Question

我有一个非常大的csv文件，我无法在内存中完整加载。所以我想逐个阅读，将其转换为numpy数组，然后进行更多处理。

我已经检查过了： Lazy Method for Reading Big File in Python?

但问题是这是一个普通的读者，我无法在csvReader中找到任何指定大小的选项。

另外，因为我想将行转换为numpy数组，所以我不想读取任何一半的行，所以不是指定大小，我想要一些我可以在读者中指定“no of rows”的东西。

是否有任何内置功能或简单的方法。

Answer 1

csv.reader不会将整个文件读入内存。当您遍历reader对象时，它会逐行地遍历文件。因此，您可以像往常一样使用reader，但是在您阅读后的迭代中使用break但是您要阅读的行数很多。您可以在C-code used to implement the reader object。

中看到这一点

Initializer for the reader objecT:
static PyObject *
csv_reader(PyObject *module, PyObject *args, PyObject *keyword_args)
{
    PyObject * iterator, * dialect = NULL;
    ReaderObj * self = PyObject_GC_New(ReaderObj, &Reader_Type);

    if (!self)
        return NULL;

    self->dialect = NULL;
    self->fields = NULL;
    self->input_iter = NULL;
    self->field = NULL;
    // stuff we dont care about here
    // ...
    self->input_iter = PyObject_GetIter(iterator);  // here we save the iterator (file object) we passed in
    if (self->input_iter == NULL) {
        PyErr_SetString(PyExc_TypeError,
                        "argument 1 must be an iterator");
        Py_DECREF(self);
        return NULL;
    }

static PyObject *
Reader_iternext(ReaderObj *self)  // This is what gets called when you call `next(reader_obj)` (which is what a for loop does internally)
{
    PyObject *fields = NULL;
    Py_UCS4 c;
    Py_ssize_t pos, linelen;
    unsigned int kind;
    void *data;
    PyObject *lineobj;

    if (parse_reset(self) < 0)
        return NULL;
    do {
        lineobj = PyIter_Next(self->input_iter);  // Equivalent to calling `next(input_iter)`
        if (lineobj == NULL) {
            /* End of input OR exception */
            if (!PyErr_Occurred() && (self->field_len != 0 ||
                                      self->state == IN_QUOTED_FIELD)) {
                if (self->dialect->strict)
                    PyErr_SetString(_csvstate_global->error_obj,
                                    "unexpected end of data");
                else if (parse_save_field(self) >= 0)
                    break;
            }
            return NULL;
        }

如您所见，next(reader_object)在内部调用next(file_object)。所以你要逐行迭代，而不是将整个内容读入内存。

Answer 2

我用过这个功能。基本思想是使生成器生成文件中的数字。

def iter_loadtxt(filename, delimiter=',', skiprows=0, read_range=None, dtype=float):
    '''
    Read the file line by line and convert it to Numpy array.
    :param delimiter: character
    :param skiprows : int
    :param read_range: [int, int] or None. set it to None and the function will read the whole file.
    :param dtype: type
    '''
    def iter_func():
        with open(filename, 'r') as infile:
            for _ in range(skiprows):
                next(infile)
            if read_range is None:
                for line in infile:
                    line = line.rstrip().split(delimiter)
                    for item in line:
                        yield dtype(item)
            else:
                counter = 0
                for line in infile:
                    if counter < read_range[0]:
                        counter += 1
                    else:
                        counter += 1
                        for item in line:
                            yield dtype(item)

                    if counter >= read_range[1]:
                        break

        iter_loadtxt.rowlength = len(line)

    data = np.fromiter(iter_func(), dtype=dtype)
    data = data.reshape((-1, iter_loadtxt.rowlength))
    return data

在python中读取大型csv文件的行

2 个答案: