`np.array()`

Creates a NumPy array from a list or tuple.

arr = np.array([1,2,4])
print(arr)

Output:

[1 2 4]

`.ndim`

Returns the number of dimensions of the array.

arr.ndim

Output:

`np.array(34)`

Creates a scalar (0D) NumPy array.

arr2 = np.array(34)
arr2.ndim

Output:

`.shape`

Returns the shape as (rows, columns).

arr3 = np.array([[1,3,4],[2,34,5]])
arr3.shape

Output:

(2, 3)

`np.arange()`

Creates numbers within a range using step size.

np.arange(1,10,2)

Output:

[1 3 5 7 9]

`np.linspace()`

Creates evenly spaced numbers between two values.

np.linspace(0,1,5)

Output:

[0.   0.25 0.5  0.75 1.  ]

`np.logspace()`

Creates numbers in logarithmic scale.

np.logspace(1,3,3)

Output:

[  10.  100. 1000.]

`np.zeros()`

Creates an array filled with zeros.

np.zeros(4)

Output:

[0. 0. 0. 0.]

2D Example

np.zeros([3,2])

Output:

[[0. 0.]
 [0. 0.]
 [0. 0.]]

`np.ones()`

Creates an array filled with ones.

np.ones([3,2])

Output:

[[1. 1.]
 [1. 1.]
 [1. 1.]]

`np.full()`

Creates an array filled with a custom value.

np.full(10,2)

Output:

[2 2 2 2 2 2 2 2 2 2]

2D Example

np.full([2,4],5)

Output:

[[5 5 5 5]
 [5 5 5 5]]

`np.empty()`

Creates an uninitialized array with random garbage values.

np.empty([2,3])

Output:

[[6.92029351e-310 6.92029351e-310 0.00000000e+000]
 [0.00000000e+000 0.00000000e+000 0.00000000e+000]]

(Output varies every time)

`np.random.rand()`

Generates random numbers between 0 and 1.

np.random.rand(5)

Output:

[0.12 0.45 0.89 0.34 0.67]

(Random output)

`np.random.randn()`

Generates random numbers from normal distribution.

np.random.randn(2,3)

Output:

[[ 0.52 -1.12  0.44]
 [ 1.32  0.87 -0.29]]

(Random output)

`np.random.randint()`

Generates random integers within a range.

np.random.randint(10,100,size=5)

Output:

[23 45 89 12 67]

(Random output)

`.dtype`

Returns datatype of array elements.

arr = np.array([1,2,3])
arr.dtype

Output:

dtype('int64')

`astype()`

Converts array datatype into another datatype.

arr.astype(np.float64)

Output:

array([1., 2., 3.])

`.size`

Returns total number of elements.

arr.size

Output:

`.itemsize`

Returns memory size of one element in bytes.

arr.itemsize

Output:

`reshape()`

Changes array shape without changing data.

arr = np.array([1,2,3,4,5,6])
arr.reshape(2,3)

Output:

[[1 2 3]
 [4 5 6]]

`ravel()`

Converts multi-dimensional array into 1D view.

arr.reshape(2,3).ravel()

Output:

[1 2 3 4 5 6]

`flatten()`

Converts array into independent 1D copy.

arr.reshape(2,3).flatten()

Output:

[1 2 3 4 5 6]

Arithmetic Operations on NumPy Arrays

NumPy supports direct arithmetic operations on arrays.

arr1 = np.array([1,2,3])
arr2 = np.array([4,5,6])

print(arr1 + arr2)
print(arr1 - arr2)
print(arr1 * arr2)
print(arr1 / arr2)

Output:

[5 7 9]
[-3 -3 -3]
[ 4 10 18]
[0.25 0.4  0.5 ]

Universal Functions (ufuncs)

Universal functions perform element-wise operations efficiently.

