I have many text files. I tried to convert the txt files into a single CSV file, but it is taking a huge time. I put the code on run mode at night and I slept, it processed only 4500 files, but still morning it is running.
There is any way to fast way to convert the text files into csv?
Here is my code:
import pandas as pd
import os
import glob
from tqdm import tqdm
# create empty dataframe
csvout = pd.DataFrame(columns =["ID","Delivery_person_ID" ,"Delivery_person_Age" ,"Delivery_person_Ratings","Restaurant_latitude","Restaurant_longitude","Delivery_location_latitude","Delivery_location_longitude","Order_Date","Time_Orderd","Time_Order_picked","Weather conditions","Road_traffic_density","Vehicle_condition","Type_of_order","Type_of_vehicle", "multiple_deliveries","Festival","City","Time_taken (min)"])
# get list of files
file_list = glob.glob(os.path.join(os.getcwd(), "train/", "*.txt"))
for filename in tqdm(file_list):
# next file/record
mydict = {}
with open(filename) as datafile:
# read each line and split on " " space
for line in tqdm(datafile):
# Note: partition result in 3 string parts, "key", " ", "value"
# array slice third parameter [::2] means steps=+2
# so only take 1st and 3rd item
name, var = line.partition(" ")[::2]
mydict[name.strip()] = var.strip()
# put dictionary in dataframe
csvout = csvout.append(mydict, ignore_index=True)
# write to csv
csvout.to_csv("train.csv", sep=";", index=False)
Here is my example text file.
ID 0xb379
Delivery_person_ID BANGRES18DEL02
Delivery_person_Age 34.000000
Delivery_person_Ratings 4.500000
Restaurant_latitude 12.913041
Restaurant_longitude 77.683237
Delivery_location_latitude 13.043041
Delivery_location_longitude 77.813237
Order_Date 25-03-2022
Time_Orderd 19:45
Time_Order_picked 19:50
Weather conditions Stormy
Road_traffic_density Jam
Vehicle_condition 2
Type_of_order Snack
Type_of_vehicle scooter
multiple_deliveries 1.000000
Festival No
City Metropolitian
Time_taken (min) 33.000000
CSV is a very simple data format for which you don't need any sophisticated tools to handle. Just text and separators.
In your hopefully simple case there is no need to use pandas and dictionaries.
Except your datafiles are corrupt missing some columns or having some additional columns to skip. But even in this case you can handle such issues better within your own code so you have more control over it and are able to get results within seconds.
Assuming your datafiles are not corrupt having all columns in the right order with no missing columns or having additional ones (so you can rely on their proper formatting), just try this code:
from time import perf_counter as T
sT = T()
filesProcessed = 0
columns =["ID","Delivery_person_ID" ,"Delivery_person_Age" ,"Delivery_person_Ratings","Restaurant_latitude","Restaurant_longitude","Delivery_location_latitude","Delivery_location_longitude","Order_Date","Time_Orderd","Time_Order_picked","Weather conditions","Road_traffic_density","Vehicle_condition","Type_of_order","Type_of_vehicle", "multiple_deliveries","Festival","City","Time_taken (min)"]
import glob, os
file_list = glob.glob(os.path.join(os.getcwd(), "train/", "*.txt"))
csv_lines = []
csv_line_counter = 0
for filename in file_list:
filesProcessed += 1
with open(filename) as datafile:
csv_line = ""
for line in datafile.read().splitlines():
# print(line)
var = line.partition(" ")[-1]
csv_line += var.strip() + ';'
csv_lines.append(str(csv_line_counter)+';'+csv_line[:-1])
csv_line_counter += 1
with open("train.csv", "w") as csvfile:
csvfile.write(';'+';'.join(columns)+'\n')
csvfile.write('\n'.join(csv_lines))
eT = T()
print(f'> {filesProcessed=}, {(eT-sT)=:8.6f}')
I guess you will get the result in a speed beyond your expectations (in seconds, not minutes or hours)
On my computer, estimating from processing time of 100 files the time required for 50.000 files will be about 3 seconds.
