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import string

from pandas import DataFrame

import numpy as np
from numpy.random import randn

import pandas.tools.plotting as plotting

from matplotlib.pylab import close

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import squarify
import matplotlib.pyplot as plt
from numpy.random import rand

fig, axes = plt.subplots(2, 3, figsize=(14, 8))
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.95, hspace=0.35)

sq = 8

def random_colors(n):

sinhrks

import string

from pandas import DataFrame

import numpy as np
from numpy.random import randn, rand

import pandas.tools.plotting as plotting

from matplotlib.pylab import close

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from pandas import Series, DataFrame

import numpy as np
from numpy.random import randn, rand

import pandas.tools.plotting as plotting
import matplotlib.pyplot as plt

s = Series(randn(6), index=range(6))

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from pandas import DataFrame, Series
from numpy.random import randn, rand

df1 = DataFrame(randn(6, 3), index=range(6),
               columns=['a', 'b', 'c'])

df2 = DataFrame(randn(6, 3), index=range(6),
               columns=['d', 'e', 'f'])

df3 = DataFrame(randn(6, 3), index=range(6),

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import string

from pandas import DataFrame, Series
from numpy.random import randn
import matplotlib.pyplot as plt

df = DataFrame(randn(7, 4),
               index=list(string.ascii_letters[:7]),
               columns=['x', 'y', 'z', 'four'])
series = Series(randn(7), index=range(7))

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# -*- coding: utf-8 -*-
import string
 
from pandas import DataFrame, Series
import datetime
import numpy as np
from numpy.random import randn, rand
 
import pandas.tools.plotting as plotting
from pandas.compat import u, string_types

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import pandas as pd
import numpy as np
import pandas.util.testing as tm
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np

series = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')

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library(forecast)
library(ggplot2)

d <- AirPassengers
d.arima <- forecast::auto.arima(d)
d.forecast <- forecast(d.arima, level = c(95), h = 50)

fortify.forecast <- function(forecast.data) {
  require(dplyr)
  forecasted <- as.data.frame(forecast.data)

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library(survival)

library(ggplot2)
library(scales)

d.survfit <- survival::survfit(survival::Surv(time, status) ~ sex,
                               data = lung)

fortify.survfit <- function(survfit.data) {
  data.frame(time = survfit.data$time,