`np.sin()`

Calculates sine value for each element.

angles = np.array([0,np.pi,np.pi/2])

print(np.sin(angles))

Output:

[0.0000000e+00 1.2246468e-16 1.0000000e+00]

Indexing and Slicing

Access Elements

arr = np.array([10,20,30,40,50])

print(arr[-1])

Output:

Slicing

arr[::2]

Output:

[10 30 50]

Reverse Slicing

a = np.array([1,2,3,4,5])

a[-1:-3:-1]

Output:

[5 4]

Multidimensional Slicing

matrix = np.array([[1,2,3],
                   [4,5,6],
                   [7,8,9]])

print(matrix[0:2])

Output:

[[1 2 3]
 [4 5 6]]

Specific Rows and Columns

print(matrix[1:,1:])

Output:

[[5 6]
 [8 9]]

Index Array using `np.take()`

Selects elements using index positions.

arr = np.array([10,20,30,40,50])

ind = [0,2]

print(np.take(arr,ind))

Output:

[10 30]

Iterating NumPy Arrays

`np.nditer()`

Iterates element by element.

arr=np.array([[1,2],[3,4]])

for x in np.nditer(arr):
  print(x, end=" ")

Output:

1 2 3 4

`np.ndenumerate()`

Returns index and value while iterating.

for ind, x in np.ndenumerate(arr):
  print(ind,x)

Output:

(0, 0) 1
(0, 1) 2
(1, 0) 3
(1, 1) 4

Views vs Copies

View

Changes affect original array.

arr = np.array([1,2,4,5,6])

view = arr[1:4]

view[1] = 3

print(arr)

Output:

[1 2 3 5 6]

Copy

Changes do not affect original array.

arr1 = np.array([1,2,4,5,6])

copy = arr1[1:4].copy()

copy[0] = 100

print(arr1)
print(copy)

Output:

[1 2 4 5 6]
[100   4   5]

Transpose of Matrix

`transpose()`

Converts rows into columns.

mat = np.array([[1,2],[3,4]])

print(mat.transpose())

Output:

[[1 3]
 [2 4]]

`swapaxes()`

Swaps two specific axes in multidimensional arrays.

mat = np.array([[[1,2],[3,4]]])

swap = np.swapaxes(mat,0,1)

print(swap.shape)

Output:

(2, 1, 2)

Concatenation and Stacking

`np.concatenate()`

Joins arrays into one array.

arr1 = np.array([1,2,3])
arr2 = np.array([4,5,6])

combine = np.concatenate((arr1,arr2))

print(combine)

Output:

[1 2 3 4 5 6]

`np.vstack()`

Stacks arrays vertically.

vstack = np.vstack((arr1,arr2))

print(vstack)

Output:

[[1 2 3]
 [4 5 6]]

`np.hstack()`

Stacks arrays horizontally.

hstack = np.hstack((arr1,arr2))

print(hstack)

Output:

[1 2 3 4 5 6]

`np.stack()`

Stacks arrays along a new axis.

stack = np.stack((arr1,arr2),axis=0)

print(stack)

Output:

[[1 2 3]
 [4 5 6]]

Splitting Arrays

`np.split()`

Splits array into equal parts.

arr = np.array([1,2,3,4])

print(np.split(arr,2))

Output:

[array([1, 2]), array([3, 4])]

Repeat vs Tile

`np.repeat()`

Repeats each element multiple times.

arr = np.array([1,2,3])

print(np.repeat(arr,3))

Output:

[1 1 1 2 2 2 3 3 3]

`np.tile()`

Repeats whole array multiple times.

print(np.tile(arr,3))

Output:

[1 2 3 1 2 3 1 2 3]

Aggregate Functions

`np.sum()`

Returns sum of elements.

print(np.sum(arr))

Output:

`np.std()`

Returns standard deviation.

np.std(arr)

Output:

0.816496580927726

`np.min()`

Returns minimum value.

np.min(arr)

Output:

Sum by Axis

matrix = np.array([[1,2,3],
                   [4,5,6],
                   [7,8,9]])

print(np.sum(matrix,axis=0))
print(np.sum(matrix,axis=1))

Output:

[12 15 18]
[ 6 15 24]

`np.cumsum()`

Returns cumulative sum.

print(np.cumsum(arr))

Output:

[1 3 6]

`np.cumprod()`

Returns cumulative product.

print(np.cumprod(arr))

Output:

[1 2 6]

Conditional Operations

`np.where()`

Applies condition and returns values.

res = np.where(arr<2,"low","high")

print(res)

Output:

['low' 'high' 'high']

`np.argwhere()`

Returns indexes where condition is true.

print(np.argwhere(arr>0))

Output:

[[0]
 [1]
 [2]]

Masking in Arrays

`np.logical_and()`

Checks multiple conditions together.

array = np.array([100,2,3,4,5,6])

mask = np.logical_and(array>3,array<6)

print(mask)

Output:

[False False False  True  True False]

`np.logical_or()`

Returns true if any condition is true.

mask = np.logical_or(array>3,array<6)

print(mask)

Output:

[ True  True  True  True  True  True]

Broadcasting

Broadcasting automatically applies operations across arrays.

image = np.array([[200,150],[100,250]])

brightness = image + 50

print(brightness)

Output:

[[250 200]
 [150 300]]

Vectorization

Applies functions efficiently without loops.

def square(x):
  return x*x

vfunc = np.vectorize(square)

arr = np.array([1,2,3])

print(vfunc(arr))

Output:

[1 4 9]

Missing Values

`np.nan`

Represents missing values.

a = np.array([1,2,3,np.nan,5])

print(a)

Output:

[ 1.  2.  3. nan  5.]

`np.isnan()`

Checks for NaN values.

print(np.isnan(a))

Output:

[False False False  True False]

Infinite Values

`np.isinf()`

Checks infinite values.

b = np.array([1,np.nan,np.inf,10.2,40])

print(np.isinf(b))

Output:

[False False  True False False]

`np.nan_to_num()`

Converts NaN and Inf into numbers.

new_b = np.nan_to_num(b)

print(new_b)

Output:

[1.00000000e+000 0.00000000e+000 1.79769313e+308 1.02000000e+001
 4.00000000e+001]

​np.array()

​.ndim

​np.array(34)

​.shape

​np.arange()

​np.linspace()

​np.logspace()

​np.zeros()

​2D Example

​np.ones()

​np.full()

​2D Example

​np.empty()

​np.random.rand()

​np.random.randn()

​np.random.randint()

​.dtype

​astype()

​.size

​.itemsize

​reshape()

​ravel()

​flatten()

​Arithmetic Operations on NumPy Arrays

​Universal Functions (ufuncs)

​np.sin()

​Indexing and Slicing

​Access Elements

​Slicing

​Reverse Slicing

​Multidimensional Slicing

​Specific Rows and Columns

​Index Array using np.take()

​Iterating NumPy Arrays

​np.nditer()

​np.ndenumerate()

​Views vs Copies

​View

​Copy

​Transpose of Matrix

​transpose()

​swapaxes()

​Concatenation and Stacking

​np.concatenate()

​np.vstack()

​np.hstack()

​np.stack()

​Splitting Arrays

​np.split()

​Repeat vs Tile

​np.repeat()

​np.tile()

​Aggregate Functions

​np.sum()

​np.std()

​np.min()

​Sum by Axis

​np.cumsum()

​np.cumprod()

​Conditional Operations

​np.where()

​np.argwhere()

​Masking in Arrays

​np.logical_and()

​np.logical_or()

​Broadcasting

​Vectorization

​Missing Values

​np.nan

​np.isnan()

​Infinite Values

​np.isinf()

​np.nan_to_num()

`np.array()`

`.ndim`

`np.array(34)`

`.shape`

`np.arange()`

`np.linspace()`

`np.logspace()`

`np.zeros()`

2D Example

`np.ones()`

`np.full()`

2D Example

`np.empty()`

`np.random.rand()`

`np.random.randn()`

`np.random.randint()`

`.dtype`

`astype()`

`.size`

`.itemsize`

`reshape()`

`ravel()`

`flatten()`

Arithmetic Operations on NumPy Arrays

Universal Functions (ufuncs)

`np.sin()`

Indexing and Slicing

Access Elements

Slicing

Reverse Slicing

Multidimensional Slicing

Specific Rows and Columns

Index Array using `np.take()`

Iterating NumPy Arrays

`np.nditer()`

`np.ndenumerate()`

Views vs Copies

View

Copy

Transpose of Matrix

`transpose()`

`swapaxes()`

Concatenation and Stacking

`np.concatenate()`

`np.vstack()`

`np.hstack()`

`np.stack()`

Splitting Arrays

`np.split()`

Repeat vs Tile

`np.repeat()`

`np.tile()`

Aggregate Functions

`np.sum()`

`np.std()`

`np.min()`

Sum by Axis

`np.cumsum()`

`np.cumprod()`

Conditional Operations

`np.where()`

`np.argwhere()`

Masking in Arrays

`np.logical_and()`

`np.logical_or()`

Broadcasting

Vectorization

Missing Values

`np.nan`

`np.isnan()`

Infinite Values

`np.isinf()`

`np.nan_to_num()`