use new example data functions in docs

Author: Conengmo

docs/advanced_guide/choropleth with Jenks natural breaks optimization.md

@@ -15,20 +15,10 @@ import requests
 ```
 
 ```{code-cell} ipython3
-url = (
-    "https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
-)
-us_states = f"{url}/us-states.json"
-
-geo_json_data = json.loads(requests.get(us_states).text)
+geo_json_data = folium.example_data.us_states_geojson()
 
-county_data = pd.read_csv(f"{url}/us_county_data.csv")
 clf = 'Civilian_labor_force_2011'
-labor_force = county_data[['State', clf]][
-    (county_data[clf].str.strip()!='') & (~county_data['State'].isin(['PR', 'US']))
-]
-labor_force[clf] = labor_force[clf].astype(int)
-labor_force = labor_force.groupby('State').sum().reset_index()
+labor_force = folium.example_data.us_labor_force_pandas_dataframe()
 
 labor_force.head()
 ```

docs/advanced_guide/colormaps.md

@@ -1,25 +1,19 @@
-# Using `folium.colormap`
-
-**A few examples of how to use `folium.colormap` in choropleths.**
-
-Let's load a GeoJSON file, and try to choropleth it.
-
 ```{code-cell} ipython3
-import json
-
+---
+nbsphinx: hidden
+---
 import folium
-import pandas as pd
-import requests
+```
 
+# Using colormaps
 
-url = (
-    "https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
-)
-us_states = f"{url}/us-states.json"
-US_Unemployment_Oct2012 = f"{url}/US_Unemployment_Oct2012.csv"
+A few examples of how to use `folium.colormap` in choropleths.
 
-geo_json_data = json.loads(requests.get(us_states).text)
-unemployment = pd.read_csv(US_Unemployment_Oct2012)
+Let's load a GeoJSON file, and try to choropleth it.
+
+```{code-cell} ipython3
+geo_json_data = folium.example_data.us_states_geojson()
+unemployment = folium.example_data.us_unemployment_pandas_dataframe()
 
 unemployment_dict = unemployment.set_index("State")["Unemployment"]
 ```

docs/advanced_guide/custom_panes.md

@@ -1,3 +1,10 @@
+```{code-cell} ipython3
+---
+nbsphinx: hidden
+---
+import folium
+```
+
 # Panes and CustomPane
 
 Panes are used to control the ordering of layers on the map. You can customise
@@ -8,16 +15,7 @@ For more info on the panes Leaflet has, see https://leafletjs.com/reference.html
 First we'll load geojson data to use in the examples:
 
 ```{code-cell} ipython3
-import json
-
-import folium
-import requests
-
-url = (
-    "https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
-)
-us_states = f"{url}/us-states.json"
-geo_json_data = json.loads(requests.get(us_states).text)
+geo_json_data = folium.example_data.us_states_geojson()
 
 style_function = lambda x: {"fillOpacity": 0.8}
 ```

docs/advanced_guide/custom_tiles.md

@@ -1,25 +1,24 @@
+```{code-cell} ipython3
+---
+nbsphinx: hidden
+---
+import folium
+```
+
 # Custom tiles
 
 ## No tiles
 
 ```{code-cell} ipython3
-import os
-import json
-
-with open("../data/us-states.json") as f:
-    states = json.load(f)
+states = folium.example_data.us_states_geojson()
 
 kw = {"location": [48, -102], "zoom_start": 3}
 ```
 
 ```{code-cell} ipython3
-import folium
-from folium import GeoJson
-
-
 m = folium.Map(tiles=None, **kw)
 
-GeoJson(states).add_to(m)
+folium.GeoJson(states).add_to(m)
 
 m
 ```
@@ -35,7 +34,7 @@ white_tile = branca.utilities.image_to_url([[1, 1], [1, 1]])
 # Create a map using this url for each tile.
 m = folium.Map(tiles=white_tile, attr="white tile", **kw)
 
-GeoJson(states).add_to(m)
+folium.GeoJson(states).add_to(m)
 
 m
 ```
@@ -49,7 +48,7 @@ tiles = branca.utilities.image_to_url(images)
 
 m = folium.Map(tiles=tiles, attr="Just because we can", **kw)
 
-GeoJson(states).add_to(m)
+folium.GeoJson(states).add_to(m)
 
 m
 ```
@@ -61,7 +60,7 @@ tiles = branca.utilities.image_to_url(images)
 
 m = folium.Map(tiles=tiles, attr="Just because we can", **kw)
 
-GeoJson(states).add_to(m)
+folium.GeoJson(states).add_to(m)
 
 m
 ```

docs/advanced_guide/piechart_icons.md

@@ -1,20 +1,18 @@
+```{code-cell} ipython3
+---
+nbsphinx: hidden
+---
+import folium
+```
+
 # Piechart icons
 
 In this example we show how you can make mini-charts on several locations.
 We'll make little piecharts showing the number of consonants and vowels in
 a couple of languages. Those piecharts will be included as icons on the map.
 
 ```{code-cell} ipython3
-import os
-import ast
-
-import pandas
-
-data = pandas.read_csv(
-    "../data/consonants_vowels.csv",
-    # To ensure that tuples are read as tuples
-    converters={"coordinates": ast.literal_eval},
-)
+data = folium.example_data.language_coordinates_and_stats_pandas_dataframe()
 
 data.head()
 ```
@@ -88,9 +86,6 @@ legend._template = branca.element.Template(legend_html)
 ## Map
 
 ```{code-cell} ipython3
-import folium
-
-
 m = folium.Map(location=(0, 0), zoom_start=2)
 
 for i, coord in enumerate(data.coordinates):
@@ -103,5 +98,6 @@ for i, coord in enumerate(data.coordinates):
     marker.add_child(popup)
     m.add_child(marker)
 m.get_root().add_child(legend)
+
 m
 ```

