<p dir="auto">xref <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="195199816" data-permission-text="Title is private" data-url="https://github.com/pandas-dev/pandas/issues/14873" data-hovercard-type="issue" data-hovercard-url="/pandas-dev/pandas/issues/14873/hovercard" href="https://github.com/pandas-dev/pandas/issues/14873">#14873</a> (boolean casts)<br>
xref <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="194730027" data-permission-text="Title is private" data-url="https://github.com/pandas-dev/pandas/issues/14849" data-hovercard-type="issue" data-hovercard-url="/pandas-dev/pandas/issues/14849/hovercard" href="https://github.com/pandas-dev/pandas/issues/14849">#14849</a> (datetime)</p>
<h4 dir="auto">A small, complete example of the issue</h4>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import pandas as pd

def predictions(tool):
    out = pd.Series(index=['p1', 'p2', 'useTime'], dtype=object)
    if 'step1' in list(tool.State):
        out['p1'] = str(tool[tool.State == 'step1'].Machine.values[0])
    if 'step2' in list(tool.State):
        out['p2'] = str(tool[tool.State == 'step2'].Machine.values[0])
        out['useTime'] = str(tool[tool.State == 'step2'].oTime.values[0])
    return out


df1 = pd.DataFrame({'Key': ['B', 'B', 'A', 'A'],
                   'State': ['step1', 'step2', 'step1', 'step2'],
                   'oTime': ['', '2016-09-19 05:24:33', '', '2016-09-19 23:59:04'],
                   'Machine': ['23', '36L', '36R', '36R']})

df2 = df1.copy()
df2.oTime = pd.to_datetime(df2.oTime)


pred1 = df1.groupby('Key').apply(predictions)
pred2 = df2.groupby('Key').apply(predictions)

print(pred1)
print(pred2)"><pre class="notranslate"><span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span>

<span class="pl-k">def</span> <span class="pl-en">predictions</span>(<span class="pl-s1">tool</span>):
    <span class="pl-s1">out</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">Series</span>(<span class="pl-s1">index</span><span class="pl-c1">=</span>[<span class="pl-s">'p1'</span>, <span class="pl-s">'p2'</span>, <span class="pl-s">'useTime'</span>], <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s1">object</span>)
    <span class="pl-k">if</span> <span class="pl-s">'step1'</span> <span class="pl-c1">in</span> <span class="pl-en">list</span>(<span class="pl-s1">tool</span>.<span class="pl-v">State</span>):
        <span class="pl-s1">out</span>[<span class="pl-s">'p1'</span>] <span class="pl-c1">=</span> <span class="pl-en">str</span>(<span class="pl-s1">tool</span>[<span class="pl-s1">tool</span>.<span class="pl-v">State</span> <span class="pl-c1">==</span> <span class="pl-s">'step1'</span>].<span class="pl-v">Machine</span>.<span class="pl-s1">values</span>[<span class="pl-c1">0</span>])
    <span class="pl-k">if</span> <span class="pl-s">'step2'</span> <span class="pl-c1">in</span> <span class="pl-en">list</span>(<span class="pl-s1">tool</span>.<span class="pl-v">State</span>):
        <span class="pl-s1">out</span>[<span class="pl-s">'p2'</span>] <span class="pl-c1">=</span> <span class="pl-en">str</span>(<span class="pl-s1">tool</span>[<span class="pl-s1">tool</span>.<span class="pl-v">State</span> <span class="pl-c1">==</span> <span class="pl-s">'step2'</span>].<span class="pl-v">Machine</span>.<span class="pl-s1">values</span>[<span class="pl-c1">0</span>])
        <span class="pl-s1">out</span>[<span class="pl-s">'useTime'</span>] <span class="pl-c1">=</span> <span class="pl-en">str</span>(<span class="pl-s1">tool</span>[<span class="pl-s1">tool</span>.<span class="pl-v">State</span> <span class="pl-c1">==</span> <span class="pl-s">'step2'</span>].<span class="pl-s1">oTime</span>.<span class="pl-s1">values</span>[<span class="pl-c1">0</span>])
    <span class="pl-k">return</span> <span class="pl-s1">out</span>


<span class="pl-s1">df1</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">DataFrame</span>({<span class="pl-s">'Key'</span>: [<span class="pl-s">'B'</span>, <span class="pl-s">'B'</span>, <span class="pl-s">'A'</span>, <span class="pl-s">'A'</span>],
                   <span class="pl-s">'State'</span>: [<span class="pl-s">'step1'</span>, <span class="pl-s">'step2'</span>, <span class="pl-s">'step1'</span>, <span class="pl-s">'step2'</span>],
                   <span class="pl-s">'oTime'</span>: [<span class="pl-s">''</span>, <span class="pl-s">'2016-09-19 05:24:33'</span>, <span class="pl-s">''</span>, <span class="pl-s">'2016-09-19 23:59:04'</span>],
                   <span class="pl-s">'Machine'</span>: [<span class="pl-s">'23'</span>, <span class="pl-s">'36L'</span>, <span class="pl-s">'36R'</span>, <span class="pl-s">'36R'</span>]})

