entityMap|blocks|key|18lbn|text|如果您的所有csv文件，然后您可以尝试下面的代码。我已经添加了header=0所以在读完之后csv可以将第一行指定为列名。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3c44c|import+pandas+as+pd
import+glob

path+=+r'C:\DRO\DCL_rawdata_files'+#+use+your+path
all_files+=+glob.glob(path+%2B+"/*.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)|code-block|syntax|javascript^0|6|3|V|8|1A|3|0^^$0|$]|1|@$2|3|4|5|6|7|8|L|9|@$A|M|B|N|C|D]|$A|O|B|P|C|D]|$A|Q|B|R|C|D]]|E|@]|F|$]]|$2|G|4|H|6|I|8|S|9|@]|E|@]|F|$J|K]]]]

If you have same columns in all your <code>csv</code> files then you can try the code below.
I have added <code>header=0</code> so that after reading <code>csv</code> first row can be assigned as the column names.

<pre><code>import pandas as pd
import glob

path = r'C:\DRO\DCL_rawdata_files' # 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)
</code></pre>

entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://stackoverflow.com/a/21232849/3888455|blocks|key|56pil|text|darindaCoder‘s答案的替代方法：|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|52jcp|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|code-block|syntax|javascript^0|0|E|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|P|G|@]|H|@$I|Q|J|R|A|S]]|6|$]]|$A|K|C|L|2|M|F|T|G|@]|H|@]|6|$N|O]]]]

An alternative to <a href="https://stackoverflow.com/a/21232849/3888455">darindaCoder's answer</a>:

<pre><code>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
</code></pre>

entityMap|blocks|key|7m86u|text|import+glob
import+os
import+pandas+as+pd+++
df+=+pd.concat(map(pd.read_csv,+glob.glob(os.path.join('',+"my_files*.csv"))))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript^0^^$0|$]|1|@$2|3|4|5|6|7|8|E|9|@]|A|@]|B|$C|D]]]]

<pre><code>import glob
import os
import pandas as pd 
df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', &quot;my_files*.csv&quot;))))
</code></pre>

entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files|1|http://dask.pydata.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.compute|blocks|key|c5k7n|text|Dask库可以从多个文件中读取数据帧：|unstyled|depth|inlineStyleRanges|entityRanges|8mb61|>>>+import+dask.dataframe+as+dd
>>>+df+=+dd.read_csv('data*.csv')|code-block|syntax|javascript|ecd3o|(来源：https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files)|offset|length|9v4vm|Dask数据帧实现了Pandas数据帧API的一个子集。如果所有数据都可以放入内存，则可以|7rosi|调用df.compute()将数据帧转换为Pandas数据帧。|style|CODE^0|0|0|4|1Z|0|0|0|2|C|2|C|1^^$0|$1|$2|3|4|5|6|$7|8]]|9|$2|3|4|5|6|$7|A]]]|B|@$C|D|E|F|2|G|H|Z|I|@]|J|@]|6|$]]|$C|K|E|L|2|M|H|10|I|@]|J|@]|6|$N|O]]|$C|P|E|Q|2|G|H|11|I|@]|J|@$R|12|S|13|C|14]]|6|$]]|$C|T|E|U|2|G|H|15|I|@]|J|@]|6|$]]|$C|V|E|W|2|G|H|16|I|@$R|17|S|18|X|Y]]|J|@$R|19|S|1A|C|1B]]|6|$]]]]

The Dask library can read a dataframe from multiple files:
<pre><code>>>> import dask.dataframe as dd
>>> df = dd.read_csv('data*.csv')
</code></pre>
(Source: <a href="https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files" rel="noreferrer">https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files</a>)
The Dask dataframes implement a subset of the Pandas dataframe API. If all the data fits into memory, you can <a href="http://dask.pydata.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.compute" rel="noreferrer">call <code>df.compute()</code></a> to convert the dataframe into a Pandas dataframe.

entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://stackoverflow.com/a/21232849/186078|blocks|key|1vnmu|text|编辑:我用谷歌搜索了https://stackoverflow.com/a/21232849/186078..。然而，最近我发现，使用numpy进行任何操作，然后将其分配给dataframe一次，而不是在迭代的基础上操作dataframe本身，这样做会更快，而且似乎在这个解决方案中也是有效的。|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|ab8jb|我真心希望点击此页面的任何人都能考虑这种方法，但不想将这一大段代码作为注释附加，从而降低其可读性。|c4fvv|您可以利用numpy来真正加速数据帧连接。|522g3|import+os
import+glob
import+pandas+as+pd
import+numpy+as+np

path+=+"my_dir_full_path"
allFiles+=+glob.glob(os.path.join(path,"*.csv"))


np_array_list+=+[]
for+file_+in+allFiles:
++++df+=+pd.read_csv(file_,index_col=None,+header=0)
++++np_array_list.append(df.as_matrix())

comb_np_array+=+np.vstack(np_array_list)
big_frame+=+pd.DataFrame(comb_np_array)

big_frame.columns+=+["col1","col2"....]|code-block|syntax|javascript|95ska|计时统计信息：|bqooq|total+files+:192
avg+lines+per+file+:8492
--approach+1+without+numpy+--+8.248656988143921+seconds+---
total+records+old+:1630571
--approach+2+with+numpy+--+2.289292573928833+seconds+---^0|A|17|0|0|0|0|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|X|G|@]|H|@$I|Y|J|Z|A|10]]|6|$]]|$A|K|C|L|2|E|F|11|G|@]|H|@]|6|$]]|$A|M|C|N|2|E|F|12|G|@]|H|@]|6|$]]|$A|O|C|P|2|Q|F|13|G|@]|H|@]|6|$R|S]]|$A|T|C|U|2|E|F|14|G|@]|H|@]|6|$]]|$A|V|C|W|2|Q|F|15|G|@]|H|@]|6|$R|S]]]]

Edit: I googled my way into <a href="https://stackoverflow.com/a/21232849/186078">https://stackoverflow.com/a/21232849/186078</a>.
However of late I am finding it faster to do any manipulation using numpy and then assigning it once to dataframe rather than manipulating the dataframe itself on an iterative basis and it seems to work in this solution too.

I do sincerely want anyone hitting this page to consider this approach, but don't want to attach this huge piece of code as a comment and making it less readable. 

You can leverage numpy to really speed up the dataframe concatenation. 

<pre><code>import os
import glob
import pandas as pd
import numpy as np

path = "my_dir_full_path"
allFiles = glob.glob(os.path.join(path,"*.csv"))


np_array_list = []
for file_ in allFiles:
 df = pd.read_csv(file_,index_col=None, header=0)
 np_array_list.append(df.as_matrix())

comb_np_array = np.vstack(np_array_list)
big_frame = pd.DataFrame(comb_np_array)

big_frame.columns = ["col1","col2"....]
</code></pre>

Timing stats:

<pre><code>total files :192
avg lines per file :8492
--approach 1 without numpy -- 8.248656988143921 seconds ---
total records old :1630571
--approach 2 with numpy -- 2.289292573928833 seconds ---
</code></pre>

entityMap|blocks|key|c5tvu|text|一个liner使用map，但是如果你想指定额外的参数，你可以这样做：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|7ndr5|import+pandas+as+pd
import+glob
import+functools

df+=+pd.concat(map(functools.partial(pd.read_csv,+sep='%7C',+compression=None),+
++++++++++++++++++++glob.glob("data/*.csv")))|code-block|syntax|javascript|dkqf|注意：map本身不允许您提供额外的参数。^0|9|3|0|0|3|3^^$0|$]|1|@$2|3|4|5|6|7|8|N|9|@$A|O|B|P|C|D]]|E|@]|F|$]]|$2|G|4|H|6|I|8|Q|9|@]|E|@]|F|$J|K]]|$2|L|4|M|6|7|8|R|9|@$A|S|B|T|C|D]]|E|@]|F|$]]]]

one liner using <code>map</code>, but if you'd like to specify additional args, you could do:

<pre class="lang-py prettyprint-override"><code>import pandas as pd
import glob
import functools

df = pd.concat(map(functools.partial(pd.read_csv, sep='|', compression=None), 
 glob.glob("data/*.csv")))
</code></pre>

Note: <code>map</code> by itself does not let you supply additional args.

entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://docs.python.org/3/library/glob.html#glob.glob|blocks|key|124bt|text|如果你想递归搜索(Python+3.5或更高版本)，您可以执行以下操作：|unstyled|depth|inlineStyleRanges|entityRanges|e5il6|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)|code-block|syntax|javascript|9uknp|请注意，最后三行可以用一行来表示单行|7p147|df+=+pd.concat((pd.read_csv(f)+for+f+in+iglob(path,+recursive=True)),+ignore_index=True)|js|9fqu8|您可以在下面的文档中找到**here..。此外，我还使用了iglob而不是glob，因为它返回一个迭代器而不是列表。|offset|length|style|CODE|1gafs|编辑:多平台递归函数：|em6t3|您可以将上述代码封装到一个多平台功能(Linux、Windows、Mac)，因此您可以：|et7fq|df+=+read_df_rec('C:\user\your\path',+*.csv)|5tqet|下面是函数：|aclrb|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)^0|0|0|0|0|C|2|T|5|11|4|E|4|0|0|0|0|0|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|18|G|@]|H|@]|6|$]]|$A|I|C|J|2|K|F|19|G|@]|H|@]|6|$L|M]]|$A|N|C|O|2|E|F|1A|G|@]|H|@]|6|$]]|$A|P|C|Q|2|K|F|1B|G|@]|H|@]|6|$L|R]]|$A|S|C|T|2|E|F|1C|G|@$U|1D|V|1E|W|X]|$U|1F|V|1G|W|X]|$U|1H|V|1I|W|X]]|H|@$U|1J|V|1K|A|1L]]|6|$]]|$A|Y|C|Z|2|E|F|1M|G|@]|H|@]|6|$]]|$A|10|C|11|2|E|F|1N|G|@]|H|@]|6|$]]|$A|12|C|13|2|K|F|1O|G|@]|H|@]|6|$L|R]]|$A|14|C|15|2|E|F|1P|G|@]|H|@]|6|$]]|$A|16|C|17|2|K|F|1Q|G|@]|H|@]|6|$L|M]]]]

If you want to search recursively (Python 3.5 or above), you can do the following:

<pre><code>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)
</code></pre>

Note that the three last lines can be expressed in one single line:

<pre><code>df = pd.concat((pd.read_csv(f) for f in iglob(path, recursive=True)), ignore_index=True)
</code></pre>

You can find the documentation of <code>**</code> <a href="https://docs.python.org/3/library/glob.html#glob.glob" rel="noreferrer">here</a>. Also, I used <code>iglob</code>instead of <code>glob</code>, as it returns an iterator instead of a list.

<hr>

<hr>

EDIT: Multiplatform recursive function:

You can wrap the above into a multiplatform function (Linux, Windows, Mac), so you can do:

<pre><code>df = read_df_rec('C:\user\your\path', *.csv)
</code></pre>

Here is the function:

<pre><code>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)
</code></pre>

entityMap|blocks|key|dhitb|text|如果多个csv文件被压缩，您可以使用zipfile读取全部并拼接，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|avho8|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)|code-block|syntax|javascript^0|0^^$0|$]|1|@$2|3|4|5|6|7|8|H|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|I|9|@]|A|@]|B|$F|G]]]]

If the multiple csv files are zipped, you may use zipfile to read all and concatenate as below:
<pre><code>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)

 
</code></pre>

entityMap|blocks|key|a2pi8|text|另一个带有列表理解的在线应用程序，它允许在读取时使用参数_csv。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3584h|df+=+pd.concat([pd.read_csv(f'dir/{f}')+for+f+in+os.listdir('dir')+if+f.endswith('.csv')])|code-block|syntax|js^0|0^^$0|$]|1|@$2|3|4|5|6|7|8|H|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|I|9|@]|A|@]|B|$F|G]]]]

Another on-liner with list comprehension which allows to use arguments with read_csv.

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

entityMap|blocks|key|cuuk5|text|另一种方法是使用pathlib库(通常优先于os.path)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|dbrmq|这种方法避免了重复使用pandas。concat()/apped()。|9uv9g|来自pandas文档：|24aff|值得注意的是，concat()+(因此append())创建了数据的完整副本，并且不断重用此函数可能会对性能造成重大影响。如果需要对多个数据集使用该操作，请使用列表理解。|euitl|import+pandas+as+pd
from+pathlib+import+Path

dir+=+Path("../relevant_directory")

df+=+(pd.read_csv(f)+for+f+in+dir.glob("*.csv"))
df+=+pd.concat(df)|code-block|syntax|javascript^0|8|7|M|7|0|I|8|R|7|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|R|9|@$A|S|B|T|C|D]|$A|U|B|V|C|D]]|E|@]|F|$]]|$2|G|4|H|6|7|8|W|9|@$A|X|B|Y|C|D]|$A|Z|B|10|C|D]]|E|@]|F|$]]|$2|I|4|J|6|7|8|11|9|@]|E|@]|F|$]]|$2|K|4|L|6|7|8|12|9|@]|E|@]|F|$]]|$2|M|4|N|6|O|8|13|9|@]|E|@]|F|$P|Q]]]]

Alternative using the <code>pathlib</code> library (often preferred over <code>os.path</code>). 

