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Commit 97ad010

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nicolaskruchtennicolaskruchten
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Merge branch 'doc-prod'
2 parents f13d299 + b336637 commit 97ad010
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‎doc/python/aggregations.md

Copy file name to clipboardExpand all lines: doc/python/aggregations.md
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -191,7 +191,7 @@ layout = dict(
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method = 'restyle',
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), dict(
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args = ['xbins.size', 'M3'],
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label = 'Quater',
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label = 'Quarter',
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method = 'restyle',
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), dict(
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args = ['xbins.size', 'M6'],

‎doc/python/builtin-colorscales.md

Copy file name to clipboardExpand all lines: doc/python/builtin-colorscales.md
+1-1Lines changed: 1 addition & 1 deletion
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@@ -41,7 +41,7 @@ jupyter:
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Many Plotly Express functions accept a `color_continuous_scale` argument and many trace
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types have a `colorscale` attribute in their schema. Plotly comes with a large number of
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built-in continuous color scales, which can be referred to in Python code when setting the above arguments,
44-
either by name in a case-insensitive string e.g. `px.scatter(continuous_color_scale="Viridis"`) or by reference e.g.
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either by name in a case-insensitive string e.g. `px.scatter(color_continuous_scale="Viridis"`) or by reference e.g.
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`go.Scatter(marker_colorscale=plotly.colors.sequential.Viridis)`. They can also be reversed by adding `_r` at the end
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e.g. `"Viridis_r"` or `plotly.colors.sequential.Viridis_r`.
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‎doc/python/dumbbell-plots.md

Copy file name to clipboardExpand all lines: doc/python/dumbbell-plots.md
+58-32Lines changed: 58 additions & 32 deletions
Original file line numberDiff line numberDiff line change
@@ -55,47 +55,59 @@ countries = (
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.unique()
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)
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data = {"x": [], "y": [], "colors": [], "years": []}
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data = {"line_x": [], "line_y": [], "1952": [], "2002": [], "colors": [], "years": [], "countries": []}
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for country in countries:
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data["x"].extend(
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data["1952"].extend([df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0]])
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data["2002"].extend([df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0]])
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data["line_x"].extend(
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[
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df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0],
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df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0],
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None,
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]
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)
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data["y"].extend([country, country, None]),
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data["colors"].extend(["green", "blue", "brown"]),
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data["years"].extend(["1952", "2002", None])
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data["line_y"].extend([country, country, None]),
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fig = go.Figure(
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data=[
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go.Scatter(
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x=data["x"],
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y=data["y"],
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x=data["line_x"],
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y=data["line_y"],
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mode="lines",
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showlegend=False,
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marker=dict(
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color="grey",
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),
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color="grey"
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)
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),
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go.Scatter(
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x=data["x"],
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y=data["y"],
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mode="markers+text",
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x=data["1952"],
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y=countries,
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mode="markers",
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name="1952",
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marker=dict(
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color=data["colors"],
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size=10,
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),
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hovertemplate="""Country: %{y} <br> Life Expectancy: %{x} <br><extra></extra>""",
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color="green",
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size=10
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)
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),
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go.Scatter(
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x=data["2002"],
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y=countries,
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mode="markers",
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name="2002",
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marker=dict(
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color="blue",
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size=10
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)
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),
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]
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)
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fig.update_layout(
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title="Life Expectancy in Europe: 1952 and 2002",
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height=1000,
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showlegend=False,
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legend_itemclick=False
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)
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fig.show()
@@ -124,48 +136,62 @@ countries = (
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.unique()
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)
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127-
data = {"x": [], "y": [], "colors": [], "years": []}
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data = {"line_x": [], "line_y": [], "1952": [], "2002": [], "colors": [], "years": [], "countries": []}
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for country in countries:
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data["x"].extend(
142+
data["1952"].extend([df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0]])
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data["2002"].extend([df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0]])
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data["line_x"].extend(
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[
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df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0],
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df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0],
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None,
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]
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)
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data["y"].extend([country, country, None]),
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data["colors"].extend(["silver", "lightskyblue", "white"]),
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data["years"].extend(["1952", "2002", None])
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data["line_y"].extend([country, country, None]),
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fig = go.Figure(
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data=[
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go.Scatter(
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x=data["x"],
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y=data["y"],
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x=data["line_x"],
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y=data["line_y"],
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mode="markers+lines",
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showlegend=False,
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marker=dict(
148-
symbol="arrow", color="black", size=16, angleref="previous", standoff=8
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),
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symbol="arrow",
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color="black",
163+
size=16,
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angleref="previous",
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standoff=8
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)
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),
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go.Scatter(
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x=data["1952"],
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y=countries,
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name="1952",
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mode="markers",
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marker=dict(
174+
color="silver",
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size=16,
176+
)
150177
),
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go.Scatter(
152-
x=data["x"],
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y=data["y"],
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text=data["years"],
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x=data["2002"],
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y=countries,
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name="2002",
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mode="markers",
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marker=dict(
157-
color=data["colors"],
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color="lightskyblue",
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size=16,
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),
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hovertemplate="""Country: %{y} <br> Life Expectancy: %{x} <br> Year: %{text} <br><extra></extra>""",
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),
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]
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)
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fig.update_layout(
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title="Life Expectancy in Europe: 1952 and 2002",
167193
height=1000,
168-
showlegend=False,
194+
legend_itemclick=False
169195
)
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171197

