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I want to change the x-axis scientific notation from e-05 to e-06

I plotted some values from my experiment output by the spider(anaconda). I want to change x axis scientific notation from 1e-05 to 1e-06. I googled and could not find a relevant solution to this problem. please help
enter code here
#Import Libraries
import numpy as np
import csv
import matplotlib.pyplot as plt
import pylab
import style
#Handling value error
def isfloat(num):
try:
float(num)
return True
except ValueError:
return False
#import csv file
with open( 'try1.csv', 'r') as i:
file01= list(csv.reader(i,delimiter=','))[2:]
file02=[[float(k) if (isfloat(k)) else 0 for k in j] for j in file01] # Picking the values only
#creating a mumpy array
Data= np.array(file02, dtype=float)
xdata= Data[:,0][::280]
ydata= Data[:,1][::280]
#Plot
plt.figure(1,dpi=800)
plt.title('Force Spectroscopy')
plt.ylabel('Vertical Deflection')
plt.xlabel('Measured Height')
plt.style.use(['seaborn_grid'])
plt.plot(xdata,ydata, color='green',label=('Experimental data'))
#Theoritical Plot
new= -(0.107e-5)*xdata
plt.plot(xdata,new, color= 'purple',label='Theoritical')
#Legend Modification
plt.legend('upper right',fontsize=20)
plt.legend()
Output image of my plot. see the axis notation 1e-5

you can use ticklabel_format() to set the tick label format. You can add the following line -> plt.ticklabel_format(style='sci', axis='x', scilimits=(-6,-6)) to your code to have the x-axis in e-06. Note that the -6 to -6 is telling matplotlib to set the format from e-06 to e-06. More info here
Your modified code sample here to demonstrate the same...
Code
import numpy as np
import matplotlib.pyplot as plt
import pylab
import style
xdata = np.array([1.21, 1.32, 2.54]) * (1e-5)
ydata = [1, 4, 15]
#Plot
plt.figure(1,dpi=800)
plt.title('Force Spectroscopy')
plt.ylabel('Vertical Deflection')
plt.xlabel('Measured Height')
plt.plot(xdata,ydata, color='green',label=('Experimental data'))
#Theoritical Plot
new= -(0.107e-5)*np.array(xdata)
plt.plot(xdata,new, color= 'purple',label='Theoritical')
plt.ticklabel_format(style='sci', axis='x', scilimits=(-6,-6))
#Legend Modification
plt.legend('upper right',fontsize=20)
plt.legend()
Output

how to plot some lists with different shape?

I have some lists that each of which has a different shape and I would like to plot all of them together in one polar scatter plot. I also tried to use iter tools but I could not find the solution.
import numpy as np
import matplotlib.pyplot as plt
a1=[1,2,3,4,5,6]
a2=[2,3,5,6]
a3=[1,2,3]
a4=[1,2,3,4,4,56,7,8]
ax1 = plt.subplot(111,polar= True)
for i in range (0,3):
theta = 4 * np.pi * np.random.rand(len(a[i]))
ax1.set_ylim(0,0.1)
ax1.set_rlabel_position(180)
for i in range (0,3):
ax1.scatter(theta,a[i], cmap='hsv', alpha=0.5)

Be carefull i modified your lists for a better visual exmaple!
I hope I understood your question correctly...
import numpy as np
import matplotlib.pyplot as plt
a1=[1,2,3,4,5,6]
a2=[2,3,5,6]
a3=[1,2,3]
a4=[1,2,3,4,4,7,7,8]
ax1 = plt.subplot(111,polar= True)
for onelist in [a1,a2,a3,a4]:
theta_list = np.linspace(0,2*np.pi,len(onelist))
ax1.plot(theta_list,onelist,marker="x")
plt.show()

matplotlib.close() does not close plot

I'm using Python 3.6 in jupyter notebook. plt.close does not close plot. I tried with plt.ion() also and many other ways.
I want to display image, then wait for pause or input() and then remove the previous image and show the new one.
import matplotlib.pyplot as plt
from time import sleep
from scipy import eye
plt.imshow(eye(3))
plt.show()
sleep(1)
plt.close()

