ENH: DataFrame eval will not work with '"True", "False", "inf", "Inf"

[weikhor]

Feature Type

• Adding new functionality to pandas

• Changing existing functionality in pandas

• Removing existing functionality in pandas

Problem Description

With version 1.5.0.dev0+1213.g4fe8b84c77, it not able to support dataframe eval when column name used is from ["True", "False", "inf", "Inf"]

import pandas as pd
import numpy as np

column = "True"
df = pd.DataFrame(np.random.randint(0, 100, size=(10, 2)),
                  columns=[column, "col1"])
expected = df[df[column] > 6]
result = df.query(f"{column}>6")

It shows this error

Traceback (most recent call last):
  File "/home/open_source/pandas_1/development/development.py", line 19, in <module>
    result = df.query(f"{column}>6")
  File "/home/open_source/pandas_1/pandas/pandas/util/_decorators.py", line 317, in wrapper
    return func(*args, **kwargs)
  File "/home/open_source/pandas_1/pandas/pandas/core/frame.py", line 4388, in query
    result = self.loc[res]
  File "/home/open_source/pandas_1/pandas/pandas/core/indexing.py", line 1071, in __getitem__
    return self._getitem_axis(maybe_callable, axis=axis)
  File "/home/open_source/pandas_1/pandas/pandas/core/indexing.py", line 1306, in _getitem_axis
    self._validate_key(key, axis)
  File "/home/open_source/pandas_1/pandas/pandas/core/indexing.py", line 1116, in _validate_key
    raise KeyError(
KeyError: 'False: boolean label can not be used without a boolean index'

Feature Description

Column name from ["True", "False", "inf", "Inf"] can support dataframe eval. For example,

import pandas as pd
import numpy as np

column = "True"
df = pd.DataFrame(np.random.randint(0, 100, size=(10, 2)),
                  columns=[column, "col1"])
expected = df[df[column] > 6]
result = df.query(f"{column}>6")

Alternative Solutions

No alternative solution.

Additional Context

It is related to #44603

[dannyi96]

1. For Python Boolean Literals - True and False

As per docs - https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html

exprstr - The query string to evaluate.
....
... You can refer to column names that are not valid Python variable names by surrounding them in backticks. 
...

Hence the above will work by using
result = df.query(f"`{column}`>6")

2. For inf and Inf
   Specifying the df name before the column name should do the trick
   result = df.query(f"@df.{column}>6")

Below is example working snippet -

import pandas as pd
import numpy as np

literal_columns = ["True", "False"]
for column in literal_columns:
    df = pd.DataFrame(np.random.randint(0, 100, size=(10, 2)),
                  columns=[column, "col1"])
    expected = df[df[column] > 6]
    result = df.query(f"`{column}`>6")
    print(result)

inf_columns = ["inf", "Inf"]
for column in inf_columns:
    df = pd.DataFrame(np.random.randint(0, 100, size=(10, 2)),
                  columns=[column, "col1"])
    expected = df[df[column] > 6]
    result = df.query(f"@df.{column}>6")
    print(result)