NumPy

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ตรวจสอบเวอร์ชันของ numpy

import numpy
print(numpy.version.version)
# 1.19.5
import numpy as np
print(np.__version__)
# 1.19.5
> conda list

Arrays

The array object in NumPy is called ndarray

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
print(type(arr))
# [1 2 3 4 5]
# <class 'numpy.ndarray'>

0-D Arrays

import numpy as np
arr = np.array(5)
print(arr)
print(type(arr))
# 5
# <class 'numpy.ndarray'>

1-D Arrays

import numpy as np
arr = np.array([1,2,3])
print(arr)
print(type(arr))
# [1 2 3]
# <class 'numpy.ndarray'>

2-D Arrays

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)
print(type(arr))
# [[1 2 3]
#  [4 5 6]]
# <class 'numpy.ndarray'>

3-D Arrays

import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(arr)
print(type(arr))
# [[[1 2 3]
#   [4 5 6]]
# 
#  [[ 7  8  9]
#   [10 11 12]]]
# <class 'numpy.ndarray'>

Number of Dimensions

import numpy as np
a = np.array(5)
b = np.array([1, 2, 3])
c = np.array([[1, 2, 3], [4, 5, 6]])
d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])

print(a.ndim)
print(b.ndim)
print(c.ndim)
print(d.ndim)
# 0
# 1
# 2
# 3

Define the number of dimensions by using the ndmin argument.

import numpy as np
arr = np.array([1, 2, 3, 4], ndmin=5)

print(arr)
print('number of dimensions :', arr.ndim)
# [[[[[1 2 3 4]]]]]
# number of dimensions : 5

NumPy Data Types

NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc.

  • i – integer
  • b – boolean
  • u – unsigned integer
  • f – float
  • c – complex float
  • m – timedelta
  • M – datetime
  • O – object
  • S – string
  • U – unicode string
  • V – fixed chunk of memory for other type ( void )
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr.dtype)
# int32
import numpy as np
arr = np.array(['apple', 'banana', 'cherry'])
print(arr.dtype)
# <U6
import numpy as np
arr = np.array([1, 2, 3, 4], dtype='S')
print(arr)
print(arr.dtype)
# [b'1' b'2' b'3' b'4']
# |S1
import numpy as np
arr = np.array([1, 2, 3, 4], dtype='i4')
print(arr)
print(arr.dtype)
# [1 2 3 4]
# int32

Converting Data Type

import numpy as np
arr = np.array([1.1, 2.1, 3.1])
newarr = arr.astype('i') # newarr = arr.astype(int)
print(newarr)
print(newarr.dtype)
# [1 2 3]
# int32
import numpy as np
arr = np.array([1, 0, 3])
newarr = arr.astype(bool)
print(newarr)
print(newarr.dtype)
# [ True False  True]
# bool

Array Copy vs View

The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.copy()
arr[0] = 42
print(arr)
print(x)
# [42  2  3  4  5]
# [1 2 3 4 5]
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
arr[0] = 42
print(arr)
print(x)
# [42  2  3  4  5]
# [42  2  3  4  5]

Check if Array Owns it’s Data

copies owns the data, and views does not own the data

Every NumPy array has the attribute base that returns None if the array owns the data.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.copy()
y = arr.view()

print(arr.base)
print(x.base)
print(y.base)
# None
# None
# [1 2 3 4 5]

Array Shape

Array Shape 

import numpy as np
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
print(arr.shape)
print(type(arr.shape))
# (2, 4)
# <class 'tuple'>
import numpy as np
arr = np.array([1, 2, 3, 4], ndmin=5)

print(arr)
print('shape of array :', arr.shape)
# [[[[[1 2 3 4]]]]]
# shape of array : (1, 1, 1, 1, 4)

Array Reshaping

Reshape From 1-D to 2-D

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)
# [[ 1  2  3]
#  [ 4  5  6]
#  [ 7  8  9]
#  [10 11 12]]

Reshape From 1-D to 3-D

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(2, 3, 2)
print(newarr)
# [[[ 1  2]
#   [ 3  4]
#   [ 5  6]]
# 
#  [[ 7  8]
#   [ 9 10]
#   [11 12]]]

Reshape return view

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(arr.reshape(2, 4).base)
# [1 2 3 4 5 6 7 8]

Unknown Dimension

You are allowed to have one “unknown” dimension.

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
newarr = arr.reshape(2, 2, -1) # newarr = arr.reshape(2, 2, 2)
print(newarr)
# [[[1 2]
#   [3 4]]
# 
#  [[5 6]
#   [7 8]]]

Flattening the arrays

Flattening array means converting a multidimensional array into a 1D array.

