| Name | mdpython JSON |
| Version |
0.0.16
JSON |
| download |
| home_page | None |
| Summary | MD Python Library |
| upload_time | 2024-10-02 06:58:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.0 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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'])
# Search for text inside JSON data
from mdpython.datautils import jsonutil
json_data = {
"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"
}
}
]
}
print(jsonutil.search(json_data, "blue"))
# Output
[['data.0.colors.1', 'blue'], ['data.1.colors.0', 'blue']]
# Get all objects for a particular key in JSON
from mdpython.datautils import jsonutil
json_data = {
"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"
}
}
]
}
print(jsonutil.find_values_by_key(json_data, "colors"))
# Output
[['red', 'blue', 'green'], ['blue', 'yellow']]
# Reconcile bills and payments
import pandas as pd
from mdpython.account import reconcile
inp_fl = r"C:\Users\Dell\Onedrive\Desktop\input.xlsx"
out_fl = r"C:\Users\Dell\Onedrive\Desktop\output.xlsx"
disc_ar = [2, 2.5, 5, 10]
bill_df = pd.read_excel(inp_fl, usecols="B:C").dropna()
pymt_df = pd.read_excel(inp_fl, usecols="G:H").dropna()
recon = reconcile.reconcile_payment(
bill_df=bill_df
, pymt_df=pymt_df
, bill_dt_col="Bill Date"
, bill_amt_col="Bill Amount"
, pymt_dt_col="Payment Date"
, pymt_amt_col="Payment Amount"
, disc_ar=disc_ar
)
print(recon.bill_dtl_df)
print(recon.pymt_dtl_df)
recon.to_excel(out_fl)
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"description": "# MD Python Library\n\nThis package contains high level functions for common Python use cases.\n\n# Compare Directories\n\n from mdpython.fileutils import compare \n \n cmp = compare.compare_dirs(r\"C:\\Users\\Dell\\OneDrive\\Desktop\\result_9th\", \n r\"C:\\Users\\Dell\\OneDrive\\Desktop\\result_9th_v2\") \n \n cmp.gen_html_report(r\"C:\\Users\\Dell\\OneDrive\\Desktop\\out.html\", [\"py\", \"txt\", \n \"json\"]) \n \n for fl in cmp.files_only_in_right: \n if fl.name.endswith(\"py\"): \n print(fl.absolute())\n\n\n# Menu based app\n\n from datetime import datetime\n from random import randint, choice\n from mdpython.uiutils import menu_based_app\n \n def show_date():\n print(datetime.now().strftime(\"%Y-%m-%d\"))\n \n \n def show_time():\n print(datetime.now().strftime(\"%H:%M:%S\"))\n \n \n def show_date_and_time():\n print(datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n \n \n def show_random_number():\n print(randint(1, 100))\n \n \n def show_random_color():\n print(choice(['red', 'blue', 'green']))\n \n \n ar = [\n [\"Random\", \"Random Integer\", show_random_number],\n [\"Random\", \"Random Color\", show_random_color],\n [\"Date\", \"Show Date\", show_date],\n [\"Date\", \"Show Time\", show_time],\n [\"Date\", \"Show Date and Time\", show_date_and_time]\n ]\n \n menu_based_app.start(ar)\n\n# Disk cleanup\n\n from mdpython.fileutils import cleanup\n \n dr = r\"C:\\Users\\Dell\\OneDrive\\Desktop\\result_9th\"\n \n info = cleanup.retrieve_info(dr)\n \n print(\"sorted by time\")\n for dtl in info.sort_by_time()[:5]:\n print(dtl)\n \n print(\"\\nsorted by size\")\n for dtl in info.sort_by_size()[:5]:\n print(dtl)\n \n print(\"\\nmodified