1、list创建
new_list1 = ['TV','Car','Cloth','Food']
new_list2 = list(['TV','Car','Cloth','Food'])
print (new_list1)
print (new_list2)
运行结果:
['TV', 'Car', 'Cloth', 'Food']
['TV', 'Car', 'Cloth', 'Food']
2、list常用的方法:
1. append
new_list1 = ['TV','Car','Cloth','Food']
print (new_list1)
new_list1.append('Water') #追加'water'
print (new_list1)
运行结果:
['TV', 'Car', 'Cloth', 'Food']
['TV', 'Car', 'Cloth', 'Food', 'Water']
2.count
new_list1 = ['TV','Car','Cloth','Food','Food']
print (new_list1.count('Food')) #统计'food'在列表中的次数 => 2次
3.extend
new_list1 = ['TV','Car','Cloth','Food','Food']
new_list1.extend([11,22,33,'Car'])
print (new_list1)
运行结果:
['TV', 'Car', 'Cloth', 'Food', 'Food', 11, 22, 33, 'Car']
4.sort
new_list1 = ['TV','Car','Cloth','Food','Food']
print (new_list1)
new_list1.sort() #正向排序
print (new_list1)
new_list1.sort(reverse=True) #反向排序
print (new_list1)
运行结果:
['TV', 'Car', 'Cloth', 'Food', 'Food']
['Car', 'Cloth', 'Food', 'Food', 'TV'] #正向排序结果
['TV', 'Food', 'Food', 'Cloth', 'Car'] #反向排序结果
5. len
new_list1 = ['TV','Car','Food','Cloth','Food']
print (len(new_list1)) => 5 #打印元素的个数
6.remove
new_list1 = ['TV','Car','Food','Cloth','Food']
new_list1.remove('Food')
print (new_list1)
运行结果:
['TV', 'Car', 'Cloth', 'Food'] #remove会删除找到的第一个相同元素
7.pop
new_list1 = ['TV','Car','Food','Cloth','Food']
new_list1.pop() #随机删除一个元素
print (new_list1)
new_list1.pop(2) #指定删除某个元素
print (new_list1)
运行结果为:
['TV', 'Car', 'Food', 'Cloth']
['TV', 'Car', 'Cloth'] #,此处删除的是第3个元素'Food'
8.index
new_list1 = ['TV','Car','Food','Cloth','Food']
print (new_list1.index('Food')) #返回指定元素的index值,相同元素返回查找到的第一个
运行结果为:2
9.insert
new_list1 = ['TV','Car','Food','Cloth','Food']
new_list1.insert(2,'HAHAHA') #向指定的位置插入元素
print (new_list1)
运行结果为:
['TV', 'Car', 'HAHAHA', 'Food', 'Cloth', 'Food']

ForhandlinglargedatasetsinPython,useNumPyarraysforbetterperformance.1)NumPyarraysarememory-efficientandfasterfornumericaloperations.2)Avoidunnecessarytypeconversions.3)Leveragevectorizationforreducedtimecomplexity.4)Managememoryusagewithefficientdata

Inpython,ListSusedynamicMemoryAllocationWithOver-Asalose,而alenumpyArraySallaySallocateFixedMemory.1)listssallocatemoremoremoremorythanneededinentientary上,respizeTized.2)numpyarsallaysallaysallocateAllocateAllocateAlcocateExactMemoryForements,OfferingPrediCtableSageButlessemageButlesseflextlessibility。

Inpython,YouCansspecthedatatAtatatPeyFelemereModeRernSpant.1)Usenpynernrump.1)Usenpynyp.dloatp.dloatp.ploatm64,formor professisconsiscontrolatatypes。

NumPyisessentialfornumericalcomputinginPythonduetoitsspeed,memoryefficiency,andcomprehensivemathematicalfunctions.1)It'sfastbecauseitperformsoperationsinC.2)NumPyarraysaremorememory-efficientthanPythonlists.3)Itoffersawiderangeofmathematicaloperation

Contiguousmemoryallocationiscrucialforarraysbecauseitallowsforefficientandfastelementaccess.1)Itenablesconstanttimeaccess,O(1),duetodirectaddresscalculation.2)Itimprovescacheefficiencybyallowingmultipleelementfetchespercacheline.3)Itsimplifiesmemorym

SlicingaPythonlistisdoneusingthesyntaxlist[start:stop:step].Here'showitworks:1)Startistheindexofthefirstelementtoinclude.2)Stopistheindexofthefirstelementtoexclude.3)Stepistheincrementbetweenelements.It'susefulforextractingportionsoflistsandcanuseneg

numpyallowsforvariousoperationsonArrays:1)basicarithmeticlikeaddition,减法,乘法和division; 2)evationAperationssuchasmatrixmultiplication; 3)element-wiseOperations wiseOperationswithOutexpliitloops; 4)

Arresinpython,尤其是Throughnumpyandpandas,weessentialFordataAnalysis,offeringSpeedAndeffied.1)NumpyArseNable efflaysenable efficefliceHandlingAtaSetSetSetSetSetSetSetSetSetSetSetsetSetSetSetSetsopplexoperationslikemovingaverages.2)


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