Plotly Visualization¶

The aim of this notebook is to proivde guidelines on how to achieve parity with Pandas' visualization methods as explained in http://pandas.pydata.org/pandas-docs/stable/visualization.html with the use of Plotly and Cufflinks

In [50]:
import pandas as pd
import numpy as np
from IPython.display import display,HTML

In [51]:
%reload_ext autoreload


Theme¶

Cufflinks can set global theme (sytle) to used. In this case we will use Matplotlib's ggplot style.

In [52]:
cf.set_config_file(theme='ggplot',sharing='public',offline=False)


Basic Plotting¶

The iplot method on Series and DataFrame is wrapper of Plotly's plot method

In [53]:
# Cufflinks can generate random data for different shapes
# Let's generate a single line with 1000 points
cf.datagen.lines(1,1000).iplot()

Out[53]:
In [54]:
# Generating 4 timeseries
df=cf.datagen.lines(4,1000)
df.iplot()

Out[54]:

You can plot one column versus another using the x and y keywords in iplot

In [55]:
df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()
df3['A'] = pd.Series(list(range(len(df3))))
df3.iplot(x='A', y='B')

Out[55]:

Bar Plots¶

In [56]:
df.ix[3].iplot(kind='bar',bargap=.5)

Out[56]:

Calling a DataFrame’s plot() method with kind='bar' produces a multiple bar plot:

In [57]:
df=pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.iplot(kind='bar')

Out[57]:

To produce a stacked bar plot, use barmode=stack

In [58]:
df.iplot(kind='bar',barmode='stack')

Out[58]:

To get horizontal bar plots, pass kind='barh'

In [59]:
df.iplot(kind='barh',barmode='stack',bargap=.1)

Out[59]:

Histograms¶

Historgrams can be used with kind='histogram'

In [60]:
df = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])


In [61]:
df.iplot(kind='histogram')

Out[61]:

Histogram can be stacked by using barmode=stack. Bin size can be changed by bin keyword.

In [62]:
df.iplot(kind='histogram',barmode='stack',bins=20)

Out[62]:

Orientation can normalization can also be set for Histograms by using orientation='horizontal' and histnorm=probability.

In [63]:
df.iplot(kind='histogram',columns=['a'],orientation='h',histnorm='probability')

Out[63]:

Histograms (and any other kind of plot) can be set in a multiple layout by using subplots=True

In [64]:
df_h=cf.datagen.histogram(4)
df_h.iplot(kind='histogram',subplots=True,bins=50)

Out[64]:

Box Plots¶

Boxplots can be drawn calling a Series and DataFrame with kind='box'

In [65]:
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.iplot(kind='box')

Out[65]:

Grouping values¶

In [66]:
df = pd.DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )
df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])


Grouping values by generating a list of figures

In [67]:
figs=[df[df['X']==d][['Col1','Col2']].iplot(kind='box',asFigure=True) for d in pd.unique(df['X']) ]

In [68]:
cf.iplot(cf.subplots(figs))

Out[68]:

Grouping values and ammending the keys

In [69]:
def by(df,category):
l=[]
for cat in pd.unique(df[category]):
_df=df[df[category]==cat]
del _df[category]
_df=_df.rename(columns=dict([(k,'{0}_{1}'.format(cat,k)) for k in _df.columns]))
l.append(_df.iplot(kind='box',asFigure=True))
return l


In [70]:
cf.iplot(cf.subplots(by(df,'X')))

Out[70]:

Area Plots¶

You can create area plots with Series.plot and DataFrame.plot by passing kind='area'. To produce stacked area plot, each column must be either all positive or all negative values.

When input data contains NaN, it will be automatically filled by 0. If you want to drop or fill by different values, use dataframe.dropna() or dataframe.fillna() before calling plot.

To fill the area you can use fill=True

In [71]:
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

In [72]:
df.iplot(kind='area',fill=True,opacity=1)

Out[72]:

For non-stacked charts you can use kind=scatter with fill=True. Alpha value is set to 0.3 unless otherwise specified:

In [73]:
df.iplot(fill=True)

Out[73]:

Scatter Plot¶

You can create scatter plots with DataFrame.plot by passing kind='scatter'. Scatter plot requires numeric columns for x and y axis. These can be specified by x and y keywords each, otherwise the DataFrame index will be used as x

In [74]:
df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])

In [75]:
df.iplot(kind='scatter',x='a',y='b',mode='markers')

Out[75]:

Colors can be assigned as either a list or dicitonary by using color. The marker symbol can be defined by using symbol

In [76]:
df.iplot(kind='scatter',mode='markers',symbol='dot',colors=['orange','teal','blue','yellow'],size=10)

Out[76]:

Bubble charts can be used with kind=bubble and by assigning one column as the size

In [77]:
df.iplot(kind='bubble',x='a',y='b',size='c')

Out[77]:

Scatter Matrix¶

You can create a scatter plot matrix using the function scatter_matrix

In [78]:
df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])

In [79]:
df.scatter_matrix()

Out[79]:

Subplots¶

Subplots can be defined with subplots=True. The shape of the output can also be determined with shape=(rows,cols). If omitted then the subplot shape will automatically defined.

