is a great way to add insight and make your chart more understandable.

path = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/iris.csv'
+path = '../../static/data/iris.csv'
+iris = pd.read_csv(path)
+
+fig, ax = plt.subplots(figsize=(8, 6))
+colors = ["#1B9E77", "#D95F02", "#7570B3"]
+for species, color in zip(iris['species'].unique(), colors):
+    subset = iris[iris['species'] == species]
+    ax.scatter(subset['sepal_length'], subset['sepal_width'], c=color, label=species, s=50)
+
+text = 'The iris dataset contains 3 species:\n<setosa>, <versicolor>, and <virginica>'
+fig_text(
+    s=text,
+    x=.5, y=1,
+    fontsize=20,
+    color='black',
+    highlight_textprops=[{"color": colors[0], 'fontweight': 'bold'},
+                         {"color": colors[1], 'fontweight': 'bold'},
+                         {"color": colors[2], 'fontweight': 'bold'}],
+    ha='center'
+)
+
+plt.show()

Going further

+

This post explains how to create nice annotations in matplotlib.

+

You might be interested in how to math in annotations or how to add an image in a plot

logo of a chart:ScatterPlot
+

Scatterplot

logo of a chart:Heatmap
+

Heatmap

logo of a chart:Correlogram
+

Correlogram

logo of a chart:BubblePlot
+

Bubble

logo of a chart:ScatterConnected
+

Connected Scatter

logo of a chart:2dDensity
+

2D Density

Contact & Edit


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥

This page is just a jupyter notebook, you can edit it here. Please help me making this website better 🙏!

Copyright © the Python Graph Gallery 2023
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+ + \ No newline at end of file diff --git a/how-to-remove-axis-in-matplotlib/index.html b/how-to-remove-axis-in-matplotlib/index.html index ba1d0c507a..61694d37f9 100644 --- a/how-to-remove-axis-in-matplotlib/index.html +++ b/how-to-remove-axis-in-matplotlib/index.html @@ -134,6 +134,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/how-to-use-rectangles-in-matplotlib-legends/index.html b/how-to-use-rectangles-in-matplotlib-legends/index.html index 09080a284a..b8e560853c 100644 --- a/how-to-use-rectangles-in-matplotlib-legends/index.html +++ b/how-to-use-rectangles-in-matplotlib-legends/index.html @@ -76,6 +76,6 @@

Problem

- \ No newline at end of file + \ No newline at end of file diff --git a/index.html b/index.html index 76fe1cd91a..6f8cc11453 100644 --- a/index.html +++ b/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/line-chart-dual-y-axis-with-matplotlib/index.html b/line-chart-dual-y-axis-with-matplotlib/index.html index e26987b0b2..8afd3eae30 100644 --- a/line-chart-dual-y-axis-with-matplotlib/index.html +++ b/line-chart-dual-y-axis-with-matplotlib/index.html @@ -101,6 +101,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/line-chart/index.html b/line-chart/index.html index 7502bc8e83..f5566d8292 100644 --- a/line-chart/index.html +++ b/line-chart/index.html @@ -25,6 +25,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/lollipop-plot/index.html b/lollipop-plot/index.html index 5f6207d0d6..c1d5bd10ff 100644 --- a/lollipop-plot/index.html +++ b/lollipop-plot/index.html @@ -27,6 +27,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/manhattan-plot-with-matplotlib/index.html b/manhattan-plot-with-matplotlib/index.html index f550ad3860..fb02b3fb8f 100644 --- a/manhattan-plot-with-matplotlib/index.html +++ b/manhattan-plot-with-matplotlib/index.html @@ -62,6 +62,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/map-read-geojson-with-python-geopandas/index.html b/map-read-geojson-with-python-geopandas/index.html index 495348d024..1a1d949ff8 100644 --- a/map-read-geojson-with-python-geopandas/index.html +++ b/map-read-geojson-with-python-geopandas/index.html @@ -40,6 +40,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/map/index.html b/map/index.html index f6f5e0264e..2220b332dd 100644 --- a/map/index.html +++ b/map/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/matplotlib/index.html b/matplotlib/index.html index 9a72cb2016..f91a85d089 100644 --- a/matplotlib/index.html +++ b/matplotlib/index.html @@ -43,7 +43,7 @@ # Add title ax.set_title('A simple barplot'); -

See how the syntax slightly differs between both options? (plt.title versus ax.set_title(). This is pretty confusing and I advise to have a look to the official documentation for a more thorough explanation. In the gallery, both APIs are used.

