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1
tappyness1 commited on
Commit
6f8fd55
·
1 Parent(s): 33f164a

added in heatmap charts

Browse files
Files changed (4) hide show
  1. app.py +32 -3
  2. notebooks/Causian_trial1.ipynb +3605 -0
  3. src/basic_plot.py +2 -2
  4. src/heatmap.py +139 -0
app.py CHANGED
@@ -6,10 +6,10 @@ import os
6
  from src.basic_plot import basic_chart
7
  from src.map_viz import calling_map_viz
8
  from src.data_ingestion import daily_average
 
9
 
10
-
11
- def main():
12
-
13
  # comment out for local testing, but be sure to include after testing
14
  dataset = load_dataset("tappyness1/causion", use_auth_token=os.environ['TOKEN'])
15
  # print (dataset)
@@ -19,7 +19,11 @@ def main():
19
  # only use this part before for local testing
20
  # once local testing is completed, comment out and use the dataset above
21
  # counts_df = pd.read_csv("data/counts_dataset.csv")
 
 
 
22
 
 
23
  # st.set_page_config(layout="wide")
24
  height = 650
25
 
@@ -34,12 +38,37 @@ def main():
34
  st.sidebar.markdown("Select Plots to show")
35
  checkbox_one = st.sidebar.checkbox('Overall Traffic', value = True) # rename as necessary
36
  checkbox_two = st.sidebar.checkbox('Traffic Map', value = True)
 
37
 
38
  if checkbox_one:
39
  st.plotly_chart(basic_chart(counts_df),use_container_width=True)
40
 
41
  if checkbox_two:
42
  st.pyplot(calling_map_viz(counts_df))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
43
 
44
  if __name__ == "__main__":
45
  main()
 
6
  from src.basic_plot import basic_chart
7
  from src.map_viz import calling_map_viz
8
  from src.data_ingestion import daily_average
9
+ from src.heatmap import HeatMap
10
 
11
+ @st.cache_data
12
+ def fetch_data():
 
13
  # comment out for local testing, but be sure to include after testing
14
  dataset = load_dataset("tappyness1/causion", use_auth_token=os.environ['TOKEN'])
15
  # print (dataset)
 
19
  # only use this part before for local testing
20
  # once local testing is completed, comment out and use the dataset above
21
  # counts_df = pd.read_csv("data/counts_dataset.csv")
22
+ return counts_df
23
+
24
+ def main():
25
 
26
+ counts_df = fetch_data()
27
  # st.set_page_config(layout="wide")
28
  height = 650
29
 
 
38
  st.sidebar.markdown("Select Plots to show")
39
  checkbox_one = st.sidebar.checkbox('Overall Traffic', value = True) # rename as necessary
40
  checkbox_two = st.sidebar.checkbox('Traffic Map', value = True)
41
+ checkbox_three = st.sidebar.checkbox('Heat Map', value = True)
42
 
43
  if checkbox_one:
44
  st.plotly_chart(basic_chart(counts_df),use_container_width=True)
45
 
46
  if checkbox_two:
47
  st.pyplot(calling_map_viz(counts_df))
48
+
49
+ if checkbox_three:
50
+
51
+ heatmap = HeatMap(counts_df)
52
+
53
+ st.plotly_chart(heatmap.vehicle_count_bar())
54
+ st.plotly_chart(heatmap.heatmap())
55
+
56
+ hour_choice = st.selectbox(
57
+ "Choose Hour",
58
+ options=[
59
+ "00:00", "01:00", "02:00", "03:00", "04:00", "05:00",
60
+ "06:00", "07:00", "08:00", "09:00", "10:00", "11:00",
61
+ "12:00", "13:00", "14:00", "15:00", "16:00", "17:00",
62
+ "18:00", "19:00", "20:00", "21:00", "22:00", "23:00",
63
+ ],
64
+ key = "hour"
65
+ )
66
+ st.plotly_chart(heatmap.update_hour_bar_chart(hour_choice))
67
+
68
+ day_choice = st.selectbox("Choose Day of the Week", ["Monday", "Tuesday", "Wednesday",
69
+ "Thursday", "Friday","Saturday", "Sunday"], key = "day")
70
+ st.plotly_chart(heatmap.update_day_bar_chart(day_choice))
71
+
72
 
