def get_candle(df, up_color=None, down_color=None, theme=None, **kwargs): ohlc = ["open", "high", "low", "close"] if not theme: theme = auth.get_config_file()["theme"] layout = getLayout(theme=theme) c_dir = _ohlc_dict(df) args = [df[c_dir[_]] for _ in ohlc] args.append(df.index) fig = py.plotly.tools.FigureFactory.create_candlestick(*args, **kwargs) candle = Figure() candle["data"] = fig["data"] candle["layout"] = layout data = candle["data"] def update_color(n, color): data[n]["fillcolor"] = normalize(color) data[n]["line"].update(color=normalize(color)) if up_color: update_color(0, up_color) if down_color: update_color(1, down_color) candle["layout"]["hovermode"] = "closest" layout = getLayout(theme=theme) candle["layout"] = merge_dict(layout, candle["layout"]) return candle
def colorgen(colors=None,n=None,scale=None,theme=None): """ Returns a generator with a list of colors and gradients of those colors Parameters: ----------- colors : list(colors) List of colors to use Example: colorgen() colorgen(['blue','red','pink']) colorgen(['#f03','rgb(23,25,25)']) """ step=.1 if not colors: if not scale: if not theme: scale = get_config_file()['colorscale'] else: scale = themes.THEMES[theme]['colorscale'] colors=get_scales(scale) dq=deque(colors) if n: step=len(dq)*0.8/n if len(dq)*8<n else .1 for i in np.arange(.2,1,step): for y in dq: yield to_rgba(y,1-i+.2) dq.rotate()
def iplot(data_or_figure,validate=True,sharing=None,filename='',online=None,**kwargs): """ Plots a figure in IPython data_or_figure : figure Plotly figure to be charted validate : bool If True then all values are validated before it is charted sharing : string Sets the sharing level permission public - anyone can see this chart private - only you can see this chart secret - only people with the link can see the chart filename : string Name to be used to save the file in the server online : bool If True then charts are rendered in the server """ valid_kwargs=['world_readable'] for key in kwargs.keys(): if key not in valid_kwargs: raise Exception("Invalid keyword : '{0}'".format(key)) if all(['world_readable' in kwargs,sharing is None]): sharing=kwargs['world_readable'] if isinstance(sharing,bool): if sharing: sharing='public' else: sharing='private' if sharing is None: sharing=auth.get_config_file()['sharing'] if offline.is_offline() and not online: show_link = auth.get_config_file()['offline_show_link'] link_text = auth.get_config_file()['offline_link_text'] return offline.py_offline.iplot(data_or_figure,show_link=show_link,link_text=link_text) else: if 'layout' in data_or_figure: validate = False if 'shapes' in data_or_figure['layout'] else validate return py.iplot(data_or_figure,validate=validate,sharing=sharing, filename=filename)
def scatter_matrix(df, theme=None, bins=10, color="grey", size=2): """ Displays a matrix with scatter plot for each pair of Series in the DataFrame. The diagonal shows a histogram for each of the Series Parameters: ----------- df : DataFrame Pandas DataFrame theme : string Theme to be used (if not the default) bins : int Number of bins to use for histogram color : string Color to be used for each scatter plot size : int Size for each marker on the scatter plot """ if not theme: theme = auth.get_config_file()["theme"] figs = [] for i in df.columns: for j in df.columns: if i == j: fig = df.iplot(kind="histogram", keys=[i], asFigure=True, bins=bins) figs.append(fig) else: figs.append( df.iplot(kind="scatter", mode="markers", x=j, y=i, asFigure=True, size=size, colors=[color]) ) layout = getLayout(theme) layout["xaxis1"].update(showgrid=False) layout["yaxis1"].update(showgrid=False) sm = subplots( figs, shape=(len(df.columns), len(df.columns)), shared_xaxes=False, shared_yaxes=False, horizontal_spacing=0.05, vertical_spacing=0.07, base_layout=layout, ) sm["layout"].update(bargap=0.02, showlegend=False) return sm