Matplotlib logoCustomizing titles

The title is a key part of a good chart. It has to summarize its key message in a few words. Always avoid to simply repeat what is displayed on each axis. Try to provide some more insight allowing to understand the main result instead.

The post #190 goes in depth on how to customize the matplotlibtitle. All the main types of change are descried: changing its color, adding a subtitle, using math formulas and so on. Give it a go!

Python chart with subtitle and custom color

Python chart with subtitle and custom color

Add a math formula in title with Matplotlib

Add a math formula in title with Matplotlib

Control the height of the matplotlib title

Control the height of the matplotlib title

Subplot, title, and margin customization

Subplot, title, and margin customization

Matplotlib logoAnnotation

Annotation is a key part of a great visualization. It transforms a simple chart in a whole piece of story telling. Before you share your graph with anyone, make sure the key insight are highlighted with labels, shapes or color.

All those kind of annotation are built thanks to various matplotlib functions like annotate() or add_patch(). The blogposts showcased below should get you started with it.

Add a label with a segment

Add a label with a segment

Add a square to highlight an area

Add a square to highlight an area

Add a circle to highlight an area

Add a circle to highlight an area

Add a segment

Add a segment

Add vertical and horizontal ablines

Add vertical and horizontal ablines

Annotate with an equation

Annotate with an equation

Subplot, title, and margin customization

Subplot, title, and margin customization

How to add an img into a matplotlib chart

How to add an img into a matplotlib chart

Matplotlib logoCustom fonts

Using custom fonts adds a nice touch to your graph and make it shine among all the other plots using defaults. The blog-post below will guide you through the process: installing a font and calling it with matplotlib functions.

Matplotlib logoAdd margin around a matplotlib chart

It is a common struggle to deal with the margins around the chart. Fortunately this is pretty straightforward thanks to the subplots_adjust() function as explained in the examples below.

Increase the bottom margin of a matplotlib chart.

Increase the bottom margin of a matplotlib chart.

Increase the top margin to fit a title

Increase the top margin to fit a title

Subplot, title, and margin customization

Subplot, title, and margin customization

Matplotlib logoSplit the figure in subplots

It can be really useful to split your graphic window in several parts, in order to display several charts in the same time. The subplot() function of matplotlib allows to do it quickly with a great level of customization.

Split the figure in 4 charts of same size.

Split the figure in 4 charts of same size.

Split the figure with a custom layout

Split the figure with a custom layout

Manage chart size on subplots

Manage chart size on subplots

Additional note: how to remove some unused entries in a grid using the ax.remove() function:

How to remove some unused entries in a chart grid.

How to remove some unused entries in a chart grid.

Matplotlib logoAvailable style sheets

Matplotlib comes with a set of 26 pre-built themes to style your chart easily. This post is dedicated to this feature, explaining how to use plt.style.use() to pick a theme up.

Matplotlib logoCustomizing axis

Matplotlib allows every kind of axis configuration. It is mainly done thanks to the tick_params(), xticks() and xlabels() functions. The post #191 will guide you through all the possibilities.

Change the axis title and its appearance

Change the axis title and its appearance

Control axis labels and ticks look

Control axis labels and ticks look

Control axis limit with the xlim() function

Control axis limit with the xlim() function

How to deal with the date format. How to represent those dates properly on the X axis.

How to deal with the date format. How to represent those dates properly on the X axis.

How to use a log scale for the X or the Y axis.

