09/13/2021 10:40 AM . 09/13/2021 10:40 AM .. 09/13/2021 10:40 AM 1,853,189 Amazon.csv 09/13/2021…

09/13/2021 10:40 AM  .
09/13/2021 10:40 AM  ..
09/13/2021 10:40 AM 1,853,189 Amazon.csv
09/13/2021 10:40 AM 102,234 Amazon_Google_perfectMapping.csv
09/13/2021 10:40 AM 155,487 Amazon_small.csv
09/13/2021 10:40 AM 1,070,774 Google.csv
09/13/2021 10:40 AM 64,413 Google_small.csv
 5 File(s) 3,246,097 bytes
 2 Dir(s) 269,292,036,096 bytes free

## Part 0: Preliminaries
Download the data and unzip the files into a folder called `data` in the same directory as this notebook. We start by processing these files with `pandas`.
For each of the data files, create DataFrames whose indexes will be the IDs:
the IDs are the zero-th column of the file (they are URLs for Google, and alphanumeric strings for Amazon). Use the "iso-8859-1" encoding while parsing the csv files, and make the price information numeric. Adjust the column names so that they are identical across all the data frames. Eventually, you should have four datafiles with identical indexes named **`id`** and identical column names `name`,`description`, `manufacturer`, `price`
Also create a `gold_standard` frame from `Amazon_Google_perfectMapping.csv` with a plain numeric index.

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