Remove smart quotes from all docstrings. (#12035)

This PR removes all "smart quotes" from the library by enforcing a pre-commit hook.

Smart quotes typically arise from copying rendered docstrings from Pandas, because Sphinx automatically transforms straight quotes into smart quotes when rendering the docs as HTML. However, the use of smart quotes is undesirable in code, and makes it difficult to do find-replace transformations if straight and smart quotes are mixed.

I have made suggestions to fix this several times before, so I am making the suggestions more permanent and automatically enforceable via a pre-commit style check:
- #12025 (comment)
- #9817 (comment)
- #9571 (comment)

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #12035

.pre-commit-config.yaml

@@ -52,6 +52,16 @@ repos:
               - id: clang-format
                 types_or: [c, c++, cuda]
                 args: ["-fallback-style=none", "-style=file", "-i"]
+      - repo: https://github.com/sirosen/texthooks
+        rev: 0.4.0
+        hooks:
+              - id: fix-smartquotes
+                exclude: |
+                  (?x)^(
+                    ^cpp/include/cudf_test/cxxopts.hpp|
+                    ^python/cudf/cudf/tests/data/subword_tokenizer_data/.*|
+                    ^python/cudf/cudf/tests/test_text.py
+                  )
       - repo: local
         hooks:
               - id: no-deprecationwarning

README.md

@@ -50,7 +50,7 @@ For additional examples, browse our complete [API documentation](https://docs.ra
 
 ## Quick Start
 
-Please see the [Demo Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize cuDF.
+Please see the [Demo Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you're running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize cuDF.
 
 ## Installation
 

docs/cudf/source/user_guide/10min.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "[Dask](https://dask.org/) is a flexible library for parallel computing in Python that makes scaling out your workflow smooth and simple. On the CPU, Dask uses Pandas to execute operations in parallel on DataFrame partitions.\n",
     "\n",
-    "[Dask-cuDF](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) extends Dask where necessary to allow its DataFrame partitions to be processed by cuDF GPU DataFrames as opposed to Pandas DataFrames. For instance, when you call dask_cudf.read_csv(...), your cluster’s GPUs do the work of parsing the CSV file(s) with underlying cudf.read_csv().\n",
+    "[Dask-cuDF](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) extends Dask where necessary to allow its DataFrame partitions to be processed by cuDF GPU DataFrames as opposed to Pandas DataFrames. For instance, when you call dask_cudf.read_csv(...), your cluster's GPUs do the work of parsing the CSV file(s) with underlying cudf.read_csv().\n",
     "\n",
     "\n",
     "### When to use cuDF and Dask-cuDF\n",

docs/cudf/source/user_guide/missing-data.ipynb

@@ -229,7 +229,7 @@
    "id": "acdf29d7",
    "metadata": {},
    "source": [
-    "One has to be mindful that in Python (and NumPy), the nan's don’t compare equal, but None's do. Note that cudf/NumPy uses the fact that `np.nan != np.nan`, and treats `None` like `np.nan`."
+    "One has to be mindful that in Python (and NumPy), the nan's don't compare equal, but None's do. Note that cudf/NumPy uses the fact that `np.nan != np.nan`, and treats `None` like `np.nan`."
    ]
   },
   {
@@ -279,7 +279,7 @@
    "id": "4fdb8bc7",
    "metadata": {},
    "source": [
-    "So as compared to above, a scalar equality comparison versus a None/np.nan doesn’t provide useful information."
+    "So as compared to above, a scalar equality comparison versus a None/np.nan doesn't provide useful information."
    ]
   },
   {

python/cudf/cudf/_lib/search.pyx

@@ -24,9 +24,9 @@ def search_sorted(
         List of columns to search in
     values : List of columns
         List of value columns to search for
-    side : str {‘left’, ‘right’} optional
-        If ‘left’, the index of the first suitable location is given.
-        If ‘right’, return the last such index
+    side : str {'left', 'right'} optional
+        If 'left', the index of the first suitable location is given.
+        If 'right', return the last such index
     """
     cdef unique_ptr[column] c_result
     cdef vector[libcudf_types.order] c_column_order

python/cudf/cudf/_lib/strings/convert/convert_urls.pyx

@@ -1,4 +1,4 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
 
 from libcpp.memory cimport unique_ptr
 from libcpp.utility cimport move
@@ -41,7 +41,7 @@ def url_encode(Column source_strings):
     """
     Encode each string in column. No format checking is performed.
     All characters are encoded except for ASCII letters, digits,
-    and these characters: ‘.’,’_’,’-‘,’~’. Encoding converts to
+    and these characters: '.','_','-','~'. Encoding converts to
     hex using UTF-8 encoded bytes.
 
