python畫樹圖
⑴ 用python畫樹中的填上的200數據怎麼來的
代碼如下：
def tree(plist,l,a,f):
if l>5:
lst=[]
for p in plist:
p.forward(l)
q=p.clone()
p.left(a)
q.right(a)
lst.append(p)
lst.append(q)
tree(lst,l*f,a,f)
⑵ 用python畫一棵樹
1、准備
1
打開我們的Python shell界面，也就是大家所說的idle界面。
⑶ python怎麼做聚類樹狀圖
#-*-coding:utf-8-*-importmathimportpylabaspl#數據集：每三個是一組分別是西瓜的編號，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"""#數據處理dataset是30個樣本（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)]#計算歐幾里得距離,a,b分別為兩個元組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))#找到距離最小的下標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)#演算法模型：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)#合並更新
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#畫圖defdraw(C):
colValue=['r','y','g','b','c','k','m']foriinrange(len(C)):
coo_X=[]#x坐標列表
coo_Y=[]#y坐標列表
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)
⑷ 如何採用Python語言繪制一棵樹
class node: left=None right=None def __init__(self, parent=None): self.parent=parent 賦值的時候對應就好了。如root=node()，a=node(root)，root.left=a，就有點像C語言里的指針了。
⑸ python怎麼動態畫出一棵樹
fromturtleimportTurtle
deftree(tList,length,angle,factor):
iflength>5:
lst=[]
fortintList:
t.forward(length);
temp=t.clone();
t.left(angle);
temp.right(angle);
lst.append(t);
lst.append(temp);
tree(lst,length*factor,angle,factor);
defmakeTree(x,y):
t=Turtle();
t.color('green');
t.pensize(5);
t.hideturtle();
#t.getscreen().tracer(30,0);
t.speed(10);
t.left(90);
t.penup();
t.goto(x,y);
t.pendown();
t=tree([t],110,65,0.6375);
makeTree(0,0)
⑹ python中樹結構使用什麼實現的
這是數據結構的問題，按照數據結構中樹的實現即可，當然，要是圖方便也可以使用dict來模擬
⑺ 如何將python生成的決策樹利用graphviz畫出來
#這里有一個示例，你可以看一下。
#http://scikit-learn.org/stable/moles/tree.html
>>>fromIPython.displayimportImage
>>>dot_data=tree.export_graphviz(clf,out_file=None,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True,rounded=True,
special_characters=True)
>>>graph=pydotplus.graph_from_dot_data(dot_data)
>>>Image(graph.create_png())
⑻ 用python畫樹中的填上的數據怎麼來的
from turtle import Turtle def tree(tList,length,angle,factor): if length > 5: lst = [] for t in tList: t.forward(length); temp = t.clone(); t.left(angle); temp.right(angle); lst.append(t); lst.append(temp); tree(lst,length*factor,angle,factor); def makeTree(x,y): t = Turtle(); t.color('green'); t.pensize(5); t.hideturtle(); #t.getscreen().tracer(30,0); t.speed(10); t.left(90); t.penup(); t.goto(x,y); t.pendown(); t = tree([t],110,65,0.6375); makeTree(0,0)
⑼ python連接到資料庫如圖所示，把資料庫中的數據做成圖形，類似這種條形樹狀的。
可以用record這個庫，支持sql結果轉dataframe，然後就可以用matplot這個庫畫圖了