Develop Reference

Adding histogram and 1 std band to residual plot

I have plotted a graph in python with a subplot of residuals and am trying to find a way to at a histogram plot of the residuals on the end of the histogram plot. I would also like to add a grey band on the residual plot showing 1 standard deviation.
also is there a way to remove the top and right-hand side boarders of the plot.
Here is a copy of the code and the graph I currently have.
fig1 = pyplot.figure(figsize =(9.6,7.2))
plt.frame1 =fig1.add_axes((0.2,0.4,.75,.6))
pyplot.errorbar(xval, yval*1000, yerr=yerr*1000, xerr=xerr, marker='x', linestyle='None')
# Axis labels
pyplot.xlabel('Height (m)', fontsize = 12)
pyplot.ylabel('dM/dt (g $s^{-1}$)', fontsize = 12)
# Generate best fit line using model function and best fit parameters, and add to plot
fit_line=model_funct(xval, [a_soln, b_soln])
pyplot.plot(xval, fit_line*1000)
# Set suitable axis limits: you will probably need to change these...
#pyplot.xlim(-1, 61)
#pyplot.ylim(65, 105)
# pyplot.show()
plt.frame2 = fig1.add_axes((0.2,0.2,.75,.2)) #start frame1 at 0.2, 0.4
plt.xlabel("Height of Water (m)", fontsize = 12)
plt.ylabel("Normalised\nResiduals", fontsize = 12) #\n is used to start a new line
plt.plot(h,normalised_residuals,"x", color = "green")
plt.axhline(0, linewidth=1, linestyle="--", color="black")
plt.savefig("Final Graph.png", dpi = 500)

The naming in your code is a bit weird, therefore I only post snippets since it is hard to try it by myself. Sometimes you use pyplot and sometimes you use plt which should be the same. Also you should name your axis like this ax = fig1.add_axes((0.2,0.4,.75,.6)). Then, if you do the plot, you should call it with the axis directly, i.e. use ax.errorbar().
To hide the borders of the axis in the top plot use:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
Adding an error band in the bottom plot is pretty easy to do. Just calculate the mean and standard deviation using np.mean() and np.std(). Afterwards, call
plt.fill_between(h, y1=np.mean(normalised_residuals) - np.std(normalised_residuals),
y2=np.mean(normalised_residuals) + np.std(normalised_residuals),
color='gray', alpha=.5)
and change the color and alpha however you want it to be.
For the histogram projection you just add another axis like you've done it two times before (let's assume it is called ax) and call
ax.hist(normalised_residuals, bins=8, orientation="horizontal")
Here, bins has to be set to a small value probably since you don't have that many data points.

Place legend above the ax at a consistent distance

I'm trying to place a legend just above the ax in matplotlib using ax.legend(loc=(0, 1.1)); however, if I change the figure size from (5,5) to (5,10) the legend shows up at a different distance from the top edge of the plot.
Is there any way to reference the top edge of the plot and offset it a set distance from it?
Thanks

There is a constant distance between the legend bounding box and the axes by default. This is set via the borderaxespad parameter. This defaults to the rc value of rcParams["legend.borderaxespad"], which is usually set to 0.5 (in units of the fontsize).
So essentially you get the behaviour you're asking for for free. Mind however that you should specify the loc to the corner of the legend from which that padding is to be taken. I.e.
import numpy as np
import matplotlib.pyplot as plt
for figsize in [(5,4), (5,9)]:
fig, ax = plt.subplots(figsize=figsize)
ax.plot([1,2,3], label="label")
ax.legend(loc="lower left", bbox_to_anchor=(0,1))
plt.show()
For more detailed explanations on how to position legend outside the axes, see How to put the legend out of the plot. Also relevant: How to specify legend position in matplotlib in graph coordinates

Matplotlib: Align text with axis ticks

I want to display a custom text next to my plot's y-axis, as in this minimal example:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.text(-0.05, 0.5, '$x$')
plt.show()
The horizontal alignment 0.05 is something I figure out by trial and error.
Unfortunately, 0.05 is only right for exactly one size of the plot window.
For the default window size, the text is where I want it:
But once I enlarge the plot window, my text gets lost in no-man's-land:
I tried ax.text(-ax.yaxis.get_tick_padding, 0.5, '$x$'), but padding appears to be measured in different units.
How can I make sure my text has the same distance from the y-axis for every window size?

