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Use pandas to Visualize Open Exchange Rates Data in Python

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

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

Connecting to Open Exchange Rates Data

Connecting to Open Exchange Rates 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 Open Exchange Rates API supports basic authentication with an App Id. After you register, your App Id is displayed in your account dashboard. Set this to the AppId connection property.

Follow the procedure below to install the required modules and start accessing Open Exchange Rates 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 Open Exchange Rates Data in Python

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

engine = create_engine("openexchangerates:///?AppId=abc1234")

Execute SQL to Open Exchange Rates

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

df = pandas.read_sql("SELECT Id, Statistics_ViewCount FROM Projects WHERE Id = 'MyProjectId'", engine)

Visualize Open Exchange Rates Data

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

df.plot(kind="bar", x="Id", y="Statistics_ViewCount")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Open Exchange Rates Python Connector to start building Python apps and scripts with connectivity to Open Exchange Rates 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("openexchangerates:///?AppId=abc1234")
df = pandas.read_sql("SELECT Id, Statistics_ViewCount FROM Projects WHERE Id = 'MyProjectId'", engine)

df.plot(kind="bar", x="Id", y="Statistics_ViewCount")
plt.show()