title: Faulty Pandas dataframe read_json sorting on python3.5.9
body: <p>Dataframe with more than 10 rows is incorrectly sorted on python3.5.9 after converting to json and back to pandas.DataFrame.</p>
  
  <pre><code>from pandas import DataFrame, read_json
  
  columns = ['a', 'b', 'c']
  data = [[1*i, 2*i, 3*i] for i in range(11)]
  df = DataFrame(columns=columns, data=data)
  print(df)
  #      a   b   c
  # 0    0   0   0
  # 1    1   2   3
  # 2    2   4   6
  # 3    3   6   9
  # 4    4   8  12
  # 5    5  10  15
  # 6    6  12  18
  # 7    7  14  21
  # 8    8  16  24
  # 9    9  18  27
  # 10  10  20  30
  
  new_df = read_json(df.to_json())
  print(new_df)
  #      a   b   c
  # 0    0   0   0
  # 1    1   2   3
  # 10  10  20  30   # this should be the last line
  # 2    2   4   6
  # 3    3   6   9
  # 4    4   8  12
  # 5    5  10  15
  # 6    6  12  18
  # 7    7  14  21
  # 8    8  16  24
  # 9    9  18  27
  </code></pre>
  
  <p>So DataFrame which was created with <code>read_json</code> seems to be sorting indexes like strings (1,10,2,3,...) instead of ints (1,2,3..). </p>
  
  <p>Behaviour generated with Python 3.5.9 (default, Jan  4 2020, 04:09:01) (docker image <a href="https://github.com/docker-library/python/blob/d2a2b4f7422aac78c7d5ea6aadc49d009d184a5f/3.5/stretch/Dockerfile" rel="nofollow noreferrer">python:3.5-stretch</a>)</p>
  
  <p>Everything seems to be working fine on my local machine (Python 3.8.1 (default, Dec 21 2019, 20:57:38)).</p>
  
  <p>pandas==0.25.3 was used on both instances.</p>
  
  <p>Is where a way to fix this without upgrading python?</p>