Axes can be shared across plots with shared_xaxes=True as well as shared_yaxes=True

In [80]:
df=cf.datagen.lines(4)

In [81]:
df.iplot(subplots=True,shape=(4,1),shared_xaxes=True,vertical_spacing=.02,fill=True)

Out[81]:

Subplot Title can be set with subplot_titles. If set to True then the column names will be used. Otherwise a list of strings can be passed.

In [82]:
df.iplot(subplots=True,subplot_titles=True,legend=False)

Out[82]:

Irregular Subplots can also be drawn using specs.
For example, for getting a charts that spans across 2 rows we can use specs=[[{'rowspan':2},{}],[None,{}]].
For a full set of advanced layout you can see help(cufflinks.subplots)

In [83]:
df=cf.datagen.bubble(10,50,mode='stocks')

In [84]:
figs=cf.figures(df,[dict(kind='histogram',keys='x',color='blue'),
dict(kind='scatter',mode='markers',x='x',y='y',size=5),
dict(kind='scatter',mode='markers',x='x',y='y',size=5,color='teal')],asList=True)
figs.append(cf.datagen.lines(1).figure(bestfit=True,colors=['blue'],bestfit_colors=['pink']))
base_layout=cf.tools.get_base_layout(figs)
sp=cf.subplots(figs,shape=(3,2),base_layout=base_layout,vertical_spacing=.15,horizontal_spacing=.03,
specs=[[{'rowspan':2},{}],[None,{}],[{'colspan':2},None]],
subplot_titles=['Histogram','Scatter 1','Scatter 2','Bestfit Line'])
sp['layout'].update(showlegend=False)

In [85]:
cf.iplot(sp)

Out[85]:

Shapes¶

Lines can be added with hline and vline for horizontal and vertical lines respectively. These can be either a list of values (relative to the axis) or a dictionary.

In [86]:
df=cf.datagen.lines(3,columns=['a','b','c'])

In [87]:
df.iplot(hline=[2,4],vline=['2015-02-10'])

Out[87]:

More advanced parameters can be passed in the form of a dictionary, including width and color and dash for the line dash type.

In [88]:
df.iplot(hline=[dict(y=-1,color='blue',width=3),dict(y=1,color='pink',dash='dash')])

Out[88]:

Shaded areas can be plotted using hspan and vspan for horizontal and vertical areas respectively.
These can be set with a list of paired tuples (v0,v1) or a list of dictionaries with further parameters.

In [89]:
df.iplot(hspan=[(-1,1),(2,5)])

Out[89]:

Extra parameters can be passed in the form of dictionaries, width, fill, color, fillcolor, opacity

In [90]:
df.iplot(vspan={'x0':'2015-02-15','x1':'2015-03-15','color':'teal','fill':True,'opacity':.4})

Out[90]:
In [91]:
# Plotting resistance lines
max_vals=df.max().values.tolist()
resistance=[dict(kind='line',y=i,color=j,width=2) for i,j in zip(max_vals,['red','blue','pink'])]
df.iplot(hline=resistance)

Out[91]:

Different shapes can also be used with shapes and identifying the kind which can be either line, rect or circle

In [92]:
# Get min to max values

df_a=df['a']
max_val=df_a.max()
min_val=df_a.min()
max_date=df_a[df_a==max_val].index[0].strftime('%Y-%m-%d')
min_date=df_a[df_a==min_val].index[0].strftime('%Y-%m-%d')
shape1=dict(kind='line',x0=max_date,y0=max_val,x1=min_date,y1=min_val,color='blue',width=2)
shape2=dict(kind='rect',x0=max_date,x1=min_date,fill=True,color='gray',opacity=.3)

In [93]:
df_a.iplot(shapes=[shape1,shape2])

Out[93]:

Other Shapes¶

In [94]:
x0 = np.random.normal(2, 0.45, 300)
y0 = np.random.normal(2, 0.45, 300)

x1 = np.random.normal(6, 0.4, 200)
y1 = np.random.normal(6, 0.4, 200)

x2 = np.random.normal(4, 0.3, 200)
y2 = np.random.normal(4, 0.3, 200)

distributions = [(x0,y0),(x1,y1),(x2,y2)]

In [95]:
dfs=[pd.DataFrame(dict(x=i,y=j)) for i,j in distributions]

In [96]:
d=cf.Data()
gen=cf.colorgen(scale='ggplot')
for df in dfs:
d_=df.figure(kind='scatter',mode='markers',x='x',y='y',size=5,colors=gen.next())['data']
for _ in d_:
d.append(_)

In [97]:
gen=cf.colorgen(scale='ggplot')
shapes=[cf.tools.get_shape(kind='circle',x0=min(x),x1=max(x),
y0=min(y),y1=max(y),color=gen.next(),fill=True,
opacity=.3,width=.4) for x,y in distributions]

In [98]:
fig=cf.Figure(data=d)
fig['layout']=cf.getLayout(shapes=shapes,legend=False,title='Distribution Comparison')
cf.iplot(fig,validate=False)

Out[98]:
In [ ]: