CLN: GH29547 format with f-strings

Author: DanBasson

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/user_guide/cookbook.rst

@@ -1166,6 +1166,25 @@ Storing Attributes to a group node
    store.close()
    os.remove('test.h5')
 
+You can create or load a HDFStore in-memory  by passing the ``driver``
+parameter to PyTables. Changes are only written to disk when the HDFStore
+is closed.
+
+.. ipython:: python
+
+   store = pd.HDFStore('test.h5', 'w', diver='H5FD_CORE')
+
+   df = pd.DataFrame(np.random.randn(8, 3))
+   store['test'] = df
+
+   # only after closing the store, data is written to disk:
+   store.close()
+
+.. ipython:: python
+   :suppress:
+
+   os.remove('test.h5')
+
 .. _cookbook.binary:
 
 Binary files

doc/source/user_guide/timeseries.rst

@@ -235,6 +235,8 @@ inferred frequency upon creation:
 
     pd.DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], freq='infer')
 
+.. _timeseries.converting.format:
+
 Providing a format argument
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -319,6 +321,12 @@ which can be specified. These are computed from the starting point specified by
    pd.to_datetime([1349720105100, 1349720105200, 1349720105300,
                    1349720105400, 1349720105500], unit='ms')
 
+.. note::
+
+   The ``unit`` parameter does not use the same strings as the ``format`` parameter
+   that was discussed :ref:`above<timeseries.converting.format>`). The
+   available units are listed on the documentation for :func:`pandas.to_datetime`.
+
 Constructing a :class:`Timestamp` or :class:`DatetimeIndex` with an epoch timestamp
 with the ``tz`` argument specified will currently localize the epoch timestamps to UTC
 first then convert the result to the specified time zone. However, this behavior

doc/source/user_guide/visualization.rst

@@ -1423,7 +1423,7 @@ Here is an example of one way to easily plot group means with standard deviation
    # Plot
    fig, ax = plt.subplots()
    @savefig errorbar_example.png
-   means.plot.bar(yerr=errors, ax=ax, capsize=4)
+   means.plot.bar(yerr=errors, ax=ax, capsize=4, rot=0)
 
 .. ipython:: python
    :suppress:
@@ -1444,9 +1444,9 @@ Plotting with matplotlib table is now supported in  :meth:`DataFrame.plot` and :
 
 .. ipython:: python
 
-   fig, ax = plt.subplots(1, 1)
+   fig, ax = plt.subplots(1, 1, figsize=(7, 6.5))
    df = pd.DataFrame(np.random.rand(5, 3), columns=['a', 'b', 'c'])
-   ax.get_xaxis().set_visible(False)   # Hide Ticks
+   ax.xaxis.tick_top()  # Display x-axis ticks on top.
 
    @savefig line_plot_table_true.png
    df.plot(table=True, ax=ax)
@@ -1463,8 +1463,9 @@ as seen in the example below.
 
 .. ipython:: python
 
-   fig, ax = plt.subplots(1, 1)
-   ax.get_xaxis().set_visible(False)   # Hide Ticks
+   fig, ax = plt.subplots(1, 1, figsize=(7, 6.75))
+   ax.xaxis.tick_top()  # Display x-axis ticks on top.
+
    @savefig line_plot_table_data.png
    df.plot(table=np.round(df.T, 2), ax=ax)
 

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`)
 

doc/source/whatsnew/v1.1.0.rst

@@ -956,6 +956,7 @@ MultiIndex
         df.loc[(['b', 'a'], [2, 1]), :]
 
 - Bug in :meth:`MultiIndex.intersection` was not guaranteed to preserve order when ``sort=False``. (:issue:`31325`)
+- Bug in :meth:`DataFrame.truncate` was dropping :class:`MultiIndex` names. (:issue:`34564`)
 
 .. ipython:: python
 

pandas/core/apply.py

@@ -220,13 +220,7 @@ def apply_empty_result(self):
 
     def apply_raw(self):
         """ apply to the values as a numpy array """
-        result, reduction_success = libreduction.compute_reduction(
-            self.values, self.f, axis=self.axis
-        )
-
-        # We expect np.apply_along_axis to give a two-dimensional result, or raise.
-        if not reduction_success:
-            result = np.apply_along_axis(self.f, self.axis, self.values)
+        result = np.apply_along_axis(self.f, self.axis, self.values)
 
         # TODO: mixed type case
         if result.ndim == 2:

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.
 

pandas/core/generic.py

@@ -10460,7 +10460,12 @@ def _add_series_or_dataframe_operations(cls):
         Add the series or dataframe only operations to the cls; evaluate
         the doc strings again.
         """
-        from pandas.core.window import EWM, Expanding, Rolling, Window
+        from pandas.core.window import (
+            Expanding,
+            ExponentialMovingWindow,
+            Rolling,
+            Window,
+        )
 
