File size: 2,861 Bytes
60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 9318eda 25b5ea6 60add22 25b5ea6 60add22 25b5ea6 60add22 4d4794f |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
# Import panel and vega datasets
import panel as pn
import vega_datasets
import pandas as pd
import altair as alt
# import numpy as np
# import pprint
import datetime as dt
from vega_datasets import data
# import matplotlib.pyplot as plt
df2=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_topline.csv")
df2['timestamp']=pd.to_datetime(df2['timestamp'])
df2=pd.melt(df2, id_vars=['president', 'subgroup', 'timestamp'], value_vars=['approve','disapprove']).rename(columns={'variable':'choice', 'value':'rate'})
# Enable Panel extensions
# pn.extension()
# pn.extension('vega', 'tabulator')
pn.extension(design='bootstrap')
pn.extension('vega')
template = pn.template.BootstrapTemplate(
title='SI649 Altair3',
)
# 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)
df2_approve = df2[df2['choice'] == 'approve']
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).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=pn.Column()
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') |