如何使用 plotly express（XYXY 格式数据）在同一张图上绘制多条线？
How to plot multiple lines on same graph using plotly express (XYXY format data)?
我有一个 excel 文件，其中包含 XYXY 格式的多列数据 Data.png. How can I use plotly express to plot multiple lines on same graph? For your reference, I achieved this using the Origin software.Result.png
评论: (1)不同线条不同颜色
(2) 将每一列的名称Y设置为每一行的名称
(3) x和y轴都有Slider Control调整数据范围
非常感谢您的帮助！
- 首先生成一个与样本数据结构相同的数据框
- 您的绘图实际上是多组 X/Y 跨列数组。将所有 X/Y 数组集合重组为行，其中一列定义集合 (name)
- 那么这是 Plotly Express
的一个非常简单的基本用例
- 还在 pandas 中提供了另一种方法，使用
wide_to_long() 为 plotly 构建数据框
import pandas as pd
import numpy as np
import plotly.express as px
# generte smaple data in required structure
ROWS = 20
COLS = 5
df = pd.DataFrame(
{
f"{c}{n+1}": a if c == "X" else np.power(a, np.linspace(3, 8, ROWS))
for n, a in enumerate(np.sort(np.random.uniform(1, 2, (COLS, ROWS))))
for c in "XY"
}
)
# generate line chart. key is restructuring dataframe to be long with a column that defines name/color
px.line(
pd.concat(
[
df.loc[:, [f"{c}{n}" for c in "XY"]]
.pipe(lambda d: d.rename(columns={c: c[0] for c in d.columns}))
.assign(name=chr(ord("A") + n * 2 - 1))
for n in range(1, (len(df.columns) // 2) + 1)
]
),
x="X",
y="Y",
color="name",
).update_layout(margin={"r":800})
示例数据
X1
Y1
X2
Y2
X3
Y3
X4
Y4
X5
Y5
1.05655
1.17941
1.00106
1.00317
1.08131
1.26432
1.02673
1.08236
1.13674
1.46889
1.12829
1.48273
1.12435
1.4659
1.17564
1.69557
1.05341
1.18507
1.17995
1.71595
1.23403
2.09918
1.1806
1.79582
1.18304
1.8089
1.11287
1.45807
1.20479
1.92892
1.25683
2.37794
1.25303
2.35086
1.25923
2.39525
1.11929
1.53275
1.20527
2.02893
1.26899
2.62586
1.26441
2.58766
1.30377
2.93
1.12895
1.63484
1.31977
3.07846
1.28209
2.92254
1.28889
2.98997
1.34024
3.53909
1.24893
2.60999
1.33975
3.53352
1.35852
4.06724
1.37546
4.30467
1.35213
3.9804
1.27209
3.01014
1.35568
4.02851
1.40013
5.10234
1.45365
6.11857
1.36741
4.55027
1.27662
3.26259
1.35574
4.36531
1.45668
6.82368
1.48403
7.5034
1.43853
6.40061
1.37053
4.99873
1.36458
4.88892
1.53722
10.0571
1.50537
8.98814
1.58023
11.6633
1.39912
6.0676
1.38998
5.85774
1.54932
11.7704
1.5474
11.6885
1.65089
16.8308
1.4697
8.74519
1.44385
7.91331
1.55716
13.6067
1.5512
13.3024
1.70078
22.8881
1.52104
11.8486
1.46135
9.358
1.61261
18.9648
1.62567
19.9306
1.71918
28.1241
1.54101
14.3378
1.50292
12.29
1.65226
25.1355
1.70023
30.207
1.78133
40.7424
1.55464
17.0006
1.61183
21.4393
1.68557
32.7817
1.72353
38.0429
1.78985
48.9649
1.58977
22.1695
1.67353
31.2474
1.80329
60.1153
1.76449
51.6846
1.79787
58.8716
1.61254
27.6471
1.68959
38.2362
1.88719
97.4484
1.88748
97.5572
1.83715
80.2828
1.76924
61.1892
1.78649
65.6223
1.9383
140.635
1.90963
125.812
1.87891
111.451
1.77838
73.8938
1.826
90.0264
1.96985
189.662
1.92214
156.894
1.87996
132.145
1.8462
114.857
1.8351
109.622
1.99305
248.972
1.94302
203.147
1.88243
157.67
1.87284
151.36
1.95582
214.111
情节
wide_to_long()
px.line(
pd.wide_to_long(df.reset_index(), stubnames=["X", "Y"], i="index", j="name")
.reset_index()
.assign(name=lambda d: d["name"].apply(lambda n: chr(ord("A") + n * 2 - 1))),
x="X",
y="Y",
color="name",
).update_layout(margin={"r": 800})
我有一个 excel 文件，其中包含 XYXY 格式的多列数据 Data.png. How can I use plotly express to plot multiple lines on same graph? For your reference, I achieved this using the Origin software.Result.png
评论: (1)不同线条不同颜色 (2) 将每一列的名称Y设置为每一行的名称 (3) x和y轴都有Slider Control调整数据范围
非常感谢您的帮助！
- 首先生成一个与样本数据结构相同的数据框
- 您的绘图实际上是多组 X/Y 跨列数组。将所有 X/Y 数组集合重组为行，其中一列定义集合 (name)
- 那么这是 Plotly Express 的一个非常简单的基本用例
- 还在 pandas 中提供了另一种方法，使用
wide_to_long()为 plotly 构建数据框
import pandas as pd
import numpy as np
import plotly.express as px
# generte smaple data in required structure
ROWS = 20
COLS = 5
df = pd.DataFrame(
{
f"{c}{n+1}": a if c == "X" else np.power(a, np.linspace(3, 8, ROWS))
for n, a in enumerate(np.sort(np.random.uniform(1, 2, (COLS, ROWS))))
for c in "XY"
}
)
# generate line chart. key is restructuring dataframe to be long with a column that defines name/color
px.line(
pd.concat(
[
df.loc[:, [f"{c}{n}" for c in "XY"]]
.pipe(lambda d: d.rename(columns={c: c[0] for c in d.columns}))
.assign(name=chr(ord("A") + n * 2 - 1))
for n in range(1, (len(df.columns) // 2) + 1)
]
),
x="X",
y="Y",
color="name",
).update_layout(margin={"r":800})
示例数据
| X1 | Y1 | X2 | Y2 | X3 | Y3 | X4 | Y4 | X5 | Y5 |
|---|---|---|---|---|---|---|---|---|---|
| 1.05655 | 1.17941 | 1.00106 | 1.00317 | 1.08131 | 1.26432 | 1.02673 | 1.08236 | 1.13674 | 1.46889 |
| 1.12829 | 1.48273 | 1.12435 | 1.4659 | 1.17564 | 1.69557 | 1.05341 | 1.18507 | 1.17995 | 1.71595 |
| 1.23403 | 2.09918 | 1.1806 | 1.79582 | 1.18304 | 1.8089 | 1.11287 | 1.45807 | 1.20479 | 1.92892 |
| 1.25683 | 2.37794 | 1.25303 | 2.35086 | 1.25923 | 2.39525 | 1.11929 | 1.53275 | 1.20527 | 2.02893 |
| 1.26899 | 2.62586 | 1.26441 | 2.58766 | 1.30377 | 2.93 | 1.12895 | 1.63484 | 1.31977 | 3.07846 |
| 1.28209 | 2.92254 | 1.28889 | 2.98997 | 1.34024 | 3.53909 | 1.24893 | 2.60999 | 1.33975 | 3.53352 |
| 1.35852 | 4.06724 | 1.37546 | 4.30467 | 1.35213 | 3.9804 | 1.27209 | 3.01014 | 1.35568 | 4.02851 |
| 1.40013 | 5.10234 | 1.45365 | 6.11857 | 1.36741 | 4.55027 | 1.27662 | 3.26259 | 1.35574 | 4.36531 |
| 1.45668 | 6.82368 | 1.48403 | 7.5034 | 1.43853 | 6.40061 | 1.37053 | 4.99873 | 1.36458 | 4.88892 |
| 1.53722 | 10.0571 | 1.50537 | 8.98814 | 1.58023 | 11.6633 | 1.39912 | 6.0676 | 1.38998 | 5.85774 |
| 1.54932 | 11.7704 | 1.5474 | 11.6885 | 1.65089 | 16.8308 | 1.4697 | 8.74519 | 1.44385 | 7.91331 |
| 1.55716 | 13.6067 | 1.5512 | 13.3024 | 1.70078 | 22.8881 | 1.52104 | 11.8486 | 1.46135 | 9.358 |
| 1.61261 | 18.9648 | 1.62567 | 19.9306 | 1.71918 | 28.1241 | 1.54101 | 14.3378 | 1.50292 | 12.29 |
| 1.65226 | 25.1355 | 1.70023 | 30.207 | 1.78133 | 40.7424 | 1.55464 | 17.0006 | 1.61183 | 21.4393 |
| 1.68557 | 32.7817 | 1.72353 | 38.0429 | 1.78985 | 48.9649 | 1.58977 | 22.1695 | 1.67353 | 31.2474 |
| 1.80329 | 60.1153 | 1.76449 | 51.6846 | 1.79787 | 58.8716 | 1.61254 | 27.6471 | 1.68959 | 38.2362 |
| 1.88719 | 97.4484 | 1.88748 | 97.5572 | 1.83715 | 80.2828 | 1.76924 | 61.1892 | 1.78649 | 65.6223 |
| 1.9383 | 140.635 | 1.90963 | 125.812 | 1.87891 | 111.451 | 1.77838 | 73.8938 | 1.826 | 90.0264 |
| 1.96985 | 189.662 | 1.92214 | 156.894 | 1.87996 | 132.145 | 1.8462 | 114.857 | 1.8351 | 109.622 |
| 1.99305 | 248.972 | 1.94302 | 203.147 | 1.88243 | 157.67 | 1.87284 | 151.36 | 1.95582 | 214.111 |
情节
wide_to_long()
px.line(
pd.wide_to_long(df.reset_index(), stubnames=["X", "Y"], i="index", j="name")
.reset_index()
.assign(name=lambda d: d["name"].apply(lambda n: chr(ord("A") + n * 2 - 1))),
x="X",
y="Y",
color="name",
).update_layout(margin={"r": 800})