2 3 Building Interactive Dashboards Explained

Key Concepts

• Interactive Elements: Components that allow user interaction.
• Data Visualization: Techniques to represent data graphically.
• Filters and Controls: Tools to manipulate and filter data.
• Layout Design: Arranging elements for optimal user experience.
• Dynamic Updates: Real-time data updates based on user actions.

Interactive Elements

Interactive elements are components that allow users to interact with the dashboard. These can include buttons, sliders, dropdowns, and more. They enable users to control the data displayed and explore different aspects of the information.

import streamlit as st

st.title("Interactive Dashboard")
user_input = st.text_input("Enter your name")
st.write(f"Hello, {user_input}!")
    

Data Visualization

Data visualization involves representing data graphically to make it easier to understand. Techniques include bar charts, line graphs, scatter plots, and heatmaps. These visualizations help users identify patterns and trends in the data.

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'year': [2010, 2011, 2012, 2013, 2014],
    'sales': [100, 150, 200, 250, 300]
})

fig, ax = plt.subplots()
ax.plot(data['year'], data['sales'])
st.pyplot(fig)
    

Filters and Controls

Filters and controls are tools that allow users to manipulate and filter data. These can include date range selectors, checkboxes, and dropdown menus. They enable users to focus on specific subsets of the data.

import streamlit as st
import pandas as pd

data = pd.DataFrame({
    'category': ['A', 'B', 'C', 'A', 'B'],
    'value': [10, 20, 30, 40, 50]
})

category = st.selectbox("Select category", data['category'].unique())
filtered_data = data[data['category'] == category]
st.write(filtered_data)
    

Layout Design

Layout design involves arranging the elements of the dashboard for optimal user experience. This includes placing interactive elements, visualizations, and text in a way that is intuitive and easy to navigate.

import streamlit as st

st.title("Dashboard Layout")
st.sidebar.header("Controls")
user_input = st.sidebar.text_input("Enter your name")
st.write(f"Hello, {user_input}!")
    

Dynamic Updates

Dynamic updates involve real-time data updates based on user actions. This can include refreshing data, updating visualizations, and changing the layout in response to user inputs.

import streamlit as st
import pandas as pd
import time

st.title("Dynamic Dashboard")
data = pd.DataFrame({
    'time': [time.time()],
    'value': [100]
})

if st.button("Update"):
    data = pd.DataFrame({
        'time': [time.time()],
        'value': [data['value'].iloc[0] + 10]
    })

st.write(data)
    

Analogies

Think of an interactive dashboard as a control room where you can monitor and manipulate various systems. Interactive elements are like buttons and levers that allow you to control the systems. Data visualization is like the screens that display real-time data. Filters and controls are like switches that let you focus on specific parts of the data. Layout design is like arranging the control room for easy access to all systems. Dynamic updates are like the real-time feedback you get from the systems as you make adjustments.

By mastering these concepts, you can create powerful and user-friendly interactive dashboards in Streamlit.