Repository: pandas-dev/pandas

Issue:
pd.groupby(pd.TimeGrouper()) mishandles null values in dates
#### Code Sample, a copy-pastable example if possible
#### The code is updated following some comments
```python
import pandas as pd
import random
from random import randint

random.seed(2)
data= [['2010-01-06', randint(1,9)],
       ['2010-08-26', randint(1,9)],
       ['2010-09-06', randint(1,9)],
       ['2010-09-16', 10],
       ['2010-09-20', 10],
       ['2010-09-23', 10],
       ['2010-09-24', randint(1,9)],
       ['2010-09-20', randint(1,9)],]

for m in range(1270):
    data.append(['2010' + '-' + str(randint(10, 12)).zfill(2) + '-' + str(randint(1, 32)).zfill(2),
                randint(1, 121111)])

df = pd.DataFrame(data)
df.columns = ['date', 'n']
df['date'] = pd.to_datetime(df['date'], errors='coerce')
df_r = df[df['date'].notnull()]

g1 = df.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()
g2 = df_r.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()
# This should print 'True' but it prints 'False'
print((g1==g2).mean() == 1)
```

#### Problem description
When a columns is used in TimeGrouper to group, null values are supposed to be ignored. This is indeed correct when dataset is small. However, the above given code demonstrates that when dataset is larger, sometimes distributes null values into some legit dates. Worst of all there was one time it inserted a value in a row and shifted the entire time series downwards. When I compare two grouped series it made me think one is leading another by 1 month, causing significant waste of resources as I was developing a financial model based on large datasets.

Updated comments after further investigation:
This same piece of code behaves different on some different versions, although none of them, including the latest 0.20.3, produces correct results.


#### Expected Output
True
#### Output of ``pd.show_versions()``


<details>

INSTALLED VERSIONS
this is also updated
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 0, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>


Discussion:
@fujiaxiang : Thanks reporting this!  Unfortunately, we can't replicate your code because `df_r` is not defined in your code.  Could you please fix that?
`# Your code here
import pandas as pd
import random

data = []
for m in range(12 * 10000):
    M = m % 12 + 1
    data += [['2017-' + str(M) + '-' + str(m % 28 + 1) if m%28!=0 else 'xx', random.randint(1, 121111)] ]
    
    
df = pd.DataFrame(data)
df.columns = ['date', 'n']
df['date'] = pd.to_datetime(df['date'], errors='coerce')

g1 = df.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()
g2 = df.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()

print((g1==g2).mean() == 1)`

Output:
True
I am getting True result by changing your code 'g2' to below:
g2 = df.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()

this is not reproducible. pls set a random seed that is fixed, or construct the frame in a non-random manner.
you also are showing an older version of pandas (there was an older bug related to this that was fixed post 0.19.2).
Hi,
Very sorry for the late reply, and for my error in the previous code. It took me quite a while to reproduce the issue too, as I can't use the original dataset to write this comment. 

The code should be:
```python
import pandas as pd
import random
from random import randint

random.seed(2)
data= [['2010-01-06', randint(1,9)],
       ['2010-08-26', randint(1,9)],
       ['2010-09-06', randint(1,9)],
       ['2010-09-16', 10],
       ['2010-09-20', 10],
       ['2010-09-23', 10],
       ['2010-09-24', randint(1,9)],
       ['2010-09-20', randint(1,9)],]

for m in range(1270):
    data.append(['2010' + '-' + str(randint(10, 12)).zfill(2) + '-' + str(randint(1, 32)).zfill(2),
                randint(1, 121111)])

df = pd.DataFrame(data)
df.columns = ['date', 'n']
df['date'] = pd.to_datetime(df['date'], errors='coerce')
df_r = df[df['date'].notnull()]

g1 = df.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()
g2 = df_r.groupby(pd.TimeGrouper(key='date', freq='M'))['n'].nunique()
# This should print 'True' but it prints 'False'
print((g1==g2).mean() == 1)
```

#### Output of ``pd.show_versions()``
Note newer versions on Cloud PC were also tested and errors persist.
The below is what I use in my local machine. 
<details>

INSTALLED VERSIONS
------------------
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 0, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>

I do note that this same piece of code behaves differently on different versions. On some versions it produces a ValueError; shape mismatch, which does not really make sense as well.

