Python pd.iterrows（）消耗所有内存并给出一个错误（进程结束，退出代码137（被信号9:SIGKILL中断））

我有一个csv文件，有超过750000行和2列（序列号，状态）
序列号是从0到750000的序列号，状态为0或1
我正在pandas中读取csv文件，然后读取与SN同名的.npy文件，然后将.npy文件附加到名为（x_train，x_val）的两个列表中
x_val应为2000元素，其中700应为state=1，其余为state=0。 剩下的就交给x_火车吧
问题是，在读取约190000行后，进程停止，RAM被消耗（PC RAM=32 GB）

我的代码是：

nodules_path = "~/cropped_nodules/"
nodules_csv = pandas.read_csv("~/cropped_nodules_2.csv")

positive = 0
negative = 0
x_val = []
x_train = []
y_train = []
y_val = []

for nodule in nodules_csv.iterrows():

    if nodule.state == 1 and positive <= 700 and len(x_val) <= 2000 :
        positive += 1
        x_val_img = str(nodule.SN) + ".npy"
        x_val.append(np.load(os.path.join(nodules_path,x_val_img)))
        y_val.append(nodule.state)

    elif nodule.state == 0 and negative <= 1300 and len(x_val) <= 2000:
        x_val_img = str(nodule.SN) + ".npy"
        negative += 1
        x_val.append(np.load(os.path.join(nodules_path,x_val_img)))
        y_val.append(nodule.state)

    else:

        if len(x_train) % 10000 == 0:
            gc.collect()
            print("gc done")
        x_train_img = str(nodule.SN) + ".npy"
        x_train.append(np.load(os.path.join(nodules_path,x_train_img)))
        y_train.append(nodule.state)
        print("x_train len= ", len(x_train))
        print("Size of list1: " + str(sys.getsizeof(x_train)) + "bytes")

结果是：

[ Top 10 ]
/home/mustafa/.local/lib/python3.8/site-packages/numpy/lib/format.py:741: size=11.3 GiB, count=204005, average=58.2 KiB
/home/mustafa/.local/lib/python3.8/site-packages/numpy/lib/format.py:771: size=4781 KiB, count=102002, average=48 B
/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py:4855: size=2391 KiB, count=102000, average=24 B
/home/mustafa/home/mustafa/project/LUNAMASK/nodule_3D_CNN.py:84: size=806 KiB, count=2, average=403 KiB
/home/mustafa/home/mustafa/project/LUNAMASK/nodule_3D_CNN.py:85: size=805 KiB, count=1, average=805 KiB
/usr/local/lib/python3.8/dist-packages/pandas/io/parsers.py:2056: size=78.0 KiB, count=2305, average=35 B
/usr/lib/python3.8/abc.py:102: size=42.5 KiB, count=498, average=87 B
/home/mustafa/.local/lib/python3.8/site-packages/numpy/core/_asarray.py:83: size=41.6 KiB, count=757, average=56 B
/usr/local/lib/python3.8/dist-packages/pandas/core/series.py:512: size=37.5 KiB, count=597, average=64 B
/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py:1880: size=16.5 KiB, count=5, average=3373 B

---

我建议你看看@XtianP谢谢你的建议，我试着实现它如下：使用pandas.read_csv（“/home/mustafa/project/LUNA16/cropped_oblosis_2.csv”，chunksize=chunksize）作为reader:for chunk in reader:for index，nomble in chunk.iterrows（）：但仍然存在相同的问题x_train len=190142列表大小1:1671784字节进程已完成，退出代码137（被信号9:SIGKILL中断）是否可以将

append

s替换为追加到文件？@accumulation没有问题，但我应该将其保存到什么类型的文件？因为我需要再次加载它并将其发送给CNN。我正在从npy文件中读取3D IMG，并将IMG附加到列表中以创建训练和测试数据。如果我理解正确，您的结节已经在单独的文件中。与其将它们全部加载以将它们添加到x_val中，为什么不只将对象的路径放入x_val中？然后，只有在需要的时候，才一个接一个地懒洋洋地加载对象？

with pandas.read_csv("~/cropped_nodules_2.csv", chunksize=chunksize) as reader:
    for chunk in reader:
        for index, nodule in chunk.iterrows():
            if nodule.state == 1 and positive <= 700 and len(x_val) <= 2000 :
........

import tracemalloc

tracemalloc.start()

# ... run your application ...

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

print("[ Top 10 ]")
for stat in top_stats[:10]:
    print(stat)

[ Top 10 ]
/home/mustafa/.local/lib/python3.8/site-packages/numpy/lib/format.py:741: size=11.3 GiB, count=204005, average=58.2 KiB
/home/mustafa/.local/lib/python3.8/site-packages/numpy/lib/format.py:771: size=4781 KiB, count=102002, average=48 B
/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py:4855: size=2391 KiB, count=102000, average=24 B
/home/mustafa/home/mustafa/project/LUNAMASK/nodule_3D_CNN.py:84: size=806 KiB, count=2, average=403 KiB
/home/mustafa/home/mustafa/project/LUNAMASK/nodule_3D_CNN.py:85: size=805 KiB, count=1, average=805 KiB
/usr/local/lib/python3.8/dist-packages/pandas/io/parsers.py:2056: size=78.0 KiB, count=2305, average=35 B
/usr/lib/python3.8/abc.py:102: size=42.5 KiB, count=498, average=87 B
/home/mustafa/.local/lib/python3.8/site-packages/numpy/core/_asarray.py:83: size=41.6 KiB, count=757, average=56 B
/usr/local/lib/python3.8/dist-packages/pandas/core/series.py:512: size=37.5 KiB, count=597, average=64 B
/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py:1880: size=16.5 KiB, count=5, average=3373 B