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Shift error bars in seaborn barplot with two categories?

I am trying to plot some error bars on tope of a seaborn bar plot made with catplot as below. DF output from .to_clipboard(sep=',', index=True).
Melted DF:
,Parameter,Output,Sobol index,Value
0,$μ_{max}$,P,Total-effect,0.956747485
1,$μ_{max}$,D,Total-effect,-3.08778755e-08
2,$μ_{max}$,I,Total-effect,0.18009523
3,$μ_{max}$,N,Total-effect,0.234568344
4,$μ_{max}$,Mean,Total-effect,0.3428527570305311
5,$m$,P,Total-effect,0.159805431
6,$m$,D,Total-effect,1.13115268
7,$m$,I,Total-effect,0.256635185
8,$m$,N,Total-effect,0.00556940644
9,$m$,Mean,Total-effect,0.38829067561
10,$\alpha_D$,P,Total-effect,0.00115455519
11,$\alpha_D$,D,Total-effect,-0.000953479004
12,$\alpha_D$,I,Total-effect,0.157263821
13,$\alpha_D$,N,Total-effect,0.197434829
14,$\alpha_D$,Mean,Total-effect,0.08872493154649999
15,$N_{max}$,P,Total-effect,0.112155751
16,$N_{max}$,D,Total-effect,0.795253131
17,$N_{max}$,I,Total-effect,0.380872014
18,$N_{max}$,N,Total-effect,0.384059029
19,$N_{max}$,Mean,Total-effect,0.41808498124999993
20,$\gamma_{cell}$,P,Total-effect,0.237220261
21,$\gamma_{cell}$,D,Total-effect,-0.00380320853
22,$\gamma_{cell}$,I,Total-effect,0.00371553068
23,$\gamma_{cell}$,N,Total-effect,0.0010752371
24,$\gamma_{cell}$,Mean,Total-effect,0.059551955062499995
25,$K_d$,P,Total-effect,0.062129271
26,$K_d$,D,Total-effect,-1.46430838e-12
27,$K_d$,I,Total-effect,0.94771372
28,$K_d$,N,Total-effect,0.985790701
29,$K_d$,Mean,Total-effect,0.4989084229996339
30,$μ_{max}$,P,First-order,0.655145193
31,$μ_{max}$,D,First-order,-6.84274992e-20
32,$μ_{max}$,I,First-order,0.00537009868
33,$μ_{max}$,N,First-order,0.000186118183
34,$μ_{max}$,Mean,First-order,0.16517535246575
35,$m$,P,First-order,0.000688037316
36,$m$,D,First-order,0.379773978
37,$m$,I,First-order,0.0065270132
38,$m$,N,First-order,0.00471697043
39,$m$,Mean,First-order,0.0979264997365
40,$\alpha_D$,P,First-order,5.88800317e-07
41,$\alpha_D$,D,First-order,3.59595179e-21
42,$\alpha_D$,I,First-order,0.00405428355
43,$\alpha_D$,N,First-order,4.23811678e-05
44,$\alpha_D$,Mean,First-order,0.00102431337952925
45,$N_{max}$,P,First-order,0.00369586495
46,$N_{max}$,D,First-order,1.71266774e-09
47,$N_{max}$,I,First-order,0.0150083874
48,$N_{max}$,N,First-order,0.00143697969
49,$N_{max}$,Mean,First-order,0.005035308438166935
50,$\gamma_{cell}$,P,First-order,0.0116538163
51,$\gamma_{cell}$,D,First-order,-8.51017704e-24
52,$\gamma_{cell}$,I,First-order,0.00180446631
53,$\gamma_{cell}$,N,First-order,-1.1891221e-07
54,$\gamma_{cell}$,Mean,First-order,0.0033645409244475
55,$K_d$,P,First-order,0.000593392401
56,$K_d$,D,First-order,1.24032496e-17
57,$K_d$,I,First-order,0.314711173
58,$K_d$,N,First-order,0.393636611
59,$K_d$,Mean,First-order,0.17723529410025002
Err DF:
,Parameter,Output,Error,value
0,$μ_{max}$,P,Total Error,0.00532374344
1,$μ_{max}$,D,Total Error,3.27057237e-08
2,$μ_{max}$,I,Total Error,0.00589710262
3,$μ_{max}$,N,Total Error,0.0114684117
4,$μ_{max}$,Mean,Total Error,0.005672322616430925
5,$m$,P,Total Error,0.00043759689
6,$m$,D,Total Error,0.20494168
7,$m$,I,Total Error,0.00562181392
8,$m$,N,Total Error,0.00280516814
9,$m$,Mean,Total Error,0.0534515647375
10,$\alpha_D$,P,Total Error,0.0005054549
11,$\alpha_D$,D,Total Error,0.00103547316
