Use zip:

columns = zip(*rows) #transpose rows to columns
print columns[0] #print the first column
#you can also do more with the columns
print columns[1] # or print the second column
columns.append([7,7,7]) #add a new column to the end
backToRows = zip(*columns) # now we are back to rows with a new column
print backToRows

You can also use numpy:

a = numpy.array(a)
print a[:,0]

Edit: zip object is not subscriptable. It need to be converted to list to access as list:

column = list(zip(*row))

You could use this:

a = ((4.0, 4, 4.0), (3.0, 3, 3.6), (3.5, 6, 4.8))
a = np.array(a)
a[:,0]
returns >>> array([4. , 3. , 3.5])

Compared the 3 methods

1. 2D list: 5.323603868484497 seconds
2. Numpy library : 0.3201274871826172 seconds
3. Zip (Thanks to Joran Beasley) : 0.12395167350769043 seconds

D2_list=[list(range(100))]*100
t1=time.time()
for i in range(10**5):
    for j in range(10):
        b=[k[j] for k in D2_list]
D2_list_time=time.time()-t1

array=np.array(D2_list)
t1=time.time()        
for i in range(10**5):
    for j in range(10):
        b=array[:,j]        
Numpy_time=time.time()-t1

D2_trans = list(zip(*D2_list)) 
t1=time.time()        
for i in range(10**5):
    for j in range(10):
        b=D2_trans[j]
Zip_time=time.time()-t1

print ('2D List:',D2_list_time)
print ('Numpy:',Numpy_time)
print ('Zip:',Zip_time)

The Zip method works best. It was quite useful when I had to do some column wise processes for mapreduce jobs in the cluster servers where numpy was not installed.

You can get it like

[ x[0] for x in a]

which will return a list of the first element of each list in a

If you have access to numpy,

import numpy as np
a_transposed = a.T
# Get first row
print(a_transposed[0])

The benefit of this method is that if you want the "second" element in a 2d list, all you have to do now is a_transposed[1]. The a_transposed object is already computed, so you do not need to recalculate.

Description

Finding the first element in a 2-D list can be rephrased as find the first column in the 2d list. Because your data structure is a list of rows, an easy way of sampling the value at the first index in every row is just by transposing the matrix and sampling the first list.

Try using

for i in a :
  print(i[0])

i represents individual row in a.So,i[0] represnts the 1st element of each row.