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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
st.set_page_config(
page_title='Widgets',
page_icon=":1234:"
)
mobility_url = 'https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv'
df = pd.read_csv(mobility_url)
st.title("Widgets in Streamlit apps")
st.markdown("""Using markdown for a reminder, we can use
widgets in Streamlit (similar to `ipywidgets`).
""")
st.subheader('Feedback Widget')
st.markdown("""We could try the
[feedback widget](https://docs.streamlit.io/develop/api-reference/widgets/st.feedback).
""")
st.markdown("Using the following code:")
st.code("""
sentiment_mapping = ["one", "two", "three", "four", "five"]
selected = st.feedback("stars")
if selected is not None:
st.markdown(f"You selected {sentiment_mapping[selected]} star(s).")
""")
# sentiment_mapping = ["one", "two", "three", "four", "five"]
# selected = st.feedback("stars")
# if selected is not None:
# st.markdown(f"You selected {sentiment_mapping[selected]} star(s).")
st.write("How are you feeling right now?")
sentiment_mapping = ["one", "two", "three", "four", "five"]
selected = st.feedback("stars")
if selected is not None: # only run if a star is selected
if selected < 1:
st.markdown("Sorry to hear you are so sad :(")
elif selected < 3:
st.markdown("A solid medium is great!")
else:
st.markdown("Fantastic to hear you are having a great day!")
st.subheader("Connecting Widgets to Plots")
st.markdown("We'll start with a static plot:")
# bins along student to teacher ratio
bins = np.linspace(df['Student_teacher_ratio'].min(),
df['Student_teacher_ratio'].max(), 10)
#bins # note -- this will be "pandas-like" in view
table = df.pivot_table(index='State',
columns=pd.cut(df['Student_teacher_ratio'],bins),
aggfunc='size')
fig, ax = plt.subplots()
extent = [bins.min(),bins.max(), 0, len(table.index)] # xmin, xmax, ymin, ymax
ax.imshow(table.values, cmap='hot', interpolation='nearest',extent=extent)
ax.set_yticks(range(len(table.index)))
ax.set_yticklabels(table.index)
st.pyplot(fig)
# trick for imshow command -- save as buffer so that its not so
# big and we can format the size the way we want
# (might not need to do this for all plots)
from io import BytesIO
fig, ax = plt.subplots(figsize=(4,8))
extent = [bins.min(),bins.max(), 0, len(table.index)] # xmin, xmax, ymin, ymax
ax.imshow(table.values, cmap='hot', interpolation='nearest',extent=extent)
ax.set_yticks(range(len(table.index)))
ax.set_yticklabels(table.index)
#st.pyplot(fig)
buf = BytesIO()
fig.tight_layout()
fig.savefig(buf, format='png')
st.image(buf, width=500)
st.markdown("""Let's make use of a
[multiselect widget](https://docs.streamlit.io/develop/api-reference/widgets/st.multiselect) """)
fig_col, controls_col = st.columns([2,1],
vertical_alignment='center')
states_selected = controls_col.multiselect("Which states do you want to view", table.index.values)
if len(states_selected) > 0:
df_subset = df[df['State'].isin(states_selected)]
#st.write(df_subset) # used to debug our selection
table_subset = df_subset.pivot_table(index='State',
columns=pd.cut(df_subset['Student_teacher_ratio'],bins),
aggfunc='size')
fig, ax = plt.subplots(figsize=(4,8))
extent = [bins.min(),bins.max(), 0, len(table_subset.index)] # xmin, xmax, ymin, ymax
ax.imshow(table_subset.values, cmap='hot', interpolation='nearest',extent=extent)
ax.set_yticks(range(len(table_subset.index)))
ax.set_yticklabels(table_subset.index)
#st.pyplot(fig)
buf = BytesIO()
fig.tight_layout()
fig.savefig(buf, format='png')
#st.image(buf, width=500)
fig_col.image(buf, width=400)
else:
#st.write(df) # used to debug selection
#pass
fig, ax = plt.subplots(figsize=(4,8))
extent = [bins.min(),bins.max(), 0, len(table.index)] # xmin, xmax, ymin, ymax
ax.imshow(table.values, cmap='hot', interpolation='nearest',extent=extent)
ax.set_yticks(range(len(table.index)))
ax.set_yticklabels(table.index)
#st.pyplot(fig)
buf = BytesIO()
fig.tight_layout()
fig.savefig(buf, format='png')
#st.image(buf, width=500)
fig_col.image(buf, width=400) |