streamlit_data_visualization.py
· 792 B · Python
Bruto
import streamlit as st
import pandas as pd
import numpy as np
st.write("Streamlit supports a wide range of data visualizations, including [Plotly, Altair, and Bokeh charts](https://docs.streamlit.io/develop/api-reference/charts). 📊 And with over 20 input widgets, you can easily make your data interactive!")
all_users = ["Alice", "Bob", "Charly"]
with st.container(border=True):
users = st.multiselect("Users", all_users, default=all_users)
rolling_average = st.toggle("Rolling average")
np.random.seed(42)
data = pd.DataFrame(np.random.randn(20, len(users)), columns=users)
if rolling_average:
data = data.rolling(7).mean().dropna()
tab1, tab2 = st.tabs(["Chart", "Dataframe"])
tab1.line_chart(data, height=250)
tab2.dataframe(data, height=250, use_container_width=True)
| 1 | import streamlit as st |
| 2 | import pandas as pd |
| 3 | import numpy as np |
| 4 | |
| 5 | st.write("Streamlit supports a wide range of data visualizations, including [Plotly, Altair, and Bokeh charts](https://docs.streamlit.io/develop/api-reference/charts). 📊 And with over 20 input widgets, you can easily make your data interactive!") |
| 6 | |
| 7 | all_users = ["Alice", "Bob", "Charly"] |
| 8 | with st.container(border=True): |
| 9 | users = st.multiselect("Users", all_users, default=all_users) |
| 10 | rolling_average = st.toggle("Rolling average") |
| 11 | |
| 12 | np.random.seed(42) |
| 13 | data = pd.DataFrame(np.random.randn(20, len(users)), columns=users) |
| 14 | if rolling_average: |
| 15 | data = data.rolling(7).mean().dropna() |
| 16 | |
| 17 | tab1, tab2 = st.tabs(["Chart", "Dataframe"]) |
| 18 | tab1.line_chart(data, height=250) |
| 19 | tab2.dataframe(data, height=250, use_container_width=True) |
| 20 |