def get_ohlc(df, up_color=None, down_color=None, theme=None, **kwargs): ohlc = ["open", "high", "low", "close"] if not theme: theme = auth.get_config_file()["theme"] c_dir = _ohlc_dict(df) args = [df[c_dir[_]] for _ in ohlc] args.append(df.index) fig = py.plotly.tools.FigureFactory.create_ohlc(*args, **kwargs) ohlc_bars = Figure() ohlc_bars["data"] = fig["data"] ohlc_bars["layout"] = fig["layout"] data = ohlc_bars["data"] if up_color: data[0]["line"].update(color=normalize(up_color)) if down_color: data[1]["line"].update(color=normalize(down_color)) ohlc_bars["layout"]["hovermode"] = "closest" layout = getLayout(theme=theme) ohlc_bars["layout"] = merge_dict(layout, ohlc_bars["layout"]) return ohlc_bars
def getName(n=1, name=3, exchange=2, columns=None, mode="abc"): if columns: if isinstance(columns, str): columns = [columns] if n != len(columns): raise CufflinksError("Length of column names needs to be the \n" "same length of traces") else: if mode is None: mode = get_config_file()["datagen_mode"] if mode == "abc": columns = list(string.ascii_letters[:n]) elif mode == "stocks": columns = [ "".join(np.random.choice(list(string.uppercase), name)) + "." + "".join(np.random.choice(list(string.uppercase), exchange)) for _ in range(n) ] else: raise CufflinksError("Unknown mode: {0}".format(mode)) return columns
A productivity tool that binds pandas and plotly. It also provides tools for color generation and transformation. Author: @jorgesantos """ import datetools import utils import datagen import tools import colors import pandastools import ta from plotlytools import * from plotly.plotly import plot from utils import pp from tools import subplots,scatter_matrix,figures from extract import to_df from auth import set_config_file,get_config_file from offline import is_offline,go_offline,go_online from version import __version__ if get_config_file()['offline']: go_offline() else: go_online()
def get_subplots( rows=1, cols=1, shared_xaxes=False, shared_yaxes=False, start_cell="top-left", theme=None, base_layout=None, **kwargs ): """ Generates a subplot view for a set of figures Parameters: ----------- rows : int Number of rows cols : int Number of cols shared_xaxes : bool Assign shared x axes. If True, subplots in the same grid column have one common shared x-axis at the bottom of the gird. shared_yaxes : bool Assign shared y axes. If True, subplots in the same grid row have one common shared y-axis on the left-hand side of the gird. start_cell : string 'bottom-left' 'top-left' Choose the starting cell in the subplot grid used to set the domains of the subplots. theme : string Layout Theme solar pearl white see cufflinks.getThemes() for all available themes horizontal_spacing : float [0,1] Space between subplot columns. vertical_spacing : float Space between subplot rows. specs : list of dicts Subplot specifications. ex1: specs=[[{}, {}], [{'colspan': 2}, None]] ex2: specs=[[{'rowspan': 2}, {}], [None, {}]] - Indices of the outer list correspond to subplot grid rows starting from the bottom. The number of rows in 'specs' must be equal to 'rows'. - Indices of the inner lists correspond to subplot grid columns starting from the left. The number of columns in 'specs' must be equal to 'cols'. - Each item in the 'specs' list corresponds to one subplot in a subplot grid. (N.B. The subplot grid has exactly 'rows' times 'cols' cells.) - Use None for blank a subplot cell (or to move pass a col/row span). - Note that specs[0][0] has the specs of the 'start_cell' subplot. - Each item in 'specs' is a dictionary. The available keys are: * is_3d (boolean, default=False): flag for 3d scenes * colspan (int, default=1): number of subplot columns for this subplot to span. * rowspan (int, default=1): number of subplot rows for this subplot to span. * l (float, default=0.0): padding left of cell * r (float, default=0.0): padding right of cell * t (float, default=0.0): padding right of cell * b (float, default=0.0): padding bottom of cell - Use 'horizontal_spacing' and 'vertical_spacing' to adjust the spacing in between the subplots. insets : list of dicts Inset specifications. - Each