Cheatsheet
# Creating arrays
np.array([1,2,3])
np.array([(1.5,2,3), (4,5,6)], dtype=float)
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
# 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=" ")
# 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
# Array Mathematics
- Arithmetic Operations
g = a-b
np.subtract(a,b)
b+a
np.add(b,a)
a/b
np.divide(a,b)
a*b
np.multiply(a,b)
np.exp(b) #exponentiation
np.sqrt(b)
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
- Comparison
a == b #element-wise comparison
a < 2 # element-wise comparison
np.array_equal(a,b) #array-wise comparison
- Aggregate Functions
a.sum() #array-wise sum
a.min() #array-wise mimimum 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
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
a[2] #select the element at the 2nd index
b[1,2] #select the element at row 1 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
Indexing
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 and columns
Array Manipulation
Transposing Array
i = np.transpose(b) #permute array dimensions
i.T #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
# Others
np.info(np.ndarray.dtype) #Help
# inf is infinity - a value that is greater than any other value. -inf is therefore smaller than any other value.
# nan stands for Not A Number, and this is not equal to 0.
a = range(3,dtype=float)
a[0] = np.nan
a[1] = np.inf
a[2] = -np.inf
a # is now [nan,inf,-inf]
np.isnan(a[0]) # True
np.isinf(a[1]) # True
np.isinf(a[2]) # True
References
https://towardsdatascience.com/the-ultimate-beginners-guide-to-numpy-f5a2f99aef54