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import numpy as np
import pandas as pd

import bqplot.pyplot as plt
from datetime import datetime as dt

from bqplot.traits import convert_to_date



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price_data = pd.DataFrame(np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,
                          columns=['Security 1', 'Security 2'],
                          index=pd.date_range(start='01-01-2007', periods=150))

numpy array of np.datetime objects



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x_data = price_data.index.values



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fig = plt.figure()
line = plt.plot(x=x_data, y=price_data['Security 1'])
fig

List of python datetimes



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import datetime
ref_date = datetime.datetime(2010, 1, 1)
num_items = range(250)

x_data = [ref_date + datetime.timedelta(x) for x in num_items]
date_x = convert_to_date(x_data)



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fig = plt.figure()
line = plt.plot(x=date_x, y=price_data['Security 1'])
fig

Array of python datetimes



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x_data = np.array(x_data)
date_x = convert_to_date(x_data)



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fig = plt.figure()
line = plt.plot(x=date_x, y=price_data['Security 1'])
fig

Array of dates converted to string



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date_format = '%m-%d-%Y'
x_data = price_data.index.values
date_x = convert_to_date(x_data)



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fig = plt.figure()
line = plt.plot(x=date_x, y=price_data['Security 1'])
fig

List of dates converted to string



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x_data = x_data.tolist()



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fig = plt.figure()
line = plt.plot(x=date_x, y=price_data['Security 1'])
fig

Multi-dimensional x and y



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date_format = '%m-%d-%Y'
x_data = price_data.index.values

data_2 = price_data.iloc[30: ,]
x_data_2 = data_2.index.values



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y_data = pd.concat([price_data['Security 1'], data_2['Security 2']], axis=1).values.T



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fig = plt.figure()
line = plt.plot(x=date_x, y=y_data, colors=['hotpink', 'orange'])
fig