<p dir="auto">Under numexpr 2.2.2 (and for some reason that's the latest version available in the anaconda distribution at the moment), I get the following kind of errors:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="&gt;&gt;&gt; data.shape
(2, 219, 104, 6)
&gt;&gt;&gt; data.dtype
dtype('float64')
&gt;&gt;&gt; df = pd.Panel(data[0]).to_frame().reset_index()
&gt;&gt;&gt; df[0].dtype
dtype('float64')
&gt;&gt;&gt; df[0].count()
711
&gt;&gt;&gt; df[0] + 1
TypeError: Could not operate 1 with block values using `oa_ndim == 0` when `op_axes` is NULL is 
deprecated. Use `oa_ndim == -1` or the MultiNew iterator for NumPy &lt;1.8 compatibility"><pre class="notranslate"><span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">data</span>.<span class="pl-s1">shape</span>
(<span class="pl-c1">2</span>, <span class="pl-c1">219</span>, <span class="pl-c1">104</span>, <span class="pl-c1">6</span>)
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">data</span>.<span class="pl-s1">dtype</span>
<span class="pl-en">dtype</span>(<span class="pl-s">'float64'</span>)
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">Panel</span>(<span class="pl-s1">data</span>[<span class="pl-c1">0</span>]).<span class="pl-en">to_frame</span>().<span class="pl-en">reset_index</span>()
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">df</span>[<span class="pl-c1">0</span>].<span class="pl-s1">dtype</span>
<span class="pl-en">dtype</span>(<span class="pl-s">'float64'</span>)
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">df</span>[<span class="pl-c1">0</span>].<span class="pl-en">count</span>()
<span class="pl-c1">711</span>
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">df</span>[<span class="pl-c1">0</span>] <span class="pl-c1">+</span> <span class="pl-c1">1</span>
<span class="pl-v">TypeError</span>: <span class="pl-v">Could</span> <span class="pl-c1">not</span> <span class="pl-s1">operate</span> <span class="pl-c1">1</span> <span class="pl-k">with</span> <span class="pl-s1">block</span> <span class="pl-s1">values</span> <span class="pl-s1">using</span> <span class="pl-s">`oa_ndim == 0`</span> <span class="pl-s1">when</span> <span class="pl-s">`op_axes`</span> <span class="pl-c1">is</span> <span class="pl-v">NULL</span> <span class="pl-c1">is</span> 
<span class="pl-s1">deprecated</span>. <span class="pl-v">Use</span> <span class="pl-s">`oa_ndim == -1`</span> <span class="pl-c1">or</span> <span class="pl-s1">the</span> <span class="pl-v">MultiNew</span> <span class="pl-s1">iterator</span> <span class="pl-s1">for</span> <span class="pl-v">NumPy</span> <span class="pl-c1">&lt;</span><span class="pl-c1">1.8</span> <span class="pl-s1">compatibility</span></pre></div>
<p dir="auto">Looks like the same as <a href="https://github.com/PyTables/PyTables/issues/259" data-hovercard-type="issue" data-hovercard-url="/PyTables/PyTables/issues/259/hovercard">here</a> and <a href="https://groups.google.com/forum/#!topic/pydata/h4ww3t2hnCQ" rel="nofollow">here</a>.</p>
<p dir="auto">Upgrading to numexpr 2.3 solves it. Should v2.3 be then strictly required by pandas?</p>
<p dir="auto">with pandas 0.16 and sqlalchemy, the to_sql method of a dataframe creates duplicate redundant constraints on each boolean column, at least when exporting to Microsoft SQL Server, because for each column it creates 2 constraints checking the field must be either 0 or 1. This is redundant and slows down the database. A short example is below. Of course you must check the output in the sql database. Also, the fact that there is no control and no documentation over the constraints created by to_sql is extremely frustrating</p>
<p dir="auto">import pandas as pd<br>
import numpy as np<br>
from sqlalchemy import create_engine, MetaData, Table, select<br>
import sqlalchemy as sqlalchemy</p>
<p dir="auto">from sqlalchemy import create_engine, MetaData, Table, select<br>
import sqlalchemy as sqlalchemy</p>
<p dir="auto">ServerName = "cdodb-wb"<br>
Database = "DealAnalysis"</p>
<p dir="auto">engine = create_engine('mssql+pyodbc://' + ServerName + '/' + Database, encoding='latin1')<br>
conn = engine.connect()</p>
<p dir="auto">metadata = MetaData(conn)</p>
<p dir="auto">df=pd.DataFrame(np.random.rand(20)&gt;0.5, columns=['random_boolean'])</p>
<p dir="auto">df.to_sql('python_test_table',engine,if_exists='replace')</p>