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python初学总结（二）

2024-09-10 15:25:17   219人阅读

（1）字典

字典是一种映射关系：键（key），值（value），key-value对

创建字典的方式：直接创建和利用dict函数创建

>>> aInfo = {‘Wangdachui‘: 3000, ‘Niuyun‘:2000, ‘Linling‘:4500, ‘Tianqi‘:8000}
>>> info = [(‘Wangdachui‘,3000), (‘Niuyun‘,2000), (‘Linling‘,4500), (‘Tianqi‘,8000)]
>>> bInfo = dict(info)
>>> cInfo = dict([[‘Wangdachui‘,3000], [‘Niuyun‘,2000], [‘Linling‘,4500], [‘Tianqi‘,8000]])
>>> dInfo = dict(Wangdachui=3000, Niuyun=2000, Linling=4500, Tianqi=8000)
>>> aDict = {}.fromkeys((‘Wangdachui‘, ‘Niuyun‘, ‘Linling‘, ‘Tianqi‘),3000)
>>> aDict
{‘Tianqi‘: 3000, ‘Wangdachui‘: 3000, ‘Niuyun‘: 3000, ‘Linling‘: 3000}
>>> sorted(aDict)
[‘Linling‘, ‘Niuyun‘, ‘Tianqi‘, ‘Wangdachui‘]


>>>names = [‘Wangdachui‘, ‘Niuyun‘, ‘Linling‘, ‘Tianqi‘]
>>>salaries = [3000, 2000, 4500, 8000]
>>>print(dict(zip(names,salaries)))
{‘Niuyun‘: 2000, ‘Linling‘: 4500, ‘Tianqi‘: 8000, ‘Wangdachui‘: 3000}

字典的基本操作：增删改查

>>> aInfo = {‘Wangdachui‘: 3000, ‘Niuyun‘:2000, ‘Linling‘:4500, ‘Tianqi‘:8000}
>>> aInfo[‘Niuyun‘]　　　　#键值查找
5000
>>> aInfo[‘Niuyun‘] = 9999          #更新
>>> aInfo
{‘Tianqi‘: 8000, ‘Wangdachui‘: 3000, ‘Linling‘: 4500, ‘Niuyun‘: 9999}
>>> aInfo[‘Fuyun‘] = 1000      #添加
>>> aInfo
{‘Tianqi‘: 8000, ‘Fuyun‘: 1000, ‘Wangdachui‘: 3000, ‘Linling‘: 4500, ‘Niuyun‘: 9999}
>>> ‘Mayun‘ in aInfo        #成员判断
False
>>> del aInfo       #删除字典
>>> aInfo
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name ‘aInfo‘ is not defined

字典的格式化字符串：

>>> aInfo = {‘Wangdachui‘: 3000, ‘Niuyun‘:2000, ‘Linling‘:4500, ‘Tianqi‘:8000}
>>> for key in aInfo.keys():
print ‘name=%s, salary=%s‘ % (key, aInfo[key])      #  %(key)格式说明符 % 字典对象名>>> "Niuyun‘s salary is %(Niuyun)s." % aInfo

"Niuyun‘s salary is 5000."

输出模板的作用

>>> aInfo = {‘Wangdachui‘: 3000, ‘Niuyun‘:2000, ‘Linling‘:4500, ‘Tianqi‘:8000}
>>> template = ‘‘‘
Welcome to the pay wall.
Niuyun‘s salary is %(Niuyun)s.
Wangdachui‘s salary is %(Wangdachui)s.
‘‘‘
>>> print template % aInfo

Welcome to the pay wall.
Niuyun‘s salary is 2000.
Wangdachui‘s salary is 3000.

