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Ghost plot being generated

I have the following code:
fig = plt.figure()
ax1 = plt.subplot(211)
ax2 = plt.subplot(212, sharex = ax1)
ax2 = ax1.twinx()
num = list111.lt(-90).sum(1)
plt.yticks(fontsize = 25)
ax = num.plot(figsize=(45,25), ax=ax2, color = 'Red')
df2.plot(y = 'Close', figsize=(45,25), ax=ax1, color = 'Green')
ax1.grid()
ax.margins(x=0)
I am trying to plot ax1 and ax2 in the same graph. What i am getting is a ghost plot:
How can i get rid of the second ghost plot and move the x axis with label to the top plot?

The statement
ax2 = plt.subplot(212, sharex = ax1)
generates a subplot located beneath the ax1 subplot. But it is in contradiction with the statement
ax2 = ax1.twinx()
which points towards a secondary y-axis on the ax1 axes.
If you want all the data to be plotted only on a single axes, you can delete the first statement and use the .twinx() method:
ax1 = plt.axes()
ax2 = ax1.twinx()
# remaining code
otherwise, use both axes separately with
ax1 = plt.subplot(211)
ax2 = plt.subplot(212, sharex = ax1)
# remaining code

Creating a 2x2 subplot from one dataset as different graphs

I have a large census dataset I am working with and am taking different data from it and representing it as a singular .png in the end. I have created the graphs individually, but when I try to add them to the subplots they get distorted or axis get messed up.
Current code:
fig = plt.figure()
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
ax4 = fig.add_subplot(2, 2, 4)
ax1.pie(df.data.valuecounts(normalize=True),labels=None,startangle-240)
ax1.legend(['a','b','c','d','e'])
ax1.axis('equal')
data2=df[['A']].dropna().values
kde=df.A.plot.kde()
binss = np.logspace(0.01,7.0)
ax2=plt.hist(hincp, normed=True, bins=binss)
ax2=plt.xscale('log')
ax3 = df.replace(np.nan,0)
ax3 = (df.groupby(['G'])['R'].sum()/1000)
ax3.plot.bar(width=0.9, color='red',title='Gs').set_ylabel('Rs')
ax3.set_ylabel('Rs')
ax3.set_xlabel('# G')
t = df[['p','o','s','y']]
ax4=plt.scatter(t.o,t.p,s=t.s,c=t.y, marker = 'o', alpha = 0.2)
plt.ylim(0, 10000)
plt.xlim(0,1200000)
cbar=plt.colorbar()
plt.title("this vs that", loc = 'center')
plt.xlabel('this')
plt.ylabel('that')
All four types of graphs should be displayed and not overlap.

You create Axes for each subplot but then you don't use them.
ax1.pie(...) looks correct but later you don't use ax2,ax3,ax4.
If you are going to to use the DataFrame plotting methods, just call plt.subplot before each new plot. Like this.
df = pd.DataFrame(np.random.random((6,3)))
plt.subplot(3,1,1)
df.loc[:,0].plot()
plt.subplot(3,1,2)
df.loc[:,1].plot()
plt.subplot(3,1,3)
df.loc[:,2].plot()
plt.show()
plt.close()
Or use the Axes that you create.
df = pd.DataFrame(np.random.random((6,3)))
fig = plt.figure()
ax1 = fig.add_subplot(3,1,1)
ax2 = fig.add_subplot(3,1,2)
ax3 = fig.add_subplot(3,1,3)
ax1.plot(df.loc[:,0])
ax2.plot(df.loc[:,1])
ax3.plot(df.loc[:,2])
plt.show()
plt.close()

Plotting grids across the subplots Python matplotlib

I have tried the following:
d = [1,2,3,4,5,6,7,8,9]
f = [0,1,0,0,1,0,1,1,0]
fig = plt.figure()
fig.set_size_inches(30,10)
ax1 = fig.add_subplot(211)
line1 = ax1.plot(d,marker='.',color='b',label="1 row")
ax2 = fig.add_subplot(212)
line1 = ax2.plot(f,marker='.',color='b',label="1 row")
ax1.grid()
ax2.grid()
plt.show()
I got the following output :
But I was expecting the following output:
How I can get the grids across the two plots?

