python类axes()的实例源码

def _maybe_right_yaxis(self, ax, axes_num):
        if not self.on_right(axes_num):
            # secondary axes may be passed via ax kw
            return self._get_ax_layer(ax)

        if hasattr(ax, 'right_ax'):
            # if it has right_ax proparty, ``ax`` must be left axes
            return ax.right_ax
        elif hasattr(ax, 'left_ax'):
            # if it has left_ax proparty, ``ax`` must be right axes
            return ax
        else:
            # otherwise, create twin axes
            orig_ax, new_ax = ax, ax.twinx()
            # TODO: use Matplotlib public API when available
            new_ax._get_lines = orig_ax._get_lines
            new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill
            orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax

            if not self._has_plotted_object(orig_ax):  # no data on left y
                orig_ax.get_yaxis().set_visible(False)
            return new_ax

def result(self):
        """
        Return result axes
        """
        if self.subplots:
            if self.layout is not None and not com.is_list_like(self.ax):
                return self.axes.reshape(*self.layout)
            else:
                return self.axes
        else:
            sec_true = isinstance(self.secondary_y, bool) and self.secondary_y
            all_sec = (com.is_list_like(self.secondary_y) and
                       len(self.secondary_y) == self.nseries)
            if (sec_true or all_sec):
                # if all data is plotted on secondary, return right axes
                return self._get_ax_layer(self.axes[0], primary=False)
            else:
                return self.axes[0]

def _make_plot(self):
        x, y, data, C = self.x, self.y, self.data, self.C
        ax = self.axes[0]
        # pandas uses colormap, matplotlib uses cmap.
        cmap = self.colormap or 'BuGn'
        cmap = self.plt.cm.get_cmap(cmap)
        cb = self.kwds.pop('colorbar', True)

        if C is None:
            c_values = None
        else:
            c_values = data[C].values

        ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap,
                  **self.kwds)
        if cb:
            img = ax.collections[0]
            self.fig.colorbar(img, ax=ax)

def bar(self, **kwds):
        """
        Vertical bar plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.Series.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='bar', **kwds)

def barh(self, **kwds):
        """
        Horizontal bar plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.Series.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='barh', **kwds)

def box(self, **kwds):
        """
        Boxplot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.Series.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='box', **kwds)

def hist(self, bins=10, **kwds):
        """
        Histogram

        .. versionadded:: 0.17.0

        Parameters
        ----------
        bins: integer, default 10
            Number of histogram bins to be used
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.Series.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='hist', bins=bins, **kwds)

def kde(self, **kwds):
        """
        Kernel Density Estimate plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.Series.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='kde', **kwds)

def pie(self, **kwds):
        """
        Pie chart

        .. versionadded:: 0.17.0

        Parameters
        ----------
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.Series.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='pie', **kwds)

def line(self, x=None, y=None, **kwds):
        """
        Line plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='line', x=x, y=y, **kwds)

def bar(self, x=None, y=None, **kwds):
        """
        Vertical bar plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='bar', x=x, y=y, **kwds)

def barh(self, x=None, y=None, **kwds):
        """
        Horizontal bar plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='barh', x=x, y=y, **kwds)

def box(self, by=None, **kwds):
        """
        Boxplot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        by : string or sequence
            Column in the DataFrame to group by.
        \*\*kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='box', by=by, **kwds)

def kde(self, **kwds):
        """
        Kernel Density Estimate plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='kde', **kwds)

def area(self, x=None, y=None, **kwds):
        """
        Area plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='area', x=x, y=y, **kwds)

def pie(self, y=None, **kwds):
        """
        Pie chart

        .. versionadded:: 0.17.0

        Parameters
        ----------
        y : label or position, optional
            Column to plot.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='pie', y=y, **kwds)

def scatter(self, x, y, s=None, c=None, **kwds):
        """
        Scatter plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        s : scalar or array_like, optional
            Size of each point.
        c : label or position, optional
            Color of each point.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds)

def generate(self):
        self._args_adjust()
        self._compute_plot_data()
        self._setup_subplots()
        self._make_plot()
        self._add_table()
        self._make_legend()
        self._adorn_subplots()

