def get_colors(colors,colorscale,keys,asList=False): if type(colors)!=dict: if not colors: if colorscale: colors=get_scales(colorscale,len(keys)) clrgen=colorgen(colors,len(keys)) if asList: colors=[clrgen.next() for _ in keys] else: colors={} for key in keys: colors[key]=next(clrgen) return colors
def get_colors(colors, colorscale, keys, asList=False): if type(colors) != dict: if not colors: if colorscale: colors = get_scales(colorscale, len(keys)) clrgen = colorgen(colors, len(keys)) if asList: colors = [clrgen.next() for _ in keys] else: colors = {} for key in keys: colors[key] = next(clrgen) return colors
def _iplot(self, data=None, layout=None, filename='', world_readable=None, kind='scatter', title='', xTitle='', yTitle='', zTitle='', theme=None, colors=None, colorscale=None, fill=False, width=None, mode='lines', symbol='dot', size=12, barmode='', sortbars=False, bargap=None, bargroupgap=None, bins=None, histnorm='', histfunc='count', orientation='v', boxpoints=False, annotations=None, keys=False, bestfit=False, bestfit_colors=None, categories='', x='', y='', z='', text='', gridcolor=None, zerolinecolor=None, margin=None, subplots=False, shape=None, asFrame=False, asDates=False, asFigure=False, asImage=False, dimensions=(1116, 587), asPlot=False, asUrl=False, online=None, **kwargs): """ Returns a plotly chart either as inline chart, image of Figure object Parameters: ----------- data : Data Plotly Data Object. If not entered then the Data object will be automatically generated from the DataFrame. data : Data Plotly Data Object. If not entered then the Data object will be automatically generated from the DataFrame. layout : Layout Plotly layout Object If not entered then the Layout objet will be automatically generated from the DataFrame. filename : string Filename to be saved as in plotly account world_readable : bool If False then it will be saved as a private file kind : string Kind of chart scatter bar box spread ratio heatmap surface histogram bubble bubble3d scatter3d title : string Chart Title xTitle : string X Axis Title yTitle : string Y Axis Title zTitle : string zTitle : string Z Axis Title Applicable only for 3d charts theme : string Layout Theme solar pearl white see cufflinks.getThemes() for all available themes colors : list or dict {key:color} to specify the color for each column [colors] to use the colors in the defined order colorscale : str Color scale name If the color name is preceded by a minus (-) then the scale is inversed Only valid if 'colors' is null See cufflinks.colors.scales() for available scales fill : bool Filled Traces width : int Line width mode : string Plotting mode for scatter trace lines markers lines+markers lines+text markers+text lines+markers+text symbol : string The symbol that is drawn on the plot for each marker Valid only when mode includes markers dot cross diamond square triangle-down triangle-left triangle-right triangle-up x size : string or int Size of marker Valid only if marker in mode barmode : string Mode when displaying bars group stack overlay * Only valid when kind='bar' sortbars : bool Sort bars in descending order * Only valid when kind='bar' bargap : float Sets the gap between bars [0,1) * Only valid when kind is 'histogram' or 'bar' bargroupgap : float Set the gap between groups [0,1) * Only valid when kind is 'histogram' or 'bar' bins : int Specifies the number of bins * Only valid when kind='histogram' histnorm : string '' (frequency) percent probability density probability density Sets the type of normalization for an histogram trace. By default the height of each bar displays the frequency of occurrence, i.e., the number of times this value was found in the corresponding bin. If set to 'percent', the height of each bar displays the percentage of total