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# HIDDEN
from datascience import *
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

%load_ext autoreload
%autoreload 2


    

    




/Users/ahemani/Development/data8/datascience/datascience/tables.py:17: MatplotlibDeprecationWarning: The 'warn' parameter of use() is deprecated since Matplotlib 3.1 and will be removed in 3.3.  If any parameter follows 'warn', they should be pass as keyword, not positionally.
  matplotlib.use('agg', warn=False)
/Users/ahemani/Development/data8/datascience/datascience/util.py:10: MatplotlibDeprecationWarning: The 'warn' parameter of use() is deprecated since Matplotlib 3.1 and will be removed in 3.3.  If any parameter follows 'warn', they should be pass as keyword, not positionally.
  matplotlib.use('agg', warn=False)

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from bokeh.sampledata.autompg import autompg as df
t = Table.from_df(df)


    

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t.group('yr').plot('yr')


    

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t.plot('weight', ['displ', 'hp'])


    

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t.with_column('big weight', t['weight'] * 1000).plot('big weight', ['hp', 'displ'])


    

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t.plot('weight', ['hp', 'displ'], overlay=False)


    

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t.group_bar('cyl')


    

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t.group('cyl').bar('cyl')


    

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t.select(['cyl', 'displ', 'hp']).group('cyl', max).bar('cyl')


    

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t.group('cyl', max).bar('cyl', [1, 2, 3])


    

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t.group('cyl', max).bar('cyl', [1, 2, 3], overlay=False)


    

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t.group_barh('cyl')


    

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t.group('cyl').barh('cyl')


    

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t.select(['cyl', 'displ', 'hp']).group('cyl', max).barh('cyl')


    

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t.group('cyl', max).barh('cyl', [1, 2, 3])


    

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t.group('cyl', max).barh('cyl', [1, 2, 3], overlay=False)


    

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t.select(['weight', 'displ', 'hp']).scatter('weight')


    

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t.select(['weight', 'displ', 'hp']).scatter('weight', fit_line=True)


    

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t.select(['weight', 'displ', 'hp']).scatter('weight', fit_line=True, overlay=False)


    

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t.scatter('weight', 'hp')


    

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t.scatter('weight', [2, 3])


    

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t.with_column('big weight', t['weight'] * 1000).scatter('big weight', 'hp')


    

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t.scatter('weight', ['displ', 'hp'])


    

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t.scatter('weight', ['displ', 'hp'], overlay=False)


    

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# Check that grouping in scatter works just like grouping in histograms and that the legends are the same format
t.scatter("weight", "hp", group="origin")


    

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t.scatter("weight", "hp", colors="origin")


    

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t.drop('name').scatter('weight', 'displ', sizes='hp', s=200, group='cyl')


    

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t.hist('mpg')


    

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f = t.hist('mpg', normed=False)


    

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t.hist('mpg', overlay=False)


    

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f = t.with_column('mpg2', t['mpg'] * 10000).hist('mpg2')


    

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f = t.with_column('mpg2', t['mpg'] * 10000).hist('mpg2', bins=100)


    

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t.hist(['displ', 'hp'])


    

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t.hist(['displ', 'hp'], overlay=False)


    

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bins = np.append(np.arange(0, 200, 20), np.arange(200, 501, 50))
t.hist('displ', bins=bins, unit='cc')


    

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t.with_column('displ-µm3', t['displ'] * 10000).hist('displ-µm3', bins=bins*10000, unit='µm3')


    

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t.with_column('weight-1000', t['weight']-1000).hist(['weight', 'weight-1000'], unit='kg', width=4, height=3)


    

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two_cats = t.with_column('cat', t['hp'] > 100)
two_cats.hist('mpg', group='cat')


    

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more_cats = t.with_column('cat', [str(c) for c in (t['hp'] > 100).astype(int) + (t['hp'] > 120).astype(int)])
more_cats.hist('mpg', group='cat')


    

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more_cats.hist('mpg', group='cat', overlay=False)


    

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more_cats.pivot_hist('cat', 'mpg')


    

    




/Users/ahemani/Development/data8/datascience/datascience/tables.py:2476: UserWarning: pivot_hist is deprecated; use hist(value_column_label, group=pivot_column_label), or with side_by_side=True if you really want side-by-side bars.
  warnings.warn("pivot_hist is deprecated; use "






    

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more_cats.hist('mpg', group='cat', side_by_side=True)


    

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more_cats.hist_of_counts('mpg')


    

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more_cats.hist_of_counts('mpg', group='cat')


    

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more_cats.hist_of_counts('mpg', group='cat', overlay=False)


    

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label_t = Table().with_columns(
    'Value', make_array(2, 3, 9),
    'Proportion', make_array(0.25, 0.5, 0.25)
)

label_t.hist(bin_column='Value', bins=np.arange(1.5, 9.6, 1))


    

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# Test numerical labels aren't showing error term
t = Table().with_column("val", np.random.uniform(size=100))
t.hist()


    

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# Testing histogram shading
t = Table().with_column("val", np.random.uniform(size=1000))
t.hist(left_end=0.5)


    

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t.hist(left_end=0.5, right_end=0.8)


    

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t.hist(right_end=0.8)


    

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bins = np.arange(0, 1.1, 0.2)
t.hist(bins=bins, left_end=0.4)


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-51-89cf0a3c69b9> in <module>
      1 bins = np.arange(0, 1.1, 0.2)
----> 2 t.hist(bins=bins, left_end=0.4)

~/Development/data8/datascience/datascience/tables.py in hist(self, overlay, bins, bin_column, unit, counts, group, side_by_side, left_end, right_end, width, height, *columns, **vargs)
   2656                     left_end = min([min(self.column(k)) for k in self.labels if np.issubdtype(self.column(k).dtype, np.number)])
   2657             elif right_end is None:
-> 2658                 if bins and bins[-1]:
   2659                     right_end = bins[-1]
   2660                 else:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

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t.hist(bins=bins, left_end=0.5)


    

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t.hist(bins=bins, left_end=0.5, right_end=0.6)


    

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t.hist(bins=bins, left_end=0.4, right_end=0.5)


    

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t.hist(bins=bins, left_end=0.45, right_end=0.55)


    

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# Testing histogram shading, pt. 2
t2 = Table().with_column("val", np.random.normal(size=1000))
t2.hist(left_end=0.5)


    

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t2.hist(left_end=-0.5, right_end=0.5)


    

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t2.hist(right_end=2)


    

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# Testing shaded histograms, pt. 3
more_cats.hist('mpg', group='cat', overlay=False, left_end=20)


    

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