Develop Reference

3D scatter with two axes in logscale

I am trying to get a scatter plot with 2 axes (x,z) in a log scale using set_scale but that doesn't work.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
x=np.linspace(0,500,10)
y=np.linspace(0,1,10)
z=np.linspace(0,100000,10)
fig = plt.figure()
ax = Axes3D(fig)
ax.plot(x, y, z,'o', c='g')
ax.grid()
#ax.set_xscale('log')
#ax.set_zscale('log')
#ax.set_xlim([min(x), max(x)])
#ax.set_zlim([min(z), max(z)])
plt.show()

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()

Python pyplot x-axis label rotation

I am trying to rotate the xaxis labels but the xticks function below has no effect and the labels overwrite each other
import matplotlib.pyplot as plt
import seaborn as sns
corrmat = X.corr()
plt.xticks(rotation=90)
plt.figure(figsize=(15,16))
ax = sns.heatmap(corrmat, vmin=0, vmax=1)
ax.xaxis.tick_top()
After using suggested code changes: I get the following but I still want to increase the size of the heatmap

setp looks to be the way to go with pyplot (inspiration from this answer). This works for me:
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np; np.random.seed(0)
data = np.random.rand(10, 12)
ax = sns.heatmap(data)
ax.xaxis.tick_top()
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.show()
Obviously I don't have your data, hence the numpy random data, but otherwise the effect is as required:

Set scale of axis in plot using matplotlib

I am unable to scale the y-axis. My code is as follows:
import matplotlib.pyplot as pt
import numpy as np
fig = pt.figure()
ax = fig.add_subplot(111)
sample = 20
x=np.arange(sample)
y=10*np.sin(2*np.pi*x/20)
pt.plot(x,y)
pt.show()
The y axis has scale of 5. I'm trying to make it 1.

You can do so using set_yticks this way:
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
sample = 20
x=np.arange(sample)
y=10*np.sin(2*np.pi*x/20)
ax.plot(x,y)
ax.set_yticks(np.arange(min(y), max(y)+1, 1.0)) # setting the ticks
ax.set_xlabel('x')
ax.set_ylabel('y')
fig.show()
Which produces this image wherein y-axis has a scale of 1.

Polar plot without grid in matplotlib

Is there a way to turn of the grid for polar plots in matplotlib? I tried matplotlib.pyplot.rgrids([], []), but it doesn't work.

From your axes instance, call grid(False).
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.grid(False)
r = np.arange(0,1,0.001)
theta = 2*2*np.pi*r
ax.plot(theta,r)
plt.show()