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from collections import OrderedDict
import pandas as pd
from bokeh._legacy_charts import TimeSeries
from bokeh.io import output_notebook, show
output_notebook()


    

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# read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])
MSFT = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=MSFT&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])
IBM = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])

xyvalues = OrderedDict(
    AAPL=AAPL['Adj Close'],
    Date=AAPL['Date'],
    MSFT=MSFT['Adj Close'],
    IBM=IBM['Adj Close'],
)


    

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ts = TimeSeries(
    xyvalues, index='Date', title="timeseries, dict input", 
    legend='top_left', ylabel='Stock Prices')
show(ts)


    

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df = pd.DataFrame(xyvalues)
ts = TimeSeries(
    df, index='Date', title="timeseries, pandas input", 
    legend='top_left', ylabel='Stock Prices')
show(ts)


    

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lindex = xyvalues.pop('Date')
lxyvalues = list(xyvalues.values())
ts = TimeSeries(
    lxyvalues, index=lindex, title="timeseries, list input", 
    ylabel='Stock Prices: 0-AAPL, 1-IBM, 2-MSFT', legend=True)
show(ts)


    

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from blaze import Data

b = Data(df)
ts = TimeSeries(b, index='Date', title="timeseries, blaze input", ylabel='Stock Prices', legend='bottom_right')
show(ts)


    

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