Map index of numpy matrix

Question

How should I map indices of a numpy matrix?

For example:

mx = np.matrix([[5,6,2],[3,3,7],[0,1,6]]

The row/column indices are 0, 1, 2.

So:

>>> mx[0,0]
5

Let s say I need to map these indices, converting 0, 1, 2 into, e.g. 10, 'A', 'B' in the way that:

mx[10,10] #returns 5
mx[10,'A'] #returns 6 and so on..

I can just set a dict and use it to access the elements, but I would like to know if it is possible to do something like what I just described.

Answer 1

I would suggest using pandas dataframe with the index and columns using the new mapping for row and col indexing respectively for ease in indexing. It allows us to select a single element or an entire row or column with the familiar colon operator.

Consider a generic (non-square 4x3 shaped matrix) -

mx = np.matrix([[5,6,2],[3,3,7],[0,1,6],[4,5,2]])

Consider the mappings for rows and columns -

row_idx = [10, 'A', 'B','C']
col_idx = [10, 'A', 'B']

Let's take a look on the workflow with the given sample -

# Get data into dataframe with given mappings
In [57]: import pandas as pd

In [58]: df = pd.DataFrame(mx,index=row_idx, columns=col_idx)

# Here's how dataframe data looks like
In [60]: df
Out[60]: 
    10  A  B
10   5  6  2
A    3  3  7
B    0  1  6
C    4  5  2

# Get one scalar element
In [61]: df.loc['C',10]
Out[61]: 4

# Get one entire col
In [63]: df.loc[:,10].values
Out[63]: array([5, 3, 0, 4])

# Get one entire row
In [65]: df.loc['A'].values
Out[65]: array([3, 3, 7])

And best of all we are not making any extra copies as the dataframe and its slices are still indexing into the original matrix/array memory space -

In [98]: np.shares_memory(mx,df.loc[:,10].values)
Out[98]: True