How to plot multiple sets of X and Y in matplotlib

Question

I want to make a plot where val1 is the X axis and val2 the Y axis. I want the plot to have both the X, Y values for model type a and b, that is, a line plot with two lines, one for each model type.

I tried this:

import pandas as pd
# df contains the dataframe above
df = df.pivot(index="alpha", columns='model', values=['val1', "val2"])

df.plot(
    figsize=(15, 13),
    xlabel = "val1",
    ylabel = "val2"
)

But that gives me a line plot for every model type for val1 and val2. That is, instead of having 2 curves that have one (val1, val2) pair for every alpha, I have 4 curves, with pairs (alpha, val_n), where n = 1:2.

How can I plot val1 against val2 by model on the same plot? No loops, please.

Extra: I've seen this question, which is similar. However, it only plots multiple lines against a common X axis. In my case, there are multiple X axis. Also note that while the X axis vary, they're all within the same range.

EDIT: Following @Trenton Mckinney's comments, I edited the table to also include cases where, for some entries of alpha, there are duplicate pairs of (val1, val2).

sample data

• I have an example dataframe defined here.

model,alpha,val1,val2
a,0.0,0.0209351377553025,1.0
a,0.0101010101010101,0.0384738461538461,0.9780976220275344
a,0.0202020202020202,0.0495397764628533,0.9430538172715894
a,0.0303030303030303,0.0579088471849865,0.8785982478097623
a,0.0404040404040404,0.0679002982961727,0.8404255319148937
a,0.0505050505050505,0.076747811488188,0.8010012515644556
a,0.0606060606060606,0.0827263982410334,0.753441802252816
a,0.0707070707070707,0.0863143318716824,0.7021276595744681
a,0.0808080808080808,0.0898876404494382,0.6608260325406758
a,0.0909090909090909,0.0911214953271028,0.6101376720901126
a,0.101010101010101,0.0931797328983586,0.571964956195244
a,0.1111111111111111,0.095023122660207,0.5400500625782227
a,0.1212121212121212,0.0966555977229601,0.5100125156445557
a,0.1313131313131313,0.0991264717052791,0.4899874843554443
a,0.1414141414141414,0.0999729437229437,0.4624530663329161
a,0.1515151515151515,0.0998256320836966,0.4299123904881101
a,0.1616161616161616,0.0981823830977163,0.3954943679599499
a,0.1717171717171717,0.0976876675603217,0.3648310387984981
a,0.1818181818181818,0.0946969696969697,0.3285356695869837
a,0.1919191919191919,0.0922956589753617,0.295369211514393
a,0.202020202020202,0.0890878090366581,0.2615769712140175
a,0.2121212121212121,0.0858516483516483,0.2346683354192741
a,0.2222222222222222,0.0851274437020539,0.2152690863579474
a,0.2323232323232323,0.0827027027027027,0.1914893617021276
a,0.2424242424242424,0.0814991076740035,0.1714643304130162
a,0.2525252525252525,0.081406707912732,0.1564455569461827
a,0.2626262626262626,0.0831261101243339,0.146433041301627
a,0.2727272727272727,0.0826287471176018,0.1345431789737171
a,0.2828282828282829,0.0833677259595542,0.1264080100125156
a,0.2929292929292929,0.082210242587601,0.1145181476846057
a,0.303030303030303,0.0843315764254911,0.1101376720901126
a,0.3131313131313131,0.0822981366459627,0.0994993742177722
a,0.3232323232323232,0.0795140806184428,0.0901126408010012
a,0.3333333333333333,0.0814991076740035,0.0857321652065081
a,0.3434343434343435,0.0807493540051679,0.0782227784730913
a,0.3535353535353536,0.0800850460666194,0.0707133917396746
a,0.3636363636363636,0.0773395204949729,0.0625782227784731
a,0.3737373737373737,0.0753246753246753,0.0544430538172715
a,0.3838383838383838,0.065155807365439,0.0431789737171464
a,0.393939393939394,0.0640756302521008,0.0381727158948685
