Merge branch 'master' into nui-regression

Author: alexhlim

LICENSES/PACKAGING_LICENSE

@@ -0,0 +1,202 @@
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asv_bench/benchmarks/algos/isin.py

@@ -9,6 +9,8 @@
     date_range,
 )
 
+from ..pandas_vb_common import tm
+
 
 class IsIn:
 
@@ -22,6 +24,9 @@ class IsIn:
         "datetime64[ns]",
         "category[object]",
         "category[int]",
+        "str",
+        "string",
+        "arrow_string",
     ]
     param_names = ["dtype"]
 
@@ -57,6 +62,15 @@ def setup(self, dtype):
             self.values = np.random.choice(arr, sample_size)
             self.series = Series(arr).astype("category")
 
+        elif dtype in ["str", "string", "arrow_string"]:
+            from pandas.core.arrays.string_arrow import ArrowStringDtype  # noqa: F401
+
+            try:
+                self.series = Series(tm.makeStringIndex(N), dtype=dtype)
+            except ImportError:
+                raise NotImplementedError
+            self.values = list(self.series[:2])
+
         else:
             self.series = Series(np.random.randint(1, 10, N)).astype(dtype)
             self.values = [1, 2]

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/io/style.py

@@ -1,6 +1,9 @@
 import numpy as np
 
-from pandas import DataFrame
+from pandas import (
+    DataFrame,
+    IndexSlice,
+)
 
 
 class Render:
@@ -31,6 +34,14 @@ def peakmem_classes_render(self, cols, rows):
         self._style_classes()
         self.st._render_html()
 
+    def time_format_render(self, cols, rows):
+        self._style_format()
+        self.st.render()
+
+    def peakmem_format_render(self, cols, rows):
+        self._style_format()
+        self.st.render()
+
     def _style_apply(self):
         def _apply_func(s):
             return [
@@ -43,3 +54,12 @@ def _style_classes(self):
         classes = self.df.applymap(lambda v: ("cls-1" if v > 0 else ""))
         classes.index, classes.columns = self.df.index, self.df.columns
         self.st = self.df.style.set_td_classes(classes)
+
+    def _style_format(self):
+        ic = int(len(self.df.columns) / 4 * 3)
+        ir = int(len(self.df.index) / 4 * 3)
+        # apply a formatting function
+        # subset is flexible but hinders vectorised solutions
+        self.st = self.df.style.format(
+            "{:,.3f}", subset=IndexSlice["row_1":f"row_{ir}", "float_1":f"float_{ic}"]
+        )

ci/code_checks.sh

@@ -15,7 +15,7 @@
 #   $ ./ci/code_checks.sh code          # checks on imported code
 #   $ ./ci/code_checks.sh doctests      # run doctests
 #   $ ./ci/code_checks.sh docstrings    # validate docstring errors
-#   $ ./ci/code_checks.sh typing	# run static type analysis
+#   $ ./ci/code_checks.sh typing        # run static type analysis
 
 [[ -z "$1" || "$1" == "lint" || "$1" == "patterns" || "$1" == "code" || "$1" == "doctests" || "$1" == "docstrings" || "$1" == "typing" ]] || \
     { echo "Unknown command $1. Usage: $0 [lint|patterns|code|doctests|docstrings|typing]"; exit 9999; }
@@ -123,6 +123,11 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
 
     MSG='Doctests for directories' ; echo $MSG
     pytest -q --doctest-modules \
+      pandas/_libs/ \
+      pandas/api/ \
+      pandas/arrays/ \
+      pandas/compat/ \
+      pandas/core/array_algos/ \
       pandas/core/arrays/ \
       pandas/core/computation/ \
       pandas/core/dtypes/ \
@@ -133,6 +138,12 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
       pandas/core/strings/ \
       pandas/core/tools/ \
       pandas/core/window/ \
+      pandas/errors/ \
+      pandas/io/clipboard/ \
+      pandas/io/json/ \
+      pandas/io/excel/ \
+      pandas/io/parsers/ \
+      pandas/io/sas/ \
       pandas/tseries/
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 

ci/deps/actions-37-db.yaml

@@ -15,7 +15,7 @@ dependencies:
   - beautifulsoup4
   - botocore>=1.11
   - dask
-  - fastparquet>=0.4.0
+  - fastparquet>=0.4.0, <=0.5.0
   - fsspec>=0.7.4
   - gcsfs>=0.6.0
   - geopandas

ci/deps/azure-windows-38.yaml

@@ -15,7 +15,7 @@ dependencies:
   # pandas dependencies
   - blosc
   - bottleneck
-  - fastparquet>=0.4.0
+  - fastparquet>=0.4.0, <=0.5.0
   - flask
   - fsspec>=0.8.0
   - matplotlib=3.1.3

doc/source/user_guide/categorical.rst

@@ -954,6 +954,7 @@ categorical (categories and ordering). So if you read back the CSV file you have
 relevant columns back to ``category`` and assign the right categories and categories ordering.
 
 .. ipython:: python
+    :okwarning:
 
     import io
 

doc/source/user_guide/options.rst

@@ -456,6 +456,10 @@ io.hdf.dropna_table                     True         drop ALL nan rows when appe
 io.parquet.engine                       None         The engine to use as a default for
                                                      parquet reading and writing. If None
                                                      then try 'pyarrow' and 'fastparquet'
+io.sql.engine                           None         The engine to use as a default for
+                                                     sql reading and writing, with SQLAlchemy
+                                                     as a higher level interface. If None
+                                                     then try 'sqlalchemy'
 mode.chained_assignment                 warn         Controls ``SettingWithCopyWarning``:
                                                      'raise', 'warn', or None. Raise an
                                                      exception, warn, or no action if