python read file
with open("file.txt", "r") as txt_file:
return txt_file.readlines()
python read file
with open("file.txt", "r") as txt_file:
return txt_file.readlines()
python read file
# Basic syntax:
with open('/path/to/filename.extension', 'open_mode') as filename:
file_data = filename.readlines() # Or filename.read()
# Where:
# - open imports the file as a file object which then needs to be read
# with one of the read options
# - readlines() imports each line of the file as an element in a list
# - read() imports the file contents as one long new-line-separated
# string
# - open_mode can be one of:
# - "r" = Read which opens a file for reading (error if the file
# doesn't exist)
# - "a" = Append which opens a file for appending (creates the
# file if it doesn't exist)
# - "w" = Write which opens a file for writing (creates the file
# if it doesn't exist)
# - "x" = Create which creates the specified file (returns an error
# if the file exists)
# Note, "with open() as" is recommended because the file is closed
# automatically so you don't have to remember to use file.close()
# Basic syntax for a delimited file with multiple fields:
import csv
with open('/path/to/filename.extension', 'open_mode') as filename:
file_data = csv.reader(filename, delimiter='delimiter')
data_as_list = list(file_data)
# Where:
# - csv.reader can be used for files that use any delimiter, not just
# commas, e.g.: 't', '|', ';', etc. (It's a bit of a misnomer)
# - csv.reader() returns a csv.reader object which can be iterated
# over, directly converted to a list, and etc.
# Importing data using Numpy:
import numpy as np
data = np.loadtxt('/path/to/filename.extension',
delimiter=',', # String used to separate values
skiprows=2, # Number of rows to skip
usecols=[0,2], # Specify which columns to read
dtype=str) # The type of the resulting array
# Importing data using Pandas:
import pandas as pd
data = pd.read_csv('/path/to/filename.extension',
nrows=5, # Number of rows of file to read
header=None, # Row number to use as column names
sep='t', # Delimiter to use
comment='#', # Character to split comments
na_values=[""]) # String to recognize as NA/NaN
# Note, pandas can also import excel files with pd.read_excel()
python open file
with open('filename', 'a') as f: # able to append data to file
f.write(var1) # Were var1 is some variable you have set previously
f.write('data')
f.close() # You can add this but it is not mandatory
with open('filename', 'r') as f: # able to read data from file ( also is the default mode when opening a file in python)
with open('filename', 'x') as f: # Creates new file, if it already exists it will cause it to fail
with open('filename', 't') as f: # opens the file in text mode (also is defualt)
with open('filename', 'b') as f: # Use if your file will contain binary data
with open('filename', 'w') as f: # Open file with ability to write, will also create the file if it does not exist (if it exists will cause it to fail)
with open('filename', '+') as f: # Opens file with reading and writing
# You can combine these as you like with the + for reading and writing
python with file.open
# Reference https://docs.python.org/3/library/functions.html#open
# Method 1
file = open("welcome.txt", "r") # mode can be r(read) w(write) and others
data = file.read()
file.close()
# Method 2 - automatic close
with open("welcome.txt") as infile:
data = file.read()
python file open
#there are many modes you can open files in. r means read.
file = open('C:Usersyournamefilesfile.txt','r')
text = file.read()
#you can write a string to it, too!
file = open('C:Usersyournamefilesfile.txt','w')
file.write('This is a typical string')
#don't forget to close it afterwards!
file.close()
how to open a file in python
#open any file that is in your folder you are using in your editor
import os
os.startfile('Gamepicture.jfif')
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