Dark background

.github/workflows/ci.yml

@@ -65,7 +65,7 @@ jobs:
       if: always()
 
     - name: Testing docstring validation script
-      run: pytest --capture=no --strict-markers scripts
+      run: pytest scripts
       if: always()
 
     - name: Running benchmarks

.github/workflows/database.yml

@@ -78,7 +78,7 @@ jobs:
       uses: ./.github/actions/build_pandas
 
     - name: Test
-      run: pytest -m "${{ env.PATTERN }}" -n 2 --dist=loadfile -s --strict-markers --durations=30 --junitxml=test-data.xml -s --cov=pandas --cov-report=xml pandas/tests/io
+      run: pytest -m "${{ env.PATTERN }}" -n 2 --dist=loadfile --cov=pandas --cov-report=xml pandas/tests/io
       if: always()
 
     - name: Build Version

.github/workflows/python-dev.yml

@@ -0,0 +1,70 @@
+name: Python Dev
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    name: actions-310-dev
+    timeout-minutes: 60
+
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        fetch-depth: 0
+
+    - name: Set up Python Dev Version
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.10-dev'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip setuptools wheel
+        pip install git+https://github.com/numpy/numpy.git
+        pip install git+https://github.com/pytest-dev/pytest.git
+        pip install git+https://github.com/nedbat/coveragepy.git
+        pip install cython python-dateutil pytz hypothesis pytest-xdist
+        pip list
+
+    - name: Build Pandas
+      run: |
+        python setup.py build_ext -q -j2
+        python -m pip install -e . --no-build-isolation --no-use-pep517
+
+    - name: Build Version
+      run: |
+        python -c "import pandas; pandas.show_versions();"
+
+    - name: Test with pytest
+      run: |
+        coverage run -m pytest -m 'not slow and not network and not clipboard' pandas
+      continue-on-error: true
+
+    - name: Publish test results
+      uses: actions/upload-artifact@master
+      with:
+        name: Test results
+        path: test-data.xml
+      if: failure()
+
+    - name: Print skipped tests
+      run: |
+        python ci/print_skipped.py
+
+    - name: Report Coverage
+      run: |
+        coverage report -m
+
+    - name: Upload coverage to Codecov
+      uses: codecov/codecov-action@v1
+      with:
+        flags: unittests
+        name: codecov-pandas
+        fail_ci_if_error: true

.gitignore

@@ -104,6 +104,7 @@ asv_bench/env/
 asv_bench/html/
 asv_bench/results/
 asv_bench/pandas/
+test-data.xml
 
 # Documentation generated files #
 #################################

.pre-commit-config.yaml

@@ -35,7 +35,7 @@ repos:
         exclude: ^pandas/_libs/src/(klib|headers)/
         args: [--quiet, '--extensions=c,h', '--headers=h', --recursive, '--filter=-readability/casting,-runtime/int,-build/include_subdir']
 -   repo: https://gitlab.com/pycqa/flake8
-    rev: 3.9.1
+    rev: 3.9.0
     hooks:
     -   id: flake8
         additional_dependencies:
@@ -75,7 +75,7 @@ repos:
     hooks:
     -   id: yesqa
         additional_dependencies:
-            - flake8==3.9.1
+            - flake8==3.9.0
             - flake8-comprehensions==3.1.0
             - flake8-bugbear==21.3.2
             - pandas-dev-flaker==0.2.0

LICENSE

@@ -3,7 +3,7 @@ BSD 3-Clause License
 Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
 All rights reserved.
 
-Copyright (c) 2011-2020, Open source contributors.
+Copyright (c) 2011-2021, Open source contributors.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:

asv_bench/benchmarks/frame_methods.py

@@ -563,6 +563,14 @@ def time_frame_nunique(self):
         self.df.nunique()
 
 
+class SeriesNuniqueWithNan:
+    def setup(self):
+        self.ser = Series(100000 * (100 * [np.nan] + list(range(100)))).astype(float)
+
+    def time_series_nunique_nan(self):
+        self.ser.nunique()
+
+
 class Duplicated:
     def setup(self):
         n = 1 << 20

asv_bench/benchmarks/groupby.py

@@ -505,6 +505,34 @@ def time_frame_agg(self, dtype, method):
         self.df.groupby("key").agg(method)
 
 
+class CumminMax:
+    param_names = ["dtype", "method"]
+    params = [
+        ["float64", "int64", "Float64", "Int64"],
+        ["cummin", "cummax"],
+    ]
+
+    def setup(self, dtype, method):
+        N = 500_000
+        vals = np.random.randint(-10, 10, (N, 5))
+        null_vals = vals.astype(float, copy=True)
+        null_vals[::2, :] = np.nan
+        null_vals[::3, :] = np.nan
+        df = DataFrame(vals, columns=list("abcde"), dtype=dtype)
+        null_df = DataFrame(null_vals, columns=list("abcde"), dtype=dtype)
+        keys = np.random.randint(0, 100, size=N)
+        df["key"] = keys
+        null_df["key"] = keys
+        self.df = df
+        self.null_df = null_df
+
+    def time_frame_transform(self, dtype, method):
+        self.df.groupby("key").transform(method)
+
+    def time_frame_transform_many_nulls(self, dtype, method):
+        self.null_df.groupby("key").transform(method)
+
+
 class RankWithTies:
     # GH 21237
     param_names = ["dtype", "tie_method"]

asv_bench/benchmarks/io/style.py

@@ -17,19 +17,19 @@ def setup(self, cols, rows):
 
     def time_apply_render(self, cols, rows):
         self._style_apply()
-        self.st.render()
+        self.st._render_html()
 
