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Work with Weka data files (.arff format). Use when (1) user needs to convert ARFF to CSV, (2) mentions Weka data files, (3) needs to extract column names from ARFF files.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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| name | dev-weka |
| description | Work with Weka data files (.arff format). Use when (1) user needs to convert ARFF to CSV, (2) mentions Weka data files, (3) needs to extract column names from ARFF files. |
Process Weka ARFF files for use in Python machine learning projects, particularly converting to CSV format with proper column headers.
ARFF (Attribute-Relation File Format) is Weka's native data format:
@relation diabetes
@attribute preg numeric
@attribute plas numeric
@attribute pres numeric
@attribute skin numeric
@attribute insu numeric
@attribute mass numeric
@attribute pedi numeric
@attribute age numeric
@attribute class {tested_negative,tested_positive}
@data
6,148,72,35,0,33.6,0.627,50,tested_positive
1,85,66,29,0,26.6,0.351,31,tested_negative
...
Method 1: Using Python (Recommended)
import pandas as pd
from scipy.io import arff
# Load ARFF file
data, meta = arff.loadarff('diabetes.arff')
df = pd.DataFrame(data)
# Convert byte strings to regular strings (for categorical columns)
for col in df.columns:
if df[col].dtype == 'object':
df[col] = df[col].str.decode('utf-8')
# Save to CSV
df.to_csv('diabetes.csv', index=False)
Method 2: Manual Extraction
Read ARFF file and extract:
@attribute lines@data sectionDiabetes Dataset:
weka/data/diabetes.arffColumn Names:
Weka uses string labels, Python ML libraries prefer numeric:
# Convert class labels to numeric
df['class'] = df['class'].map({
'tested_negative': 0,
'tested_positive': 1
})
uv add scipy pandas
After conversion, verify:
Issue: Byte strings in DataFrame
.str.decode('utf-8') for object columnsIssue: Missing column names
@attribute lines in ARFFIssue: Class labels as bytes
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