Merge remote-tracking branch 'upstream/master' into 26760-converter

Author: TomAugspurger

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.

asv_bench/benchmarks/sparse.py

@@ -1,9 +1,8 @@
-import itertools
-
 import numpy as np
 import scipy.sparse
-from pandas import (SparseSeries, SparseDataFrame, SparseArray, Series,
- date_range, MultiIndex)
+
+import pandas as pd
+from pandas import MultiIndex, Series, SparseArray, date_range
 
 
 def make_array(size, dense_proportion, fill_value, dtype):
@@ -25,10 +24,10 @@ def setup(self):
  data = np.random.randn(N)[:-i]
  idx = rng[:-i]
  data[100:] = np.nan
- self.series[i] = SparseSeries(data, index=idx)
+ self.series[i] = pd.Series(pd.SparseArray(data), index=idx)
 
  def time_series_to_frame(self):
- SparseDataFrame(self.series)
+ pd.DataFrame(self.series)
 
 
 class SparseArrayConstructor:
@@ -51,16 +50,9 @@ def setup(self):
  N = 1000
  self.arr = np.arange(N)
  self.sparse = scipy.sparse.rand(N, N, 0.005)
- self.dict = dict(zip(range(N), itertools.repeat([0])))
-
- def time_constructor(self):
- SparseDataFrame(columns=self.arr, index=self.arr)
 
  def time_from_scipy(self):
- SparseDataFrame(self.sparse)
-
- def time_from_dict(self):
- SparseDataFrame(self.dict)
+ pd.DataFrame.sparse.from_spmatrix(self.sparse)
 
 
 class FromCoo:
@@ -71,7 +63,7 @@ def setup(self):
  shape=(100, 100))
 
  def time_sparse_series_from_coo(self):
- SparseSeries.from_coo(self.matrix)
+ pd.Series.sparse.from_coo(self.matrix)
 
 
 class ToCoo:
@@ -82,12 +74,12 @@ def setup(self):
  s[100] = -1.0
  s[999] = 12.1
  s.index = MultiIndex.from_product([range(10)] * 4)
- self.ss = s.to_sparse()
+ self.ss = s.astype("Sparse")
 
  def time_sparse_series_to_coo(self):
- self.ss.to_coo(row_levels=[0, 1],
- column_levels=[2, 3],
- sort_labels=True)
+ self.ss.sparse.to_coo(row_levels=[0, 1],
+  column_levels=[2, 3],
+  sort_labels=True)
 
 
 class Arithmetic:

azure-pipelines.yml

@@ -5,6 +5,7 @@ jobs:
  parameters:
  name: macOS
  vmImage: xcode9-macos10.13
+
 - template: ci/azure/posix.yml
  parameters:
  name: Linux
@@ -134,7 +135,10 @@ jobs:
  - script: |
  export PATH=$HOME/miniconda3/bin:$PATH
  source activate pandas-dev
- doc/make.py
+ # Next we should simply have `doc/make.py --warnings-are-errors`, everything else is required because the ipython directive doesn't fail the build on errors (https://github.com/ipython/ipython/issues/11547)
+ doc/make.py --warnings-are-errors | tee sphinx.log ; SPHINX_RET=${PIPESTATUS[0]}
+ grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log ; IPY_RET=$(( $? != 1 ))
+ exit $(( $SPHINX_RET + $IPY_RET ))
  displayName: 'Build documentation'
 
  - script: |

ci/deps/azure-35-compat.yaml

@@ -3,20 +3,20 @@ channels:
  - defaults
  - conda-forge
 dependencies:
- - beautifulsoup4=4.4.1
+ - beautifulsoup4=4.6.0
  - bottleneck=1.2.1
  - jinja2=2.8
  - numexpr=2.6.2
  - numpy=1.13.3
- - openpyxl=2.4.0
+ - openpyxl=2.4.8
  - pytables=3.4.2
- - python-dateutil=2.5.0
+ - python-dateutil=2.6.1
  - python=3.5.*
- - pytz=2015.4
+ - pytz=2017.2
  - scipy=0.19.0
- - xlrd=1.0.0
- - xlsxwriter=0.7.7
- - xlwt=1.0.0
+ - xlrd=1.1.0
+ - xlsxwriter=0.9.8
+ - xlwt=1.2.0
  # universal
  - cython=0.28.2
  - hypothesis>=3.58.0

ci/deps/azure-36-locale.yaml

@@ -3,21 +3,21 @@ channels:
  - defaults
  - conda-forge
 dependencies:
- - beautifulsoup4==4.5.1
+ - beautifulsoup4==4.6.0
  - bottleneck=1.2.*
  - cython=0.28.2
  - lxml
  - matplotlib=2.2.2
  - numpy=1.14.*
- - openpyxl=2.4.0
+ - openpyxl=2.4.8
  - python-dateutil
  - python-blosc
  - python=3.6.*
- - pytz=2016.10
+ - pytz=2017.2
  - scipy
  - sqlalchemy=1.1.4
- - xlrd=1.0.0
- - xlsxwriter=0.9.4
+ - xlrd=1.1.0
+ - xlsxwriter=0.9.8
  - xlwt=1.2.0
  # universal
  - pytest>=4.0.2

ci/deps/azure-macos-35.yaml

@@ -16,13 +16,14 @@ dependencies:
  - pyarrow
  - pytables
  - python=3.5.*
+ - python-dateutil==2.6.1
  - pytz
  - xarray
  - xlrd
  - xlsxwriter
  - xlwt
  - pip:
- - python-dateutil==2.5.3
+ - pyreadstat
  # universal
  - pytest==4.5.0
  - pytest-xdist

ci/deps/azure-windows-37.yaml

@@ -30,3 +30,4 @@ dependencies:
  - pytest-mock
  - moto
  - hypothesis>=3.58.0
+ - pyreadstat

ci/deps/travis-37.yaml

@@ -19,5 +19,6 @@ dependencies:
  - hypothesis>=3.58.0
  - s3fs
  - pip
+ - pyreadstat
  - pip:
  - moto

doc/source/development/contributing.rst

@@ -221,7 +221,7 @@ You'll need to have at least python3.5 installed on your system.
  # Use an ENV_DIR of your choice. We'll use ~/virtualenvs/pandas-dev
  # Any parent directories should already exist
  python3 -m venv ~/virtualenvs/pandas-dev
- # Activate the virtulaenv
+ # Activate the virtualenv
  . ~/virtualenvs/pandas-dev/bin/activate
 
  # Install the build dependencies