item in 'insets' is a dictionary. The available keys are: * cell (tuple, default=(1,1)): (row, col) index of the subplot cell to overlay inset axes onto. * is_3d (boolean, default=False): flag for 3d scenes * l (float, default=0.0): padding left of inset in fraction of cell width * w (float or 'to_end', default='to_end') inset width in fraction of cell width ('to_end': to cell right edge) * b (float, default=0.0): padding bottom of inset in fraction of cell height * h (float or 'to_end', default='to_end') inset height in fraction of cell height ('to_end': to cell top edge) """ if not theme: theme = auth.get_config_file()["theme"] layout = base_layout if base_layout else getLayout(theme) sp = py.plotly.tools.make_subplots( rows=rows, cols=cols, shared_xaxes=shared_xaxes, shared_yaxes=shared_yaxes, print_grid=False, start_cell=start_cell, **kwargs ) for k, v in layout.items(): if not isinstance(v, XAxis) and not isinstance(v, YAxis): sp["layout"].update({k: v}) if "subplot_titles" in kwargs: if "annotations" in layout: annotation = sp["layout"]["annotations"][0] else: annotation = getLayout(theme, annotations=Annotation(text=""))["annotations"] for ann in sp["layout"]["annotations"]: ann["font"].update(color=annotation["font"]["color"]) def update_items(sp_item, layout, axis): for k, v in layout[axis].items(): sp_item.update({k: v}) for k, v in sp["layout"].items(): if isinstance(v, XAxis): update_items(v, layout, "xaxis1") elif isinstance(v, YAxis): update_items(v, layout, "xaxis1") return sp
def getLayout( theme=None, title="", xTitle="", yTitle="", zTitle="", barmode="", bargap=None, bargroupgap=None, gridcolor=None, zerolinecolor=None, margin=None, annotations=False, is3d=False, **kwargs ): """ Generates a plotly Layout Parameters: ----------- theme : string Layout Theme solar pearl white title : string Chart Title xTitle : string X Axis Title yTitle : string Y Axis Title zTitle : string Z Axis Title Applicable only for 3d charts barmode : string Mode when displaying bars group stack overlay bargap : float Sets the gap between bars [0,1) Applicabe for bar and histogram plots bargroupgap : float Set the gap between groups [0,1) Applicabe for bar and histogram plots 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 annotations : dictionary Dictionary of annotations {x_point : text} is3d : bool Indicates if the layout is for a 3D chart Other Kwargs ============ Shapes hline : int, list or dict Draws a horizontal line at the indicated y position(s) Extra parameters can be passed in the form of a dictionary (see shapes) vline : int, list or dict Draws a vertical line at the indicated x position(s) Extra parameters can be passed in the form of a dictionary (see shapes) hline : [y0,y1] Draws a horizontal rectangle at the indicated (y0,y1) positions. Extra parameters can be passed in the form of a dictionary (see shapes) vline : [x0,x1] Draws a vertical rectangle at the indicated (x0,x1) positions. Extra parameters can be passed in the form of a dictionary (see shapes) shapes : dict or list(dict) List of dictionaries with the specifications of a given shape. See help(cufflinks.tools.get_shape) for more information """ for key in kwargs.keys(): if key not in __LAYOUT_KWARGS: raise Exception("Invalid keyword : '{0}'".format(key)) if not theme: theme = auth.get_config_file()["theme"] size = None if annotations: if "font" in annotations: if "size" in annotations["font"]: size = annotations["font"]["size"] def update_annotations(annotations, font_color, arrow_color): if annotations: if isinstance(annotations, dict): annotations = [annotations] for i in annotations: i.update(dict(arrowcolor=arrow_color, font={"color": font_color})) theme_data = getTheme(theme) layout = theme_data["layout"] layout["xaxis1"].update({"title": xTitle}) layout["yaxis1"].update({"title": yTitle}) if annotations: update_annotations(annotations, theme_data["annotations"]["fontcolor"], theme_data["annotations"]["arrowcolor"]) if barmode: layout.update({"barmode": barmode}) if