<p dir="auto">upon DataFrame.insert option allow_duplicates works, but only only once.<br>
When i have 2 columns with same name, additon of third throws</p>
<blockquote>
<p dir="auto">ValueError: Wrong number of items passed 2, placement implies 1</p>
</blockquote>
<h4 dir="auto">Code Sample, a copy-pastable example if possible</h4>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="a = pd.DataFrame()
a.insert(0, &quot;qwe&quot;, [1,2,3,4], allow_duplicates=True)
a.insert(0, &quot;qwe&quot;, [1,2,3,4], allow_duplicates=True)
a.insert(0, &quot;qwe&quot;, [1,2,3,4], allow_duplicates=True)"><pre class="notranslate"><code class="notranslate">a = pd.DataFrame()
a.insert(0, "qwe", [1,2,3,4], allow_duplicates=True)
a.insert(0, "qwe", [1,2,3,4], allow_duplicates=True)
a.insert(0, "qwe", [1,2,3,4], allow_duplicates=True)
</code></pre></div>
<h4 dir="auto">Expected Output</h4>
<p dir="auto">zxc  qwe  qwe<br>
0    1    1    1<br>
1    2    2    2<br>
2    3    3    3<br>
3    4    4    4</p>
<h4 dir="auto">output of <code class="notranslate">pd.show_versions()</code></h4>
<details>
## INSTALLED VERSIONS
<p dir="auto">commit: None<br>
python: 2.7.12.final.0<br>
python-bits: 64<br>
OS: Windows<br>
OS-release: 7<br>
machine: AMD64<br>
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel<br>
byteorder: little<br>
LC_ALL: None<br>
LANG: ru_RU</p>
<p dir="auto">pandas: 0.18.1<br>
nose: 1.3.7<br>
pip: 8.1.2<br>
setuptools: 25.1.6<br>
Cython: 0.24.1<br>
numpy: 1.11.1<br>
scipy: 0.18.0<br>
statsmodels: 0.6.1<br>
xarray: None<br>
IPython: 5.1.0<br>
sphinx: 1.4.6<br>
patsy: 0.4.1<br>
dateutil: 2.5.3<br>
pytz: 2016.6.1<br>
blosc: None<br>
bottleneck: 1.0.0<br>
tables: 3.2.2<br>
numexpr: 2.6.1<br>
matplotlib: 1.5.3<br>
openpyxl: 2.3.2<br>
xlrd: 1.0.0<br>
xlwt: 1.1.2<br>
xlsxwriter: 0.9.2<br>
lxml: 3.6.4<br>
bs4: 4.4.1<br>
html5lib: None<br>
httplib2: None<br>
apiclient: None<br>
sqlalchemy: 1.0.13<br>
pymysql: 0.7.6.None<br>
psycopg2: None<br>
jinja2: 2.8<br>
boto: 2.40.0<br>
pandas_datareader: None<br>
&lt;\details&gt;</p></details>
<p dir="auto">We have a check for whether <code class="notranslate">format == '%Y%m%d'</code>, but this actually seems to be slower:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="In [86]: s = pd.Series(['20120101']*1000000)

In [87]: %timeit pd.to_datetime(s)
229 ms ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [88]: %timeit pd.to_datetime(s, format='%Y%m%d')
749 ms ± 67.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)"><pre class="notranslate"><span class="pl-v">In</span> [<span class="pl-c1">86</span>]: <span class="pl-s1">s</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">Series</span>([<span class="pl-s">'20120101'</span>]<span class="pl-c1">*</span><span class="pl-c1">1000000</span>)

<span class="pl-v">In</span> [<span class="pl-c1">87</span>]: <span class="pl-c1">%</span><span class="pl-s1">timeit</span> <span class="pl-s1">pd</span>.<span class="pl-en">to_datetime</span>(<span class="pl-s1">s</span>)
<span class="pl-c1">229</span> <span class="pl-s1">ms</span> ± <span class="pl-c1">12.5</span> <span class="pl-s1">ms</span> <span class="pl-s1">per</span> <span class="pl-en">loop</span> (<span class="pl-s1">mean</span> ± <span class="pl-s1">std</span>. <span class="pl-s1">dev</span>. <span class="pl-s1">of</span> <span class="pl-c1">7</span> <span class="pl-s1">runs</span>, <span class="pl-c1">1</span> <span class="pl-s1">loop</span> <span class="pl-s1">each</span>)

<span class="pl-v">In</span> [<span class="pl-c1">88</span>]: <span class="pl-c1">%</span><span class="pl-s1">timeit</span> <span class="pl-s1">pd</span>.<span class="pl-en">to_datetime</span>(<span class="pl-s1">s</span>, <span class="pl-s1">format</span><span class="pl-c1">=</span><span class="pl-s">'%Y%m%d'</span>)
<span class="pl-c1">749</span> <span class="pl-s1">ms</span> ± <span class="pl-c1">67.4</span> <span class="pl-s1">ms</span> <span class="pl-s1">per</span> <span class="pl-en">loop</span> (<span class="pl-s1">mean</span> ± <span class="pl-s1">std</span>. <span class="pl-s1">dev</span>. <span class="pl-s1">of</span> <span class="pl-c1">7</span> <span class="pl-s1">runs</span>, <span class="pl-c1">1</span> <span class="pl-s1">loop</span> <span class="pl-s1">each</span>)</pre></div>