pep 8 issues

Author: KrishnaSai2020

doc/redirects.csv

@@ -269,11 +269,11 @@ generated/pandas.core.resample.Resampler.std,../reference/api/pandas.core.resamp
 generated/pandas.core.resample.Resampler.sum,../reference/api/pandas.core.resample.Resampler.sum
 generated/pandas.core.resample.Resampler.transform,../reference/api/pandas.core.resample.Resampler.transform
 generated/pandas.core.resample.Resampler.var,../reference/api/pandas.core.resample.Resampler.var
-generated/pandas.core.window.EWM.corr,../reference/api/pandas.core.window.EWM.corr
-generated/pandas.core.window.EWM.cov,../reference/api/pandas.core.window.EWM.cov
-generated/pandas.core.window.EWM.mean,../reference/api/pandas.core.window.EWM.mean
-generated/pandas.core.window.EWM.std,../reference/api/pandas.core.window.EWM.std
-generated/pandas.core.window.EWM.var,../reference/api/pandas.core.window.EWM.var
+generated/pandas.core.window.ExponentialMovingWindow.corr,../reference/api/pandas.core.window.ExponentialMovingWindow.corr
+generated/pandas.core.window.ExponentialMovingWindow.cov,../reference/api/pandas.core.window.ExponentialMovingWindow.cov
+generated/pandas.core.window.ExponentialMovingWindow.mean,../reference/api/pandas.core.window.ExponentialMovingWindow.mean
+generated/pandas.core.window.ExponentialMovingWindow.std,../reference/api/pandas.core.window.ExponentialMovingWindow.std
+generated/pandas.core.window.ExponentialMovingWindow.var,../reference/api/pandas.core.window.ExponentialMovingWindow.var
 generated/pandas.core.window.Expanding.aggregate,../reference/api/pandas.core.window.Expanding.aggregate
 generated/pandas.core.window.Expanding.apply,../reference/api/pandas.core.window.Expanding.apply
 generated/pandas.core.window.Expanding.corr,../reference/api/pandas.core.window.Expanding.corr

doc/source/reference/window.rst

@@ -8,7 +8,7 @@ Window
 
 Rolling objects are returned by ``.rolling`` calls: :func:`pandas.DataFrame.rolling`, :func:`pandas.Series.rolling`, etc.
 Expanding objects are returned by ``.expanding`` calls: :func:`pandas.DataFrame.expanding`, :func:`pandas.Series.expanding`, etc.
-EWM objects are returned by ``.ewm`` calls: :func:`pandas.DataFrame.ewm`, :func:`pandas.Series.ewm`, etc.
+ExponentialMovingWindow objects are returned by ``.ewm`` calls: :func:`pandas.DataFrame.ewm`, :func:`pandas.Series.ewm`, etc.
 
 Standard moving window functions
 --------------------------------
@@ -69,11 +69,11 @@ Exponentially-weighted moving window functions
 .. autosummary::
    :toctree: api/
 
-   EWM.mean
-   EWM.std
-   EWM.var
-   EWM.corr
-   EWM.cov
+   ExponentialMovingWindow.mean
+   ExponentialMovingWindow.std
+   ExponentialMovingWindow.var
+   ExponentialMovingWindow.corr
+   ExponentialMovingWindow.cov
 
 Window indexer
 --------------

doc/source/user_guide/computation.rst

@@ -230,7 +230,7 @@ see the :ref:`groupby docs <groupby.transform.window_resample>`.
    The API for window statistics is quite similar to the way one works with ``GroupBy`` objects, see the documentation :ref:`here <groupby>`.
 
 We work with ``rolling``, ``expanding`` and ``exponentially weighted`` data through the corresponding
-objects, :class:`~pandas.core.window.Rolling`, :class:`~pandas.core.window.Expanding` and :class:`~pandas.core.window.EWM`.
+objects, :class:`~pandas.core.window.Rolling`, :class:`~pandas.core.window.Expanding` and :class:`~pandas.core.window.ExponentialMovingWindow`.
 
 .. ipython:: python
 
@@ -777,7 +777,7 @@ columns by reshaping and indexing:
 Aggregation
 -----------
 
-Once the ``Rolling``, ``Expanding`` or ``EWM`` objects have been created, several methods are available to
+Once the ``Rolling``, ``Expanding`` or ``ExponentialMovingWindow`` objects have been created, several methods are available to
 perform multiple computations on the data. These operations are similar to the :ref:`aggregating API <basics.aggregate>`,
 :ref:`groupby API <groupby.aggregate>`, and :ref:`resample API <timeseries.aggregate>`.
 