bargroupgap: layout.update({"bargroupgap": bargroupgap}) if bargap: layout.update(bargap=bargap) if title: layout.update({"title": title}) if annotations: if size: annotations["font"]["size"] = size layout.update({"annotations": annotations}) if gridcolor: for k in layout: if "axis" in k: layout[k].update(gridcolor=normalize(gridcolor)) if zerolinecolor: for k in layout: if "axis" in k: layout[k].update(zerolinecolor=normalize(zerolinecolor)) if margin: if isinstance(margin, dict): margin = margin else: margin = dict(zip(("l", "r", "b", "t"), margin)) layout.update(margin=margin) if is3d: if "3d" in theme_data: layout.update(theme_data["3d"]) zaxis = layout["xaxis1"].copy() zaxis.update(title=zTitle) scene = Scene(xaxis=layout["xaxis1"], yaxis=layout["yaxis1"], zaxis=zaxis) layout.update(scene=scene) del layout["xaxis1"] del layout["yaxis1"] ## Kwargs if "legend" in kwargs: layout["showlegend"] = kwargs["legend"] if "logy" in kwargs: if kwargs["logy"]: layout["yaxis1"]["type"] = "log" if "logx" in kwargs: if kwargs["logx"]: layout["xaxis1"]["type"] = "log" # Shapes if any(k in kwargs for k in ["vline", "hline", "shapes", "hspan", "vspan"]): shapes = [] def get_shapes(xline): orientation = xline[0] xline = kwargs[xline] if isinstance(xline, list): for x_i in xline: if isinstance(x_i, dict): x_i["kind"] = "line" shapes.append(get_shape(**x_i)) else: if orientation == "h": shapes.append(get_shape(kind="line", y=x_i)) else: shapes.append(get_shape(kind="line", x=x_i)) elif isinstance(xline, dict): shapes.append(get_shape(**xline)) else: if orientation == "h": shapes.append(get_shape(kind="line", y=xline)) else: shapes.append(get_shape(kind="line", x=xline)) def get_span(xspan): orientation = xspan[0] xspan = kwargs[xspan] if isinstance(xspan, list): for x_i in xspan: if isinstance(x_i, dict): x_i["kind"] = "rect" shapes.append(get_shape(**x_i)) else: v0, v1 = x_i if orientation == "h": shapes.append(get_shape(kind="rect", y0=v0, y1=v1, fill=True, opacity=0.5)) else: shapes.append(get_shape(kind="rect", x0=v0, x1=v1, fill=True, opacity=0.5)) elif isinstance(xspan, dict): xspan["kind"] = "rect" shapes.append(get_shape(**xspan)) elif isinstance(xspan, tuple): v0, v1 = xspan if orientation == "h": shapes.append(get_shape(kind="rect", y0=v0, y1=v1, fill=True, opacity=0.5)) else: shapes.append(get_shape(kind="rect", x0=v0, x1=v1, fill=True, opacity=0.5)) else: raise Exception("Invalid value for {0}span: {1}".format(orientation, xspan)) if "hline" in kwargs: get_shapes("hline") if "vline" in kwargs: get_shapes("vline") if "hspan" in kwargs: get_span("hspan") if "vspan" in kwargs: get_span("vspan") if "shapes" in kwargs: shapes_ = kwargs["shapes"] if isinstance(shapes_, list): for i in shapes_: shp = i if "type" in i else get_shape(**i) shapes.append(shp) elif isinstance(shapes_, dict): shp = shapes_ if "type" in shapes_ else get_shape(**shapes_) shapes.append(shp) else: raise Exception("Shapes need to be either a dict or list of dicts") layout["shapes"] = shapes def updateColors(layout): for k, v in layout.items(): if isinstance(v, dict): updateColors(v) else: if isinstance(v, list): for _ in v: if isinstance(_, dict): updateColors(_) if "color" in k.lower(): if "rgba" not in v: layout[k] = normalize(v) return layout return updateColors(layout)
def _iplot(self,data=None,layout=None,filename='',sharing=None, kind='scatter',title='',xTitle='',yTitle='',zTitle='',theme=None,colors=None,colorscale=None,fill=False,width=None, dash='solid',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,mean=False,mean_colors=None,categories='',x='',y='',z='',text='',gridcolor=None, zerolinecolor=None,margin=None,labels=None,values=None,secondary_y='',subplots=False,shape=None,error_x=None, error_y=None,error_type='data',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 sharing : string Sets the sharing level permission public - anyone can see this chart private - only you can see this chart secret - only people with the link can see the chart 