Merge branch 'master' into add-sparsearray-all

Author: Licht-T

asv_bench/benchmarks/index_object.py

@@ -199,3 +199,23 @@ def time_datetime_level_values_full(self):
 
     def time_datetime_level_values_sliced(self):
         self.mi[:10].values
+
+
+class Range(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.idx_inc = RangeIndex(start=0, stop=10**7, step=3)
+        self.idx_dec = RangeIndex(start=10**7, stop=-1, step=-3)
+
+    def time_max(self):
+        self.idx_inc.max()
+
+    def time_max_trivial(self):
+        self.idx_dec.max()
+
+    def time_min(self):
+        self.idx_dec.min()
+
+    def time_min_trivial(self):
+        self.idx_inc.min()

asv_bench/benchmarks/sparse.py

@@ -1,4 +1,4 @@
-from itertools import repeat
+import itertools
 
 from .pandas_vb_common import *
 import scipy.sparse
@@ -33,7 +33,7 @@ def time_sparse_from_scipy(self):
         SparseDataFrame(scipy.sparse.rand(1000, 1000, 0.005))
 
     def time_sparse_from_dict(self):
-        SparseDataFrame(dict(zip(range(1000), repeat([0]))))
+        SparseDataFrame(dict(zip(range(1000), itertools.repeat([0]))))
 
 
 class sparse_series_from_coo(object):

asv_bench/benchmarks/timeseries.py

@@ -56,7 +56,7 @@ def setup(self):
         self.no_freq = self.rng7[:50000].append(self.rng7[50002:])
         self.d_freq = self.rng7[:50000].append(self.rng7[50000:])
 
-        self.rng8 = date_range(start='1/1/1700', freq='B', periods=100000)
+        self.rng8 = date_range(start='1/1/1700', freq='B', periods=75000)
         self.b_freq = self.rng8[:50000].append(self.rng8[50000:])
 
     def time_add_timedelta(self):

asv_bench/benchmarks/timestamp.py

@@ -1,5 +1,7 @@
 from .pandas_vb_common import *
 from pandas import to_timedelta, Timestamp
+import pytz
+import datetime
 
 
 class TimestampProperties(object):
@@ -58,3 +60,30 @@ def time_is_leap_year(self):
 
     def time_microsecond(self):
         self.ts.microsecond
+
+
+class TimestampOps(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.ts = Timestamp('2017-08-25 08:16:14')
+        self.ts_tz = Timestamp('2017-08-25 08:16:14', tz='US/Eastern')
+
+        dt = datetime.datetime(2016, 3, 27, 1)
+        self.tzinfo = pytz.timezone('CET').localize(dt, is_dst=False).tzinfo
+        self.ts2 = Timestamp(dt)
+
+    def time_replace_tz(self):
+        self.ts.replace(tzinfo=pytz.timezone('US/Eastern'))
+
+    def time_replace_across_dst(self):
+        self.ts2.replace(tzinfo=self.tzinfo)
+
+    def time_replace_None(self):
+        self.ts_tz.replace(tzinfo=None)
+
+    def time_to_pydatetime(self):
+        self.ts.to_pydatetime()
+
+    def time_to_pydatetime_tz(self):
+        self.ts_tz.to_pydatetime()

ci/requirements-3.6_NUMPY_DEV.build

@@ -1,3 +1,2 @@
 python=3.6*
 pytz
-cython

ci/requirements-3.6_NUMPY_DEV.build.sh

@@ -14,4 +14,7 @@ pip install --pre --upgrade --timeout=60 -f $PRE_WHEELS numpy scipy
 # install dateutil from master
 pip install -U git+git://github.com/dateutil/dateutil.git
 
+# cython via pip
+pip install cython
+
 true

ci/requirements-3.6_WIN.run

@@ -8,7 +8,6 @@ xlrd
 xlwt
 scipy
 feather-format
-pyarrow
 numexpr
 pytables
 matplotlib

doc/README.rst

@@ -3,9 +3,11 @@
 Contributing to the documentation
 =================================
 
-If you're not the developer type, contributing to the documentation is still
-of huge value. You don't even have to be an expert on
-*pandas* to do so! Something as simple as rewriting small passages for clarity
+Whether you are someone who loves writing, teaching, or development,
+contributing to the documentation is a huge value. If you don't see yourself
+as a developer type, please don't stress and know that we want you to
+contribute. You don't even have to be an expert on *pandas* to do so!
+Something as simple as rewriting small passages for clarity
 as you reference the docs is a simple but effective way to contribute. The
 next person to read that passage will be in your debt!
 

doc/source/10min.rst

@@ -11,7 +11,7 @@
    np.random.seed(123456)
    np.set_printoptions(precision=4, suppress=True)
    import matplotlib
-   matplotlib.style.use('ggplot')
+   # matplotlib.style.use('default')
    pd.options.display.max_rows = 15
 
    #### portions of this were borrowed from the
@@ -95,17 +95,7 @@ will be completed:
    df2.append             df2.combine_first
    df2.apply              df2.compound
    df2.applymap           df2.consolidate
-   df2.as_blocks          df2.convert_objects
-   df2.asfreq             df2.copy
-   df2.as_matrix          df2.corr
-   df2.astype             df2.corrwith
-   df2.at                 df2.count
-   df2.at_time            df2.cov
-   df2.axes               df2.cummax
-   df2.B                  df2.cummin
-   df2.between_time       df2.cumprod
-   df2.bfill              df2.cumsum
-   df2.blocks             df2.D
+   df2.D
 
 As you can see, the columns ``A``, ``B``, ``C``, and ``D`` are automatically
 tab completed. ``E`` is there as well; the rest of the attributes have been

doc/source/advanced.rst

@@ -638,9 +638,11 @@ and allows efficient indexing and storage of an index with a large number of dup
 
 .. ipython:: python
 
+   from pandas.api.types import CategoricalDtype
+
    df = pd.DataFrame({'A': np.arange(6),
                       'B': list('aabbca')})
-   df['B'] = df['B'].astype('category', categories=list('cab'))
+   df['B'] = df['B'].astype(CategoricalDtype(list('cab')))
    df
    df.dtypes
    df.B.cat.categories