0.21.1 backports (round 1)

.travis.yml

@@ -102,8 +102,6 @@ before_install:
   - uname -a
   - git --version
   - git tag
-  - ci/before_install_travis.sh
-  - export DISPLAY=":99.0"
 
 install:
   - echo "install start"
@@ -114,6 +112,8 @@ install:
 
 before_script:
   - ci/install_db_travis.sh
+  - export DISPLAY=":99.0"
+  - ci/before_script_travis.sh
 
 script:
   - echo "script start"

appveyor.yml

@@ -22,7 +22,7 @@ environment:
       PYTHON_VERSION: "3.6"
       PYTHON_ARCH: "64"
       CONDA_PY: "36"
-      CONDA_NPY: "112"
+      CONDA_NPY: "113"
 
     - CONDA_ROOT: "C:\\Miniconda3_64"
       PYTHON_VERSION: "2.7"

asv_bench/benchmarks/plotting.py

@@ -10,22 +10,47 @@ def date_range(start=None, end=None, periods=None, freq=None):
     from pandas.tools.plotting import andrews_curves
 
 
+class Plotting(object):
+    goal_time = 0.2
+
+    def setup(self):
+        import matplotlib
+        matplotlib.use('Agg')
+        self.s = Series(np.random.randn(1000000))
+        self.df = DataFrame({'col': self.s})
+
+    def time_series_plot(self):
+        self.s.plot()
+
+    def time_frame_plot(self):
+        self.df.plot()
+
+
 class TimeseriesPlotting(object):
     goal_time = 0.2
 
     def setup(self):
         import matplotlib
         matplotlib.use('Agg')
-        self.N = 2000
-        self.M = 5
-        self.df = DataFrame(np.random.randn(self.N, self.M), index=date_range('1/1/1975', periods=self.N))
+        N = 2000
+        M = 5
+        idx = date_range('1/1/1975', periods=N)
+        self.df = DataFrame(np.random.randn(N, M), index=idx)
+
+        idx_irregular = pd.DatetimeIndex(np.concatenate((idx.values[0:10],
+                                                         idx.values[12:])))
+        self.df2 = DataFrame(np.random.randn(len(idx_irregular), M),
+                             index=idx_irregular)
 
     def time_plot_regular(self):
         self.df.plot()
 
     def time_plot_regular_compat(self):
         self.df.plot(x_compat=True)
 
+    def time_plot_irregular(self):
+        self.df2.plot()
+
 
 class Misc(object):
     goal_time = 0.6

ci/before_install_travis.sh → ci/before_script_travis.sh

@@ -4,6 +4,7 @@ echo "inside $0"
 
 if [ "${TRAVIS_OS_NAME}" == "linux" ]; then
    sh -e /etc/init.d/xvfb start
+   sleep 3
 fi
 
 # Never fail because bad things happened here.

ci/check_imports.py

@@ -9,7 +9,6 @@
     'ipython',
     'jinja2'
     'lxml',
-    'matplotlib',
     'numexpr',
     'openpyxl',
     'py',

ci/environment-dev.yaml

@@ -0,0 +1,14 @@
+name: pandas-dev
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - Cython
+  - NumPy
+  - moto
+  - pytest
+  - python-dateutil
+  - python=3
+  - pytz
+  - setuptools
+  - sphinx

ci/install_travis.sh

@@ -34,9 +34,9 @@ fi
 
 # install miniconda
 if [ "${TRAVIS_OS_NAME}" == "osx" ]; then
-    time wget http://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1
+    time wget http://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -q -O miniconda.sh || exit 1
 else
-    time wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh || exit 1
+    time wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -q -O miniconda.sh || exit 1
 fi
 time bash miniconda.sh -b -p "$MINICONDA_DIR" || exit 1
 