         @doc(Rolling)
         def rolling(
@@ -10507,7 +10512,7 @@ def expanding(self, min_periods=1, center=False, axis=0):
 
         cls.expanding = expanding
 
-        @doc(EWM)
+        @doc(ExponentialMovingWindow)
         def ewm(
             self,
             com=None,
@@ -10520,7 +10525,7 @@ def ewm(
             axis=0,
         ):
             axis = self._get_axis_number(axis)
-            return EWM(
+            return ExponentialMovingWindow(
                 self,
                 com=com,
                 span=span,

pandas/core/indexes/multi.py

@@ -3193,7 +3193,12 @@ def truncate(self, before=None, after=None):
         new_codes = [level_codes[left:right] for level_codes in self.codes]
         new_codes[0] = new_codes[0] - i
 
-        return MultiIndex(levels=new_levels, codes=new_codes, verify_integrity=False)
+        return MultiIndex(
+            levels=new_levels,
+            codes=new_codes,
+            names=self._names,
+            verify_integrity=False,
+        )
 
     def equals(self, other) -> bool:
         """

pandas/core/window/__init__.py

@@ -1,3 +1,3 @@
-from pandas.core.window.ewm import EWM  # noqa:F401
+from pandas.core.window.ewm import ExponentialMovingWindow  # noqa:F401
 from pandas.core.window.expanding import Expanding, ExpandingGroupby  # noqa:F401
 from pandas.core.window.rolling import Rolling, RollingGroupby, Window  # noqa:F401

pandas/core/window/ewm.py

@@ -59,7 +59,7 @@ def get_center_of_mass(
     return float(comass)
 
 
-class EWM(_Rolling):
+class ExponentialMovingWindow(_Rolling):
     r"""
     Provide exponential weighted (EW) functions.
 
@@ -185,7 +185,7 @@ def __init__(
 
     @property
     def _constructor(self):
-        return EWM
+        return ExponentialMovingWindow
 
     _agg_see_also_doc = dedent(
         """

pandas/io/pytables.py

@@ -447,8 +447,8 @@ class HDFStore:
 
     Parameters
     ----------
-    path : string
-        File path to HDF5 file
+    path : str
+        File path to HDF5 file.
     mode : {'a', 'w', 'r', 'r+'}, default 'a'
 
         ``'r'``
@@ -462,18 +462,20 @@ class HDFStore:
         ``'r+'``
             It is similar to ``'a'``, but the file must already exist.
     complevel : int, 0-9, default None
-            Specifies a compression level for data.
-            A value of 0 or None disables compression.
+        Specifies a compression level for data.
+        A value of 0 or None disables compression.
     complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
-            Specifies the compression library to be used.
-            As of v0.20.2 these additional compressors for Blosc are supported
-            (default if no compressor specified: 'blosc:blosclz'):
-            {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
-             'blosc:zlib', 'blosc:zstd'}.
-            Specifying a compression library which is not available issues
-            a ValueError.
+        Specifies the compression library to be used.
+        As of v0.20.2 these additional compressors for Blosc are supported
+        (default if no compressor specified: 'blosc:blosclz'):
+        {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
+         'blosc:zlib', 'blosc:zstd'}.
+        Specifying a compression library which is not available issues
+        a ValueError.
     fletcher32 : bool, default False
-            If applying compression use the fletcher32 checksum
+        If applying compression use the fletcher32 checksum.
+    **kwargs
+        These parameters will be passed to the PyTables open_file method.
 
     Examples
     --------
@@ -482,6 +484,17 @@ class HDFStore:
     >>> store['foo'] = bar   # write to HDF5
     >>> bar = store['foo']   # retrieve
     >>> store.close()
+
+    **Create or load HDF5 file in-memory**
+
+    When passing the `driver` option to the PyTables open_file method through
+    **kwargs, the HDF5 file is loaded or created in-memory and will only be
+    written when closed:
+
+    >>> bar = pd.DataFrame(np.random.randn(10, 4))
+    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
+    >>> store['foo'] = bar
+    >>> store.close()   # only now, data is written to disk
     """
 
     _handle: Optional["File"]
@@ -634,6 +647,8 @@ def open(self, mode: str = "a", **kwargs):
         ----------
         mode : {'a', 'w', 'r', 'r+'}, default 'a'
             See HDFStore docstring or tables.open_file for info about modes
+        **kwargs
+            These parameters will be passed to the PyTables open_file method.
         """
         tables = _tables()