To get started with streamlit, you need to install it in your virtual environment.

pip install streamlit

Note that it would be good to also take the time to install the other dependencies we'll use in this series of videos.

pip install pandas matplotlib scikit-learn

To run the demo after installation you need to run;

streamlit hello

This is the app.py that we end up with at the end of the video;

import streamlit as st
import numpy as np

st.title("Simulation[tm]")
st.write("Here is our super important simulation")

x = st.slider('Slope', min_value=0.01, max_value=0.10, step=0.01)
y = st.slider('Noise', min_value=0.01, max_value=0.10, step=0.01)

st.write(f"x={x} y={y}")

Note that we run this app from the command line by running;

streamlit run app.py

This is the app.py that we end up with at the end of the video;

import streamlit as st
import numpy as np

st.title("Simulation[tm]")
st.write("Here is our super important simulation")

x = st.slider('Slope', min_value=0.01, max_value=0.10, step=0.01)
y = st.slider('Noise', min_value=0.01, max_value=0.10, step=0.01)

st.write(f"x={x} y={y}")
values = np.cumsum(1 + np.random.normal(x, y, (100, 10)), axis=0)
st.line_chart(values)

Note that we run this app from the command line by running;

streamlit run app.py

This is the app.py that we end up with at the end of the video;

import streamlit as st
import numpy as np

st.title("Simulation[tm]")
st.write("Here is our super important simulation")

x = st.slider('Slope', min_value=0.01, max_value=0.10, step=0.01)
y = st.slider('Noise', min_value=0.01, max_value=0.10, step=0.01)

st.write(f"x={x} y={y}")
values = np.cumsum(1 + np.random.normal(x, y, (100, 10)), axis=0)

for i in range(values.shape[1]):
    plt.plot(values[:, i])

st.pyplot()

Note that we run this app from the command line by running;

streamlit run app.py

This is the app.py that we end up with at the end of the video;

import streamlit as st
import numpy as np
import matplotlib.pylab as plt

st.title("Simulation[tm]")
st.write("Here is our super important simulation")

st.sidebar.markdown("## Controls")
st.sidebar.markdown("You can **change** the values to change the *chart*.")
x = st.sidebar.slider('Slope', min_value=0.01, max_value=0.10, step=0.01)
y = st.sidebar.slider('Noise', min_value=0.01, max_value=0.10, step=0.01)

st.write(f"x={x} y={y}")
values = np.cumsum(1 + np.random.normal(x, y, (100, 10)), axis=0)

for i in range(values.shape[1]):
    plt.plot(values[:, i])

st.pyplot()

Note that we run this app from the command line by running;

streamlit run app.py

We're using scikit-learn in this demo. Should you be unfamilar with it, feel free to just copy and paste the code below. The goal of the video is to explain streamlit, not that we worry about understanding the algorithm in detail.

This is the app.py that we end up with at the end of the video;

import numpy as np
import streamlit as st
import matplotlib.pylab as plt
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor

n = 1000
np.random.seed(42)
x = np.linspace(0, 6, n)
X = np.linspace(0, 6, n)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + np.random.random(n) * 0.3

n_est = st.sidebar.slider("n_est", min_value=1, max_value=5_000, step=1)

mod1 = DecisionTreeRegressor(max_depth=4)
y1 = mod1.fit(X,y).predict(X)
y2 = AdaBoostRegressor(mod1, n_estimators=n_est).fit(X, y).predict(X)

plt.scatter(x, y, alpha=0.1)
plt.plot(x, y1, label="just a tree")
plt.plot(x, y2, label=f"adaboost-{n_est}")
plt.legend()

st.pyplot()

Note that we run this app from the command line by running;

streamlit run app.py

We're using scikit-learn in this demo. Should you be unfamilar with it, feel free to just copy and paste the code below. The goal of the video is to explain streamlit, not that we worry about understanding the algorithm in detail.

This is the app.py that we end up with at the end of the video;

import numpy as np
import streamlit as st
import matplotlib.pylab as plt
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor

n = 1000
np.random.seed(42)
x = np.linspace(0, 6, n)
X = np.linspace(0, 6, n)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + np.random.random(n) * 0.3

n_est = st.sidebar.slider("n_est", min_value=1, max_value=5_000, step=1)


st.markdown("This is the code that runs it all.")
with st.echo():
    mod1 = DecisionTreeRegressor(max_depth=4)
    y1 = mod1.fit(X,y).predict(X)
    y2 = AdaBoostRegressor(mod1, n_estimators=n_est).fit(X, y).predict(X)

if st.sidebar.checkbox("Show Original Data"):
    plt.scatter(x, y, alpha=0.1)
plt.plot(x, y1, label="just a tree")
plt.plot(x, y2, label=f"adaboost-{n_est}")
plt.legend()

st.pyplot()

Note that we run this app from the command line by running;

streamlit run app.py

We're using scikit-learn in this demo. Should you be unfamilar with it, feel free to just copy and paste the code below. The goal of the video is to explain streamlit, not that we worry about understanding the algorithm in detail.

This is the app.py that we end up with at the end of the video;

import numpy as np
import streamlit as st
import matplotlib.pylab as plt
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor

n = 1000
np.random.seed(42)
x = np.linspace(0, 6, n)
X = np.linspace(0, 6, n)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + np.random.random(n) * 0.3

n_est = st.sidebar.slider("n_est", min_value=1, max_value=5_000, step=1)

@st.cache
def make_predictions(n_est):
    mod1 = DecisionTreeRegressor(max_depth=4)
    y1 = mod1.fit(X,y).predict(X)
    y2 = AdaBoostRegressor(mod1, n_estimators=n_est).fit(X, y).predict(X)
    return y1, y2

y1, y2 = make_predictions(n_est=n_est)

if st.sidebar.checkbox("Toggle ScatterChart"):
    plt.scatter(x, y, alpha=0.1)
plt.plot(x, y1, label="just a tree")
plt.plot(x, y2, label=f"adaboost-{n_est}")
plt.legend()

st.pyplot()

Note that we run this app from the command line by running;

streamlit run app.py

This is what the dataset looks like in our video:

|       date | currency_code | name         | local_price |  dollar_ex | dollar_price |
| ---------- | ------------- | ------------ | ----------- | ---------- | ------------ |
| 2000-04-01 | ARS           | Argentina    |      2.500… |     1.000… |       2.500… |
| 2000-04-01 | AUD           | Australia    |      2.590… |     1.680… |       1.542… |
| 2000-04-01 | BRL           | Brazil       |      2.950… |     1.790… |       1.648… |

This is the app.py that we end up with at the end of the video;

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pylab as plt

st.title('BigMac Index')

@st.cache
def load_data():
    data = pd.read_csv("bigmac.csv")
    return data.assign(date = lambda d: pd.to_datetime(d['date']))

df = load_data()

countries = st.sidebar.multiselect(
    "Select Countries",
    df['name'].unique()
)

varname = st.sidebar.selectbox(
    "Select Column",
    ("local_price", "dollar_price")
)

subset_df = df.loc[lambda d: d['name'].isin(countries)]

for name in countries:
    plotset = subset_df.loc[lambda d: d['name'] == name]
    plt.plot(plotset['date'], plotset[varname], label=name)
plt.legend()
st.pyplot()

if st.sidebar.checkbox("Show Raw Data"):
    st.markdown("### Raw Data")
    st.write(subset_df)

Note that we run this app from the command line by running;

streamlit run app.py