Merge branch 'master' into update-groupby

Author: proost

.pre-commit-config.yaml

@@ -15,3 +15,4 @@ repos:
     hooks:
     -   id: isort
         language: python_venv
+        exclude: ^pandas/__init__\.py$|^pandas/core/api\.py$

.travis.yml

@@ -30,6 +30,12 @@ matrix:
       - python: 3.5
 
     include:
+    - dist: bionic
+      # 18.04
+      python: 3.8-dev
+      env:
+        - JOB="3.8-dev" PATTERN="(not slow and not network)"
+
     - dist: trusty
       env:
         - JOB="3.7" ENV_FILE="ci/deps/travis-37.yaml" PATTERN="(not slow and not network)"
@@ -71,24 +77,27 @@ before_install:
   # This overrides travis and tells it to look nowhere.
   - export BOTO_CONFIG=/dev/null
 
+
 install:
   - echo "install start"
   - ci/prep_cython_cache.sh
   - ci/setup_env.sh
   - 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
+  - if [ "$JOB" != "3.8-dev" ]; then sh -e /etc/init.d/xvfb start; fi
   - sleep 3
 
 script:
   - echo "script start"
-  - source activate pandas-dev
+  - echo "$JOB"
+  - if [ "$JOB" != "3.8-dev" ]; then source activate pandas-dev; fi
   - ci/run_tests.sh
 
 after_script:

README.md

@@ -225,7 +225,7 @@ 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://dev.pandas.io/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/docs/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/benchmarks/categoricals.py

@@ -282,4 +282,18 @@ def time_sort_values(self):
         self.index.sort_values(ascending=False)
 
 
+class SearchSorted:
+    def setup(self):
+        N = 10 ** 5
+        self.ci = tm.makeCategoricalIndex(N).sort_values()
+        self.c = self.ci.values
+        self.key = self.ci.categories[1]
+
+    def time_categorical_index_contains(self):
+        self.ci.searchsorted(self.key)
+
+    def time_categorical_contains(self):
+        self.c.searchsorted(self.key)
+
+
 from .pandas_vb_common import setup  # noqa: F401 isort:skip

asv_bench/benchmarks/ctors.py

@@ -67,7 +67,7 @@ class SeriesConstructors:
     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"
+                "Series constructors do not support using generators with indexes"
             )
         N = 10 ** 4
         if dtype == "float":

asv_bench/benchmarks/eval.py

@@ -27,7 +27,7 @@ def time_add(self, engine, threads):
 
     def time_and(self, engine, threads):
         pd.eval(
-            "(self.df > 0) & (self.df2 > 0) & " "(self.df3 > 0) & (self.df4 > 0)",
+            "(self.df > 0) & (self.df2 > 0) & (self.df3 > 0) & (self.df4 > 0)",
             engine=engine,
         )
 

asv_bench/benchmarks/frame_methods.py

@@ -609,4 +609,15 @@ def time_dataframe_describe(self):
         self.df.describe()
 
 
+class SelectDtypes:
+    params = [100, 1000]
+    param_names = ["n"]
+
+    def setup(self, n):
+        self.df = DataFrame(np.random.randn(10, n))
+
+    def time_select_dtypes(self, n):
+        self.df.select_dtypes(include="int")
+
+
 from .pandas_vb_common import setup  # noqa: F401 isort:skip

asv_bench/benchmarks/io/hdf.py

@@ -88,11 +88,11 @@ def time_write_store_table_dc(self):
 
     def time_query_store_table_wide(self):
         self.store.select(
-            "table_wide", where="index > self.start_wide and " "index < self.stop_wide"
+            "table_wide", where="index > self.start_wide and index < self.stop_wide"
         )
 
     def time_query_store_table(self):
-        self.store.select("table", where="index > self.start and " "index < self.stop")
+        self.store.select("table", where="index > self.start and index < self.stop")
 
     def time_store_repr(self):
         repr(self.store)

asv_bench/benchmarks/join_merge.py

@@ -273,10 +273,10 @@ def time_merge_ordered(self):
 
 
 class MergeAsof:
-    params = [["backward", "forward", "nearest"]]
-    param_names = ["direction"]
+    params = [["backward", "forward", "nearest"], [None, 5]]
+    param_names = ["direction", "tolerance"]
 
