Welcome to the Plotly Community Data Science Tutorials section! Here, you will find a variety of tutorials that cover the use of Plotly for data visualization in the field of data science.

Tutorials Overview

Introduction to Plotly

Plotly is a powerful data visualization library that allows you to create interactive and publication-quality graphs from Python. It's widely used in data science for its flexibility and ease of use.

Features of Plotly

  • Interactive plots that can be zoomed, panned, and updated in real-time.
  • Wide range of plot types, including scatter plots, bar charts, heatmaps, and more.
  • Integration with various data sources and libraries, such as Pandas and NumPy.

Creating Basic Plots

Before diving into more complex plots, it's important to understand the basics of Plotly. In this tutorial, we'll go over how to create simple plots like line charts and scatter plots.

Line Chart

A line chart is a great way to visualize the trend of data over time or any other sequential data.

import plotly.graph_objs as go

data = [go.Scatter(x=[1, 2, 3, 4, 5], y=[2, 3, 5, 7, 11], mode='lines+markers')]
layout = go.Layout(title='Line Chart Example', xaxis={'title': 'X-axis'}, yaxis={'title': 'Y-axis'})
fig = go.Figure(data=data, layout=layout)
fig.show()

Interactive Dashboards with Dash

Dash is an open-source framework for building analytical web applications. It is built on top of Plotly and Flask, making it easy to create interactive dashboards.

Getting Started with Dash

To get started with Dash, you can install it using pip:

pip install dash

Then, you can create a basic dashboard with the following code:

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([
    dcc.Graph(
        id='example-graph',
        figure={
            'data': [
                {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
                {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': 'Montgomery'}
            ],
            'layout': {
                'title': 'Dash Data Visualization'
            }
        }
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

Plotly for Time Series Analysis

Time series analysis is a key aspect of data science, and Plotly can be used to visualize time series data effectively.

Time Series Plot

A time series plot is useful for visualizing data points over time.

import pandas as pd
import plotly.graph_objs as go

df = pd.DataFrame({
    'Date': pd.date_range(start='1/1/2018', periods=100, freq='D'),
    'Close': np.random.randn(100).cumsum()
})

trace = go.Scatter(x=df['Date'], y=df['Close'], mode='lines+markers')
layout = go.Layout(title='Time Series Plot', xaxis={'title': 'Date'}, yaxis={'title': 'Close'})
fig = go.Figure(data=[trace], layout=layout)
fig.show()

More Resources

For more in-depth tutorials and examples, please visit our Plotly Data Science Tutorials.

Pandas
and
NumPy

Pandas and NumPy are essential libraries for data manipulation and analysis. Make sure to check out our tutorials on these libraries as well!