Section 1.1: Ipywidgets

Ipywidgets, often referred to simply as Widgets, is a powerful interactive package that allows you to build graphical user interfaces (GUIs) within Jupyter Notebooks. This package facilitates the creation of interactive dashboards right in your notebook cells. Below is an example of how this can be implemented.

With just a few lines of code, you can transform your Jupyter Notebook into an interactive dashboard. To get started, you need to install the required packages:

pip install ipywidgets

Next, enable Ipywidgets in your Jupyter Notebook using the following command:

jupyter nbextension enable --py widgetsnbextension

Once these steps are completed, you can begin creating your interactive dashboard. For demonstration purposes, we’ll use the Titanic dataset:

import seaborn as sns

titanic = sns.load_dataset('titanic')

titanic.head()

Now, let’s build an interactive dashboard where we can visualize the average fare grouped by different categories:

from ipywidgets import interact

@interact

def create_fare_plot(col=titanic.drop(['fare', 'age'], axis=1).columns):

sns.barplot(data=titanic, x=col, y='fare')

plt.title(f'Mean Bar Plot of the Fare grouped by {col}')

This code utilizes the @interact decorator to initiate interactivity. While Ipywidgets is fantastic for Jupyter Notebooks, it has its limitations. Now, let's explore another option.

Section 1.2: Voila

Voila is a straightforward Python package that converts a Jupyter Notebook into an interactive web application. With just one installation command, you can quickly render your notebook as a web app:

pip install voila

After installation, refresh your Jupyter Notebook to find a new Voila button.

Clicking this button will redirect you to the Voila dashboard, which presents your work without displaying the underlying code, allowing your audience to focus on the results.

While Voila is excellent for simple dashboards, you might seek more flexibility. This leads us to our next package.

Section 2.1: Streamlit

Streamlit is an open-source package designed specifically for building web applications for data science and machine learning projects. It provides an intuitive API that is beginner-friendly and ideal for developing interactive data portfolios.

To start using Streamlit, install it with:

pip install streamlit

With Streamlit, you can create an interactive dashboard like this:

import streamlit as st

import pandas as pd

import plotly.express as px

import seaborn as sns

df = sns.load_dataset('titanic')

st.title('Titanic Dashboard')

st.subheader('Dataset')

st.dataframe(df)

st.subheader('Data Numerical Statistics')

st.dataframe(df.describe())

left_column, right_column = st.columns(2)

with left_column:

num_feat = st.selectbox('Select Numerical Feature', df.select_dtypes('number').columns)

fig = px.histogram(df, x=num_feat, color='survived')

st.plotly_chart(fig, use_container_width=True)

with right_column:

cat_feat = st.selectbox('Select Categorical Feature', df.select_dtypes(exclude='number').columns)

fig = px.histogram(df, x=cat_feat, color='survived')

st.plotly_chart(fig, use_container_width=True)

To run your Streamlit app, save the file and execute:

streamlit run your_file_name.py

This simple setup creates a smooth, user-friendly interface for your audience.