Do czego służy:
import numpy as np
# jak dotychczas reprezentowaliśmy macierze (tablice 2x2):
X = [[1, 2, 3], [4, 5, 6]]
print(X)
print(type(X))
[[1, 2, 3], [4, 5, 6]] <class 'list'>
# ndarray - klasa, najważniejszy obiekt: tablica obiektów (zazwyczaj liczb)
X = np.array([1, 2, 3])
print(X)
X = np.array([[1, 2, 3], [4, 5, 6]])
print(X)
print(type(X))
[1 2 3] [[1 2 3] [4 5 6]] <class 'numpy.ndarray'>
print(X)
X[0][0] = 50
print(X)
X[0,0] = 100 # jak X[0][0]
print(X)
[[1 2 3] [4 5 6]] [[50 2 3] [ 4 5 6]] [[100 2 3] [ 4 5 6]]
# Przypomnienie: dla list i stringów slicing tworzy kopię obiektu
a = [1,2,3,4,5]
b = a[:]
a[2] = -1
print(a)
print(b)
[1, 2, -1, 4, 5] [1, 2, 3, 4, 5]
X = np.array([[1, 2, 3, 4], [4, 5, 6, 7], [2, 2, 1, 1]])
print(X)
[[1 2 3 4] [4 5 6 7] [2 2 1 1]]
Y = X[:, :] # wszystkie wiersze i kolumny
print(Y)
[[1 2 3 4] [4 5 6 7] [2 2 1 1]]
Y = X[:2, :] # wiersze < 2, wszystkie kolumny
print(Y)
[[1 2 3 4] [4 5 6 7]]
Y = X[1:3, :] # wiersze 1, 2
print(Y)
[[4 5 6 7] [2 2 1 1]]
Y = X[:2, 2:] # wiersze 0, 1, kolumny 2 i dalej
print(Y)
[[3 4] [6 7]]
Y[0,0] = 1000
print(Y)
print(X) # zmieniła się!
[[1000 4] [ 6 7]] [[ 1 2 1000 4] [ 4 5 6 7] [ 2 2 1 1]]
Y = X[1, :]
print(Y)
Y = X[:, 1]
print(Y)
Y = X[1:2, :]
print(Y)
[4 5 6 7] [2 5 2] [[4 5 6 7]]
X = np.array([[1, 2, 3], [4, 5, 6]])
print(X)
[[1 2 3] [4 5 6]]
print(np.sum(X)) # suma wszystkich elementów tablicy
print(np.sum(X, axis=0)) # sumowanie po kolumnach
print(np.sum(X, axis=1)) # sumowanie po wierszach
21 [5 7 9] [ 6 15]
print(np.cumsum(X))
print(np.cumsum(X, axis=0))
print(np.cumsum(X, axis=1))
[ 1 3 6 10 15 21] [[1 2 3] [5 7 9]] [[ 1 3 6] [ 4 9 15]]
print(np.mean(X))
print(np.mean(X, axis=0))
print(np.mean(X, axis=1))
3.5 [2.5 3.5 4.5] [2. 5.]
print(np.std(X))
print(np.std(X, axis=0))
print(np.std(X, axis=1))
1.707825127659933 [1.5 1.5 1.5] [0.81649658 0.81649658]
print(np.max(X))
print(np.max(X, axis=0))
print(np.max(X, axis=1))
6 [4 5 6] [3 6]
print(np.min(X))
print(np.min(X, axis=0))
print(np.min(X, axis=1))
1 [1 2 3] [1 4]
(macierz to dwuwymiarowa tablica)
X = np.zeros((2,3)) # macierz zerowa rozmiaru 2x3 [a 2x3x4?].
print(X)
[[0. 0. 0.] [0. 0. 0.]]
X = np.ones((2,2)) # macierz z samymi jedynkami rozmiaru 2x2.
print(X)
[[1. 1.] [1. 1.]]
X = np.full((3,3), 2024) # macierz rozmiaru 3x3 z każdą pozycją równą 2024.
print(X)
[[2024 2024 2024] [2024 2024 2024] [2024 2024 2024]]
X = np.eye(5) # macierz identycznościowa rozmiaru 5x5.
print(X)
[[1. 0. 0. 0. 0.] [0. 1. 0. 0. 0.] [0. 0. 1. 0. 0.] [0. 0. 0. 1. 0.] [0. 0. 0. 0. 1.]]
X = np.arange(-np.pi, np.pi, 0.1) # punkty [-pi, -pi+0.1, -pi+0.2, ...] z przedziału [-pi, pi).
print(X)
[-3.14159265 -3.04159265 -2.94159265 -2.84159265 -2.74159265 -2.64159265 -2.54159265 -2.44159265 -2.34159265 -2.24159265 -2.14159265 -2.04159265 -1.94159265 -1.84159265 -1.74159265 -1.64159265 -1.54159265 -1.44159265 -1.34159265 -1.24159265 -1.14159265 -1.04159265 -0.94159265 -0.84159265 -0.74159265 -0.64159265 -0.54159265 -0.44159265 -0.34159265 -0.24159265 -0.14159265 -0.04159265 0.05840735 0.15840735 0.25840735 0.35840735 0.45840735 0.55840735 0.65840735 0.75840735 0.85840735 0.95840735 1.05840735 1.15840735 1.25840735 1.35840735 1.45840735 1.55840735 1.65840735 1.75840735 1.85840735 1.95840735 2.05840735 2.15840735 2.25840735 2.35840735 2.45840735 2.55840735 2.65840735 2.75840735 2.85840735 2.95840735 3.05840735]
X = np.linspace(0,1,13) # 13 punktów równoodległych w przedziale [0,1].
print(X)
[0. 0.08333333 0.16666667 0.25 0.33333333 0.41666667 0.5 0.58333333 0.66666667 0.75 0.83333333 0.91666667 1. ]
print(np.repeat(3, 4)) # powtórz 4 razy wartość 3
print(np.repeat(np.array([1,2,3]), 2))
print(np.tile(np.array([1,2,3]), 2)) # porównaj z poprzednim.
[3 3 3 3] [1 1 2 2 3 3] [1 2 3 1 2 3]
M = np.array([[1, 2, 3], [4, 5, 6]])
print(M)
print(M.shape)
[[1 2 3] [4 5 6]] (2, 3)
print(M.T)
print(M.T.shape)
[[1 4] [2 5] [3 6]] (3, 2)
A = np.array([1, 2]) # wektor 1D
print(A)
print(A.shape)
[1 2] (2,)
B = np.array([[3], [4]]) # macierz 2D rozmiaru 2x1
print(B)
print(B.shape)
[[3] [4]] (2, 1)
print(A.T) # transpozycja wektora 1D - ten sam wektor!
print(A.T.shape)
[1 2] (2,)
print(B.T)
print(B.T.shape)
[[3 4]] (1, 2)
print(A[np.newaxis]) # wektor 1D rozszerzony do macierzy 2D rozmiaru 1x2
print(A[np.newaxis].shape)
[[1 2]] (1, 2)
print(A[np.newaxis].T)
print(A[np.newaxis].T.shape)
[[1] [2]] (2, 1)
X = np.arange(1,10)
print(X)
Y = X.reshape(3, 3) # też widok!
print(Y)
Y[1,1] = 1000
print(X)
[1 2 3 4 5 6 7 8 9] [[1 2 3] [4 5 6] [7 8 9]] [ 1 2 3 4 1000 6 7 8 9]
X = np.array([[1, 2], [3, 4]])
Y = np.array([[10, 20], [30, 40]])
print(X)
print(Y)
[[1 2] [3 4]] [[10 20] [30 40]]
print(np.vstack((X,Y)))
print("-----------------------")
print(np.hstack((X,Y)))
[[ 1 2] [ 3 4] [10 20] [30 40]] ----------------------- [[ 1 2 10 20] [ 3 4 30 40]]
# Przypomnienie: jak działa mnożenie dla list i napisów.
print("123"*10)
a = [1,2,3]
print(a * 3)
print("*"*30)
print([x*3 for x in a])
123123123123123123123123123123 [1, 2, 3, 1, 2, 3, 1, 2, 3] ****************************** [3, 6, 9]
try:
print(a+5) # błąd!
except TypeError:
print("tu padł wyjątek!")
tu padł wyjątek!
