safe-evaluation 0.1.9

package solves expressions

safe_evaluation

safe_evaluation - is an implementation of polish notation algorithm for mathematical and dataframe operations

how to use

1. Installation

   pip install safe-evaluation
   

2. Tutorial

   • import evaluation method

   from safe_evaluation import solve_expression
   

   • send string that you want to evaluate

   result = solve_expression(command="2 + 2")
   

   • method will return result

   print(result)
   

3. Examples of usage

   • solve_expression(command="2 + 2")  # 4
     

   • solve_expression(command="lambda x: x * 2")  # lambda x: x * 2
     

   • solve_expression(command="2 * x - y", local={"x": 2, "y": 3})  # 1
     

   • import pandas as pd
     
     df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
     solve_expression(command="${col1} + ${col2}", df=df)  # 0   4
                                                             1   6
                                                             dtype: int64
     

   • solve_expression(command="list(map(lambda x, y: x * 2 + y, range(5), range(3, 8)))")  # [3,6,9,12,15]
     

   • solve_expression(command="list(map(lambda x: x + y, [0,1,2,3,4]))", local={"y": 10})  # [10,11,12,13,14]
     

   • import pandas as pd
     
     df = pd.DataFrame(data={'col1': [1, 2, 4, 7], 'col2': [3, 4, 8, 9]})
     solve_expression(command="np.mean(${col1}) + np.mean(${col2})", df=df)  # 9.5
     

   • import pandas as pd
     
     df = pd.DataFrame(data={'col1': [1, 2, 4, 7], 'col2': [3, 4, 8, 9]})
     solve_expression(command="${col1}.apply(lambda v: v ** 2 > 0).all()", df=df)  # True
     

   • import pandas as pd
     
     df = pd.DataFrame(data={'col1': [1, 2, 4, 7], 'col2': [3, 4, 8, 9]})
     solve_expression(command="${col1}.apply(lambda v: 1 if v < 3 else 2)", df=df)  # 0    1
                                                                                      1    1
                                                                                      2    2
                                                                                      3    2
                                                                                      Name: col1, dtype: int64
     

   • from datetime import datetime
     
     import pandas as pd
     
     dates = [datetime(year=2022, month=11, day=11 + i) for i in range(7)]
     df = pd.DataFrame(data={'dates': dates})
     solve_expression(command="${dates}.dt.dayofweek", df=df)  # 0    4
                                                                 1    5
                                                                 2    6
                                                                 3    0
                                                                 4    1
                                                                 5    2
                                                                 6    3
                                                                 Name: dates, dtype: int64
     

   • range = solve_expression(command="pd.date_range(start='2021-02-05', end='2021-03-05', freq='1D')"")
     len(range)  # 29
     

4. Supported operations

   • in is not supported yet
   • comparation (<=, <, > , >=, !=, ==)
   • unary (+, -)
   • boolean (~, &, |, ^)
   • binary (+, -, /, *, //, %, **)

5. Supported functions

   • map, filter, list, range
   • bool, int, float, complex, str
   • numpy module functions (np.mean, etc.)
   • pandas module functions (pd.date_range, etc.)
   • anonymous functions