# noqa removal in code base

asv_bench/benchmarks/algorithms.py

@@ -183,4 +183,4 @@ def time_argsort(self, N):
         self.array.argsort()
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/attrs_caching.py

@@ -33,4 +33,4 @@ def time_cache_readonly(self):
         self.obj.prop
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/binary_ops.py

@@ -156,4 +156,4 @@ def time_add_overflow_both_arg_nan(self):
         )
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/categoricals.py

@@ -296,4 +296,4 @@ def time_categorical_contains(self):
         self.c.searchsorted(self.key)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/ctors.py

@@ -114,4 +114,4 @@ def time_multiindex_from_iterables(self):
         MultiIndex.from_product(self.iterables)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/dtypes.py

@@ -40,4 +40,4 @@ def time_pandas_dtype_invalid(self, dtype):
             pass
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/eval.py

@@ -63,4 +63,4 @@ def time_query_with_boolean_selection(self):
         self.df.query("(a >= @self.min_val) & (a <= @self.max_val)")
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/frame_ctor.py

@@ -7,7 +7,7 @@
     from pandas.tseries.offsets import Nano, Hour
 except ImportError:
     # For compatibility with older versions
-    from pandas.core.datetools import *  # noqa
+    from pandas.core.datetools import *
 
 
 class FromDicts:
@@ -105,4 +105,4 @@ def time_frame_from_lists(self):
         self.df = DataFrame(self.data)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/frame_methods.py

@@ -620,4 +620,4 @@ def time_select_dtypes(self, n):
         self.df.select_dtypes(include="int")
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/gil.py

@@ -37,7 +37,7 @@ def wrapper(fname):
         return wrapper
 
 
-from .pandas_vb_common import BaseIO  # noqa: E402 isort:skip
+from .pandas_vb_common import BaseIO
 
 
 class ParallelGroupbyMethods:
@@ -302,4 +302,4 @@ def time_loop(self, threads):
             self.loop()
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/groupby.py

@@ -625,4 +625,4 @@ def time_first(self):
         self.df_nans.groupby("key").transform("first")
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/index_object.py

@@ -245,4 +245,4 @@ def peakmem_gc_instances(self, N):
             gc.enable()
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/indexing.py

@@ -372,4 +372,4 @@ def time_chained_indexing(self, mode):
                 df2["C"] = 1.0
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/inference.py

@@ -121,4 +121,4 @@ def time_convert(self, data):
         lib.maybe_convert_numeric(data, set(), coerce_numeric=False)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/io/csv.py

@@ -407,4 +407,4 @@ def time_to_datetime_format_DD_MM_YYYY(self, cache_dates):
         to_datetime(df["date"], cache=cache_dates, format="%d-%m-%Y")
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/excel.py

@@ -69,4 +69,4 @@ def time_read_excel(self, engine):
         read_excel(fname, engine=engine)
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/hdf.py

@@ -128,4 +128,4 @@ def time_write_hdf(self, format):
         self.df.to_hdf(self.fname, "df", format=format)
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/json.py

@@ -215,4 +215,4 @@ def peakmem_float(self, frames):
             df.to_json()
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/msgpack.py

@@ -29,4 +29,4 @@ def time_write_msgpack(self):
         self.df.to_msgpack(self.fname)
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/pickle.py

@@ -26,4 +26,4 @@ def time_write_pickle(self):
         self.df.to_pickle(self.fname)
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/sql.py

@@ -142,4 +142,4 @@ def time_read_sql_table_column(self, dtype):
         read_sql_table(self.table_name, self.con, columns=[dtype])
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/io/stata.py

@@ -51,4 +51,4 @@ def setup(self, convert_dates):
         self.df.to_stata(self.fname, self.convert_dates)
 
 
-from ..pandas_vb_common import setup  # noqa: F401 isort:skip
+from ..pandas_vb_common import setup

asv_bench/benchmarks/join_merge.py

@@ -382,4 +382,4 @@ def time_series_align_left_monotonic(self):
         self.ts1.align(self.ts2, join="left")
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/multiindex_object.py

@@ -147,4 +147,4 @@ def time_categorical_level(self):
         self.df.set_index(["a", "b"])
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/offset.py

