Resample with freq less than 10T generates different column names with same command
#### Code Sample, a copy-pastable example if possible

Timings.csv (see details)

<details>

```
"Invoice Id","Created Date","Modified Date"
"154021","2019-03-08 17:12:33","2019-03-08 18:50:03"
"141105","2019-02-28 17:13:54","2019-02-28 17:24:22"
"152290","2019-03-08 07:26:37","2019-03-08 07:32:24"
"136697","2019-02-26 07:22:36","2019-02-26 07:25:49"
"142892","2019-03-01 17:01:19","2019-03-01 17:17:52"
"146085","2019-03-05 10:14:12","2019-03-05 10:26:14"
"140333","2019-02-28 12:18:34","2019-02-28 12:32:46"
"159873","2019-03-13 14:09:53","2019-03-13 14:16:10"
"156567","2019-03-12 09:12:12","2019-03-12 09:21:05"
"162058","2019-03-14 14:51:39","2019-03-14 14:59:02"
"142387","2019-03-01 13:45:42","2019-03-01 13:53:30"
"154219","2019-03-08 18:38:45","2019-03-08 19:56:57"
"155780","2019-03-11 15:00:46","2019-03-11 15:38:42"
"158857","2019-03-13 09:47:48","2019-03-13 09:51:34"
"148743","2019-03-06 12:02:09","2019-03-06 13:47:16"
"139379","2019-02-27 17:04:07","2019-02-27 17:24:31"
"154375","2019-03-09 16:31:23","2019-03-09 16:36:12"
"156033","2019-03-11 16:02:41","2019-03-11 16:56:30"
"156164","2019-03-11 17:18:56","2019-03-11 17:32:03"
"156127","2019-03-11 16:53:29","2019-03-11 17:24:55"
"160476","2019-03-13 18:37:49","2019-03-13 20:00:59"
"153955","2019-03-08 16:57:13","2019-03-08 18:23:00"
"138872","2019-02-27 13:43:00","2019-02-27 14:02:52"
"146161","2019-03-05 10:29:49","2019-03-05 11:03:36"
"160620","2019-03-14 06:54:45","2019-03-14 07:01:57"
"153812","2019-03-08 16:15:33","2019-03-08 16:22:59"
"148780","2019-03-06 12:02:13","2019-03-06 14:11:34"
"141285","2019-02-28 18:55:39","2019-02-28 19:29:14"
"157709","2019-03-12 14:00:30","2019-03-12 14:10:50"
"137192","2019-02-26 11:57:56","2019-02-26 12:06:47"
"147524","2019-03-05 16:24:33","2019-03-05 18:59:20"
"141218","2019-02-28 18:45:17","2019-02-28 18:51:15"
"158015","2019-03-12 15:18:17","2019-03-12 18:13:38"
"162111","2019-03-14 15:07:09","2019-03-14 15:12:58"
"154801","2019-03-11 09:56:23","2019-03-11 10:01:46"
"150236","2019-03-06 18:08:08","2019-03-06 23:01:01"
"149391","2019-03-06 14:07:03","2019-03-06 17:55:56"
"155924","2019-03-11 15:31:49","2019-03-11 15:37:38"
"148075","2019-03-06 09:16:12","2019-03-06 09:21:46"
"145301","2019-03-04 17:07:12","2019-03-04 17:52:05"
"142602","2019-03-01 14:58:20","2019-03-01 15:23:48"
"151617","2019-03-07 13:51:45","2019-03-07 16:42:47"
"141486","2019-03-01 05:54:25","2019-03-01 06:00:47"
"160002","2019-03-13 14:36:10","2019-03-13 17:16:32"
"161105","2019-03-14 10:37:23","2019-03-14 11:21:37"
"159895","2019-03-13 14:20:19","2019-03-13 16:24:36"
"145866","2019-03-05 08:40:06","2019-03-05 08:48:44"
"159199","2019-03-13 11:12:33","2019-03-13 11:18:28"
"161618","2019-03-14 12:32:05","2019-03-14 12:38:42"
"154061","2019-03-08 17:32:47","2019-03-08 18:59:09"

```

</details>


```python
# Your code here
import pandas as pd
df = pd.read_csv("Timings.csv")
df['Created Date'] = pd.to_datetime(df['Created Date'], errors="coerce")
df['Modified Date'] = pd.to_datetime(df['Modified Date'], errors="coerce")
df1 = df.groupby(pd.Grouper(key='Created Date', freq='5T'))['Invoice Id'].count()
df2 = df.groupby(pd.Grouper(key='Modified Date', freq='5T'))['Invoice Id'].count()
df1.subtract(df2, fill_value=0).rename('Backlog').astype(int).cumsum().reset_index().head()
#                index  Backlog
#0 2019-02-26 07:20:00        1
#1 2019-02-26 07:25:00        0
#2 2019-02-26 07:30:00        0
#3 2019-02-26 07:35:00        0
#4 2019-02-26 07:40:00        0

df3 = df.groupby(pd.Grouper(key='Created Date', freq='15T'))['Invoice Id'].count()
df4 = df.groupby(pd.Grouper(key='Modified Date', freq='15T'))['Invoice Id'].count()
df3.subtract(df4, fill_value=0).rename('Backlog').astype(int).cumsum().reset_index().head()

#         Created Date  Backlog
#0 2019-02-26 07:15:00        0
#1 2019-02-26 07:30:00        0
#2 2019-02-26 07:45:00        0
#3 2019-02-26 08:00:00        0
#4 2019-02-26 08:15:00        0
```
#### Problem description
Not sure why just changing the `freq` is causing different behavior in the output (i.e. column headers are different). The values being different is of course expected.

#### Expected Output
It would be expected to consistently result in one or the other.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>


## Update:
I've been able to reproduce it. Anything below `freq=10T` produces the same results (with `index` being the name of the first column) and anything above and including `freq=10T` creates a dataframe with the first column being `"Created Date"`