Preparing the Titanic Dataset for PyTorch

One rainy Pacific Northwest weekend, I decided to take a look at the Titanic dataset. The goal is to predict if a passenger survived (1) or died (0) based on predictor variables such as passenger-class (1st, 2nd, 3rd), sex, age, and so on.

As is always the case, data preparation is a time-consuming task.

There are multiple versions of the Titanic dataset. I started by fetching raw data from the Vanderbilt Biostatistics Datasets site at hbiostat.org/data/. I used file titanic3.xls. I saved it to my machine and then opened it up in Excel.

The raw data has 1309 rows and 14 columns: pclass, survived, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked, boat, body, dest. To predict if someone survived, only three of the variables make any sense to use: pclass (passenger class), sex, and age. So, I copied the pclass, survived, sex, and age columns to a different Excel sheet.


Top: The source raw data in Excel from the Vanderbilt site. Bottom: The intermediate filtered data.

I analyzed the filtered data in Excel using the countblank() function and discovered that 263 rows/people had no age value. I saved the filtered Excel data to a tab-delimited file named titanic3_tab_prelim.txt. I changed column order so the the dependent variable, survived, is first.

At this point the text file looks like:

sur  pclass   sex      age
1      1      female    29
1      1      male      0.92
. . . 
0      2      male      80
0      2      male      
1      3      female    24
0      3      male      32
. . .

Age values can be fractional. My goal is to convert the data to this (comma-delimited) format:

sur  pclass   sex      age
1    1 0 0    +1      0.2900
1    1 0 0    -1      0.0092
. . . . . . . . . . . . . . . 
0    0 1 0    -1      0.8000
1    0 0 1    +1      0.2400
0    0 0 1    -1      0.3200
. . .

The survived variable is 0 or 1. The pclass is one-hot encoded (1st = 100, 2nd = 010, 3rd = 001), sex is -1 = male, +1 = female, and rows with no age are deleted, and rows with age have age normalized by dividing by 100. Because there were 263 rows with missing age values, the result file named titanic_train.txt has 1309 – 263 = 1046 data items.

Here’s the Python utility program I wrote to convert the intermediate text file to the training text file:

# prepare_data.py

# the prelim text file has survived, pclass, sex, age
# age could be empty cell -- 263 instances

fin = open(".\\titanic3_tab_prelim.txt", "r")
fout = open(".\\titanic_train.txt", "w")
header = fin.readline()  # consume header line
fout.write("# survived, pclass, sex, age" + "\n")

linenum = 1
for line in fin:
  line = line.strip()  # remove trailing nl
  
  s = ""  # output line
  tokens = line.split("\t")

  if len(tokens) != 4:  # missing age
    continue
  
  survived = tokens[0]
  pclass = tokens[1]
  sex = tokens[2]
  age = tokens[3]

  s += survived + ","  # 0 or 1

  if pclass == "1": s += "1,0,0,"
  elif pclass == "2": s += "0,1,0,"
  elif pclass == "3": s += "0,0,1,"

  if sex == "male": s += "-1" + ","
  elif sex == "female": s += "+1" + ","

  a = float(age)
  a = a / 100  # normalize
  aa = "%0.4f" % a
  s += str(aa) + "\n"

  fout.write(s)
  
  linenum += 1

fin.close()
fout.close()

The utility program took me several hours. The ideas were simple but the details were surprisingly tricky. The result titanic_train.txt file data is pasted below.

My next step is to create a PyTorch prediction model, possibly preceded by splitting the data into training and test sets.



Between my Master’s degree and my Ph.D. degree, I worked on a cruise ship as an assistant cruise director (based on my time as an entertainer at Disneyland). Modern cruise ships range from small to grotesquely large floating ghetto barges. When I take a cruise, I seek out ships that have a capacity of about 2200 passengers — similar to the Titanic. Left: An illustration comparing the Titanic with a large modern ship. Right: The Eurodam is one of my favorite ships — not too small, not too big.


# survived, pclass, sex, age
1,1,0,0,+1,0.2900
1,1,0,0,-1,0.0092
0,1,0,0,+1,0.0200
0,1,0,0,-1,0.3000
0,1,0,0,+1,0.2500
1,1,0,0,-1,0.4800
1,1,0,0,+1,0.6300
0,1,0,0,-1,0.3900
1,1,0,0,+1,0.5300
0,1,0,0,-1,0.7100
0,1,0,0,-1,0.4700
1,1,0,0,+1,0.1800
1,1,0,0,+1,0.2400
1,1,0,0,+1,0.2600
1,1,0,0,-1,0.8000
0,1,0,0,-1,0.2400
1,1,0,0,+1,0.5000
1,1,0,0,+1,0.3200
0,1,0,0,-1,0.3600
1,1,0,0,-1,0.3700
1,1,0,0,+1,0.4700
1,1,0,0,-1,0.2600
1,1,0,0,+1,0.4200
1,1,0,0,+1,0.2900
0,1,0,0,-1,0.2500
1,1,0,0,-1,0.2500
1,1,0,0,+1,0.1900
1,1,0,0,+1,0.3500
1,1,0,0,-1,0.2800
0,1,0,0,-1,0.4500
1,1,0,0,-1,0.4000
1,1,0,0,+1,0.3000
1,1,0,0,+1,0.5800
0,1,0,0,-1,0.4200
1,1,0,0,+1,0.4500
1,1,0,0,+1,0.2200
0,1,0,0,-1,0.4100
0,1,0,0,-1,0.4800
1,1,0,0,+1,0.4400
1,1,0,0,+1,0.5900
1,1,0,0,+1,0.6000
1,1,0,0,+1,0.4100
0,1,0,0,-1,0.4500
1,1,0,0,-1,0.4200
1,1,0,0,+1,0.5300
1,1,0,0,-1,0.3600
1,1,0,0,+1,0.5800
0,1,0,0,-1,0.3300
0,1,0,0,-1,0.2800
0,1,0,0,-1,0.1700
1,1,0,0,-1,0.1100
1,1,0,0,+1,0.1400
1,1,0,0,-1,0.3600
1,1,0,0,+1,0.3600
0,1,0,0,-1,0.4900
0,1,0,0,-1,0.3600
1,1,0,0,+1,0.7600
0,1,0,0,-1,0.4600
1,1,0,0,+1,0.4700
1,1,0,0,-1,0.2700
1,1,0,0,+1,0.3300
1,1,0,0,+1,0.3600
1,1,0,0,+1,0.3000
1,1,0,0,-1,0.4500
0,1,0,0,-1,0.2700
1,1,0,0,+1,0.2600
1,1,0,0,+1,0.2200
0,1,0,0,-1,0.4700
1,1,0,0,+1,0.3900
0,1,0,0,-1,0.3700
1,1,0,0,+1,0.6400
1,1,0,0,+1,0.5500
0,1,0,0,-1,0.7000
1,1,0,0,+1,0.3600
1,1,0,0,+1,0.6400
0,1,0,0,-1,0.3900
1,1,0,0,+1,0.3800
1,1,0,0,-1,0.5100
1,1,0,0,-1,0.2700
1,1,0,0,+1,0.3300
0,1,0,0,-1,0.3100
1,1,0,0,+1,0.2700
1,1,0,0,-1,0.3100
1,1,0,0,+1,0.1700
1,1,0,0,-1,0.5300
1,1,0,0,-1,0.0400
1,1,0,0,+1,0.5400
0,1,0,0,-1,0.5000
1,1,0,0,+1,0.2700
1,1,0,0,+1,0.4800
1,1,0,0,+1,0.4800
1,1,0,0,-1,0.4900
0,1,0,0,-1,0.3900
1,1,0,0,+1,0.2300
1,1,0,0,+1,0.3800
1,1,0,0,+1,0.5400
0,1,0,0,+1,0.3600
1,1,0,0,-1,0.3600
0,1,0,0,-1,0.3000
1,1,0,0,+1,0.2400
1,1,0,0,+1,0.2800
1,1,0,0,+1,0.2300
0,1,0,0,-1,0.1900
0,1,0,0,-1,0.6400
1,1,0,0,+1,0.6000
