Getting Started With Streamlit For Data Science Pdf -

# --- 2. Data Loading Function with Caching --- @st.cache_data def load_data(dataset): if dataset == "Iris": url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv" elif dataset == "Tips": url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv" else: return pd.DataFrame(np.random.randn(50, 3), columns=['A', 'B', 'C']) return pd.read_csv(url)

Data science is about visualization. Streamlit supports native rendering of Matplotlib, Altair, Plotly, and Bokeh charts. getting started with streamlit for data science pdf

# Load Data data = load_data(selected_dataset) # --- 2

Within minutes, your local data science project will be a live web application accessible to anyone in the world. "Random Data") )

# Creating a select box dataset_name = st.sidebar.selectbox( "Select a Dataset", ("Iris", "Tips", "Random Data") )