How to plot data from two dataframes with the same column names

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Let's say I have two DataFrames (Samples and Controls) as follows:

df_Sample =\
{'Nuclei in individual cell region Selected - Nucleus Area [µm²]': {0: 189.48, 1: 153.736, 2: 199.219, 3: 221.4, 4: 261.648, 5: 304.089, 6: 345.935, 7: 218.935, 8: 232.601, 9: 240.912, 10: 208.125, 11: 260.713, 12: 161.112, 13: 270.181, 14: 165.888, 15: 342.077, 16: 158.376, 17: 557.035, 18: 319.913, 19: 257.297},
'Nuclei in individual cell region Selected - Nucleus Roundness': {0: 0.913951, 1: 0.93739, 2: 0.93725, 3: 0.869216, 4: 0.828391, 5: 0.978106, 6: 0.955958, 7: 0.92616, 8: 0.78398, 9: 0.977184, 10: 0.848469, 11: 0.984681, 12: 0.908689, 13: 0.910773, 14: 0.908787, 15: 0.986723, 16: 0.976819, 17: 0.95381, 18: 0.976402, 19: 0.930968},
'Nuclei in individual cell region Selected - Nucleus Width [µm]': {0: 11.4282, 1: 12.2188, 2: 13.9467, 3: 12.9901, 4: 14.3977, 5: 17.4717, 6: 17.0762, 7: 14.3598, 8: 11.9658, 9: 15.5159, 10: 14.1908, 11: 15.9906, 12: 11.1176, 13: 15.854, 14: 12.266, 15: 18.1792, 16: 12.6883, 17: 22.2749, 18: 18.5788, 19: 14.8166},
'Nuclei in individual cell region Selected - Nucleus Length [µm]': {0: 18.9918, 1: 15.8738, 2: 16.5248, 3: 19.1131, 4: 21.3145, 5: 20.084, 6: 24.1163, 7: 18.2035, 8: 22.8184, 9: 19.0128, 10: 18.5242, 11: 21.1097, 12: 16.8669, 13: 21.2989, 14: 16.8885, 15: 23.6588, 16: 15.8094, 17: 29.3571, 18: 21.1347, 19: 19.8769},
'Nuclei in individual cell region Selected - Nucleus Ratio Width to Length': {0: 0.601743, 1: 0.769748, 2: 0.843986, 3: 0.679645, 4: 0.675488, 5: 0.869933, 6: 0.708077, 7: 0.788848, 8: 0.524394, 9: 0.816074, 10: 0.766064, 11: 0.757499, 12: 0.659136, 13: 0.744356, 14: 0.726293, 15: 0.768394, 16: 0.80258, 17: 0.758756, 18: 0.879065, 19: 0.745417},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Correlation 1 px': {0: 0.98371, 1: 0.97789, 2: 0.978729, 3: 0.961711, 4: 0.976911, 5: 0.966404, 6: 0.98986, 7: 0.972134, 8: 0.970894, 9: 0.949579, 10: 0.964805, 11: 0.970876, 12: 0.966332, 13: 0.978358, 14: 0.984657, 15: 0.965988, 16: 0.989449, 17: 0.970398, 18: 0.962764, 19: 0.962354},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Contrast 1 px': {0: 0.00262663, 1: 0.00337056, 2: 0.00384226, 3: 0.00407926, 4: 0.00339842, 5: 0.00268196, 6: 0.00258363, 7: 0.0026726, 8: 0.0039011, 9: 0.0049614, 10: 0.00584036, 11: 0.00359065, 12: 0.00503498, 13: 0.00360473, 14: 0.00342672, 15: 0.00324812, 16: 0.00266534, 17: 0.00354377, 18: 0.00508052, 19: 0.00399667},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Sum Variance 1 px': {0: 0.0799574, 1: 0.075373, 2: 0.089302, 3: 0.0522426, 4: 0.0727336, 5: 0.0392431, 6: 0.12669, 7: 0.0472695, 8: 0.0660276, 9: 0.0479593, 10: 0.0815123, 11: 0.0607464, 12: 0.0735158, 13: 0.0823799, 14: 0.110817, 15: 0.0469307, 16: 0.125631, 17: 0.0589657, 