73
  if __name__ == "__main__":
74
  main()
notebooks/Causian_trial1.ipynb ADDED
@@ -0,0 +1,3605 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "id": "258c9132",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "import numpy as np\n",
11
+ "import pandas as pd\n",
12
+ "import matplotlib.pyplot as plt\n",
13
+ "import scipy as sp\n",
14
+ "import sklearn as sk\n",
15
+ "import datetime\n",
16
+ "import calendar\n",
17
+ "from jupyter_dash import JupyterDash\n",
18
+ "import dash\n",
19
+ "from dash import Dash, html, dcc, Input, Output\n",
20
+ "import plotly.express as px\n",
21
+ "from plotly.subplots import make_subplots\n",
22
+ "import plotly.graph_objects as go\n",
23
+ "import requests\n",
24
+ "import io\n"
25
+ ]
26
+ },
27
+ {
28
+ "cell_type": "code",
29
+ "execution_count": 2,
30
+ "id": "e7a1d236",
31
+ "metadata": {},
32
+ "outputs": [
33
+ {
34
+ "data": {
35
+ "text/html": [
36
+ "<div>\n",
37
+ "<style scoped>\n",
38
+ " .dataframe tbody tr th:only-of-type {\n",
39
+ " vertical-align: middle;\n",
40
+ " }\n",
41
+ "\n",
42
+ " .dataframe tbody tr th {\n",
43
+ " vertical-align: top;\n",
44
+ " }\n",
45
+ "\n",
46
+ " .dataframe thead th {\n",
47
+ " text-align: right;\n",
48
+ " }\n",
49
+ "</style>\n",
50
+ "<table border=\"1\" class=\"dataframe\">\n",
51
+ " <thead>\n",
52
+ " <tr style=\"text-align: right;\">\n",
53
+ " <th></th>\n",
54
+ " <th>date</th>\n",
55
+ " <th>time</th>\n",
56
+ " <th>view</th>\n",
57
+ " <th>car</th>\n",
58
+ " <th>motorcycle</th>\n",
59
+ " <th>large_vehicle</th>\n",
60
+ " </tr>\n",
61
+ " </thead>\n",
62
+ " <tbody>\n",
63
+ " <tr>\n",
64
+ " <th>0</th>\n",
65
+ " <td>2023-02-14</td>\n",
66
+ " <td>22:36:03</td>\n",
67
+ " <td>View_from_Second_Link_at_Tuas</td>\n",
68
+ " <td>0</td>\n",
69
+ " <td>0</td>\n",
70
+ " <td>1</td>\n",
71
+ " </tr>\n",
72
+ " <tr>\n",
73
+ " <th>1</th>\n",
74
+ " <td>2023-02-14</td>\n",
75
+ " <td>22:36:03</td>\n",
76
+ " <td>View_from_Tuas_Checkpoint</td>\n",
77
+ " <td>2</td>\n",
78
+ " <td>0</td>\n",
79
+ " <td>0</td>\n",
80
+ " </tr>\n",
81
+ " <tr>\n",
82
+ " <th>2</th>\n",
83
+ " <td>2023-02-14</td>\n",
84
+ " <td>22:36:03</td>\n",
85
+ " <td>View_from_Woodlands_Causeway_Towards_Johor</td>\n",
86
+ " <td>2</td>\n",
87
+ " <td>0</td>\n",
88
+ " <td>0</td>\n",
89
+ " </tr>\n",
90
+ " <tr>\n",
91
+ " <th>3</th>\n",
92
+ " <td>2023-02-14</td>\n",
93
+ " <td>22:36:03</td>\n",
94
+ " <td>View_from_Woodlands_Checkpoint_Towards_BKE</td>\n",
95
+ " <td>3</td>\n",
96
+ " <td>0</td>\n",
97
+ " <td>1</td>\n",
98
+ " </tr>\n",
99
+ " <tr>\n",
100
+ " <th>4</th>\n",
101
+ " <td>2023-02-14</td>\n",
102
+ " <td>23:14:34</td>\n",
103
+ " <td>View_from_Second_Link_at_Tuas</td>\n",
104
+ " <td>0</td>\n",
105
+ " <td>0</td>\n",
106
+ " <td>6</td>\n",
107
+ " </tr>\n",
108
+ " </tbody>\n",
109
+ "</table>\n",
110
+ "</div>"
111
+ ],
112
+ "text/plain": [
113
+ " date time view car \\\n",
114
+ "0 2023-02-14 22:36:03 View_from_Second_Link_at_Tuas 0 \n",
115
+ "1 2023-02-14 22:36:03 View_from_Tuas_Checkpoint 2 \n",
116
+ "2 2023-02-14 22:36:03 View_from_Woodlands_Causeway_Towards_Johor 2 \n",
117
+ "3 2023-02-14 22:36:03 View_from_Woodlands_Checkpoint_Towards_BKE 3 \n",
118
+ "4 2023-02-14 23:14:34 View_from_Second_Link_at_Tuas 0 \n",
119
+ "\n",
120
+ " motorcycle large_vehicle \n",
121
+ "0 0 1 \n",
122
+ "1 0 0 \n",
123
+ "2 0 0 \n",
124
+ "3 0 1 \n",
125
+ "4 0 6 "
126
+ ]
127
+ },
128
+ "metadata": {},
129
+ "output_type": "display_data"
130
+ },
131
+ {
132
+ "name": "stdout",
133
+ "output_type": "stream",
134
+ "text": [
135
+ "(6960, 6)\n"
136
+ ]
137
+ }
138
+ ],
139
+ "source": [
140
+ "url = \"https://raw.githubusercontent.com/tappyness1/causion/main/data/counts_dataset.csv\"\n",
141
+ "\n",
142
+ "download = requests.get(url).content\n",
143
+ "df = pd.read_csv(io.StringIO(download.decode('utf-8')))\n",
144
+ "display(df.head())\n",
145
+ "print(df.shape)"
146
+ ]
147
+ },
148
+ {
149
+ "cell_type": "code",
150
+ "execution_count": 3,
151
+ "id": "dff31b99",
152
+ "metadata": {},
153
+ "outputs": [],
154
+ "source": [
155
+ "#Data manipulation\n",
156
+ "\n",
157
+ "df['date'] = pd.to_datetime(df['date'], format = \"%Y-%m-%d\")\n",
158
+ "df['day'] = df['date'].dt.day_name()\n",
159
+ "df['hour'] = df['time'].str[:2] + ':00'\n",
160
+ "df.drop(columns=['motorcycle'], axis=1, inplace=True)\n",
161
+ "df['vehicle'] = df['car'] + df['large_vehicle']"
162
+ ]
163
+ },
164
+ {
165
+ "cell_type": "code",
166
+ "execution_count": 4,
167
+ "id": "8545eff0",
168
+ "metadata": {},
169
+ "outputs": [
170
+ {
171
+ "data": {
172
+ "text/html": [
173
+ "<div>\n",
174
+ "<style scoped>\n",
175
+ " .dataframe tbody tr th:only-of-type {\n",
176
+ " vertical-align: middle;\n",
177
+ " }\n",
178
+ "\n",
179
+ " .dataframe tbody tr th {\n",
180
+ " vertical-align: top;\n",
181
+ " }\n",
182
+ "\n",
183
+ " .dataframe thead th {\n",
184
+ " text-align: right;\n",
185
+ " }\n",
186
+ "</style>\n",
187
+ "<table border=\"1\" class=\"dataframe\">\n",
188
+ " <thead>\n",
189
+ " <tr style=\"text-align: right;\">\n",
190
+ " <th></th>\n",
191
+ " <th>date</th>\n",
192
+ " <th>time</th>\n",
193
+ " <th>view</th>\n",
194
+ " <th>car</th>\n",
195
+ " <th>large_vehicle</th>\n",
196
+ " <th>day</th>\n",
197
+ " <th>hour</th>\n",
198
+ " <th>vehicle</th>\n",
199
+ " </tr>\n",
200
+ " </thead>\n",
201
+ " <tbody>\n",
202
+ " <tr>\n",
203
+ " <th>0</th>\n",
204
+ " <td>2023-02-14</td>\n",
205
+ " <td>22:36:03</td>\n",
206
+ " <td>View_from_Second_Link_at_Tuas</td>\n",
207
+ " <td>0</td>\n",
208
+ " <td>1</td>\n",
209
+ " <td>Tuesday</td>\n",
210
+ " <td>22:00</td>\n",
211
+ " <td>1</td>\n",
212
+ " </tr>\n",
213
+ " <tr>\n",
214
+ " <th>1</th>\n",
215
+ " <td>2023-02-14</td>\n",
216
+ " <td>22:36:03</td>\n",
217
+ " <td>View_from_Tuas_Checkpoint</td>\n",
218
+ " <td>2</td>\n",
219
+ " <td>0</td>\n",
220
+ " <td>Tuesday</td>\n",
221
+ " <td>22:00</td>\n",
222
+ " <td>2</td>\n",
223
+ " </tr>\n",
224
+ " <tr>\n",
225
+ " <th>2</th>\n",
226
+ " <td>2023-02-14</td>\n",
227
+ " <td>22:36:03</td>\n",
228
+ " <td>View_from_Woodlands_Causeway_Towards_Johor</td>\n",
229
+ " <td>2</td>\n",
230
+ " <td>0</td>\n",
231
+ " <td>Tuesday</td>\n",
232
+ " <td>22:00</td>\n",
233
+ " <td>2</td>\n",
234
+ " </tr>\n",
235
+ " <tr>\n",
236
+ " <th>3</th>\n",
237
+ " <td>2023-02-14</td>\n",
238
+ " <td>22:36:03</td>\n",
239
+ " <td>View_from_Woodlands_Checkpoint_Towards_BKE</td>\n",
240
+ " <td>3</td>\n",
241
+ " <td>1</td>\n",
242
+ " <td>Tuesday</td>\n",
243
+ " <td>22:00</td>\n",
244
+ " <td>4</td>\n",
245
+ " </tr>\n",
246
+ " <tr>\n",
247
+ " <th>4</th>\n",
248
+ " <td>2023-02-14</td>\n",
249
+ " <td>23:14:34</td>\n",
250
+ " <td>View_from_Second_Link_at_Tuas</td>\n",
251
+ " <td>0</td>\n",
252
+ " <td>6</td>\n",
253
+ " <td>Tuesday</td>\n",
254
+ " <td>23:00</td>\n",
255
+ " <td>6</td>\n",
256
+ " </tr>\n",
257
+ " </tbody>\n",
258
+ "</table>\n",
259
+ "</div>"
260
+ ],
261
+ "text/plain": [
262
+ " date time view car \\\n",
263
+ "0 2023-02-14 22:36:03 View_from_Second_Link_at_Tuas 0 \n",
264
+ "1 2023-02-14 22:36:03 View_from_Tuas_Checkpoint 2 \n",
265
+ "2 2023-02-14 22:36:03 View_from_Woodlands_Causeway_Towards_Johor 2 \n",
266
+ "3 2023-02-14 22:36:03 View_from_Woodlands_Checkpoint_Towards_BKE 3 \n",
267
+ "4 2023-02-14 23:14:34 View_from_Second_Link_at_Tuas 0 \n",
268
+ "\n",
269
+ " large_vehicle day hour vehicle \n",
270
+ "0 1 Tuesday 22:00 1 \n",
271
+ "1 0 Tuesday 22:00 2 \n",
272
+ "2 0 Tuesday 22:00 2 \n",
273
+ "3 1 Tuesday 22:00 4 \n",
274
+ "4 6 Tuesday 23:00 6 "
275
+ ]
276
+ },
277
+ "metadata": {},
278
+ "output_type": "display_data"
279
+ }
280
+ ],
281
+ "source": [
282
+ "display(df.head())"
283
+ ]
284
+ },
285
+ {
286
+ "cell_type": "code",
287
+ "execution_count": 5,
288
+ "id": "e567f58d",
289
+ "metadata": {},
290
+ "outputs": [
291
+ {
292
+ "name": "stderr",
293
+ "output_type": "stream",
294
+ "text": [
295
+ "C:\\Users\\neoce\\AppData\\Local\\Temp\\ipykernel_26408\\1176758689.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
296
+ " new_df = df.groupby(['day']).sum().reset_index()\n"
297
+ ]
298
+ },
299
+ {
300
+ "data": {
301
+ "text/html": [
302
+ "<div>\n",
303
+ "<style scoped>\n",
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+ " text-align: right;\n",
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+ " }\n",
315
+ "</style>\n",
316
+ "<table border=\"1\" class=\"dataframe\">\n",
317
+ " <thead>\n",
318
+ " <tr style=\"text-align: right;\">\n",
319
+ " <th></th>\n",
320
+ " <th>day</th>\n",
321
+ " <th>car</th>\n",
322
+ " <th>large_vehicle</th>\n",
323
+ " <th>vehicle</th>\n",
324
+ " </tr>\n",
325
+ " </thead>\n",
326
+ " <tbody>\n",
327
+ " <tr>\n",
328
+ " <th>1</th>\n",
329
+ " <td>Monday</td>\n",
330
+ " <td>2406</td>\n",
331
+ " <td>1064</td>\n",
332
+ " <td>3470</td>\n",
333
+ " </tr>\n",
334
+ " <tr>\n",
335
+ " <th>5</th>\n",
336
+ " <td>Tuesday</td>\n",
337
+ " <td>2003</td>\n",
338
+ " <td>811</td>\n",
339
+ " <td>2814</td>\n",
340
+ " </tr>\n",
341
+ " <tr>\n",
342
+ " <th>6</th>\n",
343
+ " <td>Wednesday</td>\n",
344
+ " <td>1942</td>\n",
345
+ " <td>864</td>\n",
346
+ " <td>2806</td>\n",
347
+ " </tr>\n",
348
+ " <tr>\n",
349
+ " <th>4</th>\n",
350
+ " <td>Thursday</td>\n",
351
+ " <td>1976</td>\n",
352
+ " <td>903</td>\n",
353
+ " <td>2879</td>\n",
354
+ " </tr>\n",
355
+ " <tr>\n",
356
+ " <th>0</th>\n",
357
+ " <td>Friday</td>\n",
358
+ " <td>2070</td>\n",
359
+ " <td>762</td>\n",
360
+ " <td>2832</td>\n",
361
+ " </tr>\n",
362
+ " <tr>\n",
363
+ " <th>2</th>\n",
364
+ " <td>Saturday</td>\n",
365
+ " <td>2117</td>\n",
366
+ " <td>578</td>\n",
367
+ " <td>2695</td>\n",
368
+ " </tr>\n",
369
+ " <tr>\n",
370
+ " <th>3</th>\n",
371
+ " <td>Sunday</td>\n",
372
+ " <td>1515</td>\n",
373
+ " <td>428</td>\n",
374
+ " <td>1943</td>\n",
375
+ " </tr>\n",
376
+ " </tbody>\n",
377
+ "</table>\n",
378
+ "</div>"
379
+ ],
380
+ "text/plain": [
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+ " day car large_vehicle vehicle\n",
382
+ "1 Monday 2406 1064 3470\n",
383
+ "5 Tuesday 2003 811 2814\n",
384
+ "6 Wednesday 1942 864 2806\n",
385
+ "4 Thursday 1976 903 2879\n",
386
+ "0 Friday 2070 762 2832\n",
387
+ "2 Saturday 2117 578 2695\n",
388
+ "3 Sunday 1515 428 1943"
389
+ ]
390
+ },
391
+ "execution_count": 5,
392
+ "metadata": {},
393
+ "output_type": "execute_result"
394
+ }
395
+ ],
396
+ "source": [
397
+ "cat = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday','Saturday', 'Sunday']\n",
398
+ "new_df = df.groupby(['day']).sum().reset_index()\n",
399
+ "new_df = new_df.reindex([1,5,6,4,0,2,3])\n",
400
+ "new_df.head(10)"
401
+ ]
402
+ },
403
+ {
404
+ "cell_type": "code",
405
+ "execution_count": 6,
406
+ "id": "b2fc096f",
407
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408
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409
+ {
410
+ "name": "stderr",
411
+ "output_type": "stream",
412
+ "text": [
413
+ "C:\\Users\\neoce\\AppData\\Local\\Temp\\ipykernel_26408\\2911116693.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
414
+ " new = df.groupby(['hour','day']).sum().drop(columns=['car', \"large_vehicle\"]).reset_index()\n"
415
+ ]
416
+ },
417
+ {
418
+ "data": {
419
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+ " <thead>\n",
436
+ " <tr style=\"text-align: right;\">\n",
437
+ " <th></th>\n",
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+ " <tbody>\n",
444
+ " <tr>\n",
445
+ " <th>0</th>\n",
446
+ " <td>00:00</td>\n",
447
+ " <td>Friday</td>\n",
448
+ " <td>44</td>\n",
449
+ " </tr>\n",
450
+ " <tr>\n",
451
+ " <th>1</th>\n",
452
+ " <td>00:00</td>\n",
453
+ " <td>Monday</td>\n",
454
+ " <td>52</td>\n",
455
+ " </tr>\n",
456
+ " <tr>\n",
457
+ " <th>2</th>\n",
458
+ " <td>00:00</td>\n",
459
+ " <td>Saturday</td>\n",
460
+ " <td>50</td>\n",
461
+ " </tr>\n",
462
+ " <tr>\n",
463
+ " <th>3</th>\n",
464
+ " <td>00:00</td>\n",
465
+ " <td>Sunday</td>\n",
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+ " <td>61</td>\n",
467
+ " </tr>\n",
468
+ " <tr>\n",
469
+ " <th>4</th>\n",
470
+ " <td>00:00</td>\n",
471
+ " <td>Thursday</td>\n",
472
+ " <td>22</td>\n",
473
+ " </tr>\n",
474
+ " <tr>\n",
475
+ " <th>...</th>\n",
476
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+ " <td>...</td>\n",
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480
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481
+ " <th>163</th>\n",
482
+ " <td>23:00</td>\n",
483
+ " <td>Saturday</td>\n",
484
+ " <td>57</td>\n",
485
+ " </tr>\n",
486
+ " <tr>\n",
487
+ " <th>164</th>\n",
488
