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Copy file name to clipboardExpand all lines: doc/python/choropleth-maps.md
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Plotly comes with two built-in geometries which do not require an external GeoJSON file:
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1. USA States
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2. Countries as defined in the Natural Earth dataset.
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2. Countries
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**Note and disclaimer:** cultural (as opposed to physical) features are by definition subject to change, debate and dispute. Plotly includes data from Natural Earth "as-is" and defers to the [Natural Earth policy regarding disputed borders](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/) which read:
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In **Plotly.py 6.3 and later**, the built-in countries geometry is created from the following sources:
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-[UN data](https://geoportal.un.org/arcgis/sharing/rest/content/items/d7caaff3ef4b4f7c82689b7c4694ad92/data) for country borders, coastlines, land, and ocean layers.
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- Natural Earth data for lakes, rivers, and subunits layers.
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> Natural Earth Vector draws boundaries of countries according to defacto status. We show who actually controls the situation on the ground.
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In **earlier versions of Plotly.py**, the built-in countries geometry is based on Natural Earth data only. Plotly includes data from Natural Earth "as-is". This dataset draws boundaries of countries according to de facto status. See the [Natural Earth page for more details](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/).
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To use the built-in countries geometry, provide `locations` as [three-letter ISO country codes](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3).
Disabling the zoom in, zoom out, and autoscale buttons for specific axes is supported on cartesian axes using the `modebardisable` attribute. In the following example, the zoom in and zoom out buttons are disabled on the `xaxis`, meaning these buttons only zoom in and out on the `yaxis`. Disable the autoscale button using `modebardisable='autoscale'`. You can also disable the zoom and autoscale buttons using `modebardisable='zoominout+autoscale'`.
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```python
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import plotly.graph_objects as go
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import plotly.data
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df = plotly.data.stocks()
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fig = go.Figure(
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data=[
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go.Scatter(
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x=df['date'],
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y=df['GOOG'],
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mode='lines+markers',
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name='Google Stock Price'
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)
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],
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layout=go.Layout(
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title='Google Stock Price Over Time with Mode Bar Disabled',
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xaxis=dict(
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title='Date',
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# Try zooming in or out using the modebar buttons. These only apply to the yaxis in this exampe.
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modebardisable='zoominout'
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),
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yaxis=dict(
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title='Stock Price (USD)',
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)
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)
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)
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fig.show()
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```
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### Configuring Figures in Dash Apps
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The same configuration dictionary that you pass to the `config` parameter of the `show()` method can also be passed to the [`config` property of a `dcc.Graph` component](https://dash.plotly.com/dash-core-components/graph).
Customize the title shown in unified hovermode, by specifing `unifiedhovertitle.text`.
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The unified hover title is a template string that supports using variables from the data. Numbers are formatted using d3-format's syntax `%{variable:d3-format}`, for `example \"Price: %{y:$.2f}\"`. Dates are formatted using d3-time-format's syntax `%{variable|d3-time-format}`, for example `\"Day: %{2019年01月01日|%A}\"`.
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The following example uses `'x unified'` hover and specifies a unified hover title that shows the full weekday, month, day, and year.
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```python
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import plotly.graph_objects as go
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import plotly.express as px
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df = px.data.stocks()
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fig = go.Figure(
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data=[
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go.Scatter(
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x=df['date'],
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y=df['GOOG'],
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mode='lines',
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name='Google'
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),
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go.Scatter(
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x=df['date'],
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y=df['AAPL'],
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mode='lines',
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name='Apple'
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)
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],
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layout=go.Layout(
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title_text="Stock Prices with Custom Unified Hover Title",
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hovermode='x unified',
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xaxis=dict(
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title_text='Date',
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unifiedhovertitle=dict(
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text='<b>%{x|%A, %B %d, %Y}</b>'
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)
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),
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yaxis=dict(
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title_text='Price (USD)',
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tickprefix='$'
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)
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)
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)
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fig.show()
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```
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#### Control hovermode with Dash
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[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.
