new_contents: 
  # Kernel Density Estimation Plots
  import numpy as np
  import pandas as pd
  from pandas import Series,DataFrame
  from numpy.random import randn
  
  # Stats
  from scipy import stats
  
  # Plotting
  import matplotlib as mpl
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  # Command so tha the plots appear in the iPython Notebook
  %matplotlib inline
  
  dataset = randn(25)
  sns.rugplot(dataset)
  x_min = dataset.min() - 2
  x_max = dataset.max() + 2
  
  x_axis = np.linspace(x_min,x_max,100)
  
  # Bandwidth estimation
  bandwidth = ( (4*dataset.std()**5) / (3*len(dataset))) ** 0.2
  
  kernel_list = []
  
  for data_point in dataset:
      # Create a kernel for each point  & append it to kernel_list
      kernel = stats.norm(data_point,bandwidth).pdf(x_axis)
      kernel_list.append(kernel)
      
      # Scale for plotting
      kernel = kernel / kernel.max()
      kernel = kernel * 0.4
      
      plt.plot(x_axis, kernel,color='grey',alpha=0.5)
      
      plt.ylim(0,1)   
      
  
subject: Create KernelDensity.py