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 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()read function in python
Syntax: file.read(fd, n)
Parameter:
fd: A file descriptor representing the file to be read.
n: Optional. An integer value denoting the number of bytes to be read from the file associated with the given file descriptor fd.
The number of bytes to return. Default -1, which means the whole file.
Return Type: This method returns a bytestring which represents the bytes read from the file associated with the file descriptor fd.python file reading
fin = open("NAME.txt", 'r')
body = fin.read().split("n")
line = fin.readline().strip()with open as file python
>>> with open('workfile') as f:
... read_data = f.read()
>>> # We can check that the file has been automatically closed.
>>> f.closed
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