DataFrame.merge with empty frame and Int64 column gives object dtype

[josham]

Code Sample, a copy-pastable example if possible

x = pd.DataFrame({
    'date': pd.Series([pd.Timestamp('2018-01-01')]),
    'value': pd.Series([1], dtype='Int64')
})
y = x[:0]
z = x.merge(y, on='date', how='left')

# z
        date  value_x value_y
0 2018-01-01        1     NaN

# z.dtypes

date       datetime64[ns]
value_x             Int64
value_y            object
dtype: object

Problem descriptioy

The dtype of the value_y column should be Int64.

Expected Output

date       datetime64[ns]
value_x             Int64
value_y             Int64
dtype: object

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

[gfyoung]

@josham : Given that you have a NaN in value_y, it would be impossible to have Int64 (would be a floating dtype actually). That being said, uncertain as to why you're getting object though...

cc @jreback

[jreback]

I agree that this should be able to return Int types here; this was just put in place in 0.24 so may not be well handled for empty types

[josham]

@jreback I came across another merge issue with Int64. I can add to this issue or create another, let me know what you prefer.

x = pd.DataFrame({
    'date': [pd.Timestamp('2018-01-01')],
    'value': pd.Series([np.nan], dtype='Int64')
})
y = pd.DataFrame({
    'date': [pd.Timestamp('2018-01-01')],
    'value': pd.Series([4], dtype='Int64')
})
z = x.merge(y, on=['date'])

Traceback (most recent call last):
  File "scratch.py", line 23, in <module>
    z = x.merge(y, on=['date'])
  File "/python/lib/python3.6/site-packages/pandas/core/frame.py", line 6868, in merge
    copy=copy, indicator=indicator, validate=validate)
  File "/python/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 48, in merge
    return op.get_result()
  File "/python/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 560, in get_result
    concat_axis=0, copy=self.copy)
  File "/python/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 2061, in concatenate_block_managers
    concatenate_join_units(join_units, concat_axis, copy=copy),
  File "/python/lib/python3.6/site-packages/pandas/core/internals/concat.py", line 240, in concatenate_join_units
    for ju in join_units]
  File "/python/lib/python3.6/site-packages/pandas/core/internals/concat.py", line 240, in <listcomp>
    for ju in join_units]
  File "/python/lib/python3.6/site-packages/pandas/core/internals/concat.py", line 194, in get_reindexed_values
    missing_arr = np.empty(self.shape, dtype=empty_dtype)
TypeError: data type not understood

[josham]

@jreback, @TomAugspurger the following seems to fix both issues. Does this seem correct to you?

--- a/pandas/core/internals/concat.py
+++ b/pandas/core/internals/concat.py
@@ -190,6 +190,8 @@ class JoinUnit(object):
                     pass
                 elif getattr(self.block, 'is_sparse', False):
                     pass
+                elif getattr(self.block, 'is_extension', False):
+                    pass
                 else:
                     missing_arr = np.empty(self.shape, dtype=empty_dtype)
                     missing_arr.fill(fill_value)