This method avoids iterative use of pandas <code>concat()</code>/<code>apped()</code>.

From the pandas documentation: 
It is worth noting that concat() (and therefore append()) makes a full copy of the data, and that constantly reusing this function can create a significant performance hit. If you need to use the operation over several datasets, use a list comprehension.

<pre><code>import pandas as pd
from pathlib import Path

dir = Path("../relevant_directory")

df = (pd.read_csv(f) for f in dir.glob("*.csv"))
df = pd.concat(df)
</code></pre>

entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://docs.python.org/2/library/os.path.html|blocks|key|d84sf|text|基于@Sid的好答案。|unstyled|depth|inlineStyleRanges|entityRanges|6211d|在连接之前，您可以将csv文件加载到中间字典中，中间字典根据文件名提供对每个数据集的访问(格式为dict_of_df['filename.csv'])。这样的字典可以帮助您识别异构数据格式的问题，例如，当列名不对齐时。|offset|length|style|CODE|mjop|导入模块并找到文件路径：|490mo|import+os
import+glob
import+pandas
from+collections+import+OrderedDict
path+=r'C:\DRO\DCL_rawdata_files'
filenames+=+glob.glob(path+%2B+"/*.csv")|code-block|syntax|javascript|62udt|注意：OrderedDict不是必需的，但它将保持可能对分析有用的文件的顺序。|8ppfb|将csv文件加载到字典中。然后连接：|2jpvc|dict_of_df+=+OrderedDict((f,+pandas.read_csv(f))+for+f+in+filenames)
pandas.concat(dict_of_df,+sort=True)|7lj9u|密钥是文件名f值是csv文件的数据帧内容。而不是使用f作为字典键，您还可以使用os.path.basename(f)或其他os.path方法将字典中键的大小减小到仅相关的较小部分。^0|0|1C|Q|0|0|0|3|B|0|0|0|6|1|Q|1|13|J|1P|7|0^^$0|$1|$2|3|4|5|6|$7|8]]]|9|@$A|B|C|D|2|E|F|13|G|@]|H|@]|6|$]]|$A|I|C|J|2|E|F|14|G|@$K|15|L|16|M|N]]|H|@]|6|$]]|$A|O|C|P|2|E|F|17|G|@]|H|@]|6|$]]|$A|Q|C|R|2|S|F|18|G|@]|H|@]|6|$T|U]]|$A|V|C|W|2|E|F|19|G|@$K|1A|L|1B|M|N]]|H|@]|6|$]]|$A|X|C|Y|2|E|F|1C|G|@]|H|@]|6|$]]|$A|Z|C|10|2|S|F|1D|G|@]|H|@]|6|$T|U]]|$A|11|C|12|2|E|F|1E|G|@$K|1F|L|1G|M|N]|$K|1H|L|1I|M|N]|$K|1J|L|1K|M|N]]|H|@$K|1L|L|1M|A|1N]]|6|$]]]]

Based on @Sid's good answer. 

Before concatenating, you can load csv files into an intermediate dictionary which gives access to each data set based on the file name (in the form <code>dict_of_df['filename.csv']</code>). Such a dictionary can help you identify issues with heterogeneous data formats, when column names are not aligned for example. 

<h2>Import modules and locate file paths:</h2>

<pre><code>import os
import glob
import pandas
from collections import OrderedDict
path =r'C:\DRO\DCL_rawdata_files'
filenames = glob.glob(path + "/*.csv")
</code></pre>

Note: <code>OrderedDict</code> is not necessary, 
but it'll keep the order of files which might be useful for analysis.

