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]=clrgen.next()
return colors
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 _iplot(self,data=None,layout=None,filename='',world_readable=False,
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,
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
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 '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].values,y=__[y].values,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].values,y=__[y].values,z=__[z].values,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].values,y=__[y].values,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].values.tolist())
data.append(_data)
else:
if kind in ('scatter','spread','ratio','bar'):
data=self.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 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)