1,1,0,0,+1,0.3000
1,1,0,0,-1,0.5000
1,1,0,0,-1,0.4300
1,1,0,0,+1,0.2200
1,1,0,0,-1,0.6000
1,1,0,0,+1,0.4800
0,1,0,0,-1,0.3700
1,1,0,0,+1,0.3500
0,1,0,0,-1,0.4700
1,1,0,0,+1,0.3500
1,1,0,0,+1,0.2200
1,1,0,0,+1,0.4500
0,1,0,0,-1,0.2400
1,1,0,0,-1,0.4900
0,1,0,0,-1,0.7100
1,1,0,0,-1,0.5300
1,1,0,0,+1,0.1900
0,1,0,0,-1,0.3800
1,1,0,0,+1,0.5800
1,1,0,0,-1,0.2300
1,1,0,0,+1,0.4500
0,1,0,0,-1,0.4600
1,1,0,0,-1,0.2500
1,1,0,0,+1,0.2500
1,1,0,0,-1,0.4800
1,1,0,0,+1,0.4900
0,1,0,0,-1,0.4500
1,1,0,0,+1,0.3500
0,1,0,0,-1,0.4000
1,1,0,0,-1,0.2700
1,1,0,0,+1,0.2400
0,1,0,0,-1,0.5500
1,1,0,0,+1,0.5200
0,1,0,0,-1,0.4200
0,1,0,0,-1,0.5500
1,1,0,0,+1,0.1600
1,1,0,0,+1,0.4400
1,1,0,0,+1,0.5100
0,1,0,0,-1,0.4200
1,1,0,0,+1,0.3500
1,1,0,0,-1,0.3500
1,1,0,0,-1,0.3800
1,1,0,0,+1,0.3500
1,1,0,0,+1,0.3800
0,1,0,0,+1,0.5000
1,1,0,0,-1,0.4900
0,1,0,0,-1,0.4600
0,1,0,0,-1,0.5000
0,1,0,0,-1,0.3250
0,1,0,0,-1,0.5800
0,1,0,0,-1,0.4100
1,1,0,0,-1,0.4200
1,1,0,0,+1,0.4500
1,1,0,0,+1,0.3900
1,1,0,0,+1,0.4900
1,1,0,0,+1,0.3000
1,1,0,0,-1,0.3500
0,1,0,0,-1,0.4200
1,1,0,0,+1,0.5500
1,1,0,0,+1,0.1600
1,1,0,0,+1,0.5100
0,1,0,0,-1,0.2900
1,1,0,0,+1,0.2100
0,1,0,0,-1,0.3000
1,1,0,0,+1,0.5800
1,1,0,0,+1,0.1500
0,1,0,0,-1,0.3000
1,1,0,0,+1,0.1600
0,1,0,0,-1,0.1900
1,1,0,0,+1,0.1800
1,1,0,0,+1,0.2400
0,1,0,0,-1,0.4600
0,1,0,0,-1,0.5400
1,1,0,0,-1,0.3600
0,1,0,0,-1,0.2800
0,1,0,0,-1,0.6500
0,1,0,0,-1,0.4400
1,1,0,0,+1,0.3300
1,1,0,0,+1,0.3700
1,1,0,0,-1,0.3000
0,1,0,0,-1,0.5500
0,1,0,0,-1,0.4700
0,1,0,0,-1,0.3700
1,1,0,0,+1,0.3100
1,1,0,0,+1,0.2300
0,1,0,0,-1,0.5800
1,1,0,0,+1,0.1900
0,1,0,0,-1,0.6400
1,1,0,0,+1,0.3900
1,1,0,0,+1,0.2200
0,1,0,0,-1,0.6500
0,1,0,0,-1,0.2850
0,1,0,0,-1,0.4550
0,1,0,0,-1,0.2300
0,1,0,0,-1,0.2900
1,1,0,0,+1,0.2200
0,1,0,0,-1,0.1800
1,1,0,0,+1,0.1700
1,1,0,0,+1,0.3000
1,1,0,0,-1,0.5200
0,1,0,0,-1,0.4700
1,1,0,0,+1,0.5600
0,1,0,0,-1,0.3800
0,1,0,0,-1,0.2200
1,1,0,0,+1,0.4300
0,1,0,0,-1,0.3100
1,1,0,0,-1,0.4500
