File size: 2,246 Bytes
762c48d
cf58608
762c48d
 
cf58608
762c48d
 
cb5724e
22eeff0
cb5724e
22eeff0
cf58608
cb5724e
 
762c48d
cb5724e
762c48d
 
 
cb5724e
 
762c48d
 
cb5724e
 
 
 
762c48d
 
cb5724e
762c48d
 
 
cf58608
762c48d
cb5724e
762c48d
 
 
 
cf58608
762c48d
 
cf58608
 
762c48d
cb5724e
 
 
 
 
 
cf58608
 
762c48d
cb5724e
22eeff0
 
762c48d
cb5724e
 
 
 
cf58608
22eeff0
762c48d
cb5724e
 
 
 
 
22eeff0
762c48d
a1b1a93
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
# Import panel and vega datasets

import panel as pn
import vega_datasets

# Enable Panel extensions
pn.extension()

template = pn.template.BootstrapTemplate(
    title='SI649 Altair3',
)

maincol = pn.Column()

# Define a function to create and return a plot
def create_plot(subgroup, date_range, moving_av_window):

    # Apply any required transformations to the data in pandas)
    filtered_df = df2_approve[df2_approve['subgroup'] == subgroup]
    filtered_df = filtered_df[(filtered_df['timestamp'].dt.date >= date_range[0]) & (filtered_df['timestamp'].dt.date <= date_range[1])]
    filtered_df['mov_avg'] = filtered_df['rate'].rolling(window=moving_av_window, min_periods=1).mean().shift(moving_av_window//2)

    # Line chart
    line_chart = alt.Chart(filtered_df).mark_line(color='red', size=2).encode(
        x='timestamp:T',
        y='mov_avg:Q'
    )

    # Scatter plot with individual polls
    scatter_plot = alt.Chart(filtered_df).mark_point(color='grey', size=2, opacity=0.7).encode(
        x='timestamp:T',
        y='rate:Q'
    )

    # Put them togetehr
    plot = scatter_plot + line_chart
    
    # Return the combined chart
    return plot
    

# # Create the selection widget
select = pn.widgets.Select(name='Select', options=['All polls', 'Adults', 'Voters'])


# # Create the slider for the date range
date_range_slider = pn.widgets.DateRangeSlider(
    name='Date Range Slider',
    start=df2['timestamp'].dt.date.min(), end=df2['timestamp'].dt.date.max(),
    value=(df2['timestamp'].dt.date.min(), df2['timestamp'].dt.date.max()),
    step=1
)


# # Create the slider for the moving average window
moving_av_slider = pn.widgets.IntSlider(name='Moving Average Window', start=1, end=100, value=1)


# Bind the widgets to the create_plot function
final = pn.Row(pn.bind(create_plot, 
                       subgroup=select, 
                       date_range=date_range_slider, 
                       moving_av_window=moving_av_slider))


# # Combine everything in a Panel Column to create an app
maincol.append(final)
maincol.append(select)
maincol.append(date_range_slider)
maincol.append(moving_av_slider)
template.main.append(maincol)

# # set the app to be servable
template.servable(title="SI649 Altair3")