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import streamlit as st

with st.echo():
    def on_change():
        st.write(f"my_selection **(1)**: `{st.session_state.my_selection}`")

    my_selection = st.selectbox(
        "Select something", ["a", "b"], on_change=on_change, key="my_selection"
    )
    st.write(f"my_selection **(2)**: `{st.session_state.my_selection}`")

treuille

import streamlit as st
import pandas as pd
import numpy as np

n_points = 1000
sf_lat, sf_lon = 37.76, -122.4
map_data = pd.DataFrame({
    'lat': np.random.randn(n_points) / 50 + sf_lat,
    'lon': np.random.randn(n_points) / 50 + sf_lon,
    'colorR': np.random.uniform(size=n_points, high=255.0),

treuille

import streamlit as st
import pandas as pd

# Load some example data.
DATA_URL = \
    "http://s3-us-west-2.amazonaws.com/streamlit-demo-data/uber-raw-data-sep14.csv.gz"
data = st.cache(pd.read_csv)(DATA_URL, nrows=1000)

# Select some rows using st.multiselect. This will break down when you have >1000 rows.
st.write('### Full Dataset', data)

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import streamlit as st
import functools


def partial_cache(func=None, **cache_kwargs):
    """Like st.cache, but if the function returns None, then nothing is cached.

    Parameters
    ----------
    func : Callable

treuille

import streamlit as st
import pandas as pd
import numpy as np

def display_dataframe_quickly(df, max_rows=5000, **st_dataframe_kwargs):
    """Display a subset of a DataFrame or Numpy Array to speed up app renders.
    
    Parameters
    ----------
    df : DataFrame | ndarray

treuille

import streamlit as st
import base64
import textwrap

def render_svg(svg):
    """Renders the given svg string."""
    b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
    html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
    st.write(html, unsafe_allow_html=True)

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import streamlit as st
import streamlit.ReportThread as ReportThread
from streamlit.server.Server import Server
import time
import functools
import random
import string


# Copied from tvst's great gist:

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import streamlit as st
from io import BytesIO
import matplotlib.pyplot as plt

def render_latex(formula, fontsize=12, dpi=300):
    """Renders LaTeX formula into Streamlit."""
    fig = plt.figure()
    text = fig.text(0, 0, '$%s$' % formula, fontsize=fontsize)

    fig.savefig(BytesIO(), dpi=dpi)  # triggers rendering

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import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div([

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import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import time

fig, ax = plt.subplots()

max_x = 5
max_rand = 10