There is no built-in option to create inter-subplot grids. In this case I'd say an easy option is to create a third axes in the background with the same grid in x direction, such that the gridline can be seen in between the two subplots.
import matplotlib.pyplot as plt
d = [1,2,3,4,5,6,7,8,9]
f = [0,1,0,0,1,0,1,1,0]
fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True)
ax3 = fig.add_subplot(111, zorder=-1)
for _, spine in ax3.spines.items():
spine.set_visible(False)
ax3.tick_params(labelleft=False, labelbottom=False, left=False, right=False )
ax3.get_shared_x_axes().join(ax3,ax1)
ax3.grid(axis="x")
line1 = ax1.plot(d, marker='.', color='b', label="1 row")
line1 = ax2.plot(f, marker='.', color='b', label="1 row")
ax1.grid()
ax2.grid()
plt.show()

Here is my solution:
import matplotlib.pyplot as plt
x1 = [1,2,3,4,5,6,7,8,9]
x2= [0,1,0,0,1,0,1,1,0]
x3= range(-10,0)
# frameon=False removes frames
# fig, (ax1,ax2, ax3) = plt.subplots(nrows=3, sharex=True, subplot_kw=dict(frameon=False))
fig, (ax1,ax2, ax3) = plt.subplots(nrows=3, sharex=True)
# remove vertical gap between subplots
plt.subplots_adjust(hspace=.0)
ax1.grid()
ax2.grid()
ax3.grid()
ax1.plot(x1)
ax2.plot(x2)
ax3.plot(x3)
Without frames subplot_kw=dict(frameon=False):

An option is to create a single plot then just offset the data. So one set plots above the other.

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()

Pandas: plotting two histograms on the same plot

I would like to have 2 histograms to appear on the same plot (with different colors, and possibly differente alphas). I tried
import random
x = pd.DataFrame([random.gauss(3,1) for _ in range(400)])
y = pd.DataFrame([random.gauss(4,2) for _ in range(400)])
x.hist( alpha=0.5, label='x')
y.hist(alpha=0.5, label='y')
x.plot(kind='kde', style='k--')
y.plot(kind='kde', style='k--')
plt.legend(loc='upper right')
plt.show()
This produces the result in 4 different plots. How can I have them on the same one?

If I understood correctly, both hists should go into the same subplot. So it should be
fig = plt.figure()
ax = fig.add_subplot(111)
_ = ax.hist(x.values)
_ = ax.hist(y.values, color='red', alpha=.3)
You can also pass the pandas plot method an axis object, so if you want both kde's in another plot do:
fig = plt.figure()
ax = fig.add_subplot(111)
x.plot(kind='kde', ax=ax)
y.plot(kind='kde', ax=ax, color='red')
To get everything into a single plot you need two different y-scales since kde is density and histogram is frequency. For that you use the axes.twinx() command.
fig = plt.figure()
ax = fig.add_subplot(111)
_ = ax.hist(x.values)
_ = ax.hist(y.values, color='red', alpha=.3)
ax1 = ax.twinx()
x.plot(kind='kde', ax=ax1)
y.plot(kind='kde', ax=ax1, color='red')

You can use plt.figure() and the function add_subplot(): the first 2 arguments are the number of rows and cols you want in your plot, the last is the position of the subplot in the plot.
fig = plt.figure()
subplot = fig.add_subplot(1, 2, 1)
subplot.hist(x.ix[:,0], alpha=0.5)
subplot = fig.add_subplot(1, 2, 2)
subplot.hist(y.ix[:,0], alpha=0.5)