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How to set equal number of ticks for two subplots?

I have two subplots of horizontal bars done in matplotlib. For the first subplot, the number of y-axis ticks is appropriate, but I'm unable to figure out why specifying number of ticks for the second subplot is coming out to be wrong. This is the code:
import matplotlib.pyplot as plt
import numpy as np
# Plot separate subplots for genders
fig, (axes1, axes2) = plt.subplots(nrows=1, ncols=2,
sharex=False,
sharey=False,
figsize=(15,10))
labels = list(out.index)
x = ["20%", "40%", "60%", "80%", "100%"]
y = np.arange(len(out))
width = 0.5
axes1.barh(y, female_distr, width, color="olive",
align="center", alpha=0.8)
axes1.ticks_params(nbins=6)
axes1.set_yticks(y)
axes1.set_yticklabels(labels)
axes1.set_xticklabels(x)
axes1.yaxis.grid(False)
axes1.set_xlabel("Occurence (%)")
axes1.set_ylabel("Language")
axes1.set_title("Language Distribution (Women)")
axes2.barh(y, male_distr, width, color="chocolate",
align="center", alpha=0.8)
axes2.locator_params(nbins=6)
axes2.set_yticks(y)
axes2.set_yticklabels(labels)
axes2.set_xticklabels(x)
axes2.yaxis.grid(False)
axes2.set_xlabel("Occurence (%)")
axes2.set_ylabel("Language")
axes2.set_title("Language Distribution (Men)")
The rest of the objects like out are simple data frames that I don't think need to be described here. The above code returns the following plot:
I would like the second subplot to have equal number of ticks but experimenting with nbins always results in either more or fewer ticks than the first subplot.

First, if you want your two plots to have the same x-axis, why not use sharex=True?
x_ticks = [0,20,40,60,80,100]
fig, (ax1,ax2) = plt.subplots(1,2, sharex=True)
ax1.set_xticks(x_ticks)
ax1.set_xticklabels(['{:.0f}%'.format(x) for x in x_ticks])
ax1.set_xlim(0,100)
ax1.grid(True, axis='x')
ax2.grid(True, axis='x')

Superimposition of histogram and density in Pandas/Matplotlib in Python

I've got a Pandas dataframe named clean which contains a column v for which I would like to draw a histogram and superimpose a density plot. I know I can plot one under the other this way:
import pandas as pd
import matplotlib.pyplot as plt
Maxv=200
plt.subplot(211)
plt.hist(clean['v'],bins=40, range=(0, Maxv), color='g')
plt.ylabel("Number")
plt.subplot(212)
ax=clean['v'].plot(kind='density')
ax.set_xlim(0, Maxv)
plt.xlabel("Orbital velocity (km/s)")
ax.get_yaxis().set_visible(False)
But when I try to superimpose, y scales doesn't match (and I loose y axis ticks and labels):
yhist, xhist, _hist = plt.hist(clean['v'],bins=40, range=(0, Maxv), color='g')
plt.ylabel("Number")
ax=clean['v'].plot(kind='density') #I would like to insert here a normalization to max(yhist)/max(ax)
ax.set_xlim(0, Maxv)
plt.xlabel("Orbital velocity (km/s)")
ax.get_yaxis().set_visible(False)
Some hint? (Additional question: how can I change the width of density smoothing?)

Based on your code, this should work:
ax = clean.v.plot(kind='hist', bins=40, normed=True)
clean.v.plot(kind='kde', ax=ax, secondary_y=True)
ax.set(xlim=[0, Maxv])
You might not even need the secondary_y anymore.

