Question

我正在编写一个脚本，通过pandas将CSV中的值读入DataFrame。值“A”和“B”是等式的输入。该等式是从外部程序的XML输出文件中获得的。该等式为DataFrame逐行提供“A”和“B”的结果，并将这些结果放回原始DataFrame中。

如果我做了一个函数定义，明确地在定义中写出等式，并返回那个等式，事情就好了。如，

import pandas as pd

dataFrame = pd.read_csv() # Reads CSV to "dataFrame"
A = dataFrame['A'] # Defines A as row A in "dataFrame"
B = dataFrame['B'] # Defines B as row B in "dataFrame"

def Func(a,b):
    P = 2*a+3*b
    return P

outPut['P'] = Func(A, B) # Assigns a value to each row in "outPut" for each 'A' and 'B' per row of "dataFrame"

然而，我真正想做的是从XML文件“构建”相同的等式，而不是明确地输入它。所以，我基本上从xml文件中提取“术语”和“系数”，并得到方程式的字符串形式。然后我使用 sympy.sympify（）将字符串转换为可执行函数。例如，

import pandas as pd
import sympy as sy
import xml.etree.ElementTree as etree

dataFrame = pd.read_csv() # Reads CSV to "dataFrame"
A = dataFrame['A']  # Defines A as row A in "dataFrame"
B = dataFrame['B']  # Defines B as row B in "dataFrame"

tree = etree.parse('C:\...')
.
..(some XML stuff with etree)
.
equationString = "some code that grabs terms and coefficients from XML file" # Builds equation from XML  'terms' and 'coefficients'

P = sy.sympify(equationString)

def Func(A, B):
    global P    
    return P

outPut['P'] = Func(A, B) # Assigns a value to each row in "outPut" for each 'A' and 'B' per row of "dataFrame"

结果是当我调用在dataFrame上执行这个等式时，文字方程被复制到“outPut”DF而不是每个'A'和'B'的逐行结果。我不明白为什么Python会以不同的方式看待这些代码示例，也不了解如何从第一个示例中获得我想要的结果。由于某种原因， sympify（）结果不可执行。当我使用 eval（）时，似乎也会出现同样的情况。

Answer 1

阐述我的评论，以下是如何使用lambdify

解决问题

In [1]: import sympy as sp

In [2]: import pandas as pd

In [3]: import numpy as np

In [4]: df = pd.DataFrame(np.random.randn(5,2), columns=['A', 'B'])

In [5]: equationString = "2*A+3*B"

In [7]: expr = sp.S(equationString)

In [8]: expr
Out[8]: 2*A + 3*B

In [10]: f = sp.lambdify(sp.symbols("A B"), expr, modules="numpy")

In [11]: f(df['A'],df['B'])
Out[11]: 
0   -2.779739
1   -1.176580
2    3.911066
3    1.888639
4    0.745293
dtype: float64

In [12]: 2*df["A"]+3*df["B"] - f(df["A"],df["B"])
Out[12]: 
0    0
1    0
2    0
3    0
4    0
dtype: float64

根据xml文件中遇到的表达式，sympy可能有点矫枉过正。以下是eval

的使用方法

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: df = pd.DataFrame(np.ran
np.random  np.rank    

In [3]: df = pd.DataFrame(np.random.randn(5, 2), columns=['A', 'B'])

In [4]: equationString = "2*A+3*B"

In [5]: f = eval("lambda A, B: "+equationString)

In [6]: f(df['A'],df['B'])
Out[6]: 
0    1.094797
1   -1.942295
2   -5.181502
3    1.888990
4    3.069017
dtype: float64

In [7]: 2*df["A"]+3*df["B"] - f(df["A"],df["B"])
Out[7]: 
0    0
1    0
2    0
3    0
4    0
dtype: float64

Sympify（）和Eval（）方程式不在函数内执行

1 个答案: