# Introduction
This is a short overview of arrays in NumPy. It demonstrates how n-dimensional (n >= 2) arrays are represented & can be manipulated.
Import - NumPy
import numpy as np# Create Array
basic method
# 1D
arr = np.array([2.1, 3, 4, 16.4])
# multi-dimension
arr = np.arry([1, 2, 3], [4, 5, 6], ...)advanced method
arr = np.zeros((3, 4))
# Output:
array([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
arr = np.ones((2, 3, 4), dtype=np.int16)
# Output:
array([[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]]], dtype=int16)
arr = np.empty((2, 3)) # random numbers - normally: 'float64'
array([[3.73603959e-262, 6.02658058e-154, 6.55490914e-260],
[5.30498948e-313, 3.14673309e-307, 1.00000000e+000]])# Important Basic Attributes
# show contents of array
arr
# number of axes (dimensions) of the array
arr.ndim
# Output: 1
# returns count of rows & columns
arr.shape
# Output: (4,)
# returns array with modified shape
arr.reshape(2, 2)
# Output:
array([[ 2.1, 3. ],
[ 4. , 16.4]])
# arr.reshape again for converting back to 1D dimension
# total number of elements in array
arr.size
# Output: 4
# type of elements in array
arr.dtype
# Output: ('float64')
# elements: type 'float64' = 64/8 itemsize; 'complex32' = 32/8 itemsize
arr.itemsize
# Output: 8
# memory address: where element is saved in array (normally: use indexing)
arr.data
# Output: <memory at 0x7fab1405c100># Basic Functions
# create array with regularly spaced values within a range
# (START, STOP, STEPS)
np.arange(5, 50, 5)
# Output: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45])# Print Array
a = np.arange(6) # 1D array
print(a)
# Output: [0 1 2 3 4 5]# Array Operations
# +/-
a = np.array([10, 20, 30, 40])
b = np.arange(4)
c = a - b
c
# Output: array([10, 19, 28, 37])
# ^n
b**2
# Output: array([0, 1, 4, 9])
# Bool
a < 35
# Output: array([ True, True, True, False])
# .max()
# .min()
# .sum()