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Aligning y-axis label with middle of subplot?

I'm working with Matplotlib and have a large number of 1D heatmaps, each with their own label. However, the labels are misaligned with the plots and I cannot figure out to get this to work automatically.
Here's an MWE
import numpy as np
import matplotlib.pyplot as plt
data = np.random.rand(10, 1000)
dogs = ["woof", "bark", "bowwow"]
fig, axs = plt.subplots(10)
for i in range(10):
axs[i].scatter(np.linspace(0, 1, 1000), np.linspace(0,1,1000)*0, 2000,
c=data[i, :], marker="|", cmap='inferno')
axs[i].set_frame_on(False)
axs[i].set_yticklabels([])
axs[i].set_xticklabels([])
axs[i].set_xticks([])
axs[i].set_yticks([])
axs[i].set_ylabel(dogs[i%3], rotation='horizontal')
plt.show()
I experimented with
axs[i].yaxis.set_label_coords(x, y)
for various values of x and y, and nothing seems to work. I would prefer to have it align automatically, with the bottom of the text corresponding to the bottom of the individual plot.
Attached is an image showcasing the alignment issue.
Example

You could create your heatmaps via seaborn, and use yticklabels=[label_name] to set the labels. Rotating the labels to 0 degrees should have them nicely aligned. Note that the data is expected to have a shape of 1xN.
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
labels = ['Alkaid', 'Mizar', 'Alioth', 'Megrez', 'Phecda', 'Merak', 'Dubhe']
nrows = len(labels)
fig, axs = plt.subplots(nrows=nrows, figsize=(10, 5))
for ax_i, data_i, label_i in zip(axs, np.random.randn(nrows, 1, 100).cumsum(axis=2), labels):
sns.heatmap(data=data_i, xticklabels=[], yticklabels=[label_i], cmap='inferno', cbar=False, ax=ax_i)
ax_i.tick_params(axis='y', rotation=0, labelsize=22, length=0) # length means length of the tick mark
plt.tight_layout()
plt.show()

After a bit of playing around, I found that
axs[0].set_ylabel("Pseudotime", fontsize=12, rotation='horizontal', ha='right', va='center')
is sufficient for aligning the y-labels.

How to remove padding in matplotlib subplots when using make_axes_locatable

I'm trying to create a 4x2 plot on a slightly non-rectangular dataset (x-axis range is smaller than y-axis range) using plt.subplots and assign colorbars using make_axes_locatable to have the colorbars nice and flush wih the subplots. I always end up with huge paddings/margins between the two subplot columns which I suspect originate from the colorbars... I've tried multiple things from many stackoverflow questions (e.g. using fig.subplots_adjust(), constrained_layout=True etc.) but to no avail. The margins between the two columns stay really large (see img below) and make the image unreadable...
Any input would be much appreciated!
Code used to reproduce the issue:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
data = np.random.random((10, 6))
fig, axs = plt.subplots(nrows=4, ncols=2, figsize=(16, 8), sharex=True, sharey=False, constrained_layout=True)
for idx in range(4):
# plot image
im1 = axs[idx, 0].imshow(data, cmap=cm.coolwarm)
im2 = axs[idx, 1].imshow(data, cmap=cm.viridis)
# make colorbars
ax1_divider = make_axes_locatable(axs[idx, 0])
cax1 = ax1_divider.append_axes("right", size="2%", pad=0.05)
cb1 = fig.colorbar(im1, cax=cax1, orientation="vertical")
ax2_divider = make_axes_locatable(axs[idx, 1])
cax2 = ax2_divider.append_axes("right", size="2%", pad=0.05)
cb2 = fig.colorbar(im2, cax=cax2, orientation="vertical")

You are plotting multiple images (which by default it tries to keep an equal aspect ratio), in which the height is greater than the width. Therefore, total height of the images > total width of the images.
It follows that one way to reduce the white spacing between columns is to reduce the width of the figure.
Try setting this:
fig, axs = plt.subplots(nrows=4, ncols=2, figsize=(4, 8), sharex=True, sharey=False, constrained_layout=True)