How to use a log scale for the X or the Y axis.

Matplotlib logoCustomizing the legend

If you have several colors or marker on your chart, you can't escape the hassle of building a nice legend. The blogpost linked below should help for all the most common use cases like positioning the legend, adding and customizing a title to it, controling markers and labels and more.

How to add the default matplotlib legend

How to add the default matplotlib legend

Legend location, learn how to add it anywhere including outside the plot area

Legend location, learn how to add it anywhere including outside the plot area

Customize the legend label appearance

Customize the legend label appearance

Add and customize the title of the legend

Add and customize the title of the legend

Customize the marker of each element item

Customize the marker of each element item

Add a background, stroke and more legend customization

Add a background, stroke and more legend customization

How to customize the legend handle to have a different shape that the one used on the chart

How to customize the legend handle to have a different shape that the one used on the chart

Insert images in a matplotlib graph

It can be very useful to insert one or several images in a matplotlib graph. It can be used for the graph caption or even inside the chart area to provide more context.

Please find below a complete tutorial explaining how to proceed using the PIL and io libraries, and a real life usage of this technique to produce a beautiful & polished figure.

How to add an img into a matplotlib chart

How to add an img into a matplotlib chart

A clean stacked area chart with images in the caption

A clean stacked area chart with images in the caption

Use an image inside each scatterplot circle

Use an image inside each scatterplot circle

Contact


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥


Copyright © the Python Graph Gallery 2023
Privacy | License

Python chart with subtitle and custom color

Add a math formula in title with Matplotlib

Add a math formula in title with Matplotlib

Control the height of the matplotlib title

Control the height of the matplotlib title

Subplot, title, and margin customization

Subplot, title, and margin customization

Matplotlib logoAnnotation

Annotation is a key part of a great visualization. It transforms a simple chart in a whole piece of story telling. Before you share your graph with anyone, make sure the key insight are highlighted with labels, shapes or color.

All those kind of annotation are built thanks to various matplotlib functions like annotate() or add_patch(). The blogposts showcased below should get you started with it.

Add a label with a segment

Add a label with a segment

Add a square to highlight an area

Add a square to highlight an area

Add a circle to highlight an area

Add a circle to highlight an area

Add a segment

Add a segment

Add vertical and horizontal ablines

Add vertical and horizontal ablines

Annotate with an equation

Annotate with an equation

Subplot, title, and margin customization

Subplot, title, and margin customization

How to add an img into a matplotlib chart

How to add an img into a matplotlib chart

How to customize color and style of the annotation

How to customize color and style of the annotation

Matplotlib logoCustom fonts

Using custom fonts adds a nice touch to your graph and make it shine among all the other plots using defaults. The blog-post below will guide you through the process: installing a font and calling it with matplotlib functions.

Matplotlib logoAdd margin around a matplotlib chart

It is a common struggle to deal with the margins around the chart. Fortunately this is pretty straightforward thanks to the subplots_adjust() function as explained in the examples below.

Increase the bottom margin of a matplotlib chart.

Increase the bottom margin of a matplotlib chart.

Increase the top margin to fit a title

Increase the top margin to fit a title

Subplot, title, and margin customization

Subplot, title, and margin customization

Matplotlib logoSplit the figure in subplots

It can be really useful to split your graphic window in several parts, in order to display several charts in the same time. The subplot() function of matplotlib allows to do it quickly with a great level of customization.

Split the figure in 4 charts of same size.

Split the figure in 4 charts of same size.

Split the figure with a custom layout

Split the figure with a custom layout

Manage chart size on subplots

Manage chart size on subplots

Additional note: how to remove some unused entries in a grid using the ax.remove() function:

How to remove some unused entries in a chart grid.

How to remove some unused entries in a chart grid.

Matplotlib logoAvailable style sheets

Matplotlib comes with a set of 26 pre-built themes to style your chart easily. This post is dedicated to this feature, explaining how to use plt.style.use() to pick a theme up.

