removing odd non-breaking-spaces from headers

Author: nicolaskruchten

doc/python/3d-axes.md

@@ -141,7 +141,7 @@ fig.show()
 import plotly.graph_objects as go
 import numpy as np
 
-# Define random surface
+# Define random surface
 N = 50
 fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
                    y=(25*np.random.randn(N)),
@@ -150,7 +150,7 @@ fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
                    color='rgba(100,22,200,0.5)'
                   )])
 
-# Different types of customized ticks
+# Different types of customized ticks
 fig.update_layout(scene = dict(
                     xaxis = dict(
                         ticktext= ['TICKS','MESH','PLOTLY','PYTHON'],
@@ -184,7 +184,7 @@ fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
                    opacity=0.5,)])
 
 
-# xaxis.backgroundcolor is used to set background color
+# xaxis.backgroundcolor is used to set background color
 fig.update_layout(scene = dict(
                     xaxis = dict(
                          backgroundcolor="rgb(200, 200, 230)",

doc/python/box-plots.md

@@ -280,7 +280,7 @@ fig.add_trace(go.Box(
     name="All Points",
     jitter=0.3,
     pointpos=-1.8,
-    boxpoints='all', # represent all points
+    boxpoints='all', # represent all points
     marker_color='rgb(7,40,89)',
     line_color='rgb(7,40,89)'
 ))
@@ -353,7 +353,7 @@ fig.add_trace(go.Box(
 
 fig.update_layout(
     yaxis_title='normalized moisture',
-    boxmode='group' # group together boxes of the different traces for each value of x
+    boxmode='group' # group together boxes of the different traces for each value of x
 )
 fig.show()
 ```

doc/python/bubble-maps.md

@@ -50,7 +50,7 @@ fig = px.scatter_geo(df, locations="iso_alpha", color="continent",
 fig.show()
 ```
 
-### Bubble Map with animation
+### Bubble Map with animation
 
 ```python
 import plotly.express as px

doc/python/choropleth-maps.md

@@ -260,7 +260,7 @@ fig.update_layout(
     geo = dict(
         scope='usa',
         projection=go.layout.geo.Projection(type = 'albers usa'),
-        showlakes=True, # lakes
+        showlakes=True, # lakes
         lakecolor='rgb(255, 255, 255)'),
 )
 

doc/python/error-bars.md

@@ -33,7 +33,7 @@ jupyter:
     thumbnail: thumbnail/error-bar.jpg
 ---
 
-### Error Bars with Plotly Express
+### Error Bars with Plotly Express
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). For functions representing 2D data points such as [`px.scatter`](https://plot.ly/python/line-and-scatter/), [`px.line`](https://plot.ly/python/line-charts/), [`px.bar`](https://plot.ly/python/bar-charts/) etc., error bars are given as a column name which is the value of the `error_x` (for the error on x position) and `error_y` (for the error on y position).
 
@@ -46,7 +46,7 @@ fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
 fig.show()
 ```
 
-#### Asymmetric Error Bars with Plotly Express
+#### Asymmetric Error Bars with Plotly Express
 
 ```python
 import plotly.express as px

doc/python/filled-area-plots.md

@@ -70,7 +70,7 @@ import plotly.graph_objects as go
 
 fig = go.Figure()
 fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy',
-                    mode='none' # override default markers+lines
+                    mode='none' # override default markers+lines
                     ))
 fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty',
                     mode= 'none'))
@@ -92,7 +92,7 @@ fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 4, 8, 3],
 fig.add_trace(go.Scatter(
     x=[1, 2, 3, 4],
     y=[1, 6, 2, 6],
-    fill='tonexty', # fill area between trace0 and trace1
+    fill='tonexty', # fill area between trace0 and trace1
     mode='lines', line_color='indigo'))
 
 fig.show()

doc/python/histograms.md

@@ -244,7 +244,7 @@ fig.add_trace(go.Histogram(
     x=x0,
     histnorm='percent',
     name='control', # name used in legend and hover labels
-    xbins=dict( # bins used for histogram
+    xbins=dict( # bins used for histogram
         start=-4.0,
         end=3.0,
         size=0.5
@@ -266,10 +266,10 @@ fig.add_trace(go.Histogram(
 ))
 
 fig.update_layout(
-    title_text='Sampled Results', # title of plot
+    title_text='Sampled Results', # title of plot
     xaxis_title_text='Value', # xaxis label
     yaxis_title_text='Count', # yaxis label
-    bargap=0.2, # gap between bars of adjacent location coordinates
+    bargap=0.2, # gap between bars of adjacent location coordinates
     bargroupgap=0.1 # gap between bars of the same location coordinates
 )
 
@@ -338,7 +338,7 @@ trace5 = go.Histogram(x=x,
                       xbins=dict(
                       start='1969-11-15',
                       end='1972-03-31',
-                      size= 'M2'), # 2 months
+                      size= 'M2'), # 2 months
                       autobinx = False
                       )
 

doc/python/line-charts.md

@@ -70,7 +70,7 @@ fig = px.line(df, x="year", y="lifeExp", color="continent",
 fig.show()
 ```
 
