import pandas as pd
import numpy as np
import plotly
import getpass

api_key=getpass.getpass()
p = plotly.plotly('nipun.batra.1', api_key)

df=pd.DataFrame({'A':np.random.rand(100), 'B':np.random.rand(100),'C':np.random.rand(100)} ,index= np.array(range(100)))

df

df.describe()

df.plot()

def df_to_iplot(df):
    
    '''
    Coverting a Pandas Data Frame to Plotly interface
    '''
    x = df.index.values
    lines={}
    for key in df:
        lines[key]={}
        lines[key]["x"]=x
        lines[key]["y"]=df[key].values
        lines[key]["name"]=key

        #Appending all lines
    lines_plotly=[lines[key] for key in df]
    return lines_plotly

p.iplot(df_to_iplot(df))

date_rng = pd.date_range('2013-01-01 00:00','2013-01-03 10:00',freq='300s')

df2=pd.DataFrame({'A':np.random.rand(len(date_rng)), 'B':np.random.rand(len(date_rng))}, index=date_rng)

df2

df2.plot()

p.iplot(df_to_iplot(df2))

def df_to_iplot(df):
    
    '''
    Coverting a Pandas Data Frame to Plotly interface
    '''
    
    if df.index.__class__.__name__=="DatetimeIndex":
        #Convert the index to MySQL Datetime like strings
        x=df.index.format()
        #Alternatively, directly use x, since DateTime index is np.datetime64
        #see http://nbviewer.ipython.org/gist/cparmer/7721116  
        #x=df.index.values.astype('datetime64[s]')
    else:
        x = df.index.values        
        
    lines={}
    for key in df:
        lines[key]={}
        lines[key]["x"]=x
        lines[key]["y"]=df[key].values
        lines[key]["name"]=key

    #Appending all lines
    lines_plotly=[lines[key] for key in df]
    return lines_plotly

p.iplot(df_to_iplot(df2))

from IPython.display import HTML
import requests

styles = requests.get("https://raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/styles/custom.css")
HTML(styles.text)