18: 0.0669395, 19: 0.0520771},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Homogeneity 1 px': {0: 0.739913, 1: 0.68523, 2: 0.695601, 3: 0.671093, 4: 0.708442, 5: 0.753666, 6: 0.787906, 7: 0.727063, 8: 0.680108, 9: 0.634683, 10: 0.626611, 11: 0.687146, 12: 0.661779, 13: 0.678676, 14: 0.695092, 15: 0.724737, 16: 0.748956, 17: 0.697572, 18: 0.647701, 19: 0.677194},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Spot 0 px': {0: 0.005843, 1: 0.00580018, 2: 0.0071962, 3: 0.00964391, 4: 0.00578204, 5: 0.00631538, 6: 0.00591882, 7: 0.00738057, 8: 0.00797945, 9: 0.0107222, 10: 0.00789028, 11: 0.0079751, 12: 0.00720769, 13: 0.00583212, 14: 0.00612275, 15: 0.00729683, 16: 0.00605783, 17: 0.00678319, 18: 0.00903149, 19: 0.00873706},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Hole 0 px': {0: 0.0053161, 1: 0.00527502, 2: 0.00624592, 3: 0.00904184, 4: 0.00543591, 5: 0.00533345, 6: 0.00579994, 7: 0.00647572, 8: 0.00731868, 9: 0.0104302, 10: 0.00760632, 11: 0.00771892, 12: 0.00689596, 13: 0.00578755, 14: 0.00604904, 15: 0.00727409, 16: 0.00561067, 17: 0.00706209, 18: 0.00924693, 19: 0.00861305},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Edge 0 px': {0: 0.0554048, 1: 0.0704348, 2: 0.062886, 3: 0.0676434, 4: 0.0616821, 5: 0.0566622, 6: 0.0475497, 7: 0.056854, 8: 0.0712491, 9: 0.077949, 10: 0.0817617, 11: 0.0688477, 12: 0.0827153, 13: 0.0629512, 14: 0.0608878, 15: 0.0607465, 16: 0.0560636, 17: 0.0645136, 18: 0.0726108, 19: 0.066896},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Ridge 0 px': {0: 0.00924915, 1: 0.00908236, 2: 0.0118103, 3: 0.0165759, 4: 0.0101151, 5: 0.0109813, 6: 0.00959717, 7: 0.0121257, 8: 0.0136556, 9: 0.0180968, 10: 0.0136057, 11: 0.0143802, 12: 0.014296, 13: 0.00956464, 14: 0.0105358, 15: 0.0127249, 16: 0.00991149, 17: 0.012284, 18: 0.015938, 19: 0.0156756},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Valley 0 px': {0: 0.0104073, 1: 0.0108218, 2: 0.0132724, 3: 0.0186756, 4: 0.012417, 5: 0.0120152, 6: 0.0107475, 7: 0.0132826, 8: 0.0163031, 9: 0.0216996, 10: 0.0181437, 11: 0.0155132, 12: 0.018504, 13: 0.0125872, 14: 0.012248, 15: 0.0145793, 16: 0.0104176, 17: 0.0148176, 18: 0.0189796, 19: 0.0183744},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Saddle 0 px': {0: 0.0110422, 1: 0.0115229, 2: 0.0137925, 3: 0.0184715, 4: 0.012461, 5: 0.0114347, 6: 0.00987503, 7: 0.0135181, 8: 0.0158798, 9: 0.0205525, 10: 0.017767, 11: 0.0154586, 12: 0.0151242, 13: 0.0124683, 14: 0.0119072, 15: 0.0141378, 16: 0.0104225, 17: 0.0142464, 18: 0.0184273, 19: 0.0172968},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Bright 0 px': {0: 0.0131424, 1: 0.012963, 2: 0.0165551, 3: 0.0228766, 4: 0.0138591, 5: 0.0150853, 6: 0.0135239, 7: 0.0169965, 8: 0.0188593, 9: 0.0251123, 10: 0.0187394, 11: 0.0194767, 12: 0.01881, 13: 0.013414, 14: 0.0145416, 15: 0.0174515, 16: 0.0138995, 17: 0.0166307, 18: 0.0217725, 19: 0.0213088},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Dark 0 px': {0: 0.0137252, 1: 0.0140704, 2: 0.017077, 3: 0.0242349, 4: 0.0156365, 5: 0.0152167, 6: 0.0145082, 7: 0.0172853, 8: 0.0206896, 9: 0.0281842, 10: 0.0225596, 11: 0.0203449, 12: 0.0224352, 13: 0.016074, 14: 0.0160069, 15: 0.0191488, 16: 0.0139954, 17: 0.0191773, 18: 0.0247077, 19: 0.0236879},
