various

nicolaskruchten · nicolaskruchten · commit a6cdc6db0350 · 2019-07-14T22:05:35.000-04:00
diff --git a/python/choropleth-maps.md b/python/choropleth-maps.md
@@ -37,8 +37,8 @@ Below we show how to create Choropleth Maps using either `px.choropleth` (one-li
 import plotly.express as px
 
 gapminder = px.data.gapminder().query("year==2007")
-fig = px.choropleth(gapminder, locations="iso_alpha", 
-                    color="lifeExp", # lifeExp is a column of gapminder 
+fig = px.choropleth(gapminder, locations="iso_alpha",
+                    color="lifeExp", # lifeExp is a column of gapminder
                     hover_name="country", # column to add to hover information
                     color_continuous_scale=px.colors.sequential.Plasma)
 fig.show()
@@ -54,8 +54,8 @@ import plotly.graph_objects as go
 # Load data frame and tidy it.
 import pandas as pd
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')
-    
-fig = go.Figure(data=go.Choropleth(  
+
+fig = go.Figure(data=go.Choropleth(
     locations=df['code'], # Spatial coordinates
     z = df['total exports'].astype(float), # Data to be color-coded
     locationmode = 'USA-states', # set of locations match entries in `locations`
@@ -87,7 +87,7 @@ df['text'] = df['state'] + '<br>' + \
     'Fruits ' + df['total fruits'] + ' Veggies ' + df['total veggies'] + '<br>' + \
     'Wheat ' + df['wheat'] + ' Corn ' + df['corn']
 
-fig = go.Figure(data=go.Choropleth(  
+fig = go.Figure(data=go.Choropleth(
     locations=df['code'],
     z=df['total exports'].astype(float),
     locationmode='USA-states',
@@ -118,7 +118,7 @@ import pandas as pd
 
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
 
-fig = go.Figure(data=go.Choropleth( 
+fig = go.Figure(data=go.Choropleth(
     locations = df['CODE'],
     z = df['GDP (BILLIONS)'],
     text = df['COUNTRY'],
@@ -179,7 +179,7 @@ for i in range(6,10)[::-1]:
             color=colors[i-6],
             line_width=0)
         )
-    ) 
+    )
 
 df_sept = df.query('Month == 9')
 fig.data[0].update(text = df_sept['Value'].map('{:.0f}'.format).astype(str)+' '+\
@@ -272,6 +272,7 @@ fig = ff.create_choropleth(
     title_text = 'USA by Unemployment %',
     legend_title = '% unemployed'
 )
+fig.layout.template = None
 fig.show()
 ```
 
@@ -291,4 +292,4 @@ IFrame(src= "https://dash-simple-apps.plotly.host/dash-choroplethplot/code", wid
 ```
 
 #### Reference
-See https://plot.ly/python/reference/#choropleth for more information and chart attribute options!
+See https://plot.ly/python/reference/#choropleth for more information and chart attribute options!
diff --git a/python/county-choropleth.md b/python/county-choropleth.md
@@ -78,6 +78,7 @@ fips = ['06021', '06023', '06027',
 values = range(len(fips))
 
 fig = ff.create_choropleth(fips=fips, values=values)
+fig.layout.template = None
 fig.show()
 ```
 
@@ -119,6 +120,7 @@ fig = ff.create_choropleth(
     county_outline={'color': 'rgb(255,255,255)', 'width': 0.5}, round_legend_values=True,
     legend_title='Population by County', title='California and Nearby States'
 )
+fig.layout.template = None
 fig.show()
 ```
 
@@ -148,6 +150,7 @@ fig = ff.create_choropleth(
     county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
     exponent_format=True,
 )
+fig.layout.template = None
 fig.show()
 ```
 
@@ -184,6 +187,7 @@ fig.update_layout(
     annotations = {'x': -0.12, 'xanchor': 'left'}
 )
 
+fig.layout.template = None
 fig.show()
 ```
 
