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?
See pandas: IO tools for all of the available .read_ methods.
Try the following code if all of the CSV files have the same columns.
I have added header=0, so that after reading the CSV file's first row, it can be assigned as the column names.
import pandas as pd
import glob
import os
path = r'C:\DRO\DCL_rawdata_files' # use your path
all_files = glob.glob(os.path.join(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)
Or, with attribution to a comment from Sid.
all_files = glob.glob(os.path.join(path, "*.csv"))
df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)
It's often necessary to identify each sample of data, which can be accomplished by adding a new column to the dataframe.
pathlib from the standard library will be used for this example. It treats paths as objects with methods, instead of strings to be sliced.
Imports and Setup
from pathlib import Path
import pandas as pd
import numpy as np
path = r'C:\DRO\DCL_rawdata_files' # or unix / linux / mac path
# Get the files from the path provided in the OP
files = Path(path).glob('*.csv') # .rglob to get subdirectories
Option 1:
Add a new column with the file name
dfs = list()
for f in files:
data = pd.read_csv(f)
# .stem is method for pathlib objects to get the filename w/o the extension
data['file'] = f.stem
dfs.append(data)
df = pd.concat(dfs, ignore_index=True)
Option 2:
Add a new column with a generic name using enumerate
dfs = list()
for i, f in enumerate(files):
data = pd.read_csv(f)
data['file'] = f'File {i}'
dfs.append(data)
df = pd.concat(dfs, ignore_index=True)
Option 3:
Create the dataframes with a list comprehension, and then use np.repeat to add a new column.
[f'S{i}' for i in range(len(dfs))] creates a list of strings to name each dataframe.
[len(df) for df in dfs] creates a list of lengths
Attribution for this option goes to this plotting answer.
# Read the files into dataframes
dfs = [pd.read_csv(f) for f in files]
# Combine the list of dataframes
df = pd.concat(dfs, ignore_index=True)
# Add a new column
df['Source'] = np.repeat([f'S{i}' for i in range(len(dfs))], [len(df) for df in dfs])
Option 4:
One liners using .assign to create the new column, with attribution to a comment from C8H10N4O2
df = pd.concat((pd.read_csv(f).assign(filename=f.stem) for f in files), ignore_index=True)
or
df = pd.concat((pd.read_csv(f).assign(Source=f'S{i}') for i, f in enumerate(files)), ignore_index=True)
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
import glob
import os
import pandas as pd
df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "my_files*.csv"))))
Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional third-party libraries. You can do this in two lines using everything Pandas and Python (all versions) already have built in.
For a few files - one-liner
df = pd.concat(map(pd.read_csv, ['d1.csv', 'd2.csv','d3.csv']))
For many files
import os
filepaths = [f for f in os.listdir(".") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))
For No Headers
If you have specific things you want to change with pd.read_csv (i.e., no headers) you can make a separate function and call that with your map:
def f(i):
return pd.read_csv(i, header=None)
df = pd.concat(map(f, filepaths))
This pandas line, which sets the df, utilizes three things:
Python's map (function, iterable) sends to the function (the
pd.read_csv()) the iterable (our list) which is every CSV element
in filepaths).
Panda's read_csv() function reads in each CSV file as normal.
Panda's concat() brings all these under one df variable.
Easy and Fast
Import two or more CSV files without having to make a list of names.
import glob
import pandas as pd
df = pd.concat(map(pd.read_csv, glob.glob('data/*.csv')))
The Dask library can read a dataframe from multiple files:
>>> import dask.dataframe as dd
>>> df = dd.read_csv('data*.csv')
(Source: https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files)
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.
I googled my way into Gaurav Singh's answer.
However, as of late, I am finding it faster to do any manipulation using NumPy and then assigning it once to a 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 I 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 statistics:
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 ---
A one-liner using map, but if you'd like to specify additional arguments, you could do:
import pandas as pd
import glob
import functools
df = pd.concat(map(functools.partial(pd.read_csv, sep='|', compression=None),
glob.glob("data/*.csv")))
Note: map by itself does not let you supply additional arguments.
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.
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)
Inspired from MrFun's answer:
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)
Notes:
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.
Another one-liner with list comprehension which allows to use arguments with read_csv.
df = pd.concat([pd.read_csv(f'dir/{f}') for f in os.listdir('dir') if f.endswith('.csv')])
Alternative using the pathlib library (often preferred over os.path).
