def _sanitize_and_check(indexes): kinds = list({type(index) for index in indexes}) if list in kinds: if len(kinds) > 1: indexes = [Index(com.try_sort(x)) if not isinstance(x, Index) else x for x in indexes] kinds.remove(list) else: return indexes, 'list' if len(kinds) > 1 or Index not in kinds: return indexes, 'special' else: return indexes, 'array'
def _sanitize_and_check(indexes): kinds = list({type(index) for index in indexes}) if list in kinds: if len(kinds) > 1: indexes = [Index(com.try_sort(x)) if not isinstance(x, Index) else x for x in indexes] kinds.remove(list) else: return indexes, 'list' if len(kinds) > 1 or Index not in kinds: return indexes, 'special' else: return indexes, 'array'
def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds): if by is not None: axes = _grouped_hist(data, column=column, by=by, ax=ax, grid=grid, figsize=figsize, sharex=sharex, sharey=sharey, layout=layout, bins=bins, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot, **kwds) return axes if column is not None: if not isinstance(column, (list, np.ndarray, ABCIndexClass)): column = [column] data = data[column] data = data._get_numeric_data() naxes = len(data.columns) if naxes == 0: raise ValueError("hist method requires numerical columns, " "nothing to plot.") fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False, sharex=sharex, sharey=sharey, figsize=figsize, layout=layout) _axes = _flatten(axes) for i, col in enumerate(com.try_sort(data.columns)): ax = _axes[i] ax.hist(data[col].dropna().values, bins=bins, **kwds) ax.set_title(col) ax.grid(grid) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(wspace=0.3, hspace=0.3) return axes
def _sanitize_and_check(indexes): """ Verify the type of indexes and convert lists to Index. Cases: - [list, list, ...]: Return ([list, list, ...], 'list') - [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...]) Lists are sorted and converted to Index. - [Index, Index, ...]: Return ([Index, Index, ...], TYPE) TYPE = 'special' if at least one special type, 'array' otherwise. Parameters ---------- indexes : list of Index or list objects Returns ------- sanitized_indexes : list of Index or list objects type : {'list', 'array', 'special'} """ kinds = list({type(index) for index in indexes}) if list in kinds: if len(kinds) > 1: indexes = [ Index(com.try_sort(x)) if not isinstance(x, Index) else x for x in indexes ] kinds.remove(list) else: return indexes, "list" if len(kinds) > 1 or Index not in kinds: return indexes, "special" else: return indexes, "array"
def _sanitize_and_check(indexes): """ Verify the type of indexes and convert lists to Index. Cases: - [list, list, ...]: Return ([list, list, ...], 'list') - [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...]) Lists are sorted and converted to Index. - [Index, Index, ...]: Return ([Index, Index, ...], TYPE) TYPE = 'special' if at least one special type, 'array' otherwise. Parameters ---------- indexes : list of Index or list objects Returns ------- sanitized_indexes : list of Index or list objects type : {'list', 'array', 'special'} """ kinds = list({type(index) for index in indexes}) if list in kinds: if len(kinds) > 1: indexes = [Index(com.try_sort(x)) if not isinstance(x, Index) else x for x in indexes] kinds.remove(list) else: return indexes, 'list' if len(kinds) > 1 or Index not in kinds: return indexes, 'special' else: return indexes, 'array'
def hist_frame( data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds, ): # Start with empty pandas data frame derived from ed_df_bins, ed_df_weights = data._hist(num_bins=bins) converter._WARN = False # no warning for pandas plots if by is not None: raise NotImplementedError("TODO") if column is not None: if not isinstance(column, (list, np.ndarray, ABCIndexClass)): column = [column] ed_df_bins = ed_df_bins[column] ed_df_weights = ed_df_weights[column] naxes = len(ed_df_bins.columns) if naxes == 0: raise ValueError("hist method requires numerical columns, " "nothing to plot.") fig, axes = _subplots( naxes=naxes, ax=ax, squeeze=False, sharex=sharex, sharey=sharey, figsize=figsize, layout=layout, ) _axes = _flatten(axes) for i, col in enumerate(com.try_sort(data.columns)): ax = _axes[i] ax.hist( ed_df_bins[col][:-1], bins=ed_df_bins[col], weights=ed_df_weights[col], **kwds, ) ax.set_title(col) ax.grid(grid) _set_ticks_props( axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot ) fig.subplots_adjust(wspace=0.3, hspace=0.3) return axes
def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds): converter._WARN = False # no warning for pandas plots if by is not None: axes = _grouped_hist(data, column=column, by=by, ax=ax, grid=grid, figsize=figsize, sharex=sharex, sharey=sharey, layout=layout, bins=bins, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot, **kwds) return axes if column is not None: if not isinstance(column, (list, np.ndarray, ABCIndexClass)): column = [column] data = data[column] data = data._get_numeric_data() naxes = len(data.columns) if naxes == 0: raise ValueError("hist method requires numerical columns, " "nothing to plot.") fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False, sharex=sharex, sharey=sharey, figsize=figsize, layout=layout) _axes = _flatten(axes) for i, col in enumerate(com.try_sort(data.columns)): ax = _axes[i] ax.hist(data[col].dropna().values, bins=bins, **kwds) ax.set_title(col) ax.grid(grid) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(wspace=0.3, hspace=0.3) return axes