a,0.4040404040404041,0.0667447306791569,0.0356695869837296
a,0.4141414141414142,0.0600801068090787,0.0281602002503128
a,0.4242424242424242,0.061633281972265,0.0250312891113892
a,0.4343434343434343,0.0586145648312611,0.0206508135168961
a,0.4444444444444445,0.0542797494780793,0.016270337922403
a,0.4545454545454546,0.0505050505050505,0.0125156445556946
a,0.4646464646464647,0.0470219435736677,0.0093867334167709
a,0.4747474747474748,0.0595238095238095,0.0093867334167709
a,0.4848484848484848,0.0666666666666666,0.0081351689612015
a,0.494949494949495,0.064516129032258,0.0062578222778473
a,0.5050505050505051,0.064516129032258,0.0050062578222778
a,0.5151515151515152,0.0697674418604651,0.0037546933667083
a,0.5252525252525253,0.0689655172413793,0.0025031289111389
a,0.5353535353535354,0.0714285714285714,0.0018773466833541
a,0.5454545454545455,0.1153846153846153,0.0018773466833541
a,0.5555555555555556,0.1176470588235294,0.0012515644555694
a,0.5656565656565657,0.1,0.0006257822277847
a,0.5757575757575758,0.1428571428571428,0.0006257822277847
a,0.5858585858585859,0.0,0.0
a,0.595959595959596,0.0,0.0
a,0.6060606060606061,0.0,0.0
a,0.6161616161616162,0.0,0.0
a,0.6262626262626263,0.0,0.0
a,0.6363636363636365,0.0,0.0
a,0.6464646464646465,0.0,0.0
a,0.6565656565656566,0.0,0.0
a,0.6666666666666667,0.0,0.0
a,0.6767676767676768,0.0,0.0
a,0.686868686868687,0.0,0.0
a,0.696969696969697,0.0,0.0
a,0.7070707070707072,0.0,0.0
a,0.7171717171717172,0.0,0.0
a,0.7272727272727273,0.0,0.0
a,0.7373737373737375,0.0,0.0
a,0.7474747474747475,0.0,0.0
a,0.7575757575757577,0.0,0.0
a,0.7676767676767677,0.0,0.0
a,0.7777777777777778,0.0,0.0
a,0.787878787878788,0.0,0.0
a,0.797979797979798,0.0,0.0
a,0.8080808080808082,0.0,0.0
a,0.8181818181818182,0.0,0.0
a,0.8282828282828284,0.0,0.0
a,0.8383838383838385,0.0,0.0
a,0.8484848484848485,0.0,0.0
a,0.8585858585858587,0.0,0.0
a,0.8686868686868687,0.0,0.0
a,0.8787878787878789,0.0,0.0
a,0.888888888888889,0.0,0.0
a,0.8989898989898991,0.0,0.0
a,0.9090909090909092,0.0,0.0
a,0.9191919191919192,0.0,0.0
a,0.9292929292929294,0.0,0.0
a,0.9393939393939394,0.0,0.0
a,0.9494949494949496,0.0,0.0
a,0.9595959595959596,0.0,0.0
a,0.9696969696969696,0.0,0.0
a,0.97979797979798,0.0,0.0
a,0.98989898989899,0.0,0.0
a,1.0,0.0,0.0
b,0.0,0.0209351377553025,1.0
b,0.0101010101010101,0.0311796532522412,0.98811013767209
b,0.0202020202020202,0.038218896713615,0.9780976220275344
b,0.0303030303030303,0.0443462796167747,0.967459324155194
b,0.0404040404040404,0.0494074390203867,0.94180225281602
b,0.0505050505050505,0.0536487837387537,0.9067584480600752
b,0.0606060606060606,0.0579099545642296,0.8773466833541927
b,0.0707070707070707,0.0624309392265193,0.8485607008760951
b,0.0808080808080808,0.0682604272634791,0.8397997496871089
b,0.0909090909090909,0.0730481402881715,0.818523153942428
b,0.101010101010101,0.0772134940932895,0.7934918648310388
b,0.1111111111111111,0.0808107396683337,0.7684605757196495
b,0.1212121212121212,0.082314072824406,0.7327909887359199
b,0.1313131313131313,0.0842868895370578,0.7052565707133918
b,0.1414141414141414,0.0872056015276893,0.685857321652065
b,0.1515151515151515,0.0890210200150741,0.6652065081351689
b,0.1616161616161616,0.0897685103375154,0.6357947434292867
b,0.1717171717171717,0.090951405697263,0.6113892365456821
b,0.1818181818181818,0.0919013741350745,0.5901126408010012
b,0.1919191919191919,0.0931881228809205,0.567584480600751
b,0.202020202020202,0.0941756945189316,0.5494367959949937
b,0.2121212121212121,0.095206017738857,0.5306633291614519
b,0.2222222222222222,0.0958118858212447,0.5125156445556946
