NumPy Arrays
Creating Arrays
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)
Initial Placeholders
np.zeros((3,4)) #Create an array of zeros
np.zeros_like(a) #Create an array of zeros which have the same shape as a
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 evenly spaced 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
I/O
Saving & Loading On Disk
np.save('my_array', a)
np.savez('array.npz', a, b)
np.load('my_array.npy')
Saving & Loading Text Files
np.loadtxt("myfile.txt")
np.genfromtxt("my_file.csv", delimiter=',')
np.savetxt("myarray.txt", a, delimiter=" ")
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
Inspecting Your Array
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
Asking For Help
np.info(np.ndarray.dtype)
Array Mathematics
Arithmetic Operations
>>> g = a - b
array([[-0.5, 0. , 0. ],[-3. , -3. , -3. ]])
>>> np.subtract(a,b)
>>> b + a
array([[ 2.5, 4. , 6. ],[ 5. , 7. , 9. ]])
>>> np.add(b,a)
>>> a / b
array([[ 0.66666667, 1., 1. ], [0.25 , 0.4 , 0.5 ]])
>>> a * b
array([[ 1.5, 4. , 9. ],[ 4. , 10. , 18. ]])
>>> np.multiply(a,b)
>>> np.divide(a,b)
>>> np.exp(b)
>>> np.sqrt(b)
>>> np.sin(a)
>>> np.cos(b)
>>> np.log(a) # Element-wise natural logarithm
>>> e.dot(f) # Dot product
array([[ 7., 7.], [ 7., 7.]])
Comparison
>>> a == b # Element-wise comparison
array([[False, True, True],
[False, False, False]], dtype=bool)
>>> a < 2 # lement-wise comparison
array([True, False, False], dtype=bool)
>>> np.array_equal(a, b) # Array-wise comparison
Aggregate Functions
a.sum() # Array-wise sum
a.min() # Array-wise minimum value
b.max(axis=0) # Maximum value of an array row
b.cumsum(axis=1) # Cumulative sum of the elements
a.mean() # Mean
b.median() # Median
a.corrcoef() # Correlation coefficient
np.std(b) # Standard deviation
Subsetting, Slicing, Indexing
Subsetting
a[2]
b[1,2]
Slicing
>>> a[0:2]
array([1, 2])
>>> b[0:2,1]
array([ 2., 5.])
>>> b[:1]
array([[1.5, 2., 3.]])
>>> c[1,...]
array([[[3., 2., 1.],
[4., 5., 6.]]])
>>> a[ : :-1]
array([3, 2, 1])
Boolean Indexing
>>> a[a<2]
array([1])
Fancy Indexing
>>> b[[1, 0, 1, 0],[0, 1, 2, 0]]
array([4.,2.,6.,1.5])
>>> b[[1, 0, 1, 0]][:,[0,1,2,0]]
array([[4.,5.,6.,4.],
[1.5,2.,3.,1.5],
[4.,5.,6.,4.],
[1.5,2.,3.,1.5]])
Array Manipulation
Copying Arrays
>>> 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
Sorting Arrays
>>> a.sort() # Sort an array
>>> c.sort(axis=0) # Sort the elements of an array's axis
Transposing Array
>>> i = np.transpose(b)
>>> i.T
Changing Array Shape
>>> b.ravel() Flatten the array
>>> g.reshape(3,-2)
Adding/Removing Elements
>>> h.resize((2,6))
>>> np.append(h,g)
>>> np.insert(a, 1, 5)
>>> np.delete(a,[1])
Combining Arrays
>>> np.concatenate((a,d),axis=0)
array([ 1, 2, 3, 10, 15, 20])
>>> np.vstack((a,b))
array([[ 1. , 2. , 3. ],
[ 1.5, 2. , 3. ],
[4. , 5. , 6. ]])
>>> np.r_[e,f]
>>> np.hstack((e,f))
array([[ 7., 7., 1., 0.],
[ 7., 7., 0., 1.]])
>>> np.column_stack((a,d))
array([[ 1, 10],
[ 2, 15],
[ 3, 20]])
>>> np.c_[a,d]
Splitting Arrays
>>> np.hsplit(a,3)
[array([1]),array([2]),array([3])]
>>> h = a.view()
>>> np.copy(a)
>>> h = a.copy()
>>> np.vsplit(c,2)
[array([[[ 1.5, 2. , 1. ],
[ 4. , 5. , 6. ]]]),
array([[[ 3., 2., 3.],
[ 4., 5., 6.]]])]
Refercence