Matplotlib logoCustomizing axis

Matplotlib allows every kind of axis configuration. It is mainly done thanks to the tick_params(), xticks() and xlabels() functions. The post #191 will guide you through all the possibilities.

Change the axis title and its appearance

Change the axis title and its appearance

Control axis labels and ticks look

Control axis labels and ticks look

Control axis limit with the xlim() function

Control axis limit with the xlim() function

How to deal with the date format. How to represent those dates properly on the X axis.

How to deal with the date format. How to represent those dates properly on the X axis.

How to use a log scale for the X or the Y axis.

How to use a log scale for the X or the Y axis.

Matplotlib logoCustomizing the legend

If you have several colors or marker on your chart, you can't escape the hassle of building a nice legend. The blogpost linked below should help for all the most common use cases like positioning the legend, adding and customizing a title to it, controling markers and labels and more.

How to add the default matplotlib legend

How to add the default matplotlib legend

Legend location, learn how to add it anywhere including outside the plot area

Legend location, learn how to add it anywhere including outside the plot area

Customize the legend label appearance

Customize the legend label appearance

Add and customize the title of the legend

Add and customize the title of the legend

Customize the marker of each element item

Customize the marker of each element item

Add a background, stroke and more legend customization

Add a background, stroke and more legend customization

How to customize the legend handle to have a different shape that the one used on the chart

How to customize the legend handle to have a different shape that the one used on the chart

Insert images in a matplotlib graph

It can be very useful to insert one or several images in a matplotlib graph. It can be used for the graph caption or even inside the chart area to provide more context.

Please find below a complete tutorial explaining how to proceed using the PIL and io libraries, and a real life usage of this technique to produce a beautiful & polished figure.

How to add an img into a matplotlib chart

How to add an img into a matplotlib chart

A clean stacked area chart with images in the caption

A clean stacked area chart with images in the caption

Use an image inside each scatterplot circle

Use an image inside each scatterplot circle

Contact


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥


Copyright © the Python Graph Gallery 2023
Privacy | License
- \ No newline at end of file + \ No newline at end of file diff --git a/network-chart/index.html b/network-chart/index.html index c2a8bbd7e9..52cb06bbc9 100644 --- a/network-chart/index.html +++ b/network-chart/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/page-data/app-data.json b/page-data/app-data.json index adfdf73190..4f89414d55 100644 --- a/page-data/app-data.json +++ b/page-data/app-data.json @@ -1 +1 @@ -{"webpackCompilationHash":"9539be7d733e464fbb0a"} +{"webpackCompilationHash":"3d72671ec63a20827418"} diff --git a/page-data/how-to-custom-annotations-matplotlib/page-data.json b/page-data/how-to-custom-annotations-matplotlib/page-data.json new file mode 100644 index 0000000000..ad0f38f876 --- /dev/null +++ b/page-data/how-to-custom-annotations-matplotlib/page-data.json @@ -0,0 +1 @@ +{"componentChunkName":"component---src-pages-jupyter-notebook-slug-js","path":"/how-to-custom-annotations-matplotlib/","result":{"data":{"jupyterNotebook":{"html":"

Annotations

\n

In data visualization, annotations have a major role in available information to the reader.

\n

They can be used to:

\n
    \n
  • highlight specific points
    • \n
  • provide additional information
    • \n
  • simply explain the data in more depth
    • \n
\n

However, it can be quite technicaly challenging to customize annotations in matplotlib.

\n

Let's see how we can create nice annotations in a few examples.

Libraries

\n

Default tools from matplotlib are unfortunately not enough to create nice annotations. We will use the highlight-text package that simplifies the process.

\n

And we will also need to load matplotlib and numpy libraries.

import numpy as np\nimport matplotlib.pyplot as plt\nfrom highlight_text import fig_text, ax_text

Simple annotation

\n

When creating a plot with annotation in matplotlib, we mainly use 2 functions:

\n
    \n
  • annotate()
    • \n
  • text()
    • \n
\n

Concretely, the annotate() function allows to add a text at a specific position, with an arrow pointing to a specific point, while the text() function allows to add a text at a specific position, without any arrow.

