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')
All files have a name convention such as NPS_Platform_FirstLabel_Session_Language_Version.xlsx
I want to have additional columns like Platform, FirstLabel, Session, Language, Version these will column names and the values determined by filenames. I coded the following, it works but the value of added columns just came from the last file. For example, assume that the last filename is
NPS_MEM_GAIT_Science_EN_10.xlsx. Therefore, all of the added columns values are MEM, GAIT_Science, etc. Not the corresponding file names.
import glob
import os
import pandas as pd
path = "C:/Users/User/blabla"
all_files = glob.glob(os.path.join(path, "*.xlsx")) #make list of paths
df = pd.DataFrame()
for f in all_files:
data = pd.read_excel(f)
df = df.append(data)
file_name = os.path.splitext(os.path.basename(f))[0]
nameList = []
nameList = file_name.rsplit('_')
df['Platform'] = nameList[1]
df['First label']= nameList[2]
df['Session'] = nameList[3]
df['Language'] = nameList[4]
df['Version'] = nameList[5]
df
I started with nameList[1] since I don't want NPS.
Any suggestions or feedback?
I have found a solution, I leave it here since there are more views than I expected.
import glob
import os
import pandas as pd
path = "C:/Users/User/....."
all_files = glob.glob(os.path.join(path, "*.xlsx")) #make list of paths
df_files= [pd.read_excel(filename) for filename in all_files]
for dataframe, filename in zip(df_files, all_files):
filename =os.path.splitext(os.path.basename(filename))[0]
filename = filename.rsplit('_')
dataframe['Platform'] = filename[1]
dataframe['First label']= filename[2]
dataframe['Session'] = filename[3]
dataframe['Language'] = filename[4]
dataframe['Version'] = filename[5]
df= pd.concat(files_df, ignore_index=True)
I think the reason is I was just iterating over the files, not the dataframe that I was trying to build. With this, I can iterate over the dataframe and file names at the same time. I have found this solution on https://jonathansoma.com/lede/foundations-2017/classes/working-with-many-files/class/
But still if you can give explicit answer about why the first code does not work as I want, it would be great
I currently have about 700 '.csv' files and want to combine them into one. Each file has three columns: 'date', 'time' and 'var'. I have to combine them based on two columns: date and name. I currently read them as dataframe. After combining them, the final file should have columns of date, name, var1, var2,var3...var700. I currently use pandas merge function, but it is supper slow, as the data is large. Is there any efficient way to combine the files? My current code is as follows:
for filename in os.listdir(signal_path):
filepath=os.path.join(signal_path,filename)
_temp = pd.read_pickle(filepath)
data.merge(_temp, how = 'left', on=['date','name'])
I have attached a
sample data , each file has different length.
this code will combine all the csv files if the files in the same path.
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)
use this:
import pandas as pd
import os
dir = '../relevant_directory/'
first = True
for folder, subfolders, files in os.walk(dir):
for f in files:
file = str(folder)+str(f)
if file.split('.')[-1] == 'csv':
if first:
data = pd.read_csv(file)
first = False
else:
df = pd.read_csv(file)
data = pd.merge(data, df, on=['date', 'name'])
I am trying to come up with a script that will allow me to read all csv files with greater than 62 bits and print two columns into a separate excel file and create a list.
The following is one of the csv files:
FileUUID Table RowInJSON JSONVariable Error Notes SQLExecuted
ff3ca629-2e9c-45f7-85f1-a3dfc637dd81 lng02_rpt_b_calvedets 1 Duplicate entry 'ETH0007805440544' for key 'nosameanimalid' INSERT INTO lng02_rpt_b_calvedets(farmermobile,hh_id,rpt_b_calvedets_rowid,damidyesno,damid,calfdam_id,damtagid,calvdatealv,calvtype,calvtypeoth,easecalv,easecalvoth,birthtyp,sex,siretype,aiprov,othaiprov,strawidyesno,strawid) VALUES ('0974502779','1','1','0','ETH0007805440544','ETH0007805470547',NULL,'2017-09-16','1',NULL,'1',NULL,'1','2','1',NULL,NULL,NULL,NULL,NULL,'0',NULL,NULL,NULL,NULL,NULL,NULL,'0',NULL,'Tv',NULL,NULL,'Et','23',NULL,'5',NULL,NULL,NULL,'0','0')
This is my attempt to solving this problem:
path = 'csvs/'
for infile in glob.glob( os.path.join(path, '*csv') ):
output = infile + '.out'
with open(infile, 'r') as source:
readr = csv.reader(source)
with open(output,"w") as result:
writr = csv.writer(result)
for r in readr:
writr.writerow((r[4], r[2]))
Please help point me to the right direction with any alternative solution
pandas does a lot of what you are trying to achieve:
import pandas as pd
# Read a csv file to a dataframe
df = pd.read_csv("<path-to-csv>")
# Filter two columns
columns = ["FileUUID", "Table"]
df = df[columns]
# Combine multiple dataframes
df_combined = pd.concat([df1, df2, df3, ...])
# Output dataframe to excel file
df_combined.to_excel("<output-path>", index=False)
To loop through all csv files > 62bits, you can use glob.glob() and os.stat()
import os
import glob
dataframes = []
for csvfile in glob.glob("<csv-folder-path>/*.csv"):
if os.stat(csvfile).st_size > 62:
dataframes.append(pd.read_csv(csvfile))
Use the standard csv module. Don't re-invent the wheel.
https://docs.python.org/3/library/csv.html
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")