blocks|key|477971|text|我相信其他人会有更好的建议，但是有一种方法可以得到你想要的东西(不需要额外的轴艺术家)，那就是使用一个极坐标投影和一个散点图和条形图。有点像|type|unstyled|depth|inlineStyleRanges|entityRanges|data|477972|import+matplotlib.pyplot+as+plt
import+numpy+as+np

x+=+np.random.uniform(size=100)
y+=+np.random.uniform(size=100)

r+=+np.sqrt(x**2+%2B+y**2)
phi+=+np.arctan2(y,+x)

h,+b+=+np.histogram(phi,+bins=np.linspace(0,+np.pi/2,+21),+density=True)
colors+=+plt.cm.Spectral(h+/+h.max())

ax+=+plt.subplot(111,+projection='polar')
ax.scatter(phi,+r,+marker='.')
ax.bar(b[:-1],+h,+width=b[1:]+-+b[:-1],+
+++++++align='edge',+bottom=np.max(r)+%2B+0.2,++color=colors)
#+Cut+off+at+90+degrees
ax.set_thetamax(90)
#+Set+the+r+grid+to+cover+the+scatter+plot
ax.set_rgrids([0,+0.5,+1])
#+Let's+put+a+line+at+1+assuming+we+want+a+ratio+of+some+sort
ax.set_thetagrids([45],+[1])|code-block|syntax|javascript|477973|它会给|477974|📷|atomic|offset|length|477975|​|477976|它缺少斧头标签和一些美化，但它可能是一个起点。我希望这会有所帮助。|477977|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/707913b61f35f9880d53fb9ecd1ebb5b.png|imageAlt^0|0|0|0|0|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|13|8|@]|9|@$L|14|M|15|1|16]]|A|$]]|$1|N|3|O|5|6|7|17|8|@]|9|@]|A|$]]|$1|P|3|Q|5|6|7|18|8|@]|9|@]|A|$]]|$1|R|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|S|$T|$5|U|V|W|A|$X|Y|Z|-4]]]]

I'm sure that others will have better suggestions, but one method that gets something like you want (without the need for extra axes artists) is to use a polar projection with a scatter and bar chart together. Something like

<pre><code>import matplotlib.pyplot as plt
import numpy as np

x = np.random.uniform(size=100)
y = np.random.uniform(size=100)

r = np.sqrt(x**2 + y**2)
phi = np.arctan2(y, x)

h, b = np.histogram(phi, bins=np.linspace(0, np.pi/2, 21), density=True)
colors = plt.cm.Spectral(h / h.max())

ax = plt.subplot(111, projection='polar')
ax.scatter(phi, r, marker='.')
ax.bar(b[:-1], h, width=b[1:] - b[:-1], 
 align='edge', bottom=np.max(r) + 0.2, color=colors)
# Cut off at 90 degrees
ax.set_thetamax(90)
# Set the r grid to cover the scatter plot
ax.set_rgrids([0, 0.5, 1])
# Let's put a line at 1 assuming we want a ratio of some sort
ax.set_thetagrids([45], [1])
</code></pre>

which will give
<a href="https://i.stack.imgur.com/Gw4rh.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Gw4rh.png" alt="enter image description here"></a>

It is missing axes labels and some beautification, but it might be a place to start. I hope it is helpful.

blocks|key|478006|text|您可以使用两个轴在另一个上面：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|478007|import+matplotlib.pyplot+as+plt
fig+=+plt.figure(figsize=(6,6))
ax1+=+fig.add_axes([0.1,0.1,.8,.8],+label="cartesian")
ax2+=+fig.add_axes([0.1,0.1,.8,.8],+projection="polar",+label="polar")

ax2.set_rorigin(-1)
ax2.set_thetamax(90)
plt.show()|code-block|syntax|javascript|478008|​|478009|📷|atomic|offset|length|478010|478011|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/e65ecc1c559a9a9bb6b01c8a444a656c.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

You can use two axes on top of each other:

<pre><code>import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6,6))
ax1 = fig.add_axes([0.1,0.1,.8,.8], label="cartesian")
ax2 = fig.add_axes([0.1,0.1,.8,.8], projection="polar", label="polar")

ax2.set_rorigin(-1)
ax2.set_thetamax(90)
plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/0paVV.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/0paVV.png" alt="enter image description here"></a>

blocks|key|478062|text|好的。感谢尼古拉斯的回答和tomjn的回答，我有了一个可行的解决方案:)|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|478063|import+numpy+as+np
import+matplotlib.pyplot+as+plt