\n

Let's see how they behave with a concrete example:

fig, ax = plt.subplots(ncols=2, figsize=(8, 5))\nx = [1, 2, 3, 4]\ny = [10, 11, 12, 13]\n\npoint_to_highlight = (2, 11)\n\n# annotation with .text()\nax[0].plot(x, y)\nax[0].text(\n    x=point_to_highlight[0], # position on x-axis\n    y=point_to_highlight[1], # position on y-axis\n    s='Text near the point',\n    fontsize=12\n)\nax[0].set_title('text()')\n\n# annotation with .annotate()\nax[1].plot(x, y)\nax[1].annotate(\n    text='Important Point',\n    xy=point_to_highlight, # position of the point\n    xytext=(1, 14), # position of the text\n    arrowprops=dict(facecolor='black', shrink=0.05),\n    fontsize=12\n)\nax[1].set_title('        annotation()')\n\nplt.show()
\"\"

Boldness

\n

Thanks to the highlight_text package, it is possible to bold a specific part of the text. This is what will make the annotation stand out from the rest of the text.

\n

The syntax is that we have to put the part of the text that we want to bold between < and >. Then, we use the highlight_textprops argument to specify the style of the selected part.

fig, ax = plt.subplots(figsize=(8, 5))\nx = [1, 2, 3, 4]\ny = [10, 11, 12, 13]\npoint_to_highlight = (2, 11)\n\nax.plot(x, y)\ntext = 'Wow, this annotation is <much better!>'\nax_text(\n    x=point_to_highlight[0], # position on x-axis\n    y=point_to_highlight[1], # position on y-axis\n    s=text,\n    fontsize=12,\n    highlight_textprops=[{"fontweight": 'bold'}],\n    ax=ax\n)\n\nplt.show()
\"\"

Colors

\n

The highlight_text package also allows to color a specific part of the text. This is a great way to add insight and make your chart more understandable.

path = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/iris.csv'\npath = '../../static/data/iris.csv'\niris = pd.read_csv(path)\n\nfig, ax = plt.subplots(figsize=(8, 6))\ncolors = ["#1B9E77", "#D95F02", "#7570B3"]\nfor species, color in zip(iris['species'].unique(), colors):\n    subset = iris[iris['species'] == species]\n    ax.scatter(subset['sepal_length'], subset['sepal_width'], c=color, label=species, s=50)\n\ntext = 'The iris dataset contains 3 species:\\n<setosa>, <versicolor>, and <virginica>'\nfig_text(\n    s=text,\n    x=.5, y=1,\n    fontsize=20,\n    color='black',\n    highlight_textprops=[{"color": colors[0], 'fontweight': 'bold'},\n                         {"color": colors[1], 'fontweight': 'bold'},\n                         {"color": colors[2], 'fontweight': 'bold'}],\n    ha='center'\n)\n\nplt.show()
\"\"

Going further

\n

This post explains how to create nice annotations in matplotlib.

\n

You might be interested in how to math in annotations or how to add an image in a plot

