Pandas 2.2 中文官方教程和指南（十九·二）（3）-阿里云开发者社区

Pandas 2.2 中文官方教程和指南（十九·二）（2）https://developer.aliyun.com/article/1509812

Highlight Min or Max

[50]:

df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')

[50]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

Highlight Between

此方法接受浮点数范围，或者 NumPy 数组或 Series，只要索引匹配。

[51]:

left = pd.Series([1.0, 0.0, 1.0], index=["A", "B", "D"])
df2.loc[:4].style.highlight_between(left=left, right=1.5, axis=1, props='color:white; background-color:purple;')

[51]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

Highlight Quantile

用于检测最高或最低百分位值

[52]:

df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color='yellow')

[52]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

背景渐变和文本渐变

您可以使用background_gradient和text_gradient方法创建“热图”。这些需要 matplotlib，我们将使用Seaborn来获得漂亮的颜色映射。

[53]:

import seaborn as sns
cm = sns.light_palette("green", as_cmap=True)
df2.style.background_gradient(cmap=cm)

[53]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

[54]:

df2.style.text_gradient(cmap=cm)

[54]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

.background_gradient 和 .text_gradient 有许多关键字参数可用于自定义渐变和颜色。请参阅文档。

设置属性

当样式实际上不依赖于值时，请使用Styler.set_properties。这只是一个简单的.map的包装器，其中函数为所有单元格返回相同的属性。

[55]:

df2.loc[:4].style.set_properties(**{'background-color': 'black',
                           'color': 'lawngreen',
                           'border-color': 'white'})

[55]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan

条形图

您可以在 DataFrame 中包含“条形图”。

[56]:

df2.style.bar(subset=['A', 'B'], color='#d65f5f')

[56]:

	A	B	C	D
0	1.764052	0.400157	nan	2.240893
1	1.867558	-0.977278	0.950088	-0.151357
2	-0.103219	0.410599	0.144044	1.454274
3	0.761038	0.121675	0.443863	0.333674
4	1.494079	-0.205158	0.313068	nan
5	-2.552990	0.653619	0.864436	-0.742165
6	2.269755	-1.454366	0.045759	-0.187184
7	1.532779	1.469359	0.154947	0.378163
8	-0.887786	-1.980796	-0.347912	0.156349
9	1.230291	1.202380	-0.387327	-0.302303

附加关键字参数可更好地控制居中和定位，您可以传递一个[color_negative, color_positive]列表来突出显示较低和较高的值，或者一个 matplotlib 颜色映射。

为了展示一个示例，这里展示了如何使用新的align选项来更改上述内容，结合设置vmin和vmax限制，图形的width，以及单元格的底层 css props，留出空间来显示文本和条形图。我们还使用text_gradient来使用 matplotlib 颜色映射对文本着色，尽管在这种情况下，可能最好不使用这种额外效果。

[57]:

df2.style.format('{:.3f}', na_rep="")\
         .bar(align=0, vmin=-2.5, vmax=2.5, cmap="bwr", height=50,
              width=60, props="width: 120px; border-right: 1px solid black;")\
         .text_gradient(cmap="bwr", vmin=-2.5, vmax=2.5)

[57]:

	A	B	C	D
0	1.764	0.400		2.241
1	1.868	-0.977	0.950	-0.151
2	-0.103	0.411	0.144	1.454
3	0.761	0.122	0.444	0.334
4	1.494	-0.205	0.313
5	-2.553	0.654	0.864	-0.742
6	2.270	-1.454	0.046	-0.187
7	1.533	1.469	0.155	0.378
8	-0.888	-1.981	-0.348	0.156
9	1.230	1.202	-0.387	-0.302

以下示例旨在突出新对齐选项的行为：

[59]:

HTML(head)

[59]:

对齐	全负	正负混合	全正	大正数
左对齐

| |
| — |
| -100 |
| -60 |
| -30 |
| -20 |
|
| |
| — |
| -10 |
| -5 |
| 0 |
| 90 |
|
| |
| — |
| 10 |
| 20 |
| 50 |
| 100 |
|
| |
| — |
| 100 |
| 103 |
| 101 |
| 102 |
|
右对齐
| |
| — |
| -100 |
| -60 |
| -30 |
| -20 |
|
| |
| — |
| -10 |
| -5 |
| 0 |
| 90 |
|
| |
| — |
| 10 |
| 20 |
| 50 |
| 100 |
|
| |
| — |
| 100 |
| 103 |
| 101 |
| 102 |
|
零
| |
| — |
| -100 |
| -60 |
| -30 |
| -20 |
|
| |
| — |
| -10 |
| -5 |
| 0 |
| 90 |
|
| |
| — |
| 10 |
| 20 |
| 50 |
| 100 |
|
| |
| — |
| 100 |
| 103 |
| 101 |
| 102 |
|
中值
| |
| — |
| -100 |
| -60 |
| -30 |
| -20 |
|
| |
| — |
| -10 |
| -5 |
| 0 |
| 90 |
|
| |
| — |
| 10 |
| 20 |
| 50 |
| 100 |
|
| |
| — |
| 100 |
| 103 |
| 101 |
| 102 |
|
平均值
| |
| — |
| -100 |
| -60 |
| -30 |
| -20 |
|
| |
| — |
| -10 |
| -5 |
| 0 |
| 90 |
|
| |
| — |
| 10 |
| 20 |
| 50 |
| 100 |
|
| |
| — |
| 100 |
| 103 |
| 101 |
| 102 |
|
99
| |
| — |
| -100 |
| -60 |
| -30 |
| -20 |
|
| |
| — |
| -10 |
| -5 |
| 0 |
| 90 |
|
| |
| — |
| 10 |
| 20 |
| 50 |
| 100 |
|
| |
| — |
| 100 |
| 103 |
| 101 |
| 102 |
|

分享样式

假设您为 DataFrame 建立了一个可爱的样式，现在您想将相同的样式应用于第二个 DataFrame。使用df1.style.export导出样式，并使用df1.style.set在第二个 DataFrame 上导入样式

[60]:

style1 = df2.style\
            .map(style_negative, props='color:red;')\
            .map(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\
            .set_table_styles([{"selector": "th", "props": "color: blue;"}])\
            .hide(axis="index")
style1

[60]:

A	B	C	D
1.764052	0.400157	nan	2.240893
1.867558	-0.977278	0.950088	-0.151357
-0.103219	0.410599	0.144044	1.454274
0.761038	0.121675	0.443863	0.333674
1.494079	-0.205158	0.313068	nan
-2.552990	0.653619	0.864436	-0.742165
2.269755	-1.454366	0.045759	-0.187184
1.532779	1.469359	0.154947	0.378163
-0.887786	-1.980796	-0.347912	0.156349
1.230291	1.202380	-0.387327	-0.302303

[61]:

style2 = df3.style
style2.use(style1.export())
style2

[61]:

c1	c2	c3	c4
-1.048553	-1.420018	-1.706270	1.950775
-0.509652	-0.438074	-1.252795	0.777490
-1.613898	-0.212740	-0.895467	0.386902
-0.510805	-1.180632	-0.028182	0.428332

请注意，即使它们是数据感知的，您也能够共享样式。这些样式在新的 DataFrame 上重新评估，它们已经被use。

限制

仅适用于 DataFrame（使用Series.to_frame().style）
索引和列不需要唯一，但某些样式功能只能与唯一索引一起使用。
不适用大的 repr，构造性能并不好；虽然我们有一些 HTML 优化
您只能应用样式，不能插入新的 HTML 实体，除非通过子类化。

其他有趣且有用的东西

这里有一些有趣的例子。

小部件

Styler与小部件的互动效果相当不错。如果您在线查看而不是自己运行笔记本，则无法交互地调整调色板。

[62]:

from ipywidgets import widgets
@widgets.interact
def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):
    return df2.style.background_gradient(
        cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,
                                            as_cmap=True)
    )

放大

[63]:

def magnify():
    return [dict(selector="th",
                 props=[("font-size", "4pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

[64]:

np.random.seed(25)
cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)
bigdf = pd.DataFrame(np.random.randn(20, 25)).cumsum()
bigdf.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '1pt'})\
    .set_caption("Hover to magnify")\
    .format(precision=2)\
    .set_table_styles(magnify())

