python - 如何使用 andrew_curves 绘制 Pandas 数据框？

Question

我有以下 Pandas 数据框:

df = pd.read_csv('path/file/file.csv',
                 header=0, sep=',', names=['PhraseId', 'SentenceId', 'Phrase', 'Sentiment'])

我想用 andrew_curves 打印它我尝试了以下方法:

andrews_curves(df, 'Name')

知道如何绘制这个吗？这是 csv 的内容:

PhraseId, SentenceId, Phrase, Sentiment
1, 1, A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story ., 1
2, 1, A series of escapades demonstrating the adage that what is good for the goose, 2
3, 1, A series, 2
4, 1, A, 2
5, 1, series, 2
6, 1, of escapades demonstrating the adage that what is good for the goose, 2
7, 1, of, 2
8, 1, escapades demonstrating the adage that what is good for the goose, 2
9, 1, escapades, 2
10, 1, demonstrating the adage that what is good for the goose, 2
11, 1, demonstrating the adage, 2
12, 1, demonstrating, 2
13, 1, the adage, 2
14, 1, the, 2
15, 1, adage, 2
16, 1, that what is good for the goose, 2
17, 1, that, 2
18, 1, what is good for the goose, 2
19, 1, what, 2
20, 1, is good for the goose, 2
21, 1, is, 2
22, 1, good for the goose, 3
23, 1, good, 3
24, 1, for the goose, 2
25, 1, for, 2
26, 1, the goose, 2
27, 1, goose, 2
28, 1, is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story ., 2
29, 1, is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story, 2

Answer 1

在the doc page您链接到的 Iris 数据集有一个名为 'Name' 的列。当你打电话时

andrews_curves(data, 'Name')

data 的行按 Name 的值分组。这就是 Iris 的原因 数据集，你会得到三种不同颜色的线条。

在您的数据集中，您有三列:A、B、C。要在您的 df 上调用 andrews_curves，您首先需要确定要分组的值。例如，如果它是 C 列的值，则调用

andrews_curves(data, 'C')

另一方面，如果您希望按列名、A、B、C 进行分组，然后 首先融化您的 DataFrame 以将其从宽格式转换为长格式，然后 然后在 variable 列(保存值 每行的 A、B 或 C):

import numpy as np
import pandas as pd
import pandas.plotting as pdplt
import matplotlib.pyplot as plt

x = np.linspace(-1, 1, 1000)
df = pd.DataFrame({'A': np.sin(x**2)/x,
                   'B': np.sin(x)*np.exp(-x),
                   'C': np.cos(x)*x})
pdplt.andrews_curves(pd.melt(df), 'variable')
plt.show()

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