DOC: fix PR09,PR08 docstring errors in pandas.plotting

pandas/core/frame.py

@@ -2066,7 +2066,7 @@ def to_feather(self, fname):
         Parameters
         ----------
         fname : str
-            string file path
+            String file path.
         """
         from pandas.io.feather_format import to_feather
 
@@ -4772,6 +4772,7 @@ def drop_duplicates(self, subset=None, keep="first", inplace=False):
             Only consider certain columns for identifying duplicates, by
             default use all of the columns
         keep : {'first', 'last', False}, default 'first'
+            Determines which duplicates (if any) to keep.
             - ``first`` : Drop duplicates except for the first occurrence.
             - ``last`` : Drop duplicates except for the last occurrence.
             - False : Drop all duplicates.
@@ -4806,10 +4807,10 @@ def duplicated(self, subset=None, keep="first"):
             Only consider certain columns for identifying duplicates, by
             default use all of the columns
         keep : {'first', 'last', False}, default 'first'
-            - ``first`` : Mark duplicates as ``True`` except for the
-              first occurrence.
-            - ``last`` : Mark duplicates as ``True`` except for the
-              last occurrence.
+            Determines which duplicates (if any) to mark.
+
+            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
             - False : Mark all duplicates as ``True``.
 
         Returns
@@ -6233,8 +6234,8 @@ def unstack(self, level=-1, fill_value=None):
         ----------
         level : int, str, or list of these, default -1 (last level)
             Level(s) of index to unstack, can pass level name
-        fill_value : replace NaN with this value if the unstack produces
-            missing values
+        fill_value : int, string or dict
+            Replace NaN with this value if the unstack produces missing values
 
         Returns
         -------
@@ -6665,6 +6666,8 @@ def apply(
                by result_type='broadcast'.
 
         raw : bool, default False
+            Determines if row or column is passed as a Series or ndarry object:
+
             * ``False`` : passes each row or column as a Series to the
               function.
             * ``True`` : the passed function will receive ndarray objects
@@ -7357,6 +7360,8 @@ def corr(self, method="pearson", min_periods=1):
         Parameters
         ----------
         method : {'pearson', 'kendall', 'spearman'} or callable
+            Method of correlation:
+
             * pearson : standard correlation coefficient
             * kendall : Kendall Tau correlation coefficient
             * spearman : Spearman rank correlation
@@ -7556,10 +7561,13 @@ def corrwith(self, other, axis=0, drop=False, method="pearson"):
         other : DataFrame, Series
             Object with which to compute correlations.
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            0 or 'index' to compute column-wise, 1 or 'columns' for row-wise.
+            The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' for
+            row-wise.
         drop : bool, default False
             Drop missing indices from result.
         method : {'pearson', 'kendall', 'spearman'} or callable
+            Method of correlation:
+
             * pearson : standard correlation coefficient
             * kendall : Kendall Tau correlation coefficient
             * spearman : Spearman rank correlation
@@ -7939,7 +7947,7 @@ def idxmin(self, axis=0, skipna=True):
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            0 or 'index' for row-wise, 1 or 'columns' for column-wise
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise
         skipna : bool, default True
             Exclude NA/null values. If an entire row/column is NA, the result
             will be NA.
@@ -7976,7 +7984,7 @@ def idxmax(self, axis=0, skipna=True):
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0
-            0 or 'index' for row-wise, 1 or 'columns' for column-wise
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise
         skipna : bool, default True
             Exclude NA/null values. If an entire row/column is NA, the result
             will be NA.

pandas/core/generic.py

@@ -2559,10 +2559,10 @@ def to_msgpack(self, path_or_buf=None, encoding="utf-8", **kwargs):
         path : str, buffer-like, or None
             Destination for the serialized object.
             If None, return generated bytes
-        append : bool whether to append to an existing msgpack
-            (default is False)
-        compress : type of compressor (zlib or blosc), default to None (no
-            compression)
+        append : bool, default False
+            Whether to append to an existing msgpack.
+        compress : str, default None
+            Type of compressor (zlib, blosc or None).
 