Here is an example that shows a sequence of plots, each for one second. Essential are the commants plt.show(block = False) and plt.pause(1) instead of sleep(1):
import numpy as np
import matplotlib.pyplot as plt
def show_image(n):
fig, ax = plt.subplots()
x = np.linspace(0,1,100)
y = x**n
ax.plot(x,y, label = 'x**{}'.format(n))
ax.legend()
plt.show(block=False)
plt.pause(1)
plt.close(fig)
for i in range(10):
show_image(i)

If I understand correctly, what you want is to show a plot, wait 1 second, then let it close automatically.
This would be achieved as follows.
import matplotlib.pyplot as plt
from scipy import eye
plt.imshow(eye(3))
def show_and_close(sec):
timer = plt.gcf().canvas.new_timer(interval=sec*1000)
timer.add_callback(lambda : plt.close())
timer.single_shot = True
timer.start()
plt.show()
show_and_close(1)

how to modify the autocorrelation default plot style and write the output of a acorr function to a dat/txt file?

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab`
mu = np.loadtxt('my_data/corr.txt')
d = mu[:,2]
y=[]
tot=0
min=999
for i in d:
y.append(float(i))
tot=tot+float(i)
if (min>float(i)):
min=float(i)
av=tot/len(y)
z=[]
m=[]
for i in y:
z.append(i-av)
m.append(i-min)
plt.acorr(z,usevlines=True,maxlags=None,normed=True)
plt.show()
WIth this code I have the output showing a bar chart.
Now,
1) How do I change this plot style to give just the trend line? I cant modify the line properties by any means.
2) How do I write this output data to a dat or txt file?

this should be a working minimal example:
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import normal
data = normal(0, 1, 1000)
# return values are lags, correlation vector and the drawn line
lags, corr, line, rest = plt.acorr(data, marker=None, linestyle='-', color='red', usevlines=False)
plt.show()
np.savetxt("correlations.txt", np.transpose((lags, corr)), header='Lags\tCorrelation')
But i would recommand not to connect the points.

pyplot: loglog() with base e

Python (and matplotlib) newbie here coming over from R, so I hope this question is not too idiotic. I'm trying to make a loglog plot on a natural log scale. But after some googling I cannot somehow figure out how to force pyplot to use a base e scale on the axes. The code I have currently:
import matplotlib.pyplot as pyplot
import math
e = math.exp(1)
pyplot.loglog(range(1,len(degrees)+1),degrees,'o',basex=e,basey=e)
Where degrees is a vector of counts at each value of range(1,len(degrees)+1). For some reason when I run this code, pyplot keeps giving me a plot with powers of 2 on the axes. I feel like this ought to be easy, but I'm stumped...
Any advice is greatly appreciated!

When plotting using plt.loglog you can pass the keyword arguments basex and basey as shown below.
From numpy you can get the e constant with numpy.e (or np.e if you import numpy as np)
import numpy as np
import matplotlib.pyplot as plt
# Generate some data.
x = np.linspace(0, 2, 1000)
y = x**np.e
plt.loglog(x,y, basex=np.e, basey=np.e)
plt.show()
Edit
Additionally if you want pretty looking ticks you can use matplotlib.ticker to choose the format of your ticks, an example of which is given below.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
x = np.linspace(1, 4, 1000)
y = x**3
fig, ax = plt.subplots()
ax.loglog(x,y, basex=np.e, basey=np.e)
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
ax.xaxis.set_major_formatter(mtick.FuncFormatter(ticks))
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()

It can also works for semilogx and semilogy to show them in e and also change their name.
import matplotlib.ticker as mtick
fig, ax = plt.subplots()
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
plt.semilogy(Time_Series, California_Pervalence ,'gray', basey=np.e )
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
Take a look at the image.