在 Pandas 中，如何将 DataFrame 按两列分箱，而其他列更改为这些分箱内的均值？

Question

我已经使用 UMAP 将标准鸢尾花数据集向下投影到二维，并将 2D 图的 x 和 y 位置的 UMAP 维度添加为数据框的列：

import numpy as np
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_iris
import umap # pip install umap-learn

iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = pd.Series(iris.target).map(dict(zip(range(3), iris.target_names)))

_umap = umap.UMAP().fit_transform(iris.data)
iris_df['UMAP_x'] = _umap[:,0]
iris_df['UMAP_y'] = _umap[:,1]
iris_df.head()

我想将 UMAP_x 和 UMAP_y 列都放入 25 个 bin 中，然后数据框中的其他列更改为每个列中列的平均值垃圾箱。如何做到这一点？感觉 cut 或重采样可能会得出答案，但我不确定如何。

Answer 1

您可以使用 cut 定义 bin，然后使用 groupby 和 transform 计算每个 bin 的平均值。

import numpy as np
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_iris
import umap

iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = pd.Series(iris.target).map(dict(zip(range(3), iris.target_names)))

_umap = umap.UMAP().fit_transform(iris.data)
iris_df['UMAP_x'] = _umap[:,0]
iris_df['UMAP_y'] = _umap[:,1]

# Define bins for UMAP_x and UMAP_y params
iris_df['UMAP_x_bin'] = pd.cut(iris_df['UMAP_x'], bins=25)
iris_df['UMAP_y_bin'] = pd.cut(iris_df['UMAP_y'], bins=25)

# Calculate mean value for each bin
iris_df['UMAP_x_mean'] = iris_df.groupby('UMAP_x_bin')['UMAP_x'].transform('mean')
iris_df['UMAP_y_mean'] = iris_df.groupby('UMAP_y_bin')['UMAP_y'].transform('mean')

iris_df.head()

在 Pandas 中，如何将 DataFrame 按两列分箱，而其他列更改为这些分箱内的均值？

In Pandas, how can a DataFrame be binned by two columns, with the other columns changed to the means within those bins?

pandas

dataframe

cut

resampling