Category Archives: Jupyter Notebook

Building a Data Pipeline in Python – Part 4 of N – Basic Reporting

Building a report that passes tests

At this point, we have seen what our data looks like, how it is stored, and what some basic tests might look like. In this post, we start to look at how this might be created as a report to aid in the ETL process.

Many companies find themselves in positions where a CSV (or something similar) is delivered from outside of their organization. In this example, we are assuming that it is being placed into a folder called “new_data”. Our code picks up the file and compares it to what we would expect in terms of test data in order to decide whether or not to move forward with the ETL process.

This Jupyter notebook could be processed each time the file is updated and could be sent to stakeholders before data is processed. It contains a very basic level of testing and visualization, but the idea should get you started. When it runs, tests confirm whether or not the data fits within certain constraints and passes some integrity tests. The data is then plotted and a final output at the bottom shows which tests have passed / failed.

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Building a Data Pipeline in Python – Part 3 of N – Testing Data

Simple testing of data: columns, data types, values

In a previous post, we walked through data exploration / visualization and tests to see if our data fit basic requirements. The Jupyter Notebook, embedded below, loads the data and tests it against some rules that start to push us in a direction that allows for more customization and flexibility of our process.

We are establishing a baseline and framework. We are still in the very early process of ETL but we can start to see what the future holds. This notebook covers:

  • Identification that all of the columns of data we need are being read in from the new file
  • Determining which columns are worth testing
  • Utilization of basic statistics of the data to find an expected range of values
  • Testing all of the above
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