Introduction Real-world data rarely comes clean. Using Python and its libraries, I gathered data from a variety of sources and in a variety of formats, assessing its quality and tidiness, then cleaning it. This is called data wrangling. I documented my wrangling efforts in a Jupyter Notebook, plus I showcased them through analyses and visualizations using Python (and its libraries) and/or SQL.
The dataset that I wrangled (and analyzing and visualizing) 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 comment 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 me to use in this project. This archive contains basic tweet data (tweet ID, timestamp, text, etc.) for all 5000+ of their tweets as they stood on August 1, 2017.
Project Steps Overview:
Step 1: Gathering data
Step 2: Assessing data
Step 3: Cleaning data
Step 4: Storing data
Step 5: Analyzing, and visualizing data
Step 6: Reporting
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Data wrangling
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Data analysis and Visualizations