Any dict-like mappable objects in Python that only show preview of contents when __repr__?

Question

I'm looking for an object I can use to store largish dataframes and sklearn objects. Ideally I would like to store them as pd.Series because it has the behavior I'm looking for in that I can do the following:

• Get the object using some key:value pair
• Preview truncates large outputs
• Can reveal the keys inside easily

I can use pd.Series objects but don't know if the practice of storing complicated objects as values is frowned upon.

Are there any other options I can use in the builtin library, pandas, numpy, scipy, or scikit-learn?

import numpy as np
x = np.arange(100)
y = list("abcde")*20
d = {"x":x, "y":y}
d
# {'x': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), 'y': ['a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e']}

from collections import namedtuple
nt = namedtuple("Example", ["x","y"])
nt(x=x, y=y)
# Example(x=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
#        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
#        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
#        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
#        68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
#        85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), y=['a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e'])

from sklearn.utils import Bunch
b = Bunch(x=x, y=y)
b
# {'x': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), 'y': ['a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e']}

import pandas as pd
pd.Series({"x":x, "y":y})
# x    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 7...
# y    [a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d, e, a, b, c, d...
# dtype: object