Question

希望这不是一个愚蠢的问题，但是我遇到了一些问题，试图利用range()循环从保存的机器学习模型中进行计算，然后将所有计算值累加到结束。每次执行range():循环时，都会返回一个estimatedKwh的numpy数组。我试图将值存储在total_estKwh中，并且如果我从total_estKwh的零开始，如何附加total_estKwh ...然后在range()循环完成后求和？

total_rows = len(data.index)
row_num = 0
total_estKwh = 0

for i in range(total_rows):
    params = np.array(data.iloc[row_num])

    if (params.ndim == 1):
        params = np.array([params])

    estimatedKwh = load_trained_model(weights_path).predict(params)
    print("Estimated kWH:", int(estimatedKwh))

    estimatedKwh = pd.DataFrame(estimatedKwh)
    estimatedKwh.append(total_estKwh)


    row_num += 1

#total_estKwh = pd.DataFrame(total_estKwh)

#print("FINISHED, total Estimated kWH:", total_estKwh)
print(total_estKwh)

代码不起作用...这是我正在尝试的内容...任何提示都可以帮助您

Using TensorFlow backend.
Estimated kWH: 1083
Traceback (most recent call last):
  File "C:\Users\bbartling\Desktop\EC\ecPredic2.py", line 70, in <module>
    estimatedKwh.append(total_estKwh)
  File "C:\Users\bbartling\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\frame.py", line 6211, in append
    sort=sort)
  File "C:\Users\bbartling\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\concat.py", line 225, in concat
    copy=copy, sort=sort)
  File "C:\Users\bbartling\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\concat.py", line 286, in __init__
    raise TypeError(msg)
TypeError: cannot concatenate object of type "<class 'int'>"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid
>>>

Answer 1

（因为评论中的讨论似乎无济于事...）

您不能附加整数，但可以附加列表；因此，只需忘记熊猫数据框并使用列表即可：

1）将total_estKwh初始化为

total_estKwh = []

2）删除estimatedKwh = pd.DataFrame(estimatedKwh)

3）将estimatedKwh.append(total_estKwh)替换为

total_estKwh.append(estimatedKwh)

如果您确实想以熊猫daraframe结尾，可以随时在for循环之后转换列表...

Answer 2

取决于您实际要实现的目标可能是一种方法。

data = pd.DataFrame(np.arange(100))

all_estimates =[]

for i, row in data.iterrows():
    params = row.values

    if (params.ndim == 1):
        params = np.array([params])

    #estimatedKwh = load_trained_model(weights_path).predict(params)
    estimatedKwh = np.array([1.2]) # A dummy value for this example 
    all_estimates.append(estimatedKwh)

df = pd.DataFrame(all_estimates)
total_estKwh = df.sum() # Total for all_estimates

保存范围循环计算中的值

2 个答案: