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from collections import OrderedDict
import numpy as np
import pandas as pd
from bokeh._legacy_charts import Step, output_notebook, show
output_notebook()


    

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# create some example data
xyvalues = OrderedDict(
    python=[2, 3, 7, 5, 26, 81, 44, 93, 94, 105, 66, 67, 90, 83],
    pypy=[12, 20, 47, 15, 126, 121, 144, 333, 354, 225, 276, 287, 270, 230],
    jython=[22, 43, 70, 75, 76, 101, 114, 123, 194, 215, 201, 227, 139, 160],
)


    

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step = Step(
    xyvalues, title="Step Chart, dict input", 
    legend='top_left', ylabel='Performance')
show(step)


    

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df = pd.DataFrame(xyvalues)
step = Step(
    df, title="Step Chart, pandas input",legend='top_left',
    ylabel='Performance')
show(step)


    

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step = Step(
    list(xyvalues.values()), title="Step Chart, list input", 
    legend='top_left', ylabel='Performance')
show(step)


    

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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'])

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


    

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step = Step(
    sxyvalues, title="Stock Prices", 
    legend='top_left', ylabel='Price')
show(step)


    

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from blaze import Data

b = Data(df)
step = Step(b, title="Step Chart (blaze)", legend='top_left', ylabel='Performance')
show(step)


    

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