import numpy as np

data = np.array([ [1, 1.5, 2], [5, 3, 1] ]) data

data.shape

data.dtype

data.size

np.zeros((3, 6))

np.empty((2, 4))

np.arange(10)

np.arange(10, dtype = np.float64)

a1 = np.arange(10) a2 = a1.astype(np.float64) a2.dtype

a1 = np.arange(1,5)

a1 * a1

a1 + a1

1 / a1

a2 = np.arange(4,0, -1) a1 > a2

a1 = np.arange(10) a1

a1[2 : 5] = 2000

a1s = a1[2 : 5] a1s

a1

a1s[1] = 10 a1s

a1

a1s[:] = 3076 a1s

a1

a3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) a3d

a3d[1]

a3d[0][1][2] # or you can use: a3d[0,1,2]

a3d[0] = 5 a3d

a2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

a2d

a2d[:2]

a2d[:2, 1:]

a = np.arange(18).reshape(3,6) a

a.T

np.dot(a.T, a)

a.T @ a

a = np.arange(5) a

np.sqrt(a)

np.exp(a)

b = np.arange(4, -1, -1) b

np.maximum(a, b)

c = a / 3 c

i, r = np.modf(c)

i

r

%matplotlib inline

import matplotlib.pyplot as plt import numpy as np

a = np.arange(10) + 100 a

plt.plot(a)

fig = plt.figure()

ax1 = fig.add_subplot(2, 2, 1) ax1

ax2 = fig.add_subplot(2, 2, 2) ax2

fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) ax4 = fig.add_subplot(2, 2, 4)

plt.plot(np.random.standard_normal(50).cumsum(), color = 'black', linestyle = 'dashed')

fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) # ax4 = fig.add_subplot(2, 2, 4) ax3.plot(np.random.standard_normal(50).cumsum(), color = 'black', linestyle = 'dashed')

fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) # ax4 = fig.add_subplot(2, 2, 4) ax3.plot(np.random.standard_normal(50).cumsum(), color = 'black', linestyle = 'dashed') ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.standard_normal(30))

fig = plt.figure() ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 2) ax3 = fig.add_subplot(2, 2, 3) # ax4 = fig.add_subplot(2, 2, 4) ax3.plot(np.random.standard_normal(50).cumsum(), color = 'black', linestyle = 'dashed') ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.standard_normal(30)) ax1.hist(np.random.standard_normal(100), bins = 20, color = 'black', alpha = .3)

fig, axes = plt.subplots(2, 3)

axes

fig, axes = plt.subplots(2, 2, sharex = True, sharey = True) for i in range(2): for j in range(2): axes[i, j].hist(np.random.standard_normal(500), bins = 50, color = 'black', alpha = .5) fig.subplots_adjust(wspace = 0, hspace = 0)