In order to test some functionality I would like to create a DataFrame from a string. Let’s say my test data looks like:

TESTDATA="""col1;col2;col3
1;4.4;99
2;4.5;200
3;4.7;65
4;3.2;140
"""

What is the simplest way to read that data into a Pandas DataFrame?

A simple way to do this is to use StringIO.StringIO (python2) or io.StringIO (python3) and pass that to the pandas.read_csv function. E.g:

import sys
if sys.version_info[0] < 3: 
    from StringIO import StringIO
else:
    from io import StringIO

import pandas as pd

TESTDATA = StringIO("""col1;col2;col3
    1;4.4;99
    2;4.5;200
    3;4.7;65
    4;3.2;140
    """)

df = pd.read_csv(TESTDATA, sep=";")

Split Method

data = input_string
df = pd.DataFrame([x.split(';') for x in data.split('\n')])
print(df)

A quick and easy solution for interactive work is to copy-and-paste the text by loading the data from the clipboard.

Select the content of the string with your mouse:

In the Python shell use read_clipboard()

>>> pd.read_clipboard()
  col1;col2;col3
0       1;4.4;99
1      2;4.5;200
2       3;4.7;65
3      4;3.2;140

Use the appropriate separator:

>>> pd.read_clipboard(sep=';')
   col1  col2  col3
0     1   4.4    99
1     2   4.5   200
2     3   4.7    65
3     4   3.2   140

>>> df = pd.read_clipboard(sep=';') # save to dataframe

This answer applies when a string is manually entered, not when it’s read from somewhere.

A traditional variable-width CSV is unreadable for storing data as a string variable. Especially for use inside a .py file, consider fixed-width pipe-separated data instead. Various IDEs and editors may have a plugin to format pipe-separated text into a neat table.

Using read_csv

Store the following in a utility module, e.g. util/pandas.py. An example is included in the function’s docstring.

import io
import re

import pandas as pd


def read_psv(str_input: str, **kwargs) -> pd.DataFrame:
    """Read a Pandas object from a pipe-separated table contained within a string.

    Input example:
        | int_score | ext_score | eligible |
        |           | 701       | True     |
        | 221.3     | 0         | False    |
        |           | 576       | True     |
        | 300       | 600       | True     |

    The leading and trailing pipes are optional, but if one is present,
    so must be the other.

    `kwargs` are passed to `read_csv`. They must not include `sep`.

    In PyCharm, the "Pipe Table Formatter" plugin has a "Format" feature that can 
    be used to neatly format a table.

    Ref: https://stackoverflow.com/a/46471952/
    """

    substitutions = [
        ('^ *', ''),  # Remove leading spaces
        (' *$', ''),  # Remove trailing spaces
        (r' *\| *', '|'),  # Remove spaces between columns
    ]
    if all(line.lstrip().startswith('|') and line.rstrip().endswith('|') for line in str_input.strip().split('\n')):
        substitutions.extend([
            (r'^\|', ''),  # Remove redundant leading delimiter
            (r'\|$', ''),  # Remove redundant trailing delimiter
        ])
    for pattern, replacement in substitutions:
        str_input = re.sub(pattern, replacement, str_input, flags=re.MULTILINE)
    return pd.read_csv(io.StringIO(str_input), sep='|', **kwargs)

Non-working alternatives

The code below doesn’t work properly because it adds an empty column on both the left and right sides.

df = pd.read_csv(io.StringIO(df_str), sep=r'\s*\|\s*', engine='python')

As for read_fwf, it doesn’t actually use so many of the optional kwargs that read_csv accepts and uses. As such, it shouldn’t be used at all for pipe-separated data.

Simplest way is to save it to temp file and then read it:

import pandas as pd

CSV_FILE_NAME = 'temp_file.csv'  # Consider creating temp file, look URL below
with open(CSV_FILE_NAME, 'w') as outfile:
    outfile.write(TESTDATA)
df = pd.read_csv(CSV_FILE_NAME, sep=';')

Right way of creating temp file: How can I create a tmp file in Python?