semvec/pypi-packages huggingface dataset:
set-nested-iters 0.10
Finds intersections/differences in nested iterables
License: MIT
Keywords: set,nested


# Finds intersections/differences in nested iterables  


```python
# Tested with:
# Python 3.9.13
# Windows 10


$pip install set-nested-iters


from set_nested_iters import (
    present_in_L1_and_L2,
    present_in_all_lists_reduce,
    unique_in_L1_and_L2,
    unique_in_each_list_reduce,
    present_only_in_L1,
    present_only_in_L1_reduce,
)
L0 = [{2, 5}, "a", 44, "jojjo", "xxx", {77: 100}]
L1 = [
    {2, 5},
    "a",
    ["bb", ["ccc", "ddd"], "ee", "ff"],
    "g",
    "h",
    "g",
    "h",
    3,
    4,
    5,
    0.0,
    1,
]
L2 = [
    {2, 5},
    "ac",
    (98, 11),
    ["bb", ["ccc", "ddd"], "ee", "ff"],
    "gc",
    "h",
    "gc",
    "h",
    11,
    2,
    3,
    1.0,
    0.0,
]
L3 = [
    {2, 5},
    "a",
    44,
    ["bb", ["ccc", "ddd"], "ee", "ff"],
    "g",
    "h",
    "g",
    "h",
    3,
    4,
    5,
    0.0,
    1,
]
L4 = [
    {2, 5},
    "ac",
    111112,
    (98, 11),
    ["bb", ["ccc", "ddd"], "ee", "ff"],
    "gc",
    "a",
    "h",
    "gc",
    "h",
    11,
    2,
    3,
    1.0,
    0.0,
]
erg0 = present_only_in_L1(L1, L2, withindex=False)
print(erg0)
erg1 = present_only_in_L1(L2, L1, withindex=False)
print(erg1)
erg2 = unique_in_L1_and_L2(L1, L2, withindex=False)
print(erg2)
erg3 = present_in_L1_and_L2(L1, L2, withindex=False)
print(erg3)
erg0 = present_only_in_L1(L1, L2, withindex=True)
print(erg0)
erg1 = present_only_in_L1(L2, L1, withindex=True)
print(erg1)
erg2 = unique_in_L1_and_L2(L1, L2, withindex=True)
print(erg2)
erg3 = present_in_L1_and_L2(L1, L2, withindex=True)
print(erg3)
print(present_in_all_lists_reduce(L1, [L2, L3, L4]))
print(unique_in_each_list_reduce(L1, [L2, L3, L4, L0]))
print(present_only_in_L1_reduce(L0, [L2, L3, L4, L1]))



['a', 'g', 4, 5]
['ac', (98, 11), 'gc', 11, 2]
['a', 'g', 4, 5, 'ac', (98, 11), 'gc', 11, 2]
[{2, 5}, ['bb', ['ccc', 'ddd'], 'ee', 'ff'], 'h', 3, 0.0, 1]
[(1, 'a'), (3, 'g'), (6, 4), (7, 5)]
[(1, 'ac'), (2, (98, 11)), (4, 'gc'), (6, 11), (7, 2)]
[(1, 'a'), (3, 'g'), (6, 4), (7, 5), (1, 'ac'), (2, (98, 11)), (4, 'gc'), (6, 11), (7, 2)]
[(0, {2, 5}), (2, ['bb', ['ccc', 'ddd'], 'ee', 'ff']), (4, 'h'), (5, 3), (8, 0.0), (9, 1)]
[{2, 5}, ['bb', ['ccc', 'ddd'], 'ee', 'ff'], 'h', 3, 0.0, 1]
[111112, {2, 5}, 'jojjo', 'xxx', {77: 100}]
['jojjo', 'xxx', {77: 100}]
	
```
yesmaster 3.0.1
Yesmaster is a terminal mastermind game. It's possible to play normally or let's play the computer with algorithm.
License: MIT
Requires Python: >=3.6,<4.0

Yes Master
==========
Goal
----
Yesmaster is a terminal mastermind game. It's possible to play normally or let's play the computer with algorithm :
- random_idiot : totally random and idiot
- random_no_repeat : random but never the same code twice
- compatible : play random but take in consideration the verification and play only code compatible with it
- compatible_8in2 : play the two first code with 8 colors and after that play compatible
- compatible_4in1 : play like compatible but the first must be 4 differents colors
- one_step_ahead : play the move that has the highest expected elimination

Installation
------------
The program is available on [Pypi](https://pypi.org/project/yesmaster/).
You need at least python 3.6 and you can use pipx to install it.
```
pipx install yesmaster
```

Usage
-----
```
usage: yesmaster.py [-h] [--algo ALGO] [--loop LOOP] {play,auto}

positional arguments:{play,auto}  play yourself or let's computer playing

optional arguments:
  -h, --help   show this help message and exit
  --algo ALGO  Choose an algo present in the algo folder
  --loop LOOP  Number of games
```
Test your algorithm
-------------------
You can add your personnal algorithm with a python file in the algo directory (the name of the python file will be the name of the algo to call in the command line). A template file shows you how your algo must be organized. Two functions are mandatory in a class name Algo :
- get(self) : executed for choosing the code. Must return a 4 letters string with letters among ROGBYAPW
- report(self, test, good, bad) : executed with 3 arguments (test : the code that you pushed with get, good : the number of goods colors at good place, bad : the number of goods colors at wrong place). Return nothing.
lazydev-utils 0.1.6
Lazy utils with broad different function to improve python implementation
License: MIT
Requires Python: >=3.6

# LazyUtils
Repository with diverse implementations so you can reuse on others projects.

Lazy utils was created to have a lot of broad functions and implementations
 that help during the coding. Currently have the following general features:
  - Configuration file
  - File management
  - Persistance local storage feature
  - Secrets management with Local storage
  - Classes structure like Singleton and Callable
  - Randomize function to generate strings
get-consecutive-filename 0.10
Next consecutive filename in folder
License: MIT
Keywords: folder


# Next consecutive filename in folder 


```python
# Tested with:
# Python 3.9.13
# Windows 10

$pip install get-consecutive-filename

from get_consecutive_filename import get_free_filename
# What is it for?
# You have a folder "F:\testfiles" with 2 files "00000.txt", "00001.txt"
# and you want to get the next consecutive filename.

fname = get_free_filename(folder="f:\\testfiles", fileextension=".txt", leadingzeros=5)
print(fname)
# f:\testfiles\00002.txt
	
```
Pandarize 0.0.8.11
Turns data into panda dataframe
License: MIT

# Pandarize
This project aims to turn all kinds of data structure/types into a nice tabulated pandas DataFrame

# Installation
```
pip install pandarize
```

# Basic Usage Guide
```python
from pandarize.frame import Pandarizer

pdr = Pandarizer() #instantiate Pandarizer class
pdr.load(source='https://somewebsite.com/filename.bib') #it can load from url or local source
pdr.fit() #infers data types and parse it into pandas dataframe
pdr.transform() #changes pandas dataframe into different mode of data types
```

# Currently Supported Data Types
- bib