         Returns
         -------
@@ -2797,10 +2797,10 @@ def to_clipboard(self, excel=True, sep=None, **kwargs):
         Parameters
         ----------
         excel : bool, default True
-            - True, use the provided separator, writing in a csv format for
-              allowing easy pasting into excel.
-            - False, write a string representation of the object to the
-              clipboard.
+            Produce output in a csv format for easy pasting into excel.
+
+            - True, use the provided separator for csv pasting.
+            - False, write a string representation of the object to the clipboard.
 
         sep : str, default ``'\t'``
             Field delimiter.
@@ -5024,15 +5024,15 @@ def sample(
         Parameters
         ----------
         func : function
-            function to apply to the %(klass)s.
+            Function to apply to the %(klass)s.
             ``args``, and ``kwargs`` are passed into ``func``.
             Alternatively a ``(callable, data_keyword)`` tuple where
             ``data_keyword`` is a string indicating the keyword of
             ``callable`` that expects the %(klass)s.
         args : iterable, optional
-            positional arguments passed into ``func``.
+            Positional arguments passed into ``func``.
         kwargs : mapping, optional
-            a dictionary of keyword arguments passed into ``func``.
+            A dictionary of keyword arguments passed into ``func``.
 
         Returns
         -------

pandas/plotting/_core.py

@@ -514,6 +514,8 @@ class PlotAccessor(PandasObject):
         Allows plotting of one column versus another. Only used if data is a
         DataFrame.
     kind : str
+        The kind of plot to produce:
+
         - 'line' : line plot (default)
         - 'bar' : vertical bar plot
         - 'barh' : horizontal bar plot
@@ -537,7 +539,7 @@ class PlotAccessor(PandasObject):
     legend : False/True/'reverse'
         Place legend on axis subplots
     style : list or dict
-        matplotlib line style per column
+        The matplotlib line style per column
     logx : bool or 'sym', default False
         Use log scaling or symlog scaling on x axis
         .. versionchanged:: 0.25.0

pandas/plotting/_misc.py

@@ -14,9 +14,9 @@ def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
     ----------
     ax : Matplotlib axes object
     data : DataFrame or Series
-        data for table contents
-    kwargs : keywords, optional
-        keyword arguments which passed to matplotlib.table.table.
+        Data for table contents.
+    **kwargs
+        Keyword arguments to be passed to matplotlib.table.table.
         If `rowLabels` or `colLabels` is not specified, data index or column
         name will be used.
 
@@ -82,7 +82,7 @@ def scatter_matrix(
     density_kwds=None,
     hist_kwds=None,
     range_padding=0.05,
-    **kwds
+    **kwargs
 ):
     """
     Draw a matrix of scatter plots.
@@ -91,28 +91,26 @@ def scatter_matrix(
     ----------
     frame : DataFrame
     alpha : float, optional
-        amount of transparency applied
+        Amount of transparency applied.
     figsize : (float,float), optional
-        a tuple (width, height) in inches
+        A tuple (width, height) in inches.
     ax : Matplotlib axis object, optional
     grid : bool, optional
-        setting this to True will show the grid
+        Setting this to True will show the grid.
     diagonal : {'hist', 'kde'}
-        pick between 'kde' and 'hist' for
-        either Kernel Density Estimation or Histogram
-        plot in the diagonal
+        Pick between 'kde' and 'hist' for either Kernel Density Estimation or
+        Histogram plot in the diagonal.
     marker : str, optional
-        Matplotlib marker type, default '.'
-    hist_kwds : other plotting keyword arguments
-        To be passed to hist function
-    density_kwds : other plotting keyword arguments
-        To be passed to kernel density estimate plot
-    range_padding : float, optional
-        relative extension of axis range in x and y
-        with respect to (x_max - x_min) or (y_max - y_min),
-        default 0.05
-    kwds : other plotting keyword arguments
-        To be passed to scatter function
+        Matplotlib marker type, default '.'.
+    density_kwds : keywords
+        Keyword arguments to be passed to kernel density estimate plot.
+    hist_kwds : keywords
+        Keyword arguments to be passed to hist function.
+    range_padding : float, default 0.05
+        Relative extension of axis range in x and y with respect to
+        (x_max - x_min) or (y_max - y_min).
+    **kwargs
+        Keyword arguments to be passed to scatter function.
 
