modifying the code of googleMaps

vayu/googleMaps.py

@@ -15,9 +15,11 @@ def googleMaps(df, lat, long, pollutant, dataLoc):
     long: str
         Name of column in df of where longitude points are
     pollutant: str
-        Name of pollutant 
-    dataLoc: str
-        Name of df column where pollutanat values are stored
+        Name of pollutant where values of that pollutant is stored.
+    date: str
+        visualizing the pollutant of a specific date.
+    markersize: int
+        The int by which the value of pollutant will be multiplied.
 
     """
     import folium
@@ -26,56 +28,30 @@ def googleMaps(df, lat, long, pollutant, dataLoc):
     import matplotlib.pyplot as plt
     import numpy as np
     import pandas as pd
-
-    latitude = 37.0902
-    longitude = -95.7129
-    Arithmetic_Mean_map = folium.Map(location=[latitude, longitude], zoom_start=4)
+
+    def googleMaps(df, lat, long, pollutant, date, markersize):
+        df1=df
+        print(date)
+        df1=df[df['date']==date]
+        print(df1)
+
 
     # =============================================================================
     # df = pd.read_csv('interpolData.csv')
     # =============================================================================
 
-    some_value = pollutant
-    df = df.loc[df["Parameter Name"] == some_value]
-
-    some_value = "2018-05-07"
-    df = df.loc[df["Date Local"] == some_value]
-
-    df = df.sample(frac=1)
+        lat= df1[lat].values[0] 
+        long=df1[long].values[0] 
+        my_map4 = folium.Map(location = [lat, long], zoom_start = 10)
 
-    # df_train, df_test = train_test_split(df, test_size=0.2)
-    df["Arithmetic Mean Q"] = pd.qcut(df[dataLoc], 4, labels=False)
-    colordict = {0: "lightblue", 1: "lightgreen", 2: "orange", 3: "red"}
+        for lat,long,pol,st in zip(df['latitude'],df['longitude'],df[pollutant],df['station']):
+            
+            folium.CircleMarker([lat, long],radius=markersize * pol, popup=(str(st).capitalize()+"<br>"+ str(round(pol, 3))), fill=True, fill_opacity=0.7, color = 'red').add_to(my_map4)
 
-    for lat, lon, Arithmetic_Mean_Q, Arithmetic_Mean, city, AQI in zip(
-        df[lat],
-        df[long],
-        df["Arithmetic Mean Q"],
-        df[dataLoc],
-        df["City Name"],
-        df["AQI"],
-    ):
-        folium.CircleMarker(
-            [lat, lon],
-            radius=0.15 * AQI,
-            popup=(
-                "City: "
-                + str(city).capitalize()
-                + "<br>"
-                #'Bike score: ' + str(bike) + '<br>'
-                "Arithmetic_Mean level: "
-                + str(Arithmetic_Mean)
-                + "%"
-            ),
-            color="b",
-            key_on=Arithmetic_Mean_Q,
-            threshold_scale=[0, 1, 2, 3],
-            fill_color=colordict[Arithmetic_Mean_Q],
-            fill=True,
-            fill_opacity=0.7,
-        ).add_to(Arithmetic_Mean_map)
-    Arithmetic_Mean_map.save("mymap.html")
+        my_map4.save("googleMaps.html")
+        print('your map has been saved')
 
 
+#Example
 # df = pd.read_csv('interpolData.csv')
 # googleMaps(df,'Latitude','Longitude','Ozone','Arithmetic Mean')