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How to customize the location of color bar in Seaborn heatmap?

I have the following code to create a heatmap. However, it creates an overlap of the color bar and the right axis text. The text has no problems, I want it to be in that length.
How can I locate the colorbar on the right/left side of the heatmap with no overlap?
I tried with "pad" parameter in cbar_kws but it didn't help.enter image description here
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
PT=pd.DataFrame(np.random.randn(300,3), columns=list('ABC'))
miniPT=PT.iloc[:,:-1]
SMALL_SIZE = 8
MEDIUM_SIZE = 80
BIGGER_SIZE = 120
plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.figure(figsize=(10, miniPT.shape[0]/5.2))
ax =sns.heatmap(miniPT, annot=False, cmap='RdYlGn')
for _, spine in ax.spines.items():
spine.set_visible(True)
# second axis
asset_list=np.asarray(PT['C'])
asset_list=asset_list[::-1]
ax3 = ax.twinx()
ax3.set_ylim([0,ax.get_ylim()[1]])
ax3.set_yticks(ax.get_yticks())
ax3.set_yticklabels(asset_list, fontsize=MEDIUM_SIZE*0.6)
# colorbar
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(labelsize=MEDIUM_SIZE)

One way to get the overlap automatically adjusted by matplotlib, is to explicitly create subplots: one for the heatmap and another for the colorbar. sns.heatmap's cbar_ax= parameter can be set to point to this subplot. gridspec_kws= is needed to set the relative sizes. At the end, plt.tight_layout() will adjust all the paddings to make everything fit nicely.
The question's code contains some strange settings (e.g. a fontsize of 80 is immense). Also, 300 rows will inevitably lead to overlapping text (the fontsize needs to be so small that non-overlapping text wouldn't be readable). Here is some more simplified example code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
PT = pd.DataFrame(np.random.randn(100, 3), columns=list('ABC'))
fig, (ax, cbar_ax) = plt.subplots(ncols=2, figsize=(10, len(PT) / 5.2), gridspec_kw={'width_ratios': [10, 1]})
sns.heatmap(PT.iloc[:, :-1], annot=False, cmap='RdYlGn', cbar_ax=cbar_ax, ax=ax)
for _, spine in ax.spines.items():
spine.set_visible(True)
# second axis
asset_list = np.asarray(PT['C'])
ax3 = ax.twinx()
ax3.set_ylim(ax.get_ylim())
ax3.set_yticks(np.arange(len(PT)))
ax3.set_yticklabels(asset_list, fontsize=80)
# colorbar
cbar_ax.tick_params(labelsize=80)
plt.tight_layout()
plt.show()
As the plot is quite large, here only the bottom part is pasted, with a link to the full plot.
This is how it would look like with:
fontsize 80 (Note that font sizes are measured in "points per inch", standard 72 points per inch);
figure width of 20 inches (instead of 10);
300 rows
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
PT = pd.DataFrame(np.random.randn(300, 3), columns=list('ABC'))
fig, (ax, cbar_ax) = plt.subplots(ncols=2, figsize=(20, len(PT) / 5.2), gridspec_kw={'width_ratios': [15, 1]})
sns.heatmap(PT.iloc[:, :-1], annot=False, cmap='RdYlGn', cbar_ax=cbar_ax, ax=ax)
for _, spine in ax.spines.items():
spine.set_visible(True)
# second axis
asset_list = np.asarray(PT['C'])
ax3 = ax.twinx()
ax3.set_ylim(ax.get_ylim())
ax3.set_yticks(np.arange(len(PT)))
ax3.set_yticklabels(asset_list, fontsize=80)
# colorbar
cbar_ax.tick_params(labelsize=80)
plt.tight_layout()
plt.show()

My solution was eventually move the colorbar to left side. This is the code and the output:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
PT = pd.DataFrame(np.random.randn(300, 3), columns=list('ABC'))
fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, len(PT) / 5.2), gridspec_kw={'width_ratios': [15, 15]})
sns.heatmap(PT.iloc[:, :-1], annot=False, cmap='RdYlGn', cbar_ax=ax0, ax=ax1)
for _, spine in ax1.spines.items():
spine.set_visible(True)
# second axis
asset_list = np.asarray(PT['C'])
ax3 = ax1.twinx()
ax3.set_ylim(ax1.get_ylim())
ax3.set_yticks(np.arange(len(PT)))
ax3.set_yticklabels(asset_list, fontsize=80)
# colorbar
ax0.tick_params(labelsize=80)
plt.tight_layout()
plt.show()

Add different shade colors for trend and forecast , with text on the region

import numpy as np
import pandas as pd
df = pd.DataFrame({"y" : np.random.rand(20)})
ax = df.iloc[:15,:].plot(ls="-", color="b")
ax2 = ax.twinx() #Create a twin Axes sharing the xaxis
df.iloc[15:,:].plot(ls="--", color="r", ax=ax)
plt.axhline(y=0.5,linestyle="--",animated=True,label="False Alaram")
plt.show()
So, first 15 are trend and last 5 are predictions.
I want different colors for trend and pred in background.
Also, how can i add text "Historic" and "Forecast" on graph.