+ " <td>23:00</td>\n",
489
+ " <td>Sunday</td>\n",
490
+ " <td>62</td>\n",
491
+ " </tr>\n",
492
+ " <tr>\n",
493
+ " <th>165</th>\n",
494
+ " <td>23:00</td>\n",
495
+ " <td>Thursday</td>\n",
496
+ " <td>51</td>\n",
497
+ " </tr>\n",
498
+ " <tr>\n",
499
+ " <th>166</th>\n",
500
+ " <td>23:00</td>\n",
501
+ " <td>Tuesday</td>\n",
502
+ " <td>49</td>\n",
503
+ " </tr>\n",
504
+ " <tr>\n",
505
+ " <th>167</th>\n",
506
+ " <td>23:00</td>\n",
507
+ " <td>Wednesday</td>\n",
508
+ " <td>80</td>\n",
509
+ " </tr>\n",
510
+ " </tbody>\n",
511
+ "</table>\n",
512
+ "<p>168 rows × 3 columns</p>\n",
513
+ "</div>"
514
+ ],
515
+ "text/plain": [
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+ " hour day vehicle\n",
517
+ "0 00:00 Friday 44\n",
518
+ "1 00:00 Monday 52\n",
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+ "2 00:00 Saturday 50\n",
520
+ "3 00:00 Sunday 61\n",
521
+ "4 00:00 Thursday 22\n",
522
+ ".. ... ... ...\n",
523
+ "163 23:00 Saturday 57\n",
524
+ "164 23:00 Sunday 62\n",
525
+ "165 23:00 Thursday 51\n",
526
+ "166 23:00 Tuesday 49\n",
527
+ "167 23:00 Wednesday 80\n",
528
+ "\n",
529
+ "[168 rows x 3 columns]"
530
+ ]
531
+ },
532
+ "metadata": {},
533
+ "output_type": "display_data"
534
+ }
535
+ ],
536
+ "source": [
537
+ "new = df.groupby(['hour','day']).sum().drop(columns=['car', \"large_vehicle\"]).reset_index()\n",
538
+ "display(new)"
539
+ ]
540
+ },
541
+ {
542
+ "cell_type": "code",
543
+ "execution_count": 7,
544
+ "id": "b7d4291c",
545
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546
+ "outputs": [
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+ {
548
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+ "<table border=\"1\" class=\"dataframe\">\n",
565
+ " <thead>\n",
566
+ " <tr style=\"text-align: right;\">\n",
567
+ " <th>hour</th>\n",
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569
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571
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572
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574
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577
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578
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580
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583
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584
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585
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590
+ " </thead>\n",
591
+ " <tbody>\n",
592
+ " <tr>\n",
593
+ " <th>1</th>\n",
594
+ " <td>Monday</td>\n",
595
+ " <td>52</td>\n",
596
+ " <td>82</td>\n",
597
+ " <td>35</td>\n",
598
+ " <td>40</td>\n",
599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
+ " <td>214</td>\n",
610
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611
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612
+ " <td>58</td>\n",
613
+ " <td>30</td>\n",
614
+ " <td>35</td>\n",
615
+ " </tr>\n",
616
+ " <tr>\n",
617
+ " <th>5</th>\n",
618
+ " <td>Tuesday</td>\n",
619
+ " <td>58</td>\n",
620
+ " <td>30</td>\n",
621
+ " <td>19</td>\n",
622
+ " <td>14</td>\n",
623
+ " <td>15</td>\n",
624
+ " <td>35</td>\n",
625
+ " <td>85</td>\n",
626
+ " <td>144</td>\n",
627
+ " <td>47</td>\n",
628
+ " <td>...</td>\n",
629
+ " <td>186</td>\n",
630
+ " <td>202</td>\n",
631
+ " <td>243</td>\n",
632
+ " <td>207</td>\n",
633
+ " <td>265</td>\n",
634
+ " <td>168</td>\n",
635
+ " <td>49</td>\n",
636
+ " <td>40</td>\n",
637
+ " <td>46</td>\n",
638
+ " <td>49</td>\n",
639
+ " </tr>\n",
640
+ " <tr>\n",
641
+ " <th>6</th>\n",
642
+ " <td>Wednesday</td>\n",
643
+ " <td>28</td>\n",
644
+ " <td>41</td>\n",
645
+ " <td>18</td>\n",
646
+ " <td>17</td>\n",
647
+ " <td>16</td>\n",
648
+ " <td>26</td>\n",
649
+ " <td>57</td>\n",
650
+ " <td>96</td>\n",
651
+ " <td>23</td>\n",
652
+ " <td>...</td>\n",
653
+ " <td>182</td>\n",
654
+ " <td>226</td>\n",
655
+ " <td>192</td>\n",
656
+ " <td>280</td>\n",
657
+ " <td>271</td>\n",
658
+ " <td>163</td>\n",
659
+ " <td>35</td>\n",
660
+ " <td>42</td>\n",
661
+ " <td>32</td>\n",
662
+ " <td>80</td>\n",
663
+ " </tr>\n",
664
+ " <tr>\n",
665
+ " <th>4</th>\n",
666
+ " <td>Thursday</td>\n",
667
+ " <td>22</td>\n",
668
+ " <td>38</td>\n",
669
+ " <td>18</td>\n",
670
+ " <td>18</td>\n",
671
+ " <td>36</td>\n",
672
+ " <td>44</td>\n",
673
+ " <td>75</td>\n",
674
+ " <td>249</td>\n",
675
+ " <td>66</td>\n",
676
+ " <td>...</td>\n",
677
+ " <td>111</td>\n",
678
+ " <td>130</td>\n",
679
+ " <td>197</td>\n",
680
+ " <td>225</td>\n",
681
+ " <td>184</td>\n",
682
+ " <td>163</td>\n",
683
+ " <td>57</td>\n",
684
+ " <td>45</td>\n",
685
+ " <td>45</td>\n",
686
+ " <td>51</td>\n",
687
+ " </tr>\n",
688
+ " <tr>\n",
689
+ " <th>0</th>\n",
690
+ " <td>Friday</td>\n",
691
+ " <td>44</td>\n",
692
+ " <td>37</td>\n",
693
+ " <td>31</td>\n",
694
+ " <td>33</td>\n",
695
+ " <td>28</td>\n",
696
+ " <td>36</td>\n",
697
+ " <td>65</td>\n",
698
+ " <td>143</td>\n",
699
+ " <td>0</td>\n",
700
+ " <td>...</td>\n",
701
+ " <td>281</td>\n",
702
+ " <td>245</td>\n",
703
+ " <td>255</td>\n",
704
+ " <td>218</td>\n",
705
+ " <td>215</td>\n",
706
+ " <td>191</td>\n",
707
+ " <td>58</td>\n",
708
+ " <td>45</td>\n",
709
+ " <td>56</td>\n",
710
+ " <td>69</td>\n",
711
+ " </tr>\n",
712
+ " <tr>\n",
713
+ " <th>2</th>\n",
714
+ " <td>Saturday</td>\n",
715
+ " <td>50</td>\n",
716
+ " <td>43</td>\n",
717
+ " <td>24</td>\n",
718
+ " <td>21</td>\n",
719
+ " <td>30</td>\n",
720
+ " <td>48</td>\n",
721
+ " <td>53</td>\n",
722
+ " <td>189</td>\n",
723
+ " <td>37</td>\n",
724
+ " <td>...</td>\n",
725
+ " <td>170</td>\n",
726
+ " <td>214</td>\n",
727
+ " <td>170</td>\n",
728
+ " <td>209</td>\n",
729
+ " <td>271</td>\n",
730
+ " <td>164</td>\n",
731
+ " <td>61</td>\n",
732
+ " <td>54</td>\n",
733
+ " <td>48</td>\n",
734
+ " <td>57</td>\n",
735
+ " </tr>\n",
736
+ " <tr>\n",
737
+ " <th>3</th>\n",
738
+ " <td>Sunday</td>\n",
739
+ " <td>61</td>\n",
740
+ " <td>38</td>\n",
741
+ " <td>16</td>\n",
742
+ " <td>21</td>\n",
743
+ " <td>15</td>\n",
744
+ " <td>28</td>\n",
745
+ " <td>45</td>\n",
746
+ " <td>149</td>\n",
747
+ " <td>4</td>\n",
748
+ " <td>...</td>\n",
749
+ " <td>132</td>\n",
750
+ " <td>100</td>\n",
751
+ " <td>171</td>\n",
752
+ " <td>98</td>\n",
753
+ " <td>96</td>\n",
754
+ " <td>105</td>\n",
755
+ " <td>50</td>\n",
