Develop Reference

set custom tick labels on heatmap color bar

I have a list of dataframes named merged_dfs that I am looping through to get the correlation and plot subplots of heatmap correlation matrix using seaborn.
I want to customize the colorbar tick labels, but I am having trouble figuring out how to do it with my example.
Currently, my colorbar scale values from top to bottom are
[1,0.5,0,-0.5,-1]
I want to keep these values, but change the tick labels to be
[1,0.5,0,0.5,1]
for my diverging color bar.
Here is the code and my attempt:
fig, ax = plt.subplots(nrows=6, ncols=2, figsize=(20,20))
for i, (title,merging) in enumerate (zip(new_name_data,merged_dfs)):
graph = merging.corr()
colormap = sns.diverging_palette(250, 250, as_cmap=True)
a = sns.heatmap(graph.abs(), cmap=colormap, vmin=-1,vmax=1,center=0,annot = graph, ax=ax.flat[i])
cbar = fig.colorbar(a)
cbar.set_ticklabels(["1","0.5","0","0.5","1"])
fig.delaxes(ax[5,1])
plt.show()
plt.close()
I keep getting this error:
AttributeError: 'AxesSubplot' object has no attribute 'get_array'

Several things are going wrong:
fig.colorbar(...) would create a new colorbar, by default appended to the last subplot that was created.
sns.heatmap returns an ax (indicates a subplot). This is very different to matplotlib functions, e.g. plt.imshow(), which would return the graphical element that was plotted.
You can suppress the heatmap's colorbar (cbar=False), and then create it newly with the parameters you want.
fig.colorbar(...) needs a parameter ax=... when the figure contains more than one subplot.
Instead of creating a new colorbar, you can add the colorbar parameters to sns.heatmap via cbar_kws=.... The colorbar itself can be found via ax.collections[0].colobar. (ax.collections[0] is where matplotlib stored the graphical object that contains the heatmap.)
Using an index is strongly discouraged when working with Python. It's usually more readable, easier to maintain and less error-prone to include everything into the zip command.
As now your vmin now is -1, taking the absolute value for the coloring seems to be a mistake.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
merged_dfs = [pd.DataFrame(data=np.random.rand(5, 7), columns=[*'ABCDEFG']) for _ in range(5)]
new_name_data = [f'Dataset {i + 1}' for i in range(len(merged_dfs))]
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 7))
for title, merging, ax in zip(new_name_data, merged_dfs, axes.flat):
graph = merging.corr()
colormap = sns.diverging_palette(250, 250, as_cmap=True)
sns.heatmap(graph, cmap=colormap, vmin=-1, vmax=1, center=0, annot=True, ax=ax, cbar_kws={'ticks': ticks})
ax.collections[0].colorbar.set_ticklabels([abs(t) for t in ticks])
fig.delaxes(axes.flat[-1])
fig.tight_layout()
plt.show()

Plotting multiple subplots with different shapefiles in background

I am trying to plot side by side GeoPandas shapefiles using matplotlib but the titles, xlabel and ylabel are not plotting correctly.
fig, axes = plt.subplots(1,2, figsize=(10,3), sharex=True, sharey=True)
base = subs.boundary.plot(color='black', linewidth=0.1, ax=axes[0])
cluster.plot(ax=base, column='pixel', markersize=20, legend=True, zorder=2)
plt.title('THHZ')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
base = forest.boundary.plot(color='black', linewidth=0.2, ax=axes[1])
cluster.plot(ax=base, column='forest', markersize=20, legend=True, zorder=2)
plt.title('Forest')
This is what I get
This is what I want

You have a mixture of object-oriented and pyplot-style matplotlib interactions. The plt.* calls are following a logic of the current axis to act upon. More detail from the matplotlib docs here: Pyplot vs Object Oriented Interface. I don't know how that behaves with your plotting function calls (code not included in your post).
To be certain of what axes you are interacting with, use the object-oriented calls using the axes object you already have:
axes[0].set_title('THHZ')
axes[0].set_xlabel('Longitude')
axes[0].set_ylabel('Latitude')
axes[1].set_title('Forest')
You can also add fig.tight_layout() at the very end for a compacted figure layout.

use ply.ylim in matplotlib to set y-axis limit only works for the last subplot

I'm trying to set y-axis limit for a certain subplot using plt.ylim. (in my example, the plot on ax1)
However, no matter where I put the command plt.ylim((10,20)), it only works on the last subplot (in the following example, it is the plot on ax2).
fig, (ax1,ax2) = plt.subplots(2,1)
x=range(1,100)
y=range(1,100)
plt.ylim((10,20))
ax1.plot(x,y)
ax2.plot(x,y)
Only ax2 will be limited and ax1 will still be in the original range.
fig, (ax1,ax2) = plt.subplots(2,1)
x=range(1,100)
y=range(1,100)
ax1.plot(x,y)
ax2.plot(x,y)
plt.ylim((10,20))
screenshot for the result
Running the two blocks of code will produce the same result. I know I can also use other methods like plt.setp(ax1, ylim=[10,20]). But I'd like to know how to use plt.ylim properly.
Thank you very much in advance!

