docs: added ml notes

DataAnalysis/Visualization/Intro/foliumExample.py

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+
+
+# https://www.youtube.com/watch?v=xPk7S-Eb4J4
+
+# imports
+import pandas as pd 
+import folium 
+
+# this makes it so that you see all the columns in a pd.show()
+pd.set_option('display.max_columns', None)
+
+# load the data 
+# https://github.com/practicalaifab/folium/blob/codespace-practicalaifab-probable-space-umbrella-r4476xv556q356qr/data/hospitals.csv
+# https://raw.githubusercontent.com/practicalaifab/folium/codespace-practicalaifab-probable-space-umbrella-r4476xv556q356qr/data/hospitals.csv
+df = pd.read_csv("https://raw.githubusercontent.com/practicalaifab/folium/codespace-practicalaifab-probable-space-umbrella-r4476xv556q356qr/data/hospitals.csv")
+
+# get list of hoospitals 
+
+# filter for only MA hospitals 
+ma = df[df['STATE'] == 'MA']
+ma = ma[['NAME', 'LATITUDE', 'LONGITUDE']]
+
+# display 
+# map.head()
+
+
+# get the mean lat/lon for the map crteation 
+lat_mean = ma['LATITUDE'].mean()
+lon_mean = ma['LONGITUDE'].mean()
+
+# create folium map 
+map = folium.Map(location=[lat_mean, lon_mean], zoom_start=15)
+
+# need to creat list of hospitals to put them on the map 
+list_hosp = ma.values.tolist()
+
+# loop over list 
+for index in list_hosp:
+    # add to map 
+    map.add_child(folium.Marker(location=[index[1], index[2]], popup=index[0], icon=folium.Icon(color='green')))
+
+
+# save map  as html file 
+map.save("ma.html")
+
+
+# df.show()
+
+

DataAnalysis/Visualization/Intro/foliumWithFunction.py

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+
+
+# https://www.youtube.com/watch?v=xPk7S-Eb4J4
+
+# imports
+import pandas as pd 
+import folium
+
+# this makes it so that you see all the columns in a pd.show()
+pd.set_option('display.max_columns', None)
+
+# load the data 
+# https://github.com/practicalaifab/folium/blob/codespace-practicalaifab-probable-space-umbrella-r4476xv556q356qr/data/hospitals.csv
+# https://raw.githubusercontent.com/practicalaifab/folium/codespace-practicalaifab-probable-space-umbrella-r4476xv556q356qr/data/hospitals.csv
+df = pd.read_csv("https://raw.githubusercontent.com/practicalaifab/folium/codespace-practicalaifab-probable-space-umbrella-r4476xv556q356qr/data/hospitals.csv")
+
+
+# function 
+def choose_state(data, state_option):
+    state = data[data['STATE'] == state_option]
+    state = state[['NAME', 'LATITUDE', 'LONGITUDE']]
+
+    # return
+    return state
+
+def plot_state(data):
+    # get lon/lat
+    lat_mean = data['LATITUDE'].mean()
+    lon_mean = data['LONGITUDE'].mean()
+
+    # get the map
+    map = folium.Map(location=[lat_mean, lon_mean], zoom_start=15)
+
+    # populate the map 
+    list_hosp = data.values.tolist()
+    for index in list_hosp:
+        # add to map 
+        map.add_child(folium.Marker(location=[index[1], index[2]], popup=index[0], icon=folium.Icon(color='green')))
+
+    # return 
+    return map
+
+if __name__ == "__main__":
+    state = 'CT'
+    # get the state data 
+    df_state = choose_state(df, state)
+
+    print(df_state.info())
+
+    # get the map
+    map = plot_state(df_state)
+
+    # save map  as html file 
+    map.save("{}.html".format(state))
+
+
+
+# # get list of hoospitals 
+
+# # filter for only MA hospitals 
+# ma = df[df['STATE'] == 'MA']
+# ma = ma[['NAME', 'LATITUDE', 'LONGITUDE']]
+
+# # display 
+# # map.head()
+
+
+# # get the mean lat/lon for the map crteation 
+# lat_mean = ma['LATITUDE'].mean()
+# lon_mean = ma['LONGITUDE'].mean()
+
+# # create folium map 
+# map = folium.Map(location=[lat_mean, lon_mean], zoom_start=15)
+
+# # need to creat list of hospitals to put them on the map 
+# list_hosp = ma.values.tolist()
+
+# # loop over list 
+# for index in list_hosp:
+#     # add to map 
+#     map.add_child(folium.Marker(location=[index[1], index[2]], popup=index[0], icon=folium.Icon(color='green')))
+
+
+# # save map  as html file 
+# map.save("ma.html")
+
+
+# df.show()
+
+