@@ -230,6 +234,7 @@ fig = ff.create_choropleth(
     title='Oregon'
 )
 
+fig.layout.template = None
 fig.show()
 ```
 
@@ -263,6 +268,7 @@ fig = ff.create_choropleth(
     legend_title='% unemployed'
 )
 
+fig.layout.template = None
 fig.show()
 ```
 
diff --git a/python/facet-plots.md b/python/facet-plots.md
@@ -36,204 +36,36 @@ jupyter:
     title: Python Facet and Trellis Plots | plotly
 ---
 
-#### Facet by Column
-A `facet grid` is a generalization of a scatterplot matrix where we can "facet" a row and/or column by another variable. Given some tabular data, stored in a `pandas.DataFrame`, we can plot one variable against another to form a regular scatter plot, _and_ we can pick a third faceting variable to form panels along the rows and/or columns to segment the data even further, forming a bunch of panels. We can also assign a coloring rule or a heatmap based on a color variable to color the plot.
 
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
-
-fig = ff.create_facet_grid(
-    mpg,
-    x='displ',
-    y='cty',
-    facet_col='cyl',
-)
-
-fig.show()
-```
-
-#### Facet by Row
-
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
-
-fig = ff.create_facet_grid(
-    mpg,
-    x='displ',
-    y='cty',
-    facet_row='cyl',
-    marker={'color': 'rgb(86, 7, 100)'},
-)
-
-fig.show()
-```
-
-#### Facet by Row and Column
-
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
+### Facet and Trellis Plots
 
-fig = ff.create_facet_grid(
-    mpg,
-    x='displ',
-    y='cty',
-    facet_row='cyl',
-    facet_col='drv',
-    marker={'color': 'rgb(234, 239, 155)'},
-)
-
-fig.show()
-```
+Facet plots, also known as trellis plots or small multiples, are figures made up of multiple subplots which have the same set of axes, where each subplot shows a subset of the data. While it is straightforward to use `plotly`'s [subplot capabilities]
+(/python/subplots/) to make such figures, it's far easier to use the built-in `facet_row` and `facet_col` arguments in the various [Plotly Express](/python/next/plotly-express/) functions.
 
-#### Custom Colormap
+### Scatter Plot Column Facets
 
 ```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv')
-
-fig = ff.create_facet_grid(
-    tips,
-    x='total_bill',
-    y='tip',
-    color_name='sex',
-    show_boxes=False,
-    marker={'size': 10, 'opacity': 1.0},
-    colormap={'Male': 'rgb(165, 242, 242)', 'Female': 'rgb(253, 174, 216)'}
-)
+import plotly.express as px
+tips = px.data.tips()
+fig = px.scatter(tips, x="total_bill", y="tip", color="smoker", facet_col="sex")
 fig.show()
 ```
 
-#### Label Variable Name:Value
+### Bar Chart Row Facets
 
 ```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
-
-fig = ff.create_facet_grid(
-    mtcars,
-    x='mpg',
-    y='wt',
-    facet_col='cyl',
-    facet_col_labels='name',
-    facet_row_labels='name',
-)
+import plotly.express as px
+tips = px.data.tips()
+fig = px.bar(tips, x="size", y="total_bill", color="sex", facet_row="smoker")
 fig.show()
 ```
 