#+Scatter+data
n+=+50
x+=+0.3+%2B+np.random.randn(n)*0.1
y+=+0.4+%2B+np.random.randn(n)*0.02

def+radial_corner_plot(x,+y,+n_hist_bins=51):
++++"""Scatter+plot+with+radial+histogram+of+x/y+ratios"""

++++#+Axis+setup
++++fig+=+plt.figure(figsize=(6,6))
++++ax1+=+fig.add_axes([0.1,0.1,.6,.6],+label="cartesian")
++++ax2+=+fig.add_axes([0.1,0.1,.8,.8],+projection="polar",+label="polar")
++++ax2.set_rorigin(-20)
++++ax2.set_thetamax(90)

++++#+define+useful+constant
++++offset_in_radians+=+np.pi/4

++++def+rotate_hist_axis(ax):
++++++++"""rotate+so+that+0+degrees+is+pointing+up+and+right"""
++++++++ax.set_theta_offset(offset_in_radians)
++++++++ax.set_thetamin(-45)
++++++++ax.set_thetamax(45)
++++++++return+ax

++++#+Convert+scatter+data+to+histogram+data
++++r+=+np.sqrt(x**2+%2B+y**2)
++++phi+=+np.arctan2(y,+x)
++++h,+b+=+np.histogram(phi,+
++++++++++++++++++++++++bins=np.linspace(0,+np.pi/2,+n_hist_bins),
++++++++++++++++++++++++density=True)

++++#+SCATTER+PLOT+-------------------------------------------------------
++++ax1.scatter(x,y)

++++ax1.set(xlim=[0,+1],+ylim=[0,+1],+xlabel="x",+ylabel="y")
++++ax1.spines['right'].set_visible(False)
++++ax1.spines['top'].set_visible(False)


++++#+HISTOGRAM+----------------------------------------------------------
++++ax2+=+rotate_hist_axis(ax2)
++++#+rotation+of+axis+requires+rotation+in+bin+positions
++++b+=+b+-+offset_in_radians

++++#+plot+the+histogram
++++bars+=+ax2.bar(b[:-1],+h,+width=b[1:]+-+b[:-1],+align='edge')

++++def+update_hist_ticks(ax,+desired_ratios):
++++++++"""Update+tick+positions+and+corresponding+tick+labels"""
++++++++x+=+np.ones(len(desired_ratios))
++++++++y+=+1/desired_ratios
++++++++phi+=+np.arctan2(y,x)+-+offset_in_radians
++++++++#+define+ticklabels
++++++++xticklabels+=+[str(round(float(label),+2))+for+label+in+desired_ratios]
++++++++#+apply+updates
++++++++ax2.set(xticks=phi,+xticklabels=xticklabels)
++++++++return+ax

++++ax2+=+update_hist_ticks(ax2,+np.array([1/8,+1/4,+1/2,+1,+2,+4,+8]))

++++#+just+have+radial+grid+lines
++++ax2.grid(which="major",+axis="y")

++++#+remove+bin+count+labels
++++ax2.set_yticks([])

++++return+(fig,+[ax1,+ax2])

fig,+ax+=+radial_corner_plot(x,+y)|code-block|syntax|javascript|478064|谢谢你的指点！|478065|​|478066|📷|atomic|478067|478068|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/62458850/5172570|1|https://stackoverflow.com/a/62458530/5172570|2|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/1623bb2e8cb9714c35e6d7bd7ff749d7.png|imageAlt^0|A|2|0|J|2|1|0|0|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@$A|17|B|18|1|19]|$A|1A|B|1B|1|1C]]|C|$]]|$1|D|3|E|5|F|7|1D|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1E|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1F|8|@]|9|@]|C|$]]|$1|M|3|N|5|O|7|1G|8|@]|9|@$A|1H|B|1I|1|1J]]|C|$]]|$1|P|3|L|5|6|7|1K|8|@]|9|@]|C|$]]|$1|Q|3|-4|5|6|7|1L|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|T|U|V|C|$W|Z]]|10|$5|11|U|12|C|$13|14|15|-4]]]]

Ok. Thanks to the <a href="https://stackoverflow.com/a/62458850/5172570">answer</a> from Nicolas, and the <a href="https://stackoverflow.com/a/62458530/5172570">answer</a> from tomjn I have a working solution :)

<pre class="lang-py prettyprint-override"><code>import numpy as np
import matplotlib.pyplot as plt