12,$\alpha_D$,I,Total Error,0.00461107762
13,$\alpha_D$,N,Total Error,0.0048878586
14,$\alpha_D$,Mean,Total Error,0.00275996607
15,$N_{max}$,P,Total Error,0.00278612157
16,$N_{max}$,D,Total Error,0.228255264
17,$N_{max}$,I,Total Error,0.0122461237
18,$N_{max}$,N,Total Error,0.00975799636
19,$N_{max}$,Mean,Total Error,0.0632613764075
20,$\gamma_{cell}$,P,Total Error,0.00183489881
21,$\gamma_{cell}$,D,Total Error,0.00380320865
22,$\gamma_{cell}$,I,Total Error,0.000257448243
23,$\gamma_{cell}$,N,Total Error,0.0014860645
24,$\gamma_{cell}$,Mean,Total Error,0.00184540505075
25,$K_d$,P,Total Error,0.00198581744
26,$K_d$,D,Total Error,4.60436562e-12
27,$K_d$,I,Total Error,0.0061495373
28,$K_d$,N,Total Error,0.0104738223
29,$K_d$,Mean,Total Error,0.004652294261151092
30,$μ_{max}$,P,First Error,0.00251411484
31,$μ_{max}$,D,First Error,5.81033819e-20
32,$μ_{max}$,I,First Error,0.000157513959
33,$μ_{max}$,N,First Error,0.000242607282
34,$μ_{max}$,Mean,First Error,0.00072855902025
35,$m$,P,First Error,0.000102871751
36,$m$,D,First Error,0.114462613
37,$m$,I,First Error,0.000544114122
38,$m$,N,First Error,0.00208775169
39,$m$,Mean,First Error,0.029299337640750003
40,$\alpha_D$,P,First Error,2.85055333e-06
41,$\alpha_D$,D,First Error,2.97531464e-21
42,$\alpha_D$,I,First Error,0.000382494131
43,$\alpha_D$,N,First Error,6.18549533e-05
44,$\alpha_D$,Mean,First Error,0.00011179990940750001
45,$N_{max}$,P,First Error,0.000359843541
46,$N_{max}$,D,First Error,1.71266774e-09
47,$N_{max}$,I,First Error,0.00147221438
48,$N_{max}$,N,First Error,0.000931579111
49,$N_{max}$,Mean,First Error,0.000690909686166935
50,$\gamma_{cell}$,P,First Error,0.000245701873
51,$\gamma_{cell}$,D,First Error,9.57051247e-24
52,$\gamma_{cell}$,I,First Error,0.000318847918
53,$\gamma_{cell}$,N,First Error,1.1718385e-07
54,$\gamma_{cell}$,Mean,First Error,0.0001411667437125
55,$K_d$,P,First Error,0.000284942556
56,$K_d$,D,First Error,2.3820604e-18
57,$K_d$,I,First Error,0.00320241658
58,$K_d$,N,First Error,0.00880561072
59,$K_d$,Mean,First Error,0.0030732424640000006
Code:
# Get dataframes
df = analysis.sensitivity_analysis_result.get_dataframe(analysis.scenario)
err_df = df.melt(id_vars=["Parameter", "Output"], value_vars=["Total Error", "First Error"], var_name="Error")
df = df.melt(id_vars=["Parameter", "Output"], value_vars=["Total-effect", "First-order"], var_name="Sobol index", value_name="Value")
# Plot
grid = sns.catplot(data=df, x="Parameter", y="Value", col="Output", col_wrap=2, hue="Sobol index", kind="bar", aspect=1.8, legend_out=False)
# Add error lines and values
for ax, var in zip(grid.axes.ravel(), ["P", "D", "I", "N"]):
# Error bars
ax.errorbar(x=df[df["Output"] == var]["Parameter"], y=df[df["Output"] == var]["Value"],
yerr=err_df[err_df["Output"] == var]["value"], ecolor='black', linewidth=0, capsize=2)
# Value labels
for c in ax.containers:
if type(c) == matplotlib.container.BarContainer:
ax.bar_label(c, labels=[f'{v.get_height():.2f}' if v.get_height() >= 0.01 else "<0.01" for v in c], label_type='edge')
grid.tight_layout()
The issue I'm facing is that the error bar cap sizes are not centered with the actual bar. Here's the output I get. How can I fix this? The x parameter I pass to error bar is not a float but rather a column from a data frame so it is not obvious to me how to shift it.

You plot categorical values (the x values are strings), the x positions of the (center of the) bar groups and the error bars the way you plotted them are range(n) where n is the number of parameters. These x positions can also be retrieved by ax.xaxis.get_majorticklocs().
To put the error bars in the middle of the bar you need to consider the offset of each bar relative to the center of the bar group. This offset depends on the bar width (default is 0.8) and the number of bars per group. For a generic solution you'll to differentiate between odd and even numbers of bars per group.
The following shows the case for 2 hues or bars per group (df['Sobol index'].nunique() == 2):
# Plot
width = 0.8
grid = sns.catplot(data=df, x="Parameter", y="Value", col="Output", col_wrap=2, hue="Sobol index", kind="bar", aspect=1.8, legend_out=False, width=width)
# Add error lines and values
for ax, var in zip(grid.axes.ravel(), ["P", "D", "I", "N"]):
# Error bars
ticklocs = ax.xaxis.get_majorticklocs()
offset = 0.5 * width / 2 # 2 = number of hues or bars per group
ax.errorbar(x=np.append(ticklocs - offset, ticklocs + offset), y=df[df["Output"] == var]["Value"],
yerr=err_df[err_df["Output"] == var]["value"], ecolor='black', linewidth=0, capsize=2, elinewidth=1)
# Value labels
for c in ax.containers:
if type(c) == matplotlib.container.BarContainer:
ax.bar_label(c, labels=[f'{v.get_height():.2f}' if v.get_height() >= 0.01 else "<0.01" for v in c], label_type='edge')
grid.tight_layout()

editing sizes in plotly scatter

Here's a sample of my simple plotly chart:
px.scatter(data_frame=testdf,
x = 'some_x',
y = 'some_y',
size = 'some_size',
color = 'sth_colorful',
title = 'stackoverflow',
range_x = [-10, 1100],
range_y = [-0.08, 1],
hover_name = 'sth_i_want_to_check',
animation_frame = 'year_of_course'
)
It is alomost good. One thing that bothers me is that 'some_size' min value is about 20 and max is about 35 so it is hard to notice the size difference between the circles.
Is there a way I can manage the diameter of these? I'd like to keep the original values to appear in a hover.

When you pass the argument size = 'some_size' to px.scatter, this directly determines the size of the markers as they render on the plot.
However, what you can do to get around this is create a new column called "display_size" with your intended sizes for the markers, and set customdata to df['some_size'] so you can access these values in the hovertemplate.
After that, you can loop through all of the traces in your px.scatter and modify the hovertemplate to display some_size instead of display_size.
For example:
## increase the standard deviation of the size values
## so that the difference between small and large markers is more apparent
testdf["display_size"] = 2*test_df["some_size"]
fig = px.scatter(data_frame=testdf,
x = 'some_x',
y = 'some_y',
size = 'display_size',
color = 'sth_colorful',
title = 'stackoverflow',
range_x = [-10, 1100],
range_y = [-0.08, 1],
hover_name = 'sth_i_want_to_check',
animation_frame = 'year_of_course'
)
fig.update_traces(customdata=testdf["some_size"])
## replace marker.size in the hovertemplate with your customdata (the actual size values)
for trace in fig.data:
trace['hovertemplate'] = trace['hovertemplate'].replace('marker.size','customdata')