@@ -971,7 +971,7 @@ Exponentially weighted windows
 
 A related set of functions are exponentially weighted versions of several of
 the above statistics. A similar interface to ``.rolling`` and ``.expanding`` is accessed
-through the ``.ewm`` method to receive an :class:`~EWM` object.
+through the ``.ewm`` method to receive an :class:`~ExponentialMovingWindow` object.
 A number of expanding EW (exponentially weighted)
 methods are provided:
 
@@ -980,11 +980,11 @@ methods are provided:
     :header: "Function", "Description"
     :widths: 20, 80
 
-    :meth:`~EWM.mean`, EW moving average
-    :meth:`~EWM.var`, EW moving variance
-    :meth:`~EWM.std`, EW moving standard deviation
-    :meth:`~EWM.corr`, EW moving correlation
-    :meth:`~EWM.cov`, EW moving covariance
+    :meth:`~ExponentialMovingWindow.mean`, EW moving average
+    :meth:`~ExponentialMovingWindow.var`, EW moving variance
+    :meth:`~ExponentialMovingWindow.std`, EW moving standard deviation
+    :meth:`~ExponentialMovingWindow.corr`, EW moving correlation
+    :meth:`~ExponentialMovingWindow.cov`, EW moving covariance
 
 In general, a weighted moving average is calculated as
 
@@ -1090,12 +1090,12 @@ Here is an example for a univariate time series:
    @savefig ewma_ex.png
    s.ewm(span=20).mean().plot(style='k')
 
-EWM has a ``min_periods`` argument, which has the same
+ExponentialMovingWindow has a ``min_periods`` argument, which has the same
 meaning it does for all the ``.expanding`` and ``.rolling`` methods:
 no output values will be set until at least ``min_periods`` non-null values
 are encountered in the (expanding) window.
 
-EWM also has an ``ignore_na`` argument, which determines how
+ExponentialMovingWindow also has an ``ignore_na`` argument, which determines how
 intermediate null values affect the calculation of the weights.
 When ``ignore_na=False`` (the default), weights are calculated based on absolute
 positions, so that intermediate null values affect the result.

doc/source/whatsnew/v0.25.0.rst

@@ -1206,7 +1206,7 @@ Groupby/resample/rolling
 - Bug in :meth:`pandas.core.groupby.GroupBy.agg` where incorrect results are returned for uint64 columns. (:issue:`26310`)
 - Bug in :meth:`pandas.core.window.Rolling.median` and :meth:`pandas.core.window.Rolling.quantile` where MemoryError is raised with empty window (:issue:`26005`)
 - Bug in :meth:`pandas.core.window.Rolling.median` and :meth:`pandas.core.window.Rolling.quantile` where incorrect results are returned with ``closed='left'`` and ``closed='neither'`` (:issue:`26005`)
-- Improved :class:`pandas.core.window.Rolling`, :class:`pandas.core.window.Window` and :class:`pandas.core.window.EWM` functions to exclude nuisance columns from results instead of raising errors and raise a ``DataError`` only if all columns are nuisance (:issue:`12537`)
+- Improved :class:`pandas.core.window.Rolling`, :class:`pandas.core.window.Window` and :class:`pandas.core.window.ExponentialMovingWindow` functions to exclude nuisance columns from results instead of raising errors and raise a ``DataError`` only if all columns are nuisance (:issue:`12537`)
 - Bug in :meth:`pandas.core.window.Rolling.max` and :meth:`pandas.core.window.Rolling.min` where incorrect results are returned with an empty variable window (:issue:`26005`)
 - Raise a helpful exception when an unsupported weighted window function is used as an argument of :meth:`pandas.core.window.Window.aggregate` (:issue:`26597`)
 

pandas/core/frame.py

@@ -7288,7 +7288,7 @@ def _gotitem(
     core.resample.Resampler : Perform operations over resampled bins.
     core.window.Rolling : Perform operations over rolling window.
     core.window.Expanding : Perform operations over expanding window.
-    core.window.EWM : Perform operation over exponential weighted
+    core.window.ExponentialMovingWindow : Perform operation over exponential weighted
         window.
     """
     )
@@ -8171,7 +8171,7 @@ def cov(self, min_periods=None) -> "DataFrame":
         See Also
         --------
         Series.cov : Compute covariance with another Series.
-        core.window.EWM.cov: Exponential weighted sample covariance.
+        core.window.ExponentialMovingWindow.cov: Exponential weighted sample covariance.
         core.window.Expanding.cov : Expanding sample covariance.
         core.window.Rolling.cov : Rolling sample covariance.