#Тема 4 <Лазарев Данил Вячеславович>
import os
os.chdir('C:\\Users\\Dannyan\\OneDrive\\Рабочий стол\\python-labs\\TEMA4\\')
help(round)
Help on built-in function round in module builtins:

round(number, ndigits=None)
    Round a number to a given precision in decimal digits.

    The return value is an integer if ndigits is omitted or None.  Otherwise
    the return value has the same type as the number.  ndigits may be negative.

2.Стандартные функции.
2.1
round(123.456,1)
123.5
round(123.456,0)
123.0
type(round(123.456,0))
<class 'float'>
type(round(123.456,1))
<class 'float'>
round(123.456)
123
type(round(123.456))
<class 'int'>
2.2
gg=range(76,123,9)
list(gg)
[76, 85, 94, 103, 112, 121]
a = range(23)
list(a)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]
#значения от 0 до 22, шаг 1, границы 0 - 22
2.3
qq = 'Lazarev'
ww = 'Anisenkov'
rr = 'Fillipova'
ee = 'Jalnin'
ff=zip(gg,qq)
tuple(ff)
((76, 'L'), (85, 'a'), (94, 'z'), (103, 'a'), (112, 'r'), (121, 'e'))
ff[1]
Traceback (most recent call last):
  File "<pyshell#24>", line 1, in <module>
    ff[1]
TypeError: 'zip' object is not subscriptable
2.4
fff=float(input('коэффициент усиления=')); dan=eval('5*fff-156')
коэффициент усиления=5
dan
-131.0
2.5
exec(input('введите инструкции:'))
введите инструкции:perem=-123.456;gg=round(abs(perem)+98,3)
gg
221.456
2.6
abs(-36)
36
pow(2,2)
4
max([1,2,3,4,33,3,2,1,5,0])
33
min([1,2,3,4,33,3,2,1,5,0])
0
sum([1,2,3,4,33,3,2,1,5,0])
54
divmod(7,3)
(2, 1)
len([1,2,3,4,33,3,2,1,5,0])
10
o = [1,2,3,4,33,3,2,1,5,0]
def f(x):
    return x*x*x
list(map(f,o))
[1, 8, 27, 64, 35937, 27, 8, 1, 125, 0]
3. Функции из стандартного модуля math
import math
dir(math)
['__doc__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'cbrt', 'ceil', 'comb', 'copysign', 'cos', 'cosh', 'degrees', 'dist', 'e', 'erf', 'erfc', 'exp', 'exp2', 'expm1', 'fabs', 'factorial', 'floor', 'fma', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'isqrt', 'lcm', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'nextafter', 'perm', 'pi', 'pow', 'prod', 'radians', 'remainder', 'sin', 'sinh', 'sqrt', 'sumprod', 'tan', 'tanh', 'tau', 'trunc', 'ulp']
help(math.factorial)
Help on built-in function factorial in module math:

factorial(n, /)
    Find n!.
math.factorial(5)
120
math.sin(1)
0.8414709848078965
math.acos(1)
0.0
math.degrees(36)
2062.648062470964
math.radians(2062.648062470964)
36.00000000000001
math.exp(2)
7.38905609893065
math.log(100)
4.605170185988092
math.log10(100)
2.0
math.sqrt(100)
10.0
math.ceil(58.3)
59
math.floor(58.3)
58
math.pi
3.141592653589793
math.sin(((2*math.pi)/7 + math.exp(0.23)))

