pandas.DataFrame.eval#

DataFrame.eval(expr, *, inplace=False, **kwargs)[source]#

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters

exprstr: The expression string to evaluate.
inplacebool, default False: If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
**kwargs: See the documentation for eval() for complete details on the keyword arguments accepted by query().

Returns

ndarray, scalar, pandas object, or None: The result of the evaluation or None if inplace=True.

See also

DataFrame.query: Evaluates a boolean expression to query the columns of a frame.
DataFrame.assign: Can evaluate an expression or function to create new values for a column.
eval: Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

Examples

>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

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

Use inplace=True to modify the original DataFrame.

>>> df.eval('C = A + B', inplace=True)
>>> df
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7

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

>>> df.eval(
...     '''
... C = A + B
... D = 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