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NumPy.md

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NumPy

  • Numpy is a Python package that allows to efficiently store and process large array of numerical data like sound data and image data.

  • Numpy library provides a simple yet powerful data structure: the n-dimensional array. Many of Python most popular libraries are built on top of Numpy.

  • Numpy uses simple Python syntax to perform complex operations on arrays.

  • It is a common practice to import numpy as np.

import numpy as np
Methods Description
np.zeros(5) array of zeros
np.zeros((3,4)) 2D array of (3, 4)
np.array 
# create array of zeros
np.zeros(5)

# 2D array of size (3,4)
m = np.zeros((3, 4))

# initialize array from values
a = np.array([1, 2, 3, 4, 5])

# multi-dimensional array
m = np.array([[1, 2], [3, 4], [5, 6]])

  • Arithmetic operations on numpy arrays, get automatically applied to each individual element of the array. This is called vectorisation.

  • Vectorization is the process of performing the same operation in the same way for each element in an array.

  • Broadcasting is the process of extending two arrays of different shapres and figuring out how to perform a vectorized calculation between them.

x = np.array([1, 2, 3, 5])
y = np.array([6, 7, 8, 9])
z = x + y
print(z)

output:
[ 7  9 11 14]
  • Numpy has its own version of common math functions like sin, cos, exp etc that are applied to each element of the array.
a = np.array([1, 4, 9, 16])
b = np.sqrt(a)

  • Numpy arrays can be sliced just like Python lists.

  • In addition to array methods Numpy also has a large number of built-in functions.

  • All arrays have a property called .shape that returns a tuple of the size in each dimension.

  • In NumPy arrays, axes are zero-indexed and identify which dimension is which. A 2-D array has a vertical axis (| - axis 0) and a horizontal axis (-- axis 1).

import numpy as np
table = np.array([
    [5, 3, 7, 1],
    [2, 6, 7, 9],
    [1, 1, 1, 1],
    [4, 3, 2, 0],
])

table.max() # 9
table.max(axis=0) # array([5, 6, 7, 9])
table.max(axis=1) # array([7, 9, 1, 4])

  • Arrays can be broadcasted against each other if their dimensions match or if one of the arrays has a size of 1.

  • A = np.arange(32).reshape(4, 1, 8)
    A has 4 planes, each with 1 row and 8 columns.
    B = np.arange(48).reshape(1, 6, 8)
    B has 1 plane with 6 rows and 8 columns.

  • NumPy arrays use commas between axes, so that multiple axes indexed in one set of square brackets.

# Durer magic square
# add up any of the rows, columns, or diagonals, then you’ll get the same number, 34.
square = np.array([
    [16, 3, 2, 13],
    [5, 10, 11, 8],
    [9, 6, 7, 12],
    [4, 15, 14, 1]
])

Python Data Analytics

Open Data

http://datahub.io/dataset http://data.gov http://open-data.europa.eu/en/data/ http://aws.amazon.com/datasets

Cython: Compiler that translates Python code into C. Jython PyPy: interpreter is a JIT (just-in-time) compiler and it converts the Python code directly in machine code at runtime.

  • There is another distribution very similar to Anaconda and it is the Canopy distribution provided by Enthought, a company founded in 2001 and known for the SciPy project (https://www.enthought.com/products/canopy/).

  • The package manager of Canopy, as opposed to conda, is graphical.

  • SciPy (pronounced "sigh pie") is a set of open-source Python libraries specialized for scientific computing.