docs/getting_started.md

@@ -149,15 +149,15 @@ m
 GeoJSON/TopoJSON overlays
 -------------------------
 
-Folium supports both GeoJSON and TopoJSON data in various formats. Here's an example where
-the data is retrieved from a url:
+Folium supports both GeoJSON and TopoJSON data in various formats, such as urls,
+file paths and dictionaries.
 
 ```{code-cell} ipython3
 m = folium.Map(tiles="cartodbpositron")
 
-url = ("https://raw.githubusercontent.com/python-visualization/folium"
-       "/main/examples/data/world-countries.json")
-folium.GeoJson(url, name="hello world").add_to(m)
+geojson_data = folium.example_data.world_countries_geojson()
+
+folium.GeoJson(geojson_data, name="hello world").add_to(m)
 
 folium.LayerControl().add_to(m)
 
@@ -171,12 +171,8 @@ Choropleth maps
 Choropleth can be created by binding the data between Pandas DataFrames/Series and Geo/TopoJSON geometries.
 
 ```{code-cell} ipython3
-import pandas as pd
-
-url = "https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
-state_geo = f"{url}/us-states.json"
-state_unemployment = f"{url}/US_Unemployment_Oct2012.csv"
-state_data = pd.read_csv(state_unemployment)
+state_geo = folium.example_data.us_states_geojson()
+state_data = folium.example_data.us_unemployment_pandas_dataframe()
 
 m = folium.Map(location=[48, -102], zoom_start=3)
 

docs/user_guide/geojson/choropleth.md

@@ -1,21 +1,21 @@
-## Using `Choropleth`
-
 ```{code-cell} ipython3
 ---
 nbsphinx: hidden
 ---
 import folium
 ```
 
+## Using `Choropleth`
+
 Now if you want to get faster, you can use the `Choropleth` class. Have a look at it's docstring, it has several styling options.
 
 Just like the `GeoJson` class you can provide it a filename, a dict, or a geopandas object.
 
 ```{code-cell} ipython3
-us_states = "../../data/us-states.json"
-
 m = folium.Map([43, -100], zoom_start=4)
 
+us_states = folium.example_data.us_states_geojson()
+
 folium.Choropleth(
     geo_data=us_states,
     fill_opacity=0.3,
@@ -28,9 +28,7 @@ m
 Then, in playing with keyword arguments, you can get a choropleth in a few lines:
 
 ```{code-cell} ipython3
-import pandas as pd
-
-state_data = pd.read_csv("../../data/US_Unemployment_Oct2012.csv")
+state_data = folium.example_data.us_unemployment_pandas_dataframe()
 
 m = folium.Map([43, -100], zoom_start=4)
 

docs/user_guide/geojson/geojson.md

@@ -1,20 +1,18 @@
+```{code-cell} ipython3
+---
+nbsphinx: hidden
+---
+import folium
+```
+
 ## Using `GeoJson`
 
 ### Loading data
 
 Let us load a GeoJSON file representing the US states.
 
 ```{code-cell} ipython3
-import json
-
-import requests
-
-url = (
-    "https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
-)
-us_states = f"{url}/us-states.json"
-
-geo_json_data = json.loads(requests.get(us_states).text)
+geo_json_data = folium.example_data.us_states_geojson()
 ```
 
 It is a classical GeoJSON `FeatureCollection` (see https://en.wikipedia.org/wiki/GeoJSON) of the form :
@@ -44,14 +42,9 @@ It is a classical GeoJSON `FeatureCollection` (see https://en.wikipedia.org/wiki
             ]
         }
 
-+++
-
 A first way of drawing it on a map, is simply to use `folium.GeoJson` :
 
 ```{code-cell} ipython3
-import folium
-
-
 m = folium.Map([43, -100], zoom_start=4)
 
 folium.GeoJson(geo_json_data).add_to(m)
@@ -64,7 +57,9 @@ Note that you can avoid loading the file on yourself ; in simply providing a fil
 ```{code-cell} ipython3
 m = folium.Map([43, -100], zoom_start=4)
 
-folium.GeoJson(us_states).add_to(m)
+filepath = folium.example_data.get_path("us_states.json")
+
+folium.GeoJson(filepath).add_to(m)
 
 m
 ```
@@ -74,7 +69,7 @@ You can pass a geopandas object.
 ```{code-cell} ipython3
 import geopandas
 
-gdf = geopandas.read_file(us_states)
+gdf = geopandas.read_file(filepath)
 
 m = folium.Map([43, -100], zoom_start=4)
 
@@ -153,10 +148,7 @@ Let's imagine we want to draw a choropleth of unemployment in the US.
 First, we may load the data:
 
 ```{code-cell} ipython3
-import pandas as pd
-
-US_Unemployment_Oct2012 = f"{url}/US_Unemployment_Oct2012.csv"
-unemployment = pd.read_csv(US_Unemployment_Oct2012)
+unemployment = folium.example_data.us_unemployment_pandas_dataframe()
 
 unemployment.head(5)
 ```

docs/user_guide/geojson/geojson_marker.md

@@ -1,16 +1,16 @@
-# GeoJSON point features with markers
-
 ```{code-cell} ipython3
-import os
+---
+nbsphinx: hidden
+---
 import folium
-import geopandas as gpd
 ```
 
-```{code-cell} ipython3
-gdf = gpd.read_file("../../data/subwaystations.geojson")
-```
+# GeoJSON point features with markers
+
 
 ```{code-cell} ipython3
+gdf = folium.example_data.subway_stations_geodataframe()
+
 gdf.head()
 ```