use folium-example-data repo

Author: Conengmo

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

@@ -15,10 +15,14 @@ import requests
 ```
 
 ```{code-cell} ipython3
-geo_json_data = folium.example_data.us_states_geojson()
+geo_json_data = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
 
 clf = 'Civilian_labor_force_2011'
-labor_force = folium.example_data.us_labor_force_pandas_dataframe()
+labor_force = pd.read_csv(
+    "https://github.com/python-visualization/folium-example-data/raw/main/us_labor_force_2011.csv"
+)
 
 labor_force.head()
 ```

docs/advanced_guide/colormaps.md

@@ -12,8 +12,15 @@ 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
-geo_json_data = folium.example_data.us_states_geojson()
-unemployment = folium.example_data.us_unemployment_pandas_dataframe()
+import pandas
+import requests
+
+geo_json_data = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
+unemployment = pandas.read_csv(
+    "https://github.com/python-visualization/folium-example-data/raw/main/us_unemployment_oct_2012.csv"
+)
 
 unemployment_dict = unemployment.set_index("State")["Unemployment"]
 ```

docs/advanced_guide/custom_panes.md

@@ -15,7 +15,11 @@ 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
-geo_json_data = folium.example_data.us_states_geojson()
+import requests
+
+geo_json_data = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
 
 style_function = lambda x: {"fillOpacity": 0.8}
 ```

docs/advanced_guide/custom_tiles.md

@@ -10,7 +10,11 @@ import folium
 ## No tiles
 
 ```{code-cell} ipython3
-states = folium.example_data.us_states_geojson()
+import requests
+
+states = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
 
 kw = {"location": [48, -102], "zoom_start": 3}
 ```

docs/advanced_guide/piechart_icons.md

@@ -12,7 +12,13 @@ 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
-data = folium.example_data.language_coordinates_and_stats_pandas_dataframe()
+import pandas
+
+data = pandas.read_csv(
+    "https://github.com/python-visualization/folium-example-data/blob/main/consonants_vowels.csv",
+    # To ensure that tuples are read as tuples
+    converters={"coordinates": ast.literal_eval},
+)
 
 data.head()
 ```

docs/getting_started.md

@@ -153,9 +153,13 @@ Folium supports both GeoJSON and TopoJSON data in various formats, such as urls,
 file paths and dictionaries.
 
 ```{code-cell} ipython3
+import requests
+
 m = folium.Map(tiles="cartodbpositron")
 
-geojson_data = folium.example_data.world_countries_geojson()
+geojson_data = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/world_countries.json"
+).json()
 
 folium.GeoJson(geojson_data, name="hello world").add_to(m)
 
@@ -171,8 +175,14 @@ Choropleth maps
 Choropleth can be created by binding the data between Pandas DataFrames/Series and Geo/TopoJSON geometries.
 
 ```{code-cell} ipython3
-state_geo = folium.example_data.us_states_geojson()
-state_data = folium.example_data.us_unemployment_pandas_dataframe()
+import pandas
+
+state_geo = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
+state_data = pandas.read_csv(
+    "https://github.com/python-visualization/folium-example-data/raw/main/us_unemployment_oct_2012.csv"
+)
 
 m = folium.Map(location=[48, -102], zoom_start=3)
 

docs/user_guide/geojson/choropleth.md

@@ -12,9 +12,13 @@ Now if you want to get faster, you can use the `Choropleth` class. Have a look a
 Just like the `GeoJson` class you can provide it a filename, a dict, or a geopandas object.
 
 ```{code-cell} ipython3
+import requests
+
 m = folium.Map([43, -100], zoom_start=4)
 
-us_states = folium.example_data.us_states_geojson()
+us_states = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
 
 folium.Choropleth(
     geo_data=us_states,
@@ -28,7 +32,11 @@ m
 Then, in playing with keyword arguments, you can get a choropleth in a few lines:
 
 ```{code-cell} ipython3
-state_data = folium.example_data.us_unemployment_pandas_dataframe()
+import pandas
+
+state_data = pandas.read_csv(
+    "https://github.com/python-visualization/folium-example-data/raw/main/us_unemployment_oct_2012.csv"
+)
 
 m = folium.Map([43, -100], zoom_start=4)
 

docs/user_guide/geojson/geojson.md

@@ -12,7 +12,11 @@ import folium
 Let us load a GeoJSON file representing the US states.
 
 ```{code-cell} ipython3
-geo_json_data = folium.example_data.us_states_geojson()
+import requests
+
+geo_json_data = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
 ```
 
 It is a classical GeoJSON `FeatureCollection` (see https://en.wikipedia.org/wiki/GeoJSON) of the form :
@@ -52,14 +56,15 @@ folium.GeoJson(geo_json_data).add_to(m)
 m
 ```
 
-Note that you can avoid loading the file on yourself ; in simply providing a file path.
+Note that you can avoid loading the file on yourself, 
+by providing a (local) file path or a url.
 
 ```{code-cell} ipython3
 m = folium.Map([43, -100], zoom_start=4)
 
-filepath = folium.example_data.get_path("us_states.json")
+url = "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
 
-folium.GeoJson(filepath).add_to(m)
+folium.GeoJson(url).add_to(m)
 
 m
 ```
@@ -148,7 +153,11 @@ Let's imagine we want to draw a choropleth of unemployment in the US.
 First, we may load the data:
 
 ```{code-cell} ipython3
-unemployment = folium.example_data.us_unemployment_pandas_dataframe()
+import pandas
+
+unemployment = pandas.read_csv(
+    "https://github.com/python-visualization/folium-example-data/raw/main/us_unemployment_oct_2012.csv"
+)
 
 unemployment.head(5)
 ```

docs/user_guide/geojson/geojson_marker.md

@@ -9,7 +9,11 @@ import folium
 
 
 ```{code-cell} ipython3
-gdf = folium.example_data.subway_stations_geodataframe()
+import geopandas
+
+gdf = geopandas.read_file(
+    "https://github.com/python-visualization/folium-example-data/raw/main/subway_stations.geojson"
+)
 
 gdf.head()
 ```

docs/user_guide/geojson/geojson_popup_and_tooltip.md

@@ -24,8 +24,11 @@ income.head()
 
 ```{code-cell} ipython3
 import geopandas
+import requests
 
-data = folium.example_data.us_states_geojson()
+data = requests.get(
+    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
+).json()
 states = geopandas.GeoDataFrame.from_features(data, crs="EPSG:4326")
 
 states.head()