我正在尝试将MiniBatchKMeans与更大的数据集一起使用,并绘制2个不同的属性。我收到Keyerror: 2
,我相信我在for
循环中出错,但是我不确定在哪里。有人可以帮我看看我的错误是什么吗?我正在运行以下代码:
import numpy as np ##Import necessary packages
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use("ggplot")
from pandas.plotting import scatter_matrix
from 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 internet
Adult = 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 dataframe
Adult.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 data
print(Adult.shape)
print(Adult.dtypes)
np.median(Adult['fnlwgt']) #Calculating median for final weight column
TooLarge = Adult.loc[:,'fnlwgt'] > 748495 #Setting a value to replace outliers from final weight column with median
Adult.loc[TooLarge,'fnlwgt']=np.median(Adult['fnlwgt']) #replacing values from final weight Column with the median of the final weight column
Adult.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()
[当我运行for
循环时,我仅看到散布矩阵中绘制了2个数据点。我是否需要以不同于创建的数据框的方式调用这些点?
我个人的问题是,您运行了一个循环以分别绘制32,000个点中的每一个,这使Python崩溃了。我不知道你为什么这么做。您只需要制作一个正常的散点图即可。使用这些行:
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()
您的原始错误是由于熊猫索引使用不当引起的。您可以这样做来复制错误:
df = pd.DataFrame(list('dasdasas'))
df[1]