python read file line by line
with open("file.txt") as file_in: lines = [] for line in file_in: lines.append(line)
python read file line by line
with open("file.txt") as file_in: lines = [] for line in file_in: lines.append(line)
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:\Users\yourname\files\file.txt','r') text = file.read() #you can write a string to it, too! file = open('C:\Users\yourname\files\file.txt','w') file.write('This is a typical string') #don't forget to close it afterwards! file.close()
python file reading
fin = open("NAME.txt", 'r') body = fin.read().split("\n") line = fin.readline().strip()
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