I could not replicate. I took the example data file and created 5000 copies of it. Then I ran your code using tqdm and without. The below shows without:
import time
import csv
import os
import glob
import pandas as pd
from tqdm import tqdm
csvout = pd.DataFrame(columns =["ID","Delivery_person_ID" ,"Delivery_person_Age" ,"Delivery_person_Ratings","Restaurant_latitude","Restaurant_longitude","Delivery_location_latitude","Delivery_location_longitude","Order_Date","Time_Orderd","Time_Order_picked","Weather conditions","Road_traffic_density","Vehicle_condition","Type_of_order","Type_of_vehicle", "multiple_deliveries","Festival","City","Time_taken (min)"])
file_list = glob.glob(os.path.join(os.getcwd(), "sample_files/", "*.txt"))
t1 = time.time()
for filename in file_list:
# next file/record
mydict = {}
with open(filename) as datafile:
# read each line and split on " " space
for line in datafile:
# Note: partition result in 3 string parts, "key", " ", "value"
# array slice third parameter [::2] means steps=+2
# so only take 1st and 3rd item
name, var = line.partition(" ")[::2]
mydict[name.strip()] = var.strip()
# put dictionary in dataframe
csvout = csvout.append(mydict, ignore_index=True)
# write to csv
csvout.to_csv("train.csv", sep=";", index=False)
t2 = time.time()
print(t2-t1)
The times I got where:
tqdm 33 seconds
no tqdm 34 seconds
Then I ran using the csv module:
t1 = time.time()
with open('output.csv', 'a', newline='') as csv_file:
columns =["ID","Delivery_person_ID" ,"Delivery_person_Age" ,"Delivery_person_Ratings","Restaurant_latitude","Restaurant_longitude","Delivery_location_latitude","Delivery_location_longitude","Order_Date","Time_Orderd","Time_Order_picked","Weather conditions","Road_traffic_density","Vehicle_condition","Type_of_order","Type_of_vehicle", "multiple_deliveries","Festival","City","Time_taken (min)"]
mydict = {}
d_Writer = csv.DictWriter(csv_file, fieldnames=columns, delimiter=',')
d_Writer.writeheader()
for filename in file_list:
with open(filename) as datafile:
for line in datafile:
name, var = line.partition(" ")[::2]
mydict[name.strip()] = var.strip()
d_Writer.writerow(mydict)
t2 = time.time()
print(t2-t1)
The time for this was:
csv 0.32231569290161133 seconds.
Try it like this.
import glob
with open('my_file.csv', 'a') as csv_file:
for path in glob.glob('./*.txt'):
with open(path) as txt_file:
txt = txt_file.read() + '\n'
csv_file.write(txt)
I'm writing a script to track my orders from a website. I want to import the order# from a txt file and the script should repeat it self as long as there are ordernumbers.I wrote a code where the script imports this txt file and chooses a random ordernumber but the script puts all ordernumbers together and doesnt seperate them how can I fix this ?
this is my code:
f=open("Order#.txt", "r")
OrderNR = f.read()
words = OrderNR.split()
Repeat = len(words)
for i in range(Repeat):
randomlist = OrderNR
Orderrandom = random.choice(randomlist)
Mainlink = 'https://footlocker.narvar.com/footlocker/tracking/startrack?order_number=' + Orderrandom
Instead of using f.read(), try using f.readlines().
# Using readlines()
file1 = open('myfile.txt', 'r')
Lines = file1.readlines()
Try PANDAS
import pandas as pd
df = pd.read_csv('Order#.txt', delimiter='\t')
print(df)
you can see TXT file in table format
I have a CSV file where the first row in the first column is blank with some numbers in the second and third row. This whole column is useless and I need to remove it so I can convert the data into a JSON file. I just need to know how to remove the first column of data so I can parse it. Any help is greatly appreciated!
My script is as follows
#!/usr/bin/python3
import pandas as pd
import csv, json
xls = pd.ExcelFile(r'C:\Users\Andy-\Desktop\Lab2Data.xlsx')
df = xls.parse(sheetname="Sheet1", index_col=None, na_values=['NA'])
df.to_csv('file.csv')
file = open('file.csv', 'r')
lines = file.readlines()
file.close()
data = {}
with open('file.csv') as csvFile:
csvReader = csv.DictReader(csvFile)
for rows in csvReader:
id = rows['Id']
data[id] = rows
with open('Lab2.json', 'w') as jsonFile:
jsonFile.write(json.dumps(data, indent=4))
I don't know much about json files but this will remove the first column from your csv file.
with open ('new_file.csv', 'w') as out_file :
with open ('file.csv') as in_file :
for line in in_file :
test_string = line.strip ('\n').split (',')
out_file.write (','.join (test_string [1:]) + '\n')
I’m merging two text files file1.tbl and file2.tbl with a common column. I used pandas to make data frames of each and merge function to have the output.
The problem is the output file does not show me the whole data and there is a row of "..." instead and at the end it just prints [9997 rows x 5 columns].
I need a file containing the whole 9997 rows.
import pandas
with open("file1.tbl") as file:
d1 = file.read()
with open("file2.tbl") as file:
d2 = file.read()
df1 = pandas.read_table('file1.tbl', delim_whitespace=True, names=('ID', 'chromosome', 'strand'))
df2 = pandas.read_table('file2.tbl', delim_whitespace=True, names=('ID', 'NUClen', 'GCpct'))
merged_table = pandas.merge(df1, df2)
with open('merged_table.tbl', 'w') as f:
print(merged_table, file=f)
I tried to convert text file content into a .csv format by reading each and every line using python csv module and converting that to a list. But i couldn't get the expected output and it stores the first line in a row but second line will be stored in 3rd row and 5th so on. Since I am new to python i don't know how to skip the line and store it in the right order.
def FileConversion():
try:
with open('TextToCSV.txt', 'r') as textFile:
LineStripped = (eachLine.strip() for eachLine in textFile)
lines = (eachLine.split(" ") for eachLine in LineStripped if eachLine)
with open('finalReport.csv', 'w') as CSVFile:
writer = csv.writer(CSVFile)
writer.writerow(('firstName', 'secondName', 'designation', "age"))
writer.writerows(lines)
Why don't you try doing something more simple:
import pandas as pd
aux = pd.read_csv("TextToCSV.txt", sep=" ")
aux.columns=['firstName', 'secondName', 'designation', "age"]
aux.to_csv("result.csv")