1with open("file.txt") as file_in:
2 lines = []
3 for line in file_in:
4 lines.append(line)
1file = open(“testfile.txt”,”w”)
2
3file.write(“Hello World”)
4file.write(“This is our new text file”)
5file.write(“and this is another line.”)
6file.write(“Why? Because we can.”)
7
8file.close()
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()