1,1,0,0,+1,0.3300
0,1,0,0,-1,0.4600
0,1,0,0,-1,0.3600
1,1,0,0,+1,0.3300
0,1,0,0,-1,0.5500
1,1,0,0,+1,0.5400
0,1,0,0,-1,0.3300
1,1,0,0,-1,0.1300
1,1,0,0,+1,0.1800
1,1,0,0,+1,0.2100
0,1,0,0,-1,0.6100
1,1,0,0,+1,0.4800
1,1,0,0,+1,0.2400
1,1,0,0,+1,0.3500
1,1,0,0,+1,0.3000
1,1,0,0,-1,0.3400
1,1,0,0,+1,0.4000
1,1,0,0,-1,0.3500
0,1,0,0,-1,0.5000
1,1,0,0,+1,0.3900
1,1,0,0,-1,0.5600
1,1,0,0,-1,0.2800
0,1,0,0,-1,0.5600
0,1,0,0,-1,0.5600
0,1,0,0,-1,0.2400
1,1,0,0,+1,0.1800
1,1,0,0,-1,0.2400
1,1,0,0,+1,0.2300
1,1,0,0,-1,0.0600
1,1,0,0,-1,0.4500
1,1,0,0,+1,0.4000
0,1,0,0,-1,0.5700
1,1,0,0,-1,0.3200
0,1,0,0,-1,0.6200
1,1,0,0,-1,0.5400
1,1,0,0,+1,0.4300
1,1,0,0,+1,0.5200
1,1,0,0,+1,0.6200
0,1,0,0,-1,0.6700
0,1,0,0,+1,0.6300
0,1,0,0,-1,0.6100
1,1,0,0,+1,0.4800
1,1,0,0,+1,0.1800
0,1,0,0,-1,0.5200
1,1,0,0,+1,0.3900
1,1,0,0,-1,0.4800
0,1,0,0,-1,0.4900
1,1,0,0,-1,0.1700
1,1,0,0,+1,0.3900
1,1,0,0,-1,0.3100
0,1,0,0,-1,0.4000
0,1,0,0,-1,0.6100
0,1,0,0,-1,0.4700
1,1,0,0,+1,0.3500
0,1,0,0,-1,0.6400
1,1,0,0,+1,0.6000
0,1,0,0,-1,0.6000
0,1,0,0,-1,0.5400
0,1,0,0,-1,0.2100
1,1,0,0,+1,0.5500
1,1,0,0,+1,0.3100
0,1,0,0,-1,0.5700
1,1,0,0,+1,0.4500
0,1,0,0,-1,0.5000
0,1,0,0,-1,0.2700
1,1,0,0,+1,0.5000
1,1,0,0,+1,0.2100
0,1,0,0,-1,0.5100
1,1,0,0,-1,0.2100
1,1,0,0,+1,0.3100
0,1,0,0,-1,0.6200
1,1,0,0,+1,0.3600
0,0,1,0,-1,0.3000
1,0,1,0,+1,0.2800
0,0,1,0,-1,0.3000
0,0,1,0,-1,0.1800
0,0,1,0,-1,0.2500
0,0,1,0,-1,0.3400
1,0,1,0,+1,0.3600
0,0,1,0,-1,0.5700
0,0,1,0,-1,0.1800
0,0,1,0,-1,0.2300
1,0,1,0,+1,0.3600
0,0,1,0,-1,0.2800
0,0,1,0,-1,0.5100
1,0,1,0,-1,0.3200
1,0,1,0,+1,0.1900
0,0,1,0,-1,0.2800
1,0,1,0,-1,0.0100
1,0,1,0,+1,0.0400
1,0,1,0,+1,0.1200
1,0,1,0,+1,0.3600
1,0,1,0,-1,0.3400
1,0,1,0,+1,0.1900
0,0,1,0,-1,0.2300
0,0,1,0,-1,0.2600
0,0,1,0,-1,0.4200
0,0,1,0,-1,0.2700
1,0,1,0,+1,0.2400
1,0,1,0,+1,0.1500
0,0,1,0,-1,0.6000
1,0,1,0,+1,0.4000
1,0,1,0,+1,0.2000
0,0,1,0,-1,0.2500
1,0,1,0,+1,0.3600
0,0,1,0,-1,0.2500
