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updating df.query and df.eval docstrings. resolves #16283 #58749

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merged 6 commits into from
May 31, 2024
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updating df.query and df.eval docstrings. resolves #16283 #58749

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daccc58
updating df.query and df.eval docstrings. resolves #16283
May 17, 2024
0de174a
typo
May 17, 2024
5e0e65b
adding 1 example
May 21, 2024
6e9e58f
changing wording following example added
May 21, 2024
2a176f3
updating 'C C' to 'C&C' for eval
May 23, 2024
22d2c63
updating 'C C' to 'C&C' for query
May 23, 2024
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109 changes: 73 additions & 36 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4574,36 +4574,44 @@ def query(self, expr: str, *, inplace: bool = False, **kwargs) -> DataFrame | No
Examples
--------
>>> df = pd.DataFrame(
... {"A": range(1, 6), "B": range(10, 0, -2), "C C": range(10, 5, -1)}
... {"A": range(1, 6), "B": range(10, 0, -2), "C&C": range(10, 5, -1)}
... )
>>> df
A B C C
A B C&C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query("A > B")
A B C C
A B C&C
4 5 2 6

The previous expression is equivalent to

>>> df[df.A > df.B]
A B C C
A B C&C
4 5 2 6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query("B == `C C`")
A B C C
>>> df.query("B == `C&C`")
A B C&C
0 1 10 10

The previous expression is equivalent to

>>> df[df.B == df["C C"]]
A B C C
>>> df[df.B == df["C&C"]]
A B C&C
0 1 10 10

Using local variable:

>>> local_var = 2
>>> df.query("A <= @local_var")
A B C&C
0 1 10 10
1 2 8 9
"""
inplace = validate_bool_kwarg(inplace, "inplace")
if not isinstance(expr, str):
Expand Down Expand Up @@ -4644,6 +4652,13 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
----------
expr : str
The expression string to evaluate.

You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.

You can refer to column names that are not valid Python variable
names by surrounding them with backticks `````.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
Expand Down Expand Up @@ -4675,14 +4690,16 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:

Examples
--------
>>> df = pd.DataFrame({"A": range(1, 6), "B": range(10, 0, -2)})
>>> df = pd.DataFrame(
... {"A": range(1, 6), "B": range(10, 0, -2), "C&C": range(10, 5, -1)}
... )
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
A B C&C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.eval("A + B")
0 11
1 10
Expand All @@ -4694,35 +4711,55 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
Assignment is allowed though by default the original DataFrame is not
modified.

>>> df.eval("C = A + B")
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df.eval("D = A + B")
A B C&C D
0 1 10 10 11
1 2 8 9 10
2 3 6 8 9
3 4 4 7 8
4 5 2 6 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
A B C&C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
... '''
... C = A + B
... D = A - B
... D = A + B
... E = A - B
... '''
... )
A B C D
0 1 10 11 -9
1 2 8 10 -6
2 3 6 9 -3
3 4 4 8 0
4 5 2 7 3
A B C&C D E
0 1 10 10 11 -9
1 2 8 9 10 -6
2 3 6 8 9 -3
3 4 4 7 8 0
4 5 2 6 7 3

For columns with spaces in their name, you can use backtick quoting.

>>> df.eval("B * `C&C`")
0 100
1 72
2 48
3 28
4 12

Local variables shall be explicitly referenced using ``@``
character in front of the name:

>>> local_var = 2
>>> df.eval("@local_var * A")
0 2
1 4
2 6
3 8
4 10
"""
from pandas.core.computation.eval import eval as _eval

Expand Down