read_clipboard: Beginner's Guide

read_clipboard is truly a saving grace for anyone starting out to answer questions in the 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 complications in the format of the data posted.

Thankfully, read_clipboard has arguments that make handling most of these cases possible (and easy). Here are some common use cases and their corresponding arguments.

Common Use Cases

read_clipboard uses read_csv under the hood with white space separator, so a lot of the techniques for parsing data from CSV apply here, such as

• parsing columns with spaces in the data

  • use sep with regex argument. First, ensure there are at least two spaces between columns and at most one consecutive white space inside the column's data itself. Then you can use sep=r'\s{2,}' which means "separate columns by looking for at least two consecutive white spaces for the separator" (note: engine='python' is required for multicharacter or regex separators):

     df = pd.read_clipboard(..., sep=r'\s{2,}', engine='python')
    

    Also see How do you handle column names having spaces in them when using pd.read_clipboard?.

• reading a series instead of DataFrame

  • use squeeze=true, you would likely also need header=None if the first row is also data.

     s = pd.read_clipboard(..., header=None, squeeze=True)
    

    Also see Could there be an easier way to use pandas read_clipboard to read a Series?.

• loading data with custom header names

  • use names=[...] in conjunction with header=None and skiprows=[0] to ignore existing headers.

     df = pd.read_clipboard(..., header=None, names=['a', 'b', 'c'], skiprows=[0])
    

• loading data without any headers

  • use header=None

• set one or more columns as the index

  • use index_col=[...] with the appropriate label or index

• parsing dates

  • use parse_dates with the appropriate format. If parsing datetimes (i.e., columns with date separated by timestamp), you will likely also need to use sep=r'\s{2,}' while ensuring your columns are separated by at least two spaces.

See this answer by me for a more comprehensive list on read_csv arguments for other cases not covered here.

Caveats

read_clipboard is a Swiss Army knife. However, it

• cannot read data in prettytable/tabulate formats (IOW, borders make it harder)

  • See Reading in a pretty-printed/formatted dataframe using pd.read_clipboard? for solutions to tackle this.

• cannot correctly parse MultIndexes unless all elements in the index are specified.

  • See Copying MultiIndex dataframes with pd.read_clipboard? for solutions to tackle this.

• cannot ignore/handle ellipses in data

  • my suggested method is to manually remove ellipses before printing

• cannot parse columns of lists (or other objects) as anything other than string. The columns will need to be converted separately, as shown in How do you read in a dataframe with lists using pd.read_clipboard?.

• cannot read text from images (so please don't use images as a means to share your data with folks, please!)

The one weakness of this function is that it doesn't capture contents of Ctrl + C if the copy is performed from a PDF file. Testing it this way results in an empty read.

But by using a regular text editor, it goes just fine. Here is an example using randomly typed text:

>>> pd.read_clipboard()
Empty DataFrame
Columns: [sfsesfsdsxcvfsdf]
Index: []