Learn Getting Started with Plotly Express

Plotly Express is a powerful and easy-to-use part of the Plotly library, designed to help you create interactive charts with just a few lines of code. As a high-level interface, Plotly Express simplifies the process of building visualizations by handling much of the underlying complexity for you. You can quickly generate a variety of chart types, including scatter plots, bar charts, line charts, area charts, pie charts, and more. This makes Plotly Express an excellent choice when you want to explore data visually or share interactive charts without spending a lot of time on setup. Its syntax is intuitive, and you can easily switch between chart types by changing just the function name, while most parameters stay the same.


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import plotly.express as px
from IPython.display import display, HTML

# Prepare toy data
x = [1, 2, 3, 4, 5]
y = [10, 15, 13, 17, 14]

# Create a simple scatter plot
fig = px.scatter(x=x, y=y, title="Simple Scatter Plot")

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))

To understand how this scatter plot is created, start by importing the plotly.express module as px. Next, prepare the data you want to visualize; here, two Python lists, x and y, represent the coordinates of the points. The px.scatter function is then called with these lists as arguments, along with an optional title parameter for the chart. This function returns a figure object, which you display using the show() method. With just a few lines, you have an interactive chart that you can zoom, pan, and hover over for more details.


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import plotly.express as px
import pandas as pd
from IPython.display import display, HTML

# Prepare data as a dictionary
data = {
    "Fruits": ["Apple", "Banana", "Orange", "Grape"],
    "Quantity": [10, 15, 7, 12]
}

# Convert dictionary to a DataFrame
df = pd.DataFrame(data)

# Create a bar chart
fig = px.bar(df, x="Fruits", y="Quantity", title="Fruit Quantities")

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))

Switching between different chart types in Plotly Express is as simple as changing the function name. For example, to create a bar chart instead of a scatter plot, use px.bar() instead of px.scatter(). The parameters for specifying data remain consistent: you still provide the data source and the column names or data lists for the axes. This consistency makes it easy to experiment with various visualizations—just swap the chart function while keeping your data structure and parameter names the same.

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Section 1. Chapter 2

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              123456789101112
            
import plotly.express as px
from IPython.display import display, HTML

# Prepare toy data
x = [1, 2, 3, 4, 5]
y = [10, 15, 13, 17, 14]

# Create a simple scatter plot
fig = px.scatter(x=x, y=y, title="Simple Scatter Plot")

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))


              123456789101112131415161718
            
import plotly.express as px
import pandas as pd
from IPython.display import display, HTML

# Prepare data as a dictionary
data = {
    "Fruits": ["Apple", "Banana", "Orange", "Grape"],
    "Quantity": [10, 15, 7, 12]
}

# Convert dictionary to a DataFrame
df = pd.DataFrame(data)

# Create a bar chart
fig = px.bar(df, x="Fruits", y="Quantity", title="Fruit Quantities")

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 2