title: Reading back pandas dataframe saved with pyarrow gives wrong values
body: <p>I was trying to save a pandas dataframe to parquet format using Pyarrow v2, and I ran into a weird problem. The (simplified) dataframe has one string column and one nested column (list of dicts). Here is an example:</p>
  <pre><code>labels = [&quot;aa&quot;, 'bb', 'cc', 'dd']
  vals = [random.choice(labels) for _ in range(2000)]
  df = pd.DataFrame({'names': vals})
  df[&quot;name_nested&quot;] = df.names.apply(lambda x: [{&quot;label&quot;: x}])
  df.to_parquet(&quot;x.par&quot;) 
  </code></pre>
  <p>as you see the columns &quot;names&quot; and &quot;name_nested&quot; should have the same values:</p>
  <p>df.head()</p>
  <pre><code>    names   name_nested
  0   bb  [{'label': 'bb'}]
  1   aa  [{'label': 'aa'}]
  2   cc  [{'label': 'cc'}]
  3   cc  [{'label': 'cc'}]
  4   cc  [{'label': 'cc'}]
  </code></pre>
  <p>However, once I reload from disk the saved parquet file, things are weird, I don't get same result:</p>
  <pre><code>df2 = pd.read_parquet(&quot;x.par&quot;)
  df2[&quot;name_nested2&quot;] = df2.name_nested.apply(lambda x: x[0][&quot;label&quot;])
  
  len(df2[df2.name_nested2 != df2.names])
  
  # 726
  </code></pre>
  <p>Out of the 2000 entries, there are 726 entries which do not match. here is an example:</p>
  <pre><code>df2[df2.name_nested2 != df2.names] 
  
      names   name_nested name_nested2
  1025    dd  [{'label': 'cc'}]   cc
  1027    bb  [{'label': 'aa'}]   aa
  1029    aa  [{'label': 'cc'}]   cc
  1031    dd  [{'label': 'aa'}]   aa
  1035    bb  [{'label': 'dd'}]   dd
  </code></pre>
  <p>As you see the column <code>name_nested</code> is not the same as <code>names</code> anymore! This is very wrong behavior. also I noticed that this happens only if the dataframe has more than 1024 rows, and the mismatches happen only after row 1024 too.</p>
  <p>I thought this would be a known issue, but couldn't find any information about this.</p>
  <p>Once I upgraded to Pyarrow 6, this is no longer an issue, but wanted to understand the root cause of this, if anyone have seen this before.</p>