您可以将数据读入为块，并将每个块保存为pickle。

import pandas as pd 
import pickle

in_path = "" #Path where the large file is
out_path = "" #Path to save the pickle files to
chunk_size = 400000 #size of chunks relies on your available memory
separator = "~"

reader = pd.read_csv(in_path,sep=separator,chunksize=chunk_size, 
                    low_memory=False)    


for i, chunk in enumerate(reader):
    out_file = out_path + "/data_{}.pkl".format(i+1)
    with open(out_file, "wb") as f:
        pickle.dump(chunk,f,pickle.HIGHEST_PROTOCOL)

在下一步中，读入pickle并将每个pickle附加到所需的数据框架中。

import glob
pickle_path = "" #Same Path as out_path i.e. where the pickle files are

data_p_files=[]
for name in glob.glob(pickle_path + "/data_*.pkl"):
   data_p_files.append(name)


df = pd.DataFrame([])
for i in range(len(data_p_files)):
    df = df.append(pd.read_pickle(data_p_files[i]),ignore_index=True)

除了上面的答案，对于那些想要处理CSV然后导出到CSV、parquet或SQL的人来说，d6tstack是另一个不错的选择。您可以加载多个文件，它处理数据模式更改(添加/删除列)。核心支持已经被剔除。

def apply(dfg):
    # do stuff
    return dfg

c = d6tstack.combine_csv.CombinerCSV([bigfile.csv], apply_after_read=apply, sep=',', chunksize=1e6)

# or
c = d6tstack.combine_csv.CombinerCSV(glob.glob('*.csv'), apply_after_read=apply, chunksize=1e6)

# output to various formats, automatically chunked to reduce memory consumption
c.to_csv_combine(filename='out.csv')
c.to_parquet_combine(filename='out.pq')
c.to_psql_combine('postgresql+psycopg2://usr:pwd@localhost/db', 'tablename') # fast for postgres
c.to_mysql_combine('mysql+mysqlconnector://usr:pwd@localhost/db', 'tablename') # fast for mysql
c.to_sql_combine('postgresql+psycopg2://usr:pwd@localhost/db', 'tablename') # slow but flexible

您可以将数据读入为块，并将每个块保存为pickle。

import pandas as pd 
import pickle

in_path = "" #Path where the large file is
out_path = "" #Path to save the pickle files to
chunk_size = 400000 #size of chunks relies on your available memory
separator = "~"

reader = pd.read_csv(in_path,sep=separator,chunksize=chunk_size, 
                    low_memory=False)    


for i, chunk in enumerate(reader):
    out_file = out_path + "/data_{}.pkl".format(i+1)
    with open(out_file, "wb") as f:
        pickle.dump(chunk,f,pickle.HIGHEST_PROTOCOL)

在下一步中，读入pickle并将每个pickle附加到所需的数据框架中。

import glob
pickle_path = "" #Same Path as out_path i.e. where the pickle files are

data_p_files=[]
for name in glob.glob(pickle_path + "/data_*.pkl"):
   data_p_files.append(name)


df = pd.DataFrame([])
for i in range(len(data_p_files)):
    df = df.append(pd.read_pickle(data_p_files[i]),ignore_index=True)

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)

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。

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)