@@ -6,7 +6,7 @@
 import pandas as pd
 
 try:
-    import pandas.tseries.holiday  # noqa
+    import pandas.tseries.holiday
 except ImportError:
     pass
 

asv_bench/benchmarks/plotting.py

@@ -94,4 +94,4 @@ def time_plot_andrews_curves(self):
         andrews_curves(self.df, "Name")
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/reindex.py

@@ -161,4 +161,4 @@ def time_lib_fast_zip(self):
         lib.fast_zip(self.col_array_list)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/replace.py

@@ -74,4 +74,4 @@ def time_replace(self, constructor, replace_data):
         self.data.replace(self.to_replace)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/reshape.py

@@ -263,4 +263,4 @@ def time_explode(self, n_rows, max_list_length):
         self.series.explode()
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/rolling.py

@@ -144,4 +144,4 @@ def peakmem_fixed(self):
             self.roll.max()
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/series_methods.py

@@ -276,4 +276,4 @@ def time_func(self, func, N, dtype):
         self.func()
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/sparse.py

@@ -136,4 +136,4 @@ def time_division(self, fill_value):
         self.arr1 / self.arr2
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/stat_ops.py

@@ -159,4 +159,4 @@ def time_cov_series(self, use_bottleneck):
         self.s.cov(self.s2)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

asv_bench/benchmarks/timeseries.py

@@ -428,4 +428,4 @@ def time_dt_accessor_year(self, tz):
         self.series.dt.year
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+from .pandas_vb_common import setup

doc/make.py

@@ -80,7 +80,7 @@ def _process_single_doc(self, single_doc):
 
         elif single_doc.startswith("pandas."):
             try:
-                obj = pandas  # noqa: F821
+                obj = pandas
                 for name in single_doc.split("."):
                     obj = getattr(obj, name)
             except AttributeError:

doc/source/conf.py

@@ -144,7 +144,7 @@
 # built documents.
 #
 # The short X.Y version.
-import pandas  # noqa: E402 isort:skip
+import pandas
 
 # version = '%s r%s' % (pandas.__version__, svn_version())
 version = str(pandas.__version__)
@@ -435,14 +435,14 @@
 # Add custom Documenter to handle attributes/methods of an AccessorProperty
 # eg pandas.Series.str and pandas.Series.dt (see GH9322)
 
-import sphinx  # noqa: E402 isort:skip
-from sphinx.util import rpartition  # noqa: E402 isort:skip
-from sphinx.ext.autodoc import (  # noqa: E402 isort:skip
+import sphinx
+from sphinx.util import rpartition
+from sphinx.ext.autodoc import (
     AttributeDocumenter,
     Documenter,
     MethodDocumenter,
 )
-from sphinx.ext.autosummary import Autosummary  # noqa: E402 isort:skip
+from sphinx.ext.autosummary import Autosummary
 
 
 class AccessorDocumenter(MethodDocumenter):

doc/source/development/extending.rst

@@ -448,7 +448,7 @@ registers the default "matplotlib" backend as follows.
 .. code-block:: python
 
    # in setup.py
-   setup(  # noqa: F821
+   setup(
        ...,
        entry_points={
            "pandas_plotting_backends": [

doc/source/getting_started/10min.rst

@@ -65,7 +65,7 @@ will be completed:
 .. ipython::
 
    @verbatim
-   In [1]: df2.<TAB>  # noqa: E225, E999
+   In [1]: df2.<TAB>
    df2.A                  df2.bool
    df2.abs                df2.boxplot
    df2.add                df2.C

doc/source/getting_started/dsintro.rst

@@ -882,5 +882,5 @@ completion mechanism so they can be tab-completed:
 
 .. code-block:: ipython
 
-    In [5]: df.fo<TAB>  # noqa: E225, E999
+    In [5]: df.fo<TAB>
     df.foo1  df.foo2

doc/source/user_guide/advanced.rst

@@ -327,13 +327,13 @@ As usual, **both sides** of the slicers are included as this is label indexing.
 
