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# start with the setup
# supress warnings about future deprecations
# import warnings
# warnings.simplefilter(action='ignore', category=FutureWarning)
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
# Solve a javascript error by explicitly setting the renderer
# alt.renderers.enable('jupyterlab')
#load data
# df1=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_polllist.csv")
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'})
## Viz 4
# Import panel and vega datasets
# import panel as pn
# import vega_datasets
# Enable Panel extensions
pn.extension(design='bootstrap')
pn.extension('vega')
template = pn.template.BootstrapTemplate(
title='SI649 Altair Assignment 3 (ritaycw)',
)
# 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
approve_data = df2[df2['choice']=='approve']
filtered_data = approve_data[approve_data['subgroup'] == subgroup]
filtered_data = filtered_data[(filtered_data['timestamp'].dt.date >= date_range[0]) & \
(filtered_data['timestamp'].dt.date <= date_range[1])]
filtered_data['mov_avg'] = filtered_data['rate'].rolling(window=moving_av_window).mean().shift(-moving_av_window//2)
# Line chart
smoothed_line = alt.Chart(filtered_data).mark_line(color='red', size=2).encode(
x='timestamp:T',
y='mov_avg:Q'
)
# Scatter plot with individual polls
scatter4 = alt.Chart(filtered_data).mark_point(size=2, opacity=0.7, color='grey').encode(
x='timestamp:T',
y=alt.Y('rate:Q', title='approve', scale=alt.Scale(domain=[30, 60])),
)
# Put them togetehr
plot = (scatter4+smoothed_line).encode(
y=alt.Y(axis=alt.Axis(title='approve, mov_avg'))
)
# Return the combined chart
return pn.pane.Vega(plot)
# Create the selection widget
dropdown = pn.widgets.Select(options=['All polls', 'Adults', 'Voters'], name='Select')
# 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
window_size_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=dropdown,
date_range=date_range_slider,
moving_av_window=window_size_slider))
# window_size_slider
# Combine everything in a Panel Column to create an app
maincol = pn.Column()
maincol.append("# Visualization 4: Interactive smoothing (hosted on Huggingface using Panel)")
maincol.append(final)
maincol.append(dropdown)
maincol.append(date_range_slider)
maincol.append(window_size_slider)
# set the app to be servable
template.main.append(maincol)
template.servable(title="SI649 Altair Assignment 3 (ritaycw)") |