occurrences found within the corresponding bin. If set to 'probability', the height of each bar displays the probability that an event will fall into the corresponding bin. If set to 'density', the height of each bar is equal to the number of occurrences in a bin divided by the size of the bin interval such that summing the area of all bins will yield the total number of occurrences. If set to 'probability density', the height of each bar is equal to the number of probability that an event will fall into the corresponding bin divided by the size of the bin interval such that summing the area of all bins will yield 1. * Only valid when kind='histogram' histfunc : string count sum avg min max Sets the binning function used for an histogram trace. * Only valid when kind='histogram' orientation : string h v Sets the orientation of the bars. If set to 'v', the length of each | bar will run vertically. If set to 'h', the length of each bar will | run horizontally * Only valid when kind is 'histogram','bar' or 'box' boxpoints : string Displays data points in a box plot outliers all suspectedoutliers False annotations : dictionary Dictionary of annotations {x_point : text} keys : list of columns List of columns to chart. Also can be usded for custom sorting. bestfit : boolean or list If True then a best fit line will be generated for all columns. If list then a best fit line will be generated for each key on the list. bestfit_colors : list or dict {key:color} to specify the color for each column [colors] to use the colors in the defined order categories : string Name of the column that contains the categories x : string Name of the column that contains the x axis values y : string Name of the column that contains the y axis values z : string Name of the column that contains the z axis values text : string Name of the column that contains the text values gridcolor : string Grid color zerolinecolor : string Zero line color margin : dict or tuple Dictionary (l,r,b,t) or Tuple containing the left, right, bottom and top margins subplots : bool If true then each trace is placed in subplot layout shape : (rows,cols) Tuple indicating the size of rows and columns If omitted then the layout is automatically set * Only valid when subplots=True asFrame : bool If true then the data component of Figure will be of Pandas form (Series) otherwise they will be index values asDates : bool If true it truncates times from a DatetimeIndex asFigure : bool If True returns plotly Figure asImage : bool If True it returns Image * Only valid when asImage=True dimensions : tuple(int,int) Dimensions for image (width,height) asPlot : bool If True the chart opens in browser asUrl : bool If True the chart url is returned. No chart is displayed. online : bool If True then the chart is rendered on the server even when running in offline mode. """ # Look for invalid kwargs valid_kwargs = [ 'color', 'opacity', 'column', 'columns', 'labels', 'text', 'horizontal_spacing', 'vertical_spacing', 'specs', 'insets', 'start_cell', 'shared_xaxes', 'shared_yaxes', 'subplot_titles' ] valid_kwargs.extend(__LAYOUT_KWARGS) for key in kwargs.keys(): if key not in valid_kwargs: raise Exception("Invalid keyword : '{0}'".format(key)) # Setting default values if not colors: colors = kwargs['color'] if 'color' in kwargs else colors if isinstance(colors, str): colors = [colors] opacity = kwargs['opacity'] if 'opacity' in kwargs else 0.8 # Get values from config theme if theme is None: theme = auth.get_config_file()['theme'] theme_config = getTheme(theme) if colorscale is None: colorscale = theme_config[ 'colorscale'] if 'colorscale' in theme_config else 'dflt' if width is None: width = theme_config['linewidth'] if 'linewidth' in theme_config else 2 # if bargap is None: # bargap=theme_config['bargap'] if 'bargap' in theme_config else 0 # In case column was used instead of keys if 'column' in kwargs: keys = [kwargs['column']] if isinstance(kwargs['column'], str) else kwargs['column'] if 'columns' in kwargs: keys = [kwargs['columns']] if isinstance(kwargs['columns'], str) else kwargs['columns'] kind = 'line' if kind == 'lines' else kind if not layout: l_kwargs = dict([(k, kwargs[k]) for k in __LAYOUT_KWARGS if k in kwargs]) if annotations: annotations = getAnnotations(self.copy(), annotations) layout = getLayout(theme=theme, xTitle=xTitle, yTitle=yTitle, zTitle=zTitle, title=title, barmode=barmode, bargap=bargap, bargroupgap=bargroupgap, annotations=annotations, gridcolor=gridcolor, zerolinecolor=zerolinecolor, margin=margin, is3d='3d' in kind, **l_kwargs) if not data: if categories: data = Data() _keys = pd.unique(self[categories]) colors = get_colors(colors, colorscale, _keys) mode = 'markers' if 'markers' not in mode else mode for _ in _keys: __ = self[self[categories] == _].copy() if text: _text = __[text] if asFrame else __[text].values _x = __[x] if asFrame else __[x].values _y = __[y] if asFrame else __[y].values if z: _z = __[z] if asFrame else __[z].values if 'bubble' in kind: rg = __[size].values rgo = self[size].values _size = [ int(100 * (float(i) - rgo.min()) / (rgo.max() - rgo.min())) + 12 for i in rg ] else: _size = size _data = Scatter3d( x=_x, y=_y, mode=mode, name=_, marker=Marker(color=colors[_], symbol=symbol, size=_size, opacity=opacity, line=Line(width=width)), textfont=getLayout(theme=theme)['xaxis1']['titlefont']) if '3d' in kind: _data = Scatter3d( x=_x, y=_y, z=_z, mode=mode, name=_, marker=Marker(color=colors[_], symbol=symbol, size=_size, opacity=opacity, line=Line(width=width)), textfont=getLayout(theme=theme)['xaxis1']['titlefont']) else: _data = Scatter( x=_x, y=_y, mode=mode, name=_, marker=Marker(color=colors[_], symbol=symbol, size=_size, opacity=opacity, line=Line(width=width)), textfont=getLayout(theme=theme)['xaxis1']['titlefont']) if text: _data.update(text=_text) data.append(_data) else: if kind in ('scatter', 'spread', 'ratio', 'bar', 'barh', 'area', 'line'): df = self.copy() if type(df) == pd.core.series.Series: df = pd.DataFrame({df.name: df}) if x: df = df.set_index(x) if y: df = df[y] if kind == 'area': df = df.transpose().fillna(0).cumsum().transpose() data = df.to_iplot(colors=colors, colorscale=colorscale, kind=kind, fill=fill, width=width, sortbars=sortbars, keys=keys, bestfit=bestfit, bestfit_colors=bestfit_colors, asDates=asDates, mode=mode, symbol=symbol, size=size, **kwargs) if kind in ('spread', 'ratio'): if kind == 'spread': trace = self.apply(lambda x: x[0] - x[1], axis=1) positive = trace.apply(lambda x: x if x >= 0 else pd.np.nan) negative = trace.apply(lambda x: x if x < 0 else pd.np.nan) trace = pd.DataFrame({ 'positive': positive, 'negative': negative }) trace = trace.to_iplot(colors={ 'positive': 'green', 'negative': 'red' }, width=0.5) else: trace = self.apply(lambda x: x[0] * 1.0 / x[1], axis=1).to_iplot(colors=['green'], width=1) trace.update({ 'xaxis': 'x2', 'yaxis': 'y2', 'fill': 'tozeroy', 'name': kind.capitalize(), 'connectgaps': False, 'showlegend': False }) data.append(Scatter(trace[0])) if kind == 'spread': data.append(Scatter(trace[1])) layout['yaxis1'].update({'domain': [.3, 1]}) layout['yaxis2'] = copy.deepcopy(layout['yaxis1']) layout['xaxis2'] = copy.deepcopy(layout['xaxis1']) layout['yaxis2'].update(domain=[0, .25], title=kind.capitalize()) layout['xaxis2'].update(anchor='y2', showticklabels=False) layout['hovermode'] = 'x' if 'bar' in kind: if 'stack' in barmode: layout['legend'].update(traceorder='normal') orientation = 'h' if kind == 'barh' else orientation for trace in data: trace.update(orientation=orientation) if kind == 