Import multiple csv files into pandas and concaten

Question

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:

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)

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

Answer 1

Edit: I googled my way into https://stackoverflow.com/a/21232849/186078. 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.

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"....]

Timing stats:

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 ---

Answer 2

The Dask library can read a dataframe from multiple files:

>>> import dask.dataframe as dd
>>> df = dd.read_csv('data*.csv')

(Source: http://dask.pydata.org/en/latest/examples/dataframe-csv.html)

The Dask dataframes implement a subset of the Pandas dataframe API. If all the data fits into memory, you can call df.compute() to convert the dataframe into a Pandas dataframe.

Answer 3

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

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

list_ = []

for file_ in allFiles:
    df = pd.read_csv(file_,index_col=None, header=0)
    list_.append(df)

frame = pd.concat(list_, axis = 0, ignore_index = True)

Answer 4

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

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)

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

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

You can find the documentation of ** here. Also, I used iglobinstead of glob, as it returns an iterator instead of a list.

---

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EDIT: Multiplatform recursive function:

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

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

Here is the function:

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)

Answer 5

An alternative to darindaCoder's answer:

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

Answer 6

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

import zipfile
import numpy as np
import pandas as pd

ziptrain = zipfile.ZipFile('yourpath/yourfile.zip')

train=[]

for f in range(0,len(ziptrain.namelist())):
    if (f == 0):
        train = pd.read_csv(ziptrain.open(ziptrain.namelist()[f]))
    else:
        my_df = pd.read_csv(ziptrain.open(ziptrain.namelist()[f]))
        train = (pd.DataFrame(np.concatenate((train,my_df),axis=0), 
                          columns=list(my_df.columns.values)))