I believe you're looking for fill_between:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({"y" : np.random.rand(20)})
fig, ax = plt.subplots(figsize=(8,6))
df.iloc[:15,:].plot(ls="-", color="b", ax=ax)
plt.fill_between(df.iloc[:15].index.tolist(), df.iloc[:15].y.tolist(), alpha=.25, color='b')
df.iloc[15:,:].plot(ls="--", color="r", ax=ax)
plt.axhline(y=0.5,linestyle="--", animated=True, label="False Alaram")
plt.fill_between(df.iloc[15:].index.tolist(), df.iloc[15:].y.tolist(), alpha=.25, color='r')
plt.legend()
plt.show()

Remove one out of two legends from Seaborn Scatterplot

Using the 'tips' dataset as a toy model, I generate the following plot:
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
g = sns.scatterplot(x="total_bill", y="sex", hue="smoker", size = 'tip',sizes=(320, 600), data=tips)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=13)
plt.show(g)
This image is exactly what I need. However, I want to remove the size = 'tip' from the legend and only keep the smoker. Essentially, remove those black circles labeled 0.0 to 12.0. How do I ensure my legend has only one variable of my choosing?

I was able to find a fix by indexing the labels in the legend.
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
g = sns.scatterplot(x="total_bill", y="sex", hue="smoker", size = 'tip',sizes=(320, 600), data=tips)
h,l = g.get_legend_handles_labels()
plt.legend(h[0:3],l[0:3],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=13)
plt.show(g)

How to accurately plot the legend of multi-layer plot (boxplot and lineplot)?

import numpy as np
import seaborn as sns
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
data_box = np.random.random((10, 3))
data_line = np.random.random(3)
ax = sns.boxplot(data=data_box, color='red', saturation=0.5)
sns.lineplot(data=data_line, color='blue')
legend_elements = [Line2D([0], [0], color='blue', lw=4, label='box'),
Patch(facecolor='red', edgecolor='grey', linewidth=1.5,
label='line')]
ax.legend(handles=legend_elements, fontsize='xx-large')
I overlay a lineplot to a boxplot as in the image above, and draw the legend manually using matplotlib.
But seaborn sets the saturation of the colors, whose default value is 0.75 (I set it to 0.5 make the difference clear). So the legend color generated by matplotlib is not accurate. Is there any way to change the saturation of the matplotlib legend? Or how can I draw legend color accurately, except setting saturation=1.

Use seaborn's desaturate function
import numpy as np
import seaborn as sns
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
data_box = np.random.random((10, 3))
data_line = np.random.random(3)
fig, ax = plt.subplots()
ax = sns.boxplot(data=data_box, color='red', saturation=0.5)
sns.lineplot(data=data_line, color='blue')
legend_elements = [Line2D([0], [0], color='blue', lw=4, label='box'),
Patch(facecolor=sns.desaturate('red',0.5), edgecolor='grey', linewidth=1.5,
label='line')]
ax.legend(handles=legend_elements, fontsize='xx-large')

Matplotlib/Seaborn: how to plot a rugplot on the top edge of x-axis?

Suppose I draw a plot using the code below. How to plot the rug part on the top edge of x-axis?
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.distplot(np.random.normal(0, 0.1, 100), rug=True, hist=False)
plt.show()

The seaborn.rugplot creates a LineCollection with the length of the lines being defined in axes coordinates. Those are always the same, such that the plot does not change if you invert the axes.
You can create your own LineCollection from the data though. The advantage compared to using bars is that the linewidth is in points and therefore no lines will be lost independend of the data range.
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import seaborn as sns
def upper_rugplot(data, height=.05, ax=None, **kwargs):
from matplotlib.collections import LineCollection
ax = ax or plt.gca()
kwargs.setdefault("linewidth", 1)
segs = np.stack((np.c_[data, data],
np.c_[np.ones_like(data), np.ones_like(data)-height]),
axis=-1)
lc = LineCollection(segs, transform=ax.get_xaxis_transform(), **kwargs)
ax.add_collection(lc)
fig, ax = plt.subplots()
data = np.random.normal(0, 0.1, 100)
sns.distplot(data, rug=False, hist=False, ax=ax)
upper_rugplot(data, ax=ax)
plt.show()

Rugs are just thin lines at the data points. Yo can think of them as thin bars. That being said, you can have a following work around: Plot distplot without rugs and then create a twin x-axis and plot a bar chart with thin bars. Following is a working answer:
import numpy as np; np.random.seed(21)
import matplotlib.pyplot as plt
import seaborn as sns
fig, ax = plt.subplots()
data = np.random.normal(0, 0.1, 100)
sns.distplot(data, rug=False, hist=False, ax=ax)
ax1 = ax.twinx()
ax1.bar(data, height=ax.get_ylim()[1]/10, width=0.001)
ax1.set_ylim(ax.get_ylim())
ax1.invert_yaxis()
ax1.set_yticks([])
plt.show()