Real-world data rarely come clean. Using Python and its libraries, I gathered data from a variety of sources and in a variety of formats, assess its quality and tidiness, then clean it. I have documented the wrangling steps in a Jupyter Notebook, plus showcased them through analyses and visualizations using Python (and its libraries). The aforementioned dataset is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comments about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because "they're good dogs Brent." WeRateDogs has over 4 million followers and has received international media coverage. WeRateDogs downloaded their Twitter archive and sent it to Udacity via email exclusively for the students to use in one of the projects. This archive contains basic tweet data (tweet ID, timestamp, text, etc.) for all 2000+ of their tweets as they stood on August 1, 2017.