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file diff --git a/pie-plot-matplotlib-basic/index.html b/pie-plot-matplotlib-basic/index.html index 85024d0b2f..116e3c6cae 100644 --- a/pie-plot-matplotlib-basic/index.html +++ b/pie-plot-matplotlib-basic/index.html @@ -48,6 +48,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/pie-plot/index.html b/pie-plot/index.html index 44d990b9fc..22fc5d755d 100644 --- a/pie-plot/index.html +++ b/pie-plot/index.html @@ -25,6 +25,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/plotly/index.html b/plotly/index.html index 7d1de8fa8b..c7b707c6a1 100644 --- a/plotly/index.html +++ b/plotly/index.html @@ -73,6 +73,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/privacy/index.html b/privacy/index.html index 30c8fbbbd4..5b23e44966 100644 --- a/privacy/index.html +++ b/privacy/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/python-colors/index.html b/python-colors/index.html index bf8228b4dd..aa47567dd0 100644 --- a/python-colors/index.html +++ b/python-colors/index.html @@ -31,6 +31,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/radar-chart/index.html b/radar-chart/index.html index 3a504cdd54..45e8565c0b 100644 --- a/radar-chart/index.html +++ b/radar-chart/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/raincloud-plot-with-matplotlib-and-ptitprince/index.html b/raincloud-plot-with-matplotlib-and-ptitprince/index.html index 27085e391d..9b17cdbe79 100644 --- a/raincloud-plot-with-matplotlib-and-ptitprince/index.html +++ b/raincloud-plot-with-matplotlib-and-ptitprince/index.html @@ -122,6 +122,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/ridgeline-graph-plotly/index.html b/ridgeline-graph-plotly/index.html index eac02760b1..155490ac54 100644 --- a/ridgeline-graph-plotly/index.html +++ b/ridgeline-graph-plotly/index.html @@ -169,6 +169,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/ridgeline-graph-seaborn/index.html b/ridgeline-graph-seaborn/index.html index a457201d6e..18594ac6cb 100644 --- a/ridgeline-graph-seaborn/index.html +++ b/ridgeline-graph-seaborn/index.html @@ -164,6 +164,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/ridgeline/index.html b/ridgeline/index.html index 60bd26acc7..4cf5c85a71 100644 --- a/ridgeline/index.html +++ b/ridgeline/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/sankey-diagram-with-python-and-plotly/index.html b/sankey-diagram-with-python-and-plotly/index.html index 5d439cdb7a..156b2c5f6b 100644 --- a/sankey-diagram-with-python-and-plotly/index.html +++ b/sankey-diagram-with-python-and-plotly/index.html @@ -64,6 +64,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/sankey-diagram/index.html b/sankey-diagram/index.html index 876055225e..34a5b0004c 100644 --- a/sankey-diagram/index.html +++ b/sankey-diagram/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/scatter-plot/index.html b/scatter-plot/index.html index 4d2b5dca6a..317c30d6dc 100644 --- a/scatter-plot/index.html +++ b/scatter-plot/index.html @@ -19,7 +19,7 @@ # plot plt.plot( 'x_values', 'y_values', data=df, linestyle='none', marker='o') plt.show() -

Matplotlib logoScatterplots with Matplotlib

Matplotlib is another great alternative to build scatterplots with python. As often, it takes a bit more lines of code to get a decent chart, but allows more customization.

The examples below should get you covered for all the most common problems: adding markers, addinglabels, changing shapes and more.

Most basic scatterplot with Matplotlib

Most basic scatterplot with Matplotlib

Customize marker features: size, color, stroke, type and more

Customize marker features: size, color, stroke, type and more

Display a math equation on your matplotlib chart

Display a math equation on your matplotlib chart

Matplotlib: add horizontal and vertical ablines

Matplotlib: add horizontal and vertical ablines

Highlight a specific area of the chart with Matplotlib

Highlight a specific area of the chart with Matplotlib

Add an annotation to highlight a specific marker

Add an annotation to highlight a specific marker

How to build a Manhattan plot with Python and Matplotlib

How to build a Manhattan plot with Python and Matplotlib

Scatterplot with grouping and legend

Scatterplot with grouping and legend

Legend customization: how to control location, add a background, customize labels and markers and more

Legend customization: how to control location, add a background, customize labels and markers and more

How to use a log scale for the X or the Y axis.

How to use a log scale for the X or the Y axis.

How to compute and add a linear regression to a scatterplot with Python and matplotlib

How to compute and add a linear regression to a scatterplot with Python and matplotlib

Linear regression with statistics on top of a scatterplot

Linear regression with statistics on top of a scatterplot

Customized linear regression with statistics on top of a scatterplot

Customized linear regression with statistics on top of a scatterplot

Subplot, title, and margin customization

Subplot, title, and margin customization

How to add an img into a matplotlib chart

How to add an img into a matplotlib chart

Plotly logoScatterplots with Plotly

If you are looking for an interactive scatterplot, plotly is definitely the way to go.