     Parameters

python/cudf/cudf/_lib/strings/padding.pyx

@@ -59,7 +59,7 @@ def zfill(Column source_strings,
           size_type width):
     """
     Returns a Column by prepending strings in `source_strings`
-    with ‘0’ characters up to the given `width`.
+    with '0' characters up to the given `width`.
     """
     cdef unique_ptr[column] c_result
     cdef column_view source_view = source_strings.view()

python/cudf/cudf/core/column/string.py

@@ -116,8 +116,8 @@ class StringMethods(ColumnMethods):
 
     This mimics pandas ``df.str`` interface. nulls stay null
     unless handled otherwise by a particular method.
-    Patterned after Python’s string methods, with some
-    inspiration from R’s stringr package.
+    Patterned after Python's string methods, with some
+    inspiration from R's stringr package.
     """
 
     _column: StringColumn
@@ -709,7 +709,7 @@ def contains(
         >>> idx.str.contains('23', regex=False)
         GenericIndex([False, False, False, True, <NA>], dtype='bool')
 
-        Returning ‘house’ or ‘dog’ when either expression occurs in a string.
+        Returning 'house' or 'dog' when either expression occurs in a string.
 
         >>> s1.str.contains('house|dog', regex=True)
         0    False
@@ -732,7 +732,7 @@ def contains(
         Ensure ``pat`` is a not a literal pattern when ``regex`` is set
         to True. Note in the following example one might expect
         only `s2[1]` and `s2[3]` to return True. However,
-        ‘.0’ as a regex matches any character followed by a 0.
+        '.0' as a regex matches any character followed by a 0.
 
         >>> s2 = cudf.Series(['40', '40.0', '41', '41.0', '35'])
         >>> s2.str.contains('.0', regex=True)
@@ -2903,7 +2903,7 @@ def pad(
             additional characters will be filled with
             character defined in fillchar.
 
-        side : {‘left’, ‘right’, ‘both’}, default ‘left’
+        side : {'left', 'right', 'both'}, default 'left'
             Side from which to fill resulting string.
 
         fillchar : str,  default ' ' (whitespace)
@@ -2930,7 +2930,7 @@ def pad(
             Equivalent to ``Series.str.pad(side='both')``.
 
         zfill
-            Pad strings in the Series/Index by prepending ‘0’ character.
+            Pad strings in the Series/Index by prepending '0' character.
             Equivalent to ``Series.str.pad(side='left', fillchar='0')``.
 
         Examples
@@ -2970,7 +2970,7 @@ def pad(
             side = libstrings.SideType[side.upper()]
         except KeyError:
             raise ValueError(
-                "side has to be either one of {‘left’, ‘right’, ‘both’}"
+                "side has to be either one of {'left', 'right', 'both'}"
             )
 
         return self._return_or_inplace(
@@ -2979,9 +2979,9 @@ def pad(
 
     def zfill(self, width: int) -> SeriesOrIndex:
         """
-        Pad strings in the Series/Index by prepending ‘0’ characters.
+        Pad strings in the Series/Index by prepending '0' characters.
 
-        Strings in the Series/Index are padded with ‘0’ characters
+        Strings in the Series/Index are padded with '0' characters
         on the left of the string to reach a total string length
         width. Strings in the Series/Index with length greater
         or equal to width are unchanged.
@@ -2994,12 +2994,12 @@ def zfill(self, width: int) -> SeriesOrIndex:
         width : int
             Minimum length of resulting string;
             strings with length less than width
-            be prepended with ‘0’ characters.
+            be prepended with '0' characters.
 
         Returns
         -------
         Series/Index of str dtype
-            Returns Series or Index with prepended ‘0’ characters.
+            Returns Series or Index with prepended '0' characters.
 
         See Also
         --------
@@ -3405,7 +3405,7 @@ def wrap(self, width: int, **kwargs) -> SeriesOrIndex:
         `expand_tabsbool` are not yet supported and will raise a
         NotImplementedError if they are set to any value.
 
