问题

I've translated the code-related portion of your text as requested:

所以我一直在尝试编写一个通用函数，帮助我根据历史数据生成回归结果，我不想使用`statsmodels`的`.predict`函数，因为它不会让我有机会生成任何最佳拟合线，当给定任何因子和任何依赖变量来拟合时。
但问题是，当我尝试使用OLS时，它一直给我报错：

TypeError: 'float' object is not iterable

但这令人困惑，因为我尝试将我的依赖变量的类型转换为`list`和`nd.array`，但它总是变成`float`类型。我不知道如何解决这个问题。

为了澄清，我将在这里放置我的代码：

```python
#############回归###################
import csv
from scipy import stats
import statsmodels.api as sm
from scipy.optimize import curve_fit
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import adfuller
from sklearn.model_selection import train_test_split
from pmdarima.arima.utils import ndiffs
# 导入库
import pmdarima as pm
from pmdarima import auto_arima
# 在训练集上拟合SARIMAX(0, 1, 1)x(2, 1, 1, 12)
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose

#首先，使用GDP作为三个股票因素
GDP = list(df1['GDP'])
GDP = GDP[:len(stockdf)]
GDP.reverse()
print(GDP)

def param(factor, stock):
    print(type(sm.add_constant(factor)))
    model = sm.OLS(list(stock), sm.add_constant(factor))
    
    # model = sm.OLS(factor, sm.add_constant(stock))
    p = model.fit().params.tolist()
    return p[1]*x + p[0]

#def findRegression(factor,stock):
    #return list(map(param,factor,stock))
#model = findRegression(GDP, stockdf['上证指数']
print(type(stockdf['SSE']))
stock = list(stockdf['SSE'])
print(type(stock))
model = list(map(param, GDP, stock))
#print(len(stockdf['上证指数']))
#print(stockdf['上证指数'])

此外，末尾的print(type())会给出以下结果：

<class 'pandas.core.series.Series'>
<class 'list'>
<class 'numpy.ndarray'>

但当我尝试在param(factor, stock)函数中添加type(stock)时，它告诉我它是class 'float'。

任何帮助将不胜感激！

Please note that code formatting and syntax may require additional adjustments depending on your specific Python environment and the libraries being used.

<details>
<summary>英文:</summary>

So I&#39;ve been trying to write a general function to help me generate the regression results given historical data, and I didn&#39;t wanted to use the `.predict` function of statsmodels because it wouldn&#39;t give me the leeway of generating any best-fit linera line when given any factor and any dependent-variable to fit.
But the thing is, when I try to use OLS, it keeps giving me this error message:

TypeError: 'float' object is not iterable

But this is confusing because I tried to convert the type of my dependent variable to `list`list, and to `nd.array` but somehow it keeps changing into `float` type. And I don't know how to solve this.

For clarification, I am going to put my code here:

#############REGRESSION###################
import csv
from scipy import stats
import statsmodels.api as sm
from scipy.optimize import curve_fit
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import adfuller
from sklearn.model_selection import train_test_split
from pmdarima.arima.utils import ndiffs

Import the library

import pmdarima as pm
from pmdarima import auto_arima

Fit a SARIMAX(0, 1, 1)x(2, 1, 1, 12) on the training set

from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose

#First, three stock factors with GDP
GDP=list(df1['GDP'])
GDP=GDP[:len(stockdf)]
GDP.reverse()
print(GDP)

def param(factor,stock):
print(type(sm.add_constant(factor)))
model = sm.OLS(list(stock), sm.add_constant(factor))

#model=sm.OLS(factor,sm.add_constant(stock))
p=model.fit().params.tolist()
return p[1]*x+p[0]

#def findRegression(factor,stock):
#return list(map(param,factor,stock))
#model=findRegression(GDP,stockdf['上证指数']
print(type(stockdf['SSE']))
stock=list(stockdf['SSE'])
print(type(stock))
model=list(map(param,GDP,stock))
#print(len(stockdf['上证指数']))
#print(stockdf['上证指数'])

also, the `print(type())` at the end gives me: 

<class 'pandas.core.series.Series'>
<class 'list'>
<class 'numpy.ndarray'>

but when I try to add `type(stock)` in my `param(factor, stock)` function, it tells me it is `class 'float'`.

Any help would be very much appreciated!

</details>


# 答案1
**得分**: 0

model=list(map(param,GDP,stock))

当你在一个函数上使用map并传入一个列表时，（我鼓励你阅读map的文档），map会为你的列表的每个元素“映射”它。

因此，当你执行map(param,GDP,stock)时，你并没有将stock列表作为参数传递给你的函数“param”，而是传递了stock[0]，stock[1]... stock[len(stock)-1]。

示例：

```python
def testing(val1, val2):
    print(val1)
    print(val2)

a = ["1","2"]
b = ["3","4"]

model=list(map(testing,a,b))

打印结果：

1 3
2 4

因为函数testing会针对每个数组的每个元素被调用，因此出现了浮点数类型的问题，当你执行list(stock)时，实际上相当于执行list(2.5)（这是一个示例），这是不可能的。

希望这清楚了。

model=list(map(param,GDP,stock))

def testing(val1, val2):
    print(val1)
    print(val2)



a = ["1","2"]
b = ["3","4"]

model=list(map(testing,a,b))

1 3
2 4