I have two distribution with different spread, say
a=N.random.normal(0,0.5,500)
b=N.random.normal(1,3.,500)
I want to make an histogram of them where the maximum is at the same level.
If I make normalised histograms with density=True parameter, it will make that area of both histograms will be 1, bit it wont change the fact that maximums are different.
P.hist(a, histtype='step', lw=2, cumulative=True)
P.hist(b, histtype='step', color='r', lw=2, density=True)
What I want is to make a histogram where it is "normalized" with the maximum values. I mean when maximum of blue and red histogram would be the same, so it could be easy to compare them. Thanks a lot in advance for your help.
I'm not sure I would do that really, but if you want to I think the best way is to add two axes (also so that you can see how tall they really are). For example, see here: https://matplotlib.org/gallery/api/two_scales.html
import numpy as np
from matplotlib import pyplot as plt
fig, ax1 = plt.subplots()
ax1.hist(a, histtype='step', color='b', lw=2, density=True)
ax1.tick_params(axis='y', labelcolor='b')
ax2 = ax1.twinx()
ax2.hist(b, histtype='step', color='r', lw=2, density=True)
ax2.tick_params(axis='y', labelcolor='r')
This gives the following output (which, I think, looks worse than what you obtained; I also changed cumulative=True to density=True in the first plot to be in line with the plot you provided):
Also, strictly speaking this does not make sure that the maxima are really identical. If you want to do that you can force it by doing e.g.
import numpy as np
from matplotlib import pyplot as plt
fig, ax1 = plt.subplots()
n1, _, _ = ax1.hist(a, histtype='step', color='b', lw=2, density=True)
ax1.tick_params(axis='y', labelcolor='b')
ax2 = ax1.twinx()
n2, _, _ = ax2.hist(b, histtype='step', color='r', lw=2, density=True)
ax2.tick_params(axis='y', labelcolor='r')
ax1.set_ylim([0, n1.max()*1.1])
ax2.set_ylim([0, n2.max()*1.1])
The following code would give a the same max as b:
a *= b.max()/a.max()
The cumulative flag in a might break this though and it should be placed before the histograms are generated.
Related
I would like to make a paired histogram like the one shown here using the seaborn distplot.
This kind of plot can also be referred to as the back-to-back histogram shown here, or a bihistogram inverted/mirrored along the x-axis as discussed here.
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
green = np.random.normal(20,10,1000)
blue = np.random.poisson(60,1000)
fig, ax = plt.subplots(figsize=(8,6))
sns.distplot(blue, hist=True, kde=True, hist_kws={'edgecolor':'black'}, kde_kws={'linewidth':2}, bins=10, color='blue')
sns.distplot(green, hist=True, kde=True, hist_kws={'edgecolor':'black'}, kde_kws={'linewidth':2}, bins=10, color='green')
ax.set_xticks(np.arange(-20,121,20))
ax.set_yticks(np.arange(0.0,0.07,0.01))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.show()
Here is the output:
When I use the method discussed here (plt.barh), I get the bar plot shown just below, which is not what I am looking for.
Or maybe I haven't understood the workaround well enough...
A simple/short implementation of python-seaborn-distplot similar to these kinds of plots would be perfect. I edited the figure of my first plot above to show the kind of plot I hope to achieve (though y-axis not upside down):
Any leads would be greatly appreciated.
You could use two subplots and invert the y-axis of the lower one and plot with the same bins.
df = pd.DataFrame({'a': np.random.normal(0,5,1000), 'b': np.random.normal(20,5,1000)})
fig =plt.figure(figsize=(5,5))
ax = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
bins = np.arange(-20,40)
ax.hist(df['a'], bins=bins)
ax2.hist(df['b'],color='orange', bins=bins)
ax2.invert_yaxis()
edit:
improvements suggested by #mwaskom
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(5,5))
bins = np.arange(-20,40)
for ax, column, color, invert in zip(axes.ravel(), df.columns, ['teal', 'orange'], [False,True]):
ax.hist(df[column], bins=bins, color=color)
if invert:
ax.invert_yaxis()
plt.subplots_adjust(hspace=0)
Here is a possible approach using seaborn's displots.