No I try this:
ax = clean.v.plot(kind='hist', bins=40, range=(0, Maxv))
clean.v.plot(kind='kde', ax=ax, secondary_y=True)
But the range part doesn't work, and ther's still the left y-axis problem

Seaborn makes this easy
import seaborn as sns
sns.distplot(df['numeric_column'],bins=25)

Two Y axis Bar plot: custom xticks

I am trying to add custom xticks to a relatively complicated bar graph plot and I am stuck.
I am plotting from two data frames, merged_90 and merged_15:
merged_15
Volume y_err_x Area_2D y_err_y
TripDate
2015-09-22 1663.016032 199.507503 1581.591701 163.473202
merged_90
Volume y_err_x Area_2D y_err_y
TripDate
1990-06-10 1096.530711 197.377497 1531.651913 205.197493
I want to create a bar graph with two axes (i.e. Area_2D and Volume) where the Area_2D and Volume bars are grouped based on their respective data frame. An example script would look like:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
fig = plt.figure()
ax1 = fig.add_subplot(111)
merged_90.Volume.plot(ax=ax1, color='orange', kind='bar',position=2.5, yerr=merged_90['y_err_x'] ,use_index=False , width=0.1)
merged_15.Volume.plot(ax=ax1, color='red', kind='bar',position=0.9, yerr=merged_15['y_err_x'] ,use_index=False, width=0.1)
ax2 = ax1.twinx()
merged_90.Area_2D.plot(ax=ax2,color='green', kind='bar',position=3.5, yerr=merged_90['y_err_y'],use_index=False, width=0.1)
merged_15.Area_2D.plot(ax=ax2,color='blue', kind='bar',position=0, yerr=merged_15['y_err_y'],use_index=False, width=0.1)
ax1.set_xlim(-0.5,0.2)
x = scipy.arange(1)
ax2.set_xticks(x)
ax2.set_xticklabels(['2015'])
plt.tight_layout()
plt.show()
The resulting plot is:
One would think I could change:
x = scipy.arange(1)
ax2.set_xticks(x)
ax2.set_xticklabels(['2015'])
to
x = scipy.arange(2)
ax2.set_xticks(x)
ax2.set_xticklabels(['1990','2015'])
but that results in:
I would like to see the ticks ordered in chronological order (i.e. 1990,2015)
Thanks!

Have you considered dropping the second axis and plotting them as follows:
ind = np.array([0,0.3])
width = 0.1
fig, ax = plt.subplots()
Rects1 = ax.bar(ind, [merged_90.Volume.values, merged_15.Volume.values], color=['orange', 'red'] ,width=width)
Rects2 = ax.bar(ind + width, [merged_90.Area_2D.values, merged_15.Area_2D.values], color=['green', 'blue'] ,width=width)
ax.set_xticks([.1,.4])
ax.set_xticklabels(('1990','2015'))
This produces:
I omitted the error and colors but you can easily add them. That would produce a readable graph given your test data. As you mentioned in comments you would still rather have two axes, presumably for different data with proper scales. To do this you could do:
fig = plt.figure()
ax1 = fig.add_subplot(111)
merged_90.Volume.plot(ax=ax, color='orange', kind='bar',position=2.5, use_index=False , width=0.1)
merged_15.Volume.plot(ax=ax, color='red', kind='bar',position=1.0, use_index=False, width=0.1)
ax2 = ax1.twinx()
merged_90.Area_2D.plot(ax=ax,color='green', kind='bar',position=3.5,use_index=False, width=0.1)
merged_15.Area_2D.plot(ax=ax,color='blue', kind='bar',position=0,use_index=False, width=0.1)
ax1.set_xlim([-.45, .2])
ax2.set_xlim(-.45, .2])
ax1.set_xticks([-.35, 0])
ax1.set_xticklabels([1990, 2015])
This produces:
Your problem was with resetting just one axis limit and not the other, they are created as twins but do not necessarily follow the changes made to one another.