'barh': trace['x'], trace['y'] = trace['y'], trace['x'] elif kind == 'bubble': mode = 'markers' if 'markers' not in mode else mode x = self[x].values.tolist() y = self[y].values.tolist() z = size if size else z rg = self[z].values z = [ int(100 * (float(_) - rg.min()) / (rg.max() - rg.min())) + 12 for _ in rg ] text = kwargs['labels'] if 'labels' in kwargs else text labels = self[text].values.tolist() if text else '' clrs = get_colors(colors, colorscale, x).values() gen = colorgen() marker = Marker( color=clrs, size=z, symbol=symbol, line=Line(width=width), textfont=getLayout(theme=theme)['xaxis1']['titlefont']) trace = Scatter(x=x, y=y, marker=marker, mode='markers', text=labels) data = Data([trace]) elif kind in ('box', 'histogram', 'hist'): if isinstance(self, pd.core.series.Series): df = pd.DataFrame({self.name: self}) else: df = self.copy() data = Data() clrs = get_colors(colors, colorscale, df.columns) if 'hist' in kind: barmode = 'overlay' if barmode == '' else barmode layout.update(barmode=barmode) columns = keys if keys else df.columns for _ in columns: if kind == 'box': __ = Box(y=df[_].values.tolist(), marker=Marker(color=clrs[_]), name=_, line=Line(width=width), boxpoints=boxpoints) else: __ = Histogram(x=df[_].values.tolist(), marker=Marker(color=clrs[_]), name=_, line=Line(width=width), orientation=orientation, opacity=kwargs['opacity'] if 'opacity' in kwargs else .8, histfunc=histfunc, histnorm=histnorm) if orientation == 'h': __['y'] = __['x'] del __['x'] if bins: if orientation == 'h': __.update(nbinsy=bins) else: __.update(nbinsx=bins) data.append(__) elif kind in ('heatmap', 'surface'): x = self[x].values.tolist() if x else self.index.values.tolist( ) y = self[y].values.tolist( ) if y else self.columns.values.tolist() z = self[z].values.tolist() if z else self.values.transpose() scale = get_scales('rdbu') if not colorscale else get_scales( colorscale) colorscale = [[float(_) / (len(scale) - 1), scale[_]] for _ in range(len(scale))] if kind == 'heatmap': data = Data( [Heatmap(z=z, x=x, y=y, colorscale=colorscale)]) else: data = Data( [Surface(z=z, x=x, y=y, colorscale=colorscale)]) elif kind in ('scatter3d', 'bubble3d'): data = Data() keys = self[text].values if text else range(len(self)) colors = get_colors(colors, colorscale, keys, asList=True) df = self.copy() df['index'] = keys if kind == 'bubble3d': rg = self[size].values size = [ int(100 * (float(_) - rg.min()) / (rg.max() - rg.min())) + 12 for _ in rg ] else: size = [size for _ in range(len(keys))] _data = Scatter3d(x=df[x].values.tolist(), y=df[y].values.tolist(), z=df[z].values.tolist(), mode=mode, name=keys, marker=Marker(color=colors, symbol=symbol, size=size, opacity=.8)) if text: _data.update(text=keys) data.append(_data) if world_readable is None: world_readable = auth.get_config_file()['world_readable'] if not filename: if title: filename = title else: filename = 'Plotly Playground {0}'.format( time.strftime("%Y-%m-%d %H:%M:%S")) ## Figure defintion figure = Figure() figure['data'] = data figure['layout'] = layout ## Subplots if subplots: fig = tools.strip_figures(figure) kw = {} if 'horizontal_spacing' in kwargs: kw['horizontal_spacing'] = kwargs['horizontal_spacing'] if 'vertical_spacing' in kwargs: kw['vertical_spacing'] = kwargs['vertical_spacing'] if 'specs' in kwargs: kw['specs'] = kwargs['specs'] if 'shared_xaxes' in kwargs: kw['shared_xaxes'] = kwargs['shared_xaxes'] if 'shared_yaxes' in kwargs: kw['shared_yaxes'] = kwargs['shared_yaxes'] if 'subplot_titles' in kwargs: if kwargs['subplot_titles'] == True: kw['subplot_titles'] = [d['name'] for d in data] else: kw['subplot_titles'] = kwargs['subplot_titles'] if 'start_cell' in kwargs: kw['start_cell'] = kwargs['start_cell'] figure = tools.subplots(fig, shape, base_layout=layout, theme=theme, **kw) ## Exports