模拟已弃用的seaborn distplots [英] Emulating deprecated seaborn distplots

问题描述

Seaborn

那么，我们如何配置 histplot 来复制已弃用的 distplot 的输出?

解决方案

因为我花了一些时间在这个上，所以我想我分享这个以便其他人可以轻松地适应这种方法:

from matplotlib import pyplot as plt将熊猫导入为 pd将 seaborn 作为 sns 导入将 numpy 导入为 npx_list = [1, 2, 3, 4, 6, 7, 9, 9, 9, 10]df = pd.DataFrame({X": x_list, Y": range(len(x_list))})f, (ax_dist, ax_hist) = plt.subplots(2, sharex=True)sns.distplot(df[X"], ax=ax_dist)ax_dist.set_title(旧的distplot")_, FD_bins = np.histogram(x_list, bins=fd")bin_nr = min(len(FD_bins)-1, 50)sns.histplot(数据=df，x=X"，ax=ax_hist，bins=bin_nr，stat=密度"，alpha=0.4，kde=True，kde_kws={cut":3})ax_hist.set_title(新的直方图")plt.show()

样本输出:

主要变化是

• bins=bin_nr - 使用 弗里德曼Diaconis Estimator 并将上限限制为 50
• stat=密度" - 在直方图中显示密度而不是计数
• alpha=0.4 - 相同的透明度
• kde=True - 添加核密度图
• kde_kws={cut": 3} - 将核密度图扩展到直方图限制之外

关于 bins="fd" 的 bin 估计，我不确定这确实是 distplot 使用的方法.非常欢迎评论和更正.

我删除了 **{linewidth": 0} 因为 distplot 有，正如@mwaskom 在评论中指出的那样，edgecolor可以由 matplotlib 设置为默认 facecolor 的直方图条周围的 code> 线.所以，你必须根据你的风格偏好来解决这个问题.

Seaborn distplot is now deprecated and will be removed in a future version. It is suggested to use histplot (or displot as a figure-level plot) as an alternative. But the presets differ between distplot and histplot:

from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns

x_list = [1, 2, 3, 4, 6, 7, 9, 9, 9, 10]
df = pd.DataFrame({"X": x_list, "Y": range(len(x_list))})

f, (ax_dist, ax_hist) = plt.subplots(2, sharex=True)

sns.distplot(df["X"], ax=ax_dist)
ax_dist.set_title("old distplot")
sns.histplot(data=df, x="X", ax=ax_hist)
ax_hist.set_title("new histplot")

plt.show()

So, how do we have to configure histplot to replicate the output of the deprecated distplot?

解决方案

Since I spent some time on this, I thought I share this so that others can easily adapt this approach:

from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np

x_list = [1, 2, 3, 4, 6, 7, 9, 9, 9, 10]
df = pd.DataFrame({"X": x_list, "Y": range(len(x_list))})

f, (ax_dist, ax_hist) = plt.subplots(2, sharex=True)

sns.distplot(df["X"], ax=ax_dist)
ax_dist.set_title("old distplot")
_, FD_bins = np.histogram(x_list, bins="fd")
bin_nr = min(len(FD_bins)-1, 50)
sns.histplot(data=df, x="X", ax=ax_hist, bins=bin_nr, stat="density", alpha=0.4, kde=True, kde_kws={"cut": 3})
ax_hist.set_title("new histplot")

plt.show()

Sample output:

The main changes are

• bins=bin_nr - determine the histogram bins using the Freedman Diaconis Estimator and restrict the upper limit to 50
• stat="density" - show density instead of count in the histogram
• alpha=0.4 - for the same transparency
• kde=True - add a kernel density plot
• kde_kws={"cut": 3} - extend the kernel density plot beyond the histogram limits

Regarding the bin estimation with bins="fd", I am not sure that this is indeed the method used by distplot. Comments and corrections are more than welcome.

I removed **{"linewidth": 0} because distplot has, as pointed out by @mwaskom in a comment, an edgecolor line around the histogram bars that can be set by matplotlib to the default facecolor. So, you have to sort this out according to your style preferences.

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