Merge remote-tracking branch 'upstream/master' into quantile_regression

Author: rhshadrach

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ repos:
     rev: 19.10b0
     hooks:
     -   id: black
-        language_version: python3.7
+        language_version: python3
 -   repo: https://gitlab.com/pycqa/flake8
     rev: 3.7.7
     hooks:

README.md

@@ -20,7 +20,7 @@
 
 ## What is it?
 
-**pandas** is a Python package providing fast, flexible, and expressive data
+**pandas** is a Python package that provides fast, flexible, and expressive data
 structures designed to make working with "relational" or "labeled" data both
 easy and intuitive. It aims to be the fundamental high-level building block for
 doing practical, **real world** data analysis in Python. Additionally, it has
@@ -154,11 +154,11 @@ For usage questions, the best place to go to is [StackOverflow](https://stackove
 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
 
 ## Discussion and Development
-Most development discussion is taking place on github in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions.
+Most development discussions take place on github in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions.
 
 ## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)
 
-All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.
+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://pandas.pydata.org/docs/dev/development/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub.
 

asv_bench/benchmarks/arithmetic.py

@@ -466,7 +466,7 @@ def setup(self, offset):
         self.rng = rng
 
     def time_apply_index(self, offset):
-        offset.apply_index(self.rng)
+        self.rng + offset
 
 
 class BinaryOpsMultiIndex:

asv_bench/benchmarks/io/json.py

@@ -53,12 +53,18 @@ def time_read_json_lines(self, index):
     def time_read_json_lines_concat(self, index):
         concat(read_json(self.fname, orient="records", lines=True, chunksize=25000))
 
+    def time_read_json_lines_nrows(self, index):
+        read_json(self.fname, orient="records", lines=True, nrows=25000)
+
     def peakmem_read_json_lines(self, index):
         read_json(self.fname, orient="records", lines=True)
 
     def peakmem_read_json_lines_concat(self, index):
         concat(read_json(self.fname, orient="records", lines=True, chunksize=25000))
 
+    def peakmem_read_json_lines_nrows(self, index):
+        read_json(self.fname, orient="records", lines=True, nrows=15000)
+
 
 class ToJSON(BaseIO):
 

asv_bench/benchmarks/pandas_vb_common.py

@@ -33,7 +33,7 @@
     np.uint8,
 ]
 datetime_dtypes = [np.datetime64, np.timedelta64]
-string_dtypes = [np.object]
+string_dtypes = [object]
 try:
     extension_dtypes = [
         pd.Int8Dtype,

asv_bench/benchmarks/series_methods.py

@@ -58,17 +58,15 @@ def time_isin_nan_values(self):
 
 class IsInForObjects:
     def setup(self):
-        self.s_nans = Series(np.full(10 ** 4, np.nan)).astype(np.object)
-        self.vals_nans = np.full(10 ** 4, np.nan).astype(np.object)
-        self.s_short = Series(np.arange(2)).astype(np.object)
-        self.s_long = Series(np.arange(10 ** 5)).astype(np.object)
-        self.vals_short = np.arange(2).astype(np.object)
-        self.vals_long = np.arange(10 ** 5).astype(np.object)
+        self.s_nans = Series(np.full(10 ** 4, np.nan)).astype(object)
+        self.vals_nans = np.full(10 ** 4, np.nan).astype(object)
+        self.s_short = Series(np.arange(2)).astype(object)
+        self.s_long = Series(np.arange(10 ** 5)).astype(object)
+        self.vals_short = np.arange(2).astype(object)
+        self.vals_long = np.arange(10 ** 5).astype(object)
         # because of nans floats are special:
-        self.s_long_floats = Series(np.arange(10 ** 5, dtype=np.float)).astype(
-            np.object
-        )
-        self.vals_long_floats = np.arange(10 ** 5, dtype=np.float).astype(np.object)
+        self.s_long_floats = Series(np.arange(10 ** 5, dtype=np.float)).astype(object)
+        self.vals_long_floats = np.arange(10 ** 5, dtype=np.float).astype(object)
 
     def time_isin_nans(self):
         # if nan-objects are different objects,

asv_bench/benchmarks/sparse.py

@@ -32,7 +32,7 @@ def time_series_to_frame(self):
 
 class SparseArrayConstructor:
 
-    params = ([0.1, 0.01], [0, np.nan], [np.int64, np.float64, np.object])
+    params = ([0.1, 0.01], [0, np.nan], [np.int64, np.float64, object])
     param_names = ["dense_proportion", "fill_value", "dtype"]
 
     def setup(self, dense_proportion, fill_value, dtype):

ci/azure/windows.yml

@@ -13,7 +13,7 @@ jobs:
         CONDA_PY: "36"
         PATTERN: "not slow and not network"
 
-      py37_np141:
+      py37_np18:
         ENV_FILE: ci/deps/azure-windows-37.yaml
         CONDA_PY: "37"
         PATTERN: "not slow and not network"

ci/deps/azure-windows-37.yaml

@@ -22,7 +22,7 @@ dependencies:
   - matplotlib=2.2.*
   - moto
   - numexpr
-  - numpy=1.14.*
+  - numpy=1.18.*
   - openpyxl
   - pyarrow=0.14
   - pytables

ci/deps/travis-36-locale.yaml

@@ -27,7 +27,7 @@ dependencies:
   - numexpr
   - numpy
   - openpyxl
-  - pandas-gbq=0.8.0
+  - pandas-gbq=0.12.0
   - psycopg2=2.6.2
   - pymysql=0.7.11
   - pytables