running

Author: bbcho

docs/api/coords.md

@@ -8,6 +8,10 @@ Coordinate systems control how data coordinates are mapped to the plane of the g
     options:
       show_root_heading: true
 
+::: ggplotly.coords.coord_fixed.coord_fixed
+    options:
+      show_root_heading: true
+
 ::: ggplotly.coords.coord_flip.coord_flip
     options:
       show_root_heading: true

docs/api/geoms.md

@@ -68,6 +68,10 @@ Geometric objects (geoms) are the visual elements used to represent data in a pl
     options:
       show_root_heading: true
 
+::: ggplotly.geoms.geom_rect.geom_rect
+    options:
+      show_root_heading: true
+
 ## Statistical Geoms
 
 ::: ggplotly.geoms.geom_smooth.geom_smooth
@@ -88,6 +92,10 @@ Geometric objects (geoms) are the visual elements used to represent data in a pl
     options:
       show_root_heading: true
 
+::: ggplotly.geoms.geom_label.geom_label
+    options:
+      show_root_heading: true
+
 ## Reference Lines
 
 ::: ggplotly.geoms.geom_hline.geom_hline

docs/api/scales.md

@@ -22,6 +22,16 @@ Scales control how data values are mapped to visual properties like position, co
     options:
       show_root_heading: true
 
+## Reversed Scales
+
+::: ggplotly.scales.scale_x_reverse.scale_x_reverse
+    options:
+      show_root_heading: true
+
+::: ggplotly.scales.scale_y_reverse.scale_y_reverse
+    options:
+      show_root_heading: true
+
 ## Date and Time Scales
 
 ::: ggplotly.scales.scale_x_date.scale_x_date

docs/guide/coordinates.ipynb

@@ -66,13 +66,26 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "**xlim/ylim vs coord_cartesian:** `xlim()` and `ylim()` filter the data before plotting. `coord_cartesian()` zooms the view without removing data points. This matters for statistical calculations like `geom_smooth`.\n",
-    "\n",
-    "## coord_flip\n",
-    "\n",
-    "Flip x and y axes."
-   ]
+   "source": "**xlim/ylim vs coord_cartesian:** `xlim()` and `ylim()` filter the data before plotting. `coord_cartesian()` zooms the view without removing data points. This matters for statistical calculations like `geom_smooth`.\n\n## coord_fixed\n\nFixed aspect ratio coordinates. Essential for maps and any plot where x and y must have equal scaling."
+  },
+  {
+   "cell_type": "code",
+   "source": "# Square data should appear as a square with ratio=1\nsquare_df = pd.DataFrame({'x': [0, 1, 1, 0, 0], 'y': [0, 0, 1, 1, 0]})\nggplot(square_df, aes(x='x', y='y')) + geom_path(size=2) + geom_point(size=10) + coord_fixed()",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": "# With ratio=2, the square appears as a tall rectangle (y is stretched)\nggplot(square_df, aes(x='x', y='y')) + geom_path(size=2) + geom_point(size=10) + coord_fixed(ratio=2)",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": "## coord_flip\n\nFlip x and y axes. Useful for horizontal bar charts and boxplots.",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -104,11 +117,11 @@
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Pie chart\npie_df = pd.DataFrame({\n    'category': ['A', 'B', 'C', 'D'],\n    'value': [25, 30, 20, 25]\n})\npie_df['x'] = 1  # Constant x for stacked bar -> pie conversion\n\nggplot(pie_df, aes(x='x', y='value', fill='category')) + \\\n    geom_bar(stat='identity', width=1) + \\\n    coord_polar(theta='y')"
+   "source": "### coord_polar Parameters\n\n```python\ncoord_polar(\n    theta='x',      # Variable mapped to angle ('x' or 'y')\n    start=0,        # Starting angle in radians\n    direction=1     # 1 = clockwise, -1 = counter-clockwise\n)\n```\n\n## coord_sf\n\nFor geographic/spatial data with proper map projections. Requires geopandas.\n\n```python\nimport geopandas as gpd\n\n# Load geographic data\nworld = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n\n# Plot with projection\nggplot(world) + geom_sf() + coord_sf(crs='EPSG:4326')\n```\n\n### Available Projections\n\n- `'mercator'` - Web Mercator (Google Maps style)\n- `'albers usa'` - Albers USA (good for US maps)\n- `'orthographic'` - Globe view\n- `'natural earth'` - Natural Earth projection\n- `'robinson'` - Robinson projection\n\n## Coordinate Reference\n\n| Function | Description |\n|----------|-------------|\n| `coord_cartesian` | Default Cartesian, zoom without clipping |\n| `coord_fixed` | Fixed aspect ratio (ratio=1 for equal scaling) |\n| `coord_flip` | Flip x and y axes |\n| `coord_polar` | Polar coordinates |\n| `coord_sf` | Geographic projections |"
   },
   {
    "cell_type": "markdown",

