Learn Integrating Plotly with Pandas DataFrames

When working with data in Python, pandas DataFrames are one of the most powerful and flexible tools you can use. A DataFrame is a two-dimensional, labeled data structure with columns that can hold different types of values, such as numbers, strings, or dates. This format is especially useful for data manipulation, cleaning, and analysis, making it a natural fit for preparing data before visualization. By using DataFrames, you can quickly filter, aggregate, and transform your data, which streamlines the process of creating meaningful and interactive charts with Plotly Express.


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

# Create a simple DataFrame
data = {
    "Country": ["USA", "Canada", "Germany", "UK", "France"],
    "GDP": [21.43, 1.84, 3.86, 2.83, 2.72],
    "Population": [331, 38, 83, 67, 65]
}
df = pd.DataFrame(data)

# Visualize the DataFrame using a scatter plot
fig = px.scatter(df, x="GDP", y="Population", text="Country",
                 title="GDP vs Population by Country")

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

When you use Plotly Express with a pandas DataFrame, Plotly automatically detects the column names and makes them available for use as axes, colors, symbols, and more. This means you can simply refer to a column by its name when specifying parameters like x, y, or color. Plotly Express takes care of mapping the data for you, which makes the visualization process both intuitive and efficient. For example, in the previous code, specifying x="GDP" and y="Population" tells Plotly to use those columns for the respective axes, and including text="Country" adds country labels to the points.


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

# Sample sales data
data = {
    "Region": ["North", "South", "East", "West", "North", "South", "East", "West"],
    "Salesperson": ["Alice", "Bob", "Charlie", "David", "Eve", "Frank", "Grace", "Heidi"],
    "Sales": [200, 150, 300, 250, 180, 170, 320, 260]
}
df = pd.DataFrame(data)

# Group by region and sum sales
grouped = df.groupby("Region", as_index=False)["Sales"].sum()

# Plot total sales by region using a bar chart
fig = px.bar(grouped, x="Region", y="Sales", title="Total Sales by Region")

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

To get the most out of pandas and Plotly integration, always perform your data cleaning and aggregation steps within pandas before passing the DataFrame to Plotly Express. This approach ensures your visualizations are accurate and easy to interpret. Use column names directly in Plotly Express functions to keep your code readable and concise. As seen in the examples, grouping and summarizing data with pandas methods like groupby lets you create charts that highlight trends and comparisons clearly. Keeping your data preparation and visualization workflow tightly integrated with pandas and Plotly will help you efficiently create compelling, interactive charts for your analyses.

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Section 1. Chapter 8

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

# Create a simple DataFrame
data = {
    "Country": ["USA", "Canada", "Germany", "UK", "France"],
    "GDP": [21.43, 1.84, 3.86, 2.83, 2.72],
    "Population": [331, 38, 83, 67, 65]
}
df = pd.DataFrame(data)

# Visualize the DataFrame using a scatter plot
fig = px.scatter(df, x="GDP", y="Population", text="Country",
                 title="GDP vs Population by Country")

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


              1234567891011121314151617181920
            
import pandas as pd
import plotly.express as px
from IPython.display import display, HTML

# Sample sales data
data = {
    "Region": ["North", "South", "East", "West", "North", "South", "East", "West"],
    "Salesperson": ["Alice", "Bob", "Charlie", "David", "Eve", "Frank", "Grace", "Heidi"],
    "Sales": [200, 150, 300, 250, 180, 170, 320, 260]
}
df = pd.DataFrame(data)

# Group by region and sum sales
grouped = df.groupby("Region", as_index=False)["Sales"].sum()

# Plot total sales by region using a bar chart
fig = px.bar(grouped, x="Region", y="Sales", title="Total Sales by Region")

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

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 8