read_clipboard beginner's guide [DRAFT]

## Beginner's Guide to `pd.read_clipboard`

[`read_clipboard`](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#id45) is truly a saving grace for anyone starting out to answer questions in the [tag:pandas] tag. Unfortunately, pandas veterans also know that the data provided in questions isn't always easy to grok into a terminal due to various complication  such as MultiIndexes, spaces in header names, datetimes, and python objects. 

Thankfully, `read_clipboard` has arguments that make handling most of these cases possible (and easy). The purpose of this answer is to document some of those cases in finer details.

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### Spaces in column headers 


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### Read a Series instead of a DataFrame

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### Python objects 

Numeric data - simpler

String data - may need yaml

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### Other considerations 

Uses `read_csv` under the hood, so a lot of the principles for loading data from CSV apply here, such as  

- parsing datetimes (use `parse_dates`)
- no headers (use `header=None`)
- custom names (use `names=[...]`)
- set a column as the index (use `index_col=[...]`)
- read series instead of DataFrame (use `squeeze=true`)
- specify a custom separator (use `sep='...'`. If multicharacter or regex, use `engine='python'`)


And so on. See [here](https://stackoverflow.com/a/56231664/4909087) for a more comprehensive list. 

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### Limitations of `read_clipboard`

- Cannot parse prettytable/tabulate output (IOW, borders make it harder). Check out some homemade attempts at tackling this.

- Cannot ignore ellipses in data (you'll need to manually remove them)
- Cannot load data from images (if you're upto the task you can make a tesseract extension that does)
-  


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### Other useful `pd.read_clipboard` questions for unconventionally formatted data