Trying to batch analyze a folder full of .csv files, then save them out again based on the .csv name. However, I'm having trouble extracting just the file name and assigning it to the dataframe (df).
import glob
import pandas as pd
path = r'csv_in'
allFiles = glob.glob(path + '/*.csv')
for file_ in allFiles:
df = pd.read_csv(file_, header=0)
df.name = file_
print(df.name)
The print result I get is "csv_in/*.csv".
The result I'm looking for is just the csv name, "*.csv"
Create new column with [] and os.path.basename with os.path.normpath:
import os
for file_ in allFiles:
df = pd.read_csv(file_, header=0)
df['name'] = os.path.basename(os.path.normpath(file_))
#if need remove extension (csv)
#df['name'] = os.path.splitext(os.path.basename("hemanth.txt"))[0]
print(df.name)
Related
I have an Excel file that needs to be refreshed automatically every week. It must be extended by other Excel files. The problem is that these files have different names each time.
So in my opinion i can not use code like:
import pandas as pd
NG = 'NG.xlsx'
df = pd.read_excel(NG)
because the filename is not always "NG" like in this case.
Do you have any ideas?
Best Greetz
You could read all the files in your folder by doing this, because it allows you to ignore name changes:
import sys
import csv
import glob
import pandas as pd
# get data file names
path = r"C:\.......\folder_with_excel"
filenames = glob.glob(path + "/*.xlsx")
DF = []
for df in dfs:
xl_file = pd.ExcelFile(filenames)
df=xl_file.parse('Sheet1')
DF.concat(df, ignore_index=True)
Alternatively:
import os
import pandas as pd
path = os.getcwd()
files = os.listdir(path) # list all the files in you directory
files_xls = [f for f in files if f[-3:] == 'xlsx'] # make a list of the xlsx
df = pd.DataFrame()
for f in files_xls:
info = pd.read_excel(f, '<sheet name>') # remove <sheet name if you don't need it
df = df.append(info)
I have problem No objects to concatenate. I can not import .csv files from main and its subdirectories to concatenate them into one DataFrame. I am using pandas. Old answers did not help me so please do not mark as duplicated.
Folder structure is like that
main/*.csv
main/name1/name1/*.csv
main/name1/name2/*.csv
main/name2/name1/*.csv
main/name3/*.csv
import pandas as pd
import os
import glob
folder_selected = 'C:/Users/jacob/Documents/csv_files'
not works
frame = pd.concat(map(pd.read_csv, glob.iglob(os.path.join(folder_selected, "/*.csv"))))
not works
csv_paths = glob.glob('*.csv')
dfs = [pd.read_csv(folder_selected) for folder_selected in csv_paths]
df = pd.concat(dfs)
not works
all_files = []
all_files = glob.glob (folder_selected + "/*.csv")
file_path = []
for file in all_files:
df = pd.read_csv(file, index_col=None, header=0)
file_path.append(df)
frame = pd.concat(file_path, axis=0, ignore_index=False)
You need to search the subdirectories recursively.
folder = 'C:/Users/jacob/Documents/csv_files'
path = folder+"/**/*.csv"
Using glob.iglob
df = pd.concat(map(pd.read_csv, glob.iglob(path, recursive=True)))
Using glob.glob
csv_paths = glob.glob(path, recursive=True)
dfs = [pd.read_csv(csv_path) for csv_path in csv_paths]
df = pd.concat(dfs)
Using os.walk
file_paths = []
for base, dirs, files in os.walk(folder):
for file in fnmatch.filter(files, '*.csv'):
file_paths.append(os.path.join(base, file))
df = pd.concat([pd.read_csv(file) for file in file_paths])
Using pathlib
from pathlib import Path
files = Path(folder).rglob('*.csv')
df = pd.concat(map(pd.read_csv, files))
Check Dask Library as following, which reads many files to one df
>>> import dask.dataframe as dd
>>> df = dd.read_csv('data*.csv')
Read their docs
https://examples.dask.org/dataframes/01-data-access.html#Read-CSV-files
Python’s pathlib is a tool for such tasks
from pathlib import Path
FOLDER_SELECTED = 'C:/Users/jacob/Documents/csv_files'
path = Path(FOLDER_SELECTED) / Path("main")
# grab all csvs in main and subfolders
df = pd.concat(pd.read_csv(f.name) for f in path.rglob("*.csv"))
Note:
If the CSV need preprocing, you can create a read_csv function to deal with issues and place it in place of pd.read_csv
I'm trying to merge a bunch of xlsx files into a single pandas dataframe in python. Furthermore, I want to include a column that lists the source file for each row. My code is as follows:
import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile
import glob
import os
# get the path for where the xlsx files are
path = os.getcwd()
files = os.listdir(path)
files_xlsx = [f for f in files if f[-4:] == 'xlsx']
# create new dataframe
df = pd.DataFrame()
# read data from files and add into dataframe
for f in files_xlsx:
data = pd.read_excel(f, 'Sheet 1')
df['Source_file'] = f
df = df.append(data)
however, when I look at the 'Source_file' column it lists the final file it reads as the name for every row. I've spent way more time than I should trying to fix this. What am I doing wrong?