# Scatter data
n = 50
x = 0.3 + np.random.randn(n)*0.1
y = 0.4 + np.random.randn(n)*0.02

def radial_corner_plot(x, y, n_hist_bins=51):
 """Scatter plot with radial histogram of x/y ratios"""

 # Axis setup
 fig = plt.figure(figsize=(6,6))
 ax1 = fig.add_axes([0.1,0.1,.6,.6], label="cartesian")
 ax2 = fig.add_axes([0.1,0.1,.8,.8], projection="polar", label="polar")
 ax2.set_rorigin(-20)
 ax2.set_thetamax(90)

 # define useful constant
 offset_in_radians = np.pi/4

 def rotate_hist_axis(ax):
 """rotate so that 0 degrees is pointing up and right"""
 ax.set_theta_offset(offset_in_radians)
 ax.set_thetamin(-45)
 ax.set_thetamax(45)
 return ax

 # Convert scatter data to histogram data
 r = np.sqrt(x**2 + y**2)
 phi = np.arctan2(y, x)
 h, b = np.histogram(phi, 
 bins=np.linspace(0, np.pi/2, n_hist_bins),
 density=True)

 # SCATTER PLOT -------------------------------------------------------
 ax1.scatter(x,y)

 ax1.set(xlim=[0, 1], ylim=[0, 1], xlabel="x", ylabel="y")
 ax1.spines['right'].set_visible(False)
 ax1.spines['top'].set_visible(False)


 # HISTOGRAM ----------------------------------------------------------
 ax2 = rotate_hist_axis(ax2)
 # rotation of axis requires rotation in bin positions
 b = b - offset_in_radians

 # plot the histogram
 bars = ax2.bar(b[:-1], h, width=b[1:] - b[:-1], align='edge')

 def update_hist_ticks(ax, desired_ratios):
 """Update tick positions and corresponding tick labels"""
 x = np.ones(len(desired_ratios))
 y = 1/desired_ratios
 phi = np.arctan2(y,x) - offset_in_radians
 # define ticklabels
 xticklabels = [str(round(float(label), 2)) for label in desired_ratios]
 # apply updates
 ax2.set(xticks=phi, xticklabels=xticklabels)
 return ax

 ax2 = update_hist_ticks(ax2, np.array([1/8, 1/4, 1/2, 1, 2, 4, 8]))

 # just have radial grid lines
 ax2.grid(which="major", axis="y")

 # remove bin count labels
 ax2.set_yticks([])

 return (fig, [ax1, ax2])

fig, ax = radial_corner_plot(x, y)
</code></pre>

Thanks for the pointers!

<a href="https://i.stack.imgur.com/qm448.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/qm448.png" alt="enter image description here"></a>

I am attempting to produce a plot like this which combines a cartesian scatter plot and a polar histogram. (Radial lines optional)
<a href="https://i.stack.imgur.com/D1kQU.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/D1kQU.jpg" alt="enter image description here"></a>
A similar solution (by <a href="https://twitter.com/legrandni" rel="nofollow noreferrer">Nicolas Legrand</a>) exists for looking at differences in x and y (<a href="https://gist.github.com/LegrandNico/2b201863dc7ae28d568573c66047dd86" rel="nofollow noreferrer">code here</a>), but we need to look at ratios (i.e. x/y).
<a href="https://i.stack.imgur.com/E5DjS.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/E5DjS.jpg" alt="enter image description here"></a>

More specifically, this is useful when we want to look at the relative risk measure which is the ratio of two probabilities.

The scatter plot on it's own is obviously not a problem, but the polar histogram is more advanced.

The most promising lead I have found is this central example from the matplotlib gallery <a href="https://matplotlib.org/examples/axes_grid/demo_floating_axes.html" rel="nofollow noreferrer">here</a>
<a href="https://i.stack.imgur.com/gnCMk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/gnCMk.png" alt="enter image description here"></a>

I have attempted to do this, but have run up against the limits of my matplotlib skills. Any efforts moving towards this goal would be great.

scatterplot and combined polar histogram in matplotlib

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我试图生成这样一个图，它结合了笛卡儿散点图和极坐标直方图。(径向线可选)一个类似的解决方案(由)存在，用于查看x和y ()的差异，但我们需要查看比率(即x/y)。​更具体地说，当我们想看相对风险度量，即两个概率的比率时，这是很有用的。散点图本身显然不是一个问题，但极坐标直方图更高级。我发现的最有希望的线索是matplo...

问matplotlib中的散点图和组合极直方图
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