pyspark.pandas.DataFrame.eval¶

DataFrame.eval(expr: str, inplace: bool = False) → Union[DataFrame, Series, None][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.

Returns

The result of the evaluation.

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.

Examples

>>> df = ps.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

pyspark.pandas.DataFrame.diff

pyspark.pandas.DataFrame.add_prefix