如何使用def函数变量函数遍历pandas df [英] How to iterate over pandas df with a def function variable function
问题描述
我希望您能在这里为我提供指导，因为我有些迷路，并且对python编程没有真正的经验.
i hope you can guide me here, cause i am a little lost and not really experienced in python programing.
我的目标:我必须为给定的化合物"计算加合物"，两者均表示麻木，但是对于化合物"来说，有46种不同的加合物".
My goal: i have to calculate the "adducts" for a given "Compound", both represents numbes, but for eah "Compound" there are 46 different "Adducts".
每个加合物的计算如下:
Each adduct is calculated as follow:
加合物1 = [精确质量* M/电荷+加合物质量]
Adduct 1 = [Exact_mass*M/Charge + Adduct_mass]
其中，exact_mass =数字，M和Charge =根据每种加合物类型的数量(1、2、3等)，Adduct_mass =根据每种加合物的数量(正或负).
where exact_mass = number, M and Charge = number (1, 2, 3, etc) according to each type of adduct, Adduct_mass = number (positive or negative) according to each adduct.
我的数据:2个数据帧.一种带有加合物的名称，M，Charge，Adduct_mass. 另一个对应于我要迭代的化合物的Compound_name和Exact_mass(我只是放了一个小的数据集)
My data: 2 data frames. One with the Adducts names, M, Charge, Adduct_mass. The other one correspond to the Compound_name and Exact_mass of the Compounds i want to iterate over (i just put a small data set)
加成:df_al
import pandas as pd
data = [["M+3H", 3, 1, 1.007276], ["M+3Na", 3, 1, 22.989], ["M+H", 1, 1, 1.007276], ["2M+H", 1, 2, 1.007276], ["M-3H", 3, 1, -1.007276]]
df_al = pd.DataFrame(data, columns=["Ion_name", "Charge", "M", "Adduct_mass"])
化合物:df
import pandas as pd
data1 = [[1, "C3H64O7", 596.465179], [2, "C30H42O7", 514.293038], [4, "C44H56O8", 712.397498], [4, "C24H32O6S", 448.191949], [5, "C20H28O3", 316.203834]]
df = pd.DataFrame(data1, columns=["CdId", "Formula", "exact_mass"])
我的代码:
df_name = df_al["Ion_name"]
df_mass = df_al["adduct_mass"]
df_div = df_al["Div"]
df_M = df_al["M"]
然后我为每个离子定义一个使用索引来设置每个值的函数
then i defined for each ion a function using the index to set each value
def A0(x):
return x*df_M[0]/df_div[0] + df_mass[0]
def A1(x):
return x*df_M[1]/df_div[1] + df_mass[1]
def A2(x):
return x*df_M[2]/df_div[2] + df_mass[2]
def A3(x):
return x*df_M[3]/df_div[3] + df_mass[3]
def A4(x):
return x*df_M[4]/df_div[4] + df_mass[4]
def A5(x):
return x*df_M[5]/df_div[5] + df_mass[5]
def A6(x):
return x*df_M[6]/df_div[6] + df_mass[6]
依此类推，直到功能A46
and so on, till func A46
然后我将每个函数映射到每个化合物，并将每个值存储在df的新列中(这是我的另一个问题:如何在与相应的功能?)
then i .map each function to to each of the Compounds and i store each value in a new column in the df (Here is my other problem: how to add the name of each ion on the top of each column matching the corresponding function?)
df[df_name.loc[0]] = df["exact_mass"].map(A0)
df[df_name.loc[1]] = df["exact_mass"].map(A1)
df[df_name.loc[2]] = df["exact_mass"].map(A2)
df[df_name.loc[3]] = df["exact_mass"].map(A3)
df[df_name.loc[4]] = df["exact_mass"].map(A4)
df[df_name.loc[5]] = df["exact_mass"].map(A5)
df[df_name.loc[6]] = df["exact_mass"].map(A6)
. . . 以此类推，直到应用A46.
. . . and so on till applying A46.
我想这是定义函数的简单方法，它会根据每个离子而变化(也许是forloop?)，也是应用函数并获得相应名称而不用.loc的简单方法.
I thing it could be a simpler way to def the function and that it changes according each ion (maybe a forloop?) and also a simpler way to apply the function and get the corresponding name without .loc each one.
谢谢！
推荐答案
一种方法是将functools.partial与map一起使用.
One way is using functools.partial together with map.
鉴于函数调用的规律性，我会尝试以下方法:
Given the regularity of your function calls, I would try something like:
from funtools import partial
def func(x, n):
return x*df_M[n]/df_div[n] + df_mass[n]
for i in range(max_i): #change max_i with the integer you need
df[df_name.loc[i]] = map(partial(func, n=i), df["exact_mass"])
#df[df_name.loc[i]] = df["exact_mass"].map(partial(func, n=i)) should work as well
此处的更多信息 https://docs.python.org/3.7/library/functools.html#functools.partial
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