How do I print an aligned numpy array with (text) row and column labels?

Is there any elegant way to exploit the correct spacing feature of print numpy.array to get a 2D array, with proper labels, that aligns properly? For example, given an array with 4 rows and 5 columns, how can I provide the array and appropriately sized lists corresponding to the row and header columns to generate some output that looks like this?

      A   B   C   D   E
Z [[ 85  86  87  88  89]
Y  [ 90 191 192  93  94]
X  [ 95  96  97  98  99]
W  [100 101 102 103 104]]

If I naively try:

import numpy
x = numpy.array([[85, 86, 87, 88, 89], \
                 [90, 191, 192, 93, 94], \
                 [95, 96, 97, 98, 99], \
                 [100,101,102,103,104]])

row_labe开发者_JAVA技巧ls = ['Z', 'Y', 'X', 'W']


print "     A   B   C   D   E"
for row, row_index in enumerate(x):
    print row_labels[row_index], row

I get:

      A   B   C   D   E
Z  [85  86  87  88  89]
Y  [90 191 192  93  94]
X  [95  96  97  98  99]
W  [100 101 102 103 104]

Is there any way i can get things to line up intelligently? I am definitely open to using any other library if there is a better way to solve my problem.

You can use IPython notebook + Pandas for that. Type your original example in IPython notebook:

import numpy
x = numpy.array([[85, 86, 87, 88, 89], 
                 [90, 191, 192, 93, 94], 
                 [95, 96, 97, 98, 99], 
                 [100,101,102,103,104]])

row_labels = ['Z', 'Y', 'X', 'W']
column_labels = ['A', 'B', 'C', 'D', 'E']

Then create a DataFrame:

import pandas
df = pandas.DataFrame(x, columns=column_labels, index=row_labels)

And then view it:

Assuming all matrix numbers have at most 3 digits, you could replace the last part with this:

print "     A   B   C   D   E"
for row_label, row in zip(row_labels, x):
    print '%s [%s]' % (row_label, ' '.join('%03s' % i for i in row))

Which outputs:

     A   B   C   D   E
Z [ 85  86  87  88  89]
Y [ 90 191 192  93  94]
X [ 95  96  97  98  99]
W [100 101 102 103 104]

Formatting with '%03s' results in a string of length 3 with left padding (using spaces). Use '%04s' for length 4 and so on. The full format string syntax is explained in the Python documentation.

Here's a way to leverage the array printing functions. I probably wouldn't use it, but it comes pretty close to meeting your requirements!

a = np.random.rand(5,4)
x = np.array('col1 col2 col3 col4'.split())
y = np.array('row1 row2 row3 row4 row5'.split())
b = numpy.zeros((6,5),object)
b[1:,1:]=a
b[0,1:]=x
b[1:,0]=y
b[0,0]=''
printer = np.vectorize(lambda x:'{0:5}'.format(x,))
print printer(b).astype(object)

[[     col1 col2 col3 col4]
 [row1 0.95 0.71 0.03 0.56]
 [row2 0.56 0.46 0.35 0.90]
 [row3 0.24 0.08 0.29 0.40]
 [row4 0.90 0.44 0.69 0.48]
 [row5 0.27 0.10 0.62 0.04]]

This code is essentially an implementation of scoffey's above, but it doesn't have the three character limitation and is a bit more powerful. Here's my code:

    def format__1(digits,num):
        if digits<len(str(num)):
            raise Exception("digits<len(str(num))")
        return ' '*(digits-len(str(num))) + str(num)
    def printmat(arr,row_labels=[], col_labels=[]): #print a 2d numpy array (maybe) or nested list
        max_chars = max([len(str(item)) for item in flattenList(arr)+col_labels]) #the maximum number of chars required to display any item in list
        if row_labels==[] and col_labels==[]:
            for row in arr:
                print '[%s]' %(' '.join(format__1(max_chars,i) for i in row))
        elif row_labels!=[] and col_labels!=[]:
            rw = max([len(str(item)) for item in row_labels]) #max char width of row__labels
            print '%s %s' % (' '*(rw+1), ' '.join(format__1(max_chars,i) for i in col_labels))
            for row_label, row in zip(row_labels, arr):
                print '%s [%s]' % (format__1(rw,row_label), ' '.join(format__1(max_chars,i) for i in row))
        else:
            raise Exception("This case is not implemented...either both row_labels and col_labels must be given or neither.")

running

    import numpy
    x = numpy.array([[85, 86, 87, 88, 89],
                     [90, 191, 192, 93, 94],
                     [95, 96, 97, 98, 99],
                     [100,101,102,103,104]])
    row_labels = ['Z', 'Y', 'X', 'W']
    column_labels = ['A', 'B', 'C', 'D', 'E']
    printmat(x,row_labels=row_labels, col_labels=column_labels)

gives

         A   B   C   D   E
    Z [ 85  86  87  88  89]
    Y [ 90 191 192  93  94]
    X [ 95  96  97  98  99]
    W [100 101 102 103 104]

This would also be the output if 'x' were just a nested python list instead of a numpy array.