我想从目录中读取几个CSV文件到熊猫，并将它们连接到一个大的DataFrame。不过我还没弄明白。以下是我目前所掌握的:
import glob
import pandas as pd
# Get data file names
path = r'C:\DRO\DCL_rawdata_files'
filenames = glob.glob(path + "/*.csv")
dfs = []
for filename in filenames:
dfs.append(pd.read_csv(filename))
# Concatenate all data into one DataFrame
big_frame = pd.concat(dfs, ignore_index=True)
我想我在for循环中需要一些帮助?
当前回答
import os
os.system("awk '(NR == 1) || (FNR > 1)' file*.csv > merged.csv")
其中NR和FNR表示正在处理的行号。
FNR是每个文件中的当前行。
NR == 1包含第一个文件的第一行(头文件)，而FNR > 1跳过每个后续文件的第一行。
其他回答
考虑使用convtools库，它提供了大量数据处理原语，并在底层生成简单的临时代码。 它不应该比熊猫/极地快，但有时它可以。
例如，你可以连接到一个CSV文件进一步重用-这是代码:
import glob
from convtools import conversion as c
from convtools.contrib.tables import Table
import pandas as pd
def test_pandas():
df = pd.concat(
(
pd.read_csv(filename, index_col=None, header=0)
for filename in glob.glob("tmp/*.csv")
),
axis=0,
ignore_index=True,
)
df.to_csv("out.csv", index=False)
# took 20.9 s
def test_convtools():
table = None
for filename in glob.glob("tmp/*.csv"):
table_ = Table.from_csv(filename, header=False)
if table is None:
table = table_
else:
table = table.chain(table_)
table.into_csv("out_convtools.csv", include_header=False)
# took 15.8 s
当然，如果你只是想获得一个数据帧而不写入一个连接文件，它将相应地花费4.63秒和10.9秒(pandas在这里更快，因为它不需要压缩列来写入回)。
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')])
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.
如果多个CSV文件被压缩，您可以使用zipfile读取所有文件并按以下方式连接:
import zipfile
import pandas as pd
ziptrain = zipfile.ZipFile('yourpath/yourfile.zip')
train = []
train = [ pd.read_csv(ziptrain.open(f)) for f in ziptrain.namelist() ]
df = pd.concat(train)
darindaCoder的答案的替代方案:
path = r'C:\DRO\DCL_rawdata_files' # use your path
all_files = glob.glob(os.path.join(path, "*.csv")) # advisable to use os.path.join as this makes concatenation OS independent
df_from_each_file = (pd.read_csv(f) for f in all_files)
concatenated_df = pd.concat(df_from_each_file, ignore_index=True)
# doesn't create a list, nor does it append to one