Change grid interval and specify tick labels in Matplotlib

I am trying to plot counts in gridded plots, but I haven't been able to figure out how to go about it.
I want:
to have dotted grids at an interval of 5;
to have major tick labels only every 20;
for the ticks to be outside the plot; and
to have "counts" inside those grids.
I have checked for potential duplicates, such as here and here, but have not been able to figure it out.
This is my code:
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
for key, value in sorted(data.items()):
x = value[0][2]
y = value[0][3]
count = value[0][4]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.annotate(count, xy = (x, y), size = 5)
# overwrites and I only get the last data point
plt.close()
# Without this, I get a "fail to allocate bitmap" error.
plt.suptitle('Number of counts', fontsize = 12)
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.axes().set_aspect('equal')
plt.axis([0, 1000, 0, 1000])
# This gives an interval of 200.
majorLocator = MultipleLocator(20)
majorFormatter = FormatStrFormatter('%d')
minorLocator = MultipleLocator(5)
# I want the minor grid to be 5 and the major grid to be 20.
plt.grid()
filename = 'C:\Users\Owl\Desktop\Plot.png'
plt.savefig(filename, dpi = 150)
plt.close()
This is what I get.
I also have a problem with the data points being overwritten.
Could anybody PLEASE help me with this problem?

There are several problems in your code.
First the big ones:
You are creating a new figure and a new axes in every iteration of your loop →
put fig = plt.figure and ax = fig.add_subplot(1,1,1) outside of the loop.
Don't use the Locators. Call the functions ax.set_xticks() and ax.grid() with the correct keywords.
With plt.axes() you are creating a new axes again. Use ax.set_aspect('equal').
The minor things:
You should not mix the MATLAB-like syntax like plt.axis() with the objective syntax.
Use ax.set_xlim(a,b) and ax.set_ylim(a,b)
This should be a working minimal example:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Major ticks every 20, minor ticks every 5
major_ticks = np.arange(0, 101, 20)
minor_ticks = np.arange(0, 101, 5)
ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor=True)
ax.set_yticks(major_ticks)
ax.set_yticks(minor_ticks, minor=True)
# And a corresponding grid
ax.grid(which='both')
# Or if you want different settings for the grids:
ax.grid(which='minor', alpha=0.2)
ax.grid(which='major', alpha=0.5)
plt.show()
Output is this:

A subtle alternative to MaxNoe's answer where you aren't explicitly setting the ticks but instead setting the cadence.
import matplotlib.pyplot as plt
from matplotlib.ticker import (AutoMinorLocator, MultipleLocator)
fig, ax = plt.subplots(figsize=(10, 8))
# Set axis ranges; by default this will put major ticks every 25.
ax.set_xlim(0, 200)
ax.set_ylim(0, 200)
# Change major ticks to show every 20.
ax.xaxis.set_major_locator(MultipleLocator(20))
ax.yaxis.set_major_locator(MultipleLocator(20))
# Change minor ticks to show every 5. (20/4 = 5)
ax.xaxis.set_minor_locator(AutoMinorLocator(4))
ax.yaxis.set_minor_locator(AutoMinorLocator(4))
# Turn grid on for both major and minor ticks and style minor slightly
# differently.
ax.grid(which='major', color='#CCCCCC', linestyle='--')
ax.grid(which='minor', color='#CCCCCC', linestyle=':')

