We Keep Coding

Negative values in joy plot from non-negative data set

I am creating a joyplot using joypy.
All my data is between[0,1].
But I get a big range of negative values in the graph:
import joypy
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import cm
import matplotlib.ticker as ticker
import matplotlib
matplotlib.use('TkAgg')
iris = pd.read_csv("1_5.csv")
fig, axes = joypy.joyplot(iris)
x = [0,0.25,0.5,0.75,1]
plt.xticks(x)
plt.show()

It isn't clear that your xticks are in any way tied to the actual joyplot itself (ie, you've created arbitrary x-ticks and placed them on the plot).
Are tick marks not represented on the plot originally (similar plots I've seen all have them by default)?

Only show round numbers on x-axis in point plot

If I use the following code I end up with an overcrowded x-axis. I would like to show only every 10th number on the x axis. Meaning [0,10,...].
Any idea how to do this?
import pandas as pd
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
a = pd.DataFrame({'y':np.random.randn(100)})
a['time']=a.index
ax = sns.pointplot(x='time', y="y", data=a)
plt.show()

You may decide not to use a pointplot at all. A usual lineplot seems to suffice.
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
a = pd.DataFrame({'y':np.random.randn(100)})
plt.plot(a.index, a.y)
plt.show()
Now this gives ticks at steps of 20. The easiest option here would be to use
plt.xticks(range(0,101,10))
to get the steps of 10. Or equally possible,
plt.gca().locator_params(nbins=11)
to devide the axis into 11 bins.
Of course the use of an appropriate locator would be equally possible.

Joining points in multi-series seaborn pointplot

i am having some trouble with a seaborn pointplot.
I am to plot the Temperature vs. growth rate of four kinds of bacteria, so that each type has its own graph, but all four are in the same plot. The thing is, i cannot connect the individual points, i can only get the individual points.
My code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats, integrate
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
dataSorted=data.sort_values(['Temperature','Growth_rate'],ascending=[True,True])
plt.subplots()
ax2=sns.pointplot(x='Temperature',y='Growth_rate', hue='Bacteria' ,data=dataSorted,scale=0.7,join=True)
axes2=ax2.axes
axes2.set_xlim(10,60)
axes2.set_ylim(0,1.5)
axes2.set_xticks(np.arange(1,7)*10)
axes2.set_xticklabels(np.arange(1,7)*10)
The output is exactly as specified, apart from the lines between points:
My plot - without lines
I have no idea how to fix this, i have even set the "join" parameter manually, even though it is set as True by default.

matplotlib aspect ratio for narrow matrices

I have a 200x3 matrix in python which I would like to plot. However, by using Matplotlib I get the following figure. How can I plot an image which looks nicer?
my code:
import matplotlib.pyplot as plt
plt.imshow(spectrum_matrix)
plt.show()

You can use set_aspect():
import matplotlib.pyplot as plt
import numpy as np
spectrum_matrix = np.random.rand(200,3)
plt.imshow(spectrum_matrix)
plt.axes().set_aspect('auto')
plt.show()
Output:

PyLab: Plotting axes to log scale, but labelling specific points on the axes

Basically, I'm doing scalability analysis, so I'm working with numbers like 2,4,8,16,32... etc and the only way graphs look rational is using a log scale.
But instead of the usual 10^1, 10^2, etc labelling, I want to have these datapoints (2,4,8...) indicated on the axes
Any ideas?

There's more than one way to do it, depending on how flexible/fancy you want to be.
The simplest way is just to do something like this:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
x = np.exp2(np.arange(10))
plt.semilogy(x)
plt.yticks(x, x)
# Turn y-axis minor ticks off
plt.gca().yaxis.set_minor_locator(mpl.ticker.NullLocator())
plt.show()
If you want to do it in a more flexible manner, then perhaps you might use something like this:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
x = np.exp2(np.arange(10))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.semilogy(x)
ax.yaxis.get_major_locator().base(2)
ax.yaxis.get_minor_locator().base(2)
# This will place 1 minor tick halfway (in linear space) between major ticks
# (in general, use np.linspace(1, 2.0001, numticks-2))
ax.yaxis.get_minor_locator().subs([1.5])
ax.yaxis.get_major_formatter().base(2)
plt.show()
Or something like this:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
x = np.exp2(np.arange(10))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.semilogy(x)
ax.yaxis.get_major_locator().base(2)
ax.yaxis.get_minor_locator().base(2)
ax.yaxis.get_minor_locator().subs([1.5])
# This is the only difference from the last snippet, uses "regular" numbers.
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
plt.show()