① python中用字典写出树形数据结构并在控制台中打印树形数据结构
#!/usr/bin/python
importos,sys,string
classXXTree:
def__init__(self):
pass
defprintHelp(self,cmd):
print'Pleaseusethefollowingcmd:'
print''+cmd+'dir'
print'e.g.'
print''+cmd+'/home/fkong/tmp'
defgetTree(self,dir,op):
list=self.getList(dir,0,op)
treelist=[]
foriinrange(0,len(list)):
fullpath=list[i]
parpath=os.path.dirname(list[i])
filename=os.path.basename(list[i])
if(fullpath==dir):
treelist.append(fullpath)
continue
path=fullpath.replace(dir,"")
names=path.split("/")
name="`---"+names[len(names)-1]
forjinrange(1,len(names)-1):
name=""+name
treelist.append(name)
pos=name.index("`")
j=i-1
whilej>0:
name=treelist[j]
if(name[pos]=='`'orname[pos]==''):
name=name[0:pos]+"|"+name[pos+1:len(name)]
treelist[j]=name
else:
break
j=j-1
foriinrange(0,len(treelist)):
printtreelist[i]
defgetList(self,dir,layer,op):
list=[]
iflayer==0:list.append(dir)
files=os.listdir(dir)
forfileinfiles:
file=os.path.join(dir,file)
ifos.path.isdir(file):
list.append(file)
list+=self.getList(file,layer+1,op)
elifop=='-d':
pass
else:
list.append(file)
returnlist
iflen(sys.argv)<2:
t=XXTree()
t.printHelp(sys.argv[0])
else:
t=XXTree()
dir=None
iflen(sys.argv)==2:
dir=sys.argv[1]
op=None
iflen(sys.argv)==3:
op=sys.argv[1]
dir=sys.argv[2]
t.getTree(dir,op)
② python中树结构使用什么实现的
这是数据结构的问题，按照数据结构中树的实现即可，当然，要是图方便也可以使用dict来模拟
③ 如何用python将几个列表转换成树形结构
不要费那个劲了，直接用pathlib
④ python连接到数据库如图所示，把数据库中的数据做成图形，类似这种条形树状的。
可以用record这个库，支持sql结果转dataframe，然后就可以用matplot这个库画图了
⑤ 请教一下python qt treewidget的树状结构怎么向指定节点插入内容
1. 树形结构实现 qt gui中有treewidget部件,将该...同时还有删除指定节点(父节点和子节点同时删除)的...c c++ javascript
⑥ python怎么做聚类树状图
#-*-coding:utf-8-*-importmathimportpylabaspl#数据集：每三个是一组分别是西瓜的编号，密度，含糖量data="""
1,0.697,0.46,2,0.774,0.376,3,0.634,0.264,4,0.608,0.318,5,0.556,0.215,
6,0.403,0.237,7,0.481,0.149,8,0.437,0.211,9,0.666,0.091,10,0.243,0.267,
11,0.245,0.057,12,0.343,0.099,13,0.639,0.161,14,0.657,0.198,15,0.36,0.37,
16,0.593,0.042,17,0.719,0.103,18,0.359,0.188,19,0.339,0.241,20,0.282,0.257,
21,0.748,0.232,22,0.714,0.346,23,0.483,0.312,24,0.478,0.437,25,0.525,0.369,
26,0.751,0.489,27,0.532,0.472,28,0.473,0.376,29,0.725,0.445,30,0.446,0.459"""#数据处理dataset是30个样本（密度，含糖量）的列表a=data.split(',')
dataset=[(float(a[i]),float(a[i+1]))foriinrange(1,len(a)-1,3)]#计算欧几里得距离,a,b分别为两个元组defdist(a,b):
returnmath.sqrt(math.pow(a[0]-b[0],2)+math.pow(a[1]-b[1],2))#dist_mindefdist_min(Ci,Cj):
returnmin(dist(i,j)foriinCiforjinCj)#dist_maxdefdist_max(Ci,Cj):
returnmax(dist(i,j)foriinCiforjinCj)#dist_avgdefdist_avg(Ci,Cj):
returnsum(dist(i,j)foriinCiforjinCj)/(len(Ci)*len(Cj))#找到距离最小的下标deffind_Min(M):
min=1000
x=0;y=0
foriinrange(len(M)):forjinrange(len(M[i])):ifi!=jandM[i][j]<min:
min=M[i][j];x=i;y=jreturn(x,y,min)#算法模型：defAGNES(dataset,dist,k):
#初始化C和M
C=[];M=[]foriindataset:
Ci=[]
Ci.append(i)
C.append(Ci)foriinC:
Mi=[]forjinC:
Mi.append(dist(i,j))
M.append(Mi)
q=len(dataset)#合并更新
whileq>k:
x,y,min=find_Min(M)
C[x].extend(C[y])
C.remove(C[y])
M=[]foriinC:
Mi=[]forjinC:
Mi.append(dist(i,j))
M.append(Mi)
q-=1
returnC#画图defdraw(C):
colValue=['r','y','g','b','c','k','m']foriinrange(len(C)):
coo_X=[]#x坐标列表
coo_Y=[]#y坐标列表
forjinrange(len(C[i])):
coo_X.append(C[i][j][0])
coo_Y.append(C[i][j][1])
pl.scatter(coo_X,coo_Y,marker='x',color=colValue[i%len(colValue)],label=i)
pl.legend(loc='upperright')
pl.show()
C=AGNES(dataset,dist_avg,3)
draw(C)
