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test.py
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test.py
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a
# OUT: {'medians': [<matplotlib.lines.Line2D object at 0x2e2b850>, <matplotlib.lines.Line2D object at 0x2e312d0>], 'fliers': [<matplotlib.lines.Line2D object at 0x2e2bb90>, <matplotlib.lines.Line2D object at 0x2e2bed0>, <matplotlib.lines.Line2D object at 0x2e31610>, <matplotlib.lines.Line2D object at 0x2e31950>], 'whiskers': [<matplotlib.lines.Line2D object at 0x2e26850>, <matplotlib.lines.Line2D object at 0x2e26b10>, <matplotlib.lines.Line2D object at 0x2e2d250>, <matplotlib.lines.Line2D object at 0x2e2d590>], 'boxes': [<matplotlib.lines.Line2D object at 0x2e2b510>, <matplotlib.lines.Line2D object at 0x2e2df50>], 'caps': [<matplotlib.lines.Line2D object at 0x2e26e50>, <matplotlib.lines.Line2D object at 0x2e2b1d0>, <matplotlib.lines.Line2D object at 0x2e2d8d0>, <matplotlib.lines.Line2D object at 0x2e2dc10>]}
a['medians']
# OUT: [<matplotlib.lines.Line2D object at 0x2e2b850>, <matplotlib.lines.Line2D object at 0x2e312d0>]
a['medians'].value
lwater = watertown(np.where(watertown<=np.max(belmont)))
lwater = watertown[np.where(watertown<=np.max(belmont))]
lwater
# OUT: array([ 3324., 304., 0., 5772., 1888., 5404., 3912., 0.,
# OUT: 2215., -184., 6664., 2933., 3740., 3882., 0., 4218.,
# OUT: 3609., 5502., 0., 0., 3332., 1408., 4030., 6036.,
# OUT: 2580., 3845., 0., 2703., 3851., 3292., 1405., 4009.,
# OUT: 0., 2727., 3888., 3504., 4923., 0., 2667., 3774.,
# OUT: 3561., 4502., -222., 4402., 3620., 1989., 5034., 6609.,
# OUT: 3456., 3801., 0., 1259., 3307., 4930., 3403., 5505.,
# OUT: 0., 7000., 3546., 4888., 6320., 2828., 3662., 0.,
# OUT: 3827., 6828., 3882., 2029., 4455., 3845., 379., 4403.,
# OUT: 0., 3502., 0., 4421., 3102., 597., 3680., 3375.,
# OUT: 0., 2359., 3443., 2347., 0., 4005., 3709., 2615.,
# OUT: 0., 3455., 2867., 4501.])
len(lwater)
# OUT: 92
np.mean(lwater)+2*np.std(lwater)/np.sqrt(92.)
# OUT: 3362.708418187724
np.mean(lwater)-2*np.std(lwater)/np.sqrt(92.)
# OUT: 2554.313320942711
np.mean(belmont)-2*np.std(belmont)/np.sqrt(92.)
# OUT: 1460.5596994237794
np.mean(belmont)+2*np.std(belmont)/np.sqrt(92.)
# OUT: 2131.0403005762205