<p dir="auto">so it appears that [9] is wrong, should be consistent with [7],[8]</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="In [1]: index = date_range('20130101',freq='2D',periods=6)

In [2]: df = DataFrame(np.arange(20).reshape(5,4),columns=list('ABCD'),index=index.take([0,1,2,3,4]))

In [3]: df.resample('2D',how='max')
Out[3]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07  12  13  14  15
2013-01-09  16  17  18  19

In [4]: df.groupby(pd.Grouper(level=0,freq='2D')).max()
Out[4]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07  12  13  14  15
2013-01-09  16  17  18  19

In [5]: df.groupby([pd.Grouper(level=0,freq='2D')]).max()
Out[5]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07  12  13  14  15
2013-01-09  16  17  18  19

In [6]: 

In [6]: df = DataFrame(np.arange(20).reshape(5,4),columns=list('ABCD'),index=index.take([0,1,2,4,5]))

In [7]: df.resample('2D')
Out[7]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07 NaN NaN NaN NaN
2013-01-09  12  13  14  15
2013-01-11  16  17  18  19

In [8]: df.groupby(pd.Grouper(level=0,freq='2D')).max()
Out[8]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07 NaN NaN NaN NaN
2013-01-09  12  13  14  15
2013-01-11  16  17  18  19

In [9]: df.groupby([pd.Grouper(level=0,freq='2D')]).max()
Out[9]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-09  12  13  14  15
2013-01-11  16  17  18  19"><pre class="notranslate"><code class="notranslate">In [1]: index = date_range('20130101',freq='2D',periods=6)

In [2]: df = DataFrame(np.arange(20).reshape(5,4),columns=list('ABCD'),index=index.take([0,1,2,3,4]))

In [3]: df.resample('2D',how='max')
Out[3]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07  12  13  14  15
2013-01-09  16  17  18  19

In [4]: df.groupby(pd.Grouper(level=0,freq='2D')).max()
Out[4]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07  12  13  14  15
2013-01-09  16  17  18  19

In [5]: df.groupby([pd.Grouper(level=0,freq='2D')]).max()
Out[5]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07  12  13  14  15
2013-01-09  16  17  18  19

In [6]: 

In [6]: df = DataFrame(np.arange(20).reshape(5,4),columns=list('ABCD'),index=index.take([0,1,2,4,5]))

In [7]: df.resample('2D')
Out[7]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07 NaN NaN NaN NaN
2013-01-09  12  13  14  15
2013-01-11  16  17  18  19

In [8]: df.groupby(pd.Grouper(level=0,freq='2D')).max()
Out[8]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-07 NaN NaN NaN NaN
2013-01-09  12  13  14  15
2013-01-11  16  17  18  19

In [9]: df.groupby([pd.Grouper(level=0,freq='2D')]).max()
Out[9]: 
             A   B   C   D
2013-01-01   0   1   2   3
2013-01-03   4   5   6   7
2013-01-05   8   9  10  11
2013-01-09  12  13  14  15
2013-01-11  16  17  18  19
</code></pre></div>
<h4 dir="auto">Code Sample, a copy-pastable example if possible</h4>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="data_frame.groupby(pd.TimeGrouper('D', convention='start', key=time_column)).count()
data_frame.groupby([pd.TimeGrouper('D', convention='start', key=time_column)]).count()"><pre class="notranslate"><span class="pl-s1">data_frame</span>.<span class="pl-en">groupby</span>(<span class="pl-s1">pd</span>.<span class="pl-v">TimeGrouper</span>(<span class="pl-s">'D'</span>, <span class="pl-s1">convention</span><span class="pl-c1">=</span><span class="pl-s">'start'</span>, <span class="pl-s1">key</span><span class="pl-c1">=</span><span class="pl-s1">time_column</span>)).<span class="pl-en">count</span>()
<span class="pl-s1">data_frame</span>.<span class="pl-en">groupby</span>([<span class="pl-s1">pd</span>.<span class="pl-v">TimeGrouper</span>(<span class="pl-s">'D'</span>, <span class="pl-s1">convention</span><span class="pl-c1">=</span><span class="pl-s">'start'</span>, <span class="pl-s1">key</span><span class="pl-c1">=</span><span class="pl-s1">time_column</span>)]).<span class="pl-en">count</span>()</pre></div>
<p dir="auto">give below two different outputs</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="            location  price  profit  type
time                                     
2017-07-23         1      1       1     1
2017-07-24         0      0       0     0
2017-07-25         1      1       1     1
2017-07-26         1      1       1     1
2017-07-27         3      3       3     3
2017-07-28         6      6       6     6"><pre class="notranslate"><code class="notranslate">            location  price  profit  type
time                                     
2017-07-23         1      1       1     1
2017-07-24         0      0       0     0
2017-07-25         1      1       1     1
2017-07-26         1      1       1     1
2017-07-27         3      3       3     3
2017-07-28         6      6       6     6
</code></pre></div>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="            location  price  profit  type
time                                     
2017-07-23         1      1       1     1
2017-07-25         1      1       1     1
2017-07-26         1      1       1     1
2017-07-27         3      3       3     3
2017-07-28         6      6       6     6"><pre class="notranslate"><code class="notranslate">            location  price  profit  type
time                                     
2017-07-23         1      1       1     1
2017-07-25         1      1       1     1
2017-07-26         1      1       1     1
2017-07-27         3      3       3     3
2017-07-28         6      6       6     6
</code></pre></div>
<h4 dir="auto">Problem description</h4>
<p dir="auto">The origin data frame doesn't contain a record in 2017-07-24, you can see if I use time grouper with convention='start' lonely, the 2017-07-24 item has been filled, but if by group list, that item hasn't been filled</p>
<h4 dir="auto">Output of <code class="notranslate">pd.show_versions()</code></h4>
<details>
<h2 dir="auto">INSTALLED VERSIONS</h2>
<p dir="auto">commit: None<br>
python: 2.7.10.final.0<br>
python-bits: 64<br>
OS: Darwin<br>
OS-release: 16.7.0<br>
machine: x86_64<br>
processor: i386<br>
byteorder: little<br>
LC_ALL: None<br>
LANG: None<br>
LOCALE: None.None</p>
<p dir="auto">pandas: 0.20.3<br>
pytest: None<br>
pip: 9.0.1<br>
setuptools: 36.2.0<br>
Cython: None<br>
numpy: 1.13.1<br>
scipy: 0.19.1<br>
xarray: None<br>
IPython: None<br>
sphinx: None<br>
patsy: None<br>
dateutil: 2.6.1<br>
pytz: 2017.2<br>
blosc: None<br>
bottleneck: None<br>
tables: None<br>
numexpr: None<br>
feather: None<br>
matplotlib: None<br>
openpyxl: None<br>
xlrd: None<br>
xlwt: None<br>
xlsxwriter: None<br>
lxml: None<br>
bs4: None<br>
html5lib: 0.9999999<br>
sqlalchemy: None<br>
pymysql: None<br>
psycopg2: None<br>
jinja2: None<br>
s3fs: None<br>
pandas_gbq: None<br>
pandas_datareader: None</p>
</details>