You may use ax.annotate instead of ax.text as it allows a little bit more freedom. Specifically it allows to annotate a point in some coordinate system with the text being offsetted to that location in different coordinates.
The following would create an annotation at position (0, 0.5) in the coordinate system given by the yaxis_transform, i.e (axes coordinates, data coordinates). The text itself is then offsetted by -5 points from this location.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.annotate('$x$', xy=(0, 0.5), xycoords=ax.get_yaxis_transform(),
xytext=(-5,0), textcoords="offset points", ha="right", va="center")
plt.show()
Note that -5 is also just an estimate and you may again want to find the best value yourself. However, having done so, this will stay the same as long as the padding and fontsize of the labels do not change.

When tick lengths or padding have been changed, you can find out the exact offset by querying one of the ticks.
The ticks have a "pad" (between label and tick mark) and a "padding" (length of the tick mark), both measured in "points".
In the default setting, both are 3.5, giving a padding of 7.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
tick = ax.yaxis.get_major_ticks()[-1]
padding = tick.get_pad() + tick.get_tick_padding()
ax.annotate('$half$', xy=(0, 0.5), xycoords=ax.get_yaxis_transform(),
xytext=(-padding,0), textcoords="offset points", ha="right", va="center")
plt.show()

Add an extra information in a python plot?

Suppose we have a figure with three plots in it for three different parameters. But for the all three plots We have same temperature T=4K . Then how can I add this information in the figure?
I am not interested to write it in the Caption. I want it on the figure itself.

figtext would work well.
The advantage of figtext over text and annotate is that figtext defaults to using the figure coordinates, whereas the others default to using the coordinates of the axes (and therefore "T=4K" would move around if your axes are different between the different plots).
import matplotlib.pyplot as plt
plt.figure()
plt.xlim(-10, 10)
plt.ylim(0, .01)
plt.figtext(.8, .8, "T = 4K")
plt.show()

Here's a demonstration of using annotate. Check out this example for different styles of annotation.
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
fig, ax = plt.subplots()
x = np.linspace(0,4,100)
plt.plot(x,2*x)
plt.plot(x,x**2)
plt.plot(x,np.sqrt(8*x))
ax.annotate('T = 4K', xy=(2,4), xycoords='data',
xytext=(-100,60), textcoords='offset points',
arrowprops=dict(arrowstyle='fancy',fc='0.6',
connectionstyle="angle3,angleA=0,angleB=-90"))
plt.show()
raw_input()

figtext can make annotations at the bootom of multiple subplots figure like a comment independent of figures so you can make additional comments or remarks all in one picture. I was looking for this too. Thank you guys! :-)
import matplotlib.pyplot as plt
plt.figure(1)
plt.suptitle("SOME TITLE HERE")
#FIRST SUBPLOT
plt.subplot(311)
plt.ylabel(r"$a [m/s^2]$") # YOU CAN USE LaTeX TYPESETTING IN PYPLOT STRINGS!
plt.xlabel("time [s]")
plt.grid(True)
plt.plot(some_data)
# SECOND SUBPLOT
plt.subplot(312)
...
# THIRD SUBPLOT
plt.subplot(313)
...
# BOTTOM LABEL
plt.figtext(0.5, 0, "SOME LABEL BELOW ALL SUBPLOTS", ha="center", fontsize=7, bbox={"facecolor":"orange", "alpha":0.5, "pad":5})
# DRAW THE PLOT
plt.show()
Notre ha=center will center the string if x=0.5. You can also use fontsize and bbox parameters to change appearance of the string and its area.