How to set conditional color range using np where python

I have a data frame that looks like:
print(file.head())
miRNAs baseMean log2FoldChange padj
0 hsa-miR-31-5p 221.442806 -7.037259 4.360127e-27
1 hsa-miR-337-5p 14.711123 -5.790422 4.556183e-01
2 hsa-miR-196b-5p 162.278255 -5.652917 1.365264e-3
3 hsa-miR-584-5p 6.430919 -5.554578 4.077578e-04
4 hsa-miR-196a-5p 455.152841 -5.361830 1.019622e-59
What I want to do is to set the color range for the file['padj'] column like below:
file['padj'] > 0.01 #gray
file['padj'] <= 0.01 #red
file['padj'] <= 0.0001 #orange
I tried to do this using np.where but only for one of the conditions.
p = file.plot.scatter(x='baseMean',y='log2FoldChange',c=np.where(np.abs(file['padj'])>0.01, 'gray', 'b'), logx=True)
# specifying horizontal line type
plt.axhline(y = 0, color = 'r', linestyle = '-')
plt.show()
I could not manage to define multiple conditions to np.where - any help is appreciated.

You could make a color column like so:
file['color'] = 'gray'
file.loc[file['padj']<=0.01, 'color'] = 'red'
file.loc[file['padj']<=0.0001, 'color'] = 'orange'
and plot like this:
plt.scatter(file['baseMean'], file['log2FoldChange'], color=file['color'])
#edit:
hacky way to generate legend:
for label, color in zip(['>0.01', '<=0.01', '<=0.0001'], ['gray', 'red', 'orange']):
plt.scatter([],[], c=color,label=label)
plt.legend()

Wrap around continuous colormap in matplotlib [duplicate]

I'm looking for a good circular/cyclic colormap to represent phase angle information (where the values are restricted to the range [0, 2π] and where 0 and 2π represent the same phase angle).
Background: I'd like to visualize normal modes by plotting both the power spectral density and the relative phase information of the oscillations across the system.
I'll admit that previously I used the 'rainbow' colormap for the power plot and the 'hsv' colormap for the phase plot (see [1]). However, the use of the rainbow colormap is extremely discouraged because of its lack of perceptual linearity and ordering [2][3]. So I switched to the 'coolwarm' colormap for the power plot which I quite like. Unfortunately, the 'hsv' colormap seems to introduce the same kind of visual distortions as the 'rainbow' map (and it also doesn't go along very well with the 'coolwarm' map since it looks kind of ugly and flashy in comparison).
Does anyone have a good recommendation for an alternative circular colormap which I could use for the phase plots?
Requirements:
It needs to be circular so that the values 0 and 2π are represented by the same color.
It should not introduce any visual distortions; in particular, it should be perceptually linear (which the 'hsv' colormap doesn't seem to be). I don't believe that perceptual ordering is such a big deal for phase information, but it would of course not do any harm.
It should be visually appealing when combined with the 'coolwarm' colormap. However, I'm not dead set on 'coolwarm' and am happy to consider other options if there is another nice pair of colormaps to visualize amplitude and phase information.
Bonus points if the colormap is available (or can be easily created) for use in matplotlib.
Many thanks for any suggestions!
[1] http://matplotlib.org/examples/color/colormaps_reference.html
[2] http://www.renci.org/~borland/pdfs/RainbowColorMap_VisViewpoints.pdf
[3] http://medvis.org/2012/08/21/rainbow-colormaps-what-are-they-good-for-absolutely-nothing/

