refactor: update documentation for various chart examples with improved clarity and structure

Author: palewire

docs/user-guide/charts/column-charts.md

@@ -13,20 +13,19 @@ url = "https://raw.githubusercontent.com/chekos/datawrapper/main/tests/samples/c
 df = pd.read_csv(url)
 
 chart = dw.ColumnChart(
-    # Chart title with HTML line break
+    # Chart headline
     title="U.S. unemployment rate",
+    # Introductory text
+    intro="January 2016-September 2020",
     # Data source attribution
     source_name="U.S. Bureau of Labor Statistics",
     source_url="https://www.bls.gov/",
-    # Introductory text
-    intro="January 2016-September 2020",
     # Data from pandas DataFrame
     data=df,
-    # Format Y-axis grid labels with one decimal place and a percentage sign
+    # Format labels with one decimal place and a percentage sign
     y_grid_format=dw.NumberFormat.PERCENT_UP_TO_ONE_DECIMAL,
-    # Format value labels with one decimal place
     value_labels_format=dw.NumberFormat.PERCENT_UP_TO_ONE_DECIMAL,
-    # Highlight specific column in red
+    # Highlight specific columns with custom colors
     base_color="#CCCCCC",
     color_category={
         "2020/04": "rgb(21, 96, 122)",
@@ -36,6 +35,7 @@ chart = dw.ColumnChart(
         "2020/08": "rgb(21, 96, 122)",
         "2020/09": "rgb(21, 96, 122)",
     },
+    # Annotations to highlight the COVID-19 period
     range_annotations=[
         dw.RangeAnnotation(
             x0="2020/01/01",

docs/user-guide/charts/line-charts.md

@@ -1,15 +1,15 @@
 # LineChart
 
-## Example
+This example, drawn from <a href="https://www.datawrapper.de/charts/lines">the Datawrapper documentation</a>, demonstrates how to create a line chart with a shaded confidence interval around the line.
 
-This example demonstrates how to create a line chart showing global land temperature in July from 1753-2015, with confidence intervals displayed as a shaded area.
+<iframe title="Global land temperature in July, 1753-2015" aria-label="Line chart" id="datawrapper-chart-9Qlvu" src="https://datawrapper.dwcdn.net/9Qlvu/1/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="400" data-external="1"></iframe><script type="text/javascript">window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}});</script>
 
 ```python
 import pandas as pd
 import datawrapper as dw
 
 # Load temperature data from GitHub
-url = "https://raw.githubusercontent.com/palewire/datawrapper-api-classes/main/tests/samples/line/land-temps.csv"
+url = "https://raw.githubusercontent.com/chekos/datawrapper/main/tests/samples/line/land-temps.csv"
 df = pd.read_csv(url)
 
 chart = dw.LineChart(
@@ -18,56 +18,59 @@ chart = dw.LineChart(
     # Data source attribution
     source_name="Berkeley Earth",
     source_url="http://berkeleyearth.org/data/",
-    # Introductory text
-    intro="Average land temperature in July, in degrees Celsius",
-    # Chart byline
-    byline="John Burn-Murdoch",
     # Data from pandas DataFrame
     data=df,
-    # Hide X-axis grid lines
-    x_grid_display="off",
-    # Show Y-axis grid lines
-    y_grid_display="on",
-    # Format Y-axis grid labels with one decimal place
-    y_grid_format=dw.NumberFormat.ONE_DECIMAL,
-    # Use monotone interpolation for smooth curves
-    interpolation=dw.LineInterpolation.MONOTONE,
-    # Configure the main temperature line
+    # Set the suffix on our values
+    transformations=dw.Transform(
+        column_format=[
+            dw.ColumnFormat(
+                column="LandAverageTemperature",
+                number_append=" °C",
+            )
+        ]
+    ),
+    y_grid_format="0",
+    # Set the range
+    # Format Y-axis grid labels with no decimal places
+    custom_range_y=[8, 21],
+    # And now the tooltip...
+    tooltip_number_format="00.00",
+    tooltip_x_format="YYYY",
+    # Configure the main temperature line's color
+    color_category={
+        "LandAverageTemperature": "#1d81a2",
+    },
     lines=[
+        # Style the main line
         dw.Line(
-            # Column to plot
             column="LandAverageTemperature",
-            # Line color
-            color="#c71e1d",
-            # Line width
-            width=dw.LineWidth.MEDIUM,
-            # Show symbol on last data point
-            symbols=dw.LineSymbol(
-                display=dw.SymbolDisplay.LAST,
-                size=5
-            ),
-            # Show value label on last data point
-            value_labels=dw.LineValueLabel(
-                last=True
-            )
-        )
+            width=dw.LineWidth.THIN,
+            interpolation=dw.LineInterpolation.CURVED,
+        ),
+        # Hide the other two
+        dw.Line(
+            column="lower",
+            width=dw.LineWidth.INVISIBLE,
+        ),
+        dw.Line(
+            column="upper",
+            width=dw.LineWidth.INVISIBLE,
+        ),
     ],
     # Add shaded confidence interval area
     area_fills=[
-        {
-            "from": "lower",
-            "to": "upper",
-            "color": "#cccccc",
-            "opacity": 0.5
-        }
+        dw.AreaFill(
+            from_column="lower",
+            to_column="upper",
+            color="#cccccc",
+            opacity=0.45,
+        )
     ],
 )
 
 chart.create()
 ```
 