<h2>Load csv files into a dictionary. Then concatenate:</h2>

<pre><code>dict_of_df = OrderedDict((f, pandas.read_csv(f)) for f in filenames)
pandas.concat(dict_of_df, sort=True)
</code></pre>

Keys are file names <code>f</code> and values are the data frame content of csv files. 
Instead of using <code>f</code> as a dictionary key, you can also use <code>os.path.basename(f)</code> or other <a href="https://docs.python.org/2/library/os.path.html" rel="nofollow noreferrer">os.path</a> methods to reduce the size of the key in the dictionary to only the smaller part that is relevant.

entityMap|blocks|key|ac4qr|text|import+os

os.system("awk+'(NR+==+1)+%7C%7C+(FNR+>+1)'+file*.csv+>+merged.csv")|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|aesl7|在哪里NR和FNR表示正在处理的行号。|unstyled|offset|length|style|CODE|7avm4|FNR是每个文件中的当前行。|cnqg|NR+==+1包括第一个文件的第一行(标题)，而(FNR+>+1)跳过每个后续文件的第一行。^0|0|3|2|6|3|0|0|3|0|0|7^^$0|$]|1|@$2|3|4|5|6|7|8|P|9|@]|A|@]|B|$C|D]]|$2|E|4|F|6|G|8|Q|9|@$H|R|I|S|J|K]|$H|T|I|U|J|K]]|A|@]|B|$]]|$2|L|4|M|6|G|8|V|9|@$H|W|I|X|J|K]]|A|@]|B|$]]|$2|N|4|O|6|G|8|Y|9|@$H|Z|I|10|J|K]]|A|@]|B|$]]]]

<pre><code>import os

os.system(&quot;awk '(NR == 1) || (FNR &gt; 1)' file*.csv &gt; merged.csv&quot;)
</code></pre>
Where <code>NR</code> and <code>FNR</code> represent the number of the line being processed.
<code>FNR</code> is the current line within each file.
<code>NR == 1</code> includes the first line of the first file (the header), while <code>FNR &gt; 1</code> skips the first line of each subsequent file.

entityMap|blocks|key|fkju8|text|你也可以这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|93hhk|import+pandas+as+pd
import+os

new_df+=+pd.DataFrame()
for+r,+d,+f+in+os.walk(csv_folder_path):
++++for+file+in+f:
++++++++complete_file_path+=+csv_folder_path%2Bfile
++++++++read_file+=+pd.read_csv(complete_file_path)
++++++++new_df+=+new_df.append(read_file,+ignore_index=True)


new_df.shape|code-block|syntax|javascript^0|0^^$0|$]|1|@$2|3|4|5|6|7|8|H|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|I|9|@]|A|@]|B|$F|G]]]]

You can do it this way also:
<pre><code>import pandas as pd
import os

new_df = pd.DataFrame()
for r, d, f in os.walk(csv_folder_path):
 for file in f:
 complete_file_path = csv_folder_path+file
 read_file = pd.read_csv(complete_file_path)
 new_df = new_df.append(read_file, ignore_index=True)


new_df.shape
</code></pre>

entityMap|blocks|key|cc6ft|text|import+pandas+as+pd
import+glob

path+=+r'C:\DRO\DCL_rawdata_files'+#+use+your+path
file_path_list+=+glob.glob(path+%2B+"/*.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)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript^0^^$0|$]|1|@$2|3|4|5|6|7|8|E|9|@]|A|@]|B|$C|D]]]]

<pre><code>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)
</code></pre>

entityMap|blocks|key|8mqen|text|这就是在Google+Drive上使用Colab的方法|type|unstyled|depth|inlineStyleRanges|entityRanges|data|fb0ll|import+pandas+as+pd
import+glob

path+=+r'/content/drive/My+Drive/data/actual/comments_only'+#+use+your+path
all_files+=+glob.glob(path+%2B+"/*.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')|code-block|syntax|javascript^0|0^^$0|$]|1|@$2|3|4|5|6|7|8|H|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|I|9|@]|A|@]|B|$F|G]]]]

This is how you can do using Colab on Google Drive

<pre><code>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')
</code></pre>

I would like to read several csv files from a directory into pandas and concatenate them into one big DataFrame. I have not been able to figure it out though. Here is what I have so far:

<pre><code>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)
</code></pre>

I guess I need some help within the for loop???

Import multiple csv files into pandas and concatenate into one DataFrame

Windows

Python

Linux

 我想从一个目录中读取几个csv文件到pandas中，并将它们连接到一个大的DataFrame中。不过，我还没能弄明白这一点。这是我到目前为止所知道的： import globimport pandas as pd# get data file namespath =r'C:\DRO\DCL_rawdata_files'filenames = glob.glob(path + "/*.csv")d

问将多个csv文件导入到DataFrame中，并将其连接到一个pandas中
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