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Multiple label positions for same axis in Matplotlib

I have a long bar chart with lots of bars and I wanna improve its reability from axis to the bars.
Suppose I have the following graph:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
y = np.linspace(1,-1,20)
x = np.arange(0,20)
labels = [f'Test {i}' for i in x]
fig, ax = plt.subplots(figsize=(12,8))
sns.barplot(y = y, x = x, ax=ax )
ax.set_xticklabels(labels, rotation=90)
which provides me the following:
All I know is how to change the label position globally across the chart. How can I change the axis layout to be cantered in the middle and change its label position based on a condition (in this case, being higher or lower than 0)? What I want to achieve is:
Thanks in advance =)

You could remove the existing x-ticks and place texts manually:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
y = np.linspace(1,-1,20)
x = np.arange(0,20)
labels = [f'Test {i}' for i in x]
fig, ax = plt.subplots(figsize=(12,8))
sns.barplot(y = y, x = x, ax=ax )
ax.set_xticks([]) # remove existing ticks
for i, (label, height) in enumerate(zip(labels, y)):
ax.text(i, 0, ' '+ label+' ', rotation=90, ha='center', va='top' if height>0 else 'bottom' )
ax.axhline(0, color='black') # draw a new x-axis
for spine in ['top', 'right', 'bottom']:
ax.spines[spine].set_visible(False) # optionally hide spines
plt.show()
Here is another approach, I'm not sure whether it is "more pythonic".
move the existing xaxis to y=0
set the tick marks in both directions
put the ticks behind the bars
prepend some spaces to the labels to move them away from the axis
realign the tick labels depending on the bar value
fig, ax = plt.subplots(figsize=(12, 8))
sns.barplot(y=y, x=x, ax=ax)
ax.spines['bottom'].set_position('zero')
for spine in ['top', 'right']:
ax.spines[spine].set_visible(False)
ax.set_xticklabels([' ' + label for label in labels], rotation=90)
for tick, height in zip(ax.get_xticklabels(), y):
tick.set_va('top' if height > 0 else 'bottom')
ax.tick_params(axis='x', direction='inout')
ax.set_axisbelow(True) # ticks behind the bars
plt.show()

Change x-axis range

I am trying to change the range of my x-axis on my plot. The code for my plot looks like this:
dataset.plot(x='Date', y='Amount', label='Actual')
plt.plot(z, y_pred, label='Predicted')
plt.title('Graph')
plt.xlabel('Date')
plt.ylabel('Amount')
plt.xticks(x, x, rotation='90')
plt.legend()
plt.show()
My graph:
I am trying to stop the graph up till the last point on the orange line.
I have tried using:
axes = plt.gca()
axes.set_xlim([xmin,xmax])
But it creates an empty plot instead. Thank you!

I think in your plot you should be using: 'axes.set_xlim([min(z),max(z)])'.
import pandas as pd
import numpy as np
df_actual = pd.DataFrame(np.random.randint(0,20,size=(20, 1)), columns=['Amount'])
df_actual['Date'] = df.index
x_predicted = range(0,10)
y_predicted = np.random.uniform(0, 20, 10)
df_actual.plot(x='Date', y='Amount', label='Actual')
plt.plot(x_predicted, y_predicted, label='Predicted')
axes = plt.gca()
axes.set_xlim([min(x_predicted),max(x_predicted)])

How to clear matplotlib labels in legend?

Is there a way to clear matplotlib labels inside a graph's legend? This post explains how to remove the legend itself, but the labels themselves still remain, and appear again if you plot a new figure. I tried the following code, but it does not work:
handles, labels = ax.get_legend_handles_labels()
labels = []
EDIT: Here is an example
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
ax.scatter([1,2,3], [4,5,6], label = "a")
legend = ax.legend()
plt.show()
legend.remove()
handles, labels = ax.get_legend_handles_labels()
print(labels)
Output: ["a"]

Use set_visible() method:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
ax.scatter([1,2,3], [4,5,6], label = "a")
legend = ax.legend()
for text in legend.texts:
if (text.get_text() == 'a'): text.set_text('b') # change label text
text.set_visible(False) # disable label
plt.show()