Hope the above clarifies!
Attached the screenshots of my outputs.
You can see how the numbers in g1 are shifted up.
![code](https://user-images.githubusercontent.com/5236149/30596827-dfbb6122-9d87-11e7-91b1-3499e75038d6.PNG)
![g1](https://user-images.githubusercontent.com/5236149/30596832-e08b017a-9d87-11e7-9db1-1f8fd6034a3c.PNG)
![g2](https://user-images.githubusercontent.com/5236149/30596846-e6aecbae-9d87-11e7-899c-0aeb21574ce1.PNG)

yeah looks like we have an invalid comparision somewhere

These are the 'same' operations (though slightly different impl path).
```
In [9]: df.resample('M', on='date').nunique()
[16:28:10.847 WARNING] /home/jreback/pandas-dev/pandas/core/groupby.py:3167: FutureWarning: In the future, NAT != NAT will be True rather than False.
  inc = np.r_[1, val[1:] != val[:-1]]

Out[9]: 
            date    n
date                 
2010-01-31     1    1
2010-02-28     0    0
2010-03-31     0    0
2010-04-30     0    0
2010-05-31     0    0
...          ...  ...
2010-08-31     5    4
2010-09-30    31  402
2010-10-31    30  397
2010-11-30    31  417
2010-12-31     1    1

[12 rows x 2 columns]

In [10]: df_r.resample('M', on='date').nunique()
Out[10]: 
            date    n
date                 
2010-01-31     1    1
2010-02-28     0    0
2010-03-31     0    0
2010-04-30     0    0
2010-05-31     0    0
...          ...  ...
2010-08-31     1    1
2010-09-30     5    4
2010-10-31    31  402
2010-11-30    30  397
2010-12-31    31  417

[12 rows x 2 columns]
```

if you can have a look would be appreciated.

Resolving patch:
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -3177,7 +3177,13 @@ def nunique(self, dropna=True):
 
         out = np.add.reduceat(inc, idx).astype('int64', copy=False)
         if len(ids):
-            res = out if ids[0] != -1 else out[1:]
+            # NaN/NaT group exists if the head of ids is -1,
+            # so remove it from res and exclude its index from idx
+            if ids[0] == -1:
+                res = out[1:]
+                idx = idx[np.flatnonzero(idx)]
+            else:
+                res = out
         else:
             res = out[1:]
         ri = self.grouper.result_index


Test patch:
diff --git a/pandas/tests/groupby/test_timegrouper.py b/pandas/tests/groupby/test_timegrouper.py
--- a/pandas/tests/groupby/test_timegrouper.py
+++ b/pandas/tests/groupby/test_timegrouper.py
@@ -608,3 +608,16 @@ def test_first_last_max_min_on_time_data(self):
         assert_frame_equal(grouped_ref.min(), grouped_test.min())
         assert_frame_equal(grouped_ref.first(), grouped_test.first())
         assert_frame_equal(grouped_ref.last(), grouped_test.last())
+
+    def test_nunique_with_timegrouper_and_nat(self):
+        # GH 17575
+        test = pd.DataFrame({
+            'time': [Timestamp('2016-06-28 09:35:35'),
+                     pd.NaT,
+                     Timestamp('2016-06-28 16:46:28')],
+            'data': ['1', '2', '3']})
+
+        grouper = pd.TimeGrouper(key='time', freq='h')
+        result = test.groupby(grouper)['data'].nunique()
+        expected = test[test.time.notnull()].groupby(grouper)['data'].nunique()
+        tm.assert_series_equal(result, expected)