Y = np.arange(1,10).reshape((3,3))
print(Y)
print(Y * 3) # produkt Hadamarda (ukryty)
print(Y + 77)
print(Y * Y) # produkt Hadamarda
print(Y @ Y) # mnożenie macierzowe (__matmul__)
[[1 2 3] [4 5 6] [7 8 9]] [[ 3 6 9] [12 15 18] [21 24 27]] [[78 79 80] [81 82 83] [84 85 86]] [[ 1 4 9] [16 25 36] [49 64 81]] [[ 30 36 42] [ 66 81 96] [102 126 150]]
Z = np.repeat(2, 3)
print(Z)
print(Z * Y) # broadcasting + mnożenie po współrzędnych
print(Y @ Z) # mnożenie macierzowe
print(Z @ Y) # mnożenie macierzowe, Z interpretowany jako macierz 1x3
[2 2 2] [[ 2 4 6] [ 8 10 12] [14 16 18]] [12 30 48] [24 30 36]
X = np.array(range(10,20))
print(X)
Y = X % 2
print(Y)
Z = Y == 1
print(Z)
print(X[Z])
print(np.where(Z)) # pozycje na których wartość jest równa True
[10 11 12 13 14 15 16 17 18 19] [0 1 0 1 0 1 0 1 0 1] [False True False True False True False True False True] [11 13 15 17 19] (array([1, 3, 5, 7, 9], dtype=int64),)
X = np.array(range(10,20))
print(X > 14)
print(X <= 18)
print((X > 14) & (X <= 18))
print(X[(X > 14) & (X <= 18)]) # dwa warunki, & zamiast and!
[False False False False False True True True True True] [ True True True True True True True True True False] [False False False False False True True True True False] [15 16 17 18]
A = np.array([[0, 3, 2], [1, 0, 4], [0, 3, 6]])
print(A)
print(np.linalg.det(A))
[[0 3 2] [1 0 4] [0 3 6]] -12.0
B = np.linalg.inv(A)
print(B)
[[ 1. 1. -1. ] [ 0.5 0. -0.16666667] [-0.25 0. 0.25 ]]
Y = np.array([3, -1, 2])
print(B @ Y)
[ 0. 1.16666667 -0.25 ]
unique = np.unique([1, 1, 2, 2, 2, 3, 3, 67, -1])
print(unique)
unique, counts = np.unique([1, 1, 2, 2, 2, 3, 3, 67, -1], return_counts=True)
print(unique, counts)
[-1 1 2 3 67] [-1 1 2 3 67] [1 2 3 2 1]
X = np.arange(-np.pi, np.pi, 0.01)
print(X)
[-3.14159265e+00 -3.13159265e+00 -3.12159265e+00 -3.11159265e+00 -3.10159265e+00 -3.09159265e+00 -3.08159265e+00 -3.07159265e+00 -3.06159265e+00 -3.05159265e+00 -3.04159265e+00 -3.03159265e+00 -3.02159265e+00 -3.01159265e+00 -3.00159265e+00 -2.99159265e+00 -2.98159265e+00 -2.97159265e+00 -2.96159265e+00 -2.95159265e+00 -2.94159265e+00 -2.93159265e+00 -2.92159265e+00 -2.91159265e+00 -2.90159265e+00 -2.89159265e+00 -2.88159265e+00 -2.87159265e+00 -2.86159265e+00 -2.85159265e+00 -2.84159265e+00 -2.83159265e+00 -2.82159265e+00 -2.81159265e+00 -2.80159265e+00 -2.79159265e+00 -2.78159265e+00 -2.77159265e+00 -2.76159265e+00 -2.75159265e+00 -2.74159265e+00 -2.73159265e+00 -2.72159265e+00 -2.71159265e+00 -2.70159265e+00 -2.69159265e+00 -2.68159265e+00 -2.67159265e+00 -2.66159265e+00 -2.65159265e+00 -2.64159265e+00 -2.63159265e+00 -2.62159265e+00 -2.61159265e+00 -2.60159265e+00 -2.59159265e+00 -2.58159265e+00 -2.57159265e+00 -2.56159265e+00 -2.55159265e+00 -2.54159265e+00 -2.53159265e+00 -2.52159265e+00 -2.51159265e+00 -2.50159265e+00 -2.49159265e+00 -2.48159265e+00 -2.47159265e+00 -2.46159265e+00 -2.45159265e+00 -2.44159265e+00 -2.43159265e+00 -2.42159265e+00 -2.41159265e+00 -2.40159265e+00 -2.39159265e+00 -2.38159265e+00 -2.37159265e+00 -2.36159265e+00 -2.35159265e+00 -2.34159265e+00 -2.33159265e+00 -2.32159265e+00 -2.31159265e+00 -2.30159265e+00 -2.29159265e+00 -2.28159265e+00 -2.27159265e+00 -2.26159265e+00 -2.25159265e+00 -2.24159265e+00 -2.23159265e+00 -2.22159265e+00 -2.21159265e+00 -2.20159265e+00 -2.19159265e+00 -2.18159265e+00 -2.17159265e+00 -2.16159265e+00 -2.15159265e+00 -2.14159265e+00 -2.13159265e+00 -2.12159265e+00 -2.11159265e+00 -2.10159265e+00 -2.09159265e+00 -2.08159265e+00 -2.07159265e+00 -2.06159265e+00 -2.05159265e+00 -2.04159265e+00 -2.03159265e+00 -2.02159265e+00 -2.01159265e+00 -2.00159265e+00 -1.99159265e+00 -1.98159265e+00 -1.97159265e+00 -1.96159265e+00 -1.95159265e+00 -1.94159265e+00 -1.93159265e+00 -1.92159265e+00 -1.91159265e+00 -1.90159265e+00 -1.89159265e+00 -1.88159265e+00 -1.87159265e+00 -1.86159265e+00 -1.85159265e+00 -1.84159265e+00 -1.83159265e+00 -1.82159265e+00 -1.81159265e+00 -1.80159265e+00 -1.79159265e+00 -1.78159265e+00 -1.77159265e+00 -1.76159265e+00 -1.75159265e+00 -1.74159265e+00 -1.73159265e+00 -1.72159265e+00 -1.71159265e+00 -1.70159265e+00 -1.69159265e+00 -1.68159265e+00 -1.67159265e+00 -1.66159265e+00 -1.65159265e+00 -1.64159265e+00 -1.63159265e+00 -1.62159265e+00 -1.61159265e+00 -1.60159265e+00 -1.59159265e+00 -1.58159265e+00 -1.57159265e+00 -1.56159265e+00 -1.55159265e+00 -1.54159265e+00 -1.53159265e+00 -1.52159265e+00 -1.51159265e+00 -1.50159265e+00 -1.49159265e+00 -1.48159265e+00 -1.47159265e+00 -1.46159265e+00 -1.45159265e+00 -1.44159265e+00 -1.43159265e+00 -1.42159265e+00 -1.41159265e+00 -1.40159265e+00 -1.39159265e+00 -1.38159265e+00 -1.37159265e+00 -1.36159265e+00 -1.35159265e+00 -1.34159265e+00 -1.33159265e+00 -1.32159265e+00 -1.31159265e+00 -1.30159265e+00 -1.29159265e+00 -1.28159265e+00 -1.27159265e+00 -1.26159265e+00 -1.25159265e+00 -1.24159265e+00 -1.23159265e+00 -1.22159265e+00 -1.21159265e+00 -1.20159265e+00 -1.19159265e+00 -1.18159265e+00 -1.17159265e+00 -1.16159265e+00 -1.15159265e+00 -1.14159265e+00 -1.13159265e+00 -1.12159265e+00 -1.11159265e+00 -1.10159265e+00 -1.09159265e+00 -1.08159265e+00 -1.07159265e+00 -1.06159265e+00 -1.05159265e+00 -1.04159265e+00 -1.03159265e+00 -1.02159265e+00 -1.01159265e+00 -1.00159265e+00 -9.91592654e-01 -9.81592654e-01 -9.71592654e-01 -9.61592654e-01 -9.51592654e-01 -9.41592654e-01 -9.31592654e-01 -9.21592654e-01 -9.11592654e-01 -9.01592654e-01 -8.91592654e-01 -8.81592654e-01 -8.71592654e-01 -8.61592654e-01 -8.51592654e-01 -8.41592654e-01 -8.31592654e-01 -8.21592654e-01 -8.11592654e-01 -8.01592654e-01 -7.91592654e-01 -7.81592654e-01 -7.71592654e-01 -7.61592654e-01 -7.51592654e-01 -7.41592654e-01 -7.31592654e-01 -7.21592654e-01 -7.11592654e-01 -7.01592654e-01 -6.91592654e-01 -6.81592654e-01 -6.71592654e-01 -6.61592654e-01 -6.51592654e-01 -6.41592654e-01 -6.31592654e-01 -6.21592654e-01 -6.11592654e-01 -6.01592654e-01 -5.91592654e-01 -5.81592654e-01 -5.71592654e-01 -5.61592654e-01 -5.51592654e-01 -5.41592654e-01 -5.31592654e-01 -5.21592654e-01 -5.11592654e-01 -5.01592654e-01 -4.91592654e-01 -4.81592654e-01 -4.71592654e-01 -4.61592654e-01 -4.51592654e-01 -4.41592654e-01 -4.31592654e-01 -4.21592654e-01 -4.11592654e-01 -4.01592654e-01 -3.91592654e-01 -3.81592654e-01 -3.71592654e-01 -3.61592654e-01 -3.51592654e-01 -3.41592654e-01 -3.31592654e-01 -3.21592654e-01 -3.11592654e-01 -3.01592654e-01 -2.91592654e-01 -2.81592654e-01 -2.71592654e-01 -2.61592654e-01 -2.51592654e-01 -2.41592654e-01 -2.31592654e-01 -2.21592654e-01 -2.11592654e-01 -2.01592654e-01 -1.91592654e-01 -1.81592654e-01 -1.71592654e-01 -1.61592654e-01 -1.51592654e-01 -1.41592654e-01 -1.31592654e-01 -1.21592654e-01 -1.11592654e-01 -1.01592654e-01 -9.15926536e-02 -8.15926536e-02 -7.15926536e-02 -6.15926536e-02 -5.15926536e-02 -4.15926536e-02 -3.15926536e-02 -2.15926536e-02 -1.15926536e-02 -1.59265359e-03 8.40734641e-03 1.84073464e-02 2.84073464e-02 3.84073464e-02 4.84073464e-02 5.84073464e-02 6.84073464e-02 7.84073464e-02 8.84073464e-02 9.84073464e-02 1.08407346e-01 1.18407346e-01 1.28407346e-01 1.38407346e-01 1.48407346e-01 1.58407346e-01 1.68407346e-01 1.78407346e-01 1.88407346e-01 1.98407346e-01 2.08407346e-01 2.18407346e-01 2.28407346e-01 2.38407346e-01 2.48407346e-01 2.58407346e-01 2.68407346e-01 2.78407346e-01 2.88407346e-01 2.98407346e-01 3.08407346e-01 3.18407346e-01 3.28407346e-01 3.38407346e-01 3.48407346e-01 3.58407346e-01 3.68407346e-01 3.78407346e-01 3.88407346e-01 3.98407346e-01 4.08407346e-01 4.18407346e-01 4.28407346e-01 4.38407346e-01 4.48407346e-01 4.58407346e-01 4.68407346e-01 4.78407346e-01 4.88407346e-01 4.98407346e-01 5.08407346e-01 5.18407346e-01 5.28407346e-01 5.38407346e-01 5.48407346e-01 5.58407346e-01 5.68407346e-01 5.78407346e-01 5.88407346e-01 5.98407346e-01 6.08407346e-01 6.18407346e-01 6.28407346e-01 6.38407346e-01 6.48407346e-01 6.58407346e-01 6.68407346e-01 6.78407346e-01 6.88407346e-01 6.98407346e-01 7.08407346e-01 7.18407346e-01 7.28407346e-01 7.38407346e-01 7.48407346e-01 7.58407346e-01 7.68407346e-01 7.78407346e-01 7.88407346e-01 7.98407346e-01 8.08407346e-01 8.18407346e-01 8.28407346e-01 8.38407346e-01 8.48407346e-01 8.58407346e-01 8.68407346e-01 8.78407346e-01 8.88407346e-01 8.98407346e-01 9.08407346e-01 9.18407346e-01 9.28407346e-01 9.38407346e-01 9.48407346e-01 9.58407346e-01 9.68407346e-01 9.78407346e-01 9.88407346e-01 9.98407346e-01 1.00840735e+00 1.01840735e+00 1.02840735e+00 1.03840735e+00 1.04840735e+00 1.05840735e+00 1.06840735e+00 1.07840735e+00 1.08840735e+00 1.09840735e+00 1.10840735e+00 1.11840735e+00 1.12840735e+00 1.13840735e+00 1.14840735e+00 1.15840735e+00 1.16840735e+00 1.17840735e+00 1.18840735e+00 1.19840735e+00 1.20840735e+00 1.21840735e+00 1.22840735e+00 1.23840735e+00 1.24840735e+00 1.25840735e+00 1.26840735e+00 1.27840735e+00 1.28840735e+00 1.29840735e+00 1.30840735e+00 1.31840735e+00 1.32840735e+00 1.33840735e+00 1.34840735e+00 1.35840735e+00 1.36840735e+00 1.37840735e+00 1.38840735e+00 1.39840735e+00 1.40840735e+00 1.41840735e+00 1.42840735e+00 1.43840735e+00 1.44840735e+00 1.45840735e+00 1.46840735e+00 1.47840735e+00 1.48840735e+00 1.49840735e+00 1.50840735e+00 1.51840735e+00 1.52840735e+00 1.53840735e+00 1.54840735e+00 1.55840735e+00 1.56840735e+00 1.57840735e+00 1.58840735e+00 1.59840735e+00 1.60840735e+00 1.61840735e+00 1.62840735e+00 1.63840735e+00 1.64840735e+00 1.65840735e+00 1.66840735e+00 1.67840735e+00 1.68840735e+00 1.69840735e+00 1.70840735e+00 1.71840735e+00 1.72840735e+00 1.73840735e+00 1.74840735e+00 1.75840735e+00 1.76840735e+00 1.77840735e+00 1.78840735e+00 1.79840735e+00 1.80840735e+00 1.81840735e+00 1.82840735e+00 1.83840735e+00 1.84840735e+00 1.85840735e+00 1.86840735e+00 1.87840735e+00 1.88840735e+00 1.89840735e+00 1.90840735e+00 1.91840735e+00 1.92840735e+00 1.93840735e+00 1.94840735e+00 1.95840735e+00 1.96840735e+00 1.97840735e+00 1.98840735e+00 1.99840735e+00 2.00840735e+00 2.01840735e+00 2.02840735e+00 2.03840735e+00 2.04840735e+00 2.05840735e+00 2.06840735e+00 2.07840735e+00 2.08840735e+00 2.09840735e+00 2.10840735e+00 2.11840735e+00 2.12840735e+00 2.13840735e+00 2.14840735e+00 2.15840735e+00 2.16840735e+00 2.17840735e+00 2.18840735e+00 2.19840735e+00 2.20840735e+00 2.21840735e+00 2.22840735e+00 2.23840735e+00 2.24840735e+00 2.25840735e+00 2.26840735e+00 2.27840735e+00 2.28840735e+00 2.29840735e+00 2.30840735e+00 2.31840735e+00 2.32840735e+00 2.33840735e+00 2.34840735e+00 2.35840735e+00 2.36840735e+00 2.37840735e+00 2.38840735e+00 2.39840735e+00 2.40840735e+00 2.41840735e+00 2.42840735e+00 2.43840735e+00 2.44840735e+00 2.45840735e+00 2.46840735e+00 2.47840735e+00 2.48840735e+00 2.49840735e+00 2.50840735e+00 2.51840735e+00 2.52840735e+00 2.53840735e+00 2.54840735e+00 2.55840735e+00 2.56840735e+00 2.57840735e+00 2.58840735e+00 2.59840735e+00 2.60840735e+00 2.61840735e+00 2.62840735e+00 2.63840735e+00 2.64840735e+00 2.65840735e+00 2.66840735e+00 2.67840735e+00 2.68840735e+00 2.69840735e+00 2.70840735e+00 2.71840735e+00 2.72840735e+00 2.73840735e+00 2.74840735e+00 2.75840735e+00 2.76840735e+00 2.77840735e+00 2.78840735e+00 2.79840735e+00 2.80840735e+00 2.81840735e+00 2.82840735e+00 2.83840735e+00 2.84840735e+00 2.85840735e+00 2.86840735e+00 2.87840735e+00 2.88840735e+00 2.89840735e+00 2.90840735e+00 2.91840735e+00 2.92840735e+00 2.93840735e+00 2.94840735e+00 2.95840735e+00 2.96840735e+00 2.97840735e+00 2.98840735e+00 2.99840735e+00 3.00840735e+00 3.01840735e+00 3.02840735e+00 3.03840735e+00 3.04840735e+00 3.05840735e+00 3.06840735e+00 3.07840735e+00 3.08840735e+00 3.09840735e+00 3.10840735e+00 3.11840735e+00 3.12840735e+00 3.13840735e+00]