756
+ " <td>50</td>\n",
757
+ " <td>56</td>\n",
758
+ " <td>62</td>\n",
759
+ " </tr>\n",
760
+ " </tbody>\n",
761
+ "</table>\n",
762
+ "<p>7 rows × 25 columns</p>\n",
763
+ "</div>"
764
+ ],
765
+ "text/plain": [
766
+ "hour day 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 \\\n",
767
+ "1 Monday 52 82 35 40 29 60 77 233 \n",
768
+ "5 Tuesday 58 30 19 14 15 35 85 144 \n",
769
+ "6 Wednesday 28 41 18 17 16 26 57 96 \n",
770
+ "4 Thursday 22 38 18 18 36 44 75 249 \n",
771
+ "0 Friday 44 37 31 33 28 36 65 143 \n",
772
+ "2 Saturday 50 43 24 21 30 48 53 189 \n",
773
+ "3 Sunday 61 38 16 21 15 28 45 149 \n",
774
+ "\n",
775
+ "hour 08:00 ... 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 \\\n",
776
+ "1 34 ... 268 148 227 253 214 256 69 58 \n",
777
+ "5 47 ... 186 202 243 207 265 168 49 40 \n",
778
+ "6 23 ... 182 226 192 280 271 163 35 42 \n",
779
+ "4 66 ... 111 130 197 225 184 163 57 45 \n",
780
+ "0 0 ... 281 245 255 218 215 191 58 45 \n",
781
+ "2 37 ... 170 214 170 209 271 164 61 54 \n",
782
+ "3 4 ... 132 100 171 98 96 105 50 50 \n",
783
+ "\n",
784
+ "hour 22:00 23:00 \n",
785
+ "1 30 35 \n",
786
+ "5 46 49 \n",
787
+ "6 32 80 \n",
788
+ "4 45 51 \n",
789
+ "0 56 69 \n",
790
+ "2 48 57 \n",
791
+ "3 56 62 \n",
792
+ "\n",
793
+ "[7 rows x 25 columns]"
794
+ ]
795
+ },
796
+ "metadata": {},
797
+ "output_type": "display_data"
798
+ }
799
+ ],
800
+ "source": [
801
+ "#Pivot the table\n",
802
+ "\n",
803
+ "table = pd.pivot_table(new, values='vehicle', index=['day'], columns=['hour']).reset_index()\n",
804
+ "table = table.reindex([1,5,6,4,0,2,3])\n",
805
+ "display(table)"
806
+ ]
807
+ },
808
+ {
809
+ "cell_type": "code",
810
+ "execution_count": 8,
811
+ "id": "209f0452",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ " text-align: right;\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
832
+ " <thead>\n",
833
+ " <tr style=\"text-align: right;\">\n",
834
+ " <th></th>\n",
835
+ " <th>hour</th>\n",
836
+ " <th>Monday</th>\n",
837
+ " <th>Tuesday</th>\n",
838
+ " <th>Wednesday</th>\n",
839
+ " <th>Thursday</th>\n",
840
+ " <th>Friday</th>\n",
841
+ " <th>Saturday</th>\n",
842
+ " <th>Sunday</th>\n",
843
+ " </tr>\n",
844
+ " </thead>\n",
845
+ " <tbody>\n",
846
+ " <tr>\n",
847
+ " <th>0</th>\n",
848
+ " <td>00:00</td>\n",
849
+ " <td>52</td>\n",
850
+ " <td>58</td>\n",
851
+ " <td>28</td>\n",
852
+ " <td>22</td>\n",
853
+ " <td>44</td>\n",
854
+ " <td>50</td>\n",
855
+ " <td>61</td>\n",
856
+ " </tr>\n",
857
+ " <tr>\n",
858
+ " <th>1</th>\n",
859
+ " <td>01:00</td>\n",
860
+ " <td>82</td>\n",
861
+ " <td>30</td>\n",
862
+ " <td>41</td>\n",
863
+ " <td>38</td>\n",
864
+ " <td>37</td>\n",
865
+ " <td>43</td>\n",
866
+ " <td>38</td>\n",
867
+ " </tr>\n",
868
+ " <tr>\n",
869
+ " <th>2</th>\n",
870
+ " <td>02:00</td>\n",
871
+ " <td>35</td>\n",
872
+ " <td>19</td>\n",
873
+ " <td>18</td>\n",
874
+ " <td>18</td>\n",
875
+ " <td>31</td>\n",
876
+ " <td>24</td>\n",
877
+ " <td>16</td>\n",
878
+ " </tr>\n",
879
+ " <tr>\n",
880
+ " <th>3</th>\n",
881
+ " <td>03:00</td>\n",
882
+ " <td>40</td>\n",
883
+ " <td>14</td>\n",
884
+ " <td>17</td>\n",
885
+ " <td>18</td>\n",
886
+ " <td>33</td>\n",
887
+ " <td>21</td>\n",
888
+ " <td>21</td>\n",
889
+ " </tr>\n",
890
+ " <tr>\n",
891
+ " <th>4</th>\n",
892
+ " <td>04:00</td>\n",
893
+ " <td>29</td>\n",
894
+ " <td>15</td>\n",
895
+ " <td>16</td>\n",
896
+ " <td>36</td>\n",
897
+ " <td>28</td>\n",
898
+ " <td>30</td>\n",
899
+ " <td>15</td>\n",
900
+ " </tr>\n",
901
+ " </tbody>\n",
902
+ "</table>\n",
903
+ "</div>"
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+ ],
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+ "text/plain": [
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+ " hour Monday Tuesday Wednesday Thursday Friday Saturday Sunday\n",
907
+ "0 00:00 52 58 28 22 44 50 61\n",
908
+ "1 01:00 82 30 41 38 37 43 38\n",
909
+ "2 02:00 35 19 18 18 31 24 16\n",
910
+ "3 03:00 40 14 17 18 33 21 21\n",
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+ "4 04:00 29 15 16 36 28 30 15"
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+ ]
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "t = table.T\n",
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+ "t.drop('day', inplace=True)\n",
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+ "t.columns = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\",\n",
922
+ " \"Saturday\", \"Sunday\"]\n",
923
+ "t = t.reset_index()\n",
924
+ "display(t.head())"
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+ ]
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+ },
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+ "execution_count": 9,
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+ "id": "5c88c289",
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+ },
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+ "gridcolor": "white",
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+ "linecolor": "white",
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+ "ticks": ""
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+ }
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+ },
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+ },
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+ "automargin": true,
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+ "gridcolor": "white",
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+ "ticks": "",
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+ "title": {
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+ "standoff": 15
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+ },
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+ },
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+ "ticks": "",
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+ "standoff": 15
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+ },
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+ }
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+ }
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+ },
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+ "anchor": "y",
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+ "constrain": "domain",
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+ "domain": [