        for ax in self.axes:
            self._post_plot_logic_common(ax, self.data)
            self._post_plot_logic(ax, self.data)

def _setup_subplots(self):
        if self.subplots:
            fig, axes = _subplots(naxes=self.nseries,
                                  sharex=self.sharex, sharey=self.sharey,
                                  figsize=self.figsize, ax=self.ax,
                                  layout=self.layout,
                                  layout_type=self._layout_type)
        else:
            if self.ax is None:
                fig = self.plt.figure(figsize=self.figsize)
                axes = fig.add_subplot(111)
            else:
                fig = self.ax.get_figure()
                if self.figsize is not None:
                    fig.set_size_inches(self.figsize)
                axes = self.ax

        axes = _flatten(axes)

        if self.logx or self.loglog:
            [a.set_xscale('log') for a in axes]
        if self.logy or self.loglog:
            [a.set_yscale('log') for a in axes]

        self.fig = fig
        self.axes = axes

def _post_plot_logic(self, ax, data):
        """Post process for each axes. Overridden in child classes"""
        pass

def _adorn_subplots(self):
        """Common post process unrelated to data"""
        if len(self.axes) > 0:
            all_axes = self._get_subplots()
            nrows, ncols = self._get_axes_layout()
            _handle_shared_axes(axarr=all_axes, nplots=len(all_axes),
                                naxes=nrows * ncols, nrows=nrows,
                                ncols=ncols, sharex=self.sharex,
                                sharey=self.sharey)

        for ax in self.axes:
            if self.yticks is not None:
                ax.set_yticks(self.yticks)

            if self.xticks is not None:
                ax.set_xticks(self.xticks)

            if self.ylim is not None:
                ax.set_ylim(self.ylim)

            if self.xlim is not None:
                ax.set_xlim(self.xlim)

            ax.grid(self.grid)

        if self.title:
            if self.subplots:
                self.fig.suptitle(self.title)
            else:
                self.axes[0].set_title(self.title)

def _make_legend(self):
        ax, leg = self._get_ax_legend(self.axes[0])

        handles = []
        labels = []
        title = ''

        if not self.subplots:
            if leg is not None:
                title = leg.get_title().get_text()
                handles = leg.legendHandles
                labels = [x.get_text() for x in leg.get_texts()]

            if self.legend:
                if self.legend == 'reverse':
                    self.legend_handles = reversed(self.legend_handles)
                    self.legend_labels = reversed(self.legend_labels)

                handles += self.legend_handles
                labels += self.legend_labels
                if self.legend_title is not None:
                    title = self.legend_title

            if len(handles) > 0:
                ax.legend(handles, labels, loc='best', title=title)

        elif self.subplots and self.legend:
            for ax in self.axes:
                if ax.get_visible():
                    ax.legend(loc='best')

def _get_ax_layer(cls, ax, primary=True):
        """get left (primary) or right (secondary) axes"""
        if primary:
            return getattr(ax, 'left_ax', ax)
        else:
            return getattr(ax, 'right_ax', ax)

def _get_ax(self, i):
        # get the twinx ax if appropriate
        if self.subplots:
            ax = self.axes[i]
            ax = self._maybe_right_yaxis(ax, i)
            self.axes[i] = ax
        else:
            ax = self.axes[0]
            ax = self._maybe_right_yaxis(ax, i)

        ax.get_yaxis().set_visible(True)
        return ax

def _get_axes_layout(self):
        axes = self._get_subplots()
        x_set = set()
        y_set = set()
        for ax in axes:
            # check axes coordinates to estimate layout
            points = ax.get_position().get_points()
            x_set.add(points[0][0])
            y_set.add(points[0][1])
        return (len(y_set), len(x_set))

def __init__(self, data, return_type=None, **kwargs):
        # Do not call LinePlot.__init__ which may fill nan
        if return_type not in self._valid_return_types:
            raise ValueError(
                "return_type must be {None, 'axes', 'dict', 'both'}")

        self.return_type = return_type
        MPLPlot.__init__(self, data, **kwargs)

def plot_series(data, kind='line', ax=None,                    # Series unique
                figsize=None, use_index=True, title=None, grid=None,
                legend=False, style=None, logx=False, logy=False, loglog=False,
                xticks=None, yticks=None, xlim=None, ylim=None,
                rot=None, fontsize=None, colormap=None, table=False,
                yerr=None, xerr=None,
                label=None, secondary_y=False,                 # Series unique
                **kwds):

