CI: Drop Python 3.5 support

ci/azure/posix.yml

@@ -9,17 +9,12 @@ jobs:
   strategy:
     matrix:
       ${{ if eq(parameters.name, 'macOS') }}:
-        py35_macos:
-          ENV_FILE: ci/deps/azure-macos-35.yaml
-          CONDA_PY: "35"
+        py36_macos:
+          ENV_FILE: ci/deps/azure-macos-36.yaml
+          CONDA_PY: "36"
           PATTERN: "not slow and not network"
 
       ${{ if eq(parameters.name, 'Linux') }}:
-        py35_compat:
-          ENV_FILE: ci/deps/azure-35-compat.yaml
-          CONDA_PY: "35"
-          PATTERN: "not slow and not network"
-
         py36_locale_slow_old_np:
           ENV_FILE: ci/deps/azure-36-locale.yaml
           CONDA_PY: "36"

ci/deps/azure-macos-35.yaml → ci/deps/azure-macos-36.yaml

@@ -14,7 +14,7 @@ dependencies:
   - openpyxl
   - pyarrow
   - pytables
-  - python=3.5.*
+  - python=3.6.*
   - python-dateutil==2.6.1
   - pytz
   - xarray

doc/source/development/contributing.rst

@@ -236,7 +236,7 @@ Creating a Python environment (pip)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 If you aren't using conda for your development environment, follow these instructions.
-You'll need to have at least python3.5 installed on your system.
+You'll need to have at least Python 3.6 installed on your system.
 
 .. code-block:: none
 
@@ -818,29 +818,6 @@ The limitation here is that while a human can reasonably understand that ``is_nu
 
 With custom types and inference this is not always possible so exceptions are made, but every effort should be exhausted to avoid ``cast`` before going down such paths.
 
-Syntax Requirements
-~~~~~~~~~~~~~~~~~~~
-
-Because *pandas* still supports Python 3.5, :pep:`526` does not apply and variables **must** be annotated with type comments. Specifically, this is a valid annotation within pandas:
-
-.. code-block:: python
-
-   primes = []  # type: List[int]
-
-Whereas this is **NOT** allowed:
-
-.. code-block:: python
-
-   primes: List[int] = []  # not supported in Python 3.5!
-
-Note that function signatures can always be annotated per :pep:`3107`:
-
-.. code-block:: python
-
-   def sum_of_primes(primes: List[int] = []) -> int:
-       ...
-
-
 Pandas-specific Types
 ~~~~~~~~~~~~~~~~~~~~~
 
@@ -1267,7 +1244,7 @@ environment by::
 
 or, to use a specific Python interpreter,::
 
-    asv run -e -E existing:python3.5
+    asv run -e -E existing:python3.6
 
 This will display stderr from the benchmarks, and use your local
 ``python`` that comes from your ``$PATH``.

doc/source/development/policies.rst

@@ -51,7 +51,7 @@ Pandas may change the behavior of experimental features at any time.
 Python Support
 ~~~~~~~~~~~~~~
 
-Pandas will only drop support for specific Python versions (e.g. 3.5.x, 3.6.x) in
+Pandas will only drop support for specific Python versions (e.g. 3.6.x, 3.7.x) in
 pandas **major** releases.
 
 .. _SemVer: https://semver.org

doc/source/getting_started/dsintro.rst

@@ -564,53 +564,6 @@ to a column created earlier in the same :meth:`~DataFrame.assign`.
 In the second expression, ``x['C']`` will refer to the newly created column,
 that's equal to ``dfa['A'] + dfa['B']``.
 
-To write code compatible with all versions of Python, split the assignment in two.
-
-.. ipython:: python
-
-   dependent = pd.DataFrame({"A": [1, 1, 1]})
-   (dependent.assign(A=lambda x: x['A'] + 1)
-             .assign(B=lambda x: x['A'] + 2))
-
-.. warning::
-
-   Dependent assignment may subtly change the behavior of your code between
-   Python 3.6 and older versions of Python.
-
-   If you wish to write code that supports versions of python before and after 3.6,
-   you'll need to take care when passing ``assign`` expressions that
-
-   * Update an existing column
-   * Refer to the newly updated column in the same ``assign``
-
-   For example, we'll update column "A" and then refer to it when creating "B".
-
-   .. code-block:: python
-
-      >>> dependent = pd.DataFrame({"A": [1, 1, 1]})
-      >>> dependent.assign(A=lambda x: x["A"] + 1, B=lambda x: x["A"] + 2)
-
-   For Python 3.5 and earlier the expression creating ``B`` refers to the
-   "old" value of ``A``, ``[1, 1, 1]``. The output is then
-
-   .. code-block:: console
-
-         A  B
-      0  2  3
-      1  2  3
-      2  2  3
-
-   For Python 3.6 and later, the expression creating ``A`` refers to the
-   "new" value of ``A``, ``[2, 2, 2]``, which results in
-
-   .. code-block:: console
-
-         A  B
-      0  2  4
-      1  2  4
-      2  2  4
-
-
 
