How to plot multiple lines on same graph using plotly express (XYXY format data)?

I have an excel file with multiple columns data in XYXY format 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

Comments: (1) different lines with different colors (2) set the name of each column Y as the name of each line (3) both x and y axis have Slider Control to adjust the data range

Thank you so much for your help!

Solution

start by generating a dataframe of same structure as your sample data
your plot is really multiple sets of X/Y arrays across columns. restructure for all sets of X/Y arrays to be rows, with a column that defines the set (name)
then it's a very simple basic use case of Plotly Express
have also provided another way in pandas using wide_to_long() to structure dataframe for plotly

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})

sample data

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

plot

`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})