Merge branch 'doc-prod'

Author: nicolaskruchten

doc/python/histograms.md

@@ -88,7 +88,7 @@ fig.show()
 
 #### Type of normalization
 
-The default mode is to represent the count of samples in each bin. With the `histnorm` argument, it is also possible to represent the percentage or fraction of samples in each bin (`histnorm='percent'` or `probability`), or a density histogram (the sum of bars is equal to 100, `density`), or a probability density histogram (sum equal to 1, `probability density`).
+The default mode is to represent the count of samples in each bin. With the `histnorm` argument, it is also possible to represent the percentage or fraction of samples in each bin (`histnorm='percent'` or `probability`), or a density histogram (the sum of all bar areas equals the total number of sample points, `density`), or a probability density histogram (the sum of all bar areas equals 1, `probability density`).
 
 ```python
 import plotly.express as px

doc/python/lines-on-maps.md

@@ -111,6 +111,80 @@ fig.update_layout(
 
 fig.show()
 ```
+### Performance improvement: put many lines in the same trace
+For very large amounts (>1000) of lines, performance may become critcal. If you can relinquish setting individual line styles (e.g. opacity), you can put multiple paths into one trace. This makes the map render faster and reduces the script execution time and memory consumption. 
+
+Use ```None``` between path coordinates to create a break in the otherwise connected paths. 
+
+```python 
+import plotly.graph_objects as go
+import pandas as pd
+
+df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
+df_airports.head()
+
+df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
+df_flight_paths.head()
+
+fig = go.Figure()
+
+fig.add_trace(go.Scattergeo(
+    locationmode = 'USA-states',
+    lon = df_airports['long'],
+    lat = df_airports['lat'],
+    hoverinfo = 'text',
+    text = df_airports['airport'],
+    mode = 'markers',
+    marker = dict(
+        size = 2,
+        color = 'rgb(255, 0, 0)',
+        line = dict(
+            width = 3,
+            color = 'rgba(68, 68, 68, 0)'
+        )
+    )))
+
+flight_paths = []
+lons = []
+lats = []
+import numpy as np
+lons = np.empty(3 * len(df_flight_paths))
+lons[::3] = df_flight_paths['start_lon']
+lons[1::3] = df_flight_paths['end_lon']
+lons[::3] = None
+lats = np.empty(3 * len(df_flight_paths))
+lats[::3] = df_flight_paths['start_lat']
+lats[1::3] = df_flight_paths['end_lat']
+lats[::3] = None
+
+fig.add_trace(
+    go.Scattergeo(
+        locationmode = 'USA-states',
+        lon = lons,
+        lat = lats,
+        mode = 'lines',
+        line = dict(width = 1,color = 'red'),
+        opacity = 0.5 
+    )
+)
+
+fig.update_layout(
+    title_text = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
+    showlegend = False,
+    geo = go.layout.Geo(
+        scope = 'north america',
+        projection_type = 'azimuthal equal area',
+        showland = True,
+        landcolor = 'rgb(243, 243, 243)',
+        countrycolor = 'rgb(204, 204, 204)',
+    ),
+    height=700,
+)
+
+fig.show()
+
+```
+
 
 ### London to NYC Great Circle