a=np.linspace(start=0,stop=36,num=36)np.random.seed(25)b=np.random.uniform(low=0.0,high=1.0,size=36)trace=go.Scatter(x=a,y=b)data= [trace]py.iplot(data,filename='basic-file')

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Small Line chart

x= [1,2,3,4,5,6,7,8,9]y= [1,2,3,4,0.5,4,3,2,1]z= [10,9,8,7,6,5,4,3,2,1]trace0=go.Scatter(x=x,y=y,name='List Object',line=dict(width=5))trace1=go.Scatter(x=x,y=z,name='List Object 2',line=dict(width=5))data= [trace0,trace1]layout=dict(title="Double Line Chart",xaxis=dict(title="X-Axis"),yaxis=dict(title="Y-Axis"))fig=dict(data=data,layout=layout)print(fig)

{'layout': {'yaxis': {'title': 'Y-Axis'}, 'xaxis': {'title': 'X-Axis'}, 'title': 'Double Line Chart'}, 'data': [{'y': [1, 2, 3, 4, 0.5, 4, 3, 2, 1], 'type': 'scatter', 'x': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'line': {'width': 5}, 'name': 'List Object'}, {'y': [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], 'type': 'scatter', 'x': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'line': {'width': 5}, 'name': 'List Object 2'}]}

py.iplot(fig,filename="basic-line-chart")

car=pd.read_csv("https://gist.githubusercontent.com/seankross/a412dfbd88b3db70b74b/raw/5f23f993cd87c283ce766e7ac6b329ee7cc2e1d1/mtcars.csv")df=car[['cyl','wt','mpg']]layout=dict(title="Chart from pandas dataframe",xaxis=dict(title="X-Axis"),yaxis=dict(title="Y-Axis"))df.iplot(filename="Simple-line-chart",layout=layout)

Creating Bar Chart

data= [go.Bar(x=x,y=y)]layout=dict(title="Bar Chart from pandas dataframe",xaxis=dict(title="X-Axis"),yaxis=dict(title="Y-Axis"))py.iplot(data,filename="basic-barchart",layout=layout)

color_theme=dict(color= ['rgba(169,169,169,1)','rgba(255,160,122,1)','rgba(176,224,230,1)','rgba(189,183,107,1)','rgba(188,143,143,1)','rgba(221,160,221,1)','rgba(169,169,169,1)','rgba(255,160,122,1)','rgba(176,224,230,1)'])

trace0=go.Bar(x=x,y=y,marker=color_theme)data= [trace0]layout=go.Layout(title="Custom Color")fig=go.Figure(data=data,layout=layout)py.iplot(fig,filename="file-name")

Create Pie Chart

fig= {'data' : [{'labels':['bicycle','motorbike','car','van','stroller'],'values':[1,2,3,4,0.5],'type' :'pie'}],'layout':{'title':'Simple Pie Chart'}}py.iplot(fig,filename='pie chart')

StatisticsPlot

car=pd.read_csv("https://gist.githubusercontent.com/seankross/a412dfbd88b3db70b74b/raw/5f23f993cd87c283ce766e7ac6b329ee7cc2e1d1/mtcars.csv")mpg=car.mpgmpg.iplot(kind="histogram",filename="Simple Histogram")

cars_data=car.ix[:,(1,3,4)].valuescar_data_std=StandardScaler().fit_transform(cars_data)car_select=pd.DataFrame(car_data_std)car_select.columns= ['mpg','disp','hp']car_select.iplot(kind="histogram",filename="Simple car Plot")

car_select.iplot(kind="histogram",filename="Simple car Plot",subplots=True)

car_select.iplot(kind="histogram",filename="Simple car Plot",subplots=True,shape=(3,1))

Box Plot

car_select.iplot(kind="box",filename="Boxplot")

Scatter plot

fig= {'data': [{'x':car_select.mpg,'y':car_select.disp,'mode':'markers','name':'mpg'},                {'x':car_select.hp,'y':car_select.disp,'mode':'markers','name':'hp'}],'layout':{'xaxis':{'title':''},'yaxis' : {'title':'Stardardized Displacement'}}}py.iplot(fig,filename="Group Scatter Plot")