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+ 0,
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+ 1
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+ ],
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+ "scaleanchor": "y",
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+ "side": "top",
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+ "title": {
3114
+ "text": "Hour of the Day"
3115
+ }
3116
+ },
3117
+ "yaxis": {
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+ "anchor": "x",
3119
+ "autorange": "reversed",
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+ "constrain": "domain",
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+ "domain": [
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+ 0,
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+ 1
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+ ],
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+ "title": {
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+ "text": "Day of the Week"
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+ }
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+ }
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+ }
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+ }
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
3135
+ ],
3136
+ "source": [
3137
+ "new_table = table.iloc[:,1:].to_numpy()\n",
3138
+ "fig1 = px.imshow(new_table, labels=dict(x=\"Hour of the Day\", y = 'Day of the Week', color='Causeway Traffic'),\n",
3139
+ " x=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am', '12pm',\n",
3140
+ " '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', \"11pm\"],\n",
3141
+ " y=[\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\",\n",
3142
+ " \"Saturday\", \"Sunday\"], text_auto=True)\n",
3143
+ "fig1.update_xaxes(side='top')"
3144
+ ]
3145
+ },
3146
+ {
3147
+ "cell_type": "code",
3148
+ "execution_count": 11,
3149
+ "id": "928a2063",
3150
+ "metadata": {},
3151
+ "outputs": [],
3152
+ "source": [
3153
+ "#fig = make_subplots(rows=1, cols=2, specs=[[{'type':'bar'},{'type':'bar'}]],\n",
3154
+ " #subplot_titles=('Hours', 'Days'))"
3155
+ ]
3156
+ },
3157
+ {
3158
+ "cell_type": "code",
3159
+ "execution_count": 12,
3160
+ "id": "b622fd70",
3161
+ "metadata": {},
3162
+ "outputs": [
3163
+ {
3164
+ "name": "stdout",
3165
+ "output_type": "stream",
3166
+ "text": [
3167
+ "Dash is running on http://127.0.0.1:8050/\n",
3168
+ "\n"
3169
+ ]
3170
+ },
3171
+ {
3172
+ "data": {
3173
+ "text/html": [
3174
+ "\n",
3175
+ " <iframe\n",
3176
+ " width=\"100%\"\n",
3177
+ " height=\"650\"\n",
3178
+ " src=\"http://127.0.0.1:8050/\"\n",
3179
+ " frameborder=\"0\"\n",
3180
+ " allowfullscreen\n",
3181
+ " \n",
3182
+ " ></iframe>\n",
3183
+ " "
3184
+ ],
3185
+ "text/plain": [
3186
+ "<IPython.lib.display.IFrame at 0x222c7ae9f70>"
3187
+ ]
3188
+ },
3189
+ "metadata": {},
3190
+ "output_type": "display_data"
3191
+ }
3192
+ ],
3193
+ "source": [
3194
+ "app_new = JupyterDash(__name__)\n",
3195
+ "\n",
3196
+ "app_new.title = 'CSE6242 Dashboard'\n",
3197
+ "app_new.layout = html.Div([\n",
3198
+ " html.Div(html.H2(\"Causian Dashboard\"), style={'width':'250px', 'height':'60px', 'padding-left':'2%',\n",
3199
+ " 'display':'inline-block'}),\n",
3200
+ " html.Div([\n",
3201
+ " html.Label(\"Hours\"), dcc.Dropdown(id='hours_dropdown_id',\n",
3202
+ " options=['00:00', '01:00', '02:00', '03:00', '04:00', '05:00', '06:00', '07:00', '08:00', '09:00', \n",
3203
+ " '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00', '18:00','19:00',\n",
3204
+ " '20:00', '21:00', '22:00', '23:00'],\n",
3205
+ " value='07:00', clearable=False)],\n",
3206
+ " style={'width':'20%','height':'60px', 'padding-left':'2%',\n",
3207
+ " 'display':'inline-block'}),\n",
3208
+ " html.Div([html.Label(\"Day of the Week\"), dcc.Dropdown(id='days_dropdown_id', value='Monday',\n",
3209
+ " options=[\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\",\"Saturday\", \"Sunday\"],\n",
3210
+ " clearable=False)],\n",
3211
+ " style={'width':'20%','height':'60px', 'padding-left':'2%',\n",
3212
+ " 'display':'inline-block'}),\n",
3213
+ " html.Div(dcc.Graph(id='fig_hours')),\n",
3214
+ " html.Div(dcc.Graph(id='fig_days')),\n",
3215
+ " html.Div(dcc.Graph(id='fig_heatplot', figure=fig1))])\n",
3216
+ "\n",
3217
+ "@app_new.callback(Output('fig_hours', \"figure\"), Input('hours_dropdown_id', \"value\"))\n",
3218
+ "def update_hour_bar_chart(Hours):\n",
3219
+ " fig_hours = px.bar(table, x='day', y=str(Hours), color='day', text_auto=True, labels={'day':\"Day of the Week\"})\n",
3220
+ " return fig_hours\n",
3221
+ "@app_new.callback(Output('fig_days', \"figure\"), Input('days_dropdown_id', \"value\"))\n",
3222
+ "def update_day_bar_chart(day):\n",
3223
+ " fig_days = px.bar(t, x='hour', y = str(day), color=str(day), text_auto=True, labels={'hour':\"Count of Each Hour\"})\n",
3224
+ " return fig_days\n",
3225
+ "\n",
3226
+ "app_new.run_server(mode='inline')"
3227
+ ]
3228
+ },
3229
+ {
3230
+ "cell_type": "code",
3231
+ "execution_count": 13,
3232
+ "id": "66f580aa",
3233
+ "metadata": {},
3234
+ "outputs": [
3235
+ {
3236
+ "name": "stdout",
3237
+ "output_type": "stream",
3238
+ "text": [
3239
+ "<class 'pandas.core.frame.DataFrame'>\n",
3240
+ "RangeIndex: 168 entries, 0 to 167\n",
3241
+ "Data columns (total 3 columns):\n",
3242
+ " # Column Non-Null Count Dtype \n",
3243
+ "--- ------ -------------- ----- \n",
3244
+ " 0 hour 168 non-null category\n",
3245
+ " 1 day 168 non-null category\n",
3246
+ " 2 vehicle 168 non-null int64 \n",
3247
+ "dtypes: category(2), int64(1)\n",
3248
+ "memory usage: 2.8 KB\n",
3249
+ "None\n"
3250
+ ]
3251
+ }
3252
+ ],
3253
+ "source": [
3254
+ "#Trial basic Linear Regression Model\n",
3255
+ "new['hour'] = new['hour'].astype('category')\n",
3256
+ "new['day'] = new['day'].astype('category')\n",
3257
+ "print(new.info())"
3258
+ ]
3259
+ },
3260
+ {
3261
+ "cell_type": "code",
3262
+ "execution_count": null,
3263
+ "id": "4858f7d3",
3264
+ "metadata": {},
3265
+ "outputs": [],
3266
+ "source": []
3267
+ },
3268
+ {
3269
+ "cell_type": "code",
3270
+ "execution_count": 14,
3271
+ "id": "0806a79a",
3272
+ "metadata": {},
3273
+ "outputs": [
3274
+ {
3275
+ "data": {
3276
+ "text/html": [