     def peakmem_apply_render(self, cols, rows):
         self._style_apply()
-        self.st.render()
+        self.st._render_html()
 
     def time_classes_render(self, cols, rows):
         self._style_classes()
-        self.st.render()
+        self.st._render_html()
 
     def peakmem_classes_render(self, cols, rows):
         self._style_classes()
-        self.st.render()
+        self.st._render_html()
 
     def _style_apply(self):
         def _apply_func(s):

asv_bench/benchmarks/strings.py

@@ -50,91 +50,126 @@ def peakmem_cat_frame_construction(self, dtype):
 
 
 class Methods:
-    def setup(self):
-        self.s = Series(tm.makeStringIndex(10 ** 5))
+    params = ["str", "string", "arrow_string"]
+    param_names = ["dtype"]
+
+    def setup(self, dtype):
+        from pandas.core.arrays.string_arrow import ArrowStringDtype  # noqa: F401
 
-    def time_center(self):
+        try:
+            self.s = Series(tm.makeStringIndex(10 ** 5), dtype=dtype)
+        except ImportError:
+            raise NotImplementedError
+
+    def time_center(self, dtype):
         self.s.str.center(100)
 
-    def time_count(self):
+    def time_count(self, dtype):
         self.s.str.count("A")
 
-    def time_endswith(self):
+    def time_endswith(self, dtype):
         self.s.str.endswith("A")
 
-    def time_extract(self):
+    def time_extract(self, dtype):
         with warnings.catch_warnings(record=True):
             self.s.str.extract("(\\w*)A(\\w*)")
 
-    def time_findall(self):
+    def time_findall(self, dtype):
         self.s.str.findall("[A-Z]+")
 
-    def time_find(self):
+    def time_find(self, dtype):
         self.s.str.find("[A-Z]+")
 
-    def time_rfind(self):
+    def time_rfind(self, dtype):
         self.s.str.rfind("[A-Z]+")
 
-    def time_get(self):
+    def time_get(self, dtype):
         self.s.str.get(0)
 
-    def time_len(self):
+    def time_len(self, dtype):
         self.s.str.len()
 
-    def time_join(self):
+    def time_join(self, dtype):
         self.s.str.join(" ")
 
-    def time_match(self):
+    def time_match(self, dtype):
         self.s.str.match("A")
 
-    def time_normalize(self):
+    def time_normalize(self, dtype):
         self.s.str.normalize("NFC")
 
-    def time_pad(self):
+    def time_pad(self, dtype):
         self.s.str.pad(100, side="both")
 
-    def time_partition(self):
+    def time_partition(self, dtype):
         self.s.str.partition("A")
 
-    def time_rpartition(self):
+    def time_rpartition(self, dtype):
         self.s.str.rpartition("A")
 
-    def time_replace(self):
+    def time_replace(self, dtype):
         self.s.str.replace("A", "\x01\x01")
 
-    def time_translate(self):
+    def time_translate(self, dtype):
         self.s.str.translate({"A": "\x01\x01"})
 
-    def time_slice(self):
+    def time_slice(self, dtype):
         self.s.str.slice(5, 15, 2)
 
-    def time_startswith(self):
+    def time_startswith(self, dtype):
         self.s.str.startswith("A")
 
-    def time_strip(self):
+    def time_strip(self, dtype):
         self.s.str.strip("A")
 
-    def time_rstrip(self):
+    def time_rstrip(self, dtype):
         self.s.str.rstrip("A")
 
-    def time_lstrip(self):
+    def time_lstrip(self, dtype):
         self.s.str.lstrip("A")
 
-    def time_title(self):
+    def time_title(self, dtype):
         self.s.str.title()
 
-    def time_upper(self):
+    def time_upper(self, dtype):
         self.s.str.upper()
 
-    def time_lower(self):
+    def time_lower(self, dtype):
         self.s.str.lower()
 
-    def time_wrap(self):
+    def time_wrap(self, dtype):
         self.s.str.wrap(10)
 
-    def time_zfill(self):
+    def time_zfill(self, dtype):
         self.s.str.zfill(10)
 
+    def time_isalnum(self, dtype):
+        self.s.str.isalnum()
+
+    def time_isalpha(self, dtype):
+        self.s.str.isalpha()
+
+    def time_isdecimal(self, dtype):
+        self.s.str.isdecimal()
+
+    def time_isdigit(self, dtype):
+        self.s.str.isdigit()
+
+    def time_islower(self, dtype):
+        self.s.str.islower()
+
+    def time_isnumeric(self, dtype):
+        self.s.str.isnumeric()
+
+    def time_isspace(self, dtype):
+        self.s.str.isspace()
+
+    def time_istitle(self, dtype):
+        self.s.str.istitle()
+
+    def time_isupper(self, dtype):
+        self.s.str.isupper()
+
 
 class Repeat:
 
@@ -178,13 +213,18 @@ def time_cat(self, other_cols, sep, na_rep, na_frac):
 
 class Contains:
 
-    params = [True, False]
-    param_names = ["regex"]
+    params = (["str", "string", "arrow_string"], [True, False])
+    param_names = ["dtype", "regex"]
+
+    def setup(self, dtype, regex):
+        from pandas.core.arrays.string_arrow import ArrowStringDtype  # noqa: F401
 
-    def setup(self, regex):
-        self.s = Series(tm.makeStringIndex(10 ** 5))
+        try:
+            self.s = Series(tm.makeStringIndex(10 ** 5), dtype=dtype)
+        except ImportError:
+            raise NotImplementedError
 
-    def time_contains(self, regex):
+    def time_contains(self, dtype, regex):
         self.s.str.contains("A", regex=regex)