Create Pandas DataFrame from a string

Question

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?

Answer 1

A simple way to do this is to use StringIO 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=";")

Answer 2

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

The following works for me. To use it, store it into a file, e.g. pandas_util.py. An example is included in the function's docstring. If you're using a version of Python older than 3.6, delete the type annotations from the function definition line.

import re

import pandas as pd


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

    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(pd.compat.StringIO(str_input), sep='|', **kwargs)

Non-working alternative:

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

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

Answer 3

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