Merge matplotlib subplots with shared x-axis

I have two graphs to where both have the same x-axis, but with different y-axis scalings.
The plot with regular axes is the data with a trend line depicting a decay while the y semi-log scaling depicts the accuracy of the fit.
fig1 = plt.figure(figsize=(15,6))
ax1 = fig1.add_subplot(111)
# Plot of the decay model
ax1.plot(FreqTime1,DecayCount1, '.', color='mediumaquamarine')
# Plot of the optimized fit
ax1.plot(x1, y1M, '-k', label='Fitting Function: $f(t) = %.3f e^{%.3f\t} \
%+.3f$' % (aR1,kR1,bR1))
ax1.set_xlabel('Time (sec)')
ax1.set_ylabel('Count')
ax1.set_title('Run 1 of Cesium-137 Decay')
# Allows me to change scales
# ax1.set_yscale('log')
ax1.legend(bbox_to_anchor=(1.0, 1.0), prop={'size':15}, fancybox=True, shadow=True)
Now, i'm trying to figure out to implement both close together like the examples supplied by this link
http://matplotlib.org/examples/pylab_examples/subplots_demo.html
In particular, this one
When looking at the code for the example, i'm a bit confused on how to implant 3 things:
1) Scaling the axes differently
2) Keeping the figure size the same for the exponential decay graph but having a the line graph have a smaller y size and same x size.
For example:
3) Keeping the label of the function to appear in just only the decay graph.
Any help would be most appreciated.

Look at the code and comments in it:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import gridspec
# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig = plt.figure()
# set height ratios for subplots
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
# the first subplot
ax0 = plt.subplot(gs[0])
# log scale for axis Y of the first subplot
ax0.set_yscale("log")
line0, = ax0.plot(x, y, color='r')
# the second subplot
# shared axis X
ax1 = plt.subplot(gs[1], sharex = ax0)
line1, = ax1.plot(x, y, color='b', linestyle='--')
plt.setp(ax0.get_xticklabels(), visible=False)
# remove last tick label for the second subplot
yticks = ax1.yaxis.get_major_ticks()
yticks[-1].label1.set_visible(False)
# put legend on first subplot
ax0.legend((line0, line1), ('red line', 'blue line'), loc='lower left')
# remove vertical gap between subplots
plt.subplots_adjust(hspace=.0)
plt.show()

Here is my solution:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True, subplot_kw=dict(frameon=False)) # frameon=False removes frames
plt.subplots_adjust(hspace=.0)
ax1.grid()
ax2.grid()
ax1.plot(x, y, color='r')
ax2.plot(x, y, color='b', linestyle='--')
One more option is seaborn.FacetGrid but this requires Seaborn and Pandas libraries.

Here are some adaptions to show how the code could work to add a combined legend when plotting a pandas dataframe. ax=ax0 can be used to plot on a given ax and ax0.get_legend_handles_labels() gets the information for the legend.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
dates = pd.date_range('20210101', periods=100, freq='D')
df0 = pd.DataFrame({'x': np.random.normal(0.1, 1, 100).cumsum(),
'y': np.random.normal(0.3, 1, 100).cumsum()}, index=dates)
df1 = pd.DataFrame({'z': np.random.normal(0.2, 1, 100).cumsum()}, index=dates)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [2, 1], 'hspace': 0})
df0.plot(ax=ax0, color=['dodgerblue', 'crimson'], legend=False)
df1.plot(ax=ax1, color='limegreen', legend=False)
# put legend on first subplot
handles0, labels0 = ax0.get_legend_handles_labels()
handles1, labels1 = ax1.get_legend_handles_labels()
ax0.legend(handles=handles0 + handles1, labels=labels0 + labels1)
# remove last tick label for the second subplot
yticks = ax1.get_yticklabels()
yticks[-1].set_visible(False)
plt.tight_layout()
plt.show()

Matplotlib: how to adjust zorder of second legend?

Here is an example that reproduces my problem:
import matplotlib.pyplot as plt
import numpy as np
data1,data2,data3,data4 = np.random.random(100),np.random.random(100),np.random.random(100),np.random.random(100)
fig,ax = plt.subplots()
ax.plot(data1)
ax.plot(data2)
ax.plot(data3)
ax2 = ax.twinx()
ax2.plot(data4)
plt.grid('on')
ax.legend(['1','2','3'], loc='center')
ax2.legend(['4'], loc=1)
How can I get the legend in the center to plot on top of the lines?

To get exactly what you have asked for, try the following. Note I have modified your code to define the labels when you generate the plot and also the colors so you don't get a repeated blue line.
import matplotlib.pyplot as plt
import numpy as np
data1,data2,data3,data4 = (np.random.random(100),
np.random.random(100),
np.random.random(100),
np.random.random(100))
fig,ax = plt.subplots()
ax.plot(data1, label="1", color="k")
ax.plot(data2, label="2", color="r")
ax.plot(data3, label="3", color="g")
ax2 = ax.twinx()
ax2.plot(data4, label="4", color="b")
# First get the handles and labels from the axes
handles1, labels1 = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
# Add the first legend to the second axis so it displaysys 'on top'
first_legend = plt.legend(handles1, labels1, loc='center')
ax2.add_artist(first_legend)
# Add the second legend as usual
ax2.legend(handles2, labels2)
plt.show()
Now I will add that it would be clearer if you just use a single legend adding all the lines to that. This is described in this SO post and in the code above can easily be achieved with
ax2.legend(handles1+handles2, labels1+labels2)
But obviously you may have your own reasons for wanting two legends.