As of matplotlib version 3.0 there are built-in cyclic perceptually uniform colormaps. OK, just the one colormap for the time being, but with two choices of start and end along the cycle, namely twilight and twilight_shifted.
A short example to demonstrate how they look:
import matplotlib.pyplot as plt
import numpy as np
# example data: argument of complex numbers around 0
N = 100
re, im = np.mgrid[-1:1:100j, -1:1:100j]
angle = np.angle(re + 1j*im)
cmaps = 'twilight', 'twilight_shifted'
fig, axs = plt.subplots(ncols=len(cmaps), figsize=(9.5, 5.5))
for cmap, ax in zip(cmaps, axs):
cf = ax.pcolormesh(re, im, angle, shading='gouraud', cmap=cmap)
ax.set_title(cmap)
ax.set_xlabel(r'$\operatorname{Re} z$')
ax.set_ylabel(r'$\operatorname{Im} z$')
ax.axis('scaled')
cb = plt.colorbar(cf, ax=ax, orientation='horizontal')
cb.set_label(r'$\operatorname{Arg} z$')
fig.tight_layout()
The above produces the following figure:
These brand new colormaps are an amazing addition to the existing collection of perceptually uniform (sequential) colormaps, namely viridis, plasma, inferno, magma and cividis (the last one was a new addition in 2.2 which is not only perceptually uniform and thus colorblind-friendly, but it should look as close as possible to colorblind and non-colorblind people).

EDIT: Matplotlib has now nice cyclic color maps, see the answer of #andras-deak below. They use a similar approach to the color maps as in this answer, but smooth the edges in luminosity.
The issue with the hue-HUSL colormap is that it's not intuitive to read an angle from it. Therefore, I suggest to make your own colormap. Here's a few possibilities:
For the linear segmented colormap, we definine a few colors. The colormap is then a linear interpolation between the colors. This has visual distortions.
For the luminosity-HSLUV map, we use the HUSL ("HSLUV") space, however instead of just hue channel, we use two colors and the luminosity channel. This has distortions in the chroma, but has bright colors.
The luminosity-HPLUV map, we use the HPLUV color space (following #mwaskom's comment). This is the only way to really have no visual distortions, but the colors are not saturated
This is what they look like:
We see that in our custom colormaps, white stands for 0, blue stands for 1i, etc.
On the upper right, we see the hue-HUSL map for comparison. There, the color-angle assignments are random.
Also when plotting a more complex function, it's straightforward to read out the phase of the result when using one of our colormaps.
And here's the code for the plots:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as col
import seaborn as sns
import hsluv # install via pip
import scipy.special # just for the example function
##### generate custom colormaps
def make_segmented_cmap():
white = '#ffffff'
black = '#000000'
red = '#ff0000'
blue = '#0000ff'
anglemap = col.LinearSegmentedColormap.from_list(
'anglemap', [black, red, white, blue, black], N=256, gamma=1)
return anglemap
def make_anglemap( N = 256, use_hpl = True ):
h = np.ones(N) # hue
h[:N//2] = 11.6 # red
h[N//2:] = 258.6 # blue
s = 100 # saturation
l = np.linspace(0, 100, N//2) # luminosity
l = np.hstack( (l,l[::-1] ) )
colorlist = np.zeros((N,3))
for ii in range(N):
if use_hpl:
colorlist[ii,:] = hsluv.hpluv_to_rgb( (h[ii], s, l[ii]) )
else:
colorlist[ii,:] = hsluv.hsluv_to_rgb( (h[ii], s, l[ii]) )
colorlist[colorlist > 1] = 1 # correct numeric errors
colorlist[colorlist < 0] = 0
return col.ListedColormap( colorlist )
N = 256
segmented_cmap = make_segmented_cmap()
flat_huslmap = col.ListedColormap(sns.color_palette('husl',N))
hsluv_anglemap = make_anglemap( use_hpl = False )
hpluv_anglemap = make_anglemap( use_hpl = True )
##### generate data grid
x = np.linspace(-2,2,N)
y = np.linspace(-2,2,N)
z = np.zeros((len(y),len(x))) # make cartesian grid
for ii in range(len(y)):
z[ii] = np.arctan2(y[ii],x) # simple angular function
z[ii] = np.angle(scipy.special.gamma(x+1j*y[ii])) # some complex function
##### plot with different colormaps
fig = plt.figure(1)
fig.clf()
colormapnames = ['segmented map', 'hue-HUSL', 'lum-HSLUV', 'lum-HPLUV']
colormaps = [segmented_cmap, flat_huslmap, hsluv_anglemap, hpluv_anglemap]
for ii, cm in enumerate(colormaps):
ax = fig.add_subplot(2, 2, ii+1)
pmesh = ax.pcolormesh(x, y, z/np.pi,
cmap = cm, vmin=-1, vmax=1)
plt.axis([x.min(), x.max(), y.min(), y.max()])
cbar = fig.colorbar(pmesh)
cbar.ax.set_ylabel('Phase [pi]')
ax.set_title( colormapnames[ii] )
plt.show()

