NumPy
The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.
Use the following import convention:
>>> import numpy as np
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.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
Input/Output
Saving and loading on disk
>>> np.save('my_array', a)
>>> np.savez('array.npz', a, b)
>>> np.load('my_array.npy')
Saving and Loading on Textfile
>>> np.loadtxt("myfile.txt")
>>> np.genfromtxt("my_file.csv", delimiter=',')
>>> np.savetxt("myarray.txt", a, delimiter=" ")
DataTypes
>>> 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 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
Array Mathematics
Airthmetic operations
>>> 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) Element-wise cosine
>>> np.log(a) Element-wise natural logarithm
>>> e.dot(f) Dot product
Comparision
>>> a == b Element-wise comparison
>>> a < 2 Element-wise comparison
>>> np.array_equal(a, b) Array-wise comparison
Aggregation 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 element
s >>> a.mean() Mean
>>> b.median() Median
>>> a.corrcoef() Correlation coefficient
>>> np.std(b) Standard deviation
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
Subsetting ,Slicing and Indexing
Subsetting
>>> a[2] Select the element at the 2nd index
>>> b[1,2] Select the element at row 0 column 2 (equivalent to b[1][2])
Slicing
>>> a[0:2] Select items at index 0 and 1
>>> b[0:2,1] Select items at rows 0 and 1 in column 1
>>> b[:1] Select all items at row 0 (equivalent to b[0:1, :])
>>> c[1,...] Same as [1,:,:]
>>> a[ : :-1] Reversed array a
Boolean Indexing
>>> a[a<2] Select elements from a less than 2
Fancy Indexing
>>> b[[1, 0, 1, 0],[0, 1, 2, 0]] Select elements (1,0),(0,1),(1,2) and (0,0)
>>> b[[1, 0, 1, 0]][:,[0,1,2,0]] Select a subset of the matrix’s rows
Array Manupulations
Transposing Array
>>> i = np.transpose(b) Permute array dimensions
Changing Array 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 array with 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 Arrays
>>> np.concatenate((a,d),axis=0) Concatenate arrays
>>> np.vstack((a,b)) Stack arrays vertically (row-wise)
>>> np.r_[e,f] Stack arrays vertically (row-wise)
>>> np.hstack((e,f)) Stack arrays horizontally (column-wise)
>>> np.column_stack((a,d)) Create stacked column-wise arrays
>>> np.c_[a,d] Create stacked column-wise arrays
Splitting Arrays
>>> np.hsplit(a,3) Split the array horizontally at the 3rd index
>>> np.vsplit(c,2) Split the array vertically at the 2nd index
