# Отчет по теме 4 Девятова Мария, А-03-23 ## 1 Запуск IDLE ## 2 Стандартные функции ### 2.1 Функция round Округление числа с заданной точностью ``` a=round(123.456,1); a; type(a) 123.5 b=round(123.456,0); b; type(b) 123.0 c=round(123.456); c; type(c) 123 ``` ### 2.2 Функция range Создание последовательности целых чисел с заданным шагом (по умолчанию с шагом 1). ``` gg=range(76,123,9) gg range(76, 123, 9) list(gg) [76, 85, 94, 103, 112, 121] gg1=range(23); list(gg1) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22] ``` ### 2.3 Функция zip Создание общего объекта, элементами которого являются кортежи, составленные из элементов двух или более объектов-последовательностей ``` qq=['Девятова', 'Ефимова', 'Беженарь', 'Гордиевских'] ff=zip(gg,qq) ff tuple(ff) ((76, 'Девятова'), (85, 'Ефимова'), (94, 'Беженарь'), (103, 'Гордиевских')) ff[1] Traceback (most recent call last): File "", line 1, in ff[1] TypeError: 'zip' object is not subscriptable ``` ### 2.4 Функция eval Вычисление значения выражения, записанного в виде символьной строки ``` fff=float(input('коэффициент усиления=')); dan=eval('5*fff-156') коэффициент усиления=81 dan 249.0 ``` ### 2.5 Функция exec Выполнение инструкций, переданных в аргумент функции в строковом виде ``` exec(input('введите инструкции:')) введите инструкции:perem=-123.456;gg=round(abs(perem)+98,3) gg 221.456 ``` ### 2.6 Функции abs, pow, max, min, sum, divmod, len, map ``` abs(-9) 9 pow(7, 3) 343 max(7, 9, 0) 9 min(-9, 0, 8) -9 sum([1, -8, 7, 2]) 2 divmod(43, 6) (7, 1) divmod(-43, 6) (-8, 5) divmod(-43, -6) (7, -1) len(qq) 4 length_words=map(len, qq); list(length_words) [8, 7, 8, 11] ``` ## 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!. Raise a ValueError if x is negative or non-integral. math.factorial(5) 120 math.pi 3.141592653589793 math.degrees(1) 57.29577951308232 math.radians(60) 1.0471975511965976 math.sin(math.radians(30)) 0.49999999999999994 math.degrees(math.acos(0.5)) 60.00000000000001 math.degrees(math.acos(12)) Traceback (most recent call last): File "", line 1, in math.degrees(math.acos(12)) ValueError: math domain error math.exp(2) 7.38905609893065 math.log(math.e) 1.0 math.log(49, 7) 2.0 math.log(-49, 7) Traceback (most recent call last): File "", line 1, in math.log(-49, 7) ValueError: math domain error math.log10(10) 1.0 math.sqrt(121) 11.0 math.sqrt(-49) Traceback (most recent call last): File "", line 1, in math.sqrt(-49) ValueError: math domain error math.ceil(11/2) 6 math.floor(11/2) 5 ``` ## 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() random.random() 0.03177690077737194 random.random() 0.42660420133444044 random.random() 0.6029016351661705 random.uniform(3, 8) 3.349556259329991 random.randint(3, 8) 7 random.gauss() 1.3115168238883617 random.gauss(5, 8) 18.723843368888335 lst=[13, 'cat', 5-8j, (1, 7), 67.8] random.choice(lst) (1, 7) random.shuffle(lst) lst [(1, 7), 13, 'cat', (5-8j), 67.8] random.sample(lst, 3) ['cat', 67.8, (5-8j)] help(random.betavariate) Help on method betavariate in module random: betavariate(alpha, beta) method of random.Random instance Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. The mean (expected value) and variance of the random variable are: E[X] = alpha / (alpha + beta) Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1)) random.betavariate(3, 7) 0.5892004380254151 help(random.gammavariate) Help on method gammavariate in module random: gammavariate(alpha, beta) method of random.Random instance Gamma distribution. Not the gamma function! Conditions on the parameters are alpha > 0 and beta > 0. The probability distribution function is: x ** (alpha - 1) * math.exp(-x / beta) pdf(x) = -------------------------------------- math.gamma(alpha) * beta ** alpha The mean (expected value) and variance of the random variable are: E[X] = alpha * beta Var[X] = alpha * beta ** 2 random.gammavariate(4, 6) 21.738935796671953 rand=[random.uniform(1,2), random.gauss(), random.betavariate(2,5), random.gammavariate(3, 4)] rand [1.0853816485082923, -1.2263412989631917, 0.44973003958419866, 8.863321797224248] ``` ## 6 Функции из модуля time ``` 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 1761126046.1433933 c2=time.time()-c1; c2 12.040217399597168 dat=time.gmtime(); dat time.struct_time(tm_year=2025, tm_mon=10, tm_mday=22, tm_hour=9, tm_min=41, tm_sec=17, tm_wday=2, tm_yday=295, tm_isdst=0) type(dat) KeyboardInterrupt dat.tm_mon 10 dat.tm_yday 295 msc=time.localtime() msc time.struct_time(tm_year=2025, tm_mon=10, tm_mday=22, tm_hour=12, tm_min=43, tm_sec=12, tm_wday=2, tm_yday=295, tm_isdst=0) time.asctime() 'Wed Oct 22 12:57:32 2025' time.ctime() 'Wed Oct 22 12:58:08 2025' time.sleep(5) time.mktime(msc) 1761126192.0 time.localtime(c1) time.struct_time(tm_year=2025, tm_mon=10, tm_mday=22, tm_hour=12, tm_min=40, tm_sec=46, tm_wday=2, tm_yday=295, tm_isdst=0) ``` ## 7 Графические функции ``` import pylab x=list(range(-3,55,4)) t=list(range(15)) pylab.plot(t,x) [] 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() ``` ![Первый график](Ris1.png) ``` X1=[12,6,8,10,7]; X2=[5,7,9,11,13] pylab.plot(X1) [] pylab.plot(X2) [] pylab.show() ``` ![Второй график](Ris11.png) ``` region=['Центр','Урал','Сибирь','Юг'] naselen=[65,12,23,17] pylab.pie(naselen,labels=region) ([, , , ], [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() ``` ![Круговая диаграмма](Ris2.png) ``` pylab.hist([2, 3, 4, 21, 14, 0, 13, 19, 7, 9, 11], bins=4) (array([4., 2., 3., 2.]), array([ 0. , 5.25, 10.5 , 15.75, 21. ]), ) pylab.show() ``` ![Гистограмма](Ris3.png) ``` pylab.bar(region, naselen) pylab.show() ``` ![Столбчатая диаграмма](Ris4.png) ## 8 Некоторые функции статистического модуля 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'] statistics.mode(['banana', 'apple', 'strawberry', 'lemon', 'apple', 'pineapple', 'lemon', 'apple']) 'apple' statistics.mode(['banana', 'apple', 'strawberry', 'lemon', 'apple', 'pineapple', 'lemon', 'banana']) 'banana' statistics.correlation(X1, X2) -0.3939192985791677 statistics.stdev(X1) 2.4083189157584592 statistics.mean(X1) 8.6 ```