This method avoids iterative use of pandas concat()/apped().
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.
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)
If multiple CSV files are zipped, you may use zipfile to read all and concatenate as below:
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)
Based on Sid's good answer.
To identify issues of missing or unaligned columns
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 dict_of_df['filename.csv']). Such a dictionary can help you identify issues with heterogeneous data formats, when column names are not aligned for example.
Import modules and locate file paths:
import os
import glob
import pandas
from collections import OrderedDict
path =r'C:\DRO\DCL_rawdata_files'
filenames = glob.glob(path + "/*.csv")
Note: OrderedDict is not necessary, but it'll keep the order of files which might be useful for analysis.
Load CSV files into a dictionary. Then concatenate:
dict_of_df = OrderedDict((f, pandas.read_csv(f)) for f in filenames)
pandas.concat(dict_of_df, sort=True)
Keys are file names f and values are the data frame content of CSV files.
Instead of using f as a dictionary key, you can also use os.path.basename(f) or other os.path methods to reduce the size of the key in the dictionary to only the smaller part that is relevant.
import os
os.system("awk '(NR == 1) || (FNR > 1)' file*.csv > merged.csv")
Where NR and FNR represent the number of the line being processed.
FNR is the current line within each file.
NR == 1 includes the first line of the first file (the header), while FNR > 1 skips the first line of each subsequent file.
In case of an unnamed column issue, use this code for merging multiple CSV files along the x-axis.
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")
You can do it this way also:
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
Consider using convtools library, which provides lots of data processing primitives and generates simple ad hoc code under the hood.
It is not supposed to be faster than pandas/polars, but sometimes it can be.
e.g. you could concat csv files into one for further reuse - here's the code:
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
Of course if you just want to obtain a dataframe without writing a concatenated file, it will take 4.63 s and 10.9 s correspondingly (pandas is faster here because it doesn't need to zip columns for writing it back).
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)
This is how you can do it using Colaboratory on Google Drive:
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')
I am trying to select "column 3" from my files and then combine them into one file. The issue is While I am combing the columns, they are not in the same pattern as files are in the folder. For Example, I have three files in the folder "First, Second and Third". My code given below is always reading the "Second" file before the "First" file. Can Anyone help me?
import glob
import pandas as pd
import numpy as np
from tqdm import tqdm
extension = 'dat'
all_filenames = [i for i in glob.glob('*.{}'.format(extension))]
df = pd.DataFrame(np.nan, index = np.arange(1394521), columns = ["velocity-magnitude"])
for i,f in tqdm(enumerate(all_filenames)):
reader = pd.read_csv(f, sep=r"\s+")
col = reader.iloc[:,[3]]
frames = [df,col]
df = pd.concat(frames, axis=1,join="outer")
df.to_csv('combined.dat', mode='a', header = False, index = False)
glob.glob uses os.listdir internally. This explains the arbitrary order of the files. In case you want some specific sorting, then you will have to apply it yourself, e.g. using sorted(glob.glob('*.{}'.format(extension)).
Thanks NYC Coder, yeah this sorted function is the solution to my problem.
I coded how to load and save txt file using pandas in python.
import glob
filenames = sorted(glob.glob("D:/a/test*.txt"))
filenames = filenames[0:5]
import numpy as np
import pandas as pd
for f in filenames:
df = pd.read_csv(f, skiprows=[1,2,3], dtype=str, delim_whitespace=True)
df.to_csv(f'{f[:-4]}.csv', index=False)
------>There are 10 result files in a folder
- test1.txt, test2.txt, test3.txt, test4.txt, test5.txt,
test1.csv, test2.csv, test3.csv, test4.csv, test5.csv
#Result csv.file(test1.csv)
abc:,1.233e-03
1.234e-04,
1.235e-02,
1.236e-05,
1.237e-02,
1.238e-02,
But I have some problems as follows.
I don't know how to rename test1.txt, test2.txt, test3.txt, test4.txt, test5.txt into c1.csv, c2.csv, c3.csv, c4.csv, c5.csv.
I want remove 'abc:,'data in all test(1,2,3,4,5).csv files, but I don't know how to delete and replace data.
Do you know how to rename(change) file name and remove data (specific character) referred to above problems in python?
origin data
test1.txt (it is similar to other file-{test2,test3,test4, test5}.txt)
abc: 1.233e-03
def: 1.64305155216978164e+02
ghi: 4831
jkl:
1.234e-04
1.235e-02
1.236e-05
1.237e-02
1.238e-02
Expected result
(it must chage test1,2,3,4,5.txt files into c1,2,3,4,5.csv files, it only remove herder name(abc:), also def:,ghi:,jkl: rows should remove.)
