title: Cannot write pandas DataFrame to csv: __init__() got an unexpected keyword argument 'tupleize_cols'
body: <p>I'm running the following basic code:</p>
  
  <p><code><code>dfMain.to_csv('./January_filtered_International_WE.csv')</code> </code></p>
  
  <p>which used to run normally until yesterday. This morning I upgraded to <code>pandas 0.25.0</code> while running code and now I cannot write my 500k rows dataframe to a csv. I can mention that I left Jupyter Notebook running in order to do some processing, so this morning when I opened it I had the dataFrame already, processed.</p>
  
  <p>Versions (using Windows 10)</p>
  
  <pre><code>Jupyter notebook : 5.7.8
  Python : 3.6.7
  Pandas : 0.25.0
  </code></pre>
  
  <p>I would like to save my DataFrame in a fast and efficient manner as I will load it  several times in the future. I do not want to close the notebook as this will delete the dataFrame.</p>
  
  <p>I tried:</p>
  
  <ul>
  <li>downgrading to <code>Pandas 0.24.2</code> (previous version used) but still getting the <code>__init__() got an unexpected keyword argument 'tupleize_cols'</code></li>
  <li>use <code>pd.to_pickle</code> but got a <code>memoryError</code></li>
  <li>use <code>pd.to_hdf</code> but got a <code>memoryError</code></li>
  <li>using msgbox instead but apparently it does not support DataFrames (got an error)</li>
  <li>upgrade Jupyter notebook, but got the following error:</li>
  </ul>
  
  <p><code>ERROR: ipython 5.8.0 has requirement prompt-toolkit&lt;2.0.0,&gt;=1.0.4, but
  you'll have prompt-toolkit 2.0.9 which is incompatible</code></p>
  
  <ul>
  <li>so naturally I did pip install prompt-toolkit 1.0.16 but then got this message:</li>
  </ul>
  
  <p><code>ERROR: jupyter-console 6.0.0 has requirement prompt-toolkit&lt;2.1.0,&gt;=2.0.0, but you'll have prompt-toolkit 1.0.16 which is incompatible.</code></p>
  
  <p>As an alternative I went into PyCharm and took a random <code>DataFrame.to_csv</code> and it worked. This makes me think the issue is with Jupyter Notebook.</p>
  
  <p>Any help on how to save the DataFrame (~12 GB) is appreciated! </p>