Merge for update

Author: makbigc

Makefile

@@ -1,5 +1,7 @@
 .PHONY : develop build clean clean_pyc doc lint-diff black
 
+all: develop
+
 clean:
 	-python setup.py clean
 

asv_bench/benchmarks/index_cached_properties.py

@@ -0,0 +1,75 @@
+import pandas as pd
+
+
+class IndexCache:
+    number = 1
+    repeat = (3, 100, 20)
+
+    params = [
+        [
+            "DatetimeIndex",
+            "Float64Index",
+            "IntervalIndex",
+            "Int64Index",
+            "MultiIndex",
+            "PeriodIndex",
+            "RangeIndex",
+            "TimedeltaIndex",
+            "UInt64Index",
+        ]
+    ]
+    param_names = ["index_type"]
+
+    def setup(self, index_type):
+        N = 10 ** 5
+        if index_type == "MultiIndex":
+            self.idx = pd.MultiIndex.from_product(
+                [pd.date_range("1/1/2000", freq="T", periods=N // 2), ["a", "b"]]
+            )
+        elif index_type == "DatetimeIndex":
+            self.idx = pd.date_range("1/1/2000", freq="T", periods=N)
+        elif index_type == "Int64Index":
+            self.idx = pd.Index(range(N))
+        elif index_type == "PeriodIndex":
+            self.idx = pd.period_range("1/1/2000", freq="T", periods=N)
+        elif index_type == "RangeIndex":
+            self.idx = pd.RangeIndex(start=0, stop=N)
+        elif index_type == "IntervalIndex":
+            self.idx = pd.IntervalIndex.from_arrays(range(N), range(1, N + 1))
+        elif index_type == "TimedeltaIndex":
+            self.idx = pd.TimedeltaIndex(range(N))
+        elif index_type == "Float64Index":
+            self.idx = pd.Float64Index(range(N))
+        elif index_type == "UInt64Index":
+            self.idx = pd.UInt64Index(range(N))
+        else:
+            raise ValueError
+        assert len(self.idx) == N
+        self.idx._cache = {}
+
+    def time_values(self, index_type):
+        self.idx._values
+
+    def time_shape(self, index_type):
+        self.idx.shape
+
+    def time_is_monotonic(self, index_type):
+        self.idx.is_monotonic
+
+    def time_is_monotonic_decreasing(self, index_type):
+        self.idx.is_monotonic_decreasing
+
+    def time_is_monotonic_increasing(self, index_type):
+        self.idx.is_monotonic_increasing
+
+    def time_is_unique(self, index_type):
+        self.idx.is_unique
+
+    def time_engine(self, index_type):
+        self.idx._engine
+
+    def time_inferred_type(self, index_type):
+        self.idx.inferred_type
+
+    def time_is_all_dates(self, index_type):
+        self.idx.is_all_dates

asv_bench/benchmarks/io/parsers.py

@@ -10,7 +10,7 @@
     pass
 
 
-class DoesStringLookLikeDatetime(object):
+class DoesStringLookLikeDatetime:
 
     params = (["2Q2005", "0.0", "10000"],)
     param_names = ["value"]
@@ -23,7 +23,7 @@ def time_check_datetimes(self, value):
             _does_string_look_like_datetime(obj)
 
 
-class ConcatDateCols(object):
+class ConcatDateCols:
 
     params = ([1234567890, "AAAA"], [1, 2])
     param_names = ["value", "dim"]

asv_bench/benchmarks/reshape.py

@@ -240,4 +240,17 @@ def time_qcut_datetime(self, bins):
         pd.qcut(self.datetime_series, bins)
 
 
+class Explode:
+    param_names = ["n_rows", "max_list_length"]
+    params = [[100, 1000, 10000], [3, 5, 10]]
+
+    def setup(self, n_rows, max_list_length):
+
+        data = [np.arange(np.random.randint(max_list_length)) for _ in range(n_rows)]
+        self.series = pd.Series(data)
+
+    def time_explode(self, n_rows, max_list_length):
+        self.series.explode()
+
+
 from .pandas_vb_common import setup  # noqa: F401

asv_bench/benchmarks/series_methods.py

@@ -219,7 +219,7 @@ def time_series_datetimeindex_repr(self):
         getattr(self.s, "a", None)
 
 
-class All(object):
+class All:
 
     params = [[10 ** 3, 10 ** 6], ["fast", "slow"]]
     param_names = ["N", "case"]
@@ -232,7 +232,7 @@ def time_all(self, N, case):
         self.s.all()
 
 
-class Any(object):
+class Any:
 
     params = [[10 ** 3, 10 ** 6], ["fast", "slow"]]
     param_names = ["N", "case"]
@@ -245,7 +245,7 @@ def time_any(self, N, case):
         self.s.any()
 
 
-class NanOps(object):
+class NanOps:
 
     params = [
         [

asv_bench/benchmarks/timeseries.py

@@ -293,7 +293,7 @@ def time_format_YYYYMMDD(self):
         to_datetime(self.stringsD, format="%Y%m%d")
 
 
-class ToDatetimeCacheSmallCount(object):
+class ToDatetimeCacheSmallCount:
 
     params = ([True, False], [50, 500, 5000, 100000])
     param_names = ["cache", "count"]

ci/azure/posix.yml

@@ -33,6 +33,12 @@ jobs:
           PATTERN: "not slow and not network"
           LOCALE_OVERRIDE: "it_IT.UTF-8"
 
+        py36_32bit:
+          ENV_FILE: ci/deps/azure-36-32bit.yaml
+          CONDA_PY: "36"
+          PATTERN: "not slow and not network"
+          BITS32: "yes"
+
         py37_locale:
           ENV_FILE: ci/deps/azure-37-locale.yaml
           CONDA_PY: "37"

ci/code_checks.sh

@@ -156,7 +156,7 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     MSG='Check for python2 new-style classes and for empty parentheses' ; echo $MSG
-    invgrep -R --include="*.py" --include="*.pyx" -E "class\s\S*\((object)?\):" pandas scripts
+    invgrep -R --include="*.py" --include="*.pyx" -E "class\s\S*\((object)?\):" pandas asv_bench/benchmarks scripts
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     MSG='Check for backticks incorrectly rendering because of missing spaces' ; echo $MSG

ci/deps/azure-36-32bit.yaml

@@ -0,0 +1,20 @@
+name: pandas-dev
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - gcc_linux-32
+  - gcc_linux-32
+  - gxx_linux-32
+  - cython=0.28.2
+  - numpy=1.14.*
+  - python-dateutil
+  - python=3.6.*
+  - pytz=2017.2
+  # universal
+  - pytest>=4.0.2,<5.0.0
+  - pytest-xdist
+  - pytest-mock
+  - pytest-azurepipelines
+  - hypothesis>=3.58.0
+  - pip

ci/deps/azure-37-numpydev.yaml

@@ -17,4 +17,5 @@ dependencies:
     - "--pre"
     - "numpy"
     - "scipy"
-    - pytest-azurepipelines
+    # https://github.com/pandas-dev/pandas/issues/27421
+    - pytest-azurepipelines<1.0.0