in last 30 mins\")\n for dtl in info.modified_within(mins=30)[:5]:\n print(dtl)\n \n print(\"\\nmodified more than 1 day ago\")\n for dtl in info.modified_before(mins=24 * 60)[:5]:\n print(dtl)\n \n print(\"\\nsorted by number of files in directory\")\n for dtl in info.sort_by_file_count()[:5]:\n print(dtl)\n\n# Getting execution duration\n\n from mdpython.debugutils import timer\n from random import randint\n from math import factorial\n \n timer.start(\"main\")\n \n def count_elements_in_array():\n ar = list(range(randint(1000000,10000000)))\n print(len(ar))\n \n def get_factorial():\n for i in range(5):\n timer.start(\"looptest\")\n num = randint(900,1000)\n print(num, factorial(num))\n timer.stop(\"looptest\")\n \n timer.start(\"func1\")\n count_elements_in_array()\n timer.stop(\"func1\")\n \n get_factorial()\n \n timer.stop(\"main\")\n \n timer.show()\n\n -- Output\n Name Duration Start Time End Time \n ============================== ==================== ==================== ====================\n main 0:00:00.046207 2024-04-08 21:24:59 2024-04-08 21:24:59 \n func1 0:00:00.033129 2024-04-08 21:24:59 2024-04-08 21:24:59 \n looptest.4 0:00:00.010020 2024-04-08 21:24:59 2024-04-08 21:24:59 \n looptest.2 0:00:00.003058 2024-04-08 21:24:59 2024-04-08 21:24:59 \n looptest.1 0:00:00 2024-04-08 21:24:59 2024-04-08 21:24:59 \n looptest.3 0:00:00 2024-04-08 21:24:59 2024-04-08 21:24:59 \n looptest.5 0:00:00 2024-04-08 21:24:59 2024-04-08 21:24:59 \n\n# Print current position\n\n from mdpython.debugutils import curpos\n from random import randint\n from math import factorial\n \n \n def count_elements_in_array():\n ar = list(range(randint(1000000, 10000000)))\n print(len(ar))\n \n \n def get_factorial():\n for i in range(5):\n num = randint(900, 1000)\n print(num, factorial(num))\n curpos.show()\n \n \n count_elements_in_array()\n curpos.show()\n \n get_factorial()\n\n# Compare Files\n\n from mdpython.fileutils import compare\n \n compare.compare_files(r\"C:\\Users\\Dell\\OneDrive\\Desktop\\spark\\b.py\",\n r\"C:\\Users\\Dell\\OneDrive\\Desktop\\spark\\c.py\",\n r\"C:\\Users\\Dell\\OneDrive\\Desktop\\spark\\out.html\")\n\n# Track package changes\n\n from mdpython.debugutils import version\n \n version.save(\"app_dev\", r\"D:\\data\\version\")\n version.timeline(\"app_dev\", r\"D:\\data\\version\")\n version.compare(\"app_dev\", r\"D:\\data\\version\")\n\n# Search for functions\n\n import pandas as pd\n from mdpython.debugutils import find\n \n a = [1,2]\n b = {\"k\": 1}\n s = pd.Series(range(10))\n \n find.search_function(pd, \"\")\n find.list_elements(s, \"truncate\", showdoc=True)\n \n# Flatten JSON\n\n from mdpython.datautils import jsonutil\n import json\n \n json_data = {\n \"name\": \"John\",\n \"age\": 30,\n \"car\": {\n \"make\": \"Toyota\",\n \"model\": \"Camry\"\n },\n \"colors\": [\"red\", \"blue\", \"green\"],\n \"nested_list\": [\n [1, 2, 3],\n {\"hello\": \"world\"},\n [[7, 8], [9, 10]],\n [[[11, 12], [13, 14]], [[], [17, 18]]]\n ],\n \"nested_dict\": {\n \"info1\": {\"key1\": \"value1\"},\n \"info2\": {\"key2\": \"value2\"}\n },\n \"list_of_dicts\": [\n {\"item1\": \"value1\"},\n {\"item2\": \"value2\"}\n ]\n }\n \n flattened_data = jsonutil.flatten_json(json_data)\n print(json.dumps(flattened_data, indent=2))\n\n# List of JSON object to CSV file\n\n from mdpython.datautils import jsonutil\n \n out_fl = r\"C:\\Users\\Dell\\Onedrive\\Desktop\\out.csv\"\n \n json_data = [\n {\n \"name\": \"John\",\n \"age\": 30,\n \"car\": {\n \"make\": \"Toyota\",\n \"model\": \"Camry\"\n },\n \"colors\": [\"red\", \"blue\", \"green\"]\n }, {\n \"name\": \"Sheema\",\n \"age\": 25,\n \"car\": {\n \"make\": \"Audi\",\n \"model\": \"a4\",\n \"dimension\": [5000, 1850, 1433]\n },\n \"colors\": [\"blue\", \"yellow\"]\n }, {\n \"name\": \"Bruce\",\n \"car\": {\n \"make\": \"Ford\"\n }\n }\n ]\n \n jsonutil.list_to_csv(json_data, out_fl)\n\n# Extract URLs from excel file\n\n import mdpython.fileutils.excel as mdexcel\n import json\n from mdpython.datautils import jsonutil\n \n # Get URLs\n xl = mdexcel.Excel(r\"c:\\users\\dell\\onedrive\\desktop\\dummy_data.xlsx\")\n urls = xl.extract_urls([\"A\", \"B\"])\n print(json.dumps(urls['data'], indent=2))\n \n # Save as CSV\n jsonutil.list_to_csv(urls['data'], r\"c:\\users\\dell\\onedrive\\desktop\\out.csv\",\n colkey=urls['keys'])\n\n# Search for text inside JSON data\n\n from mdpython.datautils import jsonutil\n \n json_data = {\n \"data\": [\n {\n \"name\": \"John\",\n \"age\": 30,\n \"car\": {\n \"make\": \"Toyota\",\n \"model\": \"Camry\"\n },\n \"colors\": [\"red\", \"blue\", \"green\"]\n }, {\n \"name\": \"Sheema\",\n \"age\": 25,\n \"car\": {\n \"make\": \"Audi\",\n \"model\": \"a4\",\n \"dimension\": [5000, 1850, 1433]\n },\n \"colors\": [\"blue\", \"yellow\"]\n }, {\n \"name\": \"Bruce\",\n \"car\": {\n \"make\": \"Ford\"\n }\n }\n ]\n }\n \n print(jsonutil.search(json_data, \"blue\"))\n\n # Output\n [['data.0.colors.1', 'blue'], ['data.1.colors.0', 'blue']]\n \n# Get all objects for a particular key in JSON\n\n from mdpython.datautils import jsonutil\n\n json_data = {\n \"data\": [\n {\n \"name\": \"John\",\n \"age\": 30,\n \"car\": {\n \"make\": \"Toyota\",\n \"model\": \"Camry\"\n },\n \"colors\": [\"red\", \"blue\", \"green\"]\n }, {\n \"name\": \"Sheema\",\n \"age\": 25,\n \"car\": {\n \"make\": \"Audi\",\n \"model\": \"a4\",\n \"dimension\": [5000, 1850, 1433]\n },\n \"colors\": [\"blue\", \"yellow\"]\n }, {\n \"name\": \"Bruce\",\n \"car\": {\n \"make\": \"Ford\"\n }\n }\n ]\n }\n \n print(jsonutil.find_values_by_key(json_data, \"colors\"))\n\n # Output\n [['red', 'blue', 'green'], ['blue', 'yellow']]\n\n# Reconcile bills and payments\n\n import pandas as pd\n from mdpython.account import reconcile\n \n inp_fl = r\"C:\\Users\\Dell\\Onedrive\\Desktop\\input.xlsx\"\n out_fl = r\"C:\\Users\\Dell\\Onedrive\\Desktop\\output.xlsx\"\n \n disc_ar = [2, 2.5, 5, 10]\n \n bill_df = pd.read_excel(inp_fl, usecols=\"B:C\").dropna()\n pymt_df = pd.read_excel(inp_fl, usecols=\"G:H\").dropna()\n \n recon = reconcile.reconcile_payment(\n bill_df=bill_df\n , pymt_df=pymt_df\n , bill_dt_col=\"Bill Date\"\n , bill_amt_col=\"Bill Amount\"\n , pymt_dt_col=\"Payment Date\"\n , pymt_amt_col=\"Payment Amount\"\n , disc_ar=disc_ar\n )\n \n print(recon.bill_dtl_df)\n print(recon.pymt_dtl_df)\n \n recon.to_excel(out_fl)\n",
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