[64]:

悬停放大

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
0	0.23	1.03	-0.84	-0.59	-0.96	-0.22	-0.62	1.84	-2.05	0.87	-0.92	-0.23	2.15	-1.33	0.08	-1.25	1.20	-1.05	1.06	-0.42	2.29	-2.59	2.82	0.68	-1.58
1	-1.75	1.56	-1.13	-1.10	1.03	0.00	-2.46	3.45	-1.66	1.27	-0.52	-0.02	1.52	-1.09	-1.86	-1.13	-0.68	-0.81	0.35	-0.06	1.79	-2.82	2.26	0.78	0.44
2	-0.65	3.22	-1.76	0.52	2.20	-0.37	-3.00	3.73	-1.87	2.46	0.21	-0.24	-0.10	-0.78	-3.02	-0.82	-0.21	-0.23	0.86	-0.68	1.45	-4.89	3.03	1.91	0.61
3	-1.62	3.71	-2.31	0.43	4.17	-0.43	-3.86	4.16	-2.15	1.08	0.12	0.60	-0.89	0.27	-3.67	-2.71	-0.31	-1.59	1.35	-1.83	0.91	-5.80	2.81	2.11	0.28
4	-3.35	4.48	-1.86	-1.70	5.19	-1.02	-3.81	4.72	-0.72	1.08	-0.18	0.83	-0.22	-1.08	-4.27	-2.88	-0.97	-1.78	1.53	-1.80	2.21	-6.34	3.34	2.49	2.09
5	-0.84	4.23	-1.65	-2.00	5.34	-0.99	-4.13	3.94	-1.06	-0.94	1.24	0.09	-1.78	-0.11	-4.45	-0.85	-2.06	-1.35	0.80	-1.63	1.54	-6.51	2.80	2.14	3.77
6	-0.74	5.35	-2.11	-1.13	4.20	-1.85	-3.20	3.76	-3.22	-1.23	0.34	0.57	-1.82	0.54	-4.43	-1.83	-4.03	-2.62	-0.20	-4.68	1.93	-8.46	3.34	2.52	5.81
7	-0.44	4.69	-2.30	-0.21	5.93	-2.63	-1.83	5.46	-4.50	-3.16	-1.73	0.18	0.11	0.04	-5.99	-0.45	-6.20	-3.89	0.71	-3.95	0.67	-7.26	2.97	3.39	6.66
8	0.92	5.80	-3.33	-0.65	5.99	-3.19	-1.83	5.63	-3.53	-1.30	-1.61	0.82	-2.45	-0.40	-6.06	-0.52	-6.60	-3.48	-0.04	-4.60	0.51	-5.85	3.23	2.40	5.08
9	0.38	5.54	-4.49	-0.80	7.05	-2.64	-0.44	5.35	-1.96	-0.33	-0.80	0.26	-3.37	-0.82	-6.05	-2.61	-8.45	-4.45	0.41	-4.71	1.89	-6.93	2.14	3.00	5.16
10	2.06	5.84	-3.90	-0.98	7.78	-2.49	-0.59	5.59	-2.22	-0.71	-0.46	1.80	-2.79	0.48	-5.97	-3.44	-7.77	-5.49	-0.70	-4.61	-0.52	-7.72	1.54	5.02	5.81
11	1.86	4.47	-2.17	-1.38	5.90	-0.49	0.02	5.78	-1.04	-0.60	0.49	1.96	-1.47	1.88	-5.92	-4.55	-8.15	-3.42	-2.24	-4.33	-1.17	-7.90	1.36	5.31	5.83
12	3.19	4.22	-3.06	-2.27	5.93	-2.64	0.33	6.72	-2.84	-0.20	1.89	2.63	-1.53	0.75	-5.27	-4.53	-7.57	-2.85	-2.17	-4.78	-1.13	-8.99	2.11	6.42	5.60
13	2.31	4.45	-3.87	-2.05	6.76	-3.25	-2.17	7.99	-2.56	-0.80	0.71	2.33	-0.16	-0.46	-5.10	-3.79	-7.58	-4.00	0.33	-3.67	-1.05	-8.71	2.47	5.87	6.71
14	3.78	4.33	-3.88	-1.58	6.22	-3.23	-1.46	5.57	-2.93	-0.33	-0.97	1.72	3.61	0.29	-4.21	-4.10	-6.68	-4.50	-2.19	-2.43	-1.64	-9.36	3.36	6.11	7.53
15	5.64	5.31	-3.98	-2.26	5.91	-3.30	-1.03	5.68	-3.06	-0.33	-1.16	2.19	4.20	1.01	-3.22	-4.31	-5.74	-4.44	-2.30	-1.36	-1.20	-11.27	2.59	6.69	5.91
16	4.08	4.34	-2.44	-3.30	6.04	-2.52	-0.47	5.28	-4.84	1.58	0.23	0.10	5.79	1.80	-3.13	-3.85	-5.53	-2.97	-2.13	-1.15	-0.56	-13.13	2.07	6.16	4.94
17	5.64	4.57	-3.53	-3.76	6.58	-2.58	-0.75	6.58	-4.78	3.63	-0.29	0.56	5.76	2.05	-2.27	-2.31	-4.95	-3.16	-3.06	-2.43	0.84	-12.57	3.56	7.36	4.70
18	5.99	5.82	-2.85	-4.15	7.12	-3.32	-1.21	7.93	-4.85	1.44	-0.63	0.35	7.47	0.87	-1.52	-2.09	-4.23	-2.55	-2.46	-2.89	1.90	-9.74	3.43	7.07	4.39
19	4.03	6.23	-4.10	-4.11	7.19	-4.10	-1.52	6.53	-5.21	-0.24	0.01	1.16	6.43	-1.97	-2.64	-1.66	-5.20	-3.25	-2.87	-1.65	1.64	-10.66	2.83	7.48	3.94

粘性标题

如果您在笔记本中显示一个大型矩阵或数据框，但是希望始终看到列和行标题，可以使用.set_sticky 方法，该方法操作表格样式的 CSS。

[65]:

bigdf = pd.DataFrame(np.random.randn(16, 100))
bigdf.style.set_sticky(axis="index")

[65]:

| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 |
| — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| 0 | -0.773866 | -0.240521 | -0.217165 | 1.173609 | 0.686390 | 0.008358 | 0.696232 | 0.173166 | 0.620498 | 0.504067 | 0.428066 | -0.051824 | 0.719915 | 0.057165 | 0.562808 | -0.369536 | 0.483399 | 0.620765 | -0.354342 | -1.469471 | -1.937266 | 0.038031 | -1.518162 | -0.417599 | 0.386717 | 0.716193 | 0.489961 | 0.733957 | 0.914415 | 0.679894 | 0.255448 | -0.508338 | 0.332030 | -0.111107 | -0.251983 | -1.456620 | 0.409630 | 1.062320 | -0.577115 | 0.718796 | -0.399260 | -1.311389 | 0.649122 | 0.091566 | 0.628872 | 0.297894 | -0.142290 | -0.542291 | -0.914290 | 1.144514 | 0.313584 | 1.182635 | 1.214235 | -0.416446 | -1.653940 | -2.550787 | 0.442473 | 0.052127 | -0.464469 | -0.523852 | 0.989726 | -1.325539 | -0.199687 | -1.226727 | 0.290018 | 1.164574 | 0.817841 | -0.309509 | 0.496599 | 0.943536 | -0.091850 | -2.802658 | 2.126219 | -0.521161 | 0.288098 | -0.454663 | -1.676143 | -0.357661 | -0.788960 | 0.185911 | -0.017106 | 2.454020 | 1.832706 | -0.911743 | -0.655873 | -0.000514 | -2.226997 | 0.677285 | -0.140249 | -0.408407 | -0.838665 | 0.482228 | 1.243458 | -0.477394 | -0.220343 | -2.463966 | 0.237325 | -0.307380 | 1.172478 | 0.819492 |
| 1 | 0.405906 | -0.978919 | 1.267526 | 0.145250 | -1.066786 | -2.114192 | -1.128346 | -1.082523 | 0.372216 | 0.004127 | -0.211984 | 0.937326 | -0.935890 | -1.704118 | 0.611789 | -1.030015 | 0.636123 | -1.506193 | 1.736609 | 1.392958 | 1.009424 | 0.353266 | 0.697339 | -0.297424 | 0.428702 | -0.145346 | -0.333553 | -0.974699 | 0.665314 | 0.971944 | 0.121950 | -1.439668 | 1.018808 | 1.442399 | -0.199585 | -1.165916 | 0.645656 | 1.436466 | -0.921215 | 1.293906 | -2.706443 | 1.460928 | -0.823197 | 0.292952 | -1.448992 | 0.026692 | -0.975883 | 0.392823 | 0.442166 | 0.745741 | 1.187982 | -0.218570 | 0.305288 | 0.054932 | -1.476953 | -0.114434 | 0.014103 | 0.825394 | -0.060654 | -0.413688 | 0.974836 | 1.339210 | 1.034838 | 0.040775 | 0.705001 | 0.017796 | 1.867681 | -0.390173 | 2.285277 | 2.311464 | -0.085070 | -0.648115 | 0.576300 | -0.790087 | -1.183798 | -1.334558 | -0.454118 | 0.319302 | 1.706488 | 0.830429 | 0.502476 | -0.079631 | 0.414635 | 0.332511 | 0.042935 | -0.160910 | 0.918553 | -0.292697 | -1.303834 | -0.199604 | 0.871023 | -1.370681 | -0.205701 | -0.492973 | 1.123083 | -0.081842 | -0.118527 | 0.245838 | -0.315742 | -0.511806 |
| 2 | 0.011470 | -0.036104 | 1.399603 | -0.418176 | -0.412229 | -1.234783 | -1.121500 | 1.196478 | -0.569522 | 0.422022 | -0.220484 | 0.804338 | 2.892667 | -0.511055 | -0.168722 | -1.477996 | -1.969917 | 0.471354 | 1.698548 | 0.137105 | -0.762052 | 0.199379 | -0.964346 | -0.256692 | 1.265275 | 0.848762 | -0.784161 | 1.863776 | -0.355569 | 0.854552 | 0.768061 | -2.075718 | -2.501069 | 1.109868 | 0.957545 | -0.683276 | 0.307764 | 0.733073 | 1.706250 | -1.118091 | 0.374961 | -1.414503 | -0.524183 | -1.662696 | 0.687921 | 0.521732 | 1.451396 | -0.833491 | -0.362796 | -1.174444 | -0.813893 | -0.893220 | 0.770743 | 1.156647 | -0.647444 | 0.125929 | 0.513600 | -0.537874 | 1.992052 | -1.946584 | -0.104759 | 0.484779 | -0.290936 | -0.441075 | 0.542993 | -1.050038 | 1.630482 | 0.239771 | -1.177310 | 0.464804 | -0.966995 | 0.646086 | 0.486899 | 1.022196 | -2.267827 | -1.229616 | 1.313805 | 1.073292 | 2.324940 | -0.542720 | -1.504292 | 0.777643 | -0.618553 | 0.011342 | 1.385062 | 1.363552 | -0.549834 | 0.688896 | 1.361288 | -0.381137 | 0.797812 | -1.128198 | 0.369208 | 0.540132 | 0.413853 | -0.200308 | -0.969126 | 0.981293 | -0.009783 | -0.320020 |
| 3 | -0.574816 | 1.419977 | 0.434813 | -1.101217 | -1.586275 | 1.979573 | 0.378298 | 0.782326 | 2.178987 | 0.657564 | 0.683774 | -0.091000 | -0.059552 | -0.738908 | -0.907653 | -0.701936 | 0.580039 | -0.618757 | 0.453684 | 1.665382 | -0.152321 | 0.880077 | 0.571073 | -0.604736 | 0.532359 | 0.515031 | -0.959844 | -0.887184 | 0.435781 | 0.862093 | -0.956321 | -0.625909 | 0.194472 | 0.442490 | 0.526503 | -0.215274 | 0.090711 | 0.932592 | 0.811999 | -2.497026 | 0.631545 | 0.321418 | -0.425549 | -1.078832 | 0.753444 | 0.199790 | -0.360526 | -0.013448 | -0.819476 | 0.814869 | 0.442118 | -0.972048 | -0.060603 | -2.349825 | 1.265445 | -0.573257 | 0.429124 | 1.049783 | 1.954773 | 0.071883 | -0.094209 | 0.265616 | 0.948318 | 0.331645 | 1.343401 | -0.167934 | -1.105252 | -0.167077 | -0.096576 | -0.838161 | -0.208564 | 0.394534 | 0.762533 | 1.235357 | -0.207282 | -0.202946 | -0.468025 | 0.256944 | 2.587584 | 1.186697 | -1.031903 | 1.428316 | 0.658899 | -0.046582 | -0.075422 | 1.329359 | -0.684267 | -1.524182 | 2.014061 | 3.770933 | 0.647353 | -1.021377 | -0.345493 | 0.582811 | 0.797812 | 1.326020 | 1.422857 | -3.077007 | 0.184083 | 1.478935 |
| 4 | -0.600142 | 1.929561 | -2.346771 | -0.669700 | -1.165258 | 0.814788 | 0.444449 | -0.576758 | 0.353091 | 0.408893 | 0.091391 | -2.294389 | 0.485506 | -0.081304 | -0.716272 | -1.648010 | 1.005361 | -1.489603 | 0.363098 | 0.758602 | -1.373847 | -0.972057 | 1.988537 | 0.319829 | 1.169060 | 0.146585 | 1.030388 | 1.165984 | 1.369563 | 0.730984 | -1.383696 | -0.515189 | -0.808927 | -1.174651 | -1.631502 | -1.123414 | -0.478155 | -1.583067 | 1.419074 | 1.668777 | 1.567517 | 0.222103 | -0.336040 | -1.352064 | 0.251032 | -0.401695 | 0.268413 | -0.012299 | -0.918953 | 2.921208 | -0.581588 | 0.672848 | 1.251136 | 1.382263 | 1.429897 | 1.290990 | -1.272673 | -0.308611 | -0.422988 | -0.675642 | 0.874441 | 1.305736 | -0.262585 | -1.099395 | -0.667101 | -0.646737 | -0.556338 | -0.196591 | 0.119306 | -0.266455 | -0.524267 | 2.650951 | 0.097318 | -0.974697 | 0.189964 | 1.141155 | -0.064434 | 1.104971 | -1.508908 | -0.031833 | 0.803919 | -0.659221 | 0.939145 | 0.214041 | -0.531805 | 0.956060 | 0.249328 | 0.637903 | -0.510158 | 1.850287 | -0.348407 | 2.001376 | -0.389643 | -0.024786 | -0.470973 | 0.869339 | 0.170667 | 0.598062 | 1.217262 | 1.274013 |
