old_contents: import numpy as np
  import pandas as pd
  
  df = pd.read_csv("/home/adam/GitHub/RedRocksCommunityCollege/Clair-Global-Collab/Data/Test_Data/RECS2009/recs2009_public.csv")
  
  Rooms = df.TOTROOMS
  Game1 = df.PLAYSTA1
  Game2 = df.PLAYSTA2
  Game3 = df.PLAYSTA3
  
  Rooms.mean()
  
  Houses = np.array([[0,0,0,0]]) # Make an empty array. We need [0,0] for the correct shape.
  GamerHouses = np.array([[0,0,0,0]])
  # Collect up all the tuples with rooms as the first element and mainTV as the second.
  for i in range(0, Rooms.count()):
      House = np.array([[Rooms[i],Game1[i],Game2[i],Game3[i]]]) # Make each of the tuples. This is how we would add more thing that we would like to study.
      Houses =  np.append(Houses, House, axis = 0) # Add them to the new array.
  Houses = np.delete(Houses,0,0) # Remove the extra one from the front of the list.
  
  # Finds the houses with three Systems hooked up to all the TV's.
  for i in range(0, np.shape(Houses)[0]):
      if Houses[i][1] == 1 and Houses[i][2] == 1 and Houses[i][3] == 1:
          GamerHouses = np.append(GamerHouses, [Houses[i]], axis = 0)
      else:
          pass
  GamerHouses = np.delete(GamerHouses,0,0) # Remove the extra one from the front of the list.
  
  print(GamerHouses)
  
  Sum = 0
  for i in range(0,np.shape(GamerHouses)[0]):
      Sum = GamerHouses[i][0] + Sum
  
  Avg = Sum/np.shape(GamerHouses)[0]
  print(Avg)
  
new_contents: import numpy as np
  import pandas as pd
  
  df = pd.read_csv("/home/adam/GitHub/RedRocksCommunityCollege/Clair-Global-Collab/Data/Test_Data/RECS2009/recs2009_public.csv")
  
  TOTALRooms = df.TOTROOMS
  BEDRooms = df.BEDROOMS
  Game1 = df.PLAYSTA1
  Game2 = df.PLAYSTA2
  Game3 = df.PLAYSTA3
  TVNum = df.TVCOLOR
  NumHouseMem = df.NHSLDMEM
  Education = df.EDUCATION
  
  NumHouseMem.mean()
  Education.mean()
  TVNum.mean()
  BEDRooms.mean()
  TOTALRooms.mean()
  
  '''
  0. No schooling completed
  1. Kindergarten to grade 12
  2. High school diploma or GED
  3. Some college, no degree
  4. Associate's degree
  5. Bachelor's degree
  6. Master's degree
  7. Professional degree
  8. Doctorate degree
  '''
  
  Houses = np.array([[0,0,0,0,0]]) # Make an empty array. We need [0,0] for the correct shape.
  GamerHouses = np.array([[0,0,0,0,0]])
  NoGameHouses = np.array([[0,0,0,0,0]])
  # Collect up all the tuples with rooms as the first element and mainTV as the second.
  for i in range(0, TOTALRooms.count()):
      House = np.array([[TOTALRooms[i],Game1[i],Game2[i],Game3[i],Education[i]]]) # Make each of the tuples. This is how we would add more thing that we would like to study.
      Houses =  np.append(Houses, House, axis = 0) # Add them to the new array.
  Houses = np.delete(Houses,0,0) # Remove the extra one from the front of the list.
  
  # Finds the houses with three Systems hooked up to all the TV's.
  for i in range(0, np.shape(Houses)[0]):
      if Houses[i][1] == 1 and Houses[i][2] == 1 and Houses[i][3] == 1:
          GamerHouses = np.append(GamerHouses, [Houses[i]], axis = 0)
      else:
          pass
  GamerHouses = np.delete(GamerHouses,0,0) # Remove the extra one from the front of the list.
  print(GamerHouses[1:4])
  
  for i in range(0, np.shape(Houses)[0]):
      if Houses[i][1] == 0 and Houses[i][2] == 0 and Houses[i][3] == 0:
          NoGameHouses = np.append(NoGameHouses, [Houses[i]], axis = 0)
      else:
          pass
  NoGameHouses = np.delete(NoGameHouses,0,0) # Remove the extra one from the front of the list.
  
  Sum = 0
  for i in range(0,np.shape(GamerHouses)[0]):
      Sum = GamerHouses[i][4] + Sum
  
  Avg = Sum/np.shape(GamerHouses)[0]
  print(Avg)
  
  Sum2 = 0
  for i in range(0,np.shape(NoGameHouses)[0]):
      Sum2 = NoGameHouses[i][4] + Sum2
  
  Avg2 = Sum2/np.shape(NoGameHouses)[0]
  print(Avg2)
  
subject: Read more catagories. Found some averages.