Numpy-like printing for python objects

Question

While doing data analysis in Ipython I often have to look at the data by just printing its contents to the shell. Numpy have the facility to show only the margins of huge objects when they are too long themselves. I really like this feature of ndarrays but when I print internal python object (eg. dictionary with 15k objects in it) they are dumped to the screen or sometimes truncated in not very friendly fashion. So for example for a huge dictionary I would like to see in output something like this

{ '39416' : '1397',  
  '39414' : '1397',  
  '7629'  : '7227',  
  ...,  
  '31058' : '9606',  
  '21097' : '4062',  
  '32040' : '9606' }  

It would be perfect if alignment and nested data structures could be taken care of. Is their a special module which can provide such functionality for python basic classes (list, dict)? Or there are some ipython configuration tricks I know nothing about?

Answer 1

There is a good built-in library pprint. Take a look at it.

>>> from pprint import pprint
>>> pprint({x: list(range(x)) for x in range(10)})
{0: [],
 1: [0],
 2: [0, 1],
 3: [0, 1, 2],
 4: [0, 1, 2, 3],
 5: [0, 1, 2, 3, 4],
 6: [0, 1, 2, 3, 4, 5],
 7: [0, 1, 2, 3, 4, 5, 6],
 8: [0, 1, 2, 3, 4, 5, 6, 7],
 9: [0, 1, 2, 3, 4, 5, 6, 7, 8]}

Answer 2

If your dictionary is well structured, you could convert it to a Pandas dataframe for viewing.

import numpy as np
import pandas as pd

>>> pd.DataFrame({'random normal': np.random.randn(1000), 
                  'random int': np.random.randint(0, 10, 1000)})
     random int  random normal
0             6       0.850827
1             7       0.486551
2             4      -0.111008
3             9      -1.319320
4             6      -0.393774
5             1      -0.878507
..          ...            ...
995           2      -1.882813
996           3      -0.121003
997           3       0.155835
998           5       0.920318
999           2       0.216229

[1000 rows x 2 columns]