Seaborn doesn't return the created graphical elements, but the ax can be interrogated. To make sure the ax only contains the elements you want upside down, those elements can be drawn first. Then, all the patches (the rectangular bars) and the lines (the curve for the kde) can be given their height in negative. Optionally the x-axis can be set at y == 0 using ax.spines['bottom'].set_position('zero').
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
green = np.random.normal(20, 10, 1000)
blue = np.random.poisson(60, 1000)
fig, ax = plt.subplots(figsize=(8, 6))
sns.distplot(green, hist=True, kde=True, hist_kws={'edgecolor': 'black'}, kde_kws={'linewidth': 2}, bins=10,
color='green')
for p in ax.patches: # turn the histogram upside down
p.set_height(-p.get_height())
for l in ax.lines: # turn the kde curve upside down
l.set_ydata(-l.get_ydata())
sns.distplot(blue, hist=True, kde=True, hist_kws={'edgecolor': 'black'}, kde_kws={'linewidth': 2}, bins=10,
color='blue')
ax.set_xticks(np.arange(-20, 121, 20))
ax.set_yticks(np.arange(0.0, 0.07, 0.01))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
pos_ticks = np.array([t for t in ax.get_yticks() if t > 0])
ticks = np.concatenate([-pos_ticks[::-1], [0], pos_ticks])
ax.set_yticks(ticks)
ax.set_yticklabels([f'{abs(t):.2f}' for t in ticks])
ax.spines['bottom'].set_position('zero')
plt.show()
I am creating a grid by plotting several curves using one plot call as:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
x = np.array([[0,1], [0,1], [0,1]])
y = np.array([[0,0], [1,1], [2,2]])
ax.plot([0,1],[0,2], label='foo', color='b')
ax.plot(x.T, y.T, label='bar', color='k')
ax.legend()
plt.show()
The resulting legend has as many 'bar' entries as there are curves (see below). I wish that have only one legend entry per plot call (in this case only one time 'bar').
I want this such that I can have other plot commands (e.g. the one plotting the 'foo' curve) whose curves are automatically included in the legend if they have a label. I specifically want to avoid hand-selecting the handles when constructing the legend, but rather use matplotlib's feature to deal with this by yes/no including a label when plotting. How can I achieve this?
Here is one possible solution: You may use the fact that underscores do not produce legend entries. So setting all but the first label to "_" suppresses those to appear in the legend.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
x = np.array([[0,1], [0,1], [0,1]])
y = np.array([[0,0], [1,1], [2,2]])
ax.plot([0,1],[0,2], label='foo', color='b')
lines = ax.plot(x.T, y.T, label='bar', color='k')
plt.setp(lines[1:], label="_")
ax.legend()
plt.show()
Following is one way using the already existing legend handles and labels. You first get the three handles, labels and then just show the first one. This way additionally gives you a control not only on the order of putting handles but also what to show on the plot.
ax.plot(x.T, y.T, label='bar', color='k')
handles, labels = ax.get_legend_handles_labels()
ax.legend([handles[0]], [labels[0]], loc='best')
Alternative approach where the legends will only be taken from a particular plot (set of lines) -- ax1 in this case
ax1 = ax.plot(x.T, y.T, label='bar', color='k')
plt.legend(handles=[ax1[0]], loc='best')
Extending it to you problem with two figures
ax1 = ax.plot([0,1],[0,2], label='foo', color='b')
ax2 = ax.plot(x.T, y.T, label='bar', color='k')
plt.legend(handles=[ax1[0], ax2[1]], loc='best')
Another alternative using for loops as suggested by #SpghttCd
for i in range(len(x)):
ax.plot(x[i], y[i], label=('' if i==0 else '_') + 'bar', color='k')
ax.legend()
Maybe not quite elegant, but the easiest and most straightforward way is to make a second plot using a single pair of elements where you prescribe the 'label' you want!
