Python-сообщество

 QComboBox {
    border: 2px solid #f66867;
    border-radius: 30px;
	background-color: #22222e;
	color: white;
	padding-left: 35px;
}
QListView {
	background-color: #f66867;
	border-radius: 3px;
    selection-background-color: #fa4244;
}
QComboBox::drop-down {
	border-radius: 3px;
}

         self.comboBox_3.setEditable(False)
        self.comboBox_3.setObjectName("comboBox_3")
        self.comboBox_3.addItem("")
        self.comboBox_3.addItem("")
        self.comboBox_3.addItem("")
        self.comboBox_4 = QtWidgets.QComboBox(self.centralwidget)
        self.comboBox_4.setGeometry(QtCore.QRect(300, 320, 381, 61))
        font = QtGui.QFont()
        font.setFamily("Montserrat")
        font.setPointSize(14)
        font.setBold(True)
        font.setWeight(75)
        self.comboBox_4.setFont(font)
        self.comboBox_4.setStyleSheet("QComboBox {\n"
"    border: 2px solid #f66867;\n"
"    border-radius: 30px;\n"
"    background-color: #22222e;\n"
"    color: white;\n"
"    padding-left: 35px;\n"
"}\n"
"\n"
"QComboBox QListView {\n"
"    background-color: #f66867;\n"
"    border-radius: 3px;\n"
"    selection-background-color: #fa4244;\n"
"}\n"
"\n"
"QComboBox::drop-down {\n"
"    border-radius: 3px;\n"
"}")

 "QComboBox QListView {\n"
"    background-color: #f66867;\n"
"    border-radius: 3px;\n"
"    selection-background-color: #fa4244;\n"
"}\n"

   
from dataclasses import dataclass
from typing import List, Union
  
from validated_dc import ValidatedDC
  
  
# Some combinations of List and Union
  
@dataclass
class Foo(ValidatedDC):
    value: Union[int, List[int]]
  
  
@dataclass
class Bar(ValidatedDC):
    foo: Union[Foo, List[Foo]]
  
  
# --- Valid input ---
  
foo = {'value': 1}
instance = Bar(foo=foo)
assert instance.get_errors() is None
assert instance == Bar(foo=Foo(value=1))
  
foo = {'value': [1, 2]}
instance = Bar(foo=foo)
assert instance.get_errors() is None
assert instance == Bar(foo=Foo(value=[1, 2]))
  
foo = [{'value': 1}, {'value': 2}]
instance = Bar(foo=foo)
assert instance.get_errors() is None
assert instance == Bar(foo=[Foo(value=1), Foo(value=2)])
  
foo = [{'value': [1, 2]}, {'value': [3, 4]}]
instance = Bar(foo=foo)
assert instance.get_errors() is None
assert instance == Bar(foo=[Foo(value=[1, 2]), Foo(value=[3, 4])])
  
  
# --- Invalid input ---
  
foo = {'value': 'S'}
instance = Bar(foo=foo)
assert instance.get_errors()
assert instance == Bar(foo={'value': 'S'})
# fix
instance.foo['value'] = 1
assert instance.is_valid()
assert instance.get_errors() is None
assert instance == Bar(foo=Foo(value=1))
  
foo = [{'value': [1, 2]}, {'value': ['S', 4]}]
instance = Bar(foo=foo)
assert instance.get_errors()
assert instance == Bar(foo=[{'value': [1, 2]}, {'value': ['S', 4]}])
# fix
instance.foo[1]['value'][0] = 3
assert instance.is_valid()
assert instance.get_errors() is None
assert instance == Bar(foo=[Foo(value=[1, 2]), Foo(value=[3, 4])])
  
  
# --- get_errors() ---
  
foo = {'value': 'S'}
instance = Bar(foo=foo)
print(instance.get_errors())
# {
#   'foo': [
#       # An unsuccessful attempt to use the dictionary to create a Foo instance
#       InstanceValidationError(
#           value_repr="{'value': 'S'}",
#           value_type=<class 'dict'>,
#           annotation=<class '__main__.Foo'>,
#           exception=None,
#           errors={
#               'value': [
#                   BasicValidationError(  # because the str isn't an int
#                       value_repr='S', value_type=<class 'str'>,
#                       annotation=<class 'int'>, exception=None
#                   ),
#                   BasicValidationError(  # and the str is not a list of int
#                       value_repr='S', value_type=<class 'str'>,
#                       annotation=typing.List[int], exception=None
#                   )
#               ]
#           }
#       ),
#       BasicValidationError(  # the dict is not a list of Foo
#           value_repr="{'value': 'S'}",
#           value_type=<class 'dict'>,
#           annotation=typing.List[__main__.Foo],
#           exception=None
#       )
#   ]
# }

  Задача: Даны две функции y=x**2 и y=x**2+5. Выбираем интервал для x от -5 до +5 с шагом 1. Необходимо найти в этом интервале такие значения y, чтобы ломаная, построенная из полученных новых точек (x,y - целые числа, шаг 1 от -5 до +5) ломаная линия находилась строго между этими функциями и угол поворота был не меньше 110 градусов. Результат программы - можно или нет построить такую ломаную и один из вариантов такой ломаной со значениями x,y при шаге 1 по оси х. . Эта задача близка к управлению оборудованием по данным датчиков.

 import math
def f(x):
    return x * x * x + 2 * x * x - 8 * x + 1 + 2 * math.sin(x) + 15 * math.cos(x)
a = -5
b = 5
delta = 0.001
h = 0.05
x = a
while x <= b:
    if f(x) * f(x * delta) <= 0:
        print(x + delta / 2)
    x += h

 import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
pd.options.display.max_rows = 1000
data = pd.read_csv("https://video.ittensive.com/python-advanced/data-9753-2019-07-25.utf.csv", delimiter = ";")
data = data.groupby("Year").filter(lambda x : x["UnemployedTotal"].count() < 6)
data["Year"] = data["Year"].astype("category")
data_group = data.groupby("Year").mean()
x = np.array(data_group.index).reshape(len(data_group.index),1)
y = np.array(data_group["UnemployedDisabled"]/data_group["UnemployedTotal"]*100).reshape(len(data_group.index),1)
model = LinearRegression()
model.fit(x, y)
plt.scatter(x,y , color ="orange")
x = np.append(x,[2020]).reshape(len(data_group.index)+1,1)
plt.plot(x, model.predict(x), color = "blue", linewidth = 3)
plt.show()
print(model.predict(np.array(2020).reshape(1,1)))
#print(data_group)