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 byquery()
.
- 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 0 1 10 11 1 2 8 10 2 3 6 9 3 4 4 8 4 5 2 7 >>> df A B 0 1 10 1 2 8 2 3 6 3 4 4 4 5 2
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