@@ -107,7 +107,7 @@ time conda install -n pandas pytest>=3.1.0
 time pip install pytest-xdist moto
 
 if [ "$LINT" ]; then
-   conda install flake8
+   conda install flake8=3.4.1
    pip install cpplint
 fi
 

ci/requirements-2.7_BUILD_TEST.pip

@@ -1,7 +1,6 @@
 xarray
 geopandas
 seaborn
-pandas_gbq
 pandas_datareader
 statsmodels
 scikit-learn

ci/requirements-3.5.pip

@@ -1,2 +1,2 @@
 xarray==0.9.1
-pandas-gbq
+pandas_gbq

ci/requirements-3.6.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+source activate pandas
+
+echo "[install 3.6 downstream deps]"
+
+conda install -n pandas -c conda-forge pandas-datareader xarray geopandas seaborn statsmodels scikit-learn dask

ci/requirements-3.6_WIN.run

@@ -1,6 +1,6 @@
 python-dateutil
 pytz
-numpy=1.12*
+numpy=1.13*
 bottleneck
 openpyxl
 xlsxwriter

ci/requirements_all.txt → ci/requirements-optional-conda.txt

@@ -1,28 +1,27 @@
-pytest>=3.1.0
-pytest-cov
-pytest-xdist
-flake8
-sphinx=1.5*
-nbsphinx
-ipython
-python-dateutil
-pytz
-openpyxl
-xlsxwriter
-xlrd
-xlwt
-html5lib
-patsy
 beautifulsoup4
-numpy
-cython
-scipy
+blosc
+bottleneck
+fastparquet
+feather-format
+html5lib
+ipython
+ipykernel
+jinja2
+lxml
+matplotlib
+nbsphinx
 numexpr
+openpyxl
+pyarrow
+pymysql
 pytables
-matplotlib
+pytest-cov
+pytest-xdist
+s3fs
+scipy
 seaborn
-lxml
 sqlalchemy
-bottleneck
-pymysql
-Jinja2
+xarray
+xlrd
+xlsxwriter
+xlwt

ci/requirements-optional-pip.txt

@@ -0,0 +1,27 @@
+# This file was autogenerated by scripts/convert_deps.py
+# Do not modify directlybeautifulsoup4
+blosc
+bottleneck
+fastparquet
+feather-format
+html5lib
+ipython
+jinja2
+lxml
+matplotlib
+nbsphinx
+numexpr
+openpyxl
+pyarrow
+pymysql
+tables
+pytest-cov
+pytest-xdist
+s3fs
+scipy
+seaborn
+sqlalchemy
+xarray
+xlrd
+xlsxwriter
+xlwt

ci/requirements_dev.txt

@@ -1,8 +1,10 @@
+# This file was autogenerated by scripts/convert_deps.py
+# Do not modify directly
+Cython
+NumPy
+moto
+pytest
 python-dateutil
 pytz
-numpy
-cython
-pytest>=3.1.0
-pytest-cov
-flake8
-moto
+setuptools
+sphinx

ci/script_multi.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/bin/bash -e
 
 echo "[script multi]"
 

doc/source/api.rst

@@ -1794,6 +1794,7 @@ Methods
     Timestamp.strftime
     Timestamp.strptime
     Timestamp.time
+    Timestamp.timestamp
     Timestamp.timetuple
     Timestamp.timetz
     Timestamp.to_datetime64
@@ -2173,6 +2174,17 @@ Style Export and Import
    Styler.export
    Styler.use
 
+Plotting
+~~~~~~~~
+
+.. currentmodule:: pandas
+
+.. autosummary::
+   :toctree: generated/
+
+   plotting.register_matplotlib_converters
+   plotting.deregister_matplotlib_converters
+
 .. currentmodule:: pandas
 
 General utility functions

doc/source/computation.rst

@@ -346,7 +346,9 @@ The following methods are available:
     :meth:`~Window.sum`, Sum of values
     :meth:`~Window.mean`, Mean of values
 
-The weights used in the window are specified by the ``win_type`` keyword. The list of recognized types are:
+The weights used in the window are specified by the ``win_type`` keyword.
+The list of recognized types are the `scipy.signal window functions
+  <https://docs.scipy.org/doc/scipy/reference/signal.html#window-functions>`__:
 
 - ``boxcar``
 - ``triang``