Merge remote-tracking branch 'upstream/master' into libreoffice-support

Author: WillAyd

.github/FUNDING.yml

@@ -0,0 +1 @@
+custom: https://pandas.pydata.org/donate.html

.travis.yml

@@ -48,17 +48,10 @@ matrix:
       env:
         - JOB="3.6, slow" ENV_FILE="ci/deps/travis-36-slow.yaml" PATTERN="slow"
 
-    # In allow_failures
-    - dist: trusty
-      env:
-        - JOB="3.6, doc" ENV_FILE="ci/deps/travis-36-doc.yaml" DOC=true
     allow_failures:
       - dist: trusty
         env:
           - JOB="3.6, slow" ENV_FILE="ci/deps/travis-36-slow.yaml" PATTERN="slow"
-      - dist: trusty
-        env:
-          - JOB="3.6, doc" ENV_FILE="ci/deps/travis-36-doc.yaml" DOC=true
 
 before_install:
   - echo "before_install"
@@ -86,19 +79,21 @@ install:
   - ci/submit_cython_cache.sh
   - echo "install done"
 
+before_script:
+  # display server (for clipboard functionality) needs to be started here,
+  # does not work if done in install:setup_env.sh (GH-26103)
+  - export DISPLAY=":99.0"
+  - echo "sh -e /etc/init.d/xvfb start"
+  - sh -e /etc/init.d/xvfb start
+  - sleep 3
+
 script:
   - echo "script start"
   - source activate pandas-dev
-  - ci/build_docs.sh
   - ci/run_tests.sh
 
 after_script:
   - echo "after_script start"
   - source activate pandas-dev && pushd /tmp && python -c "import pandas; pandas.show_versions();" && popd
-  - if [ -e test-data-single.xml ]; then
-        ci/print_skipped.py test-data-single.xml;
-    fi
-  - if [ -e test-data-multiple.xml ]; then
-        ci/print_skipped.py test-data-multiple.xml;
-    fi
+  - ci/print_skipped.py 
   - echo "after_script done"

LICENSES/HAVEN_LICENSE

@@ -0,0 +1,2 @@
+YEAR: 2013-2016
+COPYRIGHT HOLDER: Hadley Wickham; RStudio; and Evan Miller

LICENSES/HAVEN_MIT

@@ -0,0 +1,32 @@
+Based on http://opensource.org/licenses/MIT
+
+This is a template. Complete and ship as file LICENSE the following 2
+lines (only)
+
+YEAR:
+COPYRIGHT HOLDER:
+
+and specify as
+
+License: MIT + file LICENSE
+
+Copyright (c) <YEAR>, <COPYRIGHT HOLDER>
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -224,7 +224,7 @@ Most development discussion is taking place on github in this repo. Further, the
 
 All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.
 
-A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas-docs.github.io/pandas-docs-travis/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub.
+A detailed overview on how to contribute can be found in the **[contributing guide](https://dev.pandas.io/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub.
 
 If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.
 

asv_bench/asv.conf.json

@@ -107,7 +107,7 @@
     // `asv` will cache wheels of the recent builds in each
     // environment, making them faster to install next time.  This is
     // number of builds to keep, per environment.
-    "wheel_cache_size": 8,
+    "build_cache_size": 8,
 
     // The commits after which the regression search in `asv publish`
     // should start looking for regressions. Dictionary whose keys are

asv_bench/benchmarks/ctors.py

@@ -55,7 +55,14 @@ class SeriesConstructors:
               [False, True],
               ['float', 'int']]
 
+    # Generators get exhausted on use, so run setup before every call
+    number = 1
+    repeat = (3, 250, 10)
+
     def setup(self, data_fmt, with_index, dtype):
+        if data_fmt in (gen_of_str, gen_of_tuples) and with_index:
+            raise NotImplementedError('Series constructors do not support '
+                                      'using generators with indexes')
         N = 10**4
         if dtype == 'float':
             arr = np.random.randn(N)

asv_bench/benchmarks/frame_ctor.py

@@ -72,6 +72,10 @@ class FromRecords:
     params = [None, 1000]
     param_names = ['nrows']
 
+    # Generators get exhausted on use, so run setup before every call
+    number = 1
+    repeat = (3, 250, 10)
+
     def setup(self, nrows):
         N = 100000
         self.gen = ((x, (x * 20), (x * 100)) for x in range(N))

asv_bench/benchmarks/frame_methods.py

@@ -96,6 +96,8 @@ def time_dict_rename_both_axes(self):
 
 
 class Iteration:
+    # mem_itertuples_* benchmarks are slow
+    timeout = 120
 
     def setup(self):
         N = 1000

asv_bench/benchmarks/groupby.py

@@ -1,12 +1,11 @@
 from functools import partial
 from itertools import product
 from string import ascii_letters
-import warnings
 
 import numpy as np
 
 from pandas import (
-    Categorical, DataFrame, MultiIndex, Series, TimeGrouper, Timestamp,
+    Categorical, DataFrame, MultiIndex, Series, Timestamp,
     date_range, period_range)
 import pandas.util.testing as tm
 
@@ -301,10 +300,6 @@ def setup(self):
     def time_multi_size(self):
         self.df.groupby(['key1', 'key2']).size()
 
-    def time_dt_timegrouper_size(self):
-        with warnings.catch_warnings(record=True):
-            self.df.groupby(TimeGrouper(key='dates', freq='M')).size()
-
     def time_category_size(self):
         self.draws.groupby(self.cats).size()
 

asv_bench/benchmarks/index_object.py

@@ -52,7 +52,6 @@ def time_is_dates_only(self):
 
 class Ops:
 
-    sample_time = 0.2
     params = ['float', 'int']
     param_names = ['dtype']
 