| 5 | -0.389981 | -0.752441 | -0.734871 | 3.517318 | -1.173559 | -0.004956 | 0.145419 | 2.151368 | -3.086037 | -1.569139 | 1.449784 | -0.868951 | -1.687716 | -0.994401 | 1.153266 | 1.803045 | -0.819059 | 0.847970 | 0.227102 | -0.500762 | 0.868210 | 1.823540 | 1.161007 | -0.307606 | -0.713416 | 0.363560 | -0.822162 | 2.427681 | -0.129537 | -0.078716 | 1.345644 | -1.286094 | 0.237242 | -0.136056 | 0.596664 | -1.412381 | 1.206341 | 0.299860 | 0.705238 | 0.142412 | -1.059382 | 0.833468 | 1.060015 | -0.527045 | -1.135732 | -1.140983 | -0.779540 | -0.640875 | -1.217196 | -1.675663 | 0.241263 | -0.273322 | -1.697936 | -0.594943 | 0.101154 | 1.391735 | -0.426953 | 1.008344 | -0.818577 | 1.924570 | -0.578900 | -0.457395 | -1.096705 | 0.418522 | -0.155623 | 0.169706 | -2.533706 | 0.018904 | 1.434160 | 0.744095 | 0.647626 | -0.770309 | 2.329141 | -0.141547 | -1.761594 | 0.702091 | -1.531450 | -0.788427 | -0.184622 | -1.942321 | 1.530113 | 0.503406 | 1.105845 | -0.935120 | -1.115483 | -2.249762 | 1.307135 | 0.788412 | -0.441091 | 0.073561 | 0.812101 | -0.916146 | 1.573714 | -0.309508 | 0.499987 | 0.187594 | 0.558913 | 0.903246 | 0.317901 | -0.809797 |
| 6 | 1.128248 | 1.516826 | -0.186735 | -0.668157 | 1.132259 | -0.246648 | -0.855167 | 0.732283 | 0.931802 | 1.318684 | -1.198418 | -1.149318 | 0.586321 | -1.171937 | -0.607731 | 2.753747 | 1.479287 | -1.136365 | -0.020485 | 0.320444 | -1.955755 | 0.660402 | -1.545371 | 0.200519 | -0.017263 | 1.634686 | 0.599246 | 0.462989 | 0.023721 | 0.225546 | 0.170972 | -0.027496 | -0.061233 | -0.566411 | -0.669567 | 0.601618 | 0.503656 | -0.678253 | -2.907108 | -1.717123 | 0.397631 | 1.300108 | 0.215821 | -0.593075 | -0.225944 | -0.946057 | 1.000308 | 0.393160 | 1.342074 | -0.370687 | -0.166413 | -0.419814 | -0.255931 | 1.789478 | 0.282378 | 0.742260 | -0.050498 | 1.415309 | 0.838166 | -1.400292 | -0.937976 | -1.499148 | 0.801859 | 0.224824 | 0.283572 | 0.643703 | -1.198465 | 0.527206 | 0.215202 | 0.437048 | 1.312868 | 0.741243 | 0.077988 | 0.006123 | 0.190370 | 0.018007 | -1.026036 | -2.378430 | -1.069949 | 0.843822 | 1.289216 | -1.423369 | -0.462887 | 0.197330 | -0.935076 | 0.441271 | 0.414643 | -0.377887 | -0.530515 | 0.621592 | 1.009572 | 0.569718 | 0.175291 | -0.656279 | -0.112273 | -0.392137 | -1.043558 | -0.467318 | -0.384329 | -2.009207 |
| 7 | 0.658598 | 0.101830 | -0.682781 | 0.229349 | -0.305657 | 0.404877 | 0.252244 | -0.837784 | -0.039624 | 0.329457 | 0.751694 | 1.469070 | -0.157199 | 1.032628 | -0.584639 | -0.925544 | 0.342474 | -0.969363 | 0.133480 | -0.385974 | -0.600278 | 0.281939 | 0.868579 | 1.129803 | -0.041898 | 0.961193 | 0.131521 | -0.792889 | -1.285737 | 0.073934 | -1.333315 | -1.044125 | 1.277338 | 1.492257 | 0.411379 | 1.771805 | -1.111128 | 1.123233 | -1.019449 | 1.738357 | -0.690764 | -0.120710 | -0.421359 | -0.727294 | -0.857759 | -0.069436 | -0.328334 | -0.558180 | 1.063474 | -0.519133 | -0.496902 | 1.089589 | -1.615801 | 0.080174 | -0.229938 | -0.498420 | -0.624615 | 0.059481 | -0.093158 | -1.784549 | -0.503789 | -0.140528 | 0.002653 | -0.484930 | 0.055914 | -0.680948 | -0.994271 | 1.277052 | 0.037651 | 2.155421 | -0.437589 | 0.696404 | 0.417752 | -0.544785 | 1.190690 | 0.978262 | 0.752102 | 0.504472 | 0.139853 | -0.505089 | -0.264975 | -1.603194 | 0.731847 | 0.010903 | -1.165346 | -0.125195 | -1.032685 | -0.465520 | 1.514808 | 0.304762 | 0.793414 | 0.314635 | -1.638279 | 0.111737 | -0.777037 | 0.251783 | 1.126303 | -0.808798 | 0.422064 | -0.349264 |
| 8 | -0.356362 | -0.089227 | 0.609373 | 0.542382 | -0.768681 | -0.048074 | 2.015458 | -1.552351 | 0.251552 | 1.459635 | 0.949707 | 0.339465 | -0.001372 | 1.798589 | 1.559163 | 0.231783 | 0.423141 | -0.310530 | 0.353795 | 2.173336 | -0.196247 | -0.375636 | -0.858221 | 0.258410 | 0.656430 | 0.960819 | 1.137893 | 1.553405 | 0.038981 | -0.632038 | -0.132009 | -1.834997 | -0.242576 | -0.297879 | -0.441559 | -0.769691 | 0.224077 | -0.153009 | 0.519526 | -0.680188 | 0.535851 | 0.671496 | -0.183064 | 0.301234 | 1.288256 | -2.478240 | -0.360403 | 0.424067 | -0.834659 | -0.128464 | -0.489013 | -0.014888 | -1.461230 | -1.435223 | -1.319802 | 1.083675 | 0.979140 | -0.375291 | 1.110189 | -1.011351 | 0.587886 | -0.822775 | -1.183865 | 1.455173 | 1.134328 | 0.239403 | -0.837991 | -1.130932 | 0.783168 | 1.845520 | 1.437072 | -1.198443 | 1.379098 | 2.129113 | 0.260096 | -0.011975 | 0.043302 | 0.722941 | 1.028152 | -0.235806 | 1.145245 | -1.359598 | 0.232189 | 0.503712 | -0.614264 | -0.530606 | -2.435803 | -0.255238 | -0.064423 | 0.784643 | 0.256346 | 0.128023 | 1.414103 | -1.118659 | 0.877353 | 0.500561 | 0.463651 | -2.034512 | -0.981683 | -0.691944 |
| 9 | -1.113376 | -1.169402 | 0.680539 | -1.534212 | 1.653817 | -1.295181 | -0.566826 | 0.477014 | 1.413371 | 0.517105 | 1.401153 | -0.872685 | 0.830957 | 0.181507 | -0.145616 | 0.694592 | -0.751208 | 0.324444 | 0.681973 | -0.054972 | 0.917776 | -1.024810 | -0.206446 | -0.600113 | 0.852805 | 1.455109 | -0.079769 | 0.076076 | 0.207699 | -1.850458 | -0.124124 | -0.610871 | -0.883362 | 0.219049 | -0.685094 | -0.645330 | -0.242805 | -0.775602 | 0.233070 | 2.422642 | -1.423040 | -0.582421 | 0.968304 | -0.701025 | -0.167850 | 0.277264 | 1.301231 | 0.301205 | -3.081249 | -0.562868 | 0.192944 | -0.664592 | 0.565686 | 0.190913 | -0.841858 | -1.856545 | -1.022777 | 1.295968 | 0.451921 | 0.659955 | 0.065818 | -0.319586 | 0.253495 | -1.144646 | -0.483404 | 0.555902 | 0.807069 | 0.714196 | 0.661196 | 0.053667 | 0.346833 | -1.288977 | -0.386734 | -1.262127 | 0.477495 | -0.494034 | -0.911414 | 1.152963 | -0.342365 | -0.160187 | 0.470054 | -0.853063 | -1.387949 | -0.257257 | -1.030690 | -0.110210 | 0.328911 | -0.555923 | 0.987713 | -0.501957 | 2.069887 | -0.067503 | 0.316029 | -1.506232 | 2.201621 | 0.492097 | -0.085193 | -0.977822 | 1.039147 | -0.653932 |
| 10 | -0.405638 | -1.402027 | -1.166242 | 1.306184 | 0.856283 | -1.236170 | -0.646721 | -1.474064 | 0.082960 | 0.090310 | -0.169977 | 0.406345 | 0.915427 | -0.974503 | 0.271637 | 1.539184 | -0.098866 | -0.525149 | 1.063933 | 0.085827 | -0.129622 | 0.947959 | -0.072496 | -0.237592 | 0.012549 | 1.065761 | 0.996596 | -0.172481 | 2.583139 | -0.028578 | -0.254856 | 1.328794 | -1.592951 | 2.434350 | -0.341500 | -0.307719 | -1.333273 | -1.100845 | 0.209097 | 1.734777 | 0.639632 | 0.424779 | -0.129327 | 0.905029 | -0.482909 | 1.731628 | -2.783425 | -0.333677 | -0.110895 | 1.212636 | -0.208412 | 0.427117 | 1.348563 | 0.043859 | 1.772519 | -1.416106 | 0.401155 | 0.807157 | 0.303427 | -1.246288 | 0.178774 | -0.066126 | -1.862288 | 1.241295 | 0.377021 | -0.822320 | -0.749014 | 1.463652 | 1.602268 | -1.043877 | 1.185290 | -0.565783 | -1.076879 | 1.360241 | -0.121991 | 0.991043 | 1.007952 | 0.450185 | -0.744376 | 1.388876 | -0.316847 | -0.841655 | -1.056842 | -0.500226 | 0.096959 | 1.176896 | -2.939652 | 1.792213 | 0.316340 | 0.303218 | 1.024967 | -0.590871 | -0.453326 | -0.795981 | -0.393301 | -0.374372 | -1.270199 | 1.618372 | 1.197727 | -0.914863 |