b,0.2323232323232323,0.0973819427452899,0.4981226533166458
b,0.2424242424242424,0.0987325728770595,0.4874843554443054
b,0.2525252525252525,0.1006364359586316,0.4749687108886107
b,0.2626262626262626,0.100246845858475,0.4574468085106383
b,0.2727272727272727,0.1002437975046608,0.4374217772215269
b,0.2828282828282829,0.0990276738967838,0.4142678347934919
b,0.2929292929292929,0.0981863664790494,0.392991239048811
b,0.303030303030303,0.0979558462796402,0.3748435544430538
b,0.3131313131313131,0.0970492921712433,0.3560700876095118
b,0.3232323232323232,0.0944952483414021,0.3297872340425531
b,0.3333333333333333,0.0920953101361573,0.304755944931164
b,0.3434343434343435,0.092139303482587,0.2897371714643304
b,0.3535353535353536,0.0887949260042283,0.2628285356695869
b,0.3636363636363636,0.0878679750223015,0.246558197747184
b,0.3737373737373737,0.0863935969868173,0.2296620775969962
b,0.3838383838383838,0.084931506849315,0.2133917396745932
b,0.393939393939394,0.0827823327162126,0.1958698372966207
b,0.4040404040404041,0.0818565400843881,0.1821026282853567
b,0.4141414141414142,0.0815282791817087,0.169586983729662
b,0.4242424242424242,0.0801810539928871,0.1551939924906132
b,0.4343434343434343,0.0814153211954654,0.1483103879849812
b,0.4444444444444445,0.0844346549192364,0.1439299123904881
b,0.4545454545454546,0.0827478532396565,0.1326658322903629
b,0.4646464646464647,0.0828843762950683,0.1251564455569462
b,0.4747474747474748,0.0826666666666666,0.1163954943679599
b,0.4848484848484848,0.0832171083217108,0.1120150187734668
b,0.494949494949495,0.0818452380952381,0.1032540675844806
b,0.5050505050505051,0.080824088748019,0.0957446808510638
b,0.5151515151515152,0.0801781737193764,0.0901126408010012
b,0.5252525252525253,0.079667063020214,0.0838548185231539
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b,0.7272727272727273,0.1,0.0025031289111389
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b,0.7575757575757577,0.0,0.0
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b,0.888888888888889,0.0,0.0
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b,0.9595959595959596,0.0,0.0
b,0.9696969696969696,0.0,0.0
b,0.97979797979798,0.0,0.0
b,0.98989898989899,0.0,0.0
b,1.0,0.0,0.0
c,0.0,0.0209351377553025,1.0
c,0.0101010101010101,0.0260009203865623,0.9899874843554444
c,0.0202020202020202,0.0291272929405225,0.983729662077597
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Answer 1

• For long data, as shown by the sample data, seaborn, a high-level API for matplotlib is usually the easiest option.
• The following answers use the raw dataframe, df, not the pivoted dataframe.
• Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2

Axes-Level Plot

• seaborn.scatterplot
• Has an estimator parameter for aggregating across multiple observations of the y variable at the same x level.

import seaborn as sns
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 5))
sns.scatterplot(data=df, x='val1', y='val2', hue='model', ax=ax)

Figure-Level Plot

• seaborn.relplot, which uses sns.scatterplot as the default kind

sns.relplot(data=df, x='val1', y='val2', hue='model', kind='scatter', aspect=2)

Answer 2

You can convert model column to int type, and then pass as c argument in the plot function:

df.assign(model=df.model.factorize()[0]).plot.scatter(x='val1', y='val2', c='model', colormap='viridis')

To get a line plot, one option is to pivot model into different columns, interpolate by index and then plot:

df.pivot('val1', 'model', 'val2').interpolate(method='index').plot.line()