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 dash : string Drawing style of lines solid dash dashdot dot 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 labels : string Name of the column that contains the labels. * Only valid when kind='pie' values : string Name of the column that contains the values. * Only valid when kind='pie' secondary_y : string or list(string) Name(s) of the column to be charted on the right hand side axis 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 error_x : int or float or [int or float] error values for the x axis error_y : int or float or [int or float] error values for the y axis error_type : string type of error bars 'data' 'constant' 'percent' 'sqrt' 'continuous' 'continuous_percent' 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. Other Kwargs ============ Pie charts sort : bool If True it sorts the labels by value pull : float [0-1] Pulls the slices from the centre hole : float [0-1] Sets the size of the inner hole textposition : string Sets the position of the legends for each slice outside inner textinfo : string Sets the information to be displayed on the legends label percent value * or ony combination of the above using '+' between each item ie 'label+percent' Error Bars error_trace : string Name of the column for which error should be plotted. If omitted then errors apply to all traces. error_values_minus : int or float or [int or float] Values corresponding to the span of the error bars below the trace coordinates error_color : string Color for error bars error_thickness : float Sets the line thickness of the error bars error_width : float Sets the width (in pixels) of the cross-bar at both ends of the error bars error_opacity : float [0,1] Opacity for the error bars Subplots horizontal_spacing : float [0,1] Space between subplot columns. vertical_spacing : float [0,1] Space between subplot rows. subplot_titles : bool If True, chart titles are plotted at the top of each subplot shared_xaxes : bool Assign shared x axes. If True, subplots in the same grid column have one common shared x-axis at the bottom of the grid. shared_yaxes : bool Assign shared y axes. If True, subplots in the same grid row have one common shared y-axis on the left-hand side of the grid. Shapes hline : int, list or dict Draws a horizontal line at the indicated y position(s) Extra parameters can be passed in the form of a dictionary (see shapes) vline : int, list or dict Draws a vertical line at the indicated x position(s) Extra parameters can be passed in the form of a dictionary (see shapes) hline : [y0,y1] Draws a horizontal rectangle at the indicated (y0,y1) positions. Extra parameters can be passed in the form of a dictionary (see shapes) vline : [x0,x1] Draws a vertical rectangle at the indicated (x0,x1) positions. Extra parameters can be passed in the form of a dictionary (see shapes) shapes : dict or list(dict) List of dictionaries with the specifications of a given shape. See help(cufflinks.tools.get_shape) for more information """ # Look for invalid kwargs valid_kwargs = ['color','opacity','column','columns','labels','text','world_readable'] PIE_KWARGS=['sort','pull','hole','textposition','textinfo','linecolor'] OHLC_KWARGS=['up_color','down_color'] SUBPLOT_KWARGS=['horizontal_spacing', 'vertical_spacing', 'specs', 'insets','start_cell','shared_xaxes','shared_yaxes','subplot_titles'] ERROR_KWARGS=['error_trace','error_values_minus','error_color','error_thickness', 'error_width','error_opacity'] kwargs_list = [tools.__LAYOUT_KWARGS,OHLC_KWARGS,PIE_KWARGS,SUBPLOT_KWARGS,ERROR_KWARGS] [valid_kwargs.extend(_) for _ in kwargs_list] 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=tools.getTheme(theme) if colorscale is None: colorscale=theme_config['colorscale'] if 'colorscale' in theme_config else 'dflt' if width is None: if kind != 'pie': 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 # We assume we are good citizens validate=True if not layout: l_kwargs=dict([(k,kwargs[k]) for k in tools.