Socrata was acquired by Tyler Technologies in 2018 and is now the Data and Insights division of Tyler. The platform is still powered by the same software formerly known as Socrata but you will see references to Data & Insights going forward.

Scrubbing data with Python

There’s an awesome Python package called Scrubadub that can can help you remove personally identifiable information from text data. This is a great step to take before publishing a dataset that may contain PII, in order to prevent inadvertent disclosure.

In this example, we’ll clean up some CSV data using Scrubadub, in order to prep it for loading in Socrata:

  1. First we’ll load a local CSV it into a dataframe with Pandas,
  2. Then we’ll remove names using Scrubadub,
  3. And finally write it to a CSV that can be loaded using DataSync.


Before you start, make sure you have the following installed on your machine:

  1. Python
  2. Pandas
  3. Scrubadub
  4. Socrata DataSync

Loading your CSV with Pandas

Create a dataframe from your local CSV file with Pandas:

import pandas as pd
df = pd.read_csv('~/Dallas_Police_Officer-Involved_Shootings.csv')

With Officer Names

Remove names using Scrubadub

Scrubadub is a simple package that will look for names and other identifying information, like email addresses, SSNs, and phone numbers.

import scrubadub
scrub = lambda x: scrubadub.clean(x.decode('utf-8'), replace_with='identifier')
df['Officer(s)'] = df['Officer(s)'].apply(scrub)

Without Officer Names

Data cleansing is a serious topic and you should always work with your privacy or policy officers within your organization to make sure you are taking the correct steps to protect privacy.

Write cleansed data back to CSV

Finally, we’ll write our cleansed records back out to CSV:

df.to_csv("~/Dallas_Police_Officer-Involved_Shootings.csv", encoding='utf-8', index = False)

Once you’re done, the cleaned data file can be used to update a dataset via DataSync. For more information, see its detailed documentation