你可以尝试sframe，它和pandas有相同的语法，但是允许你操作比你的RAM大的文件。

如果你有一个csv文件，有数百万个数据条目，你想要加载完整的数据集，你应该使用dask_cudf，

import dask_cudf as dc

df = dc.read_csv("large_data.csv")

分块不应该总是解决这个问题的第一步。

Is the file large due to repeated non-numeric data or unwanted columns? If so, you can sometimes see massive memory savings by reading in columns as categories and selecting required columns via pd.read_csv usecols parameter. Does your workflow require slicing, manipulating, exporting? If so, you can use dask.dataframe to slice, perform your calculations and export iteratively. Chunking is performed silently by dask, which also supports a subset of pandas API. If all else fails, read line by line via chunks. Chunk via pandas or via csv library as a last resort.

该错误表明机器没有足够的内存来读取整个 CSV一次转换成一个数据帧。假设您不需要整个数据集 内存，避免这个问题的一种方法是处理CSV在 Chunks(通过指定chunksize参数):

chunksize = 10 ** 6
for chunk in pd.read_csv(filename, chunksize=chunksize):
    process(chunk)

chunksize参数指定每个块的行数。 (当然，最后一个块可能包含少于块大小的行。)

熊猫>= 1.2

Read_csv with chunksize返回一个上下文管理器，像这样使用:

chunksize = 10 ** 6
with pd.read_csv(filename, chunksize=chunksize) as reader:
    for chunk in reader:
        process(chunk)

参见 GH38225

我是这样说的:

chunks=pd.read_table('aphro.csv',chunksize=1000000,sep=';',\
       names=['lat','long','rf','date','slno'],index_col='slno',\
       header=None,parse_dates=['date'])

df=pd.DataFrame()
%time df=pd.concat(chunk.groupby(['lat','long',chunk['date'].map(lambda x: x.year)])['rf'].agg(['sum']) for chunk in chunks)

解决方案1:

使用大数据的熊猫

解决方案2:

TextFileReader = pd.read_csv(path, chunksize=1000)  # the number of rows per chunk

dfList = []
for df in TextFileReader:
    dfList.append(df)

df = pd.concat(dfList,sort=False)