Objective of project

Udacity Data Analyst Nanodegree Data Wrangling Project
The purpose of this project is to tidy up 3 datasets using Python.

Notes given by Udacity tutors

Key points to keep in mind when data wrangling for this project:

1. You only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.

2. Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.

3. Cleaning includes merging individual pieces of data according to the rules of tidy data.

4. The fact that the rating numerators are greater than the denominators does not need to be cleaned. This unique rating system is a big part of the popularity of WeRateDogs.

5. You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.

Acceptance Criteria

Storing, Analyzing, and Visualizing Data for this Project

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.

Reporting for this Project

1. 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.

2. 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.