Develop Reference

How can I put the largest proportion on the top in my horizontal bar chart in Matplotlib? [duplicate]

I have a scatter plot graph with a bunch of random x, y coordinates. Currently the Y-Axis starts at 0 and goes up to the max value. I would like the Y-Axis to start at the max value and go up to 0.
points = [(10,5), (5,11), (24,13), (7,8)]
x_arr = []
y_arr = []
for x,y in points:
x_arr.append(x)
y_arr.append(y)
plt.scatter(x_arr,y_arr)

There is a new API that makes this even simpler.
plt.gca().invert_xaxis()
and/or
plt.gca().invert_yaxis()

DisplacedAussie's answer is correct, but usually a shorter method is just to reverse the single axis in question:
plt.scatter(x_arr, y_arr)
ax = plt.gca()
ax.set_ylim(ax.get_ylim()[::-1])
where the gca() function returns the current Axes instance and the [::-1] reverses the list.

You could also use function exposed by the axes object of the scatter plot
scatter = plt.scatter(x, y)
ax = scatter.axes
ax.invert_xaxis()
ax.invert_yaxis()

Use matplotlib.pyplot.axis()
axis([xmin, xmax, ymin, ymax])
So you could add something like this at the end:
plt.axis([min(x_arr), max(x_arr), max(y_arr), 0])
Although you might want padding at each end so that the extreme points don't sit on the border.

If you're in ipython in pylab mode, then
plt.gca().invert_yaxis()
show()
the show() is required to make it update the current figure.

Another similar method to those described above is to use plt.ylim for example:
plt.ylim(max(y_array), min(y_array))
This method works for me when I'm attempting to compound multiple datasets on Y1 and/or Y2

using ylim() might be the best approach for your purpose:
xValues = list(range(10))
quads = [x** 2 for x in xValues]
plt.ylim(max(quads), 0)
plt.plot(xValues, quads)
will result:

Alternatively, you can use the matplotlib.pyplot.axis() function, which allows you inverting any of the plot axis
ax = matplotlib.pyplot.axis()
matplotlib.pyplot.axis((ax[0],ax[1],ax[3],ax[2]))
Or if you prefer to only reverse the X-axis, then
matplotlib.pyplot.axis((ax[1],ax[0],ax[2],ax[3]))
Indeed, you can invert both axis:
matplotlib.pyplot.axis((ax[1],ax[0],ax[3],ax[2]))

If using matplotlib you can try:
matplotlib.pyplot.xlim(l, r)
matplotlib.pyplot.ylim(b, t)
These two lines set the limits of the x and y axes respectively. For the x axis, the first argument l sets the left most value, and the second argument r sets the right most value. For the y axis, the first argument b sets the bottom most value, and the second argument t sets the top most value.

How can I reduce whitespace in Python plot?

I am plotting 2 shapefiles (converted to geopandas dataframe) using this. But there is too much whitespace. How can I reduce it to fill the box more with the map? The xlim and ylim doesn't seem to have any impact
f, ax = plt.subplots(1, figsize=(8, 8))
polydatx.plot(ax = ax, column = 'Elev_Avg', cmap='OrRd', scheme='quantiles')
segdatx.plot(ax = ax)
ax.grid(False)
ax.set_ylim(47, 47.3)
plt.axis('equal');

The problem lies in calling
plt.axis('equal')
after setting the new ylim.
From the docs:
axis('equal')
changes limits of x or y axis so that equal increments of x and y have the same length; a circle is circular.:
axis('scaled')
achieves the same result by changing the dimensions of the plot box instead of the axis data limits.
In your case I would adjust the figure size to some rectangle, not a square and use axis('scaled').

Change distance between boxplots in the same figure in python [duplicate]

I'm drawing the bloxplot shown below using python and matplotlib. Is there any way I can reduce the distance between the two boxplots on the X axis?
This is the code that I'm using to get the figure above:
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
fig = plt.figure()
xlabels = ["CG", "EG"]
ax = fig.add_subplot(111)
ax.boxplot([values_cg, values_eg])
ax.set_xticks(np.arange(len(xlabels))+1)
ax.set_xticklabels(xlabels, rotation=45, ha='right')
fig.subplots_adjust(bottom=0.3)
ylabels = yticks = np.linspace(0, 20, 5)
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
ax.tick_params(axis='x', pad=10)
ax.tick_params(axis='y', pad=10)
plt.savefig(os.path.join(output_dir, "output.pdf"))
And this is an example closer to what I'd like to get visually (although I wouldn't mind if the boxplots were even a bit closer to each other):

You can either change the aspect ratio of plot or use the widths kwarg (doc) as such:
ax.boxplot([values_cg, values_eg], widths=1)
to make the boxes wider.

