How to Visualize CSV Data in Python with pandas

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

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

Connecting to CSV Data

Connecting to CSV 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 DataSource property must be set to a valid local folder name.

Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

Follow the procedure below to install the required modules and start accessing CSV 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 CSV Data in Python

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

engine = create_engine("csv:///?DataSource=MyCSVFilesFolder")

Execute SQL to CSV

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

df = pandas.read_sql("SELECT City, TotalDue FROM Customer WHERE FirstName = 'Bob'", engine)

Visualize CSV Data

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

df.plot(kind="bar", x="City", y="TotalDue")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for CSV to start building Python apps and scripts with connectivity to CSV 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("csv:///?DataSource=MyCSVFilesFolder")
df = pandas.read_sql("SELECT City, TotalDue FROM Customer WHERE FirstName = 'Bob'", engine)

df.plot(kind="bar", x="City", y="TotalDue")
plt.show()