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Python Connector Libraries for Microsoft Excel Data Connectivity. Integrate Microsoft Excel with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to Visualize Excel Data in Python with pandas

Use pandas and other modules to analyze and visualize live Excel data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Microsoft Excel, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Excel-connected Python applications and scripts for visualizing Excel data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Excel data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Excel data in Python. When you issue complex SQL queries from Excel, the driver pushes supported SQL operations, like filters and aggregations, directly to Excel and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Excel Data

Connecting to Excel data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The ExcelFile, under the Authentication section, must be set to a valid Excel File.

Follow the procedure below to install the required modules and start accessing Excel through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Excel Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Excel data.

engine = create_engine("excel:///?Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx'")

Execute SQL to Excel

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Name, Revenue FROM Sheet WHERE Name = 'Bob'", engine)

Visualize Excel Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Excel data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Name", y="Revenue")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Microsoft Excel to start building Python apps and scripts with connectivity to Excel data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("excel:///?Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx'")
df = pandas.read_sql("SELECT Name, Revenue FROM Sheet WHERE Name = 'Bob'", engine)

df.plot(kind="bar", x="Name", y="Revenue")