You could try the "husl" system, which is similar to hls/hsv but with better visual properties. It is available in seaborn and as a standalone package.
Here's a simple example:
import numpy as np
from numpy import sin, cos, pi
import matplotlib.pyplot as plt
import seaborn as sns
n = 314
theta = np.linspace(0, 2 * pi, n)
x = cos(theta)
y = sin(theta)
f = plt.figure(figsize=(10, 5))
with sns.color_palette("husl", n):
ax = f.add_subplot(121)
ax.plot([np.zeros_like(x), x], [np.zeros_like(y), y], lw=3)
ax.set_axis_off()
ax.set_title("HUSL space")
with sns.color_palette("hls", n):
ax = f.add_subplot(122)
ax.plot([np.zeros_like(x), x], [np.zeros_like(y), y], lw=3)
ax.set_axis_off()
ax.set_title("HLS space")
f.tight_layout()

I just realized that cmocean has a colormap for this purpose.
import cmocean
import matplotlib.pyplot as plt
import numpy as np
azimuths = np.arange(0, 361, 1)
zeniths = np.arange(40, 70, 1)
values = azimuths * np.ones((30, 361))
fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))
ax.pcolormesh(azimuths*np.pi/180.0, zeniths, values, cmap=cmocean.cm.phase)
ax.set_yticks([])
plt.show()
And the result is

I like the above color maps, but wanted to bootstrap some modern color theory into them. You can get a more perceptually uniform color space by using the CIECAM02 color space.
The colorspacious package is a nice one for converting into this great color space. Using this package, I bootstrapped the above answers into a custom cmap of my own.
from colorspacious import cspace_convert
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as col
import seaborn as sns
# first draw a circle in the cylindrical JCh color space.
# the third channel is hue in degrees. First is lightness and the second chroma
color_circle = np.ones((256,3))*60
color_circle[:,1] = np.ones((256))*45
color_circle[:,2] = np.arange(0,360,360/256)
color_circle_rgb = cspace_convert(color_circle, "JCh","sRGB1")
cm = col.ListedColormap(color_circle_rgb)
##### generate data grid like in above
N=256
x = np.linspace(-2,2,N)
y = np.linspace(-2,2,N)
z = np.zeros((len(y),len(x))) # make cartesian grid
for ii in range(len(y)):
z[ii] = np.arctan2(y[ii],x) # simple angular function
fig = plt.figure()
ax = plt.gca()
pmesh = ax.pcolormesh(x, y, z/np.pi,
cmap = cm, vmin=-1, vmax=1)
plt.axis([x.min(), x.max(), y.min(), y.max()])
cbar = fig.colorbar(pmesh)
cbar.ax.set_ylabel('Phase [pi]')
If you don't want to install the colorspacious package, you can just copy and paste this list of colors, and create a colormap directly:
colors = array([[0.91510904, 0.55114749, 0.67037311],
[0.91696411, 0.55081563, 0.66264366],
[0.91870995, 0.55055664, 0.65485881],
[0.92034498, 0.55037149, 0.64702356],
[0.92186763, 0.55026107, 0.63914306],
[0.92327636, 0.55022625, 0.63122259],
[0.9245696 , 0.55026781, 0.62326754],
[0.92574582, 0.5503865 , 0.6152834 ],
[0.92680349, 0.55058299, 0.6072758 ],
[0.92774112, 0.55085789, 0.59925045],
[0.9285572 , 0.55121174, 0.59121319],
[0.92925027, 0.551645 , 0.58316992],
[0.92981889, 0.55215808, 0.57512667],
[0.93026165, 0.55275127, 0.56708953],
[0.93057716, 0.5534248 , 0.55906469],
[0.93076407, 0.55417883, 0.55105838],
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[0.76321187, 0.6121065 , 0.89661877],
[0.76831596, 0.6098921 , 0.89377082],
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[0.77835901, 0.6055206 , 0.88763988],
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[0.8377048 , 0.57966009, 0.83378306],
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[0.84977422, 0.57451892, 0.81800565],
[0.85364809, 0.57289 , 0.8124808 ],
[0.85744519, 0.57130585, 0.80682595],
[0.86116418, 0.56976788, 0.80104298],
[0.86480373, 0.56827749, 0.79513394],
[0.86836249, 0.56683612, 0.789101 ],
[0.87183909, 0.56544515, 0.78294645],
[0.87523214, 0.56410599, 0.77667274],
[0.87854024, 0.56282002, 0.77028247],
[0.88176195, 0.56158863, 0.76377835],
[0.88489584, 0.56041319, 0.75716326],
[0.88794045, 0.55929505, 0.75044023],
[0.89089432, 0.55823556, 0.74361241],
[0.89375596, 0.55723605, 0.73668312],
[0.89652387, 0.55629781, 0.72965583],
[0.89919653, 0.55542215, 0.72253414],
[0.90177242, 0.55461033, 0.71532181],
[0.90425 , 0.55386358, 0.70802274],
[0.90662774, 0.55318313, 0.70064098],
[0.90890408, 0.55257016, 0.69318073],
[0.91107745, 0.55202582, 0.68564633],
[0.91314629, 0.55155124, 0.67804225]])
cmap = col.ListedColormap(colors)

