<p dir="auto"><a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="138944354" data-permission-text="Title is private" data-url="https://github.com/pandas-dev/pandas/issues/12551" data-hovercard-type="pull_request" data-hovercard-url="/pandas-dev/pandas/pull/12551/hovercard" href="https://github.com/pandas-dev/pandas/pull/12551">#12551</a> and comment: <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="137901618" data-permission-text="Title is private" data-url="https://github.com/pandas-dev/pandas/issues/12512" data-hovercard-type="pull_request" data-hovercard-url="/pandas-dev/pandas/pull/12512/hovercard?comment_id=56771668&amp;comment_type=review_comment" href="https://github.com/pandas-dev/pandas/pull/12512#discussion_r56771668">#12512 (comment)</a></p>
<p dir="auto"><code class="notranslate">usecols=['a',1]</code> should raise</p>
<p dir="auto">Hi, guys! First of all, thanks for making this open source! =D Keep up the good work!</p>
<p dir="auto"><strong>SCRIPT</strong></p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import pandas as pd

dfs = pd.read_html('https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States')

print(dfs[0])"><pre class="notranslate"><code class="notranslate">import pandas as pd

dfs = pd.read_html('https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States')

print(dfs[0])
</code></pre></div>
<p dir="auto">The output we found where  there is any colspan is considering only the first column, and the other values are pulled back in relation to the colspan amount, making the values on the tail's row as "NaN"</p>
<p dir="auto"><strong>#### Expected Output</strong><br>
In this web page, there is a colspan in the "capital" and "largest city" columns when they are the same, I think that in a general way we would expect that  the value would be duplicate on the following &lt;colspan's amount&gt; columns in the generated DataFrame</p>
<h4 dir="auto">output of <code class="notranslate">pd.show_versions()</code></h4>
<h2 dir="auto">INSTALLED VERSIONS</h2>
<p dir="auto">commit: None<br>
python: 3.5.2.final.0<br>
python-bits: 64<br>
OS: Windows<br>
OS-release: 10<br>
machine: AMD64<br>
processor: Intel64 Family 6 Model 15 Stepping 13, GenuineIntel<br>
byteorder: little<br>
LC_ALL: None<br>
LANG: None</p>
<p dir="auto">pandas: 0.18.1<br>
nose: None<br>
pip: 8.1.2<br>
setuptools: 27.2.0<br>
Cython: 0.24.1<br>
numpy: 1.11.1<br>
scipy: 0.18.0<br>
statsmodels: None<br>
xarray: None<br>
IPython: 5.1.0<br>
sphinx: None<br>
patsy: None<br>
dateutil: 2.5.3<br>
pytz: 2016.6.1<br>
blosc: None<br>
bottleneck: None<br>
tables: None<br>
numexpr: None<br>
matplotlib: 1.5.1<br>
openpyxl: None<br>
xlrd: None<br>
xlwt: None<br>
xlsxwriter: None<br>
lxml: None<br>
bs4: 4.5.1<br>
html5lib: 0.999<br>
httplib2: None<br>
apiclient: None<br>
sqlalchemy: None<br>
pymysql: None<br>
psycopg2: None<br>
jinja2: 2.8<br>
boto: None<br>
pandas_datareader: 0.2.1</p>