Python module containing various functions
Project description
rkpython
Description
This is a general use Python module. For now, it only contains some functions to make reading and writing to text files easier, as well as a function that returns information for reading csv files**.
Installation
Use
pip install rkpython
Then import it in Python with
import rkpython as rk
Functions
to_txt()
rk.to_txt(l=None, path='text_file.txt', overwrite=False, verbose = False)
--------------
Saves a list to a .txt file.
l : list, default is None
List object to be saved.
path : str, default is 'text_file.txt'
File path (with file name) you want to save to.
overwrite : bool, default is False
Overwrites the file if it exists.
verbose : boolean, default is False
Prints out a message if the operation was succesful
--------------
Examples :
rk.to_txt(var_list, './documents/vars.txt', verbose = True)
>> File successfully written to ./documents/vars.txt
read_txt()
rk.read_txt(path='text_file.txt', verbose = False)
--------------
Reads from a text file, saving the result as a list, where each line is one item.
path : str, default = 'text_file.txt'
File path (with file name) you want to read from. Can be any type of file (.txt, .csv...)
verbose : boolean, default is False
Prints out a message if the operation was succesful
--------------
Examples :
var_list = rk.read_txt('./documents/vars.txt', verbose = True)
>> File successfully read from ./documents/vars.txt
to_size()
def to_size(size_in_bytes):
Converts a number of bytes into a more readable unit, up until Terabytes, and rounds the number down to 1 decimal. Returns a string.
--------------
size_in_bytes : int
Size in bytes that needs to be converted
--------------
Examples :
get_size(124480000)
>> '124.5 Mb'
rk.get_mem()
def get_mem(nb_objects = 10):
Prints out a list of the largest objects stored in memory, as well as the total memory usage of global variables. Returns a string.
--------------
nb_objects : int, default = 10
Maximum number of items to be printed out.
--------------
Examples :
get_mem(5)
>> Total usage : 25.3 Gb
>>
>> 5 largest objects :
>> _477 : 1.7 Gb
>> _529 : 1.7 Gb
>> _437 : 1.4 Gb
>> _412 : 1.3 Gb
>> _415 : 1.3 Gb
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