我正在嘗試將 MiniBatchKMeans 與更大的數(shù)據(jù)集一起使用并繪制 2 個不同的屬性。我收到一個Keyerror: 2我相信我在for循環(huán)中出錯但我不確定在哪里。有人可以幫我看看我的錯誤是什么？我正在運行以下代碼：import numpy as np ##Import necessary packagesimport pandas as pdimport matplotlib.pyplot as pltfrom matplotlib import stylestyle.use("ggplot")from pandas.plotting import scatter_matrixfrom sklearn.preprocessing import *from sklearn.cluster import MiniBatchKMeans url2="http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" #Reading in Data from a freely and easily available source on the internetAdult = pd.read_csv(url2, header=None, skipinitialspace=True) #Decoding data by removing extra spaces in cplumns with skipinitialspace=True##Assigning reasonable column names to the dataframeAdult.columns = ["age","workclass","fnlwgt","education","educationnum","maritalstatus","occupation", "relationship","race","sex","capitalgain","capitalloss","hoursperweek","nativecountry", "less50kmoreeq50kn"]print("reviewing dataframe:")print(Adult.head()) #Getting an overview of the dataprint(Adult.shape)print(Adult.dtypes)np.median(Adult['fnlwgt']) #Calculating median for final weight columnTooLarge = Adult.loc[:,'fnlwgt'] > 748495 #Setting a value to replace outliers from final weight column with medianAdult.loc[TooLarge,'fnlwgt']=np.median(Adult['fnlwgt']) #replacing values from final weight Column with the median of the final weight columnAdult.loc[:,'fnlwgt']X = pd.DataFrame()X.loc[:,0] = Adult.loc[:,'age']X.loc[:,1] = Adult.loc[:,'hoursperweek']kmeans = MiniBatchKMeans(n_clusters = 2)kmeans.fit(X)centroids = kmeans.cluster_centers_labels = kmeans.labels_print(centroids)print(labels)colors = ["g.","r."]for i in range(len(X)): print("coordinate:",X[i], "label:", labels[i]) plt.plot(X.loc[:,0][i],X.loc[:,1][i], colors[labels[i]], markersize = 10)plt.scatter(centroids[:, 0], centroids[:, 1], marker = "x", s=150, linewidths = 5, zorder = 10)plt.show()當(dāng)我運行for循環(huán)時，我只看到散點矩陣中繪制了 2 個數(shù)據(jù)點。我是否需要以與創(chuàng)建的數(shù)據(jù)框不同的方式調(diào)用這些點？
1 回答
繁花不似錦
TA貢獻(xiàn)1851條經(jīng)驗 獲得超4個贊
您可以通過不運行循環(huán)來單獨繪制 32,000 個點中的每一個來避免此問題，這是不好的做法，也是不必要的。您可以簡單地傳遞兩個數(shù)組來plt.scatter()制作這個散點圖，不需要循環(huán)。使用這些行：
colors = ["green","red"]
plt.scatter(X.iloc[:,0], X.iloc[:,1], c=np.array(colors)[labels],
s = 10, alpha=.1)
plt.scatter(centroids[:, 0], centroids[:, 1], marker = "x", s=150,
linewidths = 5, zorder = 10, c=['green', 'red'])
plt.show()
您最初的錯誤是由于對熊貓索引的不當(dāng)使用造成的。您可以通過這樣做來復(fù)制您的錯誤：
df = pd.DataFrame(list('dasdasas'))
df[1]
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