Any of those help As far as I know there is no standard scientific journal representation for showing significance. The exact way you draw it is a matter of taste. This is probably the reason why matplotlib has no specific function for significance bars (at least to my knowledge). You could just do it manually. E.g: code :
from matplotlib.markers import TICKDOWN
def significance_bar(start,end,height,displaystring,linewidth = 1.2,markersize = 8,boxpad =0.3,fontsize = 15,color = 'k'):
# draw a line with downticks at the ends
plt.plot([start,end],[height]*2,'',color = color,lw=linewidth,marker = TICKDOWN,markeredgewidth=linewidth,markersize = markersize)
# draw the text with a bounding box covering up the line
plt.text(0.5*(start+end),height,displaystring,ha = 'center',va='center',bbox=dict(facecolor='1.', edgecolor='none',boxstyle='Square,pad='+str(boxpad)),size = fontsize)
pvals = [0.001,0.1,0.00001]
offset =1
for i,p in enumerate(pvals):
if p>=0.05:
displaystring = r'n.s.'
elif p<0.0001:
displaystring = r'***'
elif p<0.001:
displaystring = r'**'
else:
displaystring = r'*'
height = offset + max(cell_lysate_avg[i],media_avg[i])
bar_centers = index[i] + numpy.array([0.5,1.5])*bar_width
significance_bar(bar_centers[0],bar_centers[1],height,displaystring)
Share :

How to hide anova significance levels on the bottom of the table
By : Jivan Ghadage
Date : March 29 2020, 07:55 AM
Hope that helps We had a similar post the other day about not showing NAs. You could do: code :
x < as.matrix(anova(new.model, current.model))
print(x, na.print="", quote=FALSE)
x < as.matrix(anova(lm(hp~mpg+am, data=mtcars)))
print(x, na.print="", quote=FALSE)

Creating Graphs in Pythons using matplotlib: ERROR Module 6
By : user3313081
Date : March 29 2020, 07:55 AM
should help you out Are you sure you've installed six? If you have both python 2.x and 3.x installed, it may be that when running easy_install or pip, you're installing six for 2.x rather than 3.x. If you have pip, try running pip3 install six in a shell, or for easy_install, run easy_install3.4 six (replace 3.4 with your 3.x python version).

Add a list of labels in Pythons matplotlib
By : user3713704
Date : March 29 2020, 07:55 AM
it fixes the issue Would this work for you? code :
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
x_cords = [1,2,4,2,5,3,2,5,3,4,6,2,3,4,5,3,4,2,4,5]
y_cords = [6,5,3,4,5,6,3,5,4,6,3,4,5,6,3,4,5,6,3,4]
z_cords = [3,1,3,4,2,4,5,6,3,4,5,6,2,4,5,7,3,4,5,6]
classlbl= [0,2,0,1,2,0,2,0,1,2,0,1,0,2,0,2,0,1,0,2]
colors = ['r','g','b']
Labels = ['RED','GREEN','BLUE']
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#plotlabels = ['Acer campestre L',...,'Viburnum tinus']
#Creating colorlist from labels indexes
colors = np.asarray(colors)
colorslist = colors[classlbl]
Labels = np.asarray(Labels)
labellist = Labels[classlbl]
# Plot point by point
for x,y,z,c,l in zip(x_cords, y_cords, z_cords,colorslist,labellist):
ax.scatter(x, y, z, color=c,label=l)
# Get the labels and handles
handles, labels = ax.get_legend_handles_labels()
# Filter the labels and handles to remove duplicates
newLeg=dict()
for h,l in zip(handles,labels):
if l not in newLeg.keys():
newLeg[l]=h
# Create new handles and labels
handles=[]
labels=[]
for l in newLeg.keys():
handles.append(newLeg[l])
labels.append(l)
# Create new Legend
ax.legend(handles, labels)
plt.show()

ggpubr: Show significance levels (*** or n.s.) instead of pvalue in the label
By : user2148459
Date : March 29 2020, 07:55 AM
wish of those help I would like to show the significance levels (*** or n.s.) as labels in my linear regression using ggpubr in R. This seems to be done by using aes(label = ..p.signif..) as posted here: https://www.rbloggers.com/addpvaluesandsignificancelevelstoggplots/ , You can use cut: code :
ggscatter(df, x = "wt", y = "mpg",
add = "reg.line", # Add regression line
conf.int = TRUE, # Add confidence interval
color = "cyl", palette = "jco", # Color by groups "cyl"
shape = "cyl" # Change point shape by groups "cyl"
)+
stat_cor(aes(color = cyl,
label =paste(..rr.label.., cut(..p..,
breaks = c(Inf, 0.0001, 0.001, 0.01, 0.05, Inf),
labels = c("'****'", "'***'", "'**'", "'*'", "'ns'")),
sep = "~")),
label.x = 3)

numpy polyfit with data that has varying levels of statistical significance
By : jumbopark
Date : March 29 2020, 07:55 AM
may help you . One possibility is to use weighted least squares in statsmodelsroughly:

