Binning.py

def fixed_width_cut(df,feature,labels=['Low','Medium','High']):
  feature_slice, retbins = pd.cut(df[feature], len(labels) ,retbins=True, labels=labels)
  retbins = [ '%.2f' % elem for elem in retbins ]
  return feature_slice,retbins

def quartile_cut(df,feature,labels=['Low','Medium','High']):
  feature_slice, retbins = pd.qcut(df[feature], q=len(labels),retbins=True,labels=labels)
  retbins = [ '%.2f' % elem for elem in retbins ]
  return feature_slice,retbins


# Specified cuts
salary_df['average_monthly_hours_bin'], retbins = fixed_width_cut(salary_df,'average_montly_hours',labels=['Low','Medium','High'])
salary_df['average_monthly_hours_qbin'], qretbins = quartile_cut(salary_df,'average_montly_hours',labels=['Low','Medium','High'])

# Visualize
draw_plot(salary_df,['average_monthly_hours_bin','average_monthly_hours_qbin'],'Projects related (Left= fixed width cut, right = quartile cut)',type='countplot')