Efficient way to convert dictionary of list to pair list of key and value

Question

I have dictionary of list as follows (it can be more than 1M elements, also assume dictionary is sorted by key)

import scipy.sparse as sp
d = {0: [0,1], 1: [1,2,3], 
     2: [3,4,5], 3: [4,5,6], 
     4: [5,6,7], 5: [7], 
     6: [7,8,9]}

I want to know what is the most efficient way (fastest way for large dictionary) to convert it into list of row and column index like:

r_index = [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 6, 6, 6]
c_index = [0, 1, 1, 2, 3, 3, 4, 5, 4, 5, 6, 5, 6, 7, 7, 7, 8, 9]

Here are some solutions that I have so far:

1. Using iteration

   row_ind = [k for k, v in d.iteritems() for _ in range(len(v))] # or d.items() in Python 3
   col_ind = [i for ids in d.values() for i in ids]
   

2. Using pandas library

   import pandas as pd
   df = pd.DataFrame.from_dict(d, orient='index')
   df = df.stack().reset_index()
   row_ind = list(df['level_0'])
   col_ind = list(df[0])
   

3. Using itertools

   import itertools
   indices = [(x,y) for x, y in itertools.chain.from_iterable([itertools.product((k,), v) for k, v in d.items()])]
   indices = np.array(indices)
   row_ind = indices[:, 0]
   col_ind = indices[:, 1]
   

I'm not sure which way is the fastest way to deal with this problem if I have a lot of elements in my dictionary. Thanks!

Answer 1

The first rule of thumb of optimization in python is, to make sure that your innermost loop is outsourced to some library function. This only applies for cpython - pypy is a completely different story. In your case using extend is giving some significant speedup.

import time
l = range(10000)
x = dict([(k, list(l)) for k in range(1000)])

def org(d):
    row_ind = [k for k, v in d.items() for _ in range(len(v))]
    col_ind = [i for ids in d.values() for i in ids]

def ext(d):
    row_ind = [k for k, v in d.items() for _ in range(len(v))]
    col_ind = []
    for ids in d.values():
        col_ind.extend(ids)

def ext_both(d):
    row_ind = []
    for k, v in d.items():
        row_ind.extend([k] * len(v))
    col_ind = []
    for ids in d.values():
        col_ind.extend(ids)

functions = [org, ext, ext_both]
for func in functions:
    begin = time.time()
    func(x)
    elapsed = time.time() - begin
    print(func.__name__ + ": "  + str(elapsed))

Output when using python2:

org: 0.512559890747
ext: 0.340406894684
ext_both: 0.149670124054