0,0,1,0,-1,0.4200
1,0,1,0,+1,0.4200
1,0,1,0,-1,0.0083
1,0,1,0,-1,0.2600
1,0,1,0,+1,0.2200
1,0,1,0,+1,0.3500
0,0,1,0,-1,0.1900
0,0,1,0,+1,0.4400
0,0,1,0,-1,0.5400
0,0,1,0,-1,0.5200
0,0,1,0,-1,0.3700
0,0,1,0,+1,0.2900
1,0,1,0,+1,0.2500
1,0,1,0,+1,0.4500
0,0,1,0,-1,0.2900
1,0,1,0,+1,0.2800
0,0,1,0,-1,0.2900
0,0,1,0,-1,0.2800
1,0,1,0,-1,0.2400
1,0,1,0,+1,0.0800
0,0,1,0,-1,0.3100
1,0,1,0,+1,0.3100
1,0,1,0,+1,0.2200
0,0,1,0,+1,0.3000
0,0,1,0,-1,0.2100
1,0,1,0,-1,0.0800
0,0,1,0,-1,0.1800
1,0,1,0,+1,0.4800
1,0,1,0,+1,0.2800
0,0,1,0,-1,0.3200
0,0,1,0,-1,0.1700
0,0,1,0,-1,0.2900
1,0,1,0,+1,0.2400
0,0,1,0,-1,0.2500
0,0,1,0,-1,0.1800
1,0,1,0,+1,0.1800
1,0,1,0,+1,0.3400
0,0,1,0,-1,0.5400
1,0,1,0,-1,0.0800
0,0,1,0,-1,0.4200
1,0,1,0,+1,0.3400
1,0,1,0,+1,0.2700
1,0,1,0,+1,0.3000
0,0,1,0,-1,0.2300
0,0,1,0,-1,0.2100
0,0,1,0,-1,0.1800
0,0,1,0,-1,0.4000
1,0,1,0,+1,0.2900
0,0,1,0,-1,0.1800
0,0,1,0,-1,0.3600
0,0,1,0,+1,0.3800
0,0,1,0,-1,0.3500
0,0,1,0,-1,0.3800
0,0,1,0,-1,0.3400
1,0,1,0,+1,0.3400
0,0,1,0,-1,0.1600
0,0,1,0,-1,0.2600
0,0,1,0,-1,0.4700
0,0,1,0,-1,0.2100
0,0,1,0,-1,0.2100
0,0,1,0,-1,0.2400
0,0,1,0,-1,0.2400
0,0,1,0,-1,0.3400
0,0,1,0,-1,0.3000
0,0,1,0,-1,0.5200
0,0,1,0,-1,0.3000
1,0,1,0,-1,0.0067
1,0,1,0,+1,0.2400
0,0,1,0,-1,0.4400
1,0,1,0,+1,0.0600
0,0,1,0,-1,0.2800
1,0,1,0,-1,0.6200
0,0,1,0,-1,0.3000
1,0,1,0,+1,0.0700
0,0,1,0,-1,0.4300
1,0,1,0,+1,0.4500
1,0,1,0,+1,0.2400
1,0,1,0,+1,0.2400
0,0,1,0,-1,0.4900
1,0,1,0,+1,0.4800
1,0,1,0,+1,0.5500
0,0,1,0,-1,0.2400
0,0,1,0,-1,0.3200
0,0,1,0,-1,0.2100
0,0,1,0,+1,0.1800
1,0,1,0,+1,0.2000
0,0,1,0,-1,0.2300
0,0,1,0,-1,0.3600
1,0,1,0,+1,0.5400
0,0,1,0,-1,0.5000
0,0,1,0,-1,0.4400
1,0,1,0,+1,0.2900
0,0,1,0,-1,0.2100
1,0,1,0,-1,0.4200
0,0,1,0,-1,0.6300
0,0,1,0,+1,0.6000
0,0,1,0,-1,0.3300
1,0,1,0,+1,0.1700
0,0,1,0,-1,0.4200
1,0,1,0,+1,0.2400
0,0,1,0,-1,0.4700
0,0,1,0,-1,0.2400
0,0,1,0,-1,0.2200
0,0,1,0,-1,0.3200
1,0,1,0,+1,0.2300
0,0,1,0,-1,0.3400
1,0,1,0,+1,0.2400
