使用 Python 从目录中读取所有 csv 个文件

Question

I hope this is not trivial but I am wondering the following:

If I have a specific folder with n csv files, how could I iteratively read all of them, one at a time, and perform some calculations on their values?

For a single file, for example, I do something like this and perform some calculations on the x array:

import csv
import os

directoryPath=raw_input('Directory path for native csv file: ') 
csvfile = numpy.genfromtxt(directoryPath, delimiter=",")
x=csvfile[:,2] #Creates the array that will undergo a set of calculations

I know that I can check how many csv files there are in a given folder (check here ):

import glob
for files in glob.glob("*.csv"):
    print files 

But I failed to figure out how to possibly nest the numpy.genfromtxt() function in a for loop, so that I read in all the csv files of a directory that it is up to me to specify.

EDIT

The folder I have only has jpg and csv files. The latter are named eventX.csv , where X ranges from 1 to 50. The for loop I am referring to should therefore consider the file names the way they are.

Answer 1

That's how I'd do it:

import os

directory = os.path.join("c:\\","path")
for root,dirs,files in os.walk(directory):
    for file in files:
       if file.endswith(".csv"):
           f=open(file, 'r')
           #  perform calculation
           f.close()

Answer 2

Using pandas and glob as the base packages

import glob
import pandas as pd

glued_data = pd.DataFrame()
for file_name in glob.glob(directoryPath+'*.csv'):
    x = pd.read_csv(file_name, low_memory=False)
    glued_data = pd.concat([glued_data,x],axis=0)

Answer 3

I think you look for something like this

import glob

for file_name in glob.glob(directoryPath+'*.csv'):
    x = np.genfromtxt(file_name,delimiter=',')[:,2]
    # do your calculations

Edit

If you want to get all csv files from a folder (including subfolder) you could use subprocess instead of glob (note that this code only works on linux systems)

import subprocess
file_list = subprocess.check_output(['find',directoryPath,'-name','*.csv']).split('\n')[:-1]

for i,file_name in enumerate(file_list):
    x = np.genfromtxt(file_name,delimiter=',')[:,2]
    # do your calculations
    # now you can use i as an index

It first searches the folder and sub-folders for all file_names using the find command from the shell and applies your calculations afterwards.

Answer 4

According to the documentation of numpy.genfromtxt() , the first argument can be a

File, filename, or generator to read.

That would mean that you could write a generator that yields the lines of all the files like this:

def csv_merge_generator(pattern):
    for file in glob.glob(pattern):
        for line in file:
            yield line

# then using it like this

numpy.genfromtxt(csv_merge_generator('*.csv')) 

should work. (I do not have numpy installed, so cannot test easily)

Answer 5

Here's a more succinct way to do this, given some path = "/path/to/dir/" .

import glob
import pandas as pd

pd.concat([pd.read_csv(f) for f in glob.glob(path+'*.csv')])

Then you can apply your calculation to the whole dataset, or, if you want to apply it one by one:

pd.concat([process(pd.read_csv(f)) for f in glob.glob(path+'*.csv')])

Answer 6

The function below will return a dictionary containing a dataframe for each .csv file in the folder within your defined path .

import pandas as pd
import glob
import os
import ntpath

def panda_read_csv(path):
    pd_csv_dict = {}
    csv_files = glob.glob(os.path.join(path, "*.csv"))
    for csv_file in csv_files:
        file_name = ntpath.basename(csv_file)
        pd_csv_dict['pd_' + file_name] = pd.read_csv(csv_file, sep=";", encoding='mac_roman')
    locals().update(pd_csv_dict)
    return pd_csv_dict

Answer 7

Another answer using list comprehension:

from os import listdir
files= [f for f in listdir("./") if f.endswith(".csv")]

Answer 8

If you want to import your files as separate dataframes, you can try this:

import pandas as pd
import os

filenames = os.listdir("../data/") # lists all csv files in your directory

def extract_name_files(text): # removes .csv from the name of each file
    name_file = text.strip('.csv').lower()
    return name_file

names_of_files = list(map(extract_name_files,filenames)) # creates a list that will be used to name your dataframes

for i in range(0,len(names_of_files)): # saves each csv in a dataframe structure
    exec(names_of_files[i] + " =  pd.read_csv('../data/'+filenames[i])")

Answer 9

You can use pathlib glob functionality to list all .csv in a path, and pandas to read them. Then it's only a matter of applying whatever function you want (which, if systematic, can also be done within the list comprehension)

import pands as pd
from pathlib import Path

path2csv = Path("/your/path/")
csvlist = path2csv.glob("*.csv")
csvs = [pd.read_csv(g) for g in csvlist ]

Answer 10

You need to import the glob library and then use it like following:

import  glob
path='C:\\Users\\Admin\\PycharmProjects\\db_conection_screenshot\\seclectors_absent_images'
filenames = glob.glob(path + "\*.png")
print(len(filenames))