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How to force AxesSubplot to use integer tick labels

I'm struggling to correctly apply Integerlabels to the AxesSubplot Object. Somehow all the other popular solutions don't work. I presume this has to do with the nature of how Pandas plots vs how Matplotlib natively plots a dataframe.
When looking at the docs, there is a mention about preventing resolution adjustments by using the optional parameter x_compat like so:
df['A'].plot(x_compat=True)
However this only throws me an error and I also can't find this parameter in the pandas 1.1.2 docs.
This is the code that produces the plot:
def count_id(id_val):
plot = df[df['ID']==id_val]['TRANSCRIPTION_STRING'].value_counts().plot(kind='bar')
plot.set_xticklabels(plot.get_xticklabels(), rotation=40, ha ='right')
print(type(plot))
I have found this answer and it wasn't helpful for the following reasons:
It uses .gca() which throws an error for my case.
It's specifically using matplotlib instead of pandas.plot, which is the same under the hood, but hardly has very different documentation and isn't obvious to work for my case. More experienced users might differ.
It was a good suggestion and I see the similarities, but I very much tried to find it helpful before asking and it just wasn't helpful.

You can use:
from matplotlib.ticker import MaxNLocator
plot.yaxis.set_major_locator(MaxNLocator(integer=True))
MaxNLocator is the default 'locator' for the ticks. You can also force some fixed multiples, e.g. set_major_locator(MultipleLocater(1)).
PS: The usual variable name for the return value of pandas plot functions is ax. This makes it clearer that the standard matplotlib functions can be used to customize the plots.
ax = df[df['ID']==id_val]['TRANSCRIPTION_STRING'].value_counts().plot(kind='bar')
ax.set_xticklabels(plot.get_xticklabels(), rotation=40, ha ='right')
ax.yaxis.set_major_locator(MaxNLocator(integer=True))

Re-adjusting (automatically) limits on plot in matplotlib

Is there a way to let matplotlib know to recompute the optimal bounds of a plot?
My problem is that, I am manually computing a bunch of boxplots, putting them at various locations in a plot. By the end, some boxplots extend beyond the plot frame. I could hard-code some xlim and ylim's for now, but I want a more general solution.
What I was thinking was a feature where you say "ok plt I am done plotting, now please adjust the bounds so that all my data is nicely within the bounds".
Is this possible?
EDIT:
The answer is yes.
Follow-up question: Can this be done for the ticks as well?

You want to use matplotlib's automatic axis scaling. You can do this with either axes.axis with the "auto" input or axes.set_autoscale_on
ax.axis('auto')
ax.set_autoscale_on()
If you want to auto-scale only the x or y axis, you can use set_autoscaley_on or set_autoscalex_on.

matplotlib: put legend symbols on the right of the labels

It's a simple thing but I've searched for quite a while without success: I want to customise a figure legend by reversing the horizontal order of the symbols and labels.
In Gnuplot, this is simply achieved by set key reverse. Example: change x data1 to data1 x. In matplotlib, there seems to be no user-friendly solution. Thus, I thought about changing a kind of handle anchor or just shifting the handle's position, but couldn't find any point to start with.

The requested feature is already there, as the keyword markerfirst of the legend command.
plt.plot([1,2],[3,4], label='labeltext')
plt.legend(markerfirst=False)
plt.show()
If you want to make it your default behaviour, you can change the value of legend.markerfirst in rcParams, see customizing matplotlib.

Pyplot plot function

Given below is the code for plotting points using pyplot.
x1=300+p[k]*math.cos(val[thetaval])
y1=300+p[k]*math.sin(val[thetaval])
plt.plot(x1,y1,'k.')
The plotting is working fine, the problem is, if I want to plot it as a point I am specifying the dot in 'k.' inside the plot function. The output is something like:
The width of the black line/curve that I am plotting is much more that needed. How to reduce it?

It seems that you are not plotting a line but a series of small points. Maybe if you try setting the markersize argument of the plot function could work.

Looking into the documentation of plot() you can find "linewidth"
So use:
plt.plot(x1,y1,'k.', linewidth=0.1)

How to prevent numbers being changed to exponential form in a plot

I'm using Matplotlib in Python to plot simple x-y datasets. This produces nice-looking graphs, although when I "zoom in" too close on various sections of the plotted graph using the Figure View (which appears when you execute plt.show() ), the x-axis values change from standard number form (1050, 1060, 1070 etc.) to scientific form with exponential notation (e.g. 1, 1.5, 2.0 with the x-axis label given as +1.057e3).
I'd prefer my figures to retain the simple numbering of the axis, rather than using exponential form. Is there a way I can force Matplotlib to do this?

The formatting of tick labels is controlled by a Formatter object, which assuming you haven't done anything fancy will be a ScalerFormatterby default. This formatter will use a constant shift if the fractional change of the values visible is very small. To avoid this, simply turn it off:
plt.plot(arange(0,100,10) + 1000, arange(0,100,10))
ax = plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
plt.draw()
If you want to avoid scientific notation in general,
ax.get_xaxis().get_major_formatter().set_scientific(False)
Can control this with globally via the axes.formatter.useoffset rcparam.

You can use a simpler command to turn it off:
plt.ticklabel_format(useOffset=False)

You can use something like:
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))

Use the following command:
ax.ticklabel_format(useOffset=False, style='plain')
If you are using a subplot, you may experience the AttributeError: This method only works with the ScalarFormatter in which case you would add axis='y' like the below. You can change 'y' to the axis with the issues.
ax1.ticklabel_format(useOffset=False, style='plain', axis='y')
Source question and answer here. Note, the axis 'y' command use is hidden in the answer comments.

I have used below code before the graphs, and it worked seamless for me..
plt.ticklabel_format(style='plain')

Exactly I didn't want scientific numbers to be shown when I zoom in, and the following worked in my case too. I am using Lat/Lon in labeling where scientific form doesn't make sense.
plt.ticklabel_format(useOffset=False)