CODEWITHSUNDEEP
pythonpandasscikit-learnunsupervised-learning
TheTechGuy
TheTechGuy

Reputation: 1608

Unsupervised machine learning with scikit learn

I am learning about unsupervised machine learning with scikit learn. I have collected so data from online. when I try to apply scatter plot I am getting following error

IndexingError: Too many indexers

Here is the code:

data = arff.loadarff("./Data/Arrhythmia/Arrhythmia_withoutdupl_02_v01.arff")
df = pd.DataFrame(data[0])
df = df.drop(['outlier',"id"],axis=1)
X_com = df.att10
plt.scatter(X_com.iloc[:,0],X_com.iloc[:,1])
plt.show()

I want to apply here KMeans algorithm from scikit learn. What I am doing wrong ? Thanks in advance

Upvotes: 1

Views: 288

Answers (2)

Venkatachalam
Venkatachalam

Reputation: 16966

May be you want to see the spread of data, with respect to first two features. Then you have to slice it from the initial dataset instead from Series (as @Alexandre metioned)

data = arff.loadarff("./Data/Arrhythmia/Arrhythmia_withoutdupl_02_v01.arff")
df = pd.DataFrame(data[0])
df = df.drop(['outlier',"id"],axis=1)
plt.scatter(df.iloc[:,0],df.iloc[:,1])
plt.show()

Upvotes: 0

yatu
yatu

Reputation: 88285

X_com is a pd.Series, so when you're trying to slice it using .iloc, you can only specify one axis.

Upvotes: 3

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