By default, a legend can expand to fill up to half of the layout area height for a horizontal legend and the full height for a vertical legend. You can change this by specifying a `maxheight` for the legend. `maxheight` is interpreted as a ratio if it is 1 or less, and as an exact pixel value if it is greater than 1. In the following plot with many legend items, we set `maxheight` to a ratio of 0.10, giving the plot more space.
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```python
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import plotly.express as px
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from plotly import data
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df = data.gapminder().query("year==2007 and continent == 'Europe'")
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fig = px.scatter(df,
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x="gdpPercap",
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y="lifeExp",
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color="country",
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size="pop",
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size_max=45,
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title="Life Expectancy vs. GDP per Capita in 2007 (by Country)",
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labels={"gdpPercap": "GDP per Capita"},
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)
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fig.update_layout(
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xaxis=dict(
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side="top"
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),
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=-0.35,
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xanchor="center",
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x=0.5,
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maxheight=0.1, # Comment maxheight to see legend take up 0.5 of plotting area
You can control how minor log labels are displayed using the `minorloglabels` attribute. Set to `"complete"` to show complete digits, or `None` for no labels. By default, minor log labels use `"small digits"`, as shown in the previous example.
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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(
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df, x="gdpPercap",
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y="lifeExp",
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hover_name="country",
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log_x=True,
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range_x=[1,100000],
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range_y=[0,100]
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)
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fig.update_xaxes(
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minor=dict(
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ticks="inside",
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ticklen=6,
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showgrid=True
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),
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minorloglabels="complete"
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)
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fig.show()
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```
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### Logarithmic Axes with Graph Objects
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If Plotly Express does not provide a good starting point, it is also possible to use [the more generic `go.Figure` class from `plotly.graph_objects`](/python/graph-objects/).
Copy file name to clipboardExpand all lines: doc/python/map-configuration.md
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### Physical Base Maps
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Plotly Geo maps have a built-in base map layer composed of "physical" and "cultural" (i.e. administrative border) data from the [Natural Earth Dataset](https://www.naturalearthdata.com/downloads/). Various lines and area fills can be shown or hidden, and their color and line-widths specified. In the [default `plotly` template](/python/templates/), a map frame and physical features such as a coastal outline and filled land areas are shown, at a small-scale 1:110m resolution:
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Plotly Geo maps have a built-in base map layer composed of *physical* and *cultural* (i.e. administrative border) data.
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In **Plotly.py 6.3 and later**, the base map layer is created from the following sources:
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-[UN data](https://geoportal.un.org/arcgis/sharing/rest/content/items/d7caaff3ef4b4f7c82689b7c4694ad92/data) for country borders, coastlines, land, and oceans layers.
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- Natural Earth data for lakes, rivers, and subunits layers.
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In **earlier versions of Plotly.py**, the base map layer is based on Natural Earth data only. Plotly includes data from Natural Earth "as-is". This dataset draws boundaries of countries according to de facto status. See the [Natural Earth page for more details](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/).
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Various lines and area fills can be shown or hidden, and their color and line-widths specified. In the [default `plotly` template](/python/templates/), a map frame and physical features such as a coastal outline and filled land areas are shown, at a small-scale 1:110m resolution:
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```python
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import plotly.graph_objects as go
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In addition to physical base map features, a "cultural" base map is included which is composed of country borders and selected sub-country borders such as states.
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**Note and disclaimer:** cultural features are by definition subject to change, debate and dispute. Plotly includes data from Natural Earth "as-is" and defers to the [Natural Earth policy regarding disputed borders](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/) which read:
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In **Plotly.py 6.3 and later**, this base map is created from [UN data](https://geoportal.un.org/arcgis/sharing/rest/content/items/d7caaff3ef4b4f7c82689b7c4694ad92/data) for country borders, and Natural Earth data for sub-country borders.
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> Natural Earth Vector draws boundaries of countries according to defacto status. We show who actually controls the situation on the ground.
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In **earlier versions of Plotly.py**, this base map is based on Natural Earth data only. Plotly includes data from Natural Earth "as-is". This dataset draws boundaries of countries according to defacto status. See the [Natural Earth page for more details](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/).
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**To create a map with your own cultural features** please refer to our [choropleth documentation](/python/choropleth-maps/).
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