    .. code-block:: python
 
-      df.loc[(slice('A1', 'A3'), ...), :]             # noqa: E999
+      df.loc[(slice('A1', 'A3'), ...), :]
 
    You should **not** do this:
 
    .. code-block:: python
 
-      df.loc[(slice('A1', 'A3'), ...)]                # noqa: E999
+      df.loc[(slice('A1', 'A3'), ...)]
 
 .. ipython:: python
 

doc/source/user_guide/computation.rst

@@ -250,7 +250,7 @@ These object provide tab-completion of the available methods and properties.
 
 .. code-block:: ipython
 
-   In [14]: r.<TAB>                                          # noqa: E225, E999
+   In [14]: r.<TAB>
    r.agg         r.apply       r.count       r.exclusions  r.max         r.median      r.name        r.skew        r.sum
    r.aggregate   r.corr        r.cov         r.kurt        r.mean        r.min         r.quantile    r.std         r.var
 

doc/source/user_guide/enhancingperf.rst

@@ -73,7 +73,7 @@ four calls) using the `prun ipython magic function <http://ipython.org/ipython-d
 
 .. ipython:: python
 
-   %prun -l 4 df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)  # noqa E999
+   %prun -l 4 df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
 
 By far the majority of time is spend inside either ``integrate_f`` or ``f``,
 hence we'll concentrate our efforts cythonizing these two functions.
@@ -387,13 +387,13 @@ Consider the following toy example of doubling each observation:
 
 
    @numba.vectorize
-   def double_every_value_withnumba(x):  # noqa E501
+   def double_every_value_withnumba(x):
        return x * 2
 
 .. code-block:: ipython
 
    # Custom function without numba
-   In [5]: %timeit df['col1_doubled'] = df['a'].apply(double_every_value_nonumba)  # noqa E501
+   In [5]: %timeit df['col1_doubled'] = df['a'].apply(double_every_value_nonumba)
    1000 loops, best of 3: 797 us per loop
 
    # Standard implementation (faster than a custom function)

doc/source/user_guide/groupby.rst

@@ -248,7 +248,7 @@ the length of the ``groups`` dict, so it is largely just a convenience:
 .. ipython::
 
    @verbatim
-   In [1]: gb.<TAB>  # noqa: E225, E999
+   In [1]: gb.<TAB>
    gb.agg        gb.boxplot    gb.cummin     gb.describe   gb.filter     gb.get_group  gb.height     gb.last       gb.median     gb.ngroups    gb.plot       gb.rank       gb.std        gb.transform
    gb.aggregate  gb.count      gb.cumprod    gb.dtype      gb.first      gb.groups     gb.hist       gb.max        gb.min        gb.nth        gb.prod       gb.resample   gb.sum        gb.var
    gb.apply      gb.cummax     gb.cumsum     gb.fillna     gb.gender     gb.head       gb.indices    gb.mean       gb.name       gb.ohlc       gb.quantile   gb.size       gb.tail       gb.weight

doc/source/user_guide/io.rst

@@ -3191,7 +3191,7 @@ argument to ``to_excel`` and to ``ExcelWriter``. The built-in engines are:
    writer = pd.ExcelWriter('path_to_file.xlsx', engine='xlsxwriter')
 
    # Or via pandas configuration.
-   from pandas import options  # noqa: E402
+   from pandas import options
    options.io.excel.xlsx.writer = 'xlsxwriter'
 
    df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')

doc/source/user_guide/missing_data.rst

@@ -65,7 +65,7 @@ Series and DataFrame objects:
 
    .. ipython:: python
 
-      None == None                                                 # noqa: E711
+      None == None
       np.nan == np.nan
 
    So as compared to above, a scalar equality comparison versus a ``None/np.nan`` doesn't provide useful information.

doc/source/user_guide/visualization.rst

@@ -129,7 +129,7 @@ You can also create these other plots using the methods ``DataFrame.plot.<kind>`
 
     In [14]: df = pd.DataFrame()
 
-    In [15]: df.plot.<TAB>  # noqa: E225, E999
+    In [15]: df.plot.<TAB>
     df.plot.area     df.plot.barh     df.plot.density  df.plot.hist     df.plot.line     df.plot.scatter
     df.plot.bar      df.plot.box      df.plot.hexbin   df.plot.kde      df.plot.pie