1with open("file.txt") as file_in:
2 lines = []
3 for line in file_in:
4 lines.append(line)
1# Basic syntax:
2with open('/path/to/filename.extension', 'open_mode') as filename:
3 file_data = filename.readlines() # Or filename.read()
4# Where:
5# - open imports the file as a file object which then needs to be read
6# with one of the read options
7# - readlines() imports each line of the file as an element in a list
8# - read() imports the file contents as one long new-line-separated
9# string
10# - open_mode can be one of:
11# - "r" = Read which opens a file for reading (error if the file
12# doesn't exist)
13# - "a" = Append which opens a file for appending (creates the
14# file if it doesn't exist)
15# - "w" = Write which opens a file for writing (creates the file
16# if it doesn't exist)
17# - "x" = Create which creates the specified file (returns an error
18# if the file exists)
19# Note, "with open() as" is recommended because the file is closed
20# automatically so you don't have to remember to use file.close()
21
22# Basic syntax for a delimited file with multiple fields:
23import csv
24with open('/path/to/filename.extension', 'open_mode') as filename:
25 file_data = csv.reader(filename, delimiter='delimiter')
26 data_as_list = list(file_data)
27# Where:
28# - csv.reader can be used for files that use any delimiter, not just
29# commas, e.g.: '\t', '|', ';', etc. (It's a bit of a misnomer)
30# - csv.reader() returns a csv.reader object which can be iterated
31# over, directly converted to a list, and etc.
32
33# Importing data using Numpy:
34import numpy as np
35data = np.loadtxt('/path/to/filename.extension',
36 delimiter=',', # String used to separate values
37 skiprows=2, # Number of rows to skip
38 usecols=[0,2], # Specify which columns to read
39 dtype=str) # The type of the resulting array
40
41# Importing data using Pandas:
42import pandas as pd
43data = pd.read_csv('/path/to/filename.extension',
44 nrows=5, # Number of rows of file to read
45 header=None, # Row number to use as column names
46 sep='\t', # Delimiter to use
47 comment='#', # Character to split comments
48 na_values=[""]) # String to recognize as NA/NaN
49
50# Note, pandas can also import excel files with pd.read_excel()
1with open('filename', 'a') as f: # able to append data to file
2 f.write(var1) # Were var1 is some variable you have set previously
3 f.write('data')
4 f.close() # You can add this but it is not mandatory
5
6with open('filename', 'r') as f: # able to read data from file ( also is the default mode when opening a file in python)
7
8with open('filename', 'x') as f: # Creates new file, if it already exists it will cause it to fail
9
10with open('filename', 't') as f: # opens the file in text mode (also is defualt)
11
12with open('filename', 'b') as f: # Use if your file will contain binary data
13
14with 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)
15
16with open('filename', '+') as f: # Opens file with reading and writing
17
18# You can combine these as you like with the + for reading and writing
1# Reference https://docs.python.org/3/library/functions.html#open
2
3# Method 1
4file = open("welcome.txt", "r") # mode can be r(read) w(write) and others
5data = file.read()
6file.close()
7
8# Method 2 - automatic close
9with open("welcome.txt") as infile:
10 data = file.read()
11
1#there are many modes you can open files in. r means read.
2file = open('C:\Users\yourname\files\file.txt','r')
3text = file.read()
4
5#you can write a string to it, too!
6file = open('C:\Users\yourname\files\file.txt','w')
7file.write('This is a typical string')
8
9#don't forget to close it afterwards!
10file.close()
1fin = open("NAME.txt", 'r')
2body = fin.read().split("\n")
3line = fin.readline().strip()