data_analysis.py

import numpy as np
# Create 2X3 double precision array initialized to all zeroes
a = np.zeros((2,3), dtype=np.float64)

# Create array initialized by list of lists
a = np.array([[0,1,2],[3,4,5]], dtype=np.float64)

# Create array using "arange" function
a = np.arange(6, dtype=np.float64).reshape(2,3)

# Print single element of 2D array
print a[0,0]      # a scalar, not an array

# Print first row of 2D array
print a[0,:]      # 1D array

# Print last column of array
print a[:,-1]     # 1D array

# Print sub-matrix of 2D array
print a[0:2,1:3]  # 2D array

# Assign single value to single element of 2D array
a[0,0] = 25.0

# Assign 1D array to first row of 2D array
a[0,:] = np.array([10,11,12], dtype=np.float64)

# Assign 1D array to last column of 2D array
a[:,-1] = np.array([20,21], dtype=np.float64)

# Assign 2D array to sub-matrix of 2D array
a[0:2,1:3] = np.array([[10,11],[20,21]], dtype=np.float64)

# Assign scalar to first row of 2D array
a[0,:] = 10.0

# Assign 1D array to all rows of 2D array
a[:,:] = np.array([30,31,32], dtype=np.float64)

# Assign 1D array to all columns of 2D array
a[:,:] = np.array([40,41], dtype=np.float64).reshape(2,1)

# Assign scalar to sub-matrix of 2D array
a[0:2,1:3] = 100.0

# Create 1D array
a = np.arange(4, dtype=np.float64)

# Add 1D arrays elementwise
a + a

# Multiply 1D arrays elementwise
a * a

# Sum elements of 1D array
a.sum()

# Compute dot product
np.dot(a, a)    # same as: (a * a).sum()

# Compute cross product
np.dot(a.reshape(4,1), a.reshape(1,4))

from scipy import linalg
a = np.array([[1, 2], [3, 4]], dtype=np.float64)

# Compute the inverse matrix
linalg.inv(a)

# Compute singular value decomposition
linalg.svd(a)

# Compute eigenvalues
linalg.eigvals(a)

import matplotlib.pyplot as plt
x = np.linspace(0.0, 2.0, 20)

plt.plot(x, np.sqrt(x), 'ro')  # red circles
plt.show()

plt.plot(x, np.sqrt(x), 'b-')  # blue lines
plt.show()

# Three plots in one figure
plt.plot(x, x, 'g--', x, np.sqrt(x), 'ro', x, np.sqrt(x), 'b-')
plt.show()