-    def setup(self, direction):
+    def setup(self, direction, tolerance):
         one_count = 200000
         two_count = 1000000
 
@@ -303,6 +303,9 @@ def setup(self, direction):
         df1["time32"] = np.int32(df1.time)
         df2["time32"] = np.int32(df2.time)
 
+        df1["timeu64"] = np.uint64(df1.time)
+        df2["timeu64"] = np.uint64(df2.time)
+
         self.df1a = df1[["time", "value1"]]
         self.df2a = df2[["time", "value2"]]
         self.df1b = df1[["time", "key", "value1"]]
@@ -313,22 +316,52 @@ def setup(self, direction):
         self.df2d = df2[["time32", "value2"]]
         self.df1e = df1[["time", "key", "key2", "value1"]]
         self.df2e = df2[["time", "key", "key2", "value2"]]
+        self.df1f = df1[["timeu64", "value1"]]
+        self.df2f = df2[["timeu64", "value2"]]
+
+    def time_on_int(self, direction, tolerance):
+        merge_asof(
+            self.df1a, self.df2a, on="time", direction=direction, tolerance=tolerance
+        )
 
-    def time_on_int(self, direction):
-        merge_asof(self.df1a, self.df2a, on="time", direction=direction)
+    def time_on_int32(self, direction, tolerance):
+        merge_asof(
+            self.df1d, self.df2d, on="time32", direction=direction, tolerance=tolerance
+        )
 
-    def time_on_int32(self, direction):
-        merge_asof(self.df1d, self.df2d, on="time32", direction=direction)
+    def time_on_uint64(self, direction, tolerance):
+        merge_asof(
+            self.df1f, self.df2f, on="timeu64", direction=direction, tolerance=tolerance
+        )
 
-    def time_by_object(self, direction):
-        merge_asof(self.df1b, self.df2b, on="time", by="key", direction=direction)
+    def time_by_object(self, direction, tolerance):
+        merge_asof(
+            self.df1b,
+            self.df2b,
+            on="time",
+            by="key",
+            direction=direction,
+            tolerance=tolerance,
+        )
 
-    def time_by_int(self, direction):
-        merge_asof(self.df1c, self.df2c, on="time", by="key2", direction=direction)
+    def time_by_int(self, direction, tolerance):
+        merge_asof(
+            self.df1c,
+            self.df2c,
+            on="time",
+            by="key2",
+            direction=direction,
+            tolerance=tolerance,
+        )
 
-    def time_multiby(self, direction):
+    def time_multiby(self, direction, tolerance):
         merge_asof(
-            self.df1e, self.df2e, on="time", by=["key", "key2"], direction=direction
+            self.df1e,
+            self.df2e,
+            on="time",
+            by=["key", "key2"],
+            direction=direction,
+            tolerance=tolerance,
         )
 
 

asv_bench/benchmarks/rolling.py

@@ -25,6 +25,25 @@ def peakmem_rolling(self, constructor, window, dtype, method):
         getattr(self.roll, method)()
 
 
+class Apply:
+    params = (
+        ["DataFrame", "Series"],
+        [10, 1000],
+        ["int", "float"],
+        [sum, np.sum, lambda x: np.sum(x) + 5],
+        [True, False],
+    )
+    param_names = ["contructor", "window", "dtype", "function", "raw"]
+
+    def setup(self, constructor, window, dtype, function, raw):
+        N = 10 ** 5
+        arr = (100 * np.random.random(N)).astype(dtype)
+        self.roll = getattr(pd, constructor)(arr).rolling(window)
+
+    def time_rolling(self, constructor, window, dtype, function, raw):
+        self.roll.apply(function, raw=raw)
+
+
 class ExpandingMethods:
 
     params = (