In all the examples using numpy arrays that you have seen so far, these arrays have been one dimensional.
It can often be useful to collect different data together in a table.
One way to do this is by combining one-dimensional numpy arrays into larger, two-dimensional, arrays.
import numpy as np
# create an array `x` with the integers 1 to 5
x = np.arange(1,6)
# create three new arrays by performing calculations on `x`
y1 = x**2
y2 = x+3
y3 = x/2 + 1
print('x=',x)
print('y1=',y1)
print('y2=',y2)
print('y3=',y3)
You can combine numpy arrays into a table as columns using np.column_stack()
# combine x, y1, y2, and y3 as columns in a new table
column_table = np.column_stack( ( x, y1, y2, y3 ) )
print( column_table )
Or as rows using np.row_stack()
# arrange x, y1, y2, and y3 as rows in a new table
row_table = np.row_stack( ( x, y1, y2, y3 ) )
print( row_table )
Remember that a 1D numpy array can be indexed like a list.
my_1D_array = np.array( [ 1, 2, 3, 4, 5, 6] )
my_1D_array[2:5]
# [2:5] selects from 2 jumps, up to, but not including, 5 jumps
A 2D numpy array can be treated like a list of lists, and indexing returns selected rows.
row_table[1] # return the 2nd row (1 jump from the start)
Because each row is a 1D numpy array, you can use a second index to select a single entry.
row_table[1][3]
These two indices can be combined into a single bracket, where the first value selects one of more rows, and the second value selects one or more columns.
row_table[1,3]
To select a single row, we make use of the range character :. Remember, for a list or 1D array, : lets us select a range of elements, and leaving out one of the numbers selects all elements up to the start, or end, of the list.
my_list = [ 'a', 'b', 'c', 'd', 'e' ]
my_list[1:]
Leaving out both numbers extends our selection up to both ends of the list or array.
my_list[:]
For a 2D array, you can think of this as “every row” or “every column”.
print( row_table )
print()
print( row_table[:,3] ) # all rows, jump 3 columns
You can also convert between 1D and 2D numpy arrays (or even higher dimensions) using np.reshape()
a = ( [ 1, 2, 3, 4, 5, 6 ] )
print( a )
b = np.reshape( a, (3, 2) ) # reshape into a 3×2 2D array
print( b )
and back again
c = np.reshape( b, (6) )
print( c )