Skip to content

Latest commit

 

History

History
147 lines (134 loc) · 3.57 KB

Numpy_Tutorial.md

File metadata and controls

147 lines (134 loc) · 3.57 KB
Raw

Numpy Tutorial

October 2017

Introduction

  • numpy stands for numerical python
  • The fundamental idea of numpy is support for multidimensional arrays. So numpy can be considered as the base for numerical computing in Python.

Installing numpy

  • Python does not come bundled with numpy.
  • To install numpy, run the following command in the command prompt:
pip install numpy
  • To check that numpy was successfully installed, run the following within IPython:
import numpy
numpy.__version__

The ndarray Object

  • The ndarray is a fundamental object of numpy.
    • This object is an N-dimensional array, meaning that it contains a collection of elements of the same type indexed using N (dimensions of the array) integers.
  • The main attributes of ndarray are:
    • data type (dtype)
    • shape
    • size
    • itemsize
    • data
    • ndim
  • Example:
my_array = np.array(((6, 12, 93, 2), (5, 26, 78, 90), (3, 12, 16, 22), (5, 3, 1, 16)))

my_array
array([[ 6, 12, 93,  2],
       [ 5, 26, 78, 90],
       [ 3, 12, 16, 22],
       [ 5,  3,  1, 16]])
  • To return the data type of my_array:
my_array.dtype
  • To return the shape of my_array:
my_array.shape
  • The above will return a tuple of array dimensions (rows, columns).
  • To return the size (number of elements) of my_array:
my_array.size
  • To return the itemsize, meaning the size of one array element in bytes of my_array:
my_array.itemsize
  • To return the buffer object that points to my_array's place in memory:
my_array.data
  • To return the number of the array dimensions of my_array:
my_array.ndim
  • To create an ndarray with five columns and one row:
my_array = np.array([1, 2, 3, 4, 5])
  • To create an ndarray with four columns and four rows:
my_array = np.array([[6, 12, 93, 2], [5, 26, 78, 90], [3, 12, 16, 22], [5, 3, 1, 16]])

Selecting Items

  • To select the item located on the third row and fourth column:
my_array[2,3]
  • Remember that numpy uses zero indexing so the third row is index 2 and the fourth column is index 3.
  • To access items in an array the convention is array[row, column].

Empty (Uninitialized) Arrays

  • To create an empty array:
np.empty(shape, dtype, order)
  • For example:
np.empty((4,4))

Array Filled With Zeros

  • To create an array where the elements are all zeros:
np.zeroes((4,4), dtype=int)

Array Filled with Ones

  • To create an array where the elements are all ones:
np.ones((4,4), dtype=int)

Array with Evenly Spaced Values Within a Given Range

  • To create an array with evenly spaced values within a specific range:
np.arange(start, stop, step, dtype)
  • For example:
np.arange(1, 10)

Reshaping an Array

  • To give a new shape to an array without changing its data:
my_array = np.ones((4,4))
np.reshape(my_array, (8,2))

Concatenating Arrays

  • To join two or more arrays of the same shape along the rows:
np.concatenate((array1, array2), axis=0)
  • To join two or more arrays of the same shape along the columns:
np.concatenate((array1, array2), axis=1)

Splitting Arrays

  • To divide an array into multiple sub-arrays:
np.split(my_array, indices_or_sections, axis=0)
  • For example, to split the following array into three equal parts:
concatenated_array = np.array(((1, 2),
                       (3, 4),
                       (5, 6),
                       (7, 8),
                       (9, 10),
                       (11, 12)))

split_array = np.split(concatenated_array, 3)