Aligning subplots with a pyplot barplot and seaborn heatmap

I am attempting to place a Seaborn time-based heatmap on top of a bar chart, indicating the number of patients in each bin/timeframe. I can successfully make an individual heatmap and bar plot, but combining the two does not work as intended.
import pandas as pd
import numpy as np
import seaborn as sb
from matplotlib import pyplot as plt
# Mock data
patient_counts = [650, 28, 8]
missings_df = pd.DataFrame(np.array([[-15.8, 600/650, 580/650, 590/650],
[488.2, 20/23, 21/23, 21/23],
[992.2, 7/8, 8/8, 8/8]]),
columns=['time', 'Resp. (/min)', 'SpO2', 'Blood Pressure'])
missings_df.set_index('time', inplace=True)
# Plot heatmap
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(26, 16), sharex=True, gridspec_kw={'height_ratios': [5, 1]})
sb.heatmap(missings_df.T, cmap="Blues", cbar_kws={"shrink": .8}, ax=ax1, xticklabels=False)
plt.xlabel('Time (hours)')
# Plot line graph under heatmap to show nr. of patients in each bin
x_ticks = [time for time in missings_df.index]
ax2.bar([i for i, _ in enumerate(x_ticks)], patient_counts, align='center')
plt.xticks([i for i, _ in enumerate(x_ticks)], x_ticks)
plt.show()
This code gives me the graph below. As you can see, there are two issues:
The bar plot extends too far
The first and second bar are not aligned with the top graph, where the tick of the first plot does not line up with the centre of the bar either.
I've tried looking online but could not find a good resource to fix the issues.. Any ideas?

A problem is that the colorbar takes away space from the heatmap, making its plot narrower than the bar plot. You can create a 2x2 grid to make room for the colorbar, and remove the empty subplot. Change sharex=True to sharex='col' to prevent the colorbar getting the same x-axis as the heatmap.
Another problem is that the heatmap has its cell borders at positions 0, 1, 2, ..., so their centers are at 0.5, 1.5, 2.5, .... You can put the bars at these centers instead of at their default positions:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
fig, ((ax1, cbar_ax), (ax2, dummy_ax)) = plt.subplots(nrows=2, ncols=2, figsize=(26, 16), sharex='col',
gridspec_kw={'height_ratios': [5, 1], 'width_ratios': [20, 1]})
missings_df = np.random.rand(3, 3)
sns.heatmap(missings_df.T, cmap="Blues", cbar_ax=cbar_ax, xticklabels=False, linewidths=2, ax=ax1)
ax2.set_xlabel('Time (hours)')
patient_counts = np.random.randint(10, 50, 3)
x_ticks = ['Time1', 'Time2', 'Time3']
x_tick_pos = [i + 0.5 for i in range(len(x_ticks))]
ax2.bar(x_tick_pos, patient_counts, align='center')
ax2.set_xticks(x_tick_pos)
ax2.set_xticklabels(x_ticks)
dummy_ax.axis('off')
plt.tight_layout()
plt.show()
PS: Be careful not to mix the "functional" interface with the "object-oriented" interface to matplotlib. So, try not to use plt.xlabel() as it is not obvious that it will be applied to the "current" ax (ax2 in the code of the question).

How to remove or hide y-axis ticklabels from a matplotlib / seaborn plot

I made a plot that looks like this
I want to turn off the ticklabels along the y axis. And to do that I am using
plt.tick_params(labelleft=False, left=False)
And now the plot looks like this. Even though the labels are turned off the scale 1e67 still remains.
Turning off the scale 1e67 would make the plot look better. How do I do that?

seaborn is used to draw the plot, but it's just a high-level API for matplotlib.
The functions called to remove the y-axis labels and ticks are matplotlib methods.
After creating the plot, use .set().
.set(yticklabels=[]) should remove tick labels.
This doesn't work if you use .set_title(), but you can use .set(title='')
.set(ylabel=None) should remove the axis label.
.tick_params(left=False) will remove the ticks.
Similarly, for the x-axis: How to remove or hide x-axis labels from a seaborn / matplotlib plot?
Tested in python 3.11, pandas 1.5.2, matplotlib 3.6.2, seaborn 0.12.1
Example 1
import seaborn as sns
import matplotlib.pyplot as plt
# load data
exercise = sns.load_dataset('exercise')
pen = sns.load_dataset('penguins')
# create figures
fig, ax = plt.subplots(2, 1, figsize=(8, 8))
# plot data
g1 = sns.boxplot(x='time', y='pulse', hue='kind', data=exercise, ax=ax[0])
g2 = sns.boxplot(x='species', y='body_mass_g', hue='sex', data=pen, ax=ax[1])
plt.show()
Remove Labels
fig, ax = plt.subplots(2, 1, figsize=(8, 8))
g1 = sns.boxplot(x='time', y='pulse', hue='kind', data=exercise, ax=ax[0])
g1.set(yticklabels=[]) # remove the tick labels
g1.set(title='Exercise: Pulse by Time for Exercise Type') # add a title
g1.set(ylabel=None) # remove the axis label
g2 = sns.boxplot(x='species', y='body_mass_g', hue='sex', data=pen, ax=ax[1])
g2.set(yticklabels=[])
g2.set(title='Penguins: Body Mass by Species for Gender')
g2.set(ylabel=None) # remove the y-axis label
g2.tick_params(left=False) # remove the ticks
plt.tight_layout()
plt.show()
Example 2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# sinusoidal sample data
sample_length = range(1, 1+1) # number of columns of frequencies
rads = np.arange(0, 2*np.pi, 0.01)
data = np.array([(np.cos(t*rads)*10**67) + 3*10**67 for t in sample_length])
df = pd.DataFrame(data.T, index=pd.Series(rads.tolist(), name='radians'), columns=[f'freq: {i}x' for i in sample_length])
df.reset_index(inplace=True)
# plot
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot('radians', 'freq: 1x', data=df)
# or skip the previous two lines and plot df directly
# ax = df.plot(x='radians', y='freq: 1x', figsize=(8, 8), legend=False)
Remove Labels
# plot
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot('radians', 'freq: 1x', data=df)
# or skip the previous two lines and plot df directly
# ax = df.plot(x='radians', y='freq: 1x', figsize=(8, 8), legend=False)
ax.set(yticklabels=[]) # remove the tick labels
ax.tick_params(left=False) # remove the ticks