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
3293
+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>hour_00:00</th>\n",
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+ " <th>hour_01:00</th>\n",
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+ " <th>hour_02:00</th>\n",
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+ " <th>hour_04:00</th>\n",
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+ " <th>hour_05:00</th>\n",
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+ " <th>hour_06:00</th>\n",
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+ " <th>hour_07:00</th>\n",
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+ " <th>hour_08:00</th>\n",
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+ " <th>hour_09:00</th>\n",
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+ " <th>...</th>\n",
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+ " <th>hour_21:00</th>\n",
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+ " <th>hour_22:00</th>\n",
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+ " <th>hour_23:00</th>\n",
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+ " <th>day_Friday</th>\n",
3310
+ " <th>day_Monday</th>\n",
3311
+ " <th>day_Saturday</th>\n",
3312
+ " <th>day_Sunday</th>\n",
3313
+ " <th>day_Thursday</th>\n",
3314
+ " <th>day_Tuesday</th>\n",
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+ " <th>day_Wednesday</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <td>0</td>\n",
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+ " </tr>\n",
3367
+ " <tr>\n",
3368
+ " <th>2</th>\n",
3369
+ " <td>1</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
3414
+ " </tr>\n",
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+ " <tr>\n",
3416
+ " <th>4</th>\n",
3417
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+ " </tr>\n",
3439
+ " </tbody>\n",
3440
+ "</table>\n",
3441
+ "<p>5 rows × 31 columns</p>\n",
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+ "</div>"
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+ ],
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+ "0 0 0 0 0 ... 0 \n",
3454
+ "1 0 0 0 0 ... 0 \n",
3455
+ "2 0 0 0 0 ... 0 \n",
3456
+ "3 0 0 0 0 ... 0 \n",
3457
+ "4 0 0 0 0 ... 0 \n",
3458
+ "\n",
3459
+ " hour_22:00 hour_23:00 day_Friday day_Monday day_Saturday day_Sunday \\\n",
3460
+ "0 0 0 1 0 0 0 \n",
3461
+ "1 0 0 0 1 0 0 \n",
3462
+ "2 0 0 0 0 1 0 \n",
3463
+ "3 0 0 0 0 0 1 \n",
3464
+ "4 0 0 0 0 0 0 \n",
3465
+ "\n",
3466
+ " day_Thursday day_Tuesday day_Wednesday \n",
3467
+ "0 0 0 0 \n",
3468
+ "1 0 0 0 \n",
3469
+ "2 0 0 0 \n",
3470
+ "3 0 0 0 \n",
3471
+ "4 1 0 0 \n",
3472
+ "\n",
3473
+ "[5 rows x 31 columns]"
3474
+ ]
3475
+ },
3476
+ "metadata": {},
3477
+ "output_type": "display_data"
3478
+ },
3479
+ {
3480
+ "name": "stdout",
3481
+ "output_type": "stream",
3482
+ "text": [
3483
+ "(168, 31)\n",
3484
+ "(168,)\n"
3485
+ ]
3486
+ }
3487
+ ],
3488
+ "source": [
3489
+ "from sklearn.linear_model import LinearRegression\n",
3490
+ "\n",
3491
+ "X = new.loc[:,['hour', 'day']]\n",
3492
+ "y = new.loc[:,'vehicle']\n",
3493
+ "n = pd.get_dummies(X)\n",
3494
+ "display(n.head())\n",
3495
+ "print(n.shape)\n",
3496
+ "print(y.shape)"
3497
+ ]
3498
+ },
3499
+ {
3500
+ "cell_type": "code",
3501
+ "execution_count": 15,
3502
+ "id": "b6adf1cf",
3503
+ "metadata": {},
3504
+ "outputs": [
3505
+ {
3506
+ "name": "stdout",
3507
+ "output_type": "stream",
3508
+ "text": [
3509
+ "0.8552520624553817\n"
3510
+ ]
3511
+ }
3512
+ ],
3513
+ "source": [
3514
+ "reg = LinearRegression().fit(n,y)\n",
3515
+ "print(reg.score(n,y))\n",
3516
+ "y_pred = reg.predict(n)\n",
3517
+ "\n",
3518
+ "new_pred = []\n",
3519
+ "for i in y_pred:\n",
3520
+ " if i < 0:\n",
3521
+ " new_pred.append(0)\n",
3522
+ " else:\n",
3523
+ " new_pred.append(i)\n"
3524
+ ]
3525
+ },
3526
+ {
3527
+ "cell_type": "code",
3528
+ "execution_count": 16,
3529
+ "id": "4fb37621",
3530
+ "metadata": {},
3531
+ "outputs": [],
3532
+ "source": [
3533
+ "import sklearn.metrics as metrics\n",
3534
+ "def regression_results(y_true, y_pred):\n",
3535
+ "\n",
3536
+ " # Regression metrics\n",
3537
+ " explained_variance=metrics.explained_variance_score(y_true, y_pred)\n",
3538
+ " mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred) \n",
3539
+ " mse=metrics.mean_squared_error(y_true, y_pred) \n",
3540
+ " mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)\n",
3541
+ " median_absolute_error=metrics.median_absolute_error(y_true, y_pred)\n",
3542
+ " r2=metrics.r2_score(y_true, y_pred)\n",
3543
+ "\n",
3544
+ " print('explained_variance: ', round(explained_variance,4)) \n",
3545
+ " print('mean_squared_log_error: ', round(mean_squared_log_error,4))\n",
3546
+ " print('r2: ', round(r2,4))\n",
3547
+ " print('MAE: ', round(mean_absolute_error,4))\n",
3548
+ " print('MSE: ', round(mse,4))\n",
3549
+ " print('RMSE: ', round(np.sqrt(mse),4))"
3550
+ ]
3551
+ },
3552
+ {
3553
+ "cell_type": "code",
3554
+ "execution_count": 17,
3555
+ "id": "fc1bd361",
3556
+ "metadata": {},
3557
+ "outputs": [
3558
+ {
3559
+ "name": "stdout",
3560
+ "output_type": "stream",
3561
+ "text": [
3562
+ "explained_variance: 0.8567\n",
3563
+ "mean_squared_log_error: 0.3976\n",
3564
+ "r2: 0.8566\n",
3565
+ "MAE: 23.7455\n",
3566
+ "MSE: 1029.6179\n",
3567
+ "RMSE: 32.0877\n"
3568
+ ]
3569
+ }
3570
+ ],
3571
+ "source": [
3572
+ "regression_results(y, new_pred)"
3573
+ ]
3574
+ },
3575
+ {
3576
+ "cell_type": "code",
3577
+ "execution_count": null,
3578
+ "id": "49a4d65d",
3579
+ "metadata": {},
3580
+ "outputs": [],
3581
+ "source": []
3582
+ }
3583
+ ],
3584
+ "metadata": {
3585
+ "kernelspec": {
3586
+ "display_name": "Python 3 (ipykernel)",
3587
+ "language": "python",
3588
+ "name": "python3"
3589
+ },
3590
+ "language_info": {
3591
+ "codemirror_mode": {
3592
+ "name": "ipython",
3593
+ "version": 3
3594
+ },
3595
+ "file_extension": ".py",
3596
+ "mimetype": "text/x-python",
3597
+ "name": "python",
3598
+ "nbconvert_exporter": "python",
3599
+ "pygments_lexer": "ipython3",
3600
+ "version": "3.8.16"
3601
+ }
3602
+ },
3603
+ "nbformat": 4,
3604
+ "nbformat_minor": 5
3605
+ }
src/basic_plot.py CHANGED
@@ -13,10 +13,10 @@ def basic_chart(counts_df):
13
  counts_df['hour'] = counts_df['datetime'].dt.strftime('%H')
14
 