validate = False if 'shapes' in layout else True if asFigure: return figure elif asImage: py.image.save_as(figure, filename=filename, format='png', width=dimensions[0], height=dimensions[1]) return display(Image(filename + '.png')) elif asPlot: return py.plot(figure, world_readable=world_readable, filename=filename, validate=validate) elif asUrl: return py.plot(figure, world_readable=world_readable, filename=filename, validate=validate, auto_open=False) else: return iplot(figure, world_readable=world_readable, filename=filename, validate=validate, online=online)
def _iplot(self,data=None,layout=None,filename='',world_readable=None, kind='scatter',title='',xTitle='',yTitle='',zTitle='',theme=None,colors=None,colorscale=None,fill=False,width=None, mode='lines',symbol='dot',size=12,barmode='',sortbars=False,bargap=None,bargroupgap=None,bins=None,histnorm='', histfunc='count',orientation='v',boxpoints=False,annotations=None,keys=False,bestfit=False, bestfit_colors=None,categories='',x='',y='',z='',text='',gridcolor=None,zerolinecolor=None,margin=None, subplots=False,shape=None,asFrame=False,asDates=False,asFigure=False,asImage=False, dimensions=(1116,587),asPlot=False,asUrl=False,online=None,**kwargs): """ Returns a plotly chart either as inline chart, image of Figure object Parameters: ----------- data : Data Plotly Data Object. If not entered then the Data object will be automatically generated from the DataFrame. data : Data Plotly Data Object. If not entered then the Data object will be automatically generated from the DataFrame. layout : Layout Plotly layout Object If not entered then the Layout objet will be automatically generated from the DataFrame. filename : string Filename to be saved as in plotly account world_readable : bool If False then it will be saved as a private file kind : string Kind of chart scatter bar box spread ratio heatmap surface histogram bubble bubble3d scatter3d title : string Chart Title xTitle : string X Axis Title yTitle : string Y Axis Title zTitle : string zTitle : string Z Axis Title Applicable only for 3d charts theme : string Layout Theme solar pearl white see cufflinks.getThemes() for all available themes colors : list or dict {key:color} to specify the color for each column [colors] to use the colors in the defined order colorscale : str Color scale name If the color name is preceded by a minus (-) then the scale is inversed Only valid if 'colors' is null See cufflinks.colors.scales() for available scales fill : bool Filled Traces width : int Line width mode : string Plotting mode for scatter trace lines markers lines+markers lines+text markers+text lines+markers+text symbol : string The symbol that is drawn on the plot for each marker Valid only when mode includes markers dot cross diamond square triangle-down triangle-left triangle-right triangle-up x size : string or int Size of marker Valid only if marker in mode barmode : string Mode when displaying bars group stack overlay * Only valid when kind='bar' sortbars : bool Sort bars in descending order * Only valid when kind='bar' bargap : float Sets the gap between bars [0,1) * Only valid when kind is 'histogram' or 'bar' bargroupgap : float Set the gap between groups [0,1) * Only valid when kind is 'histogram' or 'bar' bins : int Specifies the number of bins * Only valid when kind='histogram' histnorm : string '' (frequency) percent probability density probability density Sets the type of normalization for an histogram trace. By default the height of each bar displays the frequency of occurrence, i.e., the number of times this value was found in the corresponding bin. If set to 'percent', the height of each bar displays the percentage of total occurrences found within the corresponding bin. If set to 'probability', the height of