如何在Python中找到KDE直方图的模式（数字）

发布于 2025-01-28 19:01:41 字数 325 浏览 2 评论 0原文

我想确定直方图中最高选择的X值。

打印直方图的代码：

fig=sns.displot(data=df, x='degrees', hue="TYPE", kind="kde",  height=6, aspect=2)
plt.xticks(np.arange(10, 20, step=0.5))
plt.xlim(10, 20)
plt.grid(axis="x")

直方图和值需要（实际上，我想要所有4个）：

原文

I want to determine the X value that has the highest pick in the histogram.

The code to print the histogram:

fig=sns.displot(data=df, x='degrees', hue="TYPE", kind="kde",  height=6, aspect=2)
plt.xticks(np.arange(10, 20, step=0.5))
plt.xlim(10, 20)
plt.grid(axis="x")

Histogram and value wanted (in fact, I would like all 4):

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傲世九天 2025-02-04 19:01:41

您需要使用matplotlib方法来检索行的基础X和Y数据。

如果您使用strendot，如摘录中，那么这是一个带有两个组的玩具数据集上的解决方案价值。 x值是首先找到最大的y值，然后使用该值的索引来找到x值。

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from seaborn import displot

np.random.RandomState(42)

d1 = pd.DataFrame({'x': np.random.normal(3, 0.2, 100), 'type': 'd1'})
d2 = pd.DataFrame({'x': np.random.normal(3.3, 0.3, 100), 'type': 'd2'})

df = pd.concat([d1,d2], axis=0, ignore_index=True)

my_kde = displot(data=df, x='x', hue='type', kind='kde')

axes = my_kde.axes.flatten()

for i, ax in enumerate(axes):
    max_xs = []
    for line in ax.lines:
        max_x = line.get_xdata()[np.argmax(line.get_ydata())]
        print(max_x)
        max_xs.append(max_x)
    for max_x in max_xs:
        ax.axvline(max_x, ls='--', color='black')

# 3.283798164938401
# 3.0426118489704757

如果您决定使用kdeplot，则语法略有不同：

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from seaborn import kdeplot

np.random.RandomState(42)

d1 = pd.DataFrame({'x': np.random.normal(3, 0.2, 100), 'type': 'd1'})
d2 = pd.DataFrame({'x': np.random.normal(3.3, 0.3, 100), 'type': 'd2'})

df = pd.concat([d1,d2], axis=0, ignore_index=True)

fig, ax = plt.subplots()

my_kde = kdeplot(data=df, x='x', hue='type', ax=ax)

lines = my_kde.get_lines()

for line in lines:
    x, y = line.get_data()
    print(x[np.argmax(y)])
    ax.axvline(x[np.argmax(y)], ls='--', color='black')

# 3.371128998664264
# 2.944974720030946

You will need to retrieve the underlying x and y data for your lines using matplotlib methods.

If you are using displot, as in your excerpt, then here is a solution on a toy dataset with two groups that both prints the x value and plots a vertical line for that value. The x value is obtained by first finding the largest y value and then using the index of that value to locate the x value.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from seaborn import displot

np.random.RandomState(42)

d1 = pd.DataFrame({'x': np.random.normal(3, 0.2, 100), 'type': 'd1'})
d2 = pd.DataFrame({'x': np.random.normal(3.3, 0.3, 100), 'type': 'd2'})

df = pd.concat([d1,d2], axis=0, ignore_index=True)

my_kde = displot(data=df, x='x', hue='type', kind='kde')

axes = my_kde.axes.flatten()

for i, ax in enumerate(axes):
    max_xs = []
    for line in ax.lines:
        max_x = line.get_xdata()[np.argmax(line.get_ydata())]
        print(max_x)
        max_xs.append(max_x)
    for max_x in max_xs:
        ax.axvline(max_x, ls='--', color='black')

# 3.283798164938401
# 3.0426118489704757

If you decide to use kdeplot, then the syntax is slightly different:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from seaborn import kdeplot

np.random.RandomState(42)

d1 = pd.DataFrame({'x': np.random.normal(3, 0.2, 100), 'type': 'd1'})
d2 = pd.DataFrame({'x': np.random.normal(3.3, 0.3, 100), 'type': 'd2'})

df = pd.concat([d1,d2], axis=0, ignore_index=True)

fig, ax = plt.subplots()

my_kde = kdeplot(data=df, x='x', hue='type', ax=ax)

lines = my_kde.get_lines()

for line in lines:
    x, y = line.get_data()
    print(x[np.argmax(y)])
    ax.axvline(x[np.argmax(y)], ls='--', color='black')

# 3.371128998664264
# 2.944974720030946

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紫轩蝶泪 2025-02-04 19:01:41

Alexk的答案非常轻微扩展：您可以获得虚线的颜色，以匹配这样的内核的颜色：

for line in lines:
    x, y = line.get_data()
    color = line.get_color()
    print(x[np.argmax(y)])
    ax.axvline(x[np.argmax(y)], ls='--', color=color)

Very slight expansion on AlexK's answer: you can get the color of the dotted vertical lines to match the colors of the kernels like this:

for line in lines:
    x, y = line.get_data()
    color = line.get_color()
    print(x[np.argmax(y)])
    ax.axvline(x[np.argmax(y)], ls='--', color=color)

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