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GeorgeSeif/dropping.py

Created January 20, 2019 22:24
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dropping.py
# Computing correlation coefficients
x_cols = [col for col in data.columns if col not in ['output']]
for col in x_cols:
corr_coeffs = np.corrcoef(data[col].values, data.output.values)
# Get the number of missing values in each column / feature variable
data.isnull().sum()
# Drop a feature variable
data = data.drop('feature_name', 1)
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