MD Python Library
Project description
MD Python Library
This package contains high level functions for common Python use cases.
Compare Directories
from mdpython.fileutils import compare
cmp = compare.compare_dirs(r"C:\Users\Dell\OneDrive\Desktop\result_9th",
r"C:\Users\Dell\OneDrive\Desktop\result_9th_v2")
cmp.gen_html_report(r"C:\Users\Dell\OneDrive\Desktop\out.html", ["py", "txt",
"json"])
for fl in cmp.files_only_in_right:
if fl.name.endswith("py"):
print(fl.absolute())
Menu based app
from datetime import datetime
from random import randint, choice
from mdpython.uiutils import menu_based_app
def show_date():
print(datetime.now().strftime("%Y-%m-%d"))
def show_time():
print(datetime.now().strftime("%H:%M:%S"))
def show_date_and_time():
print(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
def show_random_number():
print(randint(1, 100))
def show_random_color():
print(choice(['red', 'blue', 'green']))
ar = [
["Random", "Random Integer", show_random_number],
["Random", "Random Color", show_random_color],
["Date", "Show Date", show_date],
["Date", "Show Time", show_time],
["Date", "Show Date and Time", show_date_and_time]
]
menu_based_app.start(ar)
Disk cleanup
from mdpython.fileutils import cleanup
dr = r"C:\Users\Dell\OneDrive\Desktop\result_9th"
info = cleanup.retrieve_info(dr)
print("sorted by time")
for dtl in info.sort_by_time()[:5]:
print(dtl)
print("\nsorted by size")
for dtl in info.sort_by_size()[:5]:
print(dtl)
print("\nmodified in last 30 mins")
for dtl in info.modified_within(mins=30)[:5]:
print(dtl)
print("\nmodified more than 1 day ago")
for dtl in info.modified_before(mins=24 * 60)[:5]:
print(dtl)
print("\nsorted by number of files in directory")
for dtl in info.sort_by_file_count()[:5]:
print(dtl)
Getting execution duration
from mdpython.debugutils import timer
from random import randint
from math import factorial
timer.start("main")
def count_elements_in_array():
ar = list(range(randint(1000000,10000000)))
print(len(ar))
def get_factorial():
for i in range(5):
timer.start("looptest")
num = randint(900,1000)
print(num, factorial(num))
timer.stop("looptest")
timer.start("func1")
count_elements_in_array()
timer.stop("func1")
get_factorial()
timer.stop("main")
timer.show()
-- Output
Name Duration Start Time End Time
============================== ==================== ==================== ====================
main 0:00:00.046207 2024-04-08 21:24:59 2024-04-08 21:24:59
func1 0:00:00.033129 2024-04-08 21:24:59 2024-04-08 21:24:59
looptest.4 0:00:00.010020 2024-04-08 21:24:59 2024-04-08 21:24:59
looptest.2 0:00:00.003058 2024-04-08 21:24:59 2024-04-08 21:24:59
looptest.1 0:00:00 2024-04-08 21:24:59 2024-04-08 21:24:59
looptest.3 0:00:00 2024-04-08 21:24:59 2024-04-08 21:24:59
looptest.5 0:00:00 2024-04-08 21:24:59 2024-04-08 21:24:59
Print current position
from mdpython.debugutils import curpos
from random import randint
from math import factorial
def count_elements_in_array():
ar = list(range(randint(1000000, 10000000)))
print(len(ar))
def get_factorial():
for i in range(5):
num = randint(900, 1000)
print(num, factorial(num))
curpos.show()
count_elements_in_array()
curpos.show()
get_factorial()
Compare Files
from mdpython.fileutils import compare
compare.compare_files(r"C:\Users\Dell\OneDrive\Desktop\spark\b.py",
r"C:\Users\Dell\OneDrive\Desktop\spark\c.py",
r"C:\Users\Dell\OneDrive\Desktop\spark\out.html")
Track package changes
from mdpython.debugutils import version
version.save("app_dev", r"D:\data\version")
version.timeline("app_dev", r"D:\data\version")
version.compare("app_dev", r"D:\data\version")
Search for functions
import pandas as pd
from mdpython.debugutils import find
a = [1,2]
b = {"k": 1}
s = pd.Series(range(10))
find.search_function(pd, "")
find.list_elements(s, "truncate", showdoc=True)
Flatten JSON
from mdpython.datautils import jsonutil
import json
json_data = {
"name": "John",
"age": 30,
"car": {
"make": "Toyota",
"model": "Camry"
},
"colors": ["red", "blue", "green"],
"nested_list": [
[1, 2, 3],
{"hello": "world"},
[[7, 8], [9, 10]],
[[[11, 12], [13, 14]], [[], [17, 18]]]
],
"nested_dict": {
"info1": {"key1": "value1"},
"info2": {"key2": "value2"}
},
"list_of_dicts": [
{"item1": "value1"},
{"item2": "value2"}
]
}
flattened_data = jsonutil.flatten_json(json_data)
print(json.dumps(flattened_data, indent=2))
List of JSON object to CSV file
from mdpython.datautils import jsonutil
out_fl = r"C:\Users\Dell\Onedrive\Desktop\out.csv"
json_data = [
{
"name": "John",
"age": 30,
"car": {
"make": "Toyota",
"model": "Camry"
},
"colors": ["red", "blue", "green"]
}, {
"name": "Sheema",
"age": 25,
"car": {
"make": "Audi",
"model": "a4",
"dimension": [5000, 1850, 1433]
},
"colors": ["blue", "yellow"]
}, {
"name": "Bruce",
"car": {
"make": "Ford"
}
}
]
jsonutil.list_to_csv(json_data, out_fl)
Extract URLs from excel file
import mdpython.fileutils.excel as mdexcel
import json
from mdpython.datautils import jsonutil
# Get URLs
xl = mdexcel.Excel(r"c:\users\dell\onedrive\desktop\dummy_data.xlsx")
urls = xl.extract_urls(["A", "B"])
print(json.dumps(urls['data'], indent=2))
# Save as CSV
jsonutil.list_to_csv(urls['data'], r"c:\users\dell\onedrive\desktop\out.csv",
colkey=urls['keys'])
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