# b.資料科學 - matplotlib資料視覺化 ###### tags: `Data Sicnce From Scratch` ## 1.matplotlib函式庫 * <font color="#0080FF">**折線圖：一個簡單的折線圖**</font> ```python=+ """1950年-2010年的"名義GDP"(隨機)""" import random from matplotlib import pyplot as plt years = [year for year in range(1950,2011,10)] gdp = [] for _ in range(len(years)): gdp.append(random.uniform(300,15000)) plt.plot(years,gdp,color = 'green',marker = 'o',linestyle = 'solid') plt.title("Nominal GDP") plt.ylabel("Billions of $") plt.show() #或可用savefig將圖片存起來 print('years = ',years) print('gdp = ',gdp) ``` > ![](https://i.imgur.com/YHCC7ik.png)</br> > `years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]`</br> > `gdp = [6734.843618957465, 8517.466737974524, 3780.230438534374, 14288.651167138727, 11342.248822669268, 8132.800795505929, 13064.560618823463]` ## ## 2.長條圖 * <font color="#0080FF">**長條圖：一個簡單的長條圖**</font> > <font color="#EA0000" >**#長條圖：適合呈現離散項目間的數量變化**</font> ```python=+ """我喜歡的電影""" movies = ['Annie Hall','Ben-hur','Casablanca','Gandhi','West Side Story'] num_oscars = [5,11,3,8,10] #以[0,1,2,3,4]為x軸 #以[5,11,3,8,10]為y軸 plt.bar(range(len(movies)),num_oscars) plt.title("My Favorite Movies") plt.ylabel("# of Academy Awards") #得獎次數 plt.xticks(range(len(movies)),movies)#把電影名稱當作x軸的標籤 plt.show() ``` > ![](https://i.imgur.com/zHHIJ5I.png) ## * <font color="#0080FF">**長條圖：用長條圖來繪製直方圖**</font> ```python=+ """第一次考試的分數分布""" from collections import Counter #計數器 grades = [83,95,91,87,70,0,85,82,100,67,73,77,0] histogram = Counter(min(grade // 10 * 10, 90) for grade in grades) plt.bar([x + 5 for x in histogram.keys()], #將長條圖全部向右移動5個單位 histogram.values(), 10, #長條圖寬度設定為10(bar的第三個參數設定寬度) edgecolor = (0,0,0)) #將長條圖加上黑色邊框 plt.axis([-5,105,0,5]) #x軸範圍[-5到105],y軸範圍[0到5] plt.xticks([10 * i for i in range(11)]) plt.xlabel('Decile') plt.ylabel('# of students') plt.title('Distribution of Exam 1 Grades') plt.show() print(histogram) ``` > ![](https://i.imgur.com/wAnLyvu.png) > ```Counter({80: 4, 90: 3, 70: 3, 0: 2, 60: 1})``` ## * <font color="#0080FF">**長條圖：y軸會造成誤導的一張圖形**</font> ```python=+ """看看增加的量多麼大呀!""" mentions = [500,505] years = [2017,2018] plt.bar(years,mentions,0.8) plt.xticks(years) plt.ylabel("# of times I heard someone say 'data science'") #如果不這麼做的話， plt.ticklabel_format(useOffset = False) plt.axis([2016.5,2018.5,499,506]) plt.title("Look at the 'Huge' Increase") plt.show() ``` > ![](https://i.imgur.com/rbMGTqb.png) ## * <font color="#0080FF">**(續)長條圖：不在y軸刻意造成誤導效果的同一張圖形**</font> ```python=+ """看起來沒這麼大了""" mentions = [500,505] years = [2017,2018] plt.bar(years,mentions,0.8) plt.xticks(years) plt.ylabel("# of times I heard someone say 'data science'") #如果不這麼做的話，... plt.ticklabel_format(useOffset = False) plt.axis([2016.5,2018.5,0,550]) plt.title("Not So Huge Anymore") plt.show() #把y軸範圍調整較合理的座標軸 ``` >![](https://i.imgur.com/wz7LUUI.png) ## 3.