-#### Custom Labels
+### Histogram Facet Grids
 
 ```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
-
-fig = ff.create_facet_grid(
-    mtcars,
-    x='wt',
-    y='mpg',
-    facet_col='cyl',
-    facet_col_labels={4: '$2^2 = 4$', 6: '$\\frac{18}{3} = 6$', 8: '$2\cdot4 = 8$'},
-    marker={'color': 'rgb(240, 100, 2)'},
-)
-
+import plotly.express as px
+tips = px.data.tips()
+fig = px.histogram(tips, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",
+       category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})
 fig.show()
 ```
-
-#### Plot in 'ggplot2' style
-To learn more about ggplot2, check out http://ggplot2.tidyverse.org/reference/facet_grid.html
-
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv')
-
-fig = ff.create_facet_grid(
-    tips,
-    x='total_bill',
-    y='tip',
-    facet_row='sex',
-    facet_col='smoker',
-    marker={'symbol': 'circle-open', 'size': 10},
-    ggplot2=True
-)
-fig.show()
-```
-
-#### Plot with 'scattergl' traces
-
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')
-
-grid = ff.create_facet_grid(
-    mpg,
-    x='class',
-    y='displ',
-    trace_type='scattergl',
-)
-
-fig.show()
-```
-
-#### Plot with Histogram Traces
-
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv')
-
-fig = ff.create_facet_grid(
-    tips,
-    x='total_bill',
-    y='tip',
-    facet_row='sex',
-    facet_col='smoker',
-    trace_type='histogram',
-)
-
-fig.show()
-```
-
-#### Other Trace Types
-Facet Grids support `scatter`, `scattergl`, `histogram`, `bar` and `box` trace types.
-
-```python
-import plotly.figure_factory as ff
-
-import pandas as pd
-tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv')
-
-fig = ff.create_facet_grid(
-    tips,
-    y='tip',
-    facet_row='sex',
-    facet_col='smoker',
-    trace_type='box',
-)
-
-fig.show()
-```
-
-#### Reference
-
-```python
-help(ff.create_facet_grid)
-```
diff --git a/python/scatter-plots-on-maps.md b/python/scatter-plots-on-maps.md
@@ -40,7 +40,7 @@ jupyter:
 
 ### Geographical Scatter Plot with px.scatter_geo
 
-For data available as a tidy pandas DataFrame, use `px.scatter_geo` for a geographical scatter plot. The `size` argument is used to set the size of markers from a given column of the DataFrame.
+For data available as a tidy pandas DataFrame, use the [Plotly Express](/python/next/plotly-express/) function `px.scatter_geo` for a geographical scatter plot. The `size` argument is used to set the size of markers from a given column of the DataFrame.
 
 ```python
 import plotly.express as px
@@ -56,8 +56,8 @@ fig.show()
 ```python
 import plotly.express as px
 gapminder = px.data.gapminder().query("year == 2007")
-fig = px.scatter_geo(gapminder, locations="iso_alpha", 
-                     color="continent", # which column to use to set the color of markers 
+fig = px.scatter_geo(gapminder, locations="iso_alpha",
+                     color="continent", # which column to use to set the color of markers
                      hover_name="country", # column added to hover information
                      size="pop", # size of markers
                      projection="natural earth")
@@ -211,4 +211,4 @@ fig.show()
 ```
 
 #### Reference
-See  https://plot.ly/python/reference/#scattergeo and https://plot.ly/python/reference/#layout-geo for more information and chart attribute options!
+See  https://plot.ly/python/reference/#scattergeo and https://plot.ly/python/reference/#layout-geo for more information and chart attribute options!
diff --git a/python/scattermapbox.md b/python/scattermapbox.md
@@ -37,10 +37,24 @@ jupyter:
     title: Python Scatter Plots with Mapbox | Plotly
 ---
 
+GOTTA PX
+
 #### Mapbox Access Token
 
 To plot on Mapbox maps with Plotly you'll need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio) which you can add to your [Plotly settings](https://plot.ly/settings/mapbox). If you're using a Chart Studio Enterprise server, please see additional instructions here: https://help.plot.ly/mapbox-atlas/.
 
+### Basic example with Plotly Express
+
+For data available as a tidy pandas DataFrame, use the [Plotly Express](/python/next/plotly-express/) function `px.scatter_mapbox` for a scatter plot on a tile map.
+
+```python
+import plotly.express as px
+px.set_mapbox_access_token(open(".mapbox_token").read())
+carshare = px.data.carshare()
+fig = px.scatter_mapbox(carshare, lat="centroid_lat", lon="centroid_lon",     color="peak_hour", size="car_hours",
+                  color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10)
+fig.show()
+```
 
 #### Basic Example
 