‎doc/python/histograms.md

Copy file name to clipboardExpand all lines: doc/python/histograms.md
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -40,7 +40,7 @@ jupyter:
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In statistics, a [histogram](https://en.wikipedia.org/wiki/Histogram) is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented. More generally, in Plotly a histogram is an aggregated bar chart, with several possible aggregation functions (e.g. sum, average, count...) which can be used to visualize data on categorical and date axes as well as linear axes.
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43-
Alternatives to violin plots for visualizing distributions include [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
43+
Alternatives to histogram plots for visualizing distributions include [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
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> If you're looking instead for bar charts, i.e. representing *raw, unaggregated* data with rectangular
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bar, go to the [Bar Chart tutorial](/python/bar-charts/).

‎doc/python/ml-pca.md

Copy file name to clipboardExpand all lines: doc/python/ml-pca.md
+11-5Lines changed: 11 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -250,11 +250,16 @@ loadings = pca.components_.T * np.sqrt(pca.explained_variance_)
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fig = px.scatter(components, x=0, y=1, color=df['species'])
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252252
for i, feature in enumerate(features):
253-
fig.add_shape(
254-
type='line',
255-
x0=0, y0=0,
256-
x1=loadings[i, 0],
257-
y1=loadings[i, 1]
253+
fig.add_annotation(
254+
ax=0, ay=0,
255+
axref="x", ayref="y",
256+
x=loadings[i, 0],
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y=loadings[i, 1],
258+
showarrow=True,
259+
arrowsize=2,
260+
arrowhead=2,
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xanchor="right",
262+
yanchor="top"
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)
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fig.add_annotation(
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x=loadings[i, 0],
@@ -263,6 +268,7 @@ for i, feature in enumerate(features):
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xanchor="center",
264269
yanchor="bottom",
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text=feature,
271+
yshift=5,
266272
)
267273
fig.show()
268274
```

‎doc/python/shapes.md

Copy file name to clipboardExpand all lines: doc/python/shapes.md
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -51,7 +51,7 @@ The differences between these two approaches are that:
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### Shape-drawing with Scatter traces
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54-
There are two ways to draw filled shapes: scatter traces and [layout.shapes](https://plotly.com/python/reference/layout/shapes/#layout-shapes-items-shape-type) which is mostly useful for the 2d subplots, and defines the shape type to be drawn, and can be rectangle, circle, line, or path (a custom SVG path). You also can use [scatterpolar](https://plotly.com/python/polar-chart/#categorical-polar-chart), scattergeo, [scattermapbox](https://plotly.com/python/filled-area-on-mapbox/#filled-scattermapbox-trace) to draw filled shapes on any kind of subplots. To set an area to be filled with a solid color, you need to define [Scatter.fill="toself"](https://plotly.com/python/reference/scatter/#scatter-fill) that connects the endpoints of the trace into a closed shape. If `mode=line` (default value), then you need to repeat the initial point of a shape at the of the sequence to have a closed shape.
54+
There are two ways to draw filled shapes: scatter traces and [layout.shapes](https://plotly.com/python/reference/layout/shapes/#layout-shapes-items-shape-type) which is mostly useful for the 2d subplots, and defines the shape type to be drawn, and can be rectangle, circle, line, or path (a custom SVG path). You also can use [scatterpolar](https://plotly.com/python/polar-chart/#categorical-polar-chart), scattergeo, [scattermapbox](https://plotly.com/python/filled-area-on-mapbox/#filled-scattermapbox-trace) to draw filled shapes on any kind of subplots. To set an area to be filled with a solid color, you need to define [Scatter.fill="toself"](https://plotly.com/python/reference/scatter/#scatter-fill) that connects the endpoints of the trace into a closed shape. If `mode=line` (default value), then you need to repeat the initial point of a shape at the end of the sequence to have a closed shape.
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```python
5757
import plotly.graph_objects as go

‎doc/python/splom.md

Copy file name to clipboardExpand all lines: doc/python/splom.md
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -56,7 +56,7 @@ Specify the columns to be represented with the `dimensions` argument, and set co
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import plotly.express as px
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df = px.data.iris()
5858
fig = px.scatter_matrix(df,
59-
dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
59+
dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"],
6060
color="species")
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fig.show()
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```
@@ -69,7 +69,7 @@ The scatter matrix plot can be configured thanks to the parameters of `px.scatte
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import plotly.express as px
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df = px.data.iris()
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fig = px.scatter_matrix(df,
72-
dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
72+
dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"],
7373
color="species", symbol="species",
7474
title="Scatter matrix of iris data set",
7575
labels={col:col.replace('_', ' ') for col in df.columns}) # remove underscore

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