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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
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Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
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Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
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Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
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Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Create Interactive Dashboards with Streamlit and Python

Muthu
Muthu Last Updated : 18 Jul, 2024
5 min read

Introduction

In Data Visualization, Dashboard is the great Graphical User Interfaces that helps you to display the information in a highly interactive and informative way. It helps to visualize the key indicators and trends of the data.

streamlit

The various plots such as bars, pies, line charts, etc. help to explore the dataset and provide us with some useful information. Dashboards are really useful to display the key performance indicators effectively. However, creating dashboards always a tedious task for developers. In this article you will get understanding about the stramlit dashboard how to setup streamlit and import libraries also, for better understanding giving streamlit example.

This article was published as a part of the Data Science Blogathon.

Table of contents

Streamlit - logo

There are some libraries like Plotly, Bokeh in Python that lets you create a dashboard. But I didn’t find these are easy to create a perfect dashboard. Finally, I found some easy ways to create a dashboard which makes you create a quite effective and informative dashboard.

Streamlit

Streamlit is gaining popularity in Machine learning and Data Science. It is a very easy library to create a perfect dashboard by spending a little amount of time. It also comes with the inbuilt webserver and lets you deploy in the docker container.

Setting up Streamlit

Let’s first install Streamlit to our system and run the hello command to verify its working condition. We can quit the running app by using Ctrl+c.

$ pip install streamlit
$ streamlit hello

Below is the command prompt, you can see the app is running perfectly.

streamlit - command prompt

When you run the app, the localhost server will open in your browser automatically. This is the home page of the Streamlit app open-source framework.

streamlit - welcome screen

Import Libraries

Let’s import the necessary libraries using for plotting as well as displaying information.

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import matplotlib.pyplot as plt

We can display the text in different ways. Streamlit allows you to write the title, header, and also supports various functions.

  1. st.title()- to set the title
  2. st.text() to write the description for the particular graph
  3. st.markdown() to display text as markdown
  4. st.latex() to display the mathematical expressions in the dashboard.
  5. st.write() helps to display everything such as plotly graph, dataframe, functions, model, etc.
  6. st.sidebar() is used for displaying data on the sidebar.
  7. st.dataframe() to display the data frame
  8. st.map() to display the map in just a single line code etc

Setting the title and the sidebar title for Streamlit dashboard

  1. st.title(“Covid-19 Dashboard For India”)
  2. st.markdown(‘The dashboard will visualize the Covid-19 Situation in India’)
  3. st.markdown(‘Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment.’. This app gives you the real-time impact analysis of Confirmed, Deaths, active, and recovered cases of COVID-19 )
  4. st.sidebar.title(“Visualization Selector”)
  5. st.sidebar.markdown(“Select the Charts/Plots accordingly:”)

Load the dataset

Here we are going to use the COVID-19 dataset for dashboard visualization.

DATA_URL=('E:\Data science Projects\NIELIT project\covid_19_world.csv')
@st.cache(persist=True)( If you have a different use case where the data does not change so very often, you can simply use this)

def load_data():
    data=pd.read_csv(DATA_URL)
    return data

covid_data=load_data()

Data Visualization by cases

st.sidebar.checkbox("Show Analysis by State", True, key=1)
select = st.sidebar.selectbox('Select a State',df['state'])

#get the state selected in the selectbox
state_data = df[df['state'] == select]
select_status = st.sidebar.radio("Covid-19 patient's status", ('Confirmed',
'Active', 'Recovered', 'Deceased'))

We have used the Checkbox to choose the analysis by states. Selectbox will display the list of states affected by COVID-19. The radio button to choose the Active, Confirmed, Death, or Recovered cases.

Plotting the graph

def get_total_dataframe(dataset):
    total_dataframe = pd.DataFrame({
    'Status':['Confirmed', 'Recovered', 'Deaths','Active'],
    'Number of cases':(dataset.iloc[0]['confirmed'],
    dataset.iloc[0]['recovered'], 
    dataset.iloc[0]['deaths'],dataset.iloc[0]['active'])})
    return total_dataframe

state_total = get_total_dataframe(state_data)

if st.sidebar.checkbox("Show Analysis by State", True, key=2):
    st.markdown("## **State level analysis**")
    st.markdown("### Overall Confirmed, Active, Recovered and " +
    "Deceased cases in %s yet" % (select))
    if not st.checkbox('Hide Graph', False, key=1):
        state_total_graph = px.bar(
        state_total, 
        x='Status',
        y='Number of cases',
        labels={'Number of cases':'Number of cases in %s' % (select)},
        color='Status')
        st.plotly_chart(state_total_graph)

After executing this code we can select the cases according to the state you required. The method get_total_dataframe is used to get the dataset to plot the graph for the selected state.

To plot the graph, we have used plotly.express library method. And finally, show the graph using the st.plotly_chart().

The graph showing the cases for Maharashtra.

cases in maharashtra

Showing a dataframe or table

We can also view the dataframe in table view using st.dataframe() and st.table().

def get_table():
    datatable = df[['state', 'confirmed', 'recovered', 'deaths','active']].sort_values(by=['confirmed'], ascending=False)
    datatable = datatable[datatable['state'] != 'State Unassigned']
    return datatable

datatable = get_table()
st.markdown("### Covid-19 cases in India")
st.markdown("The following table gives you a real-time analysis of the confirmed, active, recovered and deceased cases of Covid-19 pertaining to each state in India.")
st.dataframe(datatable) # will display the dataframe
st.table(datatable)# will display the table
statewise covid cases in india

Conclusion

Streamlit is considered as the fastest growing Machine Learning and Data Science dashboard building platform. It is considered as one of the best dashboard libraries. You can experiment with different datasets to create interactive dashboards.

Hope you like the article and get the understanding about the streamlit dashboard and and you can setup it and import libraries hope you clear about it.

Q1. Is Plotly Dash better than Streamlit?

Streamlit: Easy & Fast for simple apps.
Plotly Dash: Powerful & Customizable for complex apps with great visuals.

Q2.What is Streamlit used for?

Streamlit: Turns Python data scripts into data dashboards and ML app prototypes in minutes.

blogathonstreamlit
Muthu
Muthu

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Responses From Readers

Clear

Mohammed Pervez Manzer
Mohammed Pervez Manzer

Very informative stuf

123

Muthu Krishnan
Muthu Krishnan

Thankyou Mohammed

123
Christian
Christian

The image you have at the top of this article.. What visualization library is this from?

123

Anis Boujemaa
Anis Boujemaa

Can you give us the csv file please ? and tell me if **df** exists in the columns or rows of the csv file ? If not can you give us the df where it should be declared and how ? Thank you so much.

123

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Building a Covid-19 Dashboard using Streamlit and Python
Building a Covid-19 Dashboard using Streamlit and Python
Developing Data Web App Using Streamlit
Developing Data Web App Using Streamlit

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