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)