'Nuclei in individual cell region Selected - Intensity Nucleus HOECHST 33342 Mean': {0: 10439.2, 1: 8599.48, 2: 11024.7, 3: 14120.2, 4: 13009.2, 5: 14328.9, 6: 8880.34, 7: 13258.0, 8: 13797.4, 9: 11089.1, 10: 8444.29, 11: 18060.7, 12: 12378.4, 13: 10022.7, 14: 11975.5, 15: 10022.7, 16: 7041.5, 17: 13130.3, 18: 16532.3, 19: 13920.7},
'Nuclei in individual cell region Selected - Intensity Nucleus HOECHST 33342 StdDev': {0: 3146.52, 1: 2589.1, 2: 3462.54, 3: 3468.93, 4: 3741.13, 5: 3113.11, 6: 3266.78, 7: 3160.88, 8: 3893.39, 9: 2664.13, 10: 2586.55, 11: 4766.58, 12: 3712.11, 13: 3047.99, 14: 4211.4, 15: 2354.91, 16: 2635.87, 17: 3371.18, 18: 4531.04, 19: 3411.83},
'Nuclei in individual cell region Selected - Individual Cell Region resized Area [µm²]': {0: 445.553, 1: 397.35, 2: 442.885, 3: 510.77, 4: 697.139, 5: 915.99, 6: 1016.63, 7: 528.905, 8: 778.639, 9: 729.705, 10: 611.068, 11: 532.118, 12: 413.038, 13: 951.751, 14: 316.65, 15: 1195.33, 16: 490.731, 17: 1677.82, 18: 1153.86, 19: 769.885},
'Nuclei in individual cell region Selected - Individual Cell Region resized Roundness': {0: 0.857263, 1: 0.795805, 2: 0.814236, 3: 0.854813, 4: 0.831398, 5: 0.777984, 6: 0.787167, 7: 0.747858, 8: 0.750062, 9: 0.762677, 10: 0.771427, 11: 0.780667, 12: 0.884383, 13: 0.666342, 14: 0.765064, 15: 0.808236, 16: 0.85367, 17: 0.79878, 18: 0.630026, 19: 0.838658},
'Nuclei in individual cell region Selected - Individual Cell Region resized Width [µm]': {0: 20.4397, 1: 18.2035, 2: 17.217, 3: 18.6955, 4: 22.8935, 5: 24.9457, 6: 27.1186, 7: 19.1837, 8: 20.5044, 9: 24.3093, 10: 19.5575, 11: 21.0186, 12: 17.3154, 13: 23.012, 14: 16.2186, 15: 26.8312, 16: 21.4016, 17: 32.6773, 18: 27.1085, 19: 25.9816},
'Nuclei in individual cell region Selected - Individual Cell Region resized Length [µm]': {0: 28.0335, 1: 28.1183, 2: 31.5599, 3: 31.9347, 4: 36.3173, 5: 51.6394, 6: 41.2543, 7: 38.9602, 8: 52.7941, 9: 43.4318, 10: 42.1264, 11: 36.0593, 12: 30.6021, 13: 50.7546, 14: 24.1592, 15: 56.6319, 16: 27.9525, 17: 61.0174, 18: 57.4963, 19: 42.2456},
'Nuclei in individual cell region Selected - Individual Cell Region resized Ratio Width to Length': {0: 0.729115, 1: 0.647391, 2: 0.545533, 3: 0.585429, 4: 0.630374, 5: 0.483074, 6: 0.65735, 7: 0.492392, 8: 0.388385, 9: 0.559713, 10: 0.464257, 11: 0.58289, 12: 0.565824, 13: 0.453397, 14: 0.671319, 15: 0.473783, 16: 0.765642, 17: 0.53554, 18: 0.471483, 19: 0.615013},
'Nuclei in individual cell region Selected - Relative Spot Intensity': {0: 0.00