-### Line Plot with go.Scatter
+### Line Plot with go.Scatter
 
 If Plotly Express does not provide a good starting point, it is possible to use the more generic `go.Scatter` function from `plotly.graph_objects`. Whereas `plotly.express` has two functions `scatter` and `line`, `go.Scatter` can be used both for plotting points (makers) or lines, depending on the value of `mode`. The different options of `go.Scatter` are documented in its [reference page](https://plot.ly/python/reference/#scatter).
 

doc/python/lines-on-maps.md

@@ -52,7 +52,7 @@ fig = px.line_geo(df, locations="iso_alpha",
 fig.show()
 ```
 
-## Lines on Maps with plotly.graph_objects
+## Lines on Maps with plotly.graph_objects
 
 ### US Flight Paths Map
 

doc/python/network-graphs.md

@@ -43,7 +43,7 @@ Install the Python library `networkx` with `pip install networkx`.
 
 
 
-### Create random graph
+### Create random graph
 
 ```python
 import plotly.graph_objects as go

doc/python/peak-finding.md

@@ -62,7 +62,7 @@ fig = go.Figure(data=go.Scatter(
 fig.show()
 ```
 
-#### Peak Detection
+#### Peak Detection
 
 We need to find the x-axis indices for the peaks in order to determine where the peaks are located.
 

doc/python/pie-charts.md

@@ -81,9 +81,9 @@ For more information about discrete colors, see the [dedicated page](/python/dis
 import plotly.express as px
 df = px.data.tips()
 fig = px.pie(df, values='tip', names='day', color='day',
-             color_discrete_map={'Thur':'lightcyan', 
-                                 'Fri':'cyan', 
-                                 'Sat':'royalblue', 
+             color_discrete_map={'Thur':'lightcyan',
+                                 'Fri':'cyan',
+                                 'Sat':'royalblue',
                                  'Sun':'darkblue'})
 fig.show()
 ```
@@ -213,7 +213,7 @@ fig.add_trace(go.Pie(labels=labels, values=[16, 15, 12, 6, 5, 4, 42], name="GHG
 fig.add_trace(go.Pie(labels=labels, values=[27, 11, 25, 8, 1, 3, 25], name="CO2 Emissions"),
               1, 2)
 
-# Use `hole` to create a donut-like pie chart
+# Use `hole` to create a donut-like pie chart
 fig.update_traces(hole=.4, hoverinfo="label+percent+name")
 
 fig.update_layout(

doc/python/polar-chart.md

@@ -257,7 +257,7 @@ fig.update_traces(mode = "lines+markers",
 # The sector is [0, 360] by default, we update it for the first plot only
 fig.update_layout(
     showlegend = False,
-    polar = dict(# setting parameters for the second plot would be polar2=dict(...)
+    polar = dict(# setting parameters for the second plot would be polar2=dict(...)
       sector = [150,210],
     ))
 

doc/python/sankey-diagram.md

@@ -39,7 +39,7 @@ A [Sankey diagram](https://en.wikipedia.org/wiki/Sankey_diagram) is a flow diagr
 
 
 ### Basic Sankey Diagram
-Sankey diagrams visualize the contributions to a flow by defining [source](https://plot.ly/python/reference/#sankey-link-source) to represent the source node, [target](https://plot.ly/python/reference/#sankey-link-target) for the target node, [value](https://plot.ly/python/reference/#sankey-link-value) to set the flow volum, and [label](https://plot.ly/python/reference/#sankey-node-label) that shows the node name. 
+Sankey diagrams visualize the contributions to a flow by defining [source](https://plot.ly/python/reference/#sankey-link-source) to represent the source node, [target](https://plot.ly/python/reference/#sankey-link-target) for the target node, [value](https://plot.ly/python/reference/#sankey-link-value) to set the flow volum, and [label](https://plot.ly/python/reference/#sankey-node-label) that shows the node name.
 
 ```python
 import plotly.graph_objects as go
@@ -74,7 +74,7 @@ data = json.loads(response.read())
 fig = go.Figure(data=[go.Sankey(
     valueformat = ".0f",
     valuesuffix = "TWh",
-    # Define nodes
+    # Define nodes
     node = dict(
       pad = 15,
       thickness = 15,
@@ -96,7 +96,7 @@ fig.show()
 ```
 
