python - 使用 chunksize 迭代地获取推断的数据帧类型

Question

How can I use pd.read_csv() to iteratively chunk through a file and retain the dtype and other meta-information as if I read in the entire dataset at once?

我需要读入一个太大而无法放入内存的数据集。我想使用 pd.read_csv 导入文件，然后立即将 block 附加到 HDFStore 中。但是，数据类型推断对后续 block 一无所知。

如果存储在表中的第一个 block 仅包含 int 而后续 block 包含 float，则会引发异常。因此，我需要首先使用 read_csv 遍历数据帧并保留最高推断类型。此外，对于对象类型，我需要保留最大长度，因为它们将作为字符串存储在表中。

是否有一种 pandonic 方法可以只保留这些信息而不读取整个数据集？

Answer 1

我没想到会这么直观，否则我不会发布这个问题。但再一次， Pandas 让事情变得轻而易举。但是，保留这个问题，因为此信息可能对其他处理大数据的人有用:

In [1]: chunker = pd.read_csv('DATASET.csv', chunksize=500, header=0)

# Store the dtypes of each chunk into a list and convert it to a dataframe:

In [2]: dtypes = pd.DataFrame([chunk.dtypes for chunk in chunker])

In [3]: dtypes.values[:5]
Out[3]:
array([[int64, int64, int64, object, int64, int64, int64, int64],
       [int64, int64, int64, int64, int64, int64, int64, int64],
       [int64, int64, int64, int64, int64, int64, int64, int64],
       [int64, int64, int64, int64, int64, int64, int64, int64],
       [int64, int64, int64, int64, int64, int64, int64, int64]], dtype=object)

# Very cool that I can take the max of these data types and it will preserve the hierarchy:

In [4]: dtypes.max().values
Out[4]: array([int64, int64, int64, object, int64, int64, int64, int64], dtype=object)

# I can now store the above into a dictionary:

types = dtypes.max().to_dict()

# And pass it into pd.read_csv fo the second run:

chunker = pd.read_csv('tree_prop_dset.csv', dtype=types, chunksize=500)