 Indexing / selection
 ~~~~~~~~~~~~~~~~~~~~

doc/source/getting_started/install.rst

@@ -18,7 +18,7 @@ Instructions for installing from source,
 Python version support
 ----------------------
 
-Officially Python 3.5.3 and above, 3.6, 3.7, and 3.8.
+Officially Python 3.6 and above, 3.7, and 3.8.
 
 Installing pandas
 -----------------
@@ -140,7 +140,7 @@ Installing with ActivePython
 Installation instructions for
 `ActivePython <https://www.activestate.com/activepython>`__ can be found
 `here <https://www.activestate.com/activepython/downloads>`__. Versions
-2.7 and 3.5 include pandas.
+2.7, 3.5 and 3.6 include pandas.
 
 Installing using your Linux distribution's package manager.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/whatsnew/v1.0.0.rst

@@ -3,6 +3,10 @@
 What's new in 1.0.0 (??)
 ------------------------
 
+.. warning::
+
+   Starting with the 1.x series of releases, pandas only supports Python 3.6 and higher.
+
 New Deprecation Policy
 ~~~~~~~~~~~~~~~~~~~~~~
 

pandas/compat/__init__.py

@@ -12,7 +12,6 @@
 import sys
 import warnings
 
-PY35 = sys.version_info[:2] == (3, 5)
 PY36 = sys.version_info >= (3, 6)
 PY37 = sys.version_info >= (3, 7)
 PY38 = sys.version_info >= (3, 8)

pandas/io/json/_json.py

@@ -12,7 +12,7 @@
 
 from pandas.core.dtypes.common import ensure_str, is_period_dtype
 
-from pandas import DataFrame, MultiIndex, Series, compat, isna, to_datetime
+from pandas import DataFrame, MultiIndex, Series, isna, to_datetime
 from pandas._typing import JSONSerializable
 from pandas.core.reshape.concat import concat
 
@@ -1115,8 +1115,6 @@ def _parse_no_numpy(self):
                 dtype=None,
                 orient="index",
             )
-            if compat.PY35:
-                self.obj = self.obj.sort_index(axis="columns").sort_index(axis="index")
         elif orient == "table":
             self.obj = parse_table_schema(json, precise_float=self.precise_float)
         else:

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -573,8 +573,6 @@ def test_agg_with_one_lambda(self):
 
         # sort for 35 and earlier
         columns = ["height_sqr_min", "height_max", "weight_max"]
-        if compat.PY35:
-            columns = ["height_max", "height_sqr_min", "weight_max"]
         expected = pd.DataFrame(
             {
                 "height_sqr_min": [82.81, 36.00],
@@ -613,22 +611,13 @@ def test_agg_multiple_lambda(self):
                 "weight": [7.9, 7.5, 9.9, 198.0],
             }
         )
-        # sort for 35 and earlier
         columns = [
             "height_sqr_min",
             "height_max",
             "weight_max",
             "height_max_2",
             "weight_min",
         ]
-        if compat.PY35:
-            columns = [
-                "height_max",
-                "height_max_2",
-                "height_sqr_min",
-                "weight_max",
-                "weight_min",
-            ]
         expected = pd.DataFrame(
             {
                 "height_sqr_min": [82.81, 36.00],

pandas/tests/io/json/test_json_table_schema.py

@@ -5,8 +5,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import PY35
-
 from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype, PeriodDtype
 
 import pandas as pd
@@ -22,14 +20,6 @@
 )
 
 
-def assert_results_equal(result, expected):
-    """Helper function for comparing deserialized JSON with Py35 compat."""
-    if PY35:
-        assert sorted(result.items()) == sorted(expected.items())
-    else:
-        assert result == expected
-
-
 class TestBuildSchema:
     def setup_method(self, method):
         self.df = DataFrame(
@@ -245,7 +235,7 @@ def test_build_series(self):
             ]
         )
 
-        assert_results_equal(result, expected)
+        assert result == expected
 
     def test_to_json(self):
         df = self.df.copy()
@@ -335,7 +325,7 @@ def test_to_json(self):
         ]
         expected = OrderedDict([("schema", schema), ("data", data)])
 
-        assert_results_equal(result, expected)
+        assert result == expected
 
     def test_to_json_float_index(self):
         data = pd.Series(1, index=[1.0, 2.0])
@@ -365,7 +355,7 @@ def test_to_json_float_index(self):
             ]
         )
 
-        assert_results_equal(result, expected)
+        assert result == expected
 
     def test_to_json_period_index(self):
         idx = pd.period_range("2016", freq="Q-JAN", periods=2)
@@ -386,7 +376,7 @@ def test_to_json_period_index(self):
         ]
         expected = OrderedDict([("schema", schema), ("data", data)])
 
-        assert_results_equal(result, expected)
+        assert result == expected
 
     def test_to_json_categorical_index(self):
         data = pd.Series(1, pd.CategoricalIndex(["a", "b"]))
@@ -421,7 +411,7 @@ def test_to_json_categorical_index(self):
             ]
         )
 
-        assert_results_equal(result, expected)
+        assert result == expected
 
     def test_date_format_raises(self):
         with pytest.raises(ValueError):
@@ -558,7 +548,7 @@ def test_categorical(self):
             ]
         )
 
-        assert_results_equal(result, expected)
+        assert result == expected
 
     @pytest.mark.parametrize(
         "idx,nm,prop",