How to plot a paired histogram using seaborn

I would like to make a paired histogram like the one shown here using the seaborn distplot.
This kind of plot can also be referred to as the back-to-back histogram shown here, or a bihistogram inverted/mirrored along the x-axis as discussed here.
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
green = np.random.normal(20,10,1000)
blue = np.random.poisson(60,1000)
fig, ax = plt.subplots(figsize=(8,6))
sns.distplot(blue, hist=True, kde=True, hist_kws={'edgecolor':'black'}, kde_kws={'linewidth':2}, bins=10, color='blue')
sns.distplot(green, hist=True, kde=True, hist_kws={'edgecolor':'black'}, kde_kws={'linewidth':2}, bins=10, color='green')
ax.set_xticks(np.arange(-20,121,20))
ax.set_yticks(np.arange(0.0,0.07,0.01))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.show()
Here is the output:
When I use the method discussed here (plt.barh), I get the bar plot shown just below, which is not what I am looking for.
Or maybe I haven't understood the workaround well enough...
A simple/short implementation of python-seaborn-distplot similar to these kinds of plots would be perfect. I edited the figure of my first plot above to show the kind of plot I hope to achieve (though y-axis not upside down):
Any leads would be greatly appreciated.

You could use two subplots and invert the y-axis of the lower one and plot with the same bins.
df = pd.DataFrame({'a': np.random.normal(0,5,1000), 'b': np.random.normal(20,5,1000)})
fig =plt.figure(figsize=(5,5))
ax = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
bins = np.arange(-20,40)
ax.hist(df['a'], bins=bins)
ax2.hist(df['b'],color='orange', bins=bins)
ax2.invert_yaxis()
edit:
improvements suggested by #mwaskom
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(5,5))
bins = np.arange(-20,40)
for ax, column, color, invert in zip(axes.ravel(), df.columns, ['teal', 'orange'], [False,True]):
ax.hist(df[column], bins=bins, color=color)
if invert:
ax.invert_yaxis()
plt.subplots_adjust(hspace=0)

Here is a possible approach using seaborn's displots.
Seaborn doesn't return the created graphical elements, but the ax can be interrogated. To make sure the ax only contains the elements you want upside down, those elements can be drawn first. Then, all the patches (the rectangular bars) and the lines (the curve for the kde) can be given their height in negative. Optionally the x-axis can be set at y == 0 using ax.spines['bottom'].set_position('zero').
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
green = np.random.normal(20, 10, 1000)
blue = np.random.poisson(60, 1000)
fig, ax = plt.subplots(figsize=(8, 6))
sns.distplot(green, hist=True, kde=True, hist_kws={'edgecolor': 'black'}, kde_kws={'linewidth': 2}, bins=10,
color='green')
for p in ax.patches: # turn the histogram upside down
p.set_height(-p.get_height())
for l in ax.lines: # turn the kde curve upside down
l.set_ydata(-l.get_ydata())
sns.distplot(blue, hist=True, kde=True, hist_kws={'edgecolor': 'black'}, kde_kws={'linewidth': 2}, bins=10,
color='blue')
ax.set_xticks(np.arange(-20, 121, 20))
ax.set_yticks(np.arange(0.0, 0.07, 0.01))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
pos_ticks = np.array([t for t in ax.get_yticks() if t > 0])
ticks = np.concatenate([-pos_ticks[::-1], [0], pos_ticks])
ax.set_yticks(ticks)
ax.set_yticklabels([f'{abs(t):.2f}' for t in ticks])
ax.spines['bottom'].set_position('zero')
plt.show()