Data needed for a Lift chart (aka Gains chart) for a predictive model created using Sklearn and Matplotlib

Calculate Model Lift

def calc_lift(x,y,clf,bins=10):
	"""
	Takes input arrays and trained SkLearn Classifier and returns a Pandas
	DataFrame with the average lift generated by the model in each bin
	
	Parameters
	-------------------
	x:    Numpy array or Pandas Dataframe with shape = [n_samples, n_features]

	y:    A 1-d Numpy array or Pandas Series with shape = [n_samples]
		  IMPORTANT: Code is only configured for binary target variable
		  of 1 for success and 0 for failure
		
	clf:  A trained SkLearn classifier object
	bins: Number of equal sized buckets to divide observations across
		  Default value is 10
	"""

    #Actual Value of y
    y_actual = y
    #Predicted Probability that y = 1
    y_prob = clf.predict_proba(x)
    #Predicted Value of Y
    y_pred = clf.predict(x)
    cols = ['ACTUAL','PROB_POSITIVE','PREDICTED']
    data = [y_actual,y_prob[:,1],y_pred]
    df = pd.DataFrame(dict(zip(cols,data)))
    
    #Observations where y=1
    total_positive_n = df['ACTUAL'].sum()
    #Total Observations
    total_n = df.index.size
    natural_positive_prob = total_positive_n/float(total_n)


    #Create Bins where First Bin has Observations with the
    #Highest Predicted Probability that y = 1
    df['BIN_POSITIVE'] = pd.qcut(df['PROB_POSITIVE'],bins,labels=False)
    
    pos_group_df = df.groupby('BIN_POSITIVE')
    #Percentage of Observations in each Bin where y = 1 
    lift_positive = pos_group_df['ACTUAL'].sum()/pos_group_df['ACTUAL'].count()
    lift_index_positive = (lift_positive/natural_positive_prob)*100
    
    
    #Consolidate Results into Output Dataframe
    lift_df = pd.DataFrame({'LIFT_POSITIVE':lift_positive,
                               'LIFT_POSITIVE_INDEX':lift_index_positive,
                               'BASELINE_POSITIVE':natural_positive_prob})
    
    return lift_df