import pandas as pd
import glob

path = r'C:\DRO\DCL_rawdata_files' # use your path
file_path_list = glob.glob(path + "/*.csv")

file_iter = iter(file_path_list)

list_df_csv = []
list_df_csv.append(pd.read_csv(next(file_iter)))

for file in file_iter:
    lsit_df_csv.append(pd.read_csv(file, header=0))
df = pd.concat(lsit_df_csv, ignore_index=True)

这是如何使用协作实验室谷歌驱动器:

import pandas as pd
import glob

path = r'/content/drive/My Drive/data/actual/comments_only' # Use your path
all_files = glob.glob(path + "/*.csv")

li = []

for filename in all_files:
    df = pd.read_csv(filename, index_col=None, header=0)
    li.append(df)

frame = pd.concat(li, axis=0, ignore_index=True,sort=True)
frame.to_csv('/content/drive/onefile.csv')

灵感来自MrFun的回答:

import glob
import pandas as pd

list_of_csv_files = glob.glob(directory_path + '/*.csv')
list_of_csv_files.sort()

df = pd.concat(map(pd.read_csv, list_of_csv_files), ignore_index=True)

注:

By default, the list of files generated through glob.glob is not sorted. On the other hand, in many scenarios, it's required to be sorted e.g. one may want to analyze number of sensor-frame-drops v/s timestamp. In pd.concat command, if ignore_index=True is not specified then it reserves the original indices from each dataframes (i.e. each individual CSV file in the list) and the main dataframe looks like timestamp id valid_frame 0 1 2 . . . 0 1 2 . . . With ignore_index=True, it looks like: timestamp id valid_frame 0 1 2 . . . 108 109 . . . IMO, this is helpful when one may want to manually create a histogram of number of frame drops v/s one minutes (or any other duration) bins and want to base the calculation on very first timestamp e.g. begin_timestamp = df['timestamp'][0] Without, ignore_index=True, df['timestamp'][0] generates the series containing very first timestamp from all the individual dataframes, it does not give just a value.

如果出现未命名列的问题，请使用此代码沿x轴合并多个CSV文件。

import glob
import os
import pandas as pd

merged_df = pd.concat([pd.read_csv(csv_file, index_col=0, header=0) for csv_file in glob.glob(
        os.path.join("data/", "*.csv"))], axis=0, ignore_index=True)

merged_df.to_csv("merged.csv")

如果你想递归搜索(Python 3.5或以上)，你可以这样做:

from glob import iglob
import pandas as pd

path = r'C:\user\your\path\**\*.csv'

all_rec = iglob(path, recursive=True)     
dataframes = (pd.read_csv(f) for f in all_rec)
big_dataframe = pd.concat(dataframes, ignore_index=True)

请注意，最后三行可以用一行表示:

df = pd.concat((pd.read_csv(f) for f in iglob(path, recursive=True)), ignore_index=True)

你可以在这里找到**的文档。另外，我使用了iglob而不是glob，因为它返回的是迭代器而不是列表。

编辑:多平台递归功能:

你可以把上面的内容包装成一个多平台函数(Linux, Windows, Mac)，所以你可以这样做:

df = read_df_rec('C:\user\your\path', *.csv)

函数如下:

from glob import iglob
from os.path import join
import pandas as pd

def read_df_rec(path, fn_regex=r'*.csv'):
    return pd.concat((pd.read_csv(f) for f in iglob(
        join(path, '**', fn_regex), recursive=True)), ignore_index=True)

另一个带有列表理解的一行程序，允许使用read_csv参数。

df = pd.concat([pd.read_csv(f'dir/{f}') for f in os.listdir('dir') if f.endswith('.csv')])