0,0,1,0,+1,0.2200
0,0,1,0,-1,0.3500
1,0,1,0,+1,0.4500
0,0,1,0,-1,0.5700
0,0,1,0,-1,0.3100
0,0,1,0,+1,0.2600
0,0,1,0,-1,0.3000
1,0,1,0,+1,0.0100
1,0,1,0,+1,0.0300
0,0,1,0,-1,0.2500
1,0,1,0,+1,0.2200
1,0,1,0,+1,0.1700
1,0,1,0,+1,0.3400
0,0,1,0,-1,0.3600
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1,0,0,1,+1,0.2100
0,0,0,1,-1,0.3900
1,0,0,1,+1,0.0100
1,0,0,1,+1,0.2400
1,0,0,1,+1,0.0400
1,0,0,1,-1,0.2500
0,0,0,1,-1,0.2000
0,0,0,1,-1,0.2450
1,0,0,1,-1,0.2900
0,0,0,1,-1,0.2200
0,0,0,1,-1,0.4000
0,0,0,1,-1,0.2100
1,0,0,1,+1,0.1800
0,0,0,1,-1,0.0400
0,0,0,1,-1,0.1000
0,0,0,1,+1,0.0900
0,0,0,1,+1,0.0200
0,0,0,1,-1,0.4000
0,0,0,1,+1,0.4500
0,0,0,1,-1,0.1900
0,0,0,1,-1,0.3000
0,0,0,1,-1,0.3200
0,0,0,1,-1,0.3300
1,0,0,1,+1,0.2300
0,0,0,1,-1,0.2100
0,0,0,1,-1,0.6050
0,0,0,1,-1,0.1900
0,0,0,1,+1,0.2200
1,0,0,1,-1,0.3100
0,0,0,1,-1,0.2700
0,0,0,1,+1,0.0200
0,0,0,1,+1,0.2900
1,0,0,1,-1,0.1600
1,0,0,1,-1,0.4400
0,0,0,1,-1,0.2500
0,0,0,1,-1,0.7400
1,0,0,1,-1,0.1400
0,0,0,1,-1,0.2400
1,0,0,1,-1,0.2500
0,0,0,1,-1,0.3400
1,0,0,1,-1,0.0042
1,0,0,1,+1,0.1600
0,0,0,1,-1,0.3200
0,0,0,1,-1,0.3050
0,0,0,1,-1,0.4400
1,0,0,1,-1,0.2500
1,0,0,1,-1,0.0700
1,0,0,1,+1,0.0900
1,0,0,1,+1,0.2900
0,0,0,1,-1,0.3600
1,0,0,1,+1,0.1800
1,0,0,1,+1,0.6300
0,0,0,1,-1,0.1150
0,0,0,1,-1,0.4050
0,0,0,1,+1,0.1000
0,0,0,1,-1,0.3600
0,0,0,1,+1,0.3000
0,0,0,1,-1,0.3300
0,0,0,1,-1,0.2800
0,0,0,1,-1,0.2800
0,0,0,1,-1,0.4700
0,0,0,1,+1,0.1800
0,0,0,1,-1,0.3100
0,0,0,1,-1,0.1600
0,0,0,1,+1,0.3100
1,0,0,1,-1,0.2200
0,0,0,1,-1,0.2000
0,0,0,1,+1,0.1400
0,0,0,1,-1,0.2200
0,0,0,1,-1,0.2200
0,0,0,1,-1,0.3250
1,0,0,1,+1,0.3800
0,0,0,1,-1,0.5100
0,0,0,1,-1,0.1800
0,0,0,1,-1,0.2100
1,0,0,1,+1,0.4700
0,0,0,1,-1,0.2850
0,0,0,1,-1,0.2100
0,0,0,1,-1,0.2700
0,0,0,1,-1,0.3600
0,0,0,1,-1,0.2700
1,0,0,1,+1,0.1500
0,0,0,1,-1,0.4550
0,0,0,1,+1,0.1450
0,0,0,1,-1,0.2650
0,0,0,1,-1,0.2700
0,0,0,1,-1,0.2900
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