cobanov

#NoEnv  ; Recommended for performance and compatibility with future AutoHotkey releases.
; #Warn  ; Enable warnings to assist with detecting common errors.
SendMode Input  ; Recommended for new scripts due to its superior speed and reliability.
SetWorkingDir %A_ScriptDir%  ; Ensures a consistent starting directory.

<^>!a::
Send, {(}
return

<^>!s::

cobanov

#NoEnv  ; Recommended for performance and compatibility with future AutoHotkey releases.
; #Warn  ; Enable warnings to assist with detecting common errors.
SendMode Input  ; Recommended for new scripts due to its superior speed and reliability.
SetWorkingDir %A_ScriptDir%  ; Ensures a consistent starting directory.

<^>!a::
Send, {(}
return

<^>!s::

cobanov

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
import argparse

# Parser 
parser = argparse.ArgumentParser()

parser.add_argument("--path","-p")
parser.add_argument("--target","-t")

cobanov

def print_return_type(func):
  def wrapper(*args, **kwargs):
    result = func(*args, **kwargs)
    print(f"{func.__name__}, {type(result)}")
    return result
  return wrapper

cobanov

from manimlib.imports import *
import math

class Shape(Scene):
    def construct(self):
        circle = Circle()

        self.play(ShowCreation(Circle()))
        self.wait(1)

cobanov

[[inputs.file]]
  files = ["avocado.csv"]
  data_format = "csv"
  csv_header_row_count = 0
  csv_column_names = []
  csv_column_types = []
  csv_skip_rows = 0
  csv_skip_columns = 1
  csv_delimiter = ","
  csv_comment = ""

cobanov

import pandas as pd
import numpy as np

d = {'col1': [1, 2, 3, 4, 5, 6, 100], 'col2': [  2,   4,   6,   8,  10,  12, 200]}
df = pd.DataFrame(d)
print(df)


def detect_outlier(df, threshold):
    outliers = []

cobanov

print("sumeyye")

cobanov

print("sumeyye")

cobanov

# -*- coding: utf-8 -*-
"""
Created on Wed Apr  3 00:47:42 2019

@author: coban
"""
import pandas as pd
import numpy as np