15
  # plot types
16
- plot = st.sidebar.selectbox('Choose Plot', options=['Day','Hour','Raw'])
17
 
18
  # view types
19
- view = st.sidebar.selectbox('Choose View', options=counts_df['view'].unique())
20
  filtered_views = counts_df[counts_df['view'] == view]
21
 
22
  # conditional views
 
13
  counts_df['hour'] = counts_df['datetime'].dt.strftime('%H')
14
 
15
  # plot types
16
+ plot = st.selectbox('Choose Plot', options=['Day','Hour','Raw'])
17
 
18
  # view types
19
+ view = st.selectbox('Choose View', options=counts_df['view'].unique())
20
  filtered_views = counts_df[counts_df['view'] == view]
21
 
22
  # conditional views
src/heatmap.py ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import pandas as pd
3
+ import matplotlib.pyplot as plt
4
+ import scipy as sp
5
+ import sklearn as sk
6
+ import plotly.express as px
7
+ from plotly.subplots import make_subplots
8
+ import plotly.graph_objects as go
9
+ import streamlit as st
10
+
11
+
12
+ def clean_data(df):
13
+
14
+ df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")
15
+ df["day"] = df["date"].dt.day_name()
16
+ df["hour"] = df["time"].str[:2] + ":00"
17
+ df.drop(columns=["motorcycle"], axis=1, inplace=True)
18
+ df["vehicle"] = df["car"] + df["large_vehicle"]
19
+
20
+ return df
21
+
22
+
23
+ class HeatMap:
24
+ def __init__(self, counts_df):
25
+ self.df = clean_data(counts_df)
26
+ new = (
27
+ self.df.groupby(["hour", "day"])
28
+ .sum()
29
+ .drop(columns=["car", "large_vehicle"])
30
+ .reset_index()
31
+ )
32
+ table = pd.pivot_table(
33
+ new, values="vehicle", index=["day"], columns=["hour"]
34
+ ).reset_index()
35
+ self.table = table.reindex([1, 5, 6, 4, 0, 2, 3])
36
+
37
+
38
+ def vehicle_count_bar(self):
39
+ new_df = self.df.groupby(["day"]).sum().reset_index()
40
+ new_df = new_df.reindex([1, 5, 6, 4, 0, 2, 3])
41
+
42
+ veh_count_fig = px.bar(
43
+ new_df,
44
+ x="day",
45
+ y="vehicle",
46
+ color="day",
47
+ text_auto=True,
48
+ labels={"day": "Day of the Week", "vehicle": "Vehicle Count"},
49
+ )
50
+
51
+ return veh_count_fig
52
+
53
+ def heatmap(self):
54
+
55
+ new_table = self.table.iloc[:, 1:].to_numpy()
56
+
57
+ hm_fig = px.imshow(
58
+ new_table,
59
+ labels=dict(
60
+ x="Hour of the Day", y="Day of the Week", color="Causeway Traffic"
61
+ ),
62
+ x=[
63
+ "12am",
64
+ "1am",
65
+ "2am",
66
+ "3am",
67
+ "4am",
68
+ "5am",
69
+ "6am",
70
+ "7am",
71
+ "8am",
72
+ "9am",
73
+ "10am",
74
+ "11am",
75
+ "12pm",
76
+ "1pm",
77
+ "2pm",
78
+ "3pm",
79
+ "4pm",
80
+ "5pm",
81
+ "6pm",
82
+ "7pm",
83
+ "8pm",
84
+ "9pm",
85
+ "10pm",
86
+ "11pm",
87
+ ],
88
+ y=[
89
+ "Monday",
90
+ "Tuesday",
91
+ "Wednesday",
92
+ "Thursday",
93
+ "Friday",
94
+ "Saturday",
95
+ "Sunday",
96
+ ],
97
+ text_auto=True,
98
+ )
99
+ hm_fig.update_xaxes(side="top")
100
+
101
+ return hm_fig
102
+
103
+ def update_hour_bar_chart(self, hour="08:00"):
104
+
105
+ fig_hours = px.bar(
106
+ self.table,
107
+ x="day",
108
+ y=str(hour),
109
+ color="day",
110
+ text_auto=True,
111
+ labels={"day": "Day of the Week"},
112
+ )
113
+ return fig_hours
114
+
115
+ def update_day_bar_chart(self, day="Saturday"):
116
+
117
+ t = self.table.T
118
+ t.drop("day", inplace=True)
119
+ t.columns = [
120
+ "Monday",
121
+ "Tuesday",
122
+ "Wednesday",
123
+ "Thursday",
124
+ "Friday",
125
+ "Saturday",
126
+ "Sunday",
127
+ ]
128
+ t = t.reset_index()
129
+
130
+ fig_days = px.bar(
131
+ t,
132
+ x="hour",
133
+ y=str(day),
134
+ color=str(day),
135
+ text_auto=True,
136
+ labels={"hour": "Count of Each Hour"},
137
+ )
138
+
139
+ return fig_days