字典的方法

clear() fromkeys()
get() has_key ()
items() pop()
setdefault() update()
values() copy()

（2）集合：无序不重复的元素的集合

可变集合：set

>>> names = [‘Wangdachui‘, ‘Niuyun‘, ‘Wangzi‘, ‘Wangdachui‘, ‘Linling‘, ‘Niuyun‘]
>>> namesSet = set(names)
>>> namesSet

{‘Wangzi‘, ‘Niuyun‘, ‘Wangdachui‘, ‘Linling‘

不可变集合：frozenset

aSet = set(‘hello‘)
print(aSet)
fSet = frozenset(‘hello‘)
print(fSet)

{‘e‘, ‘l‘, ‘h‘, ‘o‘}
frozenset({‘e‘, ‘l‘, ‘h‘, ‘o‘})

集合比较和关系运算符和集合操作

技术分享 技术分享技术分享技术分享

（3）python常用的数据结构

ndarray（N维数组）
Series（变长字典）
DataFrame（数据框）

import numpy as np
xArray = np.ones((3,4))
print(xArray)

[[ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]]

 

ndarray：

NumPy中基本的数据结构
别名为array
利于节省内存和提高CPU计算时间
有丰富的函数

ndarray的创建和输出

>>> from numpy import *
>>> aArray = array([1,2,3])
>>> aArray
array([1, 2, 3])
>>> bArray = array([(1,2,3),(4,5,6)])
>>> bArray
array([[1, 2, 3],
[4, 5, 6]])
>>> zeros((2,2))
array([[ 0., 0.],
[ 0., 0.]])
>>> arange(1,5,0.5)
array([ 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])

ndarray的基本运算符

>>> aArray = array([(5,5,5),(5,5,5)])
>>> bArray = array([(2,2,2),(2,2,2)])
>>> cArray = aArray * bArray
>>> cArray
array([[10, 10, 10],
[10, 10, 10]])
>>> aArray += bArray
>>> aArray
array([[7, 7, 7],
[7, 7, 7]])
>>> aArray > 5
array([[ True, True, True],
[True, True, True]], dtype=bool)

ndarray的属性和方法

>>> aArray = array([(1,2,3),(4,5,6)])
>>> aArray.shape
(2, 3)
>>> bArray = aArray.reshape(3,2)
>>> bArray
array([[1, 2],
[3, 4],
[5, 6]])
>>> aArray.sum()
21
>>> aArray.sum(axis = 0)
array([5, 7, 9])
>>> aArray.sum(axis = 1)
array([ 6, 15])

>>> aArray = array([1,3,7])
>>> bArray = array([3,5,8])
>>> cArray = array([9,8,7])
>>> aArray[1:]
array([3, 7])
>>> where(aArray>2, bArray, cArray)
array([9, 5, 8]

 ndarray的内建函数

>>> def fun(x,y):
return (x+1)*(y+1)
>>> arr = fromfunction(fun,(9,9))
>>> arr

array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[ 2., 4., 6., 8., 10., 12., 14., 16., 18.],
[ 3., 6., 9., 12., 15., 18., 21., 24., 27.],
[ 4., 8., 12., 16., 20., 24., 28., 32., 36.],
[ 5., 10., 15., 20., 25., 30., 35., 40., 45.],
[ 6., 12., 18., 24., 30., 36., 42., 48., 54.],
[ 7., 14., 21., 28., 35., 42., 49., 56., 63.],
[ 8., 16., 24., 32., 40., 48., 56., 64., 72.],
[ 9., 18., 27., 36., 45., 54., 63., 72., 81.]])

ndarray的ufunc函数

 

import numpy as np
>>> a = np.arange(1,5)
>>> a
array([1, 2, 3, 4])
>>> b = np.arange(2,6)
>>> b
array([2, 3, 4, 5])
>>> np.add(a,b)
array([3, 5, 7, 9])
>>> np.add.accumulate([2, 3, 8])
array([ 2, 5, 13])
>>> np.multiply.accumulate([2, 3, 8])
array([ 2, 6, 48])
Source
help(ufunc)
help(numpy)
add = <ufunc ‘add‘>

 

 

python初学总结（二）


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