<span class="pl-s1">df2</span> <span class="pl-c1">=</span> <span class="pl-s1">df1</span>.<span class="pl-en">copy</span>()
<span class="pl-s1">df2</span>.<span class="pl-s1">oTime</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-en">to_datetime</span>(<span class="pl-s1">df2</span>.<span class="pl-s1">oTime</span>)


<span class="pl-s1">pred1</span> <span class="pl-c1">=</span> <span class="pl-s1">df1</span>.<span class="pl-en">groupby</span>(<span class="pl-s">'Key'</span>).<span class="pl-en">apply</span>(<span class="pl-s1">predictions</span>)
<span class="pl-s1">pred2</span> <span class="pl-c1">=</span> <span class="pl-s1">df2</span>.<span class="pl-en">groupby</span>(<span class="pl-s">'Key'</span>).<span class="pl-en">apply</span>(<span class="pl-s1">predictions</span>)

<span class="pl-en">print</span>(<span class="pl-s1">pred1</span>)
<span class="pl-en">print</span>(<span class="pl-s1">pred2</span>)</pre></div>
<h4 dir="auto">Actual Output:</h4>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="      p1   p2              useTime
Key                               
A    36R  36R  2016-09-19 23:59:04
B     23  36L  2016-09-19 05:24:33
       p1   p2                        useTime
Key                                          
A     NaN  36R  2016-09-19T23:59:04.000000000
B    23.0  36L  2016-09-19T05:24:33.000000000"><pre class="notranslate"><code class="notranslate">      p1   p2              useTime
Key                               
A    36R  36R  2016-09-19 23:59:04
B     23  36L  2016-09-19 05:24:33
       p1   p2                        useTime
Key                                          
A     NaN  36R  2016-09-19T23:59:04.000000000
B    23.0  36L  2016-09-19T05:24:33.000000000
</code></pre></div>
<h4 dir="auto">Expected Output</h4>
<p dir="auto">pred1 and pred2 should have the same values in column p1.<br>
pred1 is correct whereas pred2 is changing type to float64.</p>
<h4 dir="auto">Output of <code class="notranslate">pd.show_versions()</code></h4>
<details>
## INSTALLED VERSIONS
<p dir="auto">commit: None<br>
python: 3.5.2.final.0<br>
python-bits: 64<br>
OS: Darwin<br>
OS-release: 14.5.0<br>
machine: x86_64<br>
processor: i386<br>
byteorder: little<br>
LC_ALL: en_US.UTF-8<br>
LANG: en_US.UTF-8<br>
LOCALE: en_US.UTF-8</p>
<p dir="auto">pandas: 0.19.0<br>
nose: None<br>
pip: 8.1.2<br>
setuptools: 27.2.0<br>
Cython: None<br>
numpy: 1.11.2<br>
scipy: 0.18.1<br>
statsmodels: None<br>
xarray: None<br>
IPython: 5.1.0<br>
sphinx: None<br>
patsy: None<br>
dateutil: 2.5.3<br>
pytz: 2016.7<br>
blosc: None<br>
bottleneck: None<br>
tables: 3.2.3.1<br>
numexpr: 2.6.1<br>
matplotlib: 1.5.3<br>
openpyxl: None<br>
xlrd: None<br>
xlwt: None<br>
xlsxwriter: None<br>
lxml: None<br>
bs4: None<br>
html5lib: None<br>
httplib2: None<br>
apiclient: None<br>
sqlalchemy: 1.1.0<br>
pymysql: None<br>
psycopg2: None<br>
jinja2: 2.8<br>
boto: None<br>
pandas_datareader: None</p>
</details>
<p dir="auto">I admit I haven't looked at the code so there may be reasons for this, but I've found myself in the need of squeezing out duplicates from a Series but keeping the results as a Series.</p>
<p dir="auto">Series.unique() however returns an array, so in my code I have to construct a Series twice:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="
series = pandas.Series([1,2,3,3])
series = pandas.Series(series.unique())"><pre class="notranslate"><span class="pl-s1">series</span> <span class="pl-c1">=</span> <span class="pl-s1">pandas</span>.<span class="pl-v">Series</span>([<span class="pl-c1">1</span>,<span class="pl-c1">2</span>,<span class="pl-c1">3</span>,<span class="pl-c1">3</span>])
<span class="pl-s1">series</span> <span class="pl-c1">=</span> <span class="pl-s1">pandas</span>.<span class="pl-v">Series</span>(<span class="pl-s1">series</span>.<span class="pl-en">unique</span>())</pre></div>
<p dir="auto">Is this by design? If so, feel free to close this bug.</p>