CODEWITHSUNDEEP
pythonpandasscikit-learncluster-analysisk-means
Dark Knight
Dark Knight

Reputation: 879

Will pandas dataframe object work with sklearn kmeans clustering?

dataset is pandas dataframe. This is sklearn.cluster.KMeans

 km = KMeans(n_clusters = n_Clusters)

 km.fit(dataset)

 prediction = km.predict(dataset)

This is how I decide which entity belongs to which cluster:

 for i in range(len(prediction)):
     cluster_fit_dict[dataset.index[i]] = prediction[i]

This is how dataset looks:

 A 1 2 3 4 5 6
 B 2 3 4 5 6 7
 C 1 4 2 7 8 1
 ...

where A,B,C are indices

Is this the correct way of using k-means?

Upvotes: 38

Views: 69372

Answers (2)

user666
user666

Reputation: 5661

Assuming all the values in the dataframe are numeric,

# Convert DataFrame to matrix
mat = dataset.values
# Using sklearn
km = sklearn.cluster.KMeans(n_clusters=5)
km.fit(mat)
# Get cluster assignment labels
labels = km.labels_
# Format results as a DataFrame
results = pandas.DataFrame([dataset.index,labels]).T

Alternatively, you could try KMeans++ for Pandas.

Upvotes: 39

ogrisel
ogrisel

Reputation: 40169

To know if your dataframe dataset has suitable content you can explicitly convert to a numpy array:

dataset_array = dataset.values
print(dataset_array.dtype)
print(dataset_array)

If the array has an homogeneous numerical dtype (typically numpy.float64) then it should be fine for scikit-learn 0.15.2 and later. You might still need to normalize the data with sklearn.preprocessing.StandardScaler for instance.

If your data frame is heterogeneously typed, the dtype of the corresponding numpy array will be object which is not suitable for scikit-learn. You need to extract a numerical representation for all the relevant features (for instance by extracting dummy variables for categorical features) and drop the columns that are not suitable features (e.g. sample identifiers).

Upvotes: 23

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