我试图读取一个大的csv文件(aprox。6 GB)在熊猫和我得到一个内存错误:
MemoryError Traceback (most recent call last)
<ipython-input-58-67a72687871b> in <module>()
----> 1 data=pd.read_csv('aphro.csv',sep=';')
...
MemoryError:
有什么帮助吗?
当前回答
函数read_csv和read_table几乎是一样的。但在程序中使用read_table函数时，必须分配分隔符“，”。
def get_from_action_data(fname, chunk_size=100000):
reader = pd.read_csv(fname, header=0, iterator=True)
chunks = []
loop = True
while loop:
try:
chunk = reader.get_chunk(chunk_size)[["user_id", "type"]]
chunks.append(chunk)
except StopIteration:
loop = False
print("Iteration is stopped")
df_ac = pd.concat(chunks, ignore_index=True)
其他回答
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.
如果您使用pandas将大文件读入块，然后逐行yield，这是我所做的
import pandas as pd
def chunck_generator(filename, header=False,chunk_size = 10 ** 5):
for chunk in pd.read_csv(filename,delimiter=',', iterator=True, chunksize=chunk_size, parse_dates=[1] ):
yield (chunk)
def _generator( filename, header=False,chunk_size = 10 ** 5):
chunk = chunck_generator(filename, header=False,chunk_size = 10 ** 5)
for row in chunk:
yield row
if __name__ == "__main__":
filename = r'file.csv'
generator = generator(filename=filename)
while True:
print(next(generator))
在使用chunksize选项之前，如果你想确定你想要在@unutbu提到的分块for循环中写入的进程函数，你可以简单地使用nrows选项。
small_df = pd.read_csv(filename, nrows=100)
一旦确定流程块准备好了，就可以将其放入整个数据帧的分块for循环中。
对于大数据，我建议你使用"dask"库，例如:
# Dataframes implement the Pandas API
import dask.dataframe as dd
df = dd.read_csv('s3://.../2018-*-*.csv')
你可以在这里阅读更多的文档。
另一个很好的选择是使用modin，因为所有的功能都与pandas相同，但它利用了分布式数据框架库，如dask。
在我的项目中，另一个高级库是数据表。
# Datatable python library
import datatable as dt
df = dt.fread("s3://.../2018-*-*.csv")
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)
