Create app3.py

app3.py

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+# supress warnings about future deprecations
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+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
+
+# Import panel and vega datasets
+import panel as pn
+import vega_datasets
+
+
+df2=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_topline.csv")
+
+# fix the time stamps and reorganize the data to combine approve and disapprove into one column
+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()
+
+# Define a function to create and return a plot
+def create_plot(subgroup='All polls', date_range=[df2['timestamp'].min(), df2['timestamp'].max()], moving_av_window=1):
+
+    # Downselection! 
+    approval_df2 = df2.copy()
+    approval_df2 = approval_df2[approval_df2['subgroup'] == subgroup]
+    approval_df2 = approval_df2[approval_df2['choice'] == 'approve']
+    # remove columns 
+    approval_df2 = approval_df2.drop(columns=['choice', 'subgroup', 'president'])
+
+    # convert date_range to datetime
+    date_range = [pd.to_datetime(date_range[0]), pd.to_datetime(date_range[1])]
+
+    # narrow down dates
+    approval_df2 = approval_df2[
+        (approval_df2['timestamp'] >= date_range[0]) &
+        (approval_df2['timestamp'] <= date_range[1])
+    ]
+    
+    # Apply any required transformations to the data in pandas
+    smooth_data = approval_df2.sort_values('timestamp').rolling(window=moving_av_window, on='timestamp').mean().shift(moving_av_window*2)
+
+    chart_data = approval_df2.copy()
+
+    # Line chart
+    line = alt.Chart(smooth_data).mark_line(color='red', size=2).encode(
+        x=alt.X('timestamp:T', scale=alt.Scale(domain=date_range)),
+        y=alt.Y('rate:Q', scale=alt.Scale(domain=[30, 60]), title='approve, mov_avg'),
+    )
+
+    # Scatter plot with individual polls
+    scatter = alt.Chart(chart_data).mark_point(size=2, opacity=0.7, color='grey').encode(
+        x=alt.X('timestamp:T', scale=alt.Scale(domain=date_range)),
+        y=alt.Y('rate:Q', scale=alt.Scale(domain=[30, 60]))
+    )
+
+    # Put them together
+    plot = alt.layer(scatter, line).resolve_scale(y='shared')
+
+    # Return the combined chart
+    return plot
+    
+# Create the selection widget
+subgroup_select = pn.widgets.Select(name='Select subgroup', options=['Adults', 'All polls', 'Voters'])
+
+# Create the slider for the date range
+date_range_slider = pn.widgets.DateRangeSlider(name='Date range slider', end=df2['timestamp'].max(), start=df2['timestamp'].min(), value=(df2['timestamp'].min(), df2['timestamp'].max()))
+
+# 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
+@pn.depends(subgroup_select.param.value, date_range_slider.param.value, moving_av_slider.param.value)
+def reactive_plot(subgroup, date_range, moving_av_window):
+    return create_plot(subgroup, date_range, moving_av_window)
+
+# Combine everything in a Panel Column to create an app
+app = pn.Column(reactive_plot, subgroup_select, date_range_slider, moving_av_slider)
+
+# Set the app to be servable
+app.servable()
+