fix asv for imports

more cleaning

asv_bench/benchmarks/pandas_vb_common.py

@@ -8,16 +8,22 @@
 import random
 import numpy as np
 import threading
+from importlib import import_module
+
 try:
     from pandas.compat import range
 except ImportError:
     pass
 
 np.random.seed(1234)
-try:
-    import pandas._tseries as lib
-except:
-    import pandas.libs.lib as lib
+
+# try em until it works!
+for imp in ['pandas_tseries', 'pandas.lib', 'pandas.libs.lib']:
+    try:
+        lib = import_module(imp)
+        break
+    except:
+        pass
 
 try:
     Panel = Panel

doc/source/whatsnew/v0.20.0.txt

@@ -5,7 +5,7 @@ v0.20.0 (????, 2017)
 
 This is a major release from 0.19 and includes a small number of API changes, several new features,
 enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
-users upgrade to that version.
+users upgrade to this version.
 
 Highlights include:
 
@@ -255,7 +255,7 @@ Possible incompat for HDF5 formats for pandas < 0.13.0
 been dropped in favor of ``pd.Series``. (:issue:``15098).
 
 This *may* cause HDF5 files that were created in prior versions to become unreadable if ``pd.TimeSeries``
-was used. This is most likely to be for pandas < 0.13.0. If you find yourself in that situation.
+was used. This is most likely to be for pandas < 0.13.0. If you find yourself in this situation.
 You can use a recent prior version of pandas to read in your HDF5 files,
 then write them out again after applying the procedure below.
 
@@ -460,16 +460,16 @@ If indicated, a deprecation warning will be issued if you reference that module.
 
     "pandas.json", "pandas.io.json.libjson", "X"
     "pandas.parser", "pandas.io.libparsers", "X"
+    "pandas.lib", "pandas.libs.lib", "X"
     "pandas.io.sas.saslib", "pandas.io.sas.libsas", ""
     "pandas.msgpack", "pandas.io.msgpack", ""
-    "pandas._testing", "pandas.util.libtesting", ""
-    "pandas._sparse", "pandas.sparse.libsparse", ""
-    "pandas._hash", "pandas.tools.libhash", ""
     "pandas.tslib", "pandas.libs.tslib", ""
     "pandas.index", "pandas.libs.index", ""
     "pandas.algos", "pandas.libs.algos", ""
-    "pandas.lib", "pandas.libs.lib", "X"
     "pandas.hashtable", "pandas.libs.hashtable", ""
+    "pandas._testing", "pandas.util.libtesting", ""
+    "pandas._sparse", "pandas.sparse.libsparse", ""
+    "pandas._hash", "pandas.tools.libhash", ""
     "pandas._window", "pandas.core.libwindow", ""
     "pandas._join", "pandas.libs.join", ""
     "pandas._period", "pandas.libs.period", ""
@@ -533,7 +533,7 @@ New Behavior:
 Other API Changes
 ^^^^^^^^^^^^^^^^^
 
-- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if that requisite is not fulfilled (:issue:`15213`).
+- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if this requisite is not fulfilled (:issue:`15213`).
 - ``CParserError`` has been renamed to ``ParserError`` in ``pd.read_csv`` and will be removed in the future (:issue:`12665`)
 - ``SparseArray.cumsum()`` and ``SparseSeries.cumsum()`` will now always return ``SparseArray`` and ``SparseSeries`` respectively (:issue:`12855`)
 - ``DataFrame.applymap()`` with an empty ``DataFrame`` will return a copy of the empty ``DataFrame`` instead of a ``Series`` (:issue:`8222`)
@@ -599,7 +599,7 @@ Performance Improvements
 Bug Fixes
 ~~~~~~~~~
 
-- Bug in ``Timestamp.replace`` now raises ``TypeError`` when incorrect argument names are given; previously that raised ``ValueError`` (:issue:`15240`)
+- Bug in ``Timestamp.replace`` now raises ``TypeError`` when incorrect argument names are given; previously this raised ``ValueError`` (:issue:`15240`)
 - Bug in ``Index`` power operations with reversed operands (:issue:`14973`)
 - Bug in ``TimedeltaIndex`` addition where overflow was being allowed without error (:issue:`14816`)
 - Bug in ``TimedeltaIndex`` raising a ``ValueError`` when boolean indexing with ``loc`` (:issue:`14946`)
@@ -674,7 +674,7 @@ Bug Fixes
 - Bug in ``DataFrame.resample().median()`` if duplicate column names are present (:issue:`14233`)
 
 - Bug in ``DataFrame.groupby().describe()`` when grouping on ``Index`` containing tuples (:issue:`14848`)
-- Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; that will now raise ``ValueError`` (:issue:`15110`)
+- Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; this will now raise ``ValueError`` (:issue:`15110`)
 - Bug in ``groupby().nunique()`` with a datetimelike-grouper where bins counts were incorrect (:issue:`13453`)
 
 - Bug in catching an overflow in ``Timestamp`` + ``Timedelta/Offset`` operations (:issue:`15126`)

pandas/core/common.py

@@ -8,8 +8,8 @@
 from functools import partial
 
 import numpy as np
-import pandas.libs.lib as lib
-import pandas.libs.tslib as tslib
+from pandas.libs import lib, tslib
+
 from pandas import compat
 from pandas.compat import long, zip, iteritems
 from pandas.core.config import get_option
@@ -476,7 +476,6 @@ def _where_compat(mask, arr1, arr2):
         new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
         return new_vals.view(_NS_DTYPE)
 
-    import pandas.libs.tslib as tslib
     if arr1.dtype == _NS_DTYPE:
         arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
     if arr2.dtype == _NS_DTYPE:

pandas/core/frame.py

@@ -93,8 +93,7 @@
 from pandas.formats.printing import pprint_thing
 import pandas.tools.plotting as gfx
 
-import pandas.libs.lib as lib
-import pandas.libs.algos as _algos
+from pandas.libs import lib, algos as _algos
 
 from pandas.core.config import get_option
 

pandas/core/generic.py

@@ -6,12 +6,9 @@
 import gc
 
 import numpy as np
-import pandas.libs.lib as lib
-from pandas.libs import tslib
-
 import pandas as pd
 
-
+from pandas.libs import tslib, lib
 from pandas.types.common import (_coerce_to_dtype,
                                  _ensure_int64,
                                  needs_i8_conversion,

pandas/core/groupby.py

@@ -58,9 +58,9 @@
 import pandas.core.algorithms as algos
 import pandas.core.common as com
 from pandas.core.config import option_context
-import pandas.libs.lib as lib
-from pandas.libs.lib import Timestamp
-from pandas.libs import tslib, algos as _algos
+
+from pandas.libs import lib, tslib, algos as _algos
+from pandas.libs.lib import Timestamp, count_level_2d
 
 _doc_template = """
 
@@ -4004,7 +4004,6 @@ def _apply_to_column_groupbys(self, func):
     def count(self):
         """ Compute count of group, excluding missing values """
         from functools import partial
-        from pandas.libs.lib import count_level_2d
         from pandas.types.missing import _isnull_ndarraylike as isnull
 
         data, _ = self._get_data_to_aggregate()

pandas/core/internals.py

@@ -53,18 +53,17 @@
 
 import pandas.core.missing as missing
 from pandas.sparse.array import _maybe_to_sparse, SparseArray
-import pandas.libs.lib as lib
-import pandas.libs.tslib as tslib
+from pandas.libs import lib, tslib
+from pandas.libs.tslib import Timedelta
+from pandas.libs.lib import BlockPlacement
+
 import pandas.computation.expressions as expressions
 from pandas.util.decorators import cache_readonly
 from pandas.util.validators import validate_bool_kwarg
 
-from pandas.libs.tslib import Timedelta
 from pandas import compat, _np_version_under1p9