 ### Style Sankey Diagram
-This example also uses [hovermode](https://plot.ly/python/reference/#layout-hovermode) to enable multiple tooltips. 
+This example also uses [hovermode](https://plot.ly/python/reference/#layout-hovermode) to enable multiple tooltips.
 
 ```python
 import plotly.graph_objects as go
@@ -147,7 +147,7 @@ fig = go.Figure(go.Sankey(
         "label": ["A", "B", "C", "D", "E", "F"],
         "x": [0.2, 0.1, 0.5, 0.7, 0.3, 0.5],
         "y": [0.7, 0.5, 0.2, 0.4, 0.2, 0.3],
-        'pad':10},  # 10 Pixels 
+        'pad':10},  # 10 Pixels
     link = {
         "source": [0, 0, 1, 2, 5, 4, 3, 5],
         "target": [5, 3, 4, 3, 0, 2, 2, 3],

doc/python/smoothing.md

@@ -97,7 +97,7 @@ fig.add_trace(go.Scatter(
     x=x,
     y=signal.savgol_filter(y,
                            53, # window size used for filtering
-                           3), # order of fitted polynomial
+                           3), # order of fitted polynomial
     mode='markers',
     marker=dict(
         size=6,

doc/python/ternary-contour.md

@@ -121,7 +121,7 @@ fig = ff.create_ternary_contour(np.array([Al, Y, Cu]), enthalpy,
 fig.show()
 ```
 
-#### Interpolation mode
+#### Interpolation mode
 
 Two modes are available in order to interpolate between data points: interpolation in Cartesian space (`interp_mode='cartesian'`) or interpolation using the [isometric log-ratio transformation](https://link.springer.com/article/10.1023/A:1023818214614) (see also [preprint](https://www.researchgate.net/profile/Leon_Parent2/post/What_is_the_best_approach_for_diagnosing_nutrient_disorders_and_formulating_fertilizer_recommendations/attachment/59d62a69c49f478072e9cf3f/AS%3A272541220835360%401441990298625/download/Egozcue+et+al+2003.pdf)),  `interp_mode='ilr'`. The `ilr` transformation preserves metrics in the [simplex](https://en.wikipedia.org/wiki/Simplex) but is not defined on its edges.
 

doc/python/time-series.md

@@ -40,7 +40,7 @@ Time series can be represented using either `plotly.express` functions (`px.line
 Plotly auto-sets the axis type to a date format when the corresponding data are either ISO-formatted date strings or if they're a [date pandas column](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html) or [datetime NumPy array](https://docs.scipy.org/doc/numpy/reference/arrays.datetime.html).
 
 ```python
-# Using plotly.express
+# Using plotly.express
 import plotly.express as px
 
 import pandas as pd
@@ -51,7 +51,7 @@ fig.show()
 ```
 
 ```python
-# Using graph_objects
+# Using graph_objects
 import plotly.graph_objects as go
 
 import pandas as pd
@@ -101,7 +101,7 @@ fig.add_trace(go.Scatter(
                 line_color='dimgray',
                 opacity=0.8))
 
-# Use date string to set xaxis range
+# Use date string to set xaxis range
 fig.update_layout(xaxis_range=['2016-07-01','2016-12-31'],
                   title_text="Manually Set Date Range")
 fig.show()

doc/python/violin.md

@@ -34,7 +34,7 @@ jupyter:
     thumbnail: thumbnail/violin.jpg
 ---
 
-## Violin Plot with Plotly Express
+## Violin Plot with Plotly Express
 
 A [violin plot](https://en.wikipedia.org/wiki/Violin_plot) is a statistical representation of numerical data. It is similar to a [box plot](https://plot.ly/python/box-plots/), with the addition of a rotated [kernel density](https://en.wikipedia.org/wiki/Kernel_density_estimation) plot on each side.
 
@@ -233,7 +233,7 @@ fig.update_layout(
 fig.show()
 ```
 
-#### Ridgeline plot
+#### Ridgeline plot
 
 A ridgeline plot ([previously known as Joy Plot](https://serialmentor.com/blog/2017/9/15/goodbye-joyplots)) shows the distribution of a numerical value for several groups. They can be used for visualizing changes in distributions over time or space.