__LAYOUT_KWARGS if k in kwargs]) if annotations: annotations=getAnnotations(self.copy(),annotations) layout=tools.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() if 'bar' in kind: df=self.copy() df=df.set_index(categories) fig=df.figure(kind=kind,colors=colors,colorscale=colorscale,fill=fill,width=width,sortbars=sortbars, asDates=asDates,mode=mode,symbol=symbol,size=size,text=text,barmode=barmode,orientation=orientation) data=fig['data'] else: _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=tools.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=tools.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=tools.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() if text: if not isinstance(text,list): text=self[text].values data=df.to_iplot(colors=colors,colorscale=colorscale,kind=kind,fill=fill,width=width,dash=dash,sortbars=sortbars,keys=keys, bestfit=bestfit,bestfit_colors=bestfit_colors,mean=mean,mean_colors=mean_colors,asDates=asDates,mode=mode,symbol=symbol,size=size, text=text,**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=colors if colors else get_scales(colorscale) clrs=[clrs] if not isinstance(clrs,list) else clrs clrs=[clrs[0]]*len(x) marker=Marker(color=clrs,size=z,symbol=symbol, line=Line(width=width),textfont=tools.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) elif kind=='pie': labels=self[labels].values.tolist() values=self[values].values.tolist() marker={'colors':get_colors(colors,colorscale,labels,asList=True)} line={} if 'linecolor' in kwargs: line['color']=kwargs['linecolor'] if width: line['width']=width if line: marker['line']=line pie={'type':'pie','values':values,'labels':labels,'name':'', 'marker':marker} for kw in ['sort','pull','hole','textposition','textinfo']: if kw in kwargs: pie[kw]=kwargs[kw] data=Data() del layout['xaxis1'] del layout['yaxis1'] data.append(pie) validate=False elif kind in ['candle','ohlc']: d=tools._ohlc_dict(self) if len(d.keys())!=4: raise Exception("OHLC type of charts require an Open, High, Low and Close column") ohlc_kwargs=check_kwargs(kwargs,OHLC_KWARGS) if kind=='candle': fig=tools.get_candle(self,theme=theme,**ohlc_kwargs) else: fig=tools.get_ohlc(self,theme=theme,**ohlc_kwargs) if bestfit: df=self.copy() bf=_to_iplot(self[d['close']],bestfit=True,bestfit_colors=bestfit_colors,asTimestamp=True) fig['data'].append(bf[1]) data=fig['data'] layout=fig['layout'] ## Sharing Values if all(['world_readable' in kwargs,sharing is None]): sharing=kwargs['world_readable'] if isinstance(sharing,bool): if sharing: sharing='public' else: sharing='private' if sharing is None: sharing=auth.get_config_file()['sharing'] 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 ## Check secondary axis if secondary_y: figure=figure.set_axis(secondary_y,side='right') ## Error Bars if kind in ('scatter','bar','barh','lines'): if any([error_x,error_y]): def set_error(axis,**kwargs): return tools.set_errors(figure,axis=axis,**kwargs) kw=check_kwargs(kwargs,ERROR_KWARGS) kw=dict([(k.replace('error_',''),v) for k,v in kw.items()]) kw['type']=error_type if error_x: kw['values']=error_x figure=set_error('x',**kw) if error_y: kw['values']=error_y figure=set_error('y',**kw) ## Subplots if subplots: fig=tools.strip_figures(figure) kw=check_kwargs(kwargs,SUBPLOT_KWARGS) 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'] figure=tools.subplots(fig,shape,base_layout=layout,theme=theme,**kw) ## Exports validate = False if 'shapes' in layout else validate if asFigure: return figure elif asImage: try: py.image.save_as(figure,filename='img/'+filename,format='png', width=dimensions[0],height=dimensions[1]) path='img/'+filename+'.png' except: py.image.save_as(figure,filename=filename,format='png', width=dimensions[0],height=dimensions[1]) path=filename+'.png' return display(Image(path)) elif asPlot: return py.plot(figure,sharing=sharing,filename=filename,validate=validate) elif asUrl: return py.plot(figure,sharing=sharing,filename=filename,validate=validate,auto_open=False) else: return iplot(figure,sharing=sharing,filename=filename,validate=validate,online=online)