x axis label disappearing in matplotlib and basic plotting in python

I am new to matplotlib, and I am finding it very confusing. I have spent quite a lot of time on the matplotlib tutorial website, but I still cannot really understand how to build a figure from scratch. To me, this means doing everything manually... not using the plt.plot() function, but always setting figure, axis handles.
Can anyone explain how to set up a figure from the ground up?
Right now, I have this code to generate a double y-axis plot. But my xlabels are disappearing and I dont' know why
fig, ax1 = plt.subplots()
ax1.plot(yearsTotal,timeseries_data1,'r-')
ax1.set_ylabel('Windspeed [m/s]')
ax1.tick_params('y',colors='r')
ax2 = ax1.twinx()
ax2.plot(yearsTotal,timeseries_data2,'b-')
ax2.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
ax2.set_xticklabels(ax1.xaxis.get_majorticklabels(), rotation=90)
ax2.set_ylabel('Open water duration [days]')
ax2.tick_params('y',colors='b')
plt.title('My title')
fig.tight_layout()
plt.savefig('plots/my_figure.png',bbox_inches='tight')
plt.show()

Because you are using a twinx, it makes sense to operate only on the original axes (ax1).
Further, the ticklabels are not defined at the point where you call ax1.xaxis.get_majorticklabels().
If you want to set the ticks and ticklabels manually, you can use your own data to do so (although I wouldn't know why you'd prefer this over using the automatic labeling) by specifying a list or array
ticks = np.arange(min(yearsTotal),max(yearsTotal)+1)
ax1.set_xticks(ticks)
ax1.set_xticklabels(ticks)
Since the ticklabels are the same as the tickpositions here, you may also just do
ax1.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
plt.setp(ax1.get_xticklabels(), rotation=70)
Complete example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
yearsTotal = np.arange(1977, 1999)
timeseries_data1 = np.cumsum(np.random.normal(size=len(yearsTotal)))+5
timeseries_data2 = np.cumsum(np.random.normal(size=len(yearsTotal)))+20
fig, ax1 = plt.subplots()
ax1.plot(yearsTotal,timeseries_data1,'r-')
ax1.set_ylabel('Windspeed [m/s]')
ax1.tick_params('y',colors='r')
ax1.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
plt.setp(ax1.get_xticklabels(), rotation=70)
ax2 = ax1.twinx()
ax2.plot(yearsTotal,timeseries_data2,'b-')
ax2.set_ylabel('Open water duration [days]')
ax2.tick_params('y',colors='b')
plt.title('My title')
fig.tight_layout()
plt.show()

Based on your code, it is not disappear, it is set (overwrite) by these two functions:
ax2.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
ax2.set_xticklabels(ax1.xaxis.get_majorticklabels(), rotation=90)
set_xticks() on the axes will set the locations and set_xticklabels() will set the xtick labels with list of strings labels.

Hide axis label only, not entire axis, in Pandas plot

I can clear the text of the xlabel in a Pandas plot with:
plt.xlabel("")
Instead, is it possible to hide the label?
May be something like .xaxis.label.set_visible(False).

From the Pandas docs -
The plot method on Series and DataFrame is just a simple wrapper around plt.plot():
This means that anything you can do with matplolib, you can do with a Pandas DataFrame plot.
pyplot has an axis() method that lets you set axis properties. Calling plt.axis('off') before calling plt.show() will turn off both axes.
df.plot()
plt.axis('off')
plt.show()
plt.close()
To control a single axis, you need to set its properties via the plot's Axes. For the x axis - (pyplot.axes().get_xaxis().....)
df.plot()
ax1 = plt.axes()
x_axis = ax1.axes.get_xaxis()
x_axis.set_visible(False)
plt.show()
plt.close()
Similarly to control an axis label, get the label and turn it off.
df.plot()
ax1 = plt.axes()
x_axis = ax1.axes.get_xaxis()
x_axis.set_label_text('foo')
x_label = x_axis.get_label()
##print isinstance(x_label, matplotlib.artist.Artist)
x_label.set_visible(False)
plt.show()
plt.close()
You can also get to the x axis like this
ax1 = plt.axes()
x_axis = ax1.xaxis
x_axis.set_label_text('foo')
x_axis.label.set_visible(False)
Or this
ax1 = plt.axes()
ax1.xaxis.set_label_text('foo')
ax1.xaxis.label.set_visible(False)
DataFrame.plot
returns a matplotlib.axes.Axes or numpy.ndarray of them
so you can get it/them when you call it.
axs = df.plot()
.set_visible() is an Artist method. The axes and their labels are Artists so they have Artist methods/attributes as well as their own. There are many ways to customize your plots. Sometimes you can find the feature you want browsing the Gallery and Examples

You can remove axis labels and ticks using xlabel= or ylabel= arguments in the plot() call. For example, to remove the xlabel, use xlabel='':
df.plot(xlabel='');
To remove the x-axis ticks, use xticks=[] (for y-axis ticks, use yticks=):
df.plot(xticks=[]);
To remove both:
df.plot(xticks=[], xlabel='');