Well, I'm not sure what you mean, but you can add text to the plot with the text() method.
Plot text in matplotlib pyplot

I suggest a grey horizontal zone around the T=4K zone
If you look at axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs) in the matplotlib documentation for axes, you can make things like that:

Hiding axis text in matplotlib plots

I'm trying to plot a figure without tickmarks or numbers on either of the axes (I use axes in the traditional sense, not the matplotlib nomenclature!). An issue I have come across is where matplotlib adjusts the x(y)ticklabels by subtracting a value N, then adds N at the end of the axis.
This may be vague, but the following simplified example highlights the issue, with '6.18' being the offending value of N:
import matplotlib.pyplot as plt
import random
prefix = 6.18
rx = [prefix+(0.001*random.random()) for i in arange(100)]
ry = [prefix+(0.001*random.random()) for i in arange(100)]
plt.plot(rx,ry,'ko')
frame1 = plt.gca()
for xlabel_i in frame1.axes.get_xticklabels():
xlabel_i.set_visible(False)
xlabel_i.set_fontsize(0.0)
for xlabel_i in frame1.axes.get_yticklabels():
xlabel_i.set_fontsize(0.0)
xlabel_i.set_visible(False)
for tick in frame1.axes.get_xticklines():
tick.set_visible(False)
for tick in frame1.axes.get_yticklines():
tick.set_visible(False)
plt.show()
The three things I would like to know are:
How to turn off this behaviour in the first place (although in most cases it is useful, it is not always!) I have looked through matplotlib.axis.XAxis and cannot find anything appropriate
How can I make N disappear (i.e. X.set_visible(False))
Is there a better way to do the above anyway? My final plot would be 4x4 subplots in a figure, if that is relevant.

Instead of hiding each element, you can hide the whole axis:
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
Or, you can set the ticks to an empty list:
frame1.axes.get_xaxis().set_ticks([])
frame1.axes.get_yaxis().set_ticks([])
In this second option, you can still use plt.xlabel() and plt.ylabel() to add labels to the axes.

If you want to hide just the axis text keeping the grid lines:
frame1 = plt.gca()
frame1.axes.xaxis.set_ticklabels([])
frame1.axes.yaxis.set_ticklabels([])
Doing set_visible(False) or set_ticks([]) will also hide the grid lines.

If you are like me and don't always retrieve the axes, ax, when plotting the figure, then a simple solution would be to do
plt.xticks([])
plt.yticks([])

I've colour coded this figure to ease the process.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
You can have full control over the figure using these commands, to complete the answer I've add also the control over the spines:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# X AXIS -BORDER
ax.spines['bottom'].set_visible(False)
# BLUE
ax.set_xticklabels([])
# RED
ax.set_xticks([])
# RED AND BLUE TOGETHER
ax.axes.get_xaxis().set_visible(False)
# Y AXIS -BORDER
ax.spines['left'].set_visible(False)
# YELLOW
ax.set_yticklabels([])
# GREEN
ax.set_yticks([])
# YELLOW AND GREEN TOGHETHER
ax.axes.get_yaxis().set_visible(False)

I was not actually able to render an image without borders or axis data based on any of the code snippets here (even the one accepted at the answer). After digging through some API documentation, I landed on this code to render my image
plt.axis('off')
plt.tick_params(axis='both', left=False, top=False, right=False, bottom=False, labelleft=False, labeltop=False, labelright=False, labelbottom=False)
plt.savefig('foo.png', dpi=100, bbox_inches='tight', pad_inches=0.0)
I used the tick_params call to basically shut down any extra information that might be rendered and I have a perfect graph in my output file.

Somewhat of an old thread but, this seems to be a faster method using the latest version of matplotlib:
set the major formatter for the x-axis
ax.xaxis.set_major_formatter(plt.NullFormatter())

One trick could be setting the color of tick labels as white to hide it!
plt.xticks(color='w')
plt.yticks(color='w')
or to be more generalized (#Armin Okić), you can set it as "None".

When using the object oriented API, the Axes object has two useful methods for removing the axis text, set_xticklabels() and set_xticks().
Say you create a plot using
fig, ax = plt.subplots(1)
ax.plot(x, y)
If you simply want to remove the tick labels, you could use
ax.set_xticklabels([])
or to remove the ticks completely, you could use
ax.set_xticks([])
These methods are useful for specifying exactly where you want the ticks and how you want them labeled. Passing an empty list results in no ticks, or no labels, respectively.

You could simply set xlabel to None, straight in your axis. Below an working example using seaborn
from matplotlib import pyplot as plt
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)
ax.set(xlabel=None)
plt.show()

Just do this in case you have subplots
fig, axs = plt.subplots(1, 2, figsize=(16, 8))
ax[0].set_yticklabels([]) # x-axis
ax[0].set_xticklabels([]) # y-axis