折線圖 * <font color="#0080FF">**折線圖：帶有圖例說明的多條折線圖**</font> > <font color="#EA0000" >**#折線圖：適合用來呈現趨勢**</font> ```python=+ """「偏差」與「變異」之間的取捨""" def sequence(times,ls = [1]): for i in range(1,times): ls.append(ls[i-1] * 2) return ls variance = sequence(9) bias_squared = sorted(variance,reverse = True) total_error = [x+y for x,y in zip(variance,bias_squared)] xs = [i for i,_ in enumerate(variance)] plt.plot(xs,variance,'g-', label = 'variance') #綠色實線 plt.plot(xs,bias_squared,'r-.', label = 'bias^2') #紅色點虛線 plt.plot(xs,total_error,'b:', label = 'total error') #藍色點線 plt.legend(loc = 9) plt.xlabel("model complexity") plt.xticks([]) plt.title("The Bias-Variance Tradeoff") plt.show() print("variance = ",variance) print("bias_squared = ",bias_squared) print("total_error = ",total_error) ``` > ![](https://i.imgur.com/F1xT2uc.png)</br> > ```variance = [1, 2, 4, 8, 16, 32, 64, 128, 256]```</br> > ```bias_squared = [256, 128, 64, 32, 16, 8, 4, 2, 1]```</br> > ```total_error = [257, 130, 68, 40, 32, 40, 68, 130, 257]``` ## 4.散點圖 * <font color="#0080FF">**散點圖：朋友數量與網站使用時間**</font> > <font color="#EA0000" >**#散點圖：適合呈現兩組成對資料的關係**</font> ```python=+ """每日分鐘數vs朋友數量""" friends = [70,65,72,63,71,64,60,64,67] minutes = [175,170,205,120,220,130,105,145,190] labels = ['a','b','c','d','e','f','g','h','i'] plt.scatter(friends,minutes) #每個點的標籤 for label,friends_count,minute_count in zip(labels,friends,minutes): plt.annotate(label, xy = (friends_count,minute_count), #把標籤放到相應的點上 xytext = (5,-5), #稍微平移一下 textcoords = 'offset points') plt.title("Daily Minutes vs. Number of Friends") plt.xlabel("# of friends") plt.ylabel("daily minutes spent on the site") plt.show() ``` >![](https://i.imgur.com/5gqDjU3.png) ## * <font color="#0080FF">**散點圖：坐標軸無法進行比較的散點圖**</font> ```python=+ """座標軸無法進行比較""" test1_grades = [99,90,85,97,80] test2_grades = [100,85,60,90,70] #比較兩次考試的成績 plt.scatter(test1_grades,test2_grades) plt.title("Axes Aren't Comparable") plt.xlabel("test 1 grade") plt.ylabel("test 2 grade") plt.show() #如果製作散點圖，讓matplotlib自動設定刻度的話，可能會產生具有誤導性的圖形 ``` >![](https://i.imgur.com/IA8ky1J.png) ## * <font color="#0080FF">**(續)散點圖：坐標軸設定為相同尺度後的同一個散點圖**</font> ```python=+ """坐標軸可以進行比較""" test1_grades = [99,90,85,97,80] test2_grades = [100,85,60,90,70] #比較兩次考試的成績 plt.scatter(test1_grades,test2_grades) plt.title("Axes Are Comparable") plt.xlabel("test 1 grade") plt.ylabel("test 2 grade") plt.axis('equal') plt.show() #使用「plt.axis('equal')」就可以讓x,y的起訖點都相同 ``` > ![](https://i.imgur.com/2WXN4ll.png) ## 時間戳記 > [name=ZEOxO][time=Sat, Nov 28 2020 13:50 PM][color=#907bf7]