Use pandas to Visualize SFTP Data in Python

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SFTP Python Connector

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



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

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

Connecting to SFTP Data

Connecting to SFTP 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.

SFTP can be used to transfer files to and from SFTP servers using the SFTP Protocol. To connect, specify the RemoteHost;. service uses the User and Password and public key authentication (SSHClientCert). Choose an SSHAuthMode and specify connection values based on your selection.

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 subfolders to report as views.
  • FileRetrievalDepth: Set this to retrieve files recursively and list them in the Root table.
Stored Procedures are available to download files, upload files, and send protocol commands. See gdatamodel for more on using SQL to interact with the server.

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

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

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

Execute SQL to SFTP

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 SFTP Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the SFTP Python Connector to start building Python apps and scripts with connectivity to SFTP 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("sftp:///?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")
plt.show()