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
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
其他回答
import glob
import os
import pandas as pd
df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "my_files*.csv"))))
Dask库可以从多个文件中读取数据帧:
>>> import dask.dataframe as dd
>>> df = dd.read_csv('data*.csv')
(来源:https://examples.dask.org/dataframes/01-data-access.html # Read-CSV-files)
Dask数据框架实现了Pandas数据框架API的一个子集。如果所有的数据都适合内存，你可以调用df.compute()将数据帧转换为Pandas数据帧。
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
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.