@@ -95,6 +94,12 @@ def time_min(self):
     def time_min_trivial(self):
         self.idx_inc.min()
 
+    def time_get_loc_inc(self):
+        self.idx_inc.get_loc(900000)
+
+    def time_get_loc_dec(self):
+        self.idx_dec.get_loc(100000)
+
 
 class IndexAppend:
 
@@ -191,8 +196,26 @@ def setup(self, N):
         self.intv = IntervalIndex.from_arrays(left, right)
         self.intv._engine
 
+        self.intv2 = IntervalIndex.from_arrays(left + 1, right + 1)
+        self.intv2._engine
+
+        self.left = IntervalIndex.from_breaks(np.arange(N))
+        self.right = IntervalIndex.from_breaks(np.arange(N - 3, 2 * N - 3))
+
     def time_monotonic_inc(self, N):
         self.intv.is_monotonic_increasing
 
+    def time_is_unique(self, N):
+        self.intv.is_unique
+
+    def time_intersection(self, N):
+        self.left.intersection(self.right)
+
+    def time_intersection_one_duplicate(self, N):
+        self.intv.intersection(self.right)
+
+    def time_intersection_both_duplicate(self, N):
+        self.intv.intersection(self.intv2)
+
 
 from .pandas_vb_common import setup  # noqa: F401

asv_bench/benchmarks/indexing.py

@@ -5,7 +5,7 @@
 from pandas import (Series, DataFrame, MultiIndex,
                     Int64Index, UInt64Index, Float64Index,
                     IntervalIndex, CategoricalIndex,
-                    IndexSlice, concat, date_range)
+                    IndexSlice, concat, date_range, option_context)
 
 
 class NumericSeriesIndexing:
@@ -335,4 +335,20 @@ def time_assign_with_setitem(self):
             self.df[i] = np.random.randn(self.N)
 
 
+class ChainIndexing:
+
+    params = [None, 'warn']
+    param_names = ['mode']
+
+    def setup(self, mode):
+        self.N = 1000000
+
+    def time_chained_indexing(self, mode):
+        with warnings.catch_warnings(record=True):
+            with option_context('mode.chained_assignment', mode):
+                df = DataFrame({'A': np.arange(self.N), 'B': 'foo'})
+                df2 = df[df.A > self.N // 2]
+                df2['C'] = 1.0
+
+
 from .pandas_vb_common import setup  # noqa: F401

asv_bench/benchmarks/io/parsers.py

@@ -1,7 +1,11 @@
 import numpy as np
 
-from pandas._libs.tslibs.parsing import (
-    _concat_date_cols, _does_string_look_like_datetime)
+try:
+    from pandas._libs.tslibs.parsing import (
+        _concat_date_cols, _does_string_look_like_datetime)
+except ImportError:
+    # Avoid whole benchmark suite import failure on asv (currently 0.4)
+    pass
 
 
 class DoesStringLookLikeDatetime(object):

asv_bench/benchmarks/multiindex_object.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 import pandas.util.testing as tm
-from pandas import date_range, MultiIndex
+from pandas import date_range, MultiIndex, DataFrame
 
 
 class GetLoc:
@@ -126,4 +126,18 @@ def time_datetime_level_values_sliced(self, mi):
         mi[:10].values
 
 
+class CategoricalLevel:
+
+    def setup(self):
+
+        self.df = DataFrame({
+            'a': np.arange(1_000_000, dtype=np.int32),
+            'b': np.arange(1_000_000, dtype=np.int64),
+            'c': np.arange(1_000_000, dtype=float),
+        }).astype({'a': 'category', 'b': 'category'})
+
+    def time_categorical_level(self):
+        self.df.set_index(['a', 'b'])
+
+
 from .pandas_vb_common import setup  # noqa: F401

asv_bench/benchmarks/offset.py

@@ -9,7 +9,7 @@
     pass
 
 hcal = pd.tseries.holiday.USFederalHolidayCalendar()
-# These offests currently raise a NotImplimentedError with .apply_index()
+# These offsets currently raise a NotImplimentedError with .apply_index()
 non_apply = [pd.offsets.Day(),
              pd.offsets.BYearEnd(),
              pd.offsets.BYearBegin(),

asv_bench/benchmarks/rolling.py

@@ -4,7 +4,6 @@
 
 class Methods:
 
-    sample_time = 0.2
     params = (['DataFrame', 'Series'],
               [10, 1000],
               ['int', 'float'],
@@ -23,7 +22,6 @@ def time_rolling(self, constructor, window, dtype, method):
 
 class ExpandingMethods:
 
-    sample_time = 0.2
     params = (['DataFrame', 'Series'],
               ['int', 'float'],
               ['median', 'mean', 'max', 'min', 'std', 'count', 'skew', 'kurt',
@@ -41,7 +39,6 @@ def time_expanding(self, constructor, dtype, method):
 
 class EWMMethods:
 
-    sample_time = 0.2
     params = (['DataFrame', 'Series'],
               [10, 1000],
               ['int', 'float'],
@@ -58,7 +55,6 @@ def time_ewm(self, constructor, window, dtype, method):
 
 
 class VariableWindowMethods(Methods):
-    sample_time = 0.2
     params = (['DataFrame', 'Series'],
               ['50s', '1h', '1d'],
               ['int', 'float'],
@@ -75,7 +71,6 @@ def setup(self, constructor, window, dtype, method):
 
 class Pairwise:
 
-    sample_time = 0.2
     params = ([10, 1000, None],
               ['corr', 'cov'],
               [True, False])
@@ -95,7 +90,6 @@ def time_pairwise(self, window, method, pairwise):
 
 
 class Quantile:
-    sample_time = 0.2
     params = (['DataFrame', 'Series'],
               [10, 1000],
               ['int', 'float'],