| 11 | -0.625210 | 0.288911 | 0.288374 | -1.372667 | -0.591395 | -0.478942 | 1.335664 | -0.459855 | -1.615975 | -1.189676 | 0.374767 | -2.488733 | 0.586656 | -1.422008 | 0.496030 | 1.911128 | -0.560660 | -0.499614 | -0.372171 | -1.833069 | 0.237124 | -0.944446 | 0.912140 | 0.359790 | -1.359235 | 0.166966 | -0.047107 | -0.279789 | -0.594454 | -0.739013 | -1.527645 | 0.401668 | 1.791252 | -2.774848 | 0.523873 | 2.207585 | 0.488999 | -0.339283 | 0.131711 | 0.018409 | 1.186551 | -0.424318 | 1.554994 | -0.205917 | -0.934975 | 0.654102 | -1.227761 | -0.461025 | -0.421201 | -0.058615 | -0.584563 | 0.336913 | -0.477102 | -1.381463 | 0.757745 | -0.268968 | 0.034870 | 1.231686 | 0.236600 | 1.234720 | -0.040247 | 0.029582 | 1.034905 | 0.380204 | -0.012108 | -0.859511 | -0.990340 | -1.205172 | -1.030178 | 0.426676 | 0.497796 | -0.876808 | 0.957963 | 0.173016 | 0.131612 | -1.003556 | -1.069908 | -1.799207 | 1.429598 | -0.116015 | -1.454980 | 0.261917 | 0.444412 | 0.273290 | 0.844115 | 0.218745 | -1.033350 | -1.188295 | 0.058373 | 0.800523 | -1.627068 | 0.861651 | 0.871018 | -0.003733 | -0.243354 | 0.947296 | 0.509406 | 0.044546 | 0.266896 | 1.337165 |
| 12 | 0.699142 | -1.928033 | 0.105363 | 1.042322 | 0.715206 | -0.763783 | 0.098798 | -1.157898 | 0.134105 | 0.042041 | 0.674826 | 0.165649 | -1.622970 | -3.131274 | 0.597649 | -1.880331 | 0.663980 | -0.256033 | -1.524058 | 0.492799 | 0.221163 | 0.429622 | -0.659584 | 1.264506 | -0.032131 | -2.114907 | -0.264043 | 0.457835 | -0.676837 | -0.629003 | 0.489145 | -0.551686 | 0.942622 | -0.512043 | -0.455893 | 0.021244 | -0.178035 | -2.498073 | -0.171292 | 0.323510 | -0.545163 | -0.668909 | -0.150031 | 0.521620 | -0.428980 | 0.676463 | 0.369081 | -0.724832 | 0.793542 | 1.237422 | 0.401275 | 2.141523 | 0.249012 | 0.486755 | -0.163274 | 0.592222 | -0.292600 | -0.547168 | 0.619104 | -0.013605 | 0.776734 | 0.131424 | 1.189480 | -0.666317 | -0.939036 | 1.105515 | 0.621452 | 1.586605 | -0.760970 | 1.649646 | 0.283199 | 1.275812 | -0.452012 | 0.301361 | -0.976951 | -0.268106 | -0.079255 | -1.258332 | 2.216658 | -1.175988 | -0.863497 | -1.653022 | -0.561514 | 0.450753 | 0.417200 | 0.094676 | -2.231054 | 1.316862 | -0.477441 | 0.646654 | -0.200252 | 1.074354 | -0.058176 | 0.120990 | 0.222522 | -0.179507 | 0.421655 | -0.914341 | -0.234178 | 0.741524 |
| 13 | 0.932714 | 1.423761 | -1.280835 | 0.347882 | -0.863171 | -0.852580 | 1.044933 | 2.094536 | 0.806206 | 0.416201 | -1.109503 | 0.145302 | -0.996871 | 0.325456 | -0.605081 | 1.175326 | 1.645054 | 0.293432 | -2.766822 | 1.032849 | 0.079115 | -1.414132 | 1.463376 | 2.335486 | 0.411951 | -0.048543 | 0.159284 | -0.651554 | -1.093128 | 1.568390 | -0.077807 | -2.390779 | -0.842346 | -0.229675 | -0.999072 | -1.367219 | -0.792042 | -1.878575 | 1.451452 | 1.266250 | -0.734315 | 0.266152 | 0.735523 | -0.430860 | 0.229864 | 0.850083 | -2.241241 | 1.063850 | 0.289409 | -0.354360 | 0.113063 | -0.173006 | 1.386998 | 1.886236 | 0.587119 | -0.961133 | 0.399295 | 1.461560 | 0.310823 | 0.280220 | -0.879103 | -1.326348 | 0.003337 | -1.085908 | -0.436723 | 2.111926 | 0.106068 | 0.615597 | 2.152996 | -0.196155 | 0.025747 | -0.039061 | 0.656823 | -0.347105 | 2.513979 | 1.758070 | 1.288473 | -0.739185 | -0.691592 | -0.098728 | -0.276386 | 0.489981 | 0.516278 | -0.838258 | 0.596673 | -0.331053 | 0.521174 | -0.145023 | 0.836693 | -1.092166 | 0.361733 | -1.169981 | 0.046731 | 0.655377 | -0.756852 | 1.285805 | -0.095019 | 0.360253 | 1.370621 | 0.083010 |
| 14 | 0.888893 | 2.288725 | -1.032332 | 0.212273 | -1.091826 | 1.692498 | 1.025367 | 0.550854 | 0.679430 | -1.335712 | -0.798341 | 2.265351 | -1.006938 | 2.059761 | 0.420266 | -1.189657 | 0.506674 | 0.260847 | -0.533145 | 0.727267 | 1.412276 | 1.482106 | -0.996258 | 0.588641 | -0.412642 | -0.920733 | -0.874691 | 0.839002 | 0.501668 | -0.342493 | -0.533806 | -2.146352 | -0.597339 | 0.115726 | 0.850683 | -0.752239 | 0.377263 | -0.561982 | 0.262783 | -0.356676 | -0.367462 | 0.753611 | -1.267414 | -1.330698 | -0.536453 | 0.840938 | -0.763108 | -0.268100 | -0.677424 | 1.606831 | 0.151732 | -2.085701 | 1.219296 | 0.400863 | 0.591165 | -1.485213 | 1.501979 | 1.196569 | -0.214154 | 0.339554 | -0.034446 | 1.176452 | 0.546340 | -1.255630 | -1.309210 | -0.445437 | 0.189437 | -0.737463 | 0.843767 | -0.605632 | -0.060777 | 0.409310 | 1.285569 | -0.622638 | 1.018193 | 0.880680 | 0.046805 | -1.818058 | -0.809829 | 0.875224 | 0.409569 | -0.116621 | -1.238919 | 3.305724 | -0.024121 | -1.756500 | 1.328958 | 0.507593 | -0.866554 | -2.240848 | -0.661376 | -0.671824 | 0.215720 | -0.296326 | 0.481402 | 0.829645 | -0.721025 | 1.263914 | 0.549047 | -1.234945 |
| 15 | -1.978838 | 0.721823 | -0.559067 | -1.235243 | 0.420716 | -0.598845 | 0.359576 | -0.619366 | -1.757772 | -1.156251 | 0.705212 | 0.875071 | -1.020376 | 0.394760 | -0.147970 | 0.230249 | 1.355203 | 1.794488 | 2.678058 | -0.153565 | -0.460959 | -0.098108 | -1.407930 | -2.487702 | 1.823014 | 0.099873 | -0.517603 | -0.509311 | -1.833175 | -0.900906 | 0.459493 | -0.655440 | 1.466122 | -1.531389 | -0.422106 | 0.421422 | 0.578615 | 0.259795 | 0.018941 | -0.168726 | 1.611107 | -1.586550 | -1.384941 | 0.858377 | 1.033242 | 1.701343 | 1.748344 | -0.371182 | -0.843575 | 2.089641 | -0.345430 | -1.740556 | 0.141915 | -2.197138 | 0.689569 | -0.150025 | 0.287456 | 0.654016 | -1.521919 | -0.918008 | -0.587528 | 0.230636 | 0.262637 | 0.615674 | 0.600044 | -0.494699 | -0.743089 | 0.220026 | -0.242207 | 0.528216 | -0.328174 | -1.536517 | -1.476640 | -1.162114 | -1.260222 | 1.106252 | -1.467408 | -0.349341 | -1.841217 | 0.031296 | -0.076475 | -0.353383 | 0.807545 | 0.779064 | -2.398417 | -0.267828 | 1.549734 | 0.814397 | 0.284770 | -0.659369 | 0.761040 | -0.722067 | 0.810332 | 1.501295 | 1.440865 | -1.367459 | -0.700301 | -1.540662 | 0.159837 | -0.625415 |