Interactivity is a real plus for scatterplots. It is very useful to have a tooltip associated to every markers to get some additional information about it. Zooming on a specific area of the scatterplot is also very valuable sometimes.

The examples below should help you get started quickly with the plotly API:

Most basic scatterplot with plotly

Most basic scatterplot with plotly

Plotly scatterplot with several levels of grouping

Plotly scatterplot with several levels of grouping

Plotly scatterplot with a trend line

Plotly scatterplot with a trend line

Pandas logoScatterplots with Pandas

Pandas, a data analysis library, also offers functions to build scatterplots. It uses matplotlib under the hood, but the syntax is more concise.

The main difference is that we have to work with Pandas objects such as Series and DataFrame.

The examples below should help you get started quickly with the pandas API:

Introduction to scatter plot with pandas

Introduction to scatter plot with pandas

Customize scatter plots with pandas

Customize scatter plots with pandas

Scatter plots grouped by color with pandas

Scatter plots grouped by color with pandas

Matplotlib logoBest python scatterplot examples

The web is full of astonishing charts made by awesome bloggers, (often using R). The Python graph gallery tries to display (or translate from R) some of the best creations and explain how their source code works.

The first example below demos how to add clean labels on a scatterplot, automatically avoiding overlapping. It also explains how to control background, fonts, titles and more.

If you want to display your work here, please drop me a word or even better, submit a Pull Request!

Learn how to use the adjustText library to add many labels with no overlaps.

Learn how to use the adjustText library to add many labels with no overlaps.

Scatterplot with regression fit, group colors and non-overlapping labels.

Scatterplot with regression fit, group colors and non-overlapping labels.

Dark mode scatterplot with custom annotations, title and explanations. All of that with a very good appearance!

Dark mode scatterplot with custom annotations, title and explanations. All of that with a very good appearance!

A scatter plot with images inside each marker to provide additional context

A scatter plot with images inside each marker to provide additional context

A scatter plot with custom annotations and colors, with some markers being circled

A scatter plot with custom annotations and colors, with some markers being circled

Contact


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥


Copyright © the Python Graph Gallery 2023
Privacy | License

Most basic scatterplot with Matplotlib

Customize marker features: size, color, stroke, type and more

Customize marker features: size, color, stroke, type and more

Display a math equation on your matplotlib chart

Display a math equation on your matplotlib chart

Matplotlib: add horizontal and vertical ablines

Matplotlib: add horizontal and vertical ablines

Highlight a specific area of the chart with Matplotlib

Highlight a specific area of the chart with Matplotlib

Add an annotation to highlight a specific marker

Add an annotation to highlight a specific marker

How to build a Manhattan plot with Python and Matplotlib

How to build a Manhattan plot with Python and Matplotlib

Scatterplot with grouping and legend

Scatterplot with grouping and legend

Legend customization: how to control location, add a background, customize labels and markers and more

Legend customization: how to control location, add a background, customize labels and markers and more

How to use a log scale for the X or the Y axis.

How to use a log scale for the X or the Y axis.

How to compute and add a linear regression to a scatterplot with Python and matplotlib

How to compute and add a linear regression to a scatterplot with Python and matplotlib

Linear regression with statistics on top of a scatterplot

Linear regression with statistics on top of a scatterplot

Customized linear regression with statistics on top of a scatterplot

Customized linear regression with statistics on top of a scatterplot

Subplot, title, and margin customization

Subplot, title, and margin customization

How to add an img into a matplotlib chart

How to add an img into a matplotlib chart

Link title and scatter plot with custom annotations

Link title and scatter plot with custom annotations

Plotly logoScatterplots with Plotly

If you are looking for an interactive scatterplot, plotly is definitely the way to go.