My CMasher package contains a large collection of scientific colormaps, including cyclic ones.
Unlike twilight, which comes close to being perceptually uniform, all of CMasher's colormaps are perceptually uniform sequential.
You can take a look at all of its colormaps in its online documentation.
CMasher's policy also states that if you are not satisfied with any of the colormaps it provides, you can request for me to create one for you that you like.

Python boxplot showing means and confidence intervals

How can I create a boxplot like the one below, in Python? I want to depict means and confidence bounds only (rather than proportions of IQRs, as in matplotlib boxplot).
I don't have any version constraints, and if your answer has some package dependency that's OK too. Thanks!

Use errorbar instead. Here is a minimal example:
import matplotlib.pyplot as plt
x = [2, 4, 3]
y = [1, 3, 5]
errors = [0.5, 0.25, 0.75]
plt.figure()
plt.errorbar(x, y, xerr=errors, fmt = 'o', color = 'k')
plt.yticks((0, 1, 3, 5, 6), ('', 'x3', 'x2', 'x1',''))
Note that boxplot is not the right approach; the conf_intervals parameter only controls the placement of the notches on the boxes (and we don't want boxes anyway, let alone notched boxes). There is no way to customize the whiskers except as a function of IQR.

Thanks to America, I propose a way to automatize this kind of graph a little bit.
Below an example of code generating 20 arrays from a normal distribution with mean=0.25 and std=0.1.
I used the formula W = t * s / sqrt(n), to calculate the margin of error of the confidence interval, with t the constant from the t distribution (see scipy.stats.t), s the standard deviation and n the number of values in an array.
list_samples=list() # making a list of arrays
for i in range(20):
list.append(np.random.normal(loc=0.25, scale=0.1, size=20))
def W_array(array, conf=0.95): # function that returns W based on the array provided
t = stats.t(df = len(array) - 1).ppf((1 + conf) /2)
W = t * np.std(array, ddof=1) / np.sqrt(len(array))
return W # the error
W_list = list()
mean_list = list()
for i in range(len(list_samples)):
W_list.append(W_array(list_samples[i])) # makes a list of W for each array
mean_list.append(np.mean(list_samples[i])) # same for the means to plot
plt.errorbar(x=mean_list, y=range(len(list_samples)), xerr=W_list, fmt='o', color='k')
plt.axvline(.25, ls='--') # this is only to demonstrate that 95%
# of the 95% CI contain the actual mean
plt.yticks([])
plt.show();