1.233e-03
1.234e-04
1.235e-02
1.236e-05
1.237e-02
1.238e-02
You can rename your file using os.rename() method or you can create a new file using f= open("file_name.extension","w+") and write the output to new file.
You can use the replace() method once you load your data into a string variable.
Here is what you can do:
import os
import glob
import pandas as pd
for f in glob.glob("*.txt"):
df = pd.read_csv(f, skiprows=[1,2,3], dtype=str, delim_whitespace=True)
df = df.replace('abc:,','')
os.rename(f,f'{f[:-4]}.csv')
df.to_csv(f'{f[:-4]}.csv', index=False)
I want to read excel file with Pandas, delete the header row and the first column and write the resultant data in an excel file with the same name. I want to do it for all the excel files in a folder. I have written the code for data reading and writing but having trouble with saving the data in a file with the same name. The code I have written is like this-
import numpy as np
import pandas as pd
import os
for filename in os.listdir ('./'):
if filename.endswith ('.xlsx'):
df = pd.read_excel ('new.xlsx', skiprows=1)
df.drop (df.columns [0], axis=1, inplace=True)
df.to_csv ('new.csv', index=False)
How can I automate my code for all the excel files in the same folder?
Use variable filename in function read_excel and then create new file names by format and for remove first column is possible use DataFrame.iloc - select all columns without first:
for filename in os.listdir ('./'):
if filename.endswith ('.xlsx'):
df = pd.read_excel (filename, skiprows=1)
df.iloc[:, 1:].to_csv('new_{}.csv'.format(filename), index=False)
Another solution with glob, there is possible specify extensions:
import glob
for filename in glob.glob('./*.xlsx'):
df = pd.read_excel (filename, skiprows=1)
df.iloc[:, 1:].to_csv('new_{}.csv'.format(filename), index=False)
#python 3.6+
#df.iloc[:, 1:].to_csv (f'new_{filename}.csv', index=False)
Try Below for reading multiple files as follows:
import pandas as pd
import glob
# Read multiple files into one dataframe along with pandas `concat`
# if you have path defined like `/home/data/` then you can use `/home/data/*.xlsx` otherwise you directly mention the path.
df = pd.concat([pd.read_excel(files, sep=',', index=False, skiprows=1) for files in glob.glob("/home/data/*.xlsx")])
Alternative:
Read multiple files into one dataframe
all_Files = glob.glob('/home/data/*.xlsx')
df = pd.concat((pd.read_excel(files, sep=',', index=False, skiprows=1) for files in all_Files))
My python code works correctly in the below example. My code combines a directory of CSV files and matches the headers. However, I want to take it a step further - how do I add a column that appends the filename of the CSV that was used?
import pandas as pd
import glob
globbed_files = glob.glob("*.csv") #creates a list of all csv files
data = [] # pd.concat takes a list of dataframes as an agrument
for csv in globbed_files:
frame = pd.read_csv(csv)
data.append(frame)
bigframe = pd.concat(data, ignore_index=True) #dont want pandas to try an align row indexes
bigframe.to_csv("Pandas_output2.csv")
This should work:
import os
for csv in globbed_files:
frame = pd.read_csv(csv)
frame['filename'] = os.path.basename(csv)
data.append(frame)
frame['filename'] creates a new column named filename and os.path.basename() turns a path like /a/d/c.txt into the filename c.txt.
Mike's answer above works perfectly. In case any googlers run into the following error:
>>> TypeError: cannot concatenate object of type "<type 'str'>";
only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid
It's possibly because the separator is not correct. I was using a custom csv file so the separator was ^. Becuase of that I needed to include the separator in the pd.read_csv call.
import os
for csv in globbed_files:
frame = pd.read_csv(csv, sep='^')
frame['filename'] = os.path.basename(csv)
data.append(frame)
files variable contains all list of csv files in your present directory. Such as
['FileName1.csv',FileName2.csv']. You also need to remove ".csv". You can use .split() function. Below is simple logic. This will work for you.
files = glob.glob("*.csv")
for i in files:
df=pd.read_csv(i)
df['New Column name'] = i.split(".")[0]
df.to_csv(i.split(".")[0]+".csv")