Interactivity is a real plus for scatterplots. It is very useful to have a tooltip associated to every markers to get some additional information about it. Zooming on a specific area of the scatterplot is also very valuable sometimes.

The examples below should help you get started quickly with the plotly API:

Most basic scatterplot with plotly

Most basic scatterplot with plotly

Plotly scatterplot with several levels of grouping

Plotly scatterplot with several levels of grouping

Plotly scatterplot with a trend line

Plotly scatterplot with a trend line

Pandas logoScatterplots with Pandas

Pandas, a data analysis library, also offers functions to build scatterplots. It uses matplotlib under the hood, but the syntax is more concise.

The main difference is that we have to work with Pandas objects such as Series and DataFrame.

The examples below should help you get started quickly with the pandas API:

Introduction to scatter plot with pandas

Introduction to scatter plot with pandas

Customize scatter plots with pandas

Customize scatter plots with pandas

Scatter plots grouped by color with pandas

Scatter plots grouped by color with pandas

Matplotlib logoBest python scatterplot examples

The web is full of astonishing charts made by awesome bloggers, (often using R). The Python graph gallery tries to display (or translate from R) some of the best creations and explain how their source code works.

The first example below demos how to add clean labels on a scatterplot, automatically avoiding overlapping. It also explains how to control background, fonts, titles and more.

If you want to display your work here, please drop me a word or even better, submit a Pull Request!

Learn how to use the adjustText library to add many labels with no overlaps.

Learn how to use the adjustText library to add many labels with no overlaps.

Scatterplot with regression fit, group colors and non-overlapping labels.

Scatterplot with regression fit, group colors and non-overlapping labels.

Dark mode scatterplot with custom annotations, title and explanations. All of that with a very good appearance!

Dark mode scatterplot with custom annotations, title and explanations. All of that with a very good appearance!

A scatter plot with images inside each marker to provide additional context

A scatter plot with images inside each marker to provide additional context

A scatter plot with custom annotations and colors, with some markers being circled

A scatter plot with custom annotations and colors, with some markers being circled

Contact


👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥


Copyright © the Python Graph Gallery 2023
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a/streamchart/index.html b/streamchart/index.html index f92eff4dd2..1d99d9f13f 100644 --- a/streamchart/index.html +++ b/streamchart/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/table/index.html b/table/index.html index 58713248d2..84e61bdfd2 100644 --- a/table/index.html +++ b/table/index.html @@ -16,6 +16,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/timeseries/index.html b/timeseries/index.html index 9f40d5175c..3016ef45c6 100644 --- a/timeseries/index.html +++ b/timeseries/index.html @@ -25,6 +25,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/treemap/index.html b/treemap/index.html index 86b528d9a1..83eaa5a1fa 100644 --- a/treemap/index.html +++ b/treemap/index.html @@ -28,6 +28,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/venn-diagram/index.html b/venn-diagram/index.html index 630f59b328..b3a1ce1c86 100644 --- a/venn-diagram/index.html +++ b/venn-diagram/index.html @@ -23,6 +23,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/violin-plot/index.html b/violin-plot/index.html index a7d6ac2c3b..096c86a81b 100644 --- a/violin-plot/index.html +++ b/violin-plot/index.html @@ -22,6 +22,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/waffle-chart/index.html b/waffle-chart/index.html index 631ea564cd..d1c71de52f 100644 --- a/waffle-chart/index.html +++ b/waffle-chart/index.html @@ -31,6 +31,6 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/web-barplot-with-annotations-and-arrows/index.html b/web-barplot-with-annotations-and-arrows/index.html index c2ca38129e..9e38786ea0 100644 --- a/web-barplot-with-annotations-and-arrows/index.html +++ b/web-barplot-with-annotations-and-arrows/index.html @@ -304,6 +304,6 @@

Add arrows

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Bonus: What is the Silverman bandwidth?

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Add arrows

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Reproduction

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Smoothing

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