     Returns
     -------
@@ -136,7 +134,7 @@ def scatter_matrix(
         density_kwds=density_kwds,
         hist_kwds=hist_kwds,
         range_padding=range_padding,
-        **kwds
+        **kwargs
     )
 
 
@@ -215,7 +213,7 @@ def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
 
 @deprecate_kwarg(old_arg_name="data", new_arg_name="frame")
 def andrews_curves(
-    frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwds
+    frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwargs
 ):
     """
     Generate a matplotlib plot of Andrews curves, for visualising clusters of
@@ -233,17 +231,17 @@ def andrews_curves(
     Parameters
     ----------
     frame : DataFrame
-        Data to be plotted, preferably normalized to (0.0, 1.0)
+        Data to be plotted, preferably normalized to (0.0, 1.0).
     class_column : Name of the column containing class names
     ax : matplotlib axes object, default None
     samples : Number of points to plot in each curve
     color : list or tuple, optional
-        Colors to use for the different classes
+        Colors to use for the different classes.
     colormap : str or matplotlib colormap object, default None
         Colormap to select colors from. If string, load colormap with that name
         from matplotlib.
-    kwds : keywords
-        Options to pass to matplotlib plotting method
+    **kwargs
+        Options to pass to matplotlib plotting method.
 
     Returns
     -------
@@ -257,7 +255,7 @@ def andrews_curves(
         samples=samples,
         color=color,
         colormap=colormap,
-        **kwds
+        **kwargs
     )
 
 
@@ -327,7 +325,7 @@ def parallel_coordinates(
     axvlines=True,
     axvlines_kwds=None,
     sort_labels=False,
-    **kwds
+    **kwargs
 ):
     """
     Parallel coordinates plotting.
@@ -336,30 +334,29 @@ def parallel_coordinates(
     ----------
     frame : DataFrame
     class_column : str
-        Column name containing class names
+        Column name containing class names.
     cols : list, optional
-        A list of column names to use
+        A list of column names to use.
     ax : matplotlib.axis, optional
-        matplotlib axis object
+        Matplotlib axis object.
     color : list or tuple, optional
-        Colors to use for the different classes
+        Colors to use for the different classes.
     use_columns : bool, optional
-        If true, columns will be used as xticks
+        If true, columns will be used as xticks.
     xticks : list or tuple, optional
-        A list of values to use for xticks
+        A list of values to use for xticks.
     colormap : str or matplotlib colormap, default None
         Colormap to use for line colors.
     axvlines : bool, optional
-        If true, vertical lines will be added at each xtick
+        If true, vertical lines will be added at each xtick.
     axvlines_kwds : keywords, optional
-        Options to be passed to axvline method for vertical lines
-    sort_labels : bool, False
-        Sort class_column labels, useful when assigning colors
+        Options to be passed to axvline method for vertical lines.
+    sort_labels : bool, default False
+        Sort class_column labels, useful when assigning colors.
 
         .. versionadded:: 0.20.0
-
-    kwds : keywords
-        Options to pass to matplotlib plotting method
+    **kwargs
+        Options to pass to matplotlib plotting method.
 
     Returns
     -------
@@ -388,7 +385,7 @@ def parallel_coordinates(
         axvlines=axvlines,
         axvlines_kwds=axvlines_kwds,
         sort_labels=sort_labels,
-        **kwds
+        **kwargs
     )
 
 
@@ -411,23 +408,23 @@ def lag_plot(series, lag=1, ax=None, **kwds):
     return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
 
 
-def autocorrelation_plot(series, ax=None, **kwds):
+def autocorrelation_plot(series, ax=None, **kwargs):
     """
     Autocorrelation plot for time series.
 
     Parameters
     ----------
     series : Time series
     ax : Matplotlib axis object, optional
-    kwds : keywords
-        Options to pass to matplotlib plotting method
+    **kwargs
+        Options to pass to matplotlib plotting method.
 
     Returns
     -------
     class:`matplotlib.axis.Axes`
     """
     plot_backend = _get_plot_backend("matplotlib")
-    return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwds)
+    return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
 
 
 def tsplot(series, plotf, ax=None, **kwargs):