    import matplotlib.pyplot as plt
    """
    If no axes is specified, check whether there are existing figures
    If there is no existing figures, _gca() will
    create a figure with the default figsize, causing the figsize=parameter to
    be ignored.
    """
    if ax is None and len(plt.get_fignums()) > 0:
        ax = _gca()
        ax = MPLPlot._get_ax_layer(ax)
    return _plot(data, kind=kind, ax=ax,
                 figsize=figsize, use_index=use_index, title=title,
                 grid=grid, legend=legend,
                 style=style, logx=logx, logy=logy, loglog=loglog,
                 xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim,
                 rot=rot, fontsize=fontsize, colormap=colormap, table=table,
                 yerr=yerr, xerr=xerr,
                 label=label, secondary_y=secondary_y,
                 **kwds)

def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True,
                  figsize=None, sharex=True, sharey=True, layout=None,
                  rot=0, ax=None, **kwargs):
    from pandas import DataFrame

    if figsize == 'default':
        # allowed to specify mpl default with 'default'
        warnings.warn("figsize='default' is deprecated. Specify figure"
                      "size by tuple instead", FutureWarning, stacklevel=4)
        figsize = None

    grouped = data.groupby(by)
    if column is not None:
        grouped = grouped[column]

    naxes = len(grouped)
    fig, axes = _subplots(naxes=naxes, figsize=figsize,
                          sharex=sharex, sharey=sharey, ax=ax,
                          layout=layout)

    _axes = _flatten(axes)

    for i, (key, group) in enumerate(grouped):
        ax = _axes[i]
        if numeric_only and isinstance(group, DataFrame):
            group = group._get_numeric_data()
        plotf(group, ax, **kwargs)
        ax.set_title(com.pprint_thing(key))

    return fig, axes

def _grouped_plot_by_column(plotf, data, columns=None, by=None,
                            numeric_only=True, grid=False,
                            figsize=None, ax=None, layout=None,
                            return_type=None, **kwargs):
    grouped = data.groupby(by)
    if columns is None:
        if not isinstance(by, (list, tuple)):
            by = [by]
        columns = data._get_numeric_data().columns.difference(by)
    naxes = len(columns)
    fig, axes = _subplots(naxes=naxes, sharex=True, sharey=True,
                          figsize=figsize, ax=ax, layout=layout)

    _axes = _flatten(axes)

    result = compat.OrderedDict()
    for i, col in enumerate(columns):
        ax = _axes[i]
        gp_col = grouped[col]
        keys, values = zip(*gp_col)
        re_plotf = plotf(keys, values, ax, **kwargs)
        ax.set_title(col)
        ax.set_xlabel(com.pprint_thing(by))
        result[col] = re_plotf
        ax.grid(grid)

    # Return axes in multiplot case, maybe revisit later # 985
    if return_type is None:
        result = axes

    byline = by[0] if len(by) == 1 else by
    fig.suptitle('Boxplot grouped by %s' % byline)
    fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)

    return result

def table(ax, data, rowLabels=None, colLabels=None,
          **kwargs):
    """
    Helper function to convert DataFrame and Series to matplotlib.table

    Parameters
    ----------
    `ax`: Matplotlib axes object
    `data`: DataFrame or Series
        data for table contents
    `kwargs`: keywords, optional
        keyword arguments which passed to matplotlib.table.table.
        If `rowLabels` or `colLabels` is not specified, data index or column
        name will be used.

    Returns
    -------
    matplotlib table object
    """
    from pandas import DataFrame
    if isinstance(data, Series):
        data = DataFrame(data, columns=[data.name])
    elif isinstance(data, DataFrame):
        pass
    else:
        raise ValueError('Input data must be DataFrame or Series')

    if rowLabels is None:
        rowLabels = data.index

    if colLabels is None:
        colLabels = data.columns

    cellText = data.values

    import matplotlib.table
    table = matplotlib.table.table(ax, cellText=cellText,
                                   rowLabels=rowLabels,
                                   colLabels=colLabels, **kwargs)
    return table