-        This method currently achieves behavior matching R’s
+        This method currently achieves behavior matching R's
         stringr library ``str_wrap`` function, the equivalent
         pandas implementation can be obtained using the
         following parameter setting:
@@ -3576,7 +3576,7 @@ def findall(self, pat: str, flags: int = 0) -> SeriesOrIndex:
         >>> import cudf
         >>> s = cudf.Series(['Lion', 'Monkey', 'Rabbit'])
 
-        The search for the pattern ‘Monkey’ returns one match:
+        The search for the pattern 'Monkey' returns one match:
 
         >>> s.str.findall('Monkey')
         0          []
@@ -3595,7 +3595,7 @@ def findall(self, pat: str, flags: int = 0) -> SeriesOrIndex:
 
         Regular expressions are supported too. For instance,
         the search for all the strings ending with
-        the word ‘on’ is shown next:
+        the word 'on' is shown next:
 
         >>> s.str.findall('on$')
         0    [on]
@@ -4228,7 +4228,7 @@ def url_encode(self) -> SeriesOrIndex:
         Returns a URL-encoded format of each string.
         No format checking is performed.
         All characters are encoded except for ASCII letters,
-        digits, and these characters: ``‘.’,’_’,’-‘,’~’``.
+        digits, and these characters: ``'.','_','-','~'``.
         Encoding converts to hex using UTF-8 encoded bytes.
 
         Returns

python/cudf/cudf/core/dataframe.py

@@ -2293,7 +2293,7 @@ def reindex(
             Return a new object, even if the passed indexes are the same.
         level : Not supported
         fill_value : Value to use for missing values.
-            Defaults to ``NA``, but can be any “compatible” value.
+            Defaults to ``NA``, but can be any "compatible" value.
         limit : Not supported
         tolerance : Not supported
 
@@ -2358,7 +2358,7 @@ def reindex(
         IE10               404       <NA>
         Konqueror          301       <NA>
 
-        Or we can use “axis-style” keyword arguments
+        Or we can use "axis-style" keyword arguments
         >>> df.reindex(columns=['http_status', 'user_agent'])
                 http_status user_agent
         Firefox            200       <NA>
@@ -3028,7 +3028,7 @@ def rename(
         """Alter column and index labels.
 
         Function / dict values must be unique (1-to-1). Labels not contained in
-        a dict / Series will be left as-is. Extra labels listed don’t throw an
+        a dict / Series will be left as-is. Extra labels listed don't throw an
         error.
 
         ``DataFrame.rename`` supports two calling conventions:
@@ -3635,8 +3635,8 @@ def merge(
             If on is None and not merging on indexes then
             this defaults to the intersection of the columns
             in both DataFrames.
-        how : {‘left’, ‘outer’, ‘inner’, 'leftsemi', 'leftanti'}, \
-            default ‘inner’
+        how : {'left', 'outer', 'inner', 'leftsemi', 'leftanti'}, \
+            default 'inner'
             Type of merge to be performed.
 
             - left : use only keys from left frame, similar to a SQL left
@@ -5363,7 +5363,7 @@ def isin(self, values):
         ----------
         values : iterable, Series, DataFrame or dict
             The result will only be true at a location if all
-            the labels match. If values is a Series, that’s the index.
+            the labels match. If values is a Series, that's the index.
             If values is a dict, the keys must be the column names,
             which must match. If values is a DataFrame, then both the
             index and column labels must match.

python/cudf/cudf/core/frame.py

@@ -1363,12 +1363,12 @@ def searchsorted(
         ----------
         value : Frame (Shape must be consistent with self)
             Values to be hypothetically inserted into Self
-        side : str {‘left’, ‘right’} optional, default ‘left‘
-            If ‘left’, the index of the first suitable location found is given
-            If ‘right’, return the last such index
+        side : str {'left', 'right'} optional, default 'left'
+            If 'left', the index of the first suitable location found is given
+            If 'right', return the last such index
         ascending : bool optional, default True
             Sorted Frame is in ascending order (otherwise descending)
-        na_position : str {‘last’, ‘first’} optional, default ‘last‘
+        na_position : str {'last', 'first'} optional, default 'last'
             Position of null values in sorted order
 
         Returns
@@ -1476,8 +1476,8 @@ def argsort(
             Has no effect but is accepted for compatibility with numpy.
         ascending : bool or list of bool, default True
             If True, sort values in ascending order, otherwise descending.
-        na_position : {‘first’ or ‘last’}, default ‘last’
-            Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs
             at the end.
 
