The dataset that you will be wrangling (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 you 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. More on this soon.
Your goal: wrangle WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations. The Twitter archive is great, but it only contains very basic tweet information. Additional gathering, then assessing and cleaning is required for "Wow!"-worthy analyses and visualizations.
The WeRateDogs Twitter archive contains basic tweet data for all 5000+ of their tweets, but not everything. One column the archive does contain though: each tweet's text, which I used to extract rating, dog name, and dog "stage" (i.e. doggo, floofer, pupper, and puppo) to make this Twitter archive "enhanced." Of the 5000+ tweets, I have filtered for tweets with ratings only (there are 2356).
Back to the basic-ness of Twitter archives: retweet count and favorite count are two of the notable column omissions. Fortunately, this additional data can be gathered by anyone from Twitter's API. Well, "anyone" who has access to data for the 3000 most recent tweets, at least. But you, because you have the WeRateDogs Twitter archive and specifically the tweet IDs within it, can gather this data for all 5000+. And guess what? You're going to query Twitter's API to gather this valuable data.
One more cool thing: I ran every image in the WeRateDogs Twitter archive through a neural network that can classify breeds of dogs*. The results: a table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images).
- Your tasks in this project are as follows:
Data wrangling, which consists of: Gathering data (downloadable file in the Resources tab in the left most panel of your classroom and linked in step 1 below). Assessing data Cleaning data Storing, analyzing, and visualizing your wrangled data Reporting on 1) your data wrangling efforts 2) your data analyses and visualizations Gathering Data for this Project
Gather each of the three pieces of data as described below in a Jupyter Notebook titled wrangle_act.ipynb:
The WeRateDogs Twitter archive. I am giving this file to you, so imagine it as a file on hand. Download this file manually (twitter_archive_enhanced.csv)
i.e., what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network. This file (image_predictions.tsv) is hosted on Udacity's servers and should be downloaded programmatically using the Requests library and the following URL: https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv (Included in here)
Each tweet's retweet count and favorite ("like") count at minimum, and any additional data you find interesting. Using the tweet IDs in the WeRateDogs Twitter archive, query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file called tweet_json.txt file. Each tweet's JSON data should be written to its own line. Then read this .txt file line by line into a pandas DataFrame with (at minimum) tweet ID, retweet count, and favorite count.
After gathering each of the above pieces of data, assess them visually and programmatically for quality and tidiness issues. Detect and document at least eight (8) quality issues and two (2) tidiness issues in your wrangle_act.ipynb Jupyter Notebook. To meet specifications, the issues that satisfy the Project Motivation (see the Key Points header on the previous page) must be assessed.
Clean each of the issues you documented while assessing. Perform this cleaning in wrangle_act.ipynb as well. The result should be a high quality and tidy master pandas DataFrame (or DataFrames, if appropriate).
Store the clean DataFrame(s) in a CSV file with the main one named twitter_archive_master.csv. If additional files exist because multiple tables are required for tidiness, name these files appropriately. Additionally, you may store the cleaned data in a SQLite database (which is to be submitted as well if you do).
Analyze and visualize your wrangled data in your wrangle_act.ipynb Jupyter Notebook. At least three (3) insights and one (1) visualization must be produced.
Create a 300-600 word written report called wrangle_report.pdf or wrangle_report.html that briefly describes your wrangling efforts. This is to be framed as an internal document.
Create a 250-word-minimum written report called act_report.pdf or act_report.html that communicates the insights and displays the visualization(s) produced from your wrangled data. This is to be framed as an external document, like a blog post or magazine article, for example.