也可以粘贴多级索引，甚至只有特定级别。

[66]:

bigdf.index = pd.MultiIndex.from_product([["A","B"],[0,1],[0,1,2,3]])
bigdf.style.set_sticky(axis="index", pixel_size=18, levels=[1,2])

[66]:

			0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99
A	0	0	-0.773866	-0.240521	-0.217165	1.173609	0.686390	0.008358	0.696232	0.173166	0.620498	0.504067	0.428066	-0.051824	0.719915	0.057165	0.562808	-0.369536	0.483399	0.620765	-0.354342	-1.469471	-1.937266	0.038031	-1.518162	-0.417599	0.386717	0.716193	0.489961	0.733957	0.914415	0.679894	0.255448	-0.508338	0.332030	-0.111107	-0.251983	-1.456620	0.409630	1.062320	-0.577115	0.718796	-0.399260	-1.311389	0.649122	0.091566	0.628872	0.297894	-0.142290	-0.542291	-0.914290	1.144514	0.313584	1.182635	1.214235	-0.416446	-1.653940	-2.550787	0.442473	0.052127	-0.464469	-0.523852	0.989726	-1.325539	-0.199687	-1.226727	0.290018	1.164574	0.817841	-0.309509	0.496599	0.943536	-0.091850	-2.802658	2.126219	-0.521161	0.288098	-0.454663	-1.676143	-0.357661	-0.788960	0.185911	-0.017106	2.454020	1.832706	-0.911743	-0.655873	-0.000514	-2.226997	0.677285	-0.140249	-0.408407	-0.838665	0.482228	1.243458	-0.477394	-0.220343	-2.463966	0.237325	-0.307380	1.172478	0.819492
1	0.405906	-0.978919	1.267526	0.145250	-1.066786	-2.114192	-1.128346	-1.082523	0.372216	0.004127	-0.211984	0.937326	-0.935890	-1.704118	0.611789	-1.030015	0.636123	-1.506193	1.736609	1.392958	1.009424	0.353266	0.697339	-0.297424	0.428702	-0.145346	-0.333553	-0.974699	0.665314	0.971944	0.121950	-1.439668	1.018808	1.442399	-0.199585	-1.165916	0.645656	1.436466	-0.921215	1.293906	-2.706443	1.460928	-0.823197	0.292952	-1.448992	0.026692	-0.975883	0.392823	0.442166	0.745741	1.187982	-0.218570	0.305288	0.054932	-1.476953	-0.114434	0.014103	0.825394	-0.060654	-0.413688	0.974836	1.339210	1.034838	0.040775	0.705001	0.017796	1.867681	-0.390173	2.285277	2.311464	-0.085070	-0.648115	0.576300	-0.790087	-1.183798	-1.334558	-0.454118	0.319302	1.706488	0.830429	0.502476	-0.079631	0.414635	0.332511	0.042935	-0.160910	0.918553	-0.292697	-1.303834	-0.199604	0.871023	-1.370681	-0.205701	-0.492973	1.123083	-0.081842	-0.118527	0.245838	-0.315742	-0.511806
2	0.011470	-0.036104	1.399603	-0.418176	-0.412229	-1.234783	-1.121500	1.196478	-0.569522	0.422022	-0.220484	0.804338	2.892667	-0.511055	-0.168722	-1.477996	-1.969917	0.471354	1.698548	0.137105	-0.762052	0.199379	-0.964346	-0.256692	1.265275	0.848762	-0.784161	1.863776	-0.355569	0.854552	0.768061	-2.075718	-2.501069	1.109868	0.957545	-0.683276	0.307764	0.733073	1.706250	-1.118091	0.374961	-1.414503	-0.524183	-1.662696	0.687921	0.521732	1.451396	-0.833491	-0.362796	-1.174444	-0.813893	-0.893220	0.770743	1.156647	-0.647444	0.125929	0.513600	-0.537874	1.992052	-1.946584	-0.104759	0.484779	-0.290936	-0.441075	0.542993	-1.050038	1.630482	0.239771	-1.177310	0.464804	-0.966995	0.646086	0.486899	1.022196	-2.267827	-1.229616	1.313805	1.073292	2.324940	-0.542720	-1.504292	0.777643	-0.618553	0.011342	1.385062	1.363552	-0.549834	0.688896	1.361288	-0.381137	0.797812	-1.128198	0.369208	0.540132	0.413853	-0.200308	-0.969126	0.981293	-0.009783	-0.320020
3	-0.574816	1.419977	0.434813	-1.101217	-1.586275	1.979573	0.378298	0.782326	2.178987	0.657564	0.683774	-0.091000	-0.059552	-0.738908	-0.907653	-0.701936	0.580039	-0.618757	0.453684	1.665382	-0.152321	0.880077	0.571073	-0.604736	0.532359	0.515031	-0.959844	-0.887184	0.435781	0.862093	-0.956321	-0.625909	0.194472	0.442490	0.526503	-0.215274	0.090711	0.932592	0.811999	-2.497026	0.631545	0.321418	-0.425549	-1.078832	0.753444	0.199790	-0.360526	-0.013448	-0.819476	0.814869	0.442118	-0.972048	-0.060603	-2.349825	1.265445	-0.573257	0.429124	1.049783	1.954773	0.071883	-0.094209	0.265616	0.948318	0.331645	1.343401	-0.167934	-1.105252	-0.167077	-0.096576	-0.838161	-0.208564	0.394534	0.762533	1.235357	-0.207282	-0.202946	-0.468025	0.256944	2.587584	1.186697	-1.031903	1.428316	0.658899	-0.046582	-0.075422	1.329359	-0.684267	-1.524182	2.014061	3.770933	0.647353	-1.021377	-0.345493	0.582811	0.797812	1.326020	1.422857	-3.077007	0.184083	1.478935
1	0	-0.600142	1.929561	-2.346771	-0.669700	-1.165258	0.814788	0.444449	-0.576758	0.353091	0.408893	0.091391	-2.294389	0.485506	-0.081304	-0.716272	-1.648010	1.005361	-1.489603	0.363098	0.758602	-1.373847	-0.972057	1.988537	0.319829	1.169060	0.146585	1.030388	1.165984	1.369563	0.730984	-1.383696	-0.515189	-0.808927	-1.174651	-1.631502	-1.123414	-0.478155	-1.583067	1.419074	1.668777	1.567517	0.222103	-0.336040	-1.352064	0.251032	-0.401695	0.268413	-0.012299	-0.918953	2.921208	-0.581588	0.672848	1.251136	1.382263	1.429897	1.290990	-1.272673	-0.308611	-0.422988	-0.675642	0.874441	1.305736	-0.262585	-1.099395	-0.667101	-0.646737	-0.556338	-0.196591	0.119306	-0.266455	-0.524267	2.650951	0.097318	-0.974697	0.189964	1.141155	-0.064434	1.104971	-1.508908	-0.031833	0.803919	-0.659221	0.939145	0.214041	-0.531805	0.956060	0.249328	0.637903	-0.510158	1.850287	-0.348407	2.001376	-0.389643	-0.024786	-0.470973	0.869339	0.170667	0.598062	1.217262	1.274013
