diff --git a/Examen.html b/Examen.html new file mode 100644 index 0000000000000000000000000000000000000000..196a11c7b91984cb9c53e237e9139902b1c2bfc5 --- /dev/null +++ b/Examen.html @@ -0,0 +1,18591 @@ + + +
+ + +#Bibliothéques :
+
+import numpy as np
+import pandas as pd
+import folium
+
#Chargement des données
+df_deaths = pd.read_csv('https://raw.githubusercontent.com/Fuenfgeld/DatamanagementAndArchiving/main/GeoCodingTutorial/cholera_deaths.csv')
+
+ | FID | +DEATHS | +LON | +LAT | +
---|---|---|---|---|
0 | +0 | +3 | +-0.137930 | +51.513418 | +
1 | +1 | +2 | +-0.137883 | +51.513361 | +
2 | +2 | +1 | +-0.137853 | +51.513317 | +
df_pumps = pd.read_csv('https://raw.githubusercontent.com/Fuenfgeld/DatamanagementAndArchiving/main/GeoCodingTutorial/johnsnow_pumps.csv')
+
# Exploration des données
+df_deaths.head()
+
+ | FID | +DEATHS | +LON | +LAT | +
---|---|---|---|---|
0 | +0 | +3 | +-0.137930 | +51.513418 | +
1 | +1 | +2 | +-0.137883 | +51.513361 | +
2 | +2 | +1 | +-0.137853 | +51.513317 | +
3 | +3 | +1 | +-0.137812 | +51.513262 | +
4 | +4 | +4 | +-0.137767 | +51.513204 | +
df_pumps.head()
+
+ | FID | +LON | +LAT | +
---|---|---|---|
0 | +250 | +-0.136668 | +51.513341 | +
1 | +251 | +-0.139586 | +51.513876 | +
2 | +252 | +-0.139671 | +51.514906 | +
3 | +253 | +-0.131630 | +51.512354 | +
4 | +254 | +-0.133594 | +51.512139 | +
# Recherche de données nulles
+
+df_deaths.info()
+
<class 'pandas.core.frame.DataFrame'> +RangeIndex: 250 entries, 0 to 249 +Data columns (total 4 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 FID 250 non-null int64 + 1 DEATHS 250 non-null int64 + 2 LON 250 non-null float64 + 3 LAT 250 non-null float64 +dtypes: float64(2), int64(2) +memory usage: 7.9 KB ++
df_pumps.info ()
+
<class 'pandas.core.frame.DataFrame'> +RangeIndex: 8 entries, 0 to 7 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 FID 8 non-null int64 + 1 LON 8 non-null float64 + 2 LAT 8 non-null float64 +dtypes: float64(2), int64(1) +memory usage: 320.0 bytes ++
df_deaths.describe()
+
+ | FID | +DEATHS | +LON | +LAT | +
---|---|---|---|---|
count | +250.000000 | +250.000000 | +250.000000 | +250.000000 | +
mean | +124.500000 | +1.956000 | +-0.136415 | +51.513416 | +
std | +72.312977 | +1.573521 | +0.001568 | +0.000753 | +
min | +0.000000 | +1.000000 | +-0.140074 | +51.511856 | +
25% | +62.250000 | +1.000000 | +-0.137633 | +51.512871 | +
50% | +124.500000 | +1.000000 | +-0.136288 | +51.513379 | +
75% | +186.750000 | +2.000000 | +-0.135333 | +51.513956 | +
max | +249.000000 | +15.000000 | +-0.132933 | +51.515834 | +
# Création liste longitude, latitude et nombre de morts
+coordinates_p = df_pumps[["LAT","LON"]].values.tolist()
+coordinates_d = df_deaths[["LAT","LON"]].values.tolist()
+totaldeaths = df_deaths[["DEATHS"]].values.tolist()
+len(totaldeaths)
+
250+
#ciblage de la carte
+import folium
+map = folium.Map(location=[51.5132119,-0.13666], tiles='Stamen Toner', zoom_start=17)
+
for i in range(0, len(coordinates_d)):
+ folium.RegularPolygonMarker(coordinates_d[i], radius = totaldeaths[i], fill_color = "red", fill_opacity = 0.5, number_of_sides = 12).add_to(map)
+
#Generation de la carte
+map
+
for i in range(0, len(coordinates_p)):
+ folium.RegularPolygonMarker(coordinates_p[i], radius = 10, \
+ fill_color = "blue", fill_opacity = 1
+ ).add_to(map)
+
map
+
+