text: import numpy as np
  import matplotlib.pyplot as plt
  import matplotlib.cbook as cbook
  
  # Load a numpy record array from yahoo csv data with fields date, open, close,
  # volume, adj_close from the mpl-data/example directory. The record array
  # stores the date as an np.datetime64 with a day unit ('D') in the date column.
  with cbook.get_sample_data('goog.npz') as datafile:
  	price_data = np.load(datafile)['price_data'].view(np.recarray)
  price_data = price_data[-250:] # get the most recent 250 trading days
  
  delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]
  
  # Marker size in units of points^2
  volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
  close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
  
  fig, ax = plt.subplots()
  ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
  
  ax.set_xlabel(r'$\Delta_i$', fontsize=15)
  ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
  ax.set_title('Volume and percent change')
  
  ax.grid(True)
  fig.tight_layout()
  
  plt.show()
  