# !pip install cufflinks import pandas as pd import numpy as np import cufflinks as cf import plotly.plotly as py import plotly.tools as tls import plotly.graph_objs as go import sklearn from sklearn.preprocessing import StandardScaler

tls.set_credentials_file(username="ashishpatel.ce", api_key='oLnw8eVRtPb9SPFkzNCJ')

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')

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)

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)

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")

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')

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

cars_data = car.ix[:,(1,3,4)].values car_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))

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

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")

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv') for col in df.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' )

import plotly.plotly as py import pandas as pd df = 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' )

import plotly.plotly as py import plotly.graph_objs as go import pandas as pd df = 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'} for i in range(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')