下面是一个例子:

chunkTemp = []
queryTemp = []
query = pd.DataFrame()

for chunk in pd.read_csv(file, header=0, chunksize=<your_chunksize>, iterator=True, low_memory=False):

    #REPLACING BLANK SPACES AT COLUMNS' NAMES FOR SQL OPTIMIZATION
    chunk = chunk.rename(columns = {c: c.replace(' ', '') for c in chunk.columns})

    #YOU CAN EITHER: 
    #1)BUFFER THE CHUNKS IN ORDER TO LOAD YOUR WHOLE DATASET 
    chunkTemp.append(chunk)

    #2)DO YOUR PROCESSING OVER A CHUNK AND STORE THE RESULT OF IT
    query = chunk[chunk[<column_name>].str.startswith(<some_pattern>)]   
    #BUFFERING PROCESSED DATA
    queryTemp.append(query)

#!  NEVER DO pd.concat OR pd.DataFrame() INSIDE A LOOP
print("Database: CONCATENATING CHUNKS INTO A SINGLE DATAFRAME")
chunk = pd.concat(chunkTemp)
print("Database: LOADED")

#CONCATENATING PROCESSED DATA
query = pd.concat(queryTemp)
print(query)

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.

如果有人还在寻找这样的东西，我发现这个名为modin的新库可以提供帮助。它使用分布式计算来帮助读取。这里有一篇不错的文章将其功能与熊猫进行了比较。它本质上使用与熊猫相同的功能。

import modin.pandas as pd
pd.read_csv(CSV_FILE_NAME)

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)

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

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。