<p dir="auto">I have <code class="notranslate">DataFrame</code>:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import pandas as pd
import datetime as datetime

a = pd.DataFrame({'id': [1, 2, 3, 2], 
              'my_date': [datetime.datetime(2017, 1, i) for i in range(1, 4)] + [datetime.datetime(2017, 1, 1)],
              'num': [2, 3, 1, 4]
        })

print (a.dtypes)
id                  int64
my_date    datetime64[ns]
num                 int64
dtype: object"><pre class="notranslate"><code class="notranslate">import pandas as pd
import datetime as datetime

a = pd.DataFrame({'id': [1, 2, 3, 2], 
              'my_date': [datetime.datetime(2017, 1, i) for i in range(1, 4)] + [datetime.datetime(2017, 1, 1)],
              'num': [2, 3, 1, 4]
        })

print (a.dtypes)
id                  int64
my_date    datetime64[ns]
num                 int64
dtype: object
</code></pre></div>
<p dir="auto">I try aggreagate by <code class="notranslate">pd.Series.nunique</code>, but get wrong output for datetimes:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="grouped_a = a.groupby('id').agg({'my_date': pd.Series.nunique, 
                                     'num': pd.Series.nunique}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']

print (grouped_a)
   id  num_unique_num            num_unique_my_date
0   1               1 1970-01-01 00:00:00.000000001
1   2               2 1970-01-01 00:00:00.000000002
2   3               1 1970-01-01 00:00:00.000000001"><pre class="notranslate"><code class="notranslate">grouped_a = a.groupby('id').agg({'my_date': pd.Series.nunique, 
                                     'num': pd.Series.nunique}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']

print (grouped_a)
   id  num_unique_num            num_unique_my_date
0   1               1 1970-01-01 00:00:00.000000001
1   2               2 1970-01-01 00:00:00.000000002
2   3               1 1970-01-01 00:00:00.000000001
</code></pre></div>
<p dir="auto">My solution is use <code class="notranslate">nunique</code> which works nice.</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="grouped_a = a.groupby('id').agg({'my_date': 'nunique', 
                                     'num': 'nunique'}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']
print (grouped_a)
   id  num_unique_num  num_unique_my_date
0   1               1                   1
1   2               2                   2
2   3               1                   1"><pre class="notranslate"><code class="notranslate">grouped_a = a.groupby('id').agg({'my_date': 'nunique', 
                                     'num': 'nunique'}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']
print (grouped_a)
   id  num_unique_num  num_unique_my_date
0   1               1                   1
1   2               2                   2
2   3               1                   1
</code></pre></div>
<p dir="auto">But why does not work first solution? Bug?</p>
<p dir="auto"><a href="http://stackoverflow.com/questions/41597179/pandas-counting-unique-datetime-values-in-group-by-gives-weird-values" rel="nofollow">SO question</a>.</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None

pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None"><pre class="notranslate"><code class="notranslate">print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None

pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None
</code></pre></div>
<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>