 from pandas.compat import range, map, zip, u
 
-from pandas.libs.lib import BlockPlacement
-
 
 class Block(PandasObject):
     """

pandas/core/missing.py

@@ -5,8 +5,8 @@
 import numpy as np
 from distutils.version import LooseVersion
 
-from pandas.libs import algos
-import pandas.libs.lib as lib
+from pandas.libs import algos, lib
+
 from pandas.compat import range, string_types
 from pandas.types.common import (is_numeric_v_string_like,
                                  is_float_dtype, is_datetime64_dtype,

pandas/core/ops.py

@@ -10,11 +10,14 @@
 import numpy as np
 import pandas as pd
 import datetime
+
+from pandas.libs import lib, index as _index, tslib, algos as _algos
+from pandas.libs.tslib import iNaT
+
 from pandas import compat
 from pandas.util.decorators import Appender
 import pandas.computation.expressions as expressions
-from pandas.libs import lib, index as _index, tslib, algos as _algos
-from pandas.libs.tslib import iNaT
+
 from pandas.compat import bind_method
 import pandas.core.missing as missing
 

pandas/core/window.py

@@ -24,9 +24,10 @@
                                  needs_i8_conversion,
                                  is_timedelta64_dtype,
                                  is_list_like,
-                                 _ensure_float64)
+                                 _ensure_float64,
+                                 is_scalar)
 import pandas as pd
-from pandas.libs.lib import isscalar
+
 from pandas.core.base import (PandasObject, SelectionMixin,
                               GroupByMixin)
 import pandas.core.common as com
@@ -154,7 +155,7 @@ def _gotitem(self, key, ndim, subset=None):
         self = self._shallow_copy(subset)
         self._reset_cache()
         if subset.ndim == 2:
-            if isscalar(key) and key in subset or is_list_like(key):
+            if is_scalar(key) and key in subset or is_list_like(key):
                 self._selection = key
         return self
 

pandas/io/pytables.py

@@ -44,8 +44,7 @@
 from pandas.core.config import get_option
 from pandas.computation.pytables import Expr, maybe_expression
 
-import pandas.libs.lib as lib
-from pandas.libs import tslib, algos
+from pandas.libs import tslib, algos, lib
 
 from distutils.version import LooseVersion
 

pandas/tests/io/data/gbq_fake_job.txt

@@ -1 +1 @@
-{u'status': {u'state': u'DONE'}, u'kind': u'bigquery#job', u'statistics': {u'query': {u'cacheHit': True, u'totalBytesProcessed': u'0'}, u'endTime': u'1377668744674', u'totalBytesProcessed': u'0', u'startTime': u'1377668744466'}, u'jobReference': {u'projectId': u'57288129629', u'jobId': u'bqjob_r5f956972f0190bdf_00000140c374bf42_2'}, u'etag': u'"4PTsVxg68bQkQs1RJ1Ndewqkgg4/oO4VmgFrAku4N6FWci9s7iFIftc"', u'configuration': {u'query': {u'createDisposition': u'CREATE_IF_NEEDED', u'query': u'SELECT * FROM [publicdata:samples.shakespeare]', u'writeDisposition': u'WRITE_TRUNCATE', u'destinationTable': {u'projectId': u'57288129629', u'tableId': u'anonb5ec450da88eeeb78a27784ea482ee75a146d442', u'datasetId': u'_d0b4f5f0d50dc68a3eb0fa6cba66a9a8687d9253'}}}, u'id': u'57288129629:bqjob_r5f956972f0190bdf_00000140c374bf42_2', u'selfLink': u'https://www.googleapis.com/bigquery/v2/projects/57288129629/jobs/bqjob_r5f956972f0190bdf_00000140c374bf42_2'}
+{u'status': {u'state': u'DONE'}, u'kind': u'bigquery#job', u'statistics': {u'query': {u'cacheHit': True, u'totalBytesProcessed': u'0'}, u'endTime': u'1377668744674', u'totalBytesProcessed': u'0', u'startTime': u'1377668744466'}, u'jobReference': {u'projectId': u'57288129629', u'jobId': u'bqjob_r5f956972f0190bdf_00000140c374bf42_2'}, u'etag': u'"4PTsVxg68bQkQs1RJ1Ndewqkgg4/oO4VmgFrAku4N6FWci9s7iFIftc"', u'configuration': {u'query': {u'createDisposition': u'CREATE_IF_NEEDED', u'query': u'SELECT * FROM [publicdata:samples.shakespeare]', u'writeDisposition': u'WRITE_TRUNCATE', u'destinationTable': {u'projectId': u'57288129629', u'tableId': u'anonb5ec450da88eeeb78a27784ea482ee75a146d442', u'datasetId': u'_d0b4f5f0d50dc68a3eb0fa6cba66a9a8687d9253'}}}, u'id': u'57288129629:bqjob_r5f956972f0190bdf_00000140c374bf42_2', u'selfLink': u'https://www.googleapis.com/bigquery/v2/projects/57288129629/jobs/bqjob_r5f956972f0190bdf_00000140c374bf42_2'}

pandas/tseries/frequencies.py

@@ -17,8 +17,8 @@
 from pandas.tseries.offsets import DateOffset
 from pandas.util.decorators import cache_readonly, deprecate_kwarg
 import pandas.tseries.offsets as offsets
-import pandas.libs.lib as lib
-import pandas.libs.tslib as tslib
+
+from pandas.libs import lib, tslib
 from pandas.libs.tslib import Timedelta
 from pytz import AmbiguousTimeError