within your for loop you are writing over each iteration of df so you'll only get back the final file,
what you need to do is delcare a list before hand and append do that,
since you called glob lets use that as well.
files = glob.glob(os.path.join(os.getcwd()) + '\*.xlsx')
dfs = [pd.read_excel(f,sheet_name='Sheet1') for f in files]
df = pd.concat(dfs)
if you want to add the filename into the df too then,
files = glob.glob(os.path.join(os.getcwd()) + '\*.xlsx')
dfs = [pd.read_excel(f,sheet_name='Sheet1') for f in files]
file_names = [os.path.basename(f) for f in files]
df = pd.concat(dfs,keys=file_names)
Using Pathlib module (recommended Python 3.4+)
from pathlib import Path
files = [f for f in Path.cwd().glob('*.xlsx')]
dfs = [pd.read_excel(f,sheet_name='Sheet1') for f in files]
file_names = [f.stem for f in files]
df = pd.concat(dfs,keys=file_names)
or as a one liner :
df = pd.concat([pd.read_excel(f) for f in Path.cwd().glob('*.xlsx')],keys=[f.stem for f in Path.cwd().glob('*.xlsx')],sort=False)
I have the following architecture of the text files in the folders and subfolders.
I want to read them all and create a df. I am using this code, but it dont work well for me as the text is not what I checked and the files are not equivalent to my counting.
l = [pd.read_csv(filename,header=None, encoding='iso-8859-1') for filename in glob.glob("2018_01_01/*.txt")]
main_df = pd.concat(l, axis=1)
main_df = main_df.T
for i in range(2):
l = [pd.read_csv(filename, header=None, encoding='iso-8859-1',quoting=csv.QUOTE_NONE) for filename in glob.glob(str(foldernames[i+1])+ '/' + '*.txt')]
df = pd.concat(l, axis=1)
df = df.T
main_df = pd.merge(main_df, df)
file
Assuming those directories contain txt files in which information have the same structure on all of them:
import os
import pandas as pd
df = pd.DataFrame(columns=['observation'])
path = '/path/to/directory/of/directories/'
for directory in os.listdir(path):
if os.path.isdir(directory):
for filename in os.listdir(directory):
with open(os.path.join(directory, filename)) as f:
observation = f.read()
current_df = pd.DataFrame({'observation': [observation]})
df = df.append(current_df, ignore_index=True)
Once all your files have been iterated, df should be the DataFrame containing all the information in your different txt files.
You can do that using a for loop. But before that, you need to give a sequenced name to all the files like 'fil_0' within 'fol_0', 'fil_1' within 'fol_1', 'fil_2' within 'fol_2' and so on. That would facilitate the use of a for loop:
dataframes = []
import pandas as pd
for var in range(1000):
name = "fol_" + str(var) + "/fil_" + str(var) + ".txt"
dataframes.append(pd.read_csv(name)) # if you need to use all the files at once
#otherwise
df = pd.read_csv(name) # you can use file one by one
It will automatically create dataframes for each file.
I have seen many questions in regards to importing multiple csv files into a pandas dataframe.
My question is how can you import multiple csv files but ignore the last csv file in your directory? I have had a hard time finding the answer to this.
Also, lets assume that the csv file names are all different which is why the code file is "/*.csv"
any resource would also be greatly appreciated. Thank you!
path =r'C:\DRO\DCL_rawdata_files' # use your path
allFiles = glob.glob(path + "/*.csv")
frame = pd.DataFrame()
list_ = []
for file_ in allFiles:
df = pd.read_csv(file_,index_col=None, header=0)
list_.append(df)
frame = pd.concat(list_)
Try this:
import os
import glob
import pandas as pd
def get_merged_csv(flist, **kwargs):
return pd.concat([pd.read_csv(f, **kwargs) for f in flist], ignore_index=True)
path =r'C:\DRO\DCL_rawdata_files' # use your path
fmask = os.path.join(path, '*.csv')
allFiles = sorted(glob.glob(fmask), key=os.path.getmtime)
frame = get_merged_csv(allFiles[:-1], index_col=None, header=0)