docs/guide/geoms.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Geoms\n\nGeoms (geometric objects) are the visual elements that represent your data. ggplotly provides 44 geoms for different visualization types."
+   "source": "# Geoms\n\nGeoms (geometric objects) are the visual elements that represent your data. ggplotly provides 46 geoms for different visualization types."
   },
   {
    "cell_type": "code",
@@ -316,6 +316,18 @@
     "ggplot(ribbon_df, aes(x='date', ymin='lower', ymax='upper')) + geom_ribbon(alpha=0.3)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": "### geom_rect\n\nRectangles defined by xmin, xmax, ymin, ymax.",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "# Rectangle data\nrect_df = pd.DataFrame({\n    'xmin': [0, 2, 4],\n    'xmax': [1, 3, 5],\n    'ymin': [0, 1, 0.5],\n    'ymax': [2, 3, 2.5],\n    'category': ['A', 'B', 'C']\n})\n\nggplot(rect_df, aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax', fill='category')) + geom_rect(alpha=0.7)",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -366,6 +378,18 @@
     "ggplot(bar_df, aes(x='category', y='count', label='count')) + geom_col() + geom_text()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": "### geom_label\n\nText labels with a background box.",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "source": "ggplot(bar_df, aes(x='category', y='count', label='count')) + geom_col() + geom_label()",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -559,7 +583,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## Complete Geom List\n\n| Geom | Description |\n|------|-------------|\n| `geom_point` | Scatter plots |\n| `geom_line` | Line plots (sorted by x) |\n| `geom_lines` | Multi-series line plots |\n| `geom_path` | Path plots (data order) |\n| `geom_bar` | Bar charts |\n| `geom_col` | Column charts |\n| `geom_histogram` | Histograms |\n| `geom_boxplot` | Box plots |\n| `geom_violin` | Violin plots |\n| `geom_density` | Density plots |\n| `geom_area` | Area plots |\n| `geom_ribbon` | Ribbon plots |\n| `geom_smooth` | Smoothed lines |\n| `geom_tile` | Heatmaps |\n| `geom_text` | Text labels |\n| `geom_errorbar` | Error bars |\n| `geom_segment` | Line segments |\n| `geom_step` | Step plots |\n| `geom_rug` | Rug plots |\n| `geom_jitter` | Jittered points |\n| `geom_vline` | Vertical lines |\n| `geom_hline` | Horizontal lines |\n| `geom_abline` | Diagonal lines |\n| `geom_contour` | Contour lines |\n| `geom_contour_filled` | Filled contours |\n| `geom_map` | Choropleth maps |\n| `geom_sf` | Simple features |\n| `geom_range` | Range plots |\n| `geom_edgebundle` | Edge bundling |\n| `geom_searoute` | Sea routes |\n| `geom_fanchart` | Fan charts for uncertainty |\n| `geom_point_3d` | 3D points |\n| `geom_surface` | 3D surfaces |\n| `geom_wireframe` | 3D wireframes |\n| `geom_candlestick` | Candlestick charts |\n| `geom_ohlc` | OHLC charts |\n| `geom_waterfall` | Waterfall charts |\n| `geom_sankey` | Sankey flow diagrams |\n| `geom_stl` | STL decomposition plots |\n| `geom_acf` | Autocorrelation plots |\n| `geom_pacf` | Partial autocorrelation plots |\n| `geom_norm` | Normal distribution overlay |\n| `geom_qq` | Q-Q plots |\n| `geom_qq_line` | Q-Q reference line |"
+   "source": "## Complete Geom List\n\n| Geom | Description |\n|------|-------------|\n| `geom_point` | Scatter plots |\n| `geom_line` | Line plots (sorted by x) |\n| `geom_lines` | Multi-series line plots |\n| `geom_path` | Path plots (data order) |\n| `geom_bar` | Bar charts |\n| `geom_col` | Column charts |\n| `geom_histogram` | Histograms |\n| `geom_boxplot` | Box plots |\n| `geom_violin` | Violin plots |\n| `geom_density` | Density plots |\n| `geom_area` | Area plots |\n| `geom_ribbon` | Ribbon plots |\n| `geom_rect` | Rectangles |\n| `geom_smooth` | Smoothed lines |\n| `geom_tile` | Heatmaps |\n| `geom_text` | Text labels |\n| `geom_label` | Text labels with background |\n| `geom_errorbar` | Error bars |\n| `geom_segment` | Line segments |\n| `geom_step` | Step plots |\n| `geom_rug` | Rug plots |\n| `geom_jitter` | Jittered points |\n| `geom_vline` | Vertical lines |\n| `geom_hline` | Horizontal lines |\n| `geom_abline` | Diagonal lines |\n| `geom_contour` | Contour lines |\n| `geom_contour_filled` | Filled contours |\n| `geom_map` | Choropleth maps |\n| `geom_sf` | Simple features |\n| `geom_range` | Range plots |\n| `geom_edgebundle` | Edge bundling |\n| `geom_searoute` | Sea routes |\n| `geom_fanchart` | Fan charts for uncertainty |\n| `geom_point_3d` | 3D points |\n| `geom_surface` | 3D surfaces |\n| `geom_wireframe` | 3D wireframes |\n| `geom_candlestick` | Candlestick charts |\n| `geom_ohlc` | OHLC charts |\n| `geom_waterfall` | Waterfall charts |\n| `geom_sankey` | Sankey flow diagrams |\n| `geom_stl` | STL decomposition plots |\n| `geom_acf` | Autocorrelation plots |\n| `geom_pacf` | Partial autocorrelation plots |\n| `geom_norm` | Normal distribution overlay |\n| `geom_qq` | Q-Q plots |\n| `geom_qq_line` | Q-Q reference line |"
   }
  ],
  "metadata": {