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

How to Visualize FTP Data in Python with pandas

Use pandas and other modules to analyze and visualize live FTP 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 FTP, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build FTP-connected Python applications and scripts for visualizing FTP data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to FTP data, execute queries, and visualize the results.

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

Connecting to FTP Data

Connecting to FTP 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.

To connect to FTP or SFTP servers, specify at least RemoteHost and FileProtocol. Specify the port with RemotePort.

Set User and Password to perform Basic authentication. Set SSHAuthMode to use SSH authentication. See the Getting Started section of the data provider help documentation for more information on authenticating via SSH.

Set SSLMode and SSLServerCert to secure connections with SSL.

The data provider lists the tables based on the available folders in your FTP server. Set the following connection properties to control the relational view of the file system:

  • RemotePath: Set this to the current working directory.
  • TableDepth: Set this to control the depth of folders to list as views.
  • FileRetrievalDepth: Set this to retrieve and list files recursively from the root table.

Stored Procedures are available to download files, upload files, and send protocol commands. See the Data Model chapter of the FTP data provider documentation for more information.

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

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

engine = create_engine("ftp:///?RemoteHost=MyFTPServer")

Execute SQL to FTP

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

df = pandas.read_sql("SELECT Filesize, Filename FROM MyDirectory WHERE FilePath = '/documents/doc.txt'", engine)

Visualize FTP Data

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

df.plot(kind="bar", x="Filesize", y="Filename")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for FTP to start building Python apps and scripts with connectivity to FTP 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("ftp:///?RemoteHost=MyFTPServer")
df = pandas.read_sql("SELECT Filesize, Filename FROM MyDirectory WHERE FilePath = '/documents/doc.txt'", engine)

df.plot(kind="bar", x="Filesize", y="Filename")