def _ta_plot(self,study,periods=14,column=None,include=True,str=None,detail=False, theme=None,sharing=None,filename='',asFigure=False,**iplot_kwargs): """ Generates a Technical Study Chart Parameters: ----------- study : string Technical Study to be charted sma - 'Simple Moving Average' rsi - 'R Strength Indicator' periods : int Number of periods column : string Name of the column on which the study will be done include : bool Indicates if the input column(s) should be included in the chart str : string Label factory for studies The following wildcards can be used: {name} : Name of the column {study} : Name of the study {period} : Period used Examples: 'study: {study} - period: {period}' detail : bool If True the supporting data/calculations are included in the chart study_colors : string or [string] Colors to be used for the studies Study Specific Parameters ------------------------- RSI rsi_upper : int (0,100] Level for the upper rsi band rsi_lower : int (0,100] Level for the lower rsi band BOLL boll_std : int or float Number of standard deviations MACD fast_period : int Number of periods for the fast moving average slow_period : int Number of periods for the slow moving average signal_period : int Number of periods for the signal CORREL how : string Method for the correlation calculation values pct_cht diff """ if 'columns' in iplot_kwargs: column=iplot_kwargs['columns'] del iplot_kwargs['columns'] if 'period' in iplot_kwargs: periods=iplot_kwargs['period'] del iplot_kwargs['period'] if 'world_readable' in iplot_kwargs: sharing=iplot_kwargs['world_readable'] del iplot_kwargs['world_readable'] if sharing is None: sharing = auth.get_config_file()['sharing'] if isinstance(sharing,bool): if sharing: sharing='public' else: sharing='private' iplot_kwargs['sharing']=sharing if theme is None: theme = auth.get_config_file()['theme'] if not filename: if 'title' in iplot_kwargs: filename=iplot_kwargs['title'] else: filename='Plotly Playground {0}'.format(time.strftime("%Y-%m-%d %H:%M:%S")) def get_subplots(figures): shape=(len(figures),1) layout=tools.get_base_layout(figures) subplots=tools.subplots(figures,shape=shape,shared_xaxes=True,base_layout=layout) if len(figures)==2: subplots['layout']['yaxis1']['domain']=[.27,1.0] subplots['layout']['yaxis2']['domain']=[0,.25] return subplots def get_study(df,func,iplot_kwargs,iplot_study_kwargs,str=None,include=False,column=None,inset=False): df=df.copy() if inset: if not column: if isinstance(df,pd.DataFrame): column=df.keys().tolist() else: df=pd.DataFrame(df) column=df.keys().tolist() if 'legend' in iplot_kwargs: iplot_study_kwargs['legend']=iplot_kwargs['legend'] fig_0=df.figure(**iplot_kwargs) df_ta=func(df,column=column,include=False,str=str,**study_kwargs) kind=iplot_kwargs['kind'] if 'kind' in iplot_kwargs else '' iplot_study_kwargs['kind']='scatter' iplot_study_kwargs['colors']='blue' if 'colors' not in iplot_study_kwargs else iplot_study_kwargs['colors'] fig_1=df_ta.figure(theme=theme,**iplot_study_kwargs) if kind in ['candle','ohlc']: for i in fig_1['data']: i['x']=[pd.Timestamp(_) for _ in i['x']] if inset: figure=tools.merge_figures([fig_0,fig_1]) if include else fig_1 else: figure=get_subplots([fig_0,fig_1]) if include else fig_1 return figure study_kwargs={} iplot_study_kwargs={} for k in iplot_kwargs.keys(): if 'study' in k: iplot_study_kwargs[k.replace('study_','')]=iplot_kwargs[k] del iplot_kwargs[k] for k in __TA_KWARGS: if k in iplot_kwargs: study_kwargs[k]=iplot_kwargs[k] del iplot_kwargs[k] if study=='rsi': study_kwargs.update({'periods':periods}) figure=get_study(self,ta.rsi,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=False) rsi_upper=study_kwargs['rsi_upper'] if 'rsi_upper' in study_kwargs else 70 rsi_lower=study_kwargs['rsi_lower'] if 'rsi_lower' in study_kwargs else 30 yref='y2' if include else 'y1' shapes=[tools.get_shape(y=i,yref=yref,color=j,dash='dash') for (i,j) in [(rsi_lower,'green'),(rsi_upper,'red')]] figure['layout']['shapes']=shapes if study=='macd': figure=get_study(self,ta.macd,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=False) if study=='sma': study_kwargs.update({'periods':periods}) figure=get_study(self,ta.sma,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=True) if study=='boll': study_kwargs.update({'periods':periods}) figure=get_study(self,ta.boll,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=True) if study=='correl': study_kwargs.update({'periods':periods}) figure=get_study(self,ta.correl,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=False) ## Exports if asFigure: return figure else: return iplot(figure,sharing=sharing,filename=filename)
def _iplot(self,data=None,layout=None,filename='',world_readable=None, kind='scatter',title='',xTitle='',yTitle='',zTitle='',theme='pearl',colors=None,colorscale=None,fill=False,width=2, mode='lines',symbol='dot',size=12,barmode='',sortbars=False,boxpoints=False,annotations=None,keys=False,bestfit=False,bestfit_colors=None, categories='',x='',y='',z='',text='',gridcolor=None,zerolinecolor=None,margin=None, asFrame=False,asDates=False,asFigure=False,asImage=False,dimensions=(1116,587), asPlot=False,asUrl=False,**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 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' 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 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. """ 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 if not layout: if annotations: annotations=getAnnotations(self.copy(),annotations) layout=getLayout(theme=theme,xTitle=xTitle,yTitle=yTitle,zTitle=zTitle,title=title,barmode=barmode, annotations=annotations,gridcolor=gridcolor,zerolinecolor=zerolinecolor,margin=margin,is3d='3d' in kind) 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)['xaxis']['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)['xaxis']['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)['xaxis']['titlefont']) if text: _data.update(text=_text) data.append(_data) else: if kind in ('scatter','spread','ratio','bar'): df=self.copy() if x: df=df.set_index(x) if y: df=df[y] 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) 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) else: trace=self.apply(lambda x:x[0]*1.0/x[1],axis=1).to_iplot() 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['yaxis'].update({'domain':[.3,1]}) layout['yaxis2']=copy.deepcopy(layout['yaxis']) layout['xaxis2']=copy.deepcopy(layout['xaxis']) layout['yaxis2'].update(domain=[0,.25],title=kind.capitalize()) layout['xaxis2'].update(anchor='y2',showticklabels=False) 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() 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)['xaxis']['titlefont']) trace=Scatter(x=x,y=y,marker=marker,mode='markers',text=labels) data=Data([trace]) elif kind in ('box','histogram'): data=Data() clrs=get_colors(colors,colorscale,self.columns) if kind=='histogram': layout.update(barmode='overlay') for _ in self.columns: if kind=='box': __=Box(y=self[_].values.tolist(),marker=Marker(color=clrs[_]),name=_, line=Line(width=width),boxpoints=boxpoints) else: __=Histogram(x=self[_].values.tolist(),marker=Marker(color=clrs[_]),name=_, line=Line(width=width), opacity=kwargs['opacity'] if 'opacity' in kwargs else .8) 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' if asFigure: return Figure(data=data,layout=layout) elif asImage: py.image.save_as(Figure(data=data,layout=layout),filename='img/'+filename,format='png', width=dimensions[0],height=dimensions[1]) return display(Image('img/'+filename+'.png')) elif asPlot: return py.plot(Figure(data=data,layout=layout),world_readable=world_readable,filename=filename) elif asUrl: return py.plot(Figure(data=data,layout=layout),world_readable=world_readable,filename=filename,auto_open=False) else: return py.iplot(Figure(data=data,layout=layout),world_readable=world_readable,filename=filename)