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
x = np.array([[0,1], [0,1], [0,1]])
y = np.array([[0,0], [1,1], [2,2]])
ax.plot([0,1],[0,2], label='foo', color='b')
ax.plot(x.T, y.T, color='k')
ax.plot(x[0].T, y[0].T, label='bar', color='k')
ax.legend()
plt.show()
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
I want to plot multiple histograms using different line styles as I cannot use color to distinguish them. I get it but still, these look very similar as two plots have very similar distribution. Can I get different markers such as dots, stars etc or some better way to distinguish these?
This is what I have
import matplotlib
matplotlib.use('PS')
import matplotlib.pyplot as plt
plt.hist(values1, histtype='step', linestyle=':',label=topic1)
plt.hist(values2, histtype='step', linestyle='--',color=color, label=topic2)
plt.hist(values3,histtype='step', linestyle='solid', label=topic3)
plt.legend(loc="upper right")
plt.legend(frameon=False)
plt.show()
plt.savefig(allplotfile)
plt.close()
You may use hatching, e.g. hatch="\\\\" in the call to hist. I'm not convinced that it looks better, but it's at least an option.
import matplotlib.pyplot as plt
plt.style.use("grayscale")
import numpy as np; np.random.seed(1)
plt.rcParams["figure.figsize"] = (4,3)
vals = np.arange(2,5.1,0.5)
p = np.array([1,.2,.36,.15,.38,.28,.4])
p = p/np.sum(p)
a = np.random.choice(vals, size=100, p=p)
b = np.random.choice(vals, size=100, p=p)
c = np.random.choice(vals, size=100, p=p)
plt.hist(a, histtype='step', linestyle=':',label="topic1", hatch="\\\\")
plt.hist(b, histtype='step', linestyle='--', label="topic2", hatch="//")
plt.hist(c,histtype='step', linestyle='solid', label="topic3", hatch="++")
plt.legend(loc="upper right")
plt.legend(frameon=False)
plt.show()
Different linewidths may also help: lw=2 etc,
(here I used 1, 2 and 3 as linewidths)
Different shades of gray, combines with alpha settings may also help:
plt.hist(a, linestyle=':', color=plt.cm.gray(0.1), alpha=0.5)
plt.hist(b, linestyle='--' ,color=plt.cm.gray(0.4), alpha=0.5)
plt.hist(c, linestyle='solid', color=plt.cm.gray(0.8), alpha=0.5)
I've got a Pandas dataframe named clean which contains a column v for which I would like to draw a histogram and superimpose a density plot. I know I can plot one under the other this way:
import pandas as pd
import matplotlib.pyplot as plt
Maxv=200
plt.subplot(211)
plt.hist(clean['v'],bins=40, range=(0, Maxv), color='g')
plt.ylabel("Number")
plt.subplot(212)
ax=clean['v'].plot(kind='density')
ax.set_xlim(0, Maxv)
plt.xlabel("Orbital velocity (km/s)")
ax.get_yaxis().set_visible(False)
But when I try to superimpose, y scales doesn't match (and I loose y axis ticks and labels):
yhist, xhist, _hist = plt.hist(clean['v'],bins=40, range=(0, Maxv), color='g')
plt.ylabel("Number")
ax=clean['v'].plot(kind='density') #I would like to insert here a normalization to max(yhist)/max(ax)
ax.set_xlim(0, Maxv)
plt.xlabel("Orbital velocity (km/s)")
ax.get_yaxis().set_visible(False)
Some hint? (Additional question: how can I change the width of density smoothing?)
Based on your code, this should work:
ax = clean.v.plot(kind='hist', bins=40, normed=True)
clean.v.plot(kind='kde', ax=ax, secondary_y=True)
ax.set(xlim=[0, Maxv])
You might not even need the secondary_y anymore.
No I try this:
ax = clean.v.plot(kind='hist', bins=40, range=(0, Maxv))
clean.v.plot(kind='kde', ax=ax, secondary_y=True)
But the range part doesn't work, and ther's still the left y-axis problem
Seaborn makes this easy
import seaborn as sns
sns.distplot(df['numeric_column'],bins=25)