Try changing the aspect ratio using
ax.set_aspect(1.5) # or some other float
The larger then number, the narrower (and taller) the plot should be:
a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_aspect

When your code writes:
ax.set_xticks(np.arange(len(xlabels))+1)
You're putting the first box plot on 0 and the second one on 1 (event though you change the tick labels afterwards), just like in the second, "wanted" example you gave they are set on 1,2,3.
So i think an alternative solution would be to play with the xticks position and the xlim of the plot.
for example using
ax.set_xlim(-1.5,2.5)
would place them closer.

positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn.
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html
This should do the job!

As #Stevie mentioned, you can use the positions kwarg (doc) to manually set the x-coordinates of the boxes:
ax.boxplot([values_cg, values_eg], positions=[1, 1.3])

Changing axis values on a plot

How can I change the data on one axis?
I'm making some spectrum analysis on some data and my x-axis is the index of some matrix. I'd like to change it so that the x-axis becomes the data itself.
I'm using the imshow() to plot the data (I have a matrix whose elements are some intensity, the y axes are their detector-source correspondent pair and the x-axis should be their frequency).
The code for it is written down here:
def pltspec(dOD, self):
idx = 0
b = plt.psd(dOD[:,idx],Fs=self.fs,NFFT=512)
B = np.zeros((2*len(self.Chan),len(b[0])))
for idx in range(2*len(self.Chan)):
b = plt.psd(dOD[:,idx],Fs=self.fs,NFFT=512)
B[idx,:] = 20*log10(b[0])
fig = plt.figure()
ax = fig.add_subplot(111)
plt.imshow(B, origin = 'lower')
plt.colorbar()
locs, labels = xticks(find(b[1]), b[1])
plt.axis('tight')
ax.xaxis.set_major_locator(MaxNLocator(5))
I think if there's a way of interchanging the index of some array with its value, my problem would be solved.
I've managed to use the line locs, labels = xticks(find(b[1]), b[1]). But with it on my graph my axis interval just isn't right... I think it has something to do with the MaxNLocator (which I used to decrease the number of ticks).
And if I use the xlim, I can set the figure to be what I want, but the x axis is still the same (on that xlim I had to use the original data to set it right).
What am I doing wrong?

Yes, you can use the xticks method exemplified in this example.
There are also more sophisticated ways of doing it. See ticker.

How to invert the x or y axis

I have a scatter plot graph with a bunch of random x, y coordinates. Currently the Y-Axis starts at 0 and goes up to the max value. I would like the Y-Axis to start at the max value and go up to 0.
points = [(10,5), (5,11), (24,13), (7,8)]
x_arr = []
y_arr = []
for x,y in points:
x_arr.append(x)
y_arr.append(y)
plt.scatter(x_arr,y_arr)

There is a new API that makes this even simpler.
plt.gca().invert_xaxis()
and/or
plt.gca().invert_yaxis()

DisplacedAussie's answer is correct, but usually a shorter method is just to reverse the single axis in question:
plt.scatter(x_arr, y_arr)
ax = plt.gca()
ax.set_ylim(ax.get_ylim()[::-1])
where the gca() function returns the current Axes instance and the [::-1] reverses the list.

You could also use function exposed by the axes object of the scatter plot
scatter = plt.scatter(x, y)
ax = scatter.axes
ax.invert_xaxis()
ax.invert_yaxis()

Use matplotlib.pyplot.axis()
axis([xmin, xmax, ymin, ymax])
So you could add something like this at the end:
plt.axis([min(x_arr), max(x_arr), max(y_arr), 0])
Although you might want padding at each end so that the extreme points don't sit on the border.

If you're in ipython in pylab mode, then
plt.gca().invert_yaxis()
show()
the show() is required to make it update the current figure.

Another similar method to those described above is to use plt.ylim for example:
plt.ylim(max(y_array), min(y_array))
This method works for me when I'm attempting to compound multiple datasets on Y1 and/or Y2

using ylim() might be the best approach for your purpose:
xValues = list(range(10))
quads = [x** 2 for x in xValues]
plt.ylim(max(quads), 0)
plt.plot(xValues, quads)
will result:

Alternatively, you can use the matplotlib.pyplot.axis() function, which allows you inverting any of the plot axis
ax = matplotlib.pyplot.axis()
matplotlib.pyplot.axis((ax[0],ax[1],ax[3],ax[2]))
Or if you prefer to only reverse the X-axis, then
matplotlib.pyplot.axis((ax[1],ax[0],ax[2],ax[3]))
Indeed, you can invert both axis:
matplotlib.pyplot.axis((ax[1],ax[0],ax[3],ax[2]))

If using matplotlib you can try:
matplotlib.pyplot.xlim(l, r)
matplotlib.pyplot.ylim(b, t)
These two lines set the limits of the x and y axes respectively. For the x axis, the first argument l sets the left most value, and the second argument r sets the right most value. For the y axis, the first argument b sets the bottom most value, and the second argument t sets the top most value.