This method is used to evaluate a Python expression as a string using various back ends. It returns ndarray, numeric scalar, DataFrame, Series.
Syntax : pandas.eval(expr, parser=’pandas’, engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False)
Arguments :
- expr : str or unicode. The expression to evaluate. This string cannot contain any Python
- parser : string, default ‘pandas’, {‘pandas’, ‘python’}.
- engine : string or None, default ‘numexpr’, {‘python’, ‘numexpr’}
- truediv : bool, optional, Whether to use true division, like in Python >= 3
- level : int, optional, The number of prior stack frames to traverse and add to the current scope. Most users will **not** need to change this parameter.
Below is the implementation of the above method with some examples :
Example 1 :
Python3
# importing package import pandas # evaluate the expressions given # in form of string print(pandas.eval("2+3")) print(pandas.eval("2+3*(5-2)")) |
Output :
5 11
Example 2 :
Python3
# importing package import pandas # creating data data = pandas.DataFrame({ "Student": ["A", "B", "C", "D"], "Physics": [89,34,23,56], "Chemistry": [34,56,98,56], "Math": [34,94,50,59] }) # view data display(data) # adding new column by existing # columns evaluation data['Total']=pandas.eval("data.Physics+data.Chemistry+data.Math") # view data display(data) # adding new column by existing # columns evaluation pandas.eval("Avg=data.Total/3",target=data,inplace=True) # view data display(data) |
Output :