Matplotlib: how to set ticks of twinned axis in log plot

In my plot, a secondary x axis is used to display the value of another variable for some data. Now, the original axis is log scaled. Unfortunaltely, the twinned axis puts the ticks (and the labels) referring to the linear scale of the original axis and not as intended to the log scale. How can this be overcome?
Here the code example that should put the ticks of the twinned axis in the same (absolute axes) position as the ones for the original axis:
def conv(x):
"""some conversion function"""
# ...
return x2
ax = plt.subplot(1,1,1)
ax.set_xscale('log')
# get the location of the ticks of ax
axlocs,axlabels = plt.xticks()
# twin axis and set limits as in ax
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
#Set the ticks, should be set referring to the log scale of ax, but are set referring to the linear scale
ax2.set_xticks(axlocs)
# put the converted labels
ax2.set_xticklabels(map(conv,axlocs))
An alternative way would be (the ticks are then not set in the same position, but that doesn't matter):
from matplotlib.ticker import FuncFormatter
ax = plt.subplot(1,1,1)
ax.set_xscale('log')
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
ax2.xaxis.set_major_formatter(FuncFormatter(lambda x,pos:conv(x)))
Both approaches work well as long as no log scale is used.
Perhaps there exists an easy fix. Is there something I missed in the documentation?
As a workaround, I tried to obtain the ax.transAxes coordinates of the ticks of ax and put the ticks at the very same position in ax2. But there does not exist something like
ax2.set_xticks(axlocs,transform=ax2.transAxes)
TypeError: set_xticks() got an unexpected keyword argument 'transform'

This has been asked a while ago, but I stumbled over it with the same question.
I eventually managed to solve the problem by introducing a logscaled (semilogx) transparent (alpha=0) dummy plot.
Example:
import numpy as np
import matplotlib.pyplot as plt
def conversion_func(x): # some arbitrary transformation function
return 2 * x**0.5 # from x to z
x = np.logspace(0, 5, 100)
y = np.sin(np.log(x))
fig = plt.figure()
ax = plt.gca()
ax.semilogx(x, y, 'k')
ax.set_xlim(x[0], x[-1]) # this is important in order that limits of both axes match
ax.set_ylabel("$y$")
ax.set_xlabel("$x$", color='C0')
ax.tick_params(axis='x', which='both', colors='C0')
ax.axvline(100, c='C0', lw=3)
ticks_x = np.logspace(0, 5, 5 + 1) # must span limits of first axis with clever spacing
ticks_z = conversion_func(ticks_x)
ax2 = ax.twiny() # get the twin axis
ax2.semilogx(ticks_z, np.ones_like(ticks_z), alpha=0) # transparent dummy plot
ax2.set_xlim(ticks_z[0], ticks_z[-1])
ax2.set_xlabel("$z \equiv f(x)$", color='C1')
ax2.xaxis.label.set_color('C1')
ax2.tick_params(axis='x', which='both', colors='C1')
ax2.axvline(20, ls='--', c='C1', lw=3) # z=20 indeed matches x=100 as desired
fig.show()
In the above example the vertical lines demonstrate that first and second axis are indeed shifted to one another as wanted. x = 100 gets shifted to z = 2*x**0.5 = 20. The colours are just to clarify which vertical line goes with which axis.

Don't need to cover them, just Eliminate the ticks!
d= [7,9,14,17,35,70];
j= [100,80,50,40,20,10];
plt.figure()
plt.xscale('log')
plt.plot(freq, freq*spec) #plot some spectrum
ax1 = plt.gca() #define my first axis
ax1.yaxis.set_ticks_position('both')
ax1.tick_params(axis='y',which='both',direction='in');
ax1.tick_params(axis='x',which='both',direction='in');
ax2 = ax1.twiny() #generates second axis (top)
ax2.set_xlim(ax1.get_xlim()); #same limits
plt.xscale('log') #make it log
ax2.set_xticks(freq[d]); #my own 'major' ticks OVERLAPS!!!
ax2.set_xticklabels(j); #change labels
ax2.tick_params(axis='x',which='major',direction='in');
ax2.tick_params(axis='x',which='minor',top=False); #REMOVE 'MINOR' TICKS
ax2.grid()

I think you can fix your issue by calling ax2.set_xscale('log').
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.semilogx(np.logspace(1.0, 5.0, 20), np.random.random([20]))
new_tick_locations = np.array([10., 100., 1000., 1.0e4])
def tick_function(X):
V = X / 1000.
return ["%.3f" % z for z in V]
ax2 = ax.twiny()
ax2.set_xscale('log')
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(tick_function(new_tick_locations))
ax2.set_xlabel(r"Modified x-axis: $X/1000$")