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
newarr = arr.reshape(-1)
print(newarr)
# [1 2 3 4 5 6]

Array Iterating

import numpy as np
arr = np.array([1, 2, 3])
for x in arr:
    print(x)
# 1
# 2
# 3
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
    print(x)
# [1 2 3]
# [4 5 6]
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
    for y in x:
        print(y)
# 1
# 2
# 3
# 4
# 5
# 6

nditer() – Iterating Arrays Using nditer()

import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in arr:
    print(x)
# [[1 2]
#  [3 4]]
# [[5 6]
#  [7 8]]
import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in np.nditer(arr):
    print(x)
# 1
# 2
# 3
# 4
# 5
# 6
# 7
# 8

nditer() – Iterating Array With Different Data Types

import numpy as np
arr = np.array([1, 2, 3])
for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']):
    print(x)
# b'1'
# b'2'
# b'3'

nditer() – Iterating With Different Step Size

import numpy as np
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
for x in np.nditer(arr[:, ::2]):
    print(x)
# 1
# 3
# 5
# 7

ndenumerate()

import numpy as np
arr = np.array([1, 2, 3])
for idx, x in np.ndenumerate(arr):
    print(idx, x)
# (0,) 1
# (1,) 2
# (2,) 3
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for idx, x in np.ndenumerate(arr):
    print(idx, x)
# (0, 0) 1
# (0, 1) 2
# (0, 2) 3
# (1, 0) 4
# (1, 1) 5
# (1, 2) 6

NumPy Joining Array

Joining NumPy Arrays

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.concatenate((arr1, arr2))
print(arr)
# [1 2 3 4 5 6]

Join two 2-D arrays along cols (axis=0)

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
arr = np.concatenate((arr1, arr2), axis=0)
print(arr)
# [[1 2]
#  [3 4]
#  [5 6]
#  [7 8]]

Join two 2-D arrays along rows (axis=1)

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
arr = np.concatenate((arr1, arr2), axis=1)
print(arr)
# [[1 2 5 6]
#  [3 4 7 8]]

stack()

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.stack((arr1, arr2), axis=0)
print(arr)
# [[1 2 3]
#  [4 5 6]]
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.stack((arr1, arr2), axis=1)
print(arr)
# [[1 4]
#  [2 5]
#  [3 6]]

hstack()

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.hstack((arr1, arr2))
print(arr)
# [1 2 3 4 5 6]

vstack()

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.vstack((arr1, arr2))
print(arr)
# [[1 2 3]
#  [4 5 6]]

dstack()

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.dstack((arr1, arr2))
print(arr)
# [[[1 4]
#   [2 5]
#   [3 6]]]

Splitting Array

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
newarr = np.array_split(arr, 3)

print(type(newarr))
print(newarr)
# <class 'list'>
# [array([1, 2]), array([3, 4]), array([5, 6])]

print(newarr[0])
print(type(newarr[0]))
# [1 2]
# <class 'numpy.ndarray'>
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
newarr = np.array_split(arr, 4)
print(newarr)
# [array([1, 2]), array([3, 4]), array([5]), array([6])]
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
newarr = np.array_split(arr, 5)
print(newarr)
# [array([1, 2]), array([3]), array([4]), array([5]), array([6])]

Splitting 2-D Arrays

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
print(arr)
# [[ 1  2]
#  [ 3  4]
#  [ 5  6]
#  [ 7  8]
#  [ 9 10]
#  [11 12]]

newarr = np.array_split(arr, 3) # newarr = np.array_split(arr, 3, axis=0)
print(newarr[0])
print(newarr[1])
print(newarr[2])
# [[1 2]
#  [3 4]]
# [[5 6]
#  [7 8]]
# [[ 9 10]
#  [11 12]]
import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
print(arr)
newarr = np.array_split(arr, 3, axis=1)
print(newarr[0])
print(newarr[1])
# [[ 1]
#  [ 3]
#  [ 5]
#  [ 7]
#  [ 9]
#  [11]]
# [[ 2]
#  [ 4]
#  [ 6]
#  [ 8]
#  [10]
#  [12]]
import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
newarr = np.hsplit(arr, 2)
print(newarr[0])
print(newarr[1])
# [[ 1]
#  [ 3]
#  [ 5]
#  [ 7]
#  [ 9]
#  [11]]
# [[ 2]
#  [ 4]
#  [ 6]
#  [ 8]
#  [10]
#  [12]]