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How to print the heatmap in a square shape using seaborn?

When I run the code below I notice that the heatmap does not have a square shape knowing that I have used square=True but it did not work! Any idea how can I print the heatmap in a square format? Thank you!
The code:
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib as plt
import os
import seaborn as sns
temp_hourly_A5_A7_AX_ASHRAE=pd.read_csv('C:\\Users\\cvaa4\\Desktop\\projects\\s\\temp_hourly_A5_A7_AX_ASHRAE.csv',index_col=0, parse_dates=True, dayfirst=True, skiprows=2)
sns.heatmap(temp_hourly_A5_A7_AX_ASHRAE,cmap="YlGnBu", vmin=18, vmax=27, square=True, cbar=False, linewidth=0.0001);
The result:

square=True should work to have square cells, below is a working example:
import pandas as pd
import numpy as np
import seaborn as sns
df = pd.DataFrame(np.tile([0,1], 15*15).reshape(-1,15))
sns.heatmap(df, square=True)
If you want a square shape of the plot however, you can use set_aspect and the shape of the data:
ax = sns.heatmap(df)
ax.set_aspect(df.shape[1]/df.shape[0]) # here 0.5 Y/X ratio

You can use matplotlib and set a figsize before plotting heatmap.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
rnd = np.random.default_rng(12345)
data = rnd.uniform(-100, 100, [100, 50])
plt.figure(figsize=(6, 5))
sns.heatmap(data, cmap='viridis');
Note that I used figsize=(6, 5) rather than a square figsize=(5, 5). This is because on a given figsize, seaborn also puts the colorbar, which might cause the heatmap to be squished a bit. You might want to change those figsizes too depending on what you need.

How to make horizontal linechart with categorical variables and timeseries?

I want to replicate plots from this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5000555/pdf/nihms774453.pdf I'm particularly interested in plot on page 16, right panel. I tried to do this in matplotlib but it seems to me that there is no way to access lines in linecollection.
I don't know how to change the color of the each line, according to the value at every index. I'd like to eventually get something like here: https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/multicolored_line.html but for every line, according to the data.
this is what I tried:
the data in numpy array: https://pastebin.com/B1wJu9Nd
import pandas as pd, numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib import colors as mcolors
%matplotlib inline
base_range = np.arange(qq.index.max()+1)
fig, ax = plt.subplots(figsize=(12,8))
ax.set_xlim(qq.index.min(), qq.index.max())
# ax.set_ylim(qq.columns[0], qq.columns[-1])
ax.set_ylim(-5, len(qq.columns) +5)
line_segments = LineCollection([np.column_stack([base_range, [y]*len(qq.index)]) for y in range(len(qq.columns))],
cmap='viridis',
linewidths=(5),
linestyles='solid',
)
line_segments.set_array(base_range)
ax.add_collection(line_segments)
axcb = fig.colorbar(line_segments)
plt.show()
my result:
what I want to achieve:

Pyplot set_xticks doesn't work as expected

I want to set the x tick density by specifying how many ticks to skip each time. For example, if the x axis is labelled by 100 consecutive dates, and I want to skip every 10 dates, then I will do something like
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ts = pd.period_range("20060101", periods=100).strftime("%Y%m%d")
y = np.random.randn(100)
ax = plt.subplot(1, 1, 1)
ax.plot(ts, y)
xticks = ax.get_xticks()
ax.set_xticks(xticks[::10])
plt.xticks(rotation="vertical")
plt.show()
However the output is out of place. Pyplot only picks the first few ticks and place them all in the wrong positions, although the spacing is correct:
What can I do to get the desired output? Namely the ticks should be instead:
['20060101' '20060111' '20060121' '20060131' '20060210' '20060220'
'20060302' '20060312' '20060322' '20060401']
#klim's answer seems to put the correct marks on the axis, but the labels still won't show. An example where the date axis is correctly marked yet without labels:

Set xticklabels also. Like this.
xticks = ax.get_xticks()
xticklabels = ax.get_xticklabels()
ax.set_xticks(xticks[::10])
ax.set_xticklabels(xticklabels[::10], rotation=90)
Forget the above, which doesn't work.
How about this?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ts = pd.period_range("20060101", periods=100).strftime("%Y%m%d")
x = np.arange(len(ts))
y = np.random.randn(100)
ax = plt.subplot(1, 1, 1)
ax.plot(x, y)
ax.set_xticks(x[::10])
ax.set_xticklabels(ts[::10], rotation="vertical")
plt.show()
This works on my machine.

Using a colormap for a pandas Series

I have pandas series of complex numbers, which I would like to plot. Currently, I am looping through each point and assigning it a color. I would prefer to generate the plot without the need to loop over each point... Using Series.plot() would be preferable. Converting series to numpy is ok though.
Here is an example of what I currently have:
import pandas as pd
import numpy as np
from matplotlib import pyplot
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
cmap = pyplot.cm.viridis
for i, val in enumerate(s):
pyplot.plot(np.real(val), np.imag(val), 'o', ms=10, color=cmap(i/(len(s)-1)))
pyplot.show()

You can use pyplot.scatter, which allows coloring of points based on a value.
pyplot.scatter(np.real(s), np.imag(s), s=50, c=np.arange(len(s)), cmap='viridis')
Here, we set c to an increasing sequence to get the same result as in the question.

You can simply plot the real and imaginary part of the series without a loop.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.plot(s.values.real,s.values.imag, marker="o", ls="")
plt.show()
However, you need to use a scatter plot if you want to have different colors:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.scatter(s.values.real,s.values.imag, c = range(len(s)), cmap=plt.cm.viridis)
plt.show()

How to change plot properties of statsmodels qqplot? (Python)

So I am plotting a normal Q-Q plot using statsmodels.graphics.gofplots.qqplot().
The module uses matplotlib.pyplot to create figure instance. It plots the graph well.
However, I would like to plot the markers with alpha=0.3.
Is there a way to do this?
Here is a sample of code:
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
test = np.random.normal(0,1, 1000)
sm.qqplot(test, line='45')
plt.show()
And the output figure:

You can use statsmodels.graphics.gofplots.ProbPlot class which has qqplot method to pass matplotlib pyplot.plot **kwargs.
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
test = np.random.normal(0, 1, 1000)
pp = sm.ProbPlot(test, fit=True)
qq = pp.qqplot(marker='.', markerfacecolor='k', markeredgecolor='k', alpha=0.3)
sm.qqline(qq.axes[0], line='45', fmt='k--')
plt.show()

qqplot returns a figure object which can be used to get the lines which can then be modified using set_alpha
fig = sm.qqplot(test, line='45');
# Grab the lines with blue dots
dots = fig.findobj(lambda x: hasattr(x, 'get_color') and x.get_color() == 'b')
[d.set_alpha(0.3) for d in dots]
Obviously you have a bit of overlap of the dots so even though they have a low alpha value, where they are piled on top of one another they look to be more opaque.