1	-0.389981	-0.752441	-0.734871	3.517318	-1.173559	-0.004956	0.145419	2.151368	-3.086037	-1.569139	1.449784	-0.868951	-1.687716	-0.994401	1.153266	1.803045	-0.819059	0.847970	0.227102	-0.500762	0.868210	1.823540	1.161007	-0.307606	-0.713416	0.363560	-0.822162	2.427681	-0.129537	-0.078716	1.345644	-1.286094	0.237242	-0.136056	0.596664	-1.412381	1.206341	0.299860	0.705238	0.142412	-1.059382	0.833468	1.060015	-0.527045	-1.135732	-1.140983	-0.779540	-0.640875	-1.217196	-1.675663	0.241263	-0.273322	-1.697936	-0.594943	0.101154	1.391735	-0.426953	1.008344	-0.818577	1.924570	-0.578900	-0.457395	-1.096705	0.418522	-0.155623	0.169706	-2.533706	0.018904	1.434160	0.744095	0.647626	-0.770309	2.329141	-0.141547	-1.761594	0.702091	-1.531450	-0.788427	-0.184622	-1.942321	1.530113	0.503406	1.105845	-0.935120	-1.115483	-2.249762	1.307135	0.788412	-0.441091	0.073561	0.812101	-0.916146	1.573714	-0.309508	0.499987	0.187594	0.558913	0.903246	0.317901	-0.809797
2	1.128248	1.516826	-0.186735	-0.668157	1.132259	-0.246648	-0.855167	0.732283	0.931802	1.318684	-1.198418	-1.149318	0.586321	-1.171937	-0.607731	2.753747	1.479287	-1.136365	-0.020485	0.320444	-1.955755	0.660402	-1.545371	0.200519	-0.017263	1.634686	0.599246	0.462989	0.023721	0.225546	0.170972	-0.027496	-0.061233	-0.566411	-0.669567	0.601618	0.503656	-0.678253	-2.907108	-1.717123	0.397631	1.300108	0.215821	-0.593075	-0.225944	-0.946057	1.000308	0.393160	1.342074	-0.370687	-0.166413	-0.419814	-0.255931	1.789478	0.282378	0.742260	-0.050498	1.415309	0.838166	-1.400292	-0.937976	-1.499148	0.801859	0.224824	0.283572	0.643703	-1.198465	0.527206	0.215202	0.437048	1.312868	0.741243	0.077988	0.006123	0.190370	0.018007	-1.026036	-2.378430	-1.069949	0.843822	1.289216	-1.423369	-0.462887	0.197330	-0.935076	0.441271	0.414643	-0.377887	-0.530515	0.621592	1.009572	0.569718	0.175291	-0.656279	-0.112273	-0.392137	-1.043558	-0.467318	-0.384329	-2.009207
3	0.658598	0.101830	-0.682781	0.229349	-0.305657	0.404877	0.252244	-0.837784	-0.039624	0.329457	0.751694	1.469070	-0.157199	1.032628	-0.584639	-0.925544	0.342474	-0.969363	0.133480	-0.385974	-0.600278	0.281939	0.868579	1.129803	-0.041898	0.961193	0.131521	-0.792889	-1.285737	0.073934	-1.333315	-1.044125	1.277338	1.492257	0.411379	1.771805	-1.111128	1.123233	-1.019449	1.738357	-0.690764	-0.120710	-0.421359	-0.727294	-0.857759	-0.069436	-0.328334	-0.558180	1.063474	-0.519133	-0.496902	1.089589	-1.615801	0.080174	-0.229938	-0.498420	-0.624615	0.059481	-0.093158	-1.784549	-0.503789	-0.140528	0.002653	-0.484930	0.055914	-0.680948	-0.994271	1.277052	0.037651	2.155421	-0.437589	0.696404	0.417752	-0.544785	1.190690	0.978262	0.752102	0.504472	0.139853	-0.505089	-0.264975	-1.603194	0.731847	0.010903	-1.165346	-0.125195	-1.032685	-0.465520	1.514808	0.304762	0.793414	0.314635	-1.638279	0.111737	-0.777037	0.251783	1.126303	-0.808798	0.422064	-0.349264
B	0	0	-0.356362	-0.089227	0.609373	0.542382	-0.768681	-0.048074	2.015458	-1.552351	0.251552	1.459635	0.949707	0.339465	-0.001372	1.798589	1.559163	0.231783	0.423141	-0.310530	0.353795	2.173336	-0.196247	-0.375636	-0.858221	0.258410	0.656430	0.960819	1.137893	1.553405	0.038981	-0.632038	-0.132009	-1.834997	-0.242576	-0.297879	-0.441559	-0.769691	0.224077	-0.153009	0.519526	-0.680188	0.535851	0.671496	-0.183064	0.301234	1.288256	-2.478240	-0.360403	0.424067	-0.834659	-0.128464	-0.489013	-0.014888	-1.461230	-1.435223	-1.319802	1.083675	0.979140	-0.375291	1.110189	-1.011351	0.587886	-0.822775	-1.183865	1.455173	1.134328	0.239403	-0.837991	-1.130932	0.783168	1.845520	1.437072	-1.198443	1.379098	2.129113	0.260096	-0.011975	0.043302	0.722941	1.028152	-0.235806	1.145245	-1.359598	0.232189	0.503712	-0.614264	-0.530606	-2.435803	-0.255238	-0.064423	0.784643	0.256346	0.128023	1.414103	-1.118659	0.877353	0.500561	0.463651	-2.034512	-0.981683	-0.691944
1	-1.113376	-1.169402	0.680539	-1.534212	1.653817	-1.295181	-0.566826	0.477014	1.413371	0.517105	1.401153	-0.872685	0.830957	0.181507	-0.145616	0.694592	-0.751208	0.324444	0.681973	-0.054972	0.917776	-1.024810	-0.206446	-0.600113	0.852805	1.455109	-0.079769	0.076076	0.207699	-1.850458	-0.124124	-0.610871	-0.883362	0.219049	-0.685094	-0.645330	-0.242805	-0.775602	0.233070	2.422642	-1.423040	-0.582421	0.968304	-0.701025	-0.167850	0.277264	1.301231	0.301205	-3.081249	-0.562868	0.192944	-0.664592	0.565686	0.190913	-0.841858	-1.856545	-1.022777	1.295968	0.451921	0.659955	0.065818	-0.319586	0.253495	-1.144646	-0.483404	0.555902	0.807069	0.714196	0.661196	0.053667	0.346833	-1.288977	-0.386734	-1.262127	0.477495	-0.494034	-0.911414	1.152963	-0.342365	-0.160187	0.470054	-0.853063	-1.387949	-0.257257	-1.030690	-0.110210	0.328911	-0.555923	0.987713	-0.501957	2.069887	-0.067503	0.316029	-1.506232	2.201621	0.492097	-0.085193	-0.977822	1.039147	-0.653932
2	-0.405638	-1.402027	-1.166242	1.306184	0.856283	-1.236170	-0.646721	-1.474064	0.082960	0.090310	-0.169977	0.406345	0.915427	-0.974503	0.271637	1.539184	-0.098866	-0.525149	1.063933	0.085827	-0.129622	0.947959	-0.072496	-0.237592	0.012549	1.065761	0.996596	-0.172481	2.583139	-0.028578	-0.254856	1.328794	-1.592951	2.434350	-0.341500	-0.307719	-1.333273	-1.100845	0.209097	1.734777	0.639632	0.424779	-0.129327	0.905029	-0.482909	1.731628	-2.783425	-0.333677	-0.110895	1.212636	-0.208412	0.427117	1.348563	0.043859	1.772519	-1.416106	0.401155	0.807157	0.303427	-1.246288	0.178774	-0.066126	-1.862288	1.241295	0.377021	-0.822320	-0.749014	1.463652	1.602268	-1.043877	1.185290	-0.565783	-1.076879	1.360241	-0.121991	0.991043	1.007952	0.450185	-0.744376	1.388876	-0.316847	-0.841655	-1.056842	-0.500226	0.096959	1.176896	-2.939652	1.792213	0.316340	0.303218	1.024967	-0.590871	-0.453326	-0.795981	-0.393301	-0.374372	-1.270199	1.618372	1.197727	-0.914863