Map Plot

1.Cloropleth Map

2.Point Map

1.Cloropleth Map

df=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')forcolindf.columns:df[col]=df[col].astype(str)scl= [[0.0,'rgb(242,240,247)'],[0.2,'rgb(218,218,235)'],[0.4,'rgb(188,189,220)'],\            [0.6,'rgb(158,154,200)'],[0.8,'rgb(117,107,177)'],[1.0,'rgb(84,39,143)']]df['text']=df['state']+'<br>'+\'Beef '+df['beef']+' Dairy '+df['dairy']+'<br>'+\'Fruits '+df['total fruits']+' Veggies '+df['total veggies']+'<br>'+\'Wheat '+df['wheat']+' Corn '+df['corn']data= [dict(type='choropleth',colorscale=scl,autocolorscale=False,locations=df['code'],z=df['total exports'].astype(float),locationmode='USA-states',text=df['text'],marker=dict(line=dict (color='rgb(255,255,255)',width=2            ) ),colorbar=dict(title="Millions USD")        ) ]layout=dict(title='2011 US Agriculture Exports by State<br>(Hover for breakdown)',geo=dict(scope='usa',projection=dict(type='albers usa' ),showlakes=True,lakecolor='rgb(255, 255, 255)'),             )fig=dict(data=data,layout=layout )py.iplot(fig,filename='d3-cloropleth-map' )

World Map

importplotly.plotlyaspyimportpandasaspddf=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')data= [dict(type='choropleth',locations=df['CODE'],z=df['GDP (BILLIONS)'],text=df['COUNTRY'],colorscale= [[0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\            [0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]],autocolorscale=False,reversescale=True,marker=dict(line=dict (color='rgb(180,180,180)',width=0.5            ) ),colorbar=dict(autotick=False,tickprefix='$',title='GDP<br>Billions US$'),      ) ]layout=dict(title='2014 Global GDP<br>Source:\            <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\            CIA World Factbook</a>',geo=dict(showframe=False,showcoastlines=False,projection=dict(type='Mercator'        )    ))fig=dict(data=data,layout=layout )py.iplot(fig,validate=False,filename='d3-world-map' )

Charoplath Map

importplotly.plotlyaspyimportplotly.graph_objsasgoimportpandasaspddf=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')df.head()cases= []colors= ['rgb(239,243,255)','rgb(189,215,231)','rgb(107,174,214)','rgb(33,113,181)']months= {6:'June',7:'July',8:'Aug',9:'Sept'}foriinrange(6,10)[::-1]:cases.append(go.Scattergeo(lon=df[df['Month']==i ]['Lon'],#-(max(range(6,10))-i),lat=df[df['Month']==i ]['Lat'],text=df[df['Month']==i ]['Value'],name=months[i],marker=dict(size=df[df['Month']==i ]['Value']/50,color=colors[i-6],line=dict(width=0)        ),    ) )cases[0]['text']=df[df['Month']==9 ]['Value'].map('{:.0f}'.format).astype(str)+' '+\df[df['Month']==9 ]['Country']cases[0]['mode']='markers+text'cases[0]['textposition']='bottom center'inset= [go.Choropleth(locationmode='country names',locations=df[df['Month']==9 ]['Country'],z=df[df['Month']==9 ]['Value'],text=df[df['Month']==9 ]['Country'],colorscale= [[0,'rgb(0, 0, 0)'],[1,'rgb(0, 0, 0)']],autocolorscale=False,showscale=False,geo='geo2'    ),go.Scattergeo(lon= [21.0936],lat= [7.1881],text= ['Africa'],mode='text',showlegend=False,geo='geo2'    )]layout=go.Layout(title='Ebola cases reported by month in West Africa 2014<br>\Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">\HDX</a>',geo=dict(resolution=50,scope='africa',showframe=False,showcoastlines=True,showland=True,landcolor="rgb(229, 229, 229)",countrycolor="rgb(255, 255, 255)" ,coastlinecolor="rgb(255, 255, 255)",projection=dict(type='Mercator'        ),lonaxis=dict(range= [-15.0,-5.0 ] ),lataxis=dict(range= [0.0,12.0 ] ),domain=dict(x= [0,1 ],y= [0,1 ]        )    ),geo2=dict(scope='africa',showframe=False,showland=True,landcolor="rgb(229, 229, 229)",showcountries=False,domain=dict(x= [0,0.6 ],y= [0,0.6 ]        ),bgcolor='rgba(255, 255, 255, 0.0)',    ),legend=dict(traceorder='reversed'    ))fig=go.Figure(layout=layout,data=cases+inset)py.iplot(fig,validate=False,filename='West Africa Ebola cases 2014')