那麼在SchemDraw模組當中呢,有六個元素用來代表流程圖的主要節點的,橢圓形代表的是決策的開始和結束,程式碼如下:
import schemdraw from schemdraw.flow import * with schemdraw.Drawing() as d: d += Start().label("Start")
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#箭頭表示的是決策的走向,用來連接各個節點的,程式碼如下:
with schemdraw.Drawing() as d: d += Arrow(w = 5).right().label("Connector")
output
平行四邊形代表的是你所要去處理和解決的問題,而長方形所代表的是你所要為此做出的努力或是過程,程式碼如下:
with schemdraw.Drawing() as d: d += Data(w = 5).label("What's the problem")
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with schemdraw.Drawing() as d: d += Process(w = 5).label("Processing")
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#而菱形代表的則是決策的具體情況,程式碼如下:
with schemdraw.Drawing() as d: d += Decision(w = 5).label("Decisions")
output
我們來繪製一個簡單的流程圖,假如週末的時候我們想著要不要出去露營(Camping),那既然要去露營的話,我們肯定是需要查看一下天氣,看一下是否是晴天(Sunny),如果是下雨天(Rainy )的話,就不去,按照這種邏輯,我們來繪製一下流程圖,程式碼如下:
import schemdraw from schemdraw.flow import * with schemdraw.Drawing() as d: d+= Start().label("Start") d+= Arrow().down(d.unit/2) # 具体是啥问题嘞 d+= Data(w = 4).label("Go camping or not") d+= Arrow().down(d.unit/2) # 第一步 查看天气 d+= Box(w = 4).label("Check weather first") d+= Arrow().down(d.unit/2) # 是否是晴天 d+= (decision := Decision(w = 5, h= 5, S = "True", E = "False").label("See if it's sunny")) # 如果是真的话 d+= Arrow().length(d.unit/2) d+= (true := Box(w = 5).label("Sunny, go camping")) d+= Arrow().length(d.unit/2) # 结束 d+= (end := Ellipse().label("End")) # 如果不是晴天的话 d+= Arrow().right(d.unit).at(decision.E) # 那如果是下雨天的话,就不能去露营咯 d+= (false := Box(w = 5).label("Rainy, stay at home")) # 决策的走向 d+= Arrow().down(d.unit*2.5).at(false.S) # 决策的走向 d+= Arrow().left(d.unit*2.15) d.save("palindrome flowchart.jpeg", dpi = 300)
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######Networkx# ########Networkx模組用來創建和處理複雜的圖網絡結構,產生多種隨機網絡和經典網絡,分析網絡結構和建立網絡模型,例如在繪製人脈關係網的案例當中就可以用到networkx模組,#########而例如一個公司的組織架構圖,也可以用到該模組,來簡單直觀的繪製公司的整體架構,程式碼如下:###import networkx as nx import matplotlib.pyplot as plt import numpy as np G = nx.DiGraph() nodes = np.arange(0, 8).tolist() G.add_nodes_from(nodes) # 节点连接的信息,哪些节点的是相连接的 G.add_edges_from([(0,1), (0,2), (1,3), (1, 4), (2, 5), (2, 6), (2,7)]) # 节点的位置 pos = {0:(10, 10), 1:(7.5, 7.5), 2:(12.5, 7.5), 3:(6, 6), 4:(9, 6), 5:(11, 6), 6:(14, 6), 7:(17, 6)} # 节点的标记 labels = {0:"CEO", 1: "Team A Lead", 2: "Team B Lead", 3: "Staff A", 4: "Staff B", 5: "Staff C", 6: "Staff D", 7: "Staff E"} nx.draw_networkx(G, pos = pos, labels = labels, arrows = True, node_shape = "s", node_color = "white") plt.title("Company Structure") plt.show()###output ##################看到這裡,大家可能會覺得會指出來的結果有點簡單,想要添加上去些許顏色,程式碼如下:###
nx.draw_networkx(G, pos = pos, labels = labels, bbox = dict(facecolor = "skyblue", boxstyle = "round", ec = "silver", pad = 0.3), edge_color = "gray" ) plt.title("Company Structure") plt.show()## #output#############
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