-<div style="min-height:433px" id="datawrapper-vis-BsBaq"><script type="text/javascript" defer src="https://datawrapper.dwcdn.net/BsBaq/embed.js" charset="utf-8" data-target="#datawrapper-vis-BsBaq"></script><noscript><img src="https://datawrapper.dwcdn.net/BsBaq/full.png" alt="" /></noscript></div>
-
 ## Reference
 
 ```{eval-rst}

docs/user-guide/charts/multiple-column-charts.md

@@ -1,170 +1,5 @@
 # MultipleColumnChart
 
-## Example
-
-This example demonstrates how to create a multiple column chart showing the population of the world's largest cities from 1950 to 2035. The chart uses 8 panels (one for each city) with custom HTML-styled titles, highlights the year 2025 in red, and includes text and range annotations.
-
-```python
-import pandas as pd
-import datawrapper as dw
-
-# Load data from GitHub
-url = "https://raw.githubusercontent.com/palewire/datawrapper-api-classes/main/tests/samples/multiple_column/population.csv"
-df = pd.read_csv(url)
-
-chart = dw.MultipleColumnChart(
-    # Chart metadata
-    title="Population of the world's largest cities, 1950 to 2035",
-    # Data source attribution
-    source_name="Our World in Data",
-    source_url="https://ourworldindata.org/urbanization",
-    # Data
-    data=df,
-    # Grid layout - 4 columns
-    grid_column=4,
-    # Fixed row height
-    grid_row_height=140,
-    # Y-axis format - abbreviated numbers (e.g., "10M")
-    y_grid_format=dw.NumberFormat.ABBREVIATED_ONE_DECIMAL,
-    # Y-axis labels inside the plot area
-    y_grid_labels="inside",
-    # X-axis and Y-axis grid lines off
-    x_grid_display="off",
-    y_grid_display="off",
-    # Base color for columns
-    base_color="#c9a291",
-    # Highlight 2025 in red
-    column_color={"2025": "#c71e1d"},
-    # Category labels for legend
-    category_labels={"2025": "...for 2025"},
-    # Exclude all other years from the legend
-    exclude_from_color_key=[
-        "1950", "1955", "1960", "1965", "1970", "1975", "1980", "1985",
-        "1990", "1995", "2000", "2005", "2010", "2015", "2020", "2030", "2035"
-    ],
-    # Hide the color key
-    show_color_key=False,
-    # Panel titles with HTML styling
-    panel_titles={
-        "Beijing": "Beijing, <span style=\"color:gray; font-weight: normal;\">China</span>",
-        "Delhi": "Delhi, <span style=\"color:gray; font-weight: normal;\">India</span>",
-        "Dhaka": "Dhaka, <span style=\"color:gray; font-weight: normal;\"> Bangladesh </span>",
-        "Lagos": "Lagos, <span style=\"color:gray; font-weight: normal;\">Nigeria</span>",
-        "Mumbai (Bombay)": "Mumbai, <span style=\"color:gray; font-weight: normal;\">India</span>",
-        "New York-Newark": "New York/Newark, <span style=\"color:gray; font-weight: normal;\">U.S.</span>",
-        "Paris": "Paris, <span style=\"color:gray; font-weight: normal;\">France</span>",
-        "Tokyo": "Tokyo, <span style=\"color:gray; font-weight: normal;\">Japan</span>",
-    },
-    # Text annotations
-    text_annotations=[
-        # Projection label for Mumbai
-        dw.TextAnnotation(
-            text="Projection",
-            x="2018/04/30 15:23",
-            y=36224085.8571,
-            plot="Mumbai (Bombay)",
-            dx=-21,
-            dy=-8,
-            align="tr",
-            size=13,
-            color="#858585",
