_04e_boxplot_demo Namespace Reference

A matplotlib example. More...

Functions
def	fake_bootstrapper (n)
	plt.show() More...

Variables
int	spread = np.random.rand(50) * 100
int	center = np.ones(25) * 50
int	flier_high = np.random.rand(10) * 100 + 100
int	flier_low = np.random.rand(10) * -100
	data = np.concatenate((spread, center, flier_high, flier_low))
	fig
	axs
	left
	right
	bottom = -5
	top = 40
	hspace
	wspace
	d2 = np.concatenate((spread, center, flier_high, flier_low))
	ax
list	random_dists
	plt.show() More...
int	N = 500
	norm = np.random.normal(1, 1, N)
	logn = np.random.lognormal(1, 1, N)
	expo = np.random.exponential(1, N)
	gumb = np.random.gumbel(6, 4, N)
	tria = np.random.triangular(2, 9, 11, N)
	bootstrap_indices = np.random.randint(0, N, N)
	ax1
	figsize
	bp = ax1.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
	color
	marker
	True
	linestyle
	which
	alpha
	axisbelow
	title
	xlabel
	ylabel
list	box_colors = ['darkkhaki', 'royalblue']
	num_boxes = len(data)
	medians = np.empty(num_boxes)
	box = bp['boxes'][i]
list	box_x = []
list	box_y = []
	box_coords = np.column_stack([box_x, box_y])
	med = bp['medians'][i]
list	median_x = []
list	median_y = []
	markeredgecolor
	rotation
	fontsize
int	pos = np.arange(num_boxes) + 1
list	upper_labels = [str(round(s, 2)) for s in medians]
list	weights = ['bold', 'semibold']
int	k = tick % 2
	transform
	horizontalalignment
	size
	weight
	backgroundcolor
float	inc = 0.1
	e1 = np.random.normal(0, 1, size=500)
	e2 = np.random.normal(0, 1, size=500)
	e3 = np.random.normal(0, 1 + inc, size=500)
	e4 = np.random.normal(0, 1 + 2 * inc, size=500)
list	treatments = [e1, e2, e3, e4]
	med1
	ci1
	med2
	ci2
list	conf_intervals = [None, None, ci1, ci2]
	markersize

Detailed Description

A matplotlib example.

Author

Paulo Roma

Since

03/11/2021

See also

https://matplotlib.org/stable/gallery/statistics/boxplot_demo.html#sphx-glr-gallery-statistics-boxplot-demo-py

Function Documentation

fake_bootstrapper()

def _04e_boxplot_demo.fake_bootstrapper

(

n

)

plt.show()

This is just a placeholder for the user's method of
bootstrapping the median and its confidence intervals.

Returns an arbitrary median and confidence interval packed into a tuple.

Variable Documentation

alpha

_04e_boxplot_demo.alpha

ax

_04e_boxplot_demo.ax

ax1

_04e_boxplot_demo.ax1

axisbelow

_04e_boxplot_demo.axisbelow

axs

_04e_boxplot_demo.axs

backgroundcolor

_04e_boxplot_demo.backgroundcolor

bootstrap_indices

_04e_boxplot_demo.bootstrap_indices = np.random.randint(0, N, N)

bottom

int _04e_boxplot_demo.bottom = -5

box

_04e_boxplot_demo.box = bp['boxes'][i]

box_colors

list _04e_boxplot_demo.box_colors = ['darkkhaki', 'royalblue']

box_coords

_04e_boxplot_demo.box_coords = np.column_stack([box_x, box_y])

box_x

list _04e_boxplot_demo.box_x = []

box_y

list _04e_boxplot_demo.box_y = []

bp

_04e_boxplot_demo.bp = ax1.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)

center

int _04e_boxplot_demo.center = np.ones(25) * 50

ci1

_04e_boxplot_demo.ci1

ci2

_04e_boxplot_demo.ci2

color

_04e_boxplot_demo.color

conf_intervals

list _04e_boxplot_demo.conf_intervals = [None, None, ci1, ci2]

d2

_04e_boxplot_demo.d2 = np.concatenate((spread, center, flier_high, flier_low))

data

list _04e_boxplot_demo.data = np.concatenate((spread, center, flier_high, flier_low))

e1

_04e_boxplot_demo.e1 = np.random.normal(0, 1, size=500)

e2

_04e_boxplot_demo.e2 = np.random.normal(0, 1, size=500)

e3

_04e_boxplot_demo.e3 = np.random.normal(0, 1 + inc, size=500)

e4

_04e_boxplot_demo.e4 = np.random.normal(0, 1 + 2 * inc, size=500)

expo

_04e_boxplot_demo.expo = np.random.exponential(1, N)

fig

_04e_boxplot_demo.fig

figsize

_04e_boxplot_demo.figsize

flier_high

int _04e_boxplot_demo.flier_high = np.random.rand(10) * 100 + 100

flier_low

int _04e_boxplot_demo.flier_low = np.random.rand(10) * -100

fontsize

_04e_boxplot_demo.fontsize

gumb

_04e_boxplot_demo.gumb = np.random.gumbel(6, 4, N)

horizontalalignment

_04e_boxplot_demo.horizontalalignment

hspace

_04e_boxplot_demo.hspace

inc

float _04e_boxplot_demo.inc = 0.1

k

int _04e_boxplot_demo.k = tick % 2

left

_04e_boxplot_demo.left

linestyle

_04e_boxplot_demo.linestyle

logn

_04e_boxplot_demo.logn = np.random.lognormal(1, 1, N)

marker

_04e_boxplot_demo.marker

markeredgecolor

_04e_boxplot_demo.markeredgecolor

markersize

_04e_boxplot_demo.markersize

med

_04e_boxplot_demo.med = bp['medians'][i]

med1

_04e_boxplot_demo.med1

med2

_04e_boxplot_demo.med2

median_x

list _04e_boxplot_demo.median_x = []

median_y

list _04e_boxplot_demo.median_y = []

medians

list _04e_boxplot_demo.medians = np.empty(num_boxes)

N

int _04e_boxplot_demo.N = 500

norm

_04e_boxplot_demo.norm = np.random.normal(1, 1, N)

num_boxes

_04e_boxplot_demo.num_boxes = len(data)

pos

int _04e_boxplot_demo.pos = np.arange(num_boxes) + 1

random_dists

list _04e_boxplot_demo.random_dists

Initial value:

=  ['Normal(1, 1)', 'Lognormal(1, 1)', 'Exp(1)', 'Gumbel(6, 4)',
                'Triangular(2, 9, 11)']

plt.show()

right

_04e_boxplot_demo.right

rotation

_04e_boxplot_demo.rotation

size

_04e_boxplot_demo.size

spread

int _04e_boxplot_demo.spread = np.random.rand(50) * 100

title

_04e_boxplot_demo.title

Referenced by _08c_clock.clock.paint_hms().

top

int _04e_boxplot_demo.top = 40

transform

_04e_boxplot_demo.transform

treatments

list _04e_boxplot_demo.treatments = [e1, e2, e3, e4]

tria

_04e_boxplot_demo.tria = np.random.triangular(2, 9, 11, N)

True

_04e_boxplot_demo.True

upper_labels

list _04e_boxplot_demo.upper_labels = [str(round(s, 2)) for s in medians]

weight

_04e_boxplot_demo.weight

weights

list _04e_boxplot_demo.weights = ['bold', 'semibold']

which

_04e_boxplot_demo.which

wspace

_04e_boxplot_demo.wspace

xlabel

_04e_boxplot_demo.xlabel

ylabel

_04e_boxplot_demo.ylabel