Includes datasets and files I used for my first ever wrangling mini project.
DESCRIPTION
The project files includes datasets and files I used for my first ever wrangling mini project.
I use Python and its libraries, to gather data from a variety of sources and in a variety of formats, assess its quality and tidiness, then clean it. This is called data wrangling. I document all wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python (and its libraries).
The dataset I wrangle (as well as analyze and visualize) 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!" WeRateDogs has over 4 million followers and has received international media coverage.
This dataset was obtained from Udacity classroom projects. This archive contains basic tweet data (tweet ID, timestamp, text, etc.) for all 5000+ of their tweets as they stood on August 1, 2017 (which were later trimmed as part of wranglng efforts).
HOW TO INSTALL AND RUN PROJECTS
The entirety of this project can be completed in this repository. If you want to work on a local machine however, the following software requirements apply:
You need to be able to work in a Jupyter Notebook on your computer. The following packages (libraries) need to be installed.
pandas
NumPy
requests
tweepy
json
You need to be able to create written documents that contain images and you need to be able to export these documents as PDF files. This task can be done in a Jupyter Notebook, but you might prefer to use a word processor like Google Docs, which is free, or Microsoft Word.
A text editor, like Sublime, which is free, will be useful but is not required.