         Returns

python/cudf/cudf/core/groupby/groupby.py

@@ -52,9 +52,9 @@ def _quantile_75(x):
 ----------
 by : mapping, function, label, or list of labels
     Used to determine the groups for the groupby. If by is a
-    function, it’s called on each value of the object’s index.
+    function, it's called on each value of the object's index.
     If a dict or Series is passed, the Series or dict VALUES will
-    be used to determine the groups (the Series’ values are first
+    be used to determine the groups (the Series' values are first
     aligned; see .align() method). If an cupy array is passed, the
     values are used as-is determine the groups. A label or list
     of labels may be passed to group by the columns in self.
@@ -65,7 +65,7 @@ def _quantile_75(x):
 as_index : bool, default True
     For aggregated output, return object with group labels as
     the index. Only relevant for DataFrame input.
-    as_index=False is effectively “SQL-style” grouped output.
+    as_index=False is effectively "SQL-style" grouped output.
 sort : bool, default False
     Sort result by group key. Differ from Pandas, cudf defaults to
     ``False`` for better performance. Note this does not influence
@@ -717,7 +717,7 @@ def _normalize_aggs(
     def pipe(self, func, *args, **kwargs):
         """
         Apply a function `func` with arguments to this GroupBy
-        object and return the function’s result.
+        object and return the function's result.
 
         Parameters
         ----------
@@ -1103,13 +1103,13 @@ def func(x):
     def describe(self, include=None, exclude=None):
         """
         Generate descriptive statistics that summarizes the central tendency,
-        dispersion and shape of a dataset’s distribution, excluding NaN values.
+        dispersion and shape of a dataset's distribution, excluding NaN values.
 
         Analyzes numeric DataFrames only
 
         Parameters
         ----------
-        include: ‘all’, list-like of dtypes or None (default), optional
+        include: 'all', list-like of dtypes or None (default), optional
             list of data types to include in the result.
             Ignored for Series.
 

python/cudf/cudf/core/index.py

@@ -1062,7 +1062,7 @@ def equals(self, other, **kwargs):
         Returns
         -------
         out: bool
-            True if “other” is an Index and it has the same elements
+            True if "other" is an Index and it has the same elements
             as calling index; False otherwise.
         """
         if (
@@ -1414,8 +1414,8 @@ def argsort(
             Has no effect but is accepted for compatibility with numpy.
         ascending : bool or list of bool, default True
             If True, sort values in ascending order, otherwise descending.
-        na_position : {‘first’ or ‘last’}, default ‘last’
-            Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs
             at the end.
 
         Returns
@@ -1853,7 +1853,7 @@ class DatetimeIndex(GenericIndex):
         This is not yet supported
     tz : pytz.timezone or dateutil.tz.tzfile
         This is not yet supported
-    ambiguous : ‘infer’, bool-ndarray, ‘NaT’, default ‘raise’
+    ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
         This is not yet supported
     name : object
         Name to be stored in the index.
@@ -2547,7 +2547,7 @@ class CategoricalIndex(GenericIndex):
         Whether or not this categorical is treated as an ordered categorical.
         If not given here or in dtype, the resulting categorical will be
         unordered.
-    dtype : CategoricalDtype or “category”, optional
+    dtype : CategoricalDtype or "category", optional
         If CategoricalDtype, cannot be used together with categories or
         ordered.
     copy : bool, default False

python/cudf/cudf/core/indexed_frame.py

@@ -562,8 +562,8 @@ def replace(
             * dict:
                 - Dicts can be used to specify different replacement values
                   for different existing values. For example, {'a': 'b',
-                  'y': 'z'} replaces the value ‘a’ with ‘b’ and
-                  ‘y’ with ‘z’.
+                  'y': 'z'} replaces the value 'a' with 'b' and
+                  'y' with 'z'.
                   To use a dict in this way the ``value`` parameter should
                   be ``None``.
         value : scalar, dict, list-like, str, default None
@@ -1865,7 +1865,7 @@ def sort_values(
             Sort ascending vs. descending. Specify list for multiple sort
             orders. If this is a list of bools, must match the length of the
             by.
-        na_position : {‘first’, ‘last’}, default ‘last’
+        na_position : {'first', 'last'}, default 'last'
             'first' puts nulls at the beginning, 'last' puts nulls at the end
         ignore_index : bool, default False
             If True, index will not be sorted.

python/cudf/cudf/core/reshape.py

@@ -484,7 +484,7 @@ def melt(
     4  b        C      4
     5  c        C      6
 
-    The names of ‘variable’ and ‘value’ columns can be customized:
+    The names of 'variable' and 'value' columns can be customized:
 
     >>> cudf.melt(df, id_vars=['A'], value_vars=['B'],
     ...         var_name='myVarname', value_name='myValname')