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Commit d124170

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Merge pull request #3931 from plotly/docs-oct
Documentation updates for 5.11
2 parents b14f8ad + 79c49c1 commit d124170

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‎doc/python/dumbbell-plots.md

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---
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jupyter:
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jupytext:
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notebook_metadata_filter: all
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text_representation:
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extension: .md
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format_name: markdown
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format_version: '1.3'
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jupytext_version: 1.14.1
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kernelspec:
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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language_info:
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codemirror_mode:
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name: ipython
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version: 3
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file_extension: .py
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mimetype: text/x-python
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name: python
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nbconvert_exporter: python
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pygments_lexer: ipython3
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version: 3.8.0
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plotly:
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description: How to create dumbbell plots in Python with Plotly.
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display_as: basic
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language: python
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layout: base
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name: Dumbbell Plots
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order: 19
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page_type: example_index
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permalink: python/dumbbell-plots/
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thumbnail: thumbnail/dumbbell-plot.jpg
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---
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## Basic Dumbbell Plot
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Dumbbell plots are useful for demonstrating change between two sets of data points, for example, the population change for a selection of countries for two different years
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In this example, we compare life expectancy in 1952 with life expectancy in 2002 for countries in Europe.
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```python
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import plotly.graph_objects as go
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from plotly import data
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import pandas as pd
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df = data.gapminder()
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df = df.loc[(df.continent == "Europe") & (df.year.isin([1952, 2002]))]
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countries = (
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df.loc[(df.continent == "Europe") & (df.year.isin([2002]))]
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.sort_values(by=["lifeExp"], ascending=True)["country"]
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.unique()
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)
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data = {"x": [], "y": [], "colors": [], "years": []}
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for country in countries:
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data["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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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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mode="lines",
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marker=dict(
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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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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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),
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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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width=1000,
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height=1000,
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showlegend=False,
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)
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fig.show()
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```
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## Dumbbell Plot with Arrow Markers
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*Note: The `arrow`, `angleref`, and `standoff` properties used on the `marker` in this example are new in 5.11*
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In this example, we add arrow markers to the plot. The first trace adds the lines connecting the data points and arrow markers.
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The second trace adds circle markers. On the first trace, we use `standoff=8` to position the arrow marker back from the data point.
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For the arrow marker to point directly at the circle marker, this value should be half the circle marker size.
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```python
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import pandas as pd
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import plotly.graph_objects as go
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from plotly import data
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df = data.gapminder()
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df = df.loc[(df.continent == "Europe") & (df.year.isin([1952, 2002]))]
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countries = (
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df.loc[(df.continent == "Europe") & (df.year.isin([2002]))]
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.sort_values(by=["lifeExp"], ascending=True)["country"]
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.unique()
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)
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data = {"x": [], "y": [], "colors": [], "years": []}
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for country in countries:
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data["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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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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mode="markers+lines",
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marker=dict(
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symbol="arrow", color="black", size=16, angleref="previous", standoff=8
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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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text=data["years"],
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mode="markers",
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marker=dict(
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color=data["colors"],
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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",
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width=1000,
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height=1000,
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showlegend=False,
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)
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fig.show()
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```

‎doc/python/legend.md

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format_version: '1.3'
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jupytext_version: 1.14.1
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kernelspec:
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display_name: Python 3
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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language_info:
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name: python
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nbconvert_exporter: python
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pygments_lexer: ipython3
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version: 3.8.8
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version: 3.8.0
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plotly:
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description: How to configure and style the legend in Plotly with Python.
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display_as: file_settings
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fig.show()
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```
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#### Horizontal Legend Entry Width
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*New in 5.11*
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Set the width of hozitonal legend entries by setting `entrywidth`. Here we set it to `70` pixels. Pixels is the default unit for `entrywidth`, but you can set it to be a fraction of the plot width using `entrywidthmode='fraction`.
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```python
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import plotly.express as px
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df = px.data.gapminder().query("year==2007")
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fig = px.scatter(df, x="gdpPercap", y="lifeExp", color="continent",
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size="pop", size_max=45, log_x=True)
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fig.update_layout(legend=dict(
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orientation="h",
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entrywidth=70,
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1
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))
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fig.show()
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```
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#### Styling Legends
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Legends support many styling options.

‎doc/python/mapbox-layers.md

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text_representation:
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extension: .md
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format_name: markdown
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format_version: '1.2'
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jupytext_version: 1.3.1
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format_version: '1.3'
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jupytext_version: 1.14.1
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kernelspec:
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display_name: Python 3
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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language_info:
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name: python
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nbconvert_exporter: python
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pygments_lexer: ipython3
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version: 3.6.8
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version: 3.8.0
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plotly:
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description: How to make Mapbox maps in Python with various base layers, with
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or without needing a Mapbox Access token.
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See the example in the [plotly and datashader tutorial](/python/datashader).
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#### Setting Map Bounds
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*New in 5.11*
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Set bounds for a map to specify an area outside which a user interacting with the map can't pan or zoom. Here we set a maximum longitude of `-180`, a minimum longitude of `-50`, a maximum latitude of `90`, and a minimum latitude of `20`.
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```python
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import plotly.express as px
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import pandas as pd
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us_cities = pd.read_csv(
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"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv"
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)
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fig = px.scatter_mapbox(
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us_cities,
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lat="lat",
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lon="lon",
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hover_name="City",
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hover_data=["State", "Population"],
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color_discrete_sequence=["fuchsia"],
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zoom=3,
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height=300,
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)
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fig.update_layout(mapbox_style="open-street-map")
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fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
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fig.update_layout(mapbox_bounds={"west": -180, "east": -50, "south": 20, "north": 90})
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fig.show()
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```
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#### Reference
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See https://plotly.com/python/reference/layout/mapbox/ for more information and options!

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