0.8334902641414562
4.Функции из модуля cmath
import cmath
dir(cmath)
['__doc__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cos', 'cosh', 'e', 'exp', 'inf', 'infj', 'isclose', 'isfinite', 'isinf', 'isnan', 'log', 'log10', 'nan', 'nanj', 'phase', 'pi', 'polar', 'rect', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau']
cmath.sqrt(1.2-0.5j)
(1.118033988749895-0.22360679774997896j)
cmath.phase(1-0.5j)
-0.4636476090008061
5. Стандартный модуль random
import random
dir(random)
['BPF', 'LOG4', 'NV_MAGICCONST', 'RECIP_BPF', 'Random', 'SG_MAGICCONST', 'SystemRandom', 'TWOPI', '_ONE', '_Sequence', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_accumulate', '_acos', '_bisect', '_ceil', '_cos', '_e', '_exp', '_fabs', '_floor', '_index', '_inst', '_isfinite', '_lgamma', '_log', '_log2', '_os', '_parse_args', '_pi', '_random', '_repeat', '_sha512', '_sin', '_sqrt', '_test', '_test_generator', '_urandom', 'betavariate', 'binomialvariate', 'choice', 'choices', 'expovariate', 'gammavariate', 'gauss', 'getrandbits', 'getstate', 'lognormvariate', 'main', 'normalvariate', 'paretovariate', 'randbytes', 'randint', 'random', 'randrange', 'sample', 'seed', 'setstate', 'shuffle', 'triangular', 'uniform', 'vonmisesvariate', 'weibullvariate']
help(random.seed)
Help on method seed in module random:

seed(a=None, version=2) method of random.Random instance
    Initialize internal state from a seed.

    The only supported seed types are None, int, float,
    str, bytes, and bytearray.

    None or no argument seeds from current time or from an operating
    system specific randomness source if available.

    If *a* is an int, all bits are used.

    For version 2 (the default), all of the bits are used if *a* is a str,
    bytes, or bytearray.  For version 1 (provided for reproducing random
    sequences from older versions of Python), the algorithm for str and
    bytes generates a narrower range of seeds.
random.seed()
r = random.random()
r
0.7602265821504725
a = random.uniform(1,3)
               
a     
1.6506391388455899

b = random.randint(1, 19)
               
b            
8

c = random.gauss()
               
c        
-2.8896415965159985

d = random.gauss(1,0)
               
d          
1.0

d = random.gauss(1,1)
               
d             
0.05266125395944099

b =[2,3,1,2,3,4,6]
               
e = random.choice(b)
               
e        
2

random.shuffle(b)
               
b           
[3, 2, 2, 6, 3, 1, 4]

k =  random.sample(b,4)
               
k           
[3, 2, 2, 4]

p = random.betavariate(1,2)
               
p              
0.48596997669197367

p = random.gammavariate(1,1)
               
p             
1.2802780104125937

Создадим свой список с заданными распределениями
f = [ random.uniform(1,3),  random.gauss(1,2), random.betavariate(2,2), random.gammavariate(2,3)]
               