-            connector_line=dw.ConnectorLine(
-                type=dw.ConnectorLineType.STRAIGHT,
-                stroke=dw.StrokeWidth.THIN
-            )
-        ),
-        # Paris 2035 label
-        dw.TextAnnotation(
-            text="Paris 2035",
-            x="1947/07/03 01:00",
-            y=16409201.2857,
-            plot="Delhi",
-            align="tl",
-            size=13,
-            color="#858585"
-        ),
-        # Delhi 2025 population callout
-        dw.TextAnnotation(
-            text="<b style=\"color:#b73229\">2025 population:</b>\n34.7M",
-            x="2023/07/14 22:34",
-            y=33128010.1429,
-            plot="Delhi",
-            dx=-21,
-            dy=-29,
-            align="tr",
-            size=13,
-            color="#494949",
-            bg=True,
-            connector_line=dw.ConnectorLine(
-                type=dw.ConnectorLineType.STRAIGHT,
-                stroke=dw.StrokeWidth.THIN,
-                inherit_color=False
-            )
-        ),
-        # Paris growth note
-        dw.TextAnnotation(
-            text="Paris grew the most between 1851 to 1856 (by over 20%).",
-            x="1947/10/03 18:05",
-            y=43097373.9429,
-            plot="Paris",
-            align="tl",
-            size=13,
-            color="#494949",
-            bg=True
-        ),
-        # Lagos projection note (hidden on mobile/desktop, shown as fallback)
-        dw.TextAnnotation(
-            text="Lagos is expected to double in size by 2035 compared to 2015.",
-            x="1947/07/03 01:00",
-            y=43096075.7522,
-            plot="Lagos",
-            align="tl",
-            size=13,
-            color="#494949",
-            bg=True,
-            show_mobile=False,
-            show_desktop=False
-        ),
-    ],
-    # Range annotations
-    range_annotations=[
-        # Horizontal line in Paris panel
-        dw.RangeAnnotation(
-            x0="2009/01/23 14:50",
-            x1="2009/01/23 14:50",
-            y0=11765087.7143,
-            y1=13622733.1429,
-            plot="Paris",
-            range_type="y",
-            display="line",
-            color="#888",
-            opacity=76,
-            stroke_type="dotted",
-            stroke_width=2,
-            show_in_all_plots=True
-        ),
-        # Shaded projection period in Mumbai panel
-        dw.RangeAnnotation(
-            x0="2018/01/01 13:22",
-            x1="2037/07/02 01:00",
-            y0=26626251.1429,
-            y1=27245466.2857,
-            plot="Mumbai (Bombay)",
-            range_type="x",
-            display="range",
-            color="#888",
-            opacity=18,
-            show_in_all_plots=True
-        ),
-    ],
-)
-
-chart.create()
-```
-
-<iframe title="Population of the world's largest cities, 1950 to 2035" aria-label="Multiple Columns" id="datawrapper-chart-ZEBmw" src="https://datawrapper.dwcdn.net/ZEBmw/1/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="460" data-external="1"></iframe><script type="text/javascript">!function(){"use strict";window.addEventListener("message",(function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r=0;r<e.length;r++)if(e[r].contentWindow===a.source){var i=a.data["datawrapper-height"][t]+"px";e[r].style.height=i}}}))}();
-</script>
-
 ## Reference
 
 ```{eval-rst}