each bar displays the probability that an event will fall into the corresponding bin. If set to 'density', the height of each bar is equal to the number of occurrences in a bin divided by the size of the bin interval such that summing the area of all bins will yield the total number of occurrences. If set to 'probability density', the height of each bar is equal to the number of probability that an event will fall into the corresponding bin divided by the size of the bin interval such that summing the area of all bins will yield 1. * Only valid when kind='histogram' histfunc : string count sum avg min max Sets the binning function used for an histogram trace. * Only valid when kind='histogram' orientation : string h v Sets the orientation of the bars. If set to 'v', the length of each | bar will run vertically. If set to 'h', the length of each bar will | run horizontally * Only valid when kind is 'histogram','bar' or 'box' boxpoints : string Displays data points in a box plot outliers all suspectedoutliers False annotations : dictionary Dictionary of annotations {x_point : text} keys : list of columns List of columns to chart. Also can be usded for custom sorting. bestfit : boolean or list If True then a best fit line will be generated for all columns. If list then a best fit line will be generated for each key on the list. bestfit_colors : list or dict {key:color} to specify the color for each column [colors] to use the colors in the defined order categories : string Name of the column that contains the categories x : string Name of the column that contains the x axis values y : string Name of the column that contains the y axis values z : string Name of the column that contains the z axis values text : string Name of the column that contains the text values gridcolor : string Grid color zerolinecolor : string Zero line color margin : dict or tuple Dictionary (l,r,b,t) or Tuple containing the left, right, bottom and top margins subplots : bool If true then each trace is placed in subplot layout shape : (rows,cols) Tuple indicating the size of rows and columns If omitted then the layout is automatically set * Only valid when subplots=True asFrame : bool If true then the data component of Figure will be of Pandas form (Series) otherwise they will be index values asDates : bool If true it truncates times from a DatetimeIndex asFigure : bool If True returns plotly Figure asImage : bool If True it returns Image * Only valid when asImage=True dimensions : tuple(int,int) Dimensions for image (width,height) asPlot : bool If True the chart opens in browser asUrl : bool If True the chart url is returned. No chart is displayed. online : bool If True then the chart is rendered on the server even when running in offline mode. """ # Look for invalid kwargs valid_kwargs = ['color','opacity','column','columns','labels','text','horizontal_spacing', 'vertical_spacing', 'specs', 'insets','start_cell','shared_xaxes','shared_yaxes','subplot_titles'] valid_kwargs.extend(__LAYOUT_KWARGS) for key in kwargs.keys(): if key not in valid_kwargs: raise Exception("Invalid keyword : '{0}'".format(key)) # Setting default values if not colors: colors=kwargs['color'] if 'color' in kwargs else colors if isinstance(colors,str): colors=[colors] opacity=kwargs['opacity'] if 'opacity' in kwargs else 0.8 # Get values from config theme if theme is None: theme = auth.get_config_file()['theme'] theme_config=getTheme(theme) if colorscale is None: colorscale=theme_config['colorscale'] if 'colorscale' in theme_config else 'dflt' if width is None: width=theme_config['linewidth'] if 'linewidth' in theme_config else 2 # if bargap is None: # bargap=theme_config['bargap'] if 'bargap' in theme_config else 0 # In case column was used instead of keys if 'column' in kwargs: keys=[kwargs['column']] if isinstance(kwargs['column'],str) else kwargs['column'] if 'columns' in kwargs: keys=[kwargs['columns']] if isinstance(kwargs['columns'],str) else kwargs['columns'] kind='line' if kind=='lines' else kind if not layout: l_kwargs=dict([(k,kwargs[k]) for k in __LAYOUT_KWARGS if k in kwargs]) if annotations: annotations=getAnnotations(self.copy(),annotations) layout=getLayout(theme=theme,xTitle=xTitle,yTitle=yTitle,zTitle=zTitle,title=title,barmode=barmode, bargap=bargap,bargroupgap=bargroupgap,annotations=annotations,gridcolor=gridcolor, zerolinecolor=zerolinecolor,margin=margin,is3d='3d' in kind,**l_kwargs) if not data: if categories: data=Data() _keys=pd.unique(self[categories]) colors=get_colors(colors,colorscale,_keys) mode='markers' if 'markers' not in mode else mode for _ in _keys: __=self[self[categories]==_].copy() if text: _text=__[text] if asFrame else __[text].values _x=__[x] if asFrame else __[x].values _y=__[y] if asFrame else __[y].values if z: _z=__[z] if asFrame else __[z].values if 'bubble' in kind: rg=__[size].values rgo=self[size].values _size=[int(100*(float(i)-rgo.min())/(rgo.max()-rgo.min()))+12 for i in rg] else: _size=size _data=Scatter3d(x=_x,y=_y,mode=mode,name=_, marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity, line=Line(width=width)),textfont=getLayout(theme=theme)['xaxis1']['titlefont']) if '3d' in kind: _data=Scatter3d(x=_x,y=_y,z=_z,mode=mode,name=_, marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity, line=Line(width=width)),textfont=getLayout(theme=theme)['xaxis1']['titlefont']) else: _data=Scatter(x=_x,y=_y,mode=mode,name=_, marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity, line=Line(width=width)),textfont=getLayout(theme=theme)['xaxis1']['titlefont']) if text: _data.update(text=_text) data.append(_data) else: if kind in ('scatter','spread','ratio','bar','barh','area','line'): df=self.copy() if type(df)==pd.core.series.Series: df=pd.DataFrame({df.name:df}) if x: df=df.set_index(x) if y: df=df[y] if kind=='area': df=df.transpose().fillna(0).cumsum().transpose() data=df.to_iplot(colors=colors,colorscale=colorscale,kind=kind,fill=fill,width=width,sortbars=sortbars,keys=keys, bestfit=bestfit,bestfit_colors=bestfit_colors,asDates=asDates,mode=mode,symbol=symbol,size=size,**kwargs) if kind in ('spread','ratio'): if kind=='spread': trace=self.apply(lambda x:x[0]-x[1],axis=1) positive=trace.apply(lambda x:x if x>=0 else pd.np.nan) negative=trace.apply(lambda x:x if x<0 else pd.np.nan) trace=pd.DataFrame({'positive':positive,'negative':negative}) trace=trace.to_iplot(colors={'positive':'green','negative':'red'},width=0.5) else: trace=self.apply(lambda x:x[0]*1.0/x[1],axis=1).to_iplot(colors=['green'],width=1) trace.update({'xaxis':'x2','yaxis':'y2','fill':'tozeroy', 'name':kind.capitalize(),'connectgaps':False,'showlegend':False}) data.append(Scatter(trace[0])) if kind=='spread': data.append(Scatter(trace[1])) layout['yaxis1'].update({'domain':[.3,1]}) layout['yaxis2']=copy.deepcopy(layout['yaxis1']) layout['xaxis2']=copy.deepcopy(layout['xaxis1']) layout['yaxis2'].update(domain=[0,.25],title=kind.capitalize()) layout['xaxis2'].update(anchor='y2',showticklabels=False) layout['hovermode']='x' if 'bar' in kind: if 'stack' in barmode: layout['legend'].update(traceorder='normal') orientation = 'h' if kind=='barh' else orientation for trace in data: trace.update(orientation=orientation) if kind=='barh': trace['x'],trace['y']=trace['y'],trace['x'] elif kind=='bubble': mode='markers' if 'markers' not in mode else mode x=self[x].values.tolist() y=self[y].values.tolist() z=size if size else z rg=self[z].values z=[int(100*(float(_)-rg.min())/(rg.max()-rg.min()))+12 for _ in rg] text=kwargs['labels'] if 'labels' in kwargs else text labels=self[text].values.tolist() if