f             
[2.247730869060465, 3.871209301585315, 0.39715861559850196, 3.959028762460888]
6. Функции модуля time - работа с календарем и со временем
import time
dir(time)
['_STRUCT_TM_ITEMS', '__doc__', '__loader__', '__name__', '__package__', '__spec__', 'altzone', 'asctime', 'ctime', 'daylight', 'get_clock_info', 'gmtime', 'localtime', 'mktime', 'monotonic', 'monotonic_ns', 'perf_counter', 'perf_counter_ns', 'process_time', 'process_time_ns', 'sleep', 'strftime', 'strptime', 'struct_time', 'thread_time', 'thread_time_ns', 'time', 'time_ns', 'timezone', 'tzname']
c1=time.time()
c1
1761161531.581906
c2=time.time()-c1
c2
20.391149044036865
dat=time.gmtime()
dat
time.struct_time(tm_year=2025, tm_mon=10, tm_mday=22, tm_hour=19, tm_min=33, tm_sec=7, tm_wday=2, tm_yday=295, tm_isdst=0)
dat.tm_mon
10
dat.tm_year
2025
dat.tm_hour
19
dat.tm_min
33
c1 = time.localtime
c1
<built-in function localtime>
c1 = time.localtime()
c1
time.struct_time(tm_year=2025, tm_mon=10, tm_mday=22, tm_hour=22, tm_min=38, tm_sec=57, tm_wday=2, tm_yday=295, tm_isdst=0)
c2 = time.asctime(c1)
c2
'Wed Oct 22 22:38:57 2025'
t1 = time.ctime()
t1
'Wed Oct 22 22:44:33 2025'
time.sleep(2)
t2 = time.mktime(c1)
t2
1761161937.0
7. Графические функции.
import pylab
x=list(range(-3,55,4))
t=list(range(15))
pylab.plot(t,x)
[<matplotlib.lines.Line2D object at 0x000001F66C044B90>]
pylab.title('Первый график')
Text(0.5, 1.0, 'Первый график')
pylab.xlabel('время')
Text(0.5, 0, 'время')
pylab.ylabel('сигнал')
Text(0, 0.5, 'сигнал')
pylab.show()
X1=[12,6,8,10,7]
X2=[5,7,9,11,13]
pylab.plot(X1)
[<matplotlib.lines.Line2D object at 0x000001F66CE951D0>]
pylab.plot(X2)
[<matplotlib.lines.Line2D object at 0x000001F66CE95310>]
pylab.show()
region=['Центр','Урал','Сибирь','Юг']
naselen=[65,12,23,17]
pylab.pie(naselen,labels=region)
([<matplotlib.patches.Wedge object at 0x000001F66BB4DA90>, <matplotlib.patches.Wedge object at 0x000001F66C0FDD10>, <matplotlib.patches.Wedge object at 0x000001F66C0FE850>, <matplotlib.patches.Wedge object at 0x000001F66C0FE5D0>], [Text(-0.191013134139045, 1.0832885038559115, 'Центр'), Text(-0.861328292412156, -0.6841882582231001, 'Урал'), Text(0.04429273995539947, -1.0991078896938387, 'Сибирь'), Text(0.9873750693480946, -0.48486129194837324, 'Юг')])
pylab.show()
a = [1,2,1,3,2,4,5,8,5,5,5,2,2,2,2,2]
pylab.hist(a,bins = 6)
(array([9., 1., 1., 4., 0., 1.]), array([1.        , 2.16666667, 3.33333333, 4.5       , 5.66666667,
       6.83333333, 8.        ]), <BarContainer object of 6 artists>)
pylab.title('Гистограмма')
Text(0.5, 1.0, 'Гистограмма')
pylab.show()
marks = ['BMW','Audi','Lada','Reno']
cost = [20,17,5,10]
pylab.bar(marks,cost)
<BarContainer object of 4 artists>
pylab.title('Stolb')
Text(0.5, 1.0, 'Stolb')
pylab.show()
8. Модуль statistics.
import statistics
dir(statistics)
['Counter', 'Decimal', 'Fraction', 'LinearRegression', 'NormalDist', 'StatisticsError', '_SQRT2', '__all__', '__annotations__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_coerce', '_convert', '_decimal_sqrt_of_frac', '_exact_ratio', '_fail_neg', '_float_sqrt_of_frac', '_integer_sqrt_of_frac_rto', '_isfinite', '_kernel_invcdfs', '_mean_stdev', '_newton_raphson', '_normal_dist_inv_cdf', '_quartic_invcdf', '_quartic_invcdf_estimate', '_random', '_rank', '_sqrt_bit_width', '_sqrtprod', '_ss', '_sum', '_triweight_invcdf', '_triweight_invcdf_estimate', 'acos', 'asin', 'atan', 'bisect_left', 'bisect_right', 'correlation', 'cos', 'cosh', 'count', 'covariance', 'defaultdict', 'erf', 'exp', 'fabs', 'fmean', 'fsum', 'geometric_mean', 'groupby', 'harmonic_mean', 'hypot', 'isfinite', 'isinf', 'itemgetter', 'kde', 'kde_random', 'linear_regression', 'log', 'math', 'mean', 'median', 'median_grouped', 'median_high', 'median_low', 'mode', 'multimode', 'namedtuple', 'numbers', 'pi', 'pstdev', 'pvariance', 'quantiles', 'random', 'reduce', 'repeat', 'sin', 'sqrt', 'stdev', 'sumprod', 'sys', 'tan', 'tau', 'variance']
a = [1,2,3,4,5,6,7,8,9,10,9,8,7,6,5,4,3,2,1]
statistics.mean(a)
5.2631578947368425
statistics.median(a)
5
b = [1,2,1,1,1,2,3,4,5,6,9]
statistics.mode(b)
1
c = statistics.stdev(a)
c
2.8253240770486627
d = [1,2,3,2,1]
v =statistics.variance(d)
v
0.7