unable to read a tab delimited file into a numpy 2-D array

Question

I am quite new to nympy and I am trying to read a tab(\t) delimited text file into an numpy array matrix using the following code:

train_data = np.genfromtxt('training.txt', dtype=None, delimiter='\t')

File contents:

38   Private    215646   HS-grad    9    Divorced    Handlers-cleaners   Not-in-family   White   Male   0   0   40   United-States   <=50K
53   Private    234721   11th   7    Married-civ-spouse  Handlers-cleaners   Husband     Black   Male   0   0   40   United-States   <=50K
30   State-gov  141297   Bachelors  13   Married-civ-spouse  Prof-specialty  Husband     Asian-Pac-Islander  Male   0   0   40   India   >50K

what I expect is a 2-D array matrix of shape (3, 15)

but with my above code I only get a single row array of shape (3,)

I am not sure why those fifteen fields of each row are not assigned a column each.

I also tried using numpy's loadtxt() but it could not handle type conversions on my data i.e even though I gave dtype=None it tried to convert the strings to default float type and failed at it.

Tried code:

train_data = np.loadtxt('try.txt', dtype=None, delimiter='\t')

Error:
ValueError: could not convert string to float: State-gov

Any pointers?

Thanks

Answer 1

Actually the issue here is that np.genfromtxt and np.loadtxt both return a structured array if the dtype is structured (i.e., has multiple types). Your array reports to have a shape of (3,), because technically it is a 1d array of 'records'. These 'records' hold all your columns but you can access all the data as if it were 2d.

You are loading it correctly:

In [82]: d = np.genfromtxt('tmp',dtype=None)

As you reported, it has a 1d shape:

In [83]: d.shape
Out[83]: (3,)

But all your data is there:

In [84]: d
Out[84]: 
array([ (38, 'Private', 215646, 'HS-grad', 9, 'Divorced', 'Handlers-cleaners', 'Not-in-family', 'White', 'Male', 0, 0, 40, 'United-States', '<=50K'),
       (53, 'Private', 234721, '11th', 7, 'Married-civ-spouse', 'Handlers-cleaners', 'Husband', 'Black', 'Male', 0, 0, 40, 'United-States', '<=50K'),
       (30, 'State-gov', 141297, 'Bachelors', 13, 'Married-civ-spouse', 'Prof-specialty', 'Husband', 'Asian-Pac-Islander', 'Male', 0, 0, 40, 'India', '>50K')], 
      dtype=[('f0', '<i8'), ('f1', 'S9'), ('f2', '<i8'), ('f3', 'S9'), ('f4', '<i8'), ('f5', 'S18'), ('f6', 'S17'), ('f7', 'S13'), ('f8', 'S18'), ('f9', 'S4'), ('f10', '<i8'), ('f11', '<i8'), ('f12', '<i8'), ('f13', 'S13'), ('f14', 'S5')])

The dtype of the array is structured as so:

In [85]: d.dtype
Out[85]: dtype([('f0', '<i8'), ('f1', 'S9'), ('f2', '<i8'), ('f3', 'S9'), ('f4', '<i8'), ('f5', 'S18'), ('f6', 'S17'), ('f7', 'S13'), ('f8', 'S18'), ('f9', 'S4'), ('f10', '<i8'), ('f11', '<i8'), ('f12', '<i8'), ('f13', 'S13'), ('f14', 'S5')])

And you can still access "columns" (known as fields) using the names given in the dtype:

In [86]: d['f0']
Out[86]: array([38, 53, 30])

In [87]: d['f1']
Out[87]: 
array(['Private', 'Private', 'State-gov'], 
      dtype='|S9')

It's more convenient to give proper names to the fields:

In [104]: names = "age,military,id,edu,a,marital,job,fam,ethnicity,gender,b,c,d,country,income"

In [105]: d = np.genfromtxt('tmp',dtype=None, names=names)

So you can now access the 'age' field, etc.:

In [106]: d['age']
Out[106]: array([38, 53, 30])

In [107]: d['income']
Out[107]: 
array(['<=50K', '<=50K', '>50K'], 
      dtype='|S5')

Or the incomes of people under 35

In [108]: d[d['age'] < 35]['income']
Out[108]: 
array(['>50K'], 
      dtype='|S5')

and over 35

In [109]: d[d['age'] > 35]['income']
Out[109]: 
array(['<=50K', '<=50K'], 
      dtype='|S5')