np.set_printoptions(suppress=True) # pozbycie się notacji naukowej
print(X)
[-3.14159265 -3.13159265 -3.12159265 -3.11159265 -3.10159265 -3.09159265 -3.08159265 -3.07159265 -3.06159265 -3.05159265 -3.04159265 -3.03159265 -3.02159265 -3.01159265 -3.00159265 -2.99159265 -2.98159265 -2.97159265 -2.96159265 -2.95159265 -2.94159265 -2.93159265 -2.92159265 -2.91159265 -2.90159265 -2.89159265 -2.88159265 -2.87159265 -2.86159265 -2.85159265 -2.84159265 -2.83159265 -2.82159265 -2.81159265 -2.80159265 -2.79159265 -2.78159265 -2.77159265 -2.76159265 -2.75159265 -2.74159265 -2.73159265 -2.72159265 -2.71159265 -2.70159265 -2.69159265 -2.68159265 -2.67159265 -2.66159265 -2.65159265 -2.64159265 -2.63159265 -2.62159265 -2.61159265 -2.60159265 -2.59159265 -2.58159265 -2.57159265 -2.56159265 -2.55159265 -2.54159265 -2.53159265 -2.52159265 -2.51159265 -2.50159265 -2.49159265 -2.48159265 -2.47159265 -2.46159265 -2.45159265 -2.44159265 -2.43159265 -2.42159265 -2.41159265 -2.40159265 -2.39159265 -2.38159265 -2.37159265 -2.36159265 -2.35159265 -2.34159265 -2.33159265 -2.32159265 -2.31159265 -2.30159265 -2.29159265 -2.28159265 -2.27159265 -2.26159265 -2.25159265 -2.24159265 -2.23159265 -2.22159265 -2.21159265 -2.20159265 -2.19159265 -2.18159265 -2.17159265 -2.16159265 -2.15159265 -2.14159265 -2.13159265 -2.12159265 -2.11159265 -2.10159265 -2.09159265 -2.08159265 -2.07159265 -2.06159265 -2.05159265 -2.04159265 -2.03159265 -2.02159265 -2.01159265 -2.00159265 -1.99159265 -1.98159265 -1.97159265 -1.96159265 -1.95159265 -1.94159265 -1.93159265 -1.92159265 -1.91159265 -1.90159265 -1.89159265 -1.88159265 -1.87159265 -1.86159265 -1.85159265 -1.84159265 -1.83159265 -1.82159265 -1.81159265 -1.80159265 -1.79159265 -1.78159265 -1.77159265 -1.76159265 -1.75159265 -1.74159265 -1.73159265 -1.72159265 -1.71159265 -1.70159265 -1.69159265 -1.68159265 -1.67159265 -1.66159265 -1.65159265 -1.64159265 -1.63159265 -1.62159265 -1.61159265 -1.60159265 -1.59159265 -1.58159265 -1.57159265 -1.56159265 -1.55159265 -1.54159265 -1.53159265 -1.52159265 -1.51159265 -1.50159265 -1.49159265 -1.48159265 -1.47159265 -1.46159265 -1.45159265 -1.44159265 -1.43159265 -1.42159265 -1.41159265 -1.40159265 -1.39159265 -1.38159265 -1.37159265 -1.36159265 -1.35159265 -1.34159265 -1.33159265 -1.32159265 -1.31159265 -1.30159265 -1.29159265 -1.28159265 -1.27159265 -1.26159265 -1.25159265 -1.24159265 -1.23159265 -1.22159265 -1.21159265 -1.20159265 -1.19159265 -1.18159265 -1.17159265 -1.16159265 -1.15159265 -1.14159265 -1.13159265 -1.12159265 -1.11159265 -1.10159265 -1.09159265 -1.08159265 -1.07159265 -1.06159265 -1.05159265 -1.04159265 -1.03159265 -1.02159265 -1.01159265 -1.00159265 -0.99159265 -0.98159265 -0.97159265 -0.96159265 -0.95159265 -0.94159265 -0.93159265 -0.92159265 -0.91159265 -0.90159265 -0.89159265 -0.88159265 -0.87159265 -0.86159265 -0.85159265 -0.84159265 -0.83159265 -0.82159265 -0.81159265 -0.80159265 -0.79159265 -0.78159265 -0.77159265 -0.76159265 -0.75159265 -0.74159265 -0.73159265 -0.72159265 -0.71159265 -0.70159265 -0.69159265 -0.68159265 -0.67159265 -0.66159265 -0.65159265 -0.64159265 -0.63159265 -0.62159265 -0.61159265 -0.60159265 -0.59159265 -0.58159265 -0.57159265 -0.56159265 -0.55159265 -0.54159265 -0.53159265 -0.52159265 -0.51159265 -0.50159265 -0.49159265 -0.48159265 -0.47159265 -0.46159265 -0.45159265 -0.44159265 -0.43159265 -0.42159265 -0.41159265 -0.40159265 -0.39159265 -0.38159265 -0.37159265 -0.36159265 -0.35159265 -0.34159265 -0.33159265 -0.32159265 -0.31159265 -0.30159265 -0.29159265 -0.28159265 -0.27159265 -0.26159265 -0.25159265 -0.24159265 -0.23159265 -0.22159265 -0.21159265 -0.20159265 -0.19159265 -0.18159265 -0.17159265 -0.16159265 -0.15159265 -0.14159265 -0.13159265 -0.12159265 -0.11159265 -0.10159265 -0.09159265 -0.08159265 -0.07159265 -0.06159265 -0.05159265 -0.04159265 -0.03159265 -0.02159265 -0.01159265 -0.00159265 0.00840735 0.01840735 0.02840735 0.03840735 0.04840735 0.05840735 0.06840735 0.07840735 0.08840735 0.09840735 0.10840735 0.11840735 0.12840735 0.13840735 0.14840735 0.15840735 0.16840735 0.17840735 0.18840735 0.19840735 0.20840735 0.21840735 0.22840735 0.23840735 0.24840735 0.25840735 0.26840735 0.27840735 0.28840735 0.29840735 0.30840735 0.31840735 0.32840735 0.33840735 0.34840735 0.35840735 0.36840735 0.37840735 0.38840735 0.39840735 0.40840735 0.41840735 0.42840735 0.43840735 0.44840735 0.45840735 0.46840735 0.47840735 0.48840735 0.49840735 0.50840735 0.51840735 0.52840735 0.53840735 0.54840735 0.55840735 0.56840735 0.57840735 0.58840735 0.59840735 0.60840735 0.61840735 0.62840735 0.63840735 0.64840735 0.65840735 0.66840735 0.67840735 0.68840735 0.69840735 0.70840735 0.71840735 0.72840735 0.73840735 0.74840735 0.75840735 0.76840735 0.77840735 0.78840735 0.79840735 0.80840735 0.81840735 0.82840735 0.83840735 0.84840735 0.85840735 0.86840735 0.87840735 0.88840735 0.89840735 0.90840735 0.91840735 0.92840735 0.93840735 0.94840735 0.95840735 0.96840735 0.97840735 0.98840735 0.99840735 1.00840735 1.01840735 1.02840735 1.03840735 1.04840735 1.05840735 1.06840735 1.07840735 1.08840735 1.09840735 1.10840735 1.11840735 