python学习笔记

reference: http://cs231n.github.io/python-numpy-tutorial/

Basic Grammar

Basic data types

• X unary increment (x++) or decrement (x--) operators.
• and instead of &&, or instead of ||, not instead of ! and != for XOR

String

• length - len(str)

• formatting - '%s %s %d' % (str1, str2, 1)

• capitalize - str.capitalize()

• uppercase - str.upper()

• Right-justify (右对齐) - str.rjust(totallength)

  totallength = len(str) + space in left

• Center(居中) - str.center(totallength)

• Replace - str.replace(str1, str2), all str1 in str will be replaced by str2

• Strip leading and trailing whitespace - str.strip()

Containers

Lists

• Negative index count from the end - list[-1]
• can contain elements of different types
• add new ele - list.append(newEle)
• remove and return the last ele - list.pop()

Slicing

• get sublist from index x to index y - nums[x:y]
  • no x: from the start
  • no y: to the end
  • x and y can be negative

Loops

• get elements in the list - for item in list
• get both indices and elements in the list - for index, item in list

List comprehensions

• transform a list of elements to another: newList = [(do something to item) for item in oldList]

• With conditions: newList = [(do something to item) for item in oldList if (in some condition)]

Dictionaries

• key-value set
• access with a default value - dic.get(key, defaultValue)
• delete - del d[key]

Loops

• get keys in the list - for key in dic
• get both keys and value in the list - for key, value in dic.items()

Dictionary comprehensions

• same with list, and change the value part of the dictionary
• use {}, X []

Sets

• an unordered collection of distinct elements

• judge whether an element is in the set - ele in set

• add

• remove

• len

Loops

• when access both indices and elements, the order is not as assumptions

Set comprehensions

• same, use {}

Tuples

• ordered list of values
• similar to list, but tuples can be used as keys in dictionaries and as element of sets

Functions

• ```
  def functionName(para1, para2, …):

  ### Classes
  
  - ```python
    class className():
    	# constructor
    	def __init__(self, para1, para2, ...):
    		# do sth
            
        # methods, definition of functions

Numpy

Arrays

• all elements are of the same type

• indexed by a tuple of nonnegative integers

• rank - number of dimensions of the array

• shape - a tuple of integers giving the size of the array along each dimension

• initialize -

  np.array([1,2,3])
  np.array([[1,2,3],[4,5,6]])
  # more ranks

  • all zeros - np.zeros(shape)
  • all ones - np.ones(shape)
  • all specific number - np.ones(shape, specificNumber)
  • idenity matrix - np.eye(dimension)
    • the shape of this matrix is dimension * dimension

Array indexing

Slicing

• slice indexing - specify the range for each dimension - array[range in the first dimension, range in the second dimention, ...]

Accessing elements

• integer indexing - array[index in the first dimension, index in the second dimension]

mix of slicing by index and range

• if use only slice indexing, the returning array will be of the same rank

import numpy as np

# Create the following rank 2 array with shape (3, 4)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Two ways of accessing the data in the middle row of the array.
# Mixing integer indexing with slices yields an array of lower rank,
# while using only slices yields an array of the same rank as the
# original array:
row_r1 = a[1, :]    # Rank 1 view of the second row of a
row_r2 = a[1:2, :]  # Rank 2 view of the second row of a
print(row_r1, row_r1.shape)  # Prints "[5 6 7 8] (4,)"
print(row_r2, row_r2.shape)  # Prints "[[5 6 7 8]] (1, 4)"

# We can make the same distinction when accessing columns of an array:
col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print(col_r1, col_r1.shape)  # Prints "[ 2  6 10] (3,)"
print(col_r2, col_r2.shape)  # Prints "[[ 2]
                             #          [ 6]
                             #          [10]] (3, 1)"

• if use integer indexing, arbitrary arrays can be construct.

• a[[0,1,2]] => index 0, 1 and 2 in first dimension

• Select one element from each row of a using the indices in b(an array of indices, [0, 2, 0, 1])

  a[np.arange(4), b]

  The output is a[0, 0], a[1, 2], a[2, 0], a[2, 1].

Boolean array indexing

a = np.array([[1,2], [3, 4], [5, 6]])
bool_idx = (a > 2)
# [[False False]
#  [ True  True]
#  [ True  True]]

Datatypes

np.array(ARRAYDATA, dtype=ARRAYTYPE) 

• * and / is elementwise

• matrix multiplication or vector multiplication - dot

• As a function: np.dot(x, y)

• As an instance method: x.dot(y)

• Transpose - v.T

• `

SciPy

Image operations

• read from file into a numpy arrays - imread(FILEPATH)

• Computing distances between sets of points - scipy.spatial.distance.pdist

Matplotlib

• plot, subplot and imshow