NumPy
import numpy as np
Array 만들기
a = np.array([1, 2, 3])
b = np.array([(1.5, 2, 3), (4, 5, 6)], dtype = float)
c = np.array([[(1.5, 2, 3), (4, 5, 6)], [(3, 2, 1), (4, 5, 6)]], dtype = float)
np.zeros((3, 4)) # Create an array of zeros
np.ones((2, 3, 4), dtype=np.int16) # Create an array of ones
d = np.arange(10, 25, 5) # Create an array of evenly spaced values (step value)
np.linspace(0, 2, 9) # Create an array of evenlyspaced values (number of samples)
e = np.full((2, 2), 7) # Create a constant array
f = np.eye(2) # Create a 2X2 identity matrix
np.random.random((2, 2)) # Create an array with random values
np.empty((3, 2)) # Create an empty array
File
np.loadtxt("myfile.txt")
np.genfromtxt("my_file.csv", delimiter= ',')
np.savetxt( "myarray.txt", a, delimiter= " ")
Array 정보
a.shape # Array dimensions
len(a) # Length of array
b.ndim # Number of array dimensions
e.size # Number of array elements
b.dtype # Data type of array elements
b.dtype.name # Name of data type
b.astype(int) # Convert an array to a different type
Data type
np.int64 # Signed 64-bit integer types
np.float32 # Standard double-precision floating point
np.complex # Complex numbers represented by 128 floats
np.bool # Boolean type storing TRUE and FALSE values
np.object # Python object type
np.string_ # Fixed-length string type
np.unicode_ # Fixed-length unicode type
Operation
a = np.array([1, 2, 3])
b = np.array([(1.5, 2, 3), (4, 5, 6)], dtype = float)
g = a - b # Subtraction : array([[-0.5, 0., 0.], [-3., -3., -3.]])
np.subtract(a, b) # Subtraction
b + a # Addition : array([[2.5, 4., 6.], [5., 7., 9.]])
np.add(b,a) # Addition
a/b # Division : array([[0.66666667, 1., 1.], [0.25, 0.4, 0.5]])
np.divide(a,b) # Division
a * b # Multiplication : array([[1.5, 4., 9.], [4., 10., 18.]])
np.multiply(a, b) # Multiplication
np.exp(b) # Exponentiation
np.sqrt(b) # Square root
np.sin(a) # Print sines of an array
np.cos(b) # Elementwise cosine
np.log(a) # Elementwise natural logarithm
e.dot(f) # Dot product : array([[7., 7.], [7., 7.]])
Comparison
a == b # Elementwise comparison : array([[False, True, True], [False, False, False]], dtype=bool)
a < 2 # Elementwise comparison : array([True, False, False], dtype=bool)
np.array_equal(a, b) # Arraywise comparison
Copy
h = a.view() # Create a view of the array with the same data
np.copy(a) # Create a copy of the array
h = a.copy() # Create a deep copy of the array
Soring
a.sort() # Sort an array
c.sort(axis=0) # Sort the elements of an array's axis
Subsetting, Slicing, Indexing
Subsetting
a[2] # Select the element at the 2nd index : 3
b[1, 2] # Select the element at row 1 column 2(equivalent to b[1][2]) : 6.0
Slicing
a[0:2] # Select items at index 0 and 1 : array([1, 2])
b[0:2, 1] # Select items at rows 0 and 1 in column 1 : array([2., 5.])
b[:1] # Select all items at row0(equivalent to b[0:1, :]) : array([[1.5, 2., 3.]])
c[1,...] # Same as[1,:,:] : array([[[3., 2., 1.], [4., 5., 6.]]])
a[::-1] # Reversed array a array([3, 2, 1])
Indexing
a[a<2] # Select elements from a less than 2 : array([1])
b[[1, 0, 1, 0], [0, 1, 2, 0]] # Select elements(1,0),(0,1),(1,2) and(0,0)
# array([4., 2., 6., 1.5])
b[[1, 0, 1, 0]][:, [0, 1, 2, 0]] # Select a subset of the matrix’s rows and columns
# array([[4., 5., 6., 4.], [1.5, 2., 3., 1.5], [4., 5., 6., 4.], [1.5, 2., 3., 1.5]])
Manipulation
Transposing
i = np.transpose(b) # Permute array dimensions
i.T # Permute array dimensions
Changing shape
b.ravel() # Flatten the array
g.reshape(3, -2) # Reshape, but don’t change data
Adding/Removing elements
h.resize((2, 6)) # Return a new arraywith shape(2,6)
np.append(h, g) # Append items to an array
np.insert(a, 1, 5) # Insert items in an array
np.delete(a, [1]) # Delete items from an array
Combining
np.concatenate((a, d), axis=0) # Concatenate arrays
# array([1, 2, 3, 10, 15, 20])
np.vstack((a, b) # Stack arrays vertically(row wise)
# array([[1., 2., 3.], [1.5, 2., 3.], [4., 5., 6.]])
np.r_[e, f] # Stack arrays vertically(row wise)
np.hstack((e, f)) # Stack arrays horizontally(column wise)
# array([[7., 7., 1., 0.], [7., 7., 0., 1.]])
np.column_stack((a, d)) # Create stacked column wise arrays
# array([[1, 10], [2, 15], [3, 20]])
np.c_[a, d] # Create stacked column wise arrays
Spliting
np.hsplit(a, 3) # Split the array horizontally at the 3rd index
# [array([1]), array([2]), array([3])]
np.vsplit(c, 2) # Split the array vertically at the 2nd index
# [array([[[1.5, 2., 1.], [4., 5., 6.]]]),
# array([[[3., 2., 3.], [4., 5., 6.]]])]