1.12840735 1.13840735 1.14840735 1.15840735 1.16840735 1.17840735 1.18840735 1.19840735 1.20840735 1.21840735 1.22840735 1.23840735 1.24840735 1.25840735 1.26840735 1.27840735 1.28840735 1.29840735 1.30840735 1.31840735 1.32840735 1.33840735 1.34840735 1.35840735 1.36840735 1.37840735 1.38840735 1.39840735 1.40840735 1.41840735 1.42840735 1.43840735 1.44840735 1.45840735 1.46840735 1.47840735 1.48840735 1.49840735 1.50840735 1.51840735 1.52840735 1.53840735 1.54840735 1.55840735 1.56840735 1.57840735 1.58840735 1.59840735 1.60840735 1.61840735 1.62840735 1.63840735 1.64840735 1.65840735 1.66840735 1.67840735 1.68840735 1.69840735 1.70840735 1.71840735 1.72840735 1.73840735 1.74840735 1.75840735 1.76840735 1.77840735 1.78840735 1.79840735 1.80840735 1.81840735 1.82840735 1.83840735 1.84840735 1.85840735 1.86840735 1.87840735 1.88840735 1.89840735 1.90840735 1.91840735 1.92840735 1.93840735 1.94840735 1.95840735 1.96840735 1.97840735 1.98840735 1.99840735 2.00840735 2.01840735 2.02840735 2.03840735 2.04840735 2.05840735 2.06840735 2.07840735 2.08840735 2.09840735 2.10840735 2.11840735 2.12840735 2.13840735 2.14840735 2.15840735 2.16840735 2.17840735 2.18840735 2.19840735 2.20840735 2.21840735 2.22840735 2.23840735 2.24840735 2.25840735 2.26840735 2.27840735 2.28840735 2.29840735 2.30840735 2.31840735 2.32840735 2.33840735 2.34840735 2.35840735 2.36840735 2.37840735 2.38840735 2.39840735 2.40840735 2.41840735 2.42840735 2.43840735 2.44840735 2.45840735 2.46840735 2.47840735 2.48840735 2.49840735 2.50840735 2.51840735 2.52840735 2.53840735 2.54840735 2.55840735 2.56840735 2.57840735 2.58840735 2.59840735 2.60840735 2.61840735 2.62840735 2.63840735 2.64840735 2.65840735 2.66840735 2.67840735 2.68840735 2.69840735 2.70840735 2.71840735 2.72840735 2.73840735 2.74840735 2.75840735 2.76840735 2.77840735 2.78840735 2.79840735 2.80840735 2.81840735 2.82840735 2.83840735 2.84840735 2.85840735 2.86840735 2.87840735 2.88840735 2.89840735 2.90840735 2.91840735 2.92840735 2.93840735 2.94840735 2.95840735 2.96840735 2.97840735 2.98840735 2.99840735 3.00840735 3.01840735 3.02840735 3.03840735 3.04840735 3.05840735 3.06840735 3.07840735 3.08840735 3.09840735 3.10840735 3.11840735 3.12840735 3.13840735]
np.set_printoptions(precision=5) # wyświetlanie tylko 5 miejsc po przecinku
print(X)
[-3.14159 -3.13159 -3.12159 -3.11159 -3.10159 -3.09159 -3.08159 -3.07159 -3.06159 -3.05159 -3.04159 -3.03159 -3.02159 -3.01159 -3.00159 -2.99159 -2.98159 -2.97159 -2.96159 -2.95159 -2.94159 -2.93159 -2.92159 -2.91159 -2.90159 -2.89159 -2.88159 -2.87159 -2.86159 -2.85159 -2.84159 -2.83159 -2.82159 -2.81159 -2.80159 -2.79159 -2.78159 -2.77159 -2.76159 -2.75159 -2.74159 -2.73159 -2.72159 -2.71159 -2.70159 -2.69159 -2.68159 -2.67159 -2.66159 -2.65159 -2.64159 -2.63159 -2.62159 -2.61159 -2.60159 -2.59159 -2.58159 -2.57159 -2.56159 -2.55159 -2.54159 -2.53159 -2.52159 -2.51159 -2.50159 -2.49159 -2.48159 -2.47159 -2.46159 -2.45159 -2.44159 -2.43159 -2.42159 -2.41159 -2.40159 -2.39159 -2.38159 -2.37159 -2.36159 -2.35159 -2.34159 -2.33159 -2.32159 -2.31159 -2.30159 -2.29159 -2.28159 -2.27159 -2.26159 -2.25159 -2.24159 -2.23159 -2.22159 -2.21159 -2.20159 -2.19159 -2.18159 -2.17159 -2.16159 -2.15159 -2.14159 -2.13159 -2.12159 -2.11159 -2.10159 -2.09159 -2.08159 -2.07159 -2.06159 -2.05159 -2.04159 -2.03159 -2.02159 -2.01159 -2.00159 -1.99159 -1.98159 -1.97159 -1.96159 -1.95159 -1.94159 -1.93159 -1.92159 -1.91159 -1.90159 -1.89159 -1.88159 -1.87159 -1.86159 -1.85159 -1.84159 -1.83159 -1.82159 -1.81159 -1.80159 -1.79159 -1.78159 -1.77159 -1.76159 -1.75159 -1.74159 -1.73159 -1.72159 -1.71159 -1.70159 -1.69159 -1.68159 -1.67159 -1.66159 -1.65159 -1.64159 -1.63159 -1.62159 -1.61159 -1.60159 -1.59159 -1.58159 -1.57159 -1.56159 -1.55159 -1.54159 -1.53159 -1.52159 -1.51159 -1.50159 -1.49159 -1.48159 -1.47159 -1.46159 -1.45159 -1.44159 -1.43159 -1.42159 -1.41159 -1.40159 -1.39159 -1.38159 -1.37159 -1.36159 -1.35159 -1.34159 -1.33159 -1.32159 -1.31159 -1.30159 -1.29159 -1.28159 -1.27159 -1.26159 -1.25159 -1.24159 -1.23159 -1.22159 -1.21159 -1.20159 -1.19159 -1.18159 -1.17159 -1.16159 -1.15159 -1.14159 -1.13159 -1.12159 -1.11159 -1.10159 -1.09159 -1.08159 -1.07159 -1.06159 -1.05159 -1.04159 -1.03159 -1.02159 -1.01159 -1.00159 -0.99159 -0.98159 -0.97159 -0.96159 -0.95159 -0.94159 -0.93159 -0.92159 -0.91159 -0.90159 -0.89159 -0.88159 -0.87159 -0.86159 -0.85159 -0.84159 -0.83159 -0.82159 -0.81159 -0.80159 -0.79159 -0.78159 -0.77159 -0.76159 -0.75159 -0.74159 -0.73159 -0.72159 -0.71159 -0.70159 -0.69159 -0.68159 -0.67159 -0.66159 -0.65159 -0.64159 -0.63159 -0.62159 -0.61159 -0.60159 -0.59159 -0.58159 -0.57159 -0.56159 -0.55159 -0.54159 -0.53159 -0.52159 -0.51159 -0.50159 -0.49159 -0.48159 -0.47159 -0.46159 -0.45159 -0.44159 -0.43159 -0.42159 -0.41159 -0.40159 -0.39159 -0.38159 -0.37159 -0.36159 -0.35159 -0.34159 -0.33159 -0.32159 -0.31159 -0.30159 -0.29159 -0.28159 -0.27159 -0.26159 -0.25159 -0.24159 -0.23159 -0.22159 -0.21159 -0.20159 -0.19159 -0.18159 -0.17159 -0.16159 -0.15159 -0.14159 -0.13159 -0.12159 -0.11159 -0.10159 -0.09159 -0.08159 -0.07159 -0.06159 -0.05159 -0.04159 -0.03159 -0.02159 -0.01159 -0.00159 0.00841 0.01841 0.02841 0.03841 0.04841 0.05841 0.06841 0.07841 0.08841 0.09841 0.10841 0.11841 0.12841 0.13841 0.14841 0.15841 0.16841 0.17841 0.18841 0.19841 0.20841 0.21841 0.22841 0.23841 0.24841 0.25841 0.26841 0.27841 0.28841 0.29841 0.30841 0.31841 0.32841 0.33841 0.34841 0.35841 0.36841 0.37841 0.38841 0.39841 0.40841 0.41841 0.42841 0.43841 0.44841 0.45841 0.46841 0.47841 