3	-0.625210	0.288911	0.288374	-1.372667	-0.591395	-0.478942	1.335664	-0.459855	-1.615975	-1.189676	0.374767	-2.488733	0.586656	-1.422008	0.496030	1.911128	-0.560660	-0.499614	-0.372171	-1.833069	0.237124	-0.944446	0.912140	0.359790	-1.359235	0.166966	-0.047107	-0.279789	-0.594454	-0.739013	-1.527645	0.401668	1.791252	-2.774848	0.523873	2.207585	0.488999	-0.339283	0.131711	0.018409	1.186551	-0.424318	1.554994	-0.205917	-0.934975	0.654102	-1.227761	-0.461025	-0.421201	-0.058615	-0.584563	0.336913	-0.477102	-1.381463	0.757745	-0.268968	0.034870	1.231686	0.236600	1.234720	-0.040247	0.029582	1.034905	0.380204	-0.012108	-0.859511	-0.990340	-1.205172	-1.030178	0.426676	0.497796	-0.876808	0.957963	0.173016	0.131612	-1.003556	-1.069908	-1.799207	1.429598	-0.116015	-1.454980	0.261917	0.444412	0.273290	0.844115	0.218745	-1.033350	-1.188295	0.058373	0.800523	-1.627068	0.861651	0.871018	-0.003733	-0.243354	0.947296	0.509406	0.044546	0.266896	1.337165
1	0	0.699142	-1.928033	0.105363	1.042322	0.715206	-0.763783	0.098798	-1.157898	0.134105	0.042041	0.674826	0.165649	-1.622970	-3.131274	0.597649	-1.880331	0.663980	-0.256033	-1.524058	0.492799	0.221163	0.429622	-0.659584	1.264506	-0.032131	-2.114907	-0.264043	0.457835	-0.676837	-0.629003	0.489145	-0.551686	0.942622	-0.512043	-0.455893	0.021244	-0.178035	-2.498073	-0.171292	0.323510	-0.545163	-0.668909	-0.150031	0.521620	-0.428980	0.676463	0.369081	-0.724832	0.793542	1.237422	0.401275	2.141523	0.249012	0.486755	-0.163274	0.592222	-0.292600	-0.547168	0.619104	-0.013605	0.776734	0.131424	1.189480	-0.666317	-0.939036	1.105515	0.621452	1.586605	-0.760970	1.649646	0.283199	1.275812	-0.452012	0.301361	-0.976951	-0.268106	-0.079255	-1.258332	2.216658	-1.175988	-0.863497	-1.653022	-0.561514	0.450753	0.417200	0.094676	-2.231054	1.316862	-0.477441	0.646654	-0.200252	1.074354	-0.058176	0.120990	0.222522	-0.179507	0.421655	-0.914341	-0.234178	0.741524
1	0.932714	1.423761	-1.280835	0.347882	-0.863171	-0.852580	1.044933	2.094536	0.806206	0.416201	-1.109503	0.145302	-0.996871	0.325456	-0.605081	1.175326	1.645054	0.293432	-2.766822	1.032849	0.079115	-1.414132	1.463376	2.335486	0.411951	-0.048543	0.159284	-0.651554	-1.093128	1.568390	-0.077807	-2.390779	-0.842346	-0.229675	-0.999072	-1.367219	-0.792042	-1.878575	1.451452	1.266250	-0.734315	0.266152	0.735523	-0.430860	0.229864	0.850083	-2.241241	1.063850	0.289409	-0.354360	0.113063	-0.173006	1.386998	1.886236	0.587119	-0.961133	0.399295	1.461560	0.310823	0.280220	-0.879103	-1.326348	0.003337	-1.085908	-0.436723	2.111926	0.106068	0.615597	2.152996	-0.196155	0.025747	-0.039061	0.656823	-0.347105	2.513979	1.758070	1.288473	-0.739185	-0.691592	-0.098728	-0.276386	0.489981	0.516278	-0.838258	0.596673	-0.331053	0.521174	-0.145023	0.836693	-1.092166	0.361733	-1.169981	0.046731	0.655377	-0.756852	1.285805	-0.095019	0.360253	1.370621	0.083010
2	0.888893	2.288725	-1.032332	0.212273	-1.091826	1.692498	1.025367	0.550854	0.679430	-1.335712	-0.798341	2.265351	-1.006938	2.059761	0.420266	-1.189657	0.506674	0.260847	-0.533145	0.727267	1.412276	1.482106	-0.996258	0.588641	-0.412642	-0.920733	-0.874691	0.839002	0.501668	-0.342493	-0.533806	-2.146352	-0.597339	0.115726	0.850683	-0.752239	0.377263	-0.561982	0.262783	-0.356676	-0.367462	0.753611	-1.267414	-1.330698	-0.536453	0.840938	-0.763108	-0.268100	-0.677424	1.606831	0.151732	-2.085701	1.219296	0.400863	0.591165	-1.485213	1.501979	1.196569	-0.214154	0.339554	-0.034446	1.176452	0.546340	-1.255630	-1.309210	-0.445437	0.189437	-0.737463	0.843767	-0.605632	-0.060777	0.409310	1.285569	-0.622638	1.018193	0.880680	0.046805	-1.818058	-0.809829	0.875224	0.409569	-0.116621	-1.238919	3.305724	-0.024121	-1.756500	1.328958	0.507593	-0.866554	-2.240848	-0.661376	-0.671824	0.215720	-0.296326	0.481402	0.829645	-0.721025	1.263914	0.549047	-1.234945
3	-1.978838	0.721823	-0.559067	-1.235243	0.420716	-0.598845	0.359576	-0.619366	-1.757772	-1.156251	0.705212	0.875071	-1.020376	0.394760	-0.147970	0.230249	1.355203	1.794488	2.678058	-0.153565	-0.460959	-0.098108	-1.407930	-2.487702	1.823014	0.099873	-0.517603	-0.509311	-1.833175	-0.900906	0.459493	-0.655440	1.466122	-1.531389	-0.422106	0.421422	0.578615	0.259795	0.018941	-0.168726	1.611107	-1.586550	-1.384941	0.858377	1.033242	1.701343	1.748344	-0.371182	-0.843575	2.089641	-0.345430	-1.740556	0.141915	-2.197138	0.689569	-0.150025	0.287456	0.654016	-1.521919	-0.918008	-0.587528	0.230636	0.262637	0.615674	0.600044	-0.494699	-0.743089	0.220026	-0.242207	0.528216	-0.328174	-1.536517	-1.476640	-1.162114	-1.260222	1.106252	-1.467408	-0.349341	-1.841217	0.031296	-0.076475	-0.353383	0.807545	0.779064	-2.398417	-0.267828	1.549734	0.814397	0.284770	-0.659369	0.761040	-0.722067	0.810332	1.501295	1.440865	-1.367459	-0.700301	-1.540662	0.159837	-0.625415

Pandas 2.2 中文官方教程和指南（十九·二）（4）https://developer.aliyun.com/article/1509814

Pandas 2.2 中文官方教程和指南（十九·二）（3）

Highlight Min or Max

Highlight Between

Highlight Quantile

背景渐变和文本渐变

设置属性

条形图

分享样式

限制

其他有趣且有用的东西

小部件

放大

粘性标题

热门文章

最新文章

相关课程

相关电子书

探索云世界

热门

云计算

大数据

云原生

人工智能

数据库

开发与运维

活动广场

任务中心

训练营

直播

乘风者计划

下载

镜像站

技术资料

Pandas 2.2 中文官方教程和指南（十九·二）（3）

Highlight Min or Max

Highlight Between

Highlight Quantile

背景渐变和文本渐变

设置属性

条形图

分享样式

限制

其他有趣且有用的东西

小部件

放大

粘性标题

热门文章

最新文章

相关课程

相关电子书