text else '' clrs=get_colors(colors,colorscale,x).values() gen=colorgen() marker=Marker(color=clrs,size=z,symbol=symbol, line=Line(width=width),textfont=getLayout(theme=theme)['xaxis1']['titlefont']) trace=Scatter(x=x,y=y,marker=marker,mode='markers',text=labels) data=Data([trace]) elif kind in ('box','histogram','hist'): if isinstance(self,pd.core.series.Series): df=pd.DataFrame({self.name:self}) else: df=self.copy() data=Data() clrs=get_colors(colors,colorscale,df.columns) if 'hist' in kind: barmode = 'overlay' if barmode=='' else barmode layout.update(barmode=barmode) columns=keys if keys else df.columns for _ in columns: if kind=='box': __=Box(y=df[_].values.tolist(),marker=Marker(color=clrs[_]),name=_, line=Line(width=width),boxpoints=boxpoints) else: __=Histogram(x=df[_].values.tolist(),marker=Marker(color=clrs[_]),name=_, line=Line(width=width),orientation=orientation, opacity=kwargs['opacity'] if 'opacity' in kwargs else .8, histfunc=histfunc, histnorm=histnorm) if orientation=='h': __['y']=__['x'] del __['x'] if bins: if orientation=='h': __.update(nbinsy=bins) else: __.update(nbinsx=bins) data.append(__) elif kind in ('heatmap','surface'): x=self[x].values.tolist() if x else self.index.values.tolist() y=self[y].values.tolist() if y else self.columns.values.tolist() z=self[z].values.tolist() if z else self.values.transpose() scale=get_scales('rdbu') if not colorscale else get_scales(colorscale) colorscale=[[float(_)/(len(scale)-1),scale[_]] for _ in range(len(scale))] if kind=='heatmap': data=Data([Heatmap(z=z,x=x,y=y,colorscale=colorscale)]) else: data=Data([Surface(z=z,x=x,y=y,colorscale=colorscale)]) elif kind in ('scatter3d','bubble3d'): data=Data() keys=self[text].values if text else range(len(self)) colors=get_colors(colors,colorscale,keys,asList=True) df=self.copy() df['index']=keys if kind=='bubble3d': rg=self[size].values size=[int(100*(float(_)-rg.min())/(rg.max()-rg.min()))+12 for _ in rg] else: size=[size for _ in range(len(keys))] _data=Scatter3d(x=df[x].values.tolist(),y=df[y].values.tolist(),z=df[z].values.tolist(),mode=mode,name=keys, marker=Marker(color=colors,symbol=symbol,size=size,opacity=.8)) if text: _data.update(text=keys) data.append(_data) if world_readable is None: world_readable = auth.get_config_file()['world_readable'] if not filename: if title: filename=title else: filename='Plotly Playground {0}'.format(time.strftime("%Y-%m-%d %H:%M:%S")) ## Figure defintion figure=Figure() figure['data']=data figure['layout']=layout ## Subplots if subplots: fig=tools.strip_figures(figure) kw={} if 'horizontal_spacing' in kwargs: kw['horizontal_spacing']=kwargs['horizontal_spacing'] if 'vertical_spacing' in kwargs: kw['vertical_spacing']=kwargs['vertical_spacing'] if 'specs' in kwargs: kw['specs']=kwargs['specs'] if 'shared_xaxes' in kwargs: kw['shared_xaxes']=kwargs['shared_xaxes'] if 'shared_yaxes' in kwargs: kw['shared_yaxes']=kwargs['shared_yaxes'] if 'subplot_titles' in kwargs: if kwargs['subplot_titles']==True: kw['subplot_titles']=[d['name'] for d in data] else: kw['subplot_titles']=kwargs['subplot_titles'] if 'start_cell' in kwargs: kw['start_cell']=kwargs['start_cell'] figure=tools.subplots(fig,shape,base_layout=layout,theme=theme,**kw) ## Exports validate = False if 'shapes' in layout else True if asFigure: return figure elif asImage: py.image.save_as(figure,filename=filename,format='png', width=dimensions[0],height=dimensions[1]) return display(Image(filename+'.png')) elif asPlot: return py.plot(figure,world_readable=world_readable,filename=filename,validate=validate) elif asUrl: return py.plot(figure,world_readable=world_readable,filename=filename,validate=validate,auto_open=False) else: return iplot(figure,world_readable=world_readable,filename=filename,validate=validate,online=online)