0.48841 0.49841 0.50841 0.51841 0.52841 0.53841 0.54841 0.55841 0.56841 0.57841 0.58841 0.59841 0.60841 0.61841 0.62841 0.63841 0.64841 0.65841 0.66841 0.67841 0.68841 0.69841 0.70841 0.71841 0.72841 0.73841 0.74841 0.75841 0.76841 0.77841 0.78841 0.79841 0.80841 0.81841 0.82841 0.83841 0.84841 0.85841 0.86841 0.87841 0.88841 0.89841 0.90841 0.91841 0.92841 0.93841 0.94841 0.95841 0.96841 0.97841 0.98841 0.99841 1.00841 1.01841 1.02841 1.03841 1.04841 1.05841 1.06841 1.07841 1.08841 1.09841 1.10841 1.11841 1.12841 1.13841 1.14841 1.15841 1.16841 1.17841 1.18841 1.19841 1.20841 1.21841 1.22841 1.23841 1.24841 1.25841 1.26841 1.27841 1.28841 1.29841 1.30841 1.31841 1.32841 1.33841 1.34841 1.35841 1.36841 1.37841 1.38841 1.39841 1.40841 1.41841 1.42841 1.43841 1.44841 1.45841 1.46841 1.47841 1.48841 1.49841 1.50841 1.51841 1.52841 1.53841 1.54841 1.55841 1.56841 1.57841 1.58841 1.59841 1.60841 1.61841 1.62841 1.63841 1.64841 1.65841 1.66841 1.67841 1.68841 1.69841 1.70841 1.71841 1.72841 1.73841 1.74841 1.75841 1.76841 1.77841 1.78841 1.79841 1.80841 1.81841 1.82841 1.83841 1.84841 1.85841 1.86841 1.87841 1.88841 1.89841 1.90841 1.91841 1.92841 1.93841 1.94841 1.95841 1.96841 1.97841 1.98841 1.99841 2.00841 2.01841 2.02841 2.03841 2.04841 2.05841 2.06841 2.07841 2.08841 2.09841 2.10841 2.11841 2.12841 2.13841 2.14841 2.15841 2.16841 2.17841 2.18841 2.19841 2.20841 2.21841 2.22841 2.23841 2.24841 2.25841 2.26841 2.27841 2.28841 2.29841 2.30841 2.31841 2.32841 2.33841 2.34841 2.35841 2.36841 2.37841 2.38841 2.39841 2.40841 2.41841 2.42841 2.43841 2.44841 2.45841 2.46841 2.47841 2.48841 2.49841 2.50841 2.51841 2.52841 2.53841 2.54841 2.55841 2.56841 2.57841 2.58841 2.59841 2.60841 2.61841 2.62841 2.63841 2.64841 2.65841 2.66841 2.67841 2.68841 2.69841 2.70841 2.71841 2.72841 2.73841 2.74841 2.75841 2.76841 2.77841 2.78841 2.79841 2.80841 2.81841 2.82841 2.83841 2.84841 2.85841 2.86841 2.87841 2.88841 2.89841 2.90841 2.91841 2.92841 2.93841 2.94841 2.95841 2.96841 2.97841 2.98841 2.99841 3.00841 3.01841 3.02841 3.03841 3.04841 3.05841 3.06841 3.07841 3.08841 3.09841 3.10841 3.11841 3.12841 3.13841]
wines = np.genfromtxt("wine.txt", delimiter=',', skip_header=1)
print(wines)
print(wines.dtype)
[[ 1. 14.23 1.71 ... 1.04 3.92 1065. ] [ 1. 13.2 1.78 ... 1.05 3.4 1050. ] [ 1. 13.16 2.36 ... 1.03 3.17 1185. ] ... [ 3. 13.27 4.28 ... 0.59 1.56 835. ] [ 3. 13.17 2.59 ... 0.6 1.62 840. ] [ 3. 14.13 4.1 ... 0.61 1.6 560. ]] float64
mushrooms = np.genfromtxt("mushrooms.txt", delimiter=',', skip_header=1, dtype=str)
print(mushrooms)
print(mushrooms.dtype)
[['p' 'x' 's' ... 'k' 's' 'u'] ['e' 'x' 's' ... 'n' 'n' 'g'] ['e' 'b' 's' ... 'n' 'n' 'm'] ... ['e' 'f' 's' ... 'b' 'c' 'l'] ['p' 'k' 'y' ... 'w' 'v' 'l'] ['e' 'x' 's' ... 'o' 'c' 'l']] <U1
X = np.linspace(0,1,13)
print(X)
print('-------------------')
np.savetxt('test.txt', X)
print(np.loadtxt('test.txt'))
[0. 0.08333 0.16667 0.25 0.33333 0.41667 0.5 0.58333 0.66667 0.75 0.83333 0.91667 1. ] ------------------- [0. 0.08333 0.16667 0.25 0.33333 0.41667 0.5 0.58333 0.66667 0.75 0.83333 0.91667 1. ]
from matplotlib import pyplot as plt
# od 3.6 też w standardowym random:
X = np.random.choice([1,2,3,4,5,6], 1000, p=[0.1, 0.2, 0.3, 0.2, 0.1, 0.1]) # losowanie 1000 liczb z rozkładu opisanego przez p
print(X)
[3 3 3 4 5 4 5 3 4 6 2 4 4 3 2 2 6 1 3 4 3 1 5 6 1 3 6 4 3 4 3 5 3 4 3 1 4 1 4 1 5 5 3 4 2 4 6 3 3 1 2 3 3 3 1 4 3 6 3 4 3 6 5 5 4 2 5 3 3 2 5 4 5 3 1 3 1 3 4 2 5 1 3 2 3 4 2 2 3 4 3 3 3 3 2 5 5 2 2 2 1 2 4 6 4 6 5 2 2 2 3 4 1 5 4 2 3 3 6 3 3 3 3 3 3 2 3 6 3 2 5 4 4 4 4 4 3 6 4 3 3 4 4 4 2 3 4 6 4 3 2 3 2 3 6 2 4 4 6 2 6 4 4 3 5 5 3 5 1 4 5 3 5 4 3 5 3 4 3 3 3 2 3 3 6 4 6 1 3 4 4 3 4 4 3 3 4 3 6 2 6 3 1 1 1 3 5 6 5 2 5 2 2 2 1 2 5 4 5 3 3 3 2 4 3 1 2 6 3 6 3 4 4 3 3 4 3 2 4 4 5 5 1 3 3 2 2 3 3 4 2 2 3 2 4 5 6 1 4 2 4 6 3 2 3 3 1 4 2 3 3 5 3 2 1 2 5 2 2 5 5 3 3 4 2 4 3 4 5 3 2 5 2 2 1 2 2 5 3 3 4 3 2 2 5 1 4 1 4 2 6 3 3 4 6 1 3 3 3 1 2 4 4 2 3 4 4 3 1 6 3 3 4 5 3 6 1 6 1 2 2 4 1 4 2 3 1 2 5 3 4 2 2 2 3 5 2 2 2 1 3 3 1 6 3 1 3 3 4 3 1 3 3 2 2 6 3 4 2 2 4 4 1 3 4 3 3 4 4 1 4 3 4 3 2 2 1 4 3 2 3 3 6 1 4 3 3 6 4 6 6 4 3 5 2 3 3 3 2 4 2 4 6 3 3 3 4 5 2 6 3 3 4 2 2 2 2 3 6 2 2 2 1 5 6 1 4 3 3 3 1 2 2 1 3 3 6 3 2 3 2 2 2 6 2 5 2 2 4 1 6 2 3 4 3 2 1 2 4 6 5 2 4 4 1 2 5 2 3 2 4 3 6 2 2 1 3 2 1 5 5 2 6 1 3 3 4 5 4 4 4 6 5 5 3 6 4 4 1 3 6 6 6 2 2 5 2 4 1 2 2 3 2 2 5 3 2 4 5 3 6 6 4 4 1 3 3 4 3 5 5 5 3 6 6 5 3 5 1 5 2 4 2 3 2 3 2 3 4 2 2 5 3 5 3 5 6 3 3 2 4 1 3 3 3 2 3 3 4 3 2 1 4 2 4 4 2 4 3 4 6 1 3 1 2 2 3 2 3 2 3 3 6 4 3 2 2 2 6 4 3 4 3 2 2 1 3 5 3 6 4 4 1 2 3 2 3 4 5 2 5 2 3 4 6 3 4 2 1 6 5 3 5 3 6 1 5 1 5 5 5 2 3 3 3 2 2 2 3 3 3 4 5 3 5 5 3 2 2 4 3 3 3 2 3 6 3 5 2 2 3 5 5 2 5 4 3 3 1 2 6 1 1 1 2 2 4 2 3 4 3 3 3 3 3 1 6 4 5 3 1 5 1 5 6 3 3 3 2 5 3 2 3 1 2 5 6 3 6 6 5 4 3 5 5 4 2 3 2 2 4 4 2 6 4 3 5 2 5 1 3 2 5 2 3 5 4 2 6 2 6 2 3 1 6 4 3 5 3 6 3 1 2 3 4 1 2 6 4 1 4 2 4 1 2 2 2 2 4 3 3 3 5 4 3 4 2 1 1 3 5 5 2 4 4 2 2 1 5 5 2 6 4 3 1 5 4 4 4 1 5 1 6 3 5 1 3 2 5 1 3 3 6 1 3 1 4 1 4 3 5 4 2 3 1 4 6 2 3 4 3 2 4 1 5 1 4 3 4 3 2 3 5 4 4 3 2 5 6 3 5 5 1 2 3 3 6 1 4 2 4 5 5 3 6 2 3 4 3 2 4 2 1 2 3 4 2 3 6 2 2 1 5 3 2 4 5 1 6 2 4 5 3 6 3 3 3 3 4 3 4 3 1 3 5 6 2 3 6 3 2 1 1 3 2 3 3 4 4 3 6 3 1 3 4 1 4 3 2 1 3 3 3 2 4 2 1 3 3 2 3 6 4 4 4 3 5 2 6 2 3 2 4 1 2 3 6 3 4 2 6 3 4 6 4 2 5 3 3 2]
plt.hist(X, bins=6)
plt.show()
X = np.random.normal(1, 3, 10000) # rozkład normalny o średniej 1 i odchyleniu standardowym 3
plt.hist(X, bins=200)
plt.show()
X = np.random.uniform(2, 4, 10000) # rozkład jednostajny na odcinku [2, 4).
plt.hist(X, bins=20)
plt.show()
X = np.linspace(-2*np.pi, 2*np.pi, 1000)
# print(np.sin(X))
print(np.exp(X))
[ 0.00187 0.00189 0.00192 0.00194 0.00196 0.00199 0.00201 0.00204 0.00207 0.00209 0.00212 0.00214 0.00217 0.0022 0.00223 0.00226 0.00228 0.00231 0.00234 0.00237 0.0024 0.00243 0.00246 0.00249 0.00253 0.00256 0.00259 0.00262 0.00266 0.00269 0.00272 0.00276 0.00279 0.00283 0.00286 0.0029 0.00294 0.00297 0.00301 0.00305 0.00309 0.00313 0.00317 0.00321 0.00325 0.00329 0.00333 0.00337 0.00342 0.00346 0.0035 0.00355 0.00359 0.00364 0.00368 0.00373 0.00378 0.00383 0.00387 0.00392 0.00397 0.00402 0.00407 0.00412 0.00418 0.00423 0.00428 0.00434 0.00439 0.00445 0.0045 0.00456 0.00462 0.00468 0.00474 0.0048 0.00486 0.00492 0.00498 0.00504 0.00511 0.00517 0.00524 0.0053 0.00537 0.00544 0.00551 0.00558 0.00565 0.00572 0.00579 0.00587 0.00594 0.00602 0.00609 0.00617 0.00625 0.00633 0.00641 0.00649 0.00657 0.00665 0.00674 0.00682 0.00691 0.007 0.00708 0.00717 0.00727 0.00736 0.00745 0.00754 0.00764 0.00774 0.00783 0.00793 0.00803 0.00814 0.00824 0.00834 0.00845 0.00856 0.00866 0.00877 0.00888 0.009 0.00911 0.00923 0.00934 0.00946 0.00958 0.0097 0.00983 0.00995 0.01008 0.0102 0.01033 0.01046 0.0106 0.01073 0.01087 0.011 0.01114 0.01128 0.01143 0.01157 0.01172 0.01187 0.01202 0.01217 0.01232 0.01248 0.01264 0.0128 0.01296 0.01312 0.01329 0.01346 0.01363 0.0138 0.01397 0.01415 0.01433 0.01451 0.0147 0.01488 0.01507 0.01526 0.01545 0.01565 0.01585 0.01605 0.01625 0.01646 0.01666 0.01688 0.01709 0.01731 0.01752 0.01775 0.01797 0.0182 0.01843 0.01866 0.0189 0.01914 0.01938 0.01963 0.01987 0.02013 0.02038 0.02064 0.0209 0.02116 0.02143 0.0217 0.02198 0.02226 0.02254 0.02282 0.02311 0.0234 0.0237 0.024 0.0243 0.02461 0.02492 0.02524 0.02556 0.02588 0.02621 0.02654 0.02688 0.02722 0.02756 0.02791 0.02826 0.02862 0.02899 0.02935 0.02972 0.0301 0.03048 0.03087 0.03126 0.03165 0.03205 0.03246 0.03287 0.03329 0.03371 0.03413 0.03457 0.035 0.03545 0.0359 0.03635 0.03681 0.03728 0.03775 0.03823 0.03871 0.0392 0.0397 0.0402 0.04071 0.04122 0.04174 0.04227 0.04281 0.04335 0.0439 0.04445 0.04502 0.04559 0.04616 0.04675 0.04734 0.04794 0.04855 0.04916 0.04978 0.05041 0.05105 0.0517 0.05235 0.05301 0.05369 0.05437 0.05505 0.05575 0.05646 0.05717 0.05789 0.05863 0.05937 0.06012 0.06088 0.06165 0.06243 0.06322 0.06402 0.06483 0.06565 0.06649 0.06733 0.06818 0.06904 0.06992 0.0708 0.0717 0.07261 0.07352 0.07446 0.0754 0.07635 0.07732 0.0783 0.07929 0.08029 0.08131 0.08234 0.08338 0.08444 0.0855 0.08659 0.08768 0.08879 0.08992 0.09106 0.09221 0.09337 0.09456 0.09575 0.09697 0.09819 0.09944 0.1007 0.10197 0.10326 0.10457 0.10589 0.10723 0.10859 0.10996 0.11136 0.11277 0.11419 0.11564 0.1171 0.11858 0.12009 0.12161 0.12314 0.1247 0.12628 0.12788 0.1295 0.13114 0.1328 0.13448 0.13618 0.13791 0.13965 0.14142 0.14321 0.14502 0.14686 0.14872 0.1506 0.15251 0.15444 0.15639 0.15837 0.16038 0.16241 0.16446 0.16654 0.16865 0.17079 0.17295 0.17514 0.17735 0.1796 0.18187 0.18418 0.18651 0.18887 0.19126 0.19368 0.19613 0.19861 0.20113 0.20367 0.20625 0.20886 0.21151 0.21418 0.21689 0.21964 0.22242 0.22524 0.22809 0.23097 0.2339 0.23686 0.23986 0.24289 0.24597 0.24908 0.25223 0.25543 0.25866 0.26194 0.26525 0.26861 0.27201 0.27545 0.27894 0.28247 0.28605 0.28967 0.29333 0.29705 0.30081 0.30461 0.30847 0.31237 0.31633 0.32033 0.32439 0.32849 0.33265 0.33686 0.34113 0.34545 0.34982 0.35425 0.35873 0.36327 0.36787 0.37253 0.37724 0.38202 0.38685 0.39175 0.39671 0.40173 0.40682 0.41197 0.41718 0.42246 0.42781 0.43322 0.43871 0.44426 0.44988 0.45558 0.46135 0.46719 0.4731 0.47909 0.48515 0.49129 0.49751 0.50381 0.51019 0.51665 0.52319 0.52981 0.53652 0.54331 0.55019 0.55715 0.5642 0.57134 0.57858 0.5859 0.59332 0.60083 0.60843 0.61613 0.62393 0.63183 0.63983 0.64793 0.65613 0.66444 0.67285 0.68136 0.68999 0.69872 0.70757 0.71652 0.7256 0.73478 0.74408 0.7535 0.76304 0.7727 0.78248 0.79238 0.80241 0.81257 0.82286 0.83327 0.84382 0.8545 0.86532 0.87627 0.88736 0.8986 0.90997 0.92149 0.93315 0.94497 0.95693 0.96904 0.98131 0.99373 1.00631 1.01905 1.03195 1.04501 1.05824 1.07163 1.0852 1.09894 1.11285 1.12693 1.1412 1.15564 1.17027 1.18509 1.20009 1.21528 1.23066 1.24624 1.26202 1.27799 1.29417 1.31055 1.32714 1.34394 1.36095 1.37818 1.39562 1.41329 1.43118 1.4493 1.46764 1.48622 1.50503 1.52409 1.54338 1.56292 1.5827 1.60273 1.62302 1.64357 1.66437 1.68544 1.70677 1.72838 1.75026 1.77241 1.79485 1.81757 1.84058 1.86388 1.88747 1.91136 1.93556 1.96006 1.98487 2.00999 2.03544 2.0612 2.08729 2.11372 2.14047 2.16757 2.19501 2.22279 2.25093 2.27942 2.30827 2.33749 2.36708 2.39705 2.42739 2.45812 2.48923 2.52074 2.55265 2.58496 2.61768 2.65082 2.68437 2.71835 2.75276 2.78761 2.8229 2.85863 2.89482 2.93146 2.96857 3.00614 3.0442 3.08273 3.12175 3.16127 3.20129 3.24181 3.28285 3.3244 3.36648 3.4091 3.45225 3.49595 3.5402 3.58502 3.6304 3.67635 3.72289 3.77002 3.81774 3.86606 3.915 3.96456 4.01475 4.06557 4.11703 4.16914 4.22192 4.27536 4.32948 4.38429 4.43978 4.49598 4.5529 4.61053 4.66889 4.72799 4.78784 4.84845 4.90982 4.97197 5.03491 5.09864 5.16318 5.22854 5.29472 5.36175 5.42962 5.49835 5.56795 5.63843 5.7098 5.78208 5.85527 5.92939 6.00445 6.08045 6.15742 6.23537 6.3143 6.39422 6.47516 6.55713 6.64013 6.72419 6.8093 6.8955 6.98278 7.07117 7.16068 7.25133 7.34312 7.43607 7.5302 7.62552 7.72205 7.81979 7.91878 8.01902 8.12053 8.22332 8.32741 8.43283 8.53957 8.64767 8.75714 8.86799 8.98024 9.09392 9.20903 9.3256 9.44365 9.56319 9.68425 9.80683 9.93097 10.05668 10.18398 10.3129 10.44344 10.57564 10.70951 10.84507 10.98235 11.12137 11.26215 11.40471 11.54908 11.69527 11.84332 11.99323 12.14505 12.29878 12.45447 12.61212 12.77177 12.93344 13.09716 13.26295 13.43083 13.60085 13.77301 13.94736 14.12391 14.30269 14.48374 14.66708 14.85274 15.04076 15.23115 15.42395 15.61919 15.81691 16.01712 16.21987 16.42519 16.63311 16.84366 17.05687 17.27278 17.49143 17.71284 17.93706 18.16411 18.39404 18.62688 18.86267 19.10144 19.34323 19.58809 19.83604 20.08713 20.3414 20.59889 20.85964 21.12369 21.39108 21.66186 21.93607 22.21374 22.49493 22.77968 23.06804 23.36004 23.65574 23.95518 24.25842 24.56549 24.87645 25.19135 25.51023 25.83315 26.16015 26.4913 26.82664 27.16622 27.5101 27.85833 28.21098 28.56808 28.92971 29.29591 29.66675 30.04228 30.42257 30.80767 31.19765 31.59256 31.99247 32.39744 32.80754 33.22284 33.64338 34.06925 34.50052 34.93724 35.37949 35.82734 36.28085 36.74011 37.20518 37.67614 38.15306 38.63601 39.12508 39.62034 40.12188 40.62975 41.14406 41.66488 42.19229 42.72638 43.26722 43.81492 44.36954 44.93119 45.49995 46.0759 46.65915 47.24978 47.84789 48.45357 49.06691 49.68802 50.31699 50.95392 51.59892 52.25208 52.9135 53.5833 54.26158 54.94845 55.64401 56.34837 57.06165 57.78396 58.51541 59.25612 60.00621 60.76579 61.53499 62.31393 63.10272 63.9015 64.71039 65.52952 66.35902 67.19902 68.04965 68.91105 69.78335 70.6667 71.56122 72.46708 73.38439 74.31332 75.25401 76.2066 77.17126 78.14812 79.13735 80.1391 81.15354 82.18081 83.22109 84.27453 85.34131 86.4216 87.51556 88.62336 89.74519 90.88122 92.03163 93.19661 94.37633 95.57098 96.78076 98.00584 99.24644 100.50274 101.77495 103.06325 104.36787 105.689 107.02685 108.38164 109.75358 111.14288 112.54977 113.97447 115.41721 116.87821 118.3577 119.85591 121.3731 122.90949 124.46532 126.04086 127.63633 129.252 130.88813 132.54496 134.22277 135.92181 137.64236 139.3847 141.14908 142.9358 144.74514 146.57738 148.43282 150.31174 152.21445 154.14124 156.09242 158.0683 160.06919 162.09541 164.14728 166.22512 168.32926 170.46004 172.61779 174.80285 177.01558 179.25631 181.52541 183.82323 186.15014 188.5065 190.89269 193.30909 195.75607 198.23403 200.74335 203.28444 205.8577 208.46353 211.10234 213.77456 216.4806 219.2209 221.99588 224.806 227.65168 230.53338 233.45157 236.40669 239.39922 242.42963 245.4984 248.60602 251.75297 254.93976 258.16689 261.43487 264.74421 268.09545 271.48911 274.92573 278.40585 281.93002 285.4988 289.11276 292.77246 296.47849 300.23143 304.03188 307.88044 311.77771 315.72432 319.72088 323.76804 327.86642 332.01669 336.21948 340.47548 344.78536 349.14979 353.56946 358.04509 362.57736 367.16701 371.81476 376.52134 381.28749 386.11398 391.00157 395.95102 400.96313 406.03868 411.17848 416.38334 421.65408 426.99155 432.39657 437.87002 443.41276 449.02565 454.7096 460.46549 466.29425 472.19678 478.17404 484.22695 490.35649 496.56362 502.84932 509.21458 515.66043 522.18786 528.79792 535.49166]
import math
# print(math.sin(X)) # błąd!
# print(math.exp(X)) # błąd!
def f(x):
if x < 0:
return 78
elif x % 2 == 1:
return -1
else:
return 20
X = np.arange(-10, 11)
print(X)
[-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10]
# f(X) # błąd!
# I. sposób
f1 = np.vectorize(f)
print(f1(X))
[78 78 78 78 78 78 78 78 78 78 20 -1 20 -1 20 -1 20 -1 20 -1 20]
# II. sposób (oznacza dokładnie to samo)
@np.vectorize
def f1(x):
if x < 0:
return 78
elif x % 2 == 1:
return -1
else:
return 20
print(f1(X))
[78 78 78 78 78 78 78 78 78 78 20 -1 20 -1 20 -1 20 -1 20 -1 20]
X = np.linspace(-2*np.pi, 2*np.pi, 1000)
plt.plot(X, np.sin(X))
plt.show()
T = np.arange(0, 2.5, 0.1)
y1 = np.sin(np.pi*T)
y2 = np.sin(np.pi*T + np.pi/2)
y3 = np.sin(np.pi*T - np.pi/2)
plt.plot(T, y1, 'b--', T, y2, 'g', T, y3, 'r-')
plt.show()
plt.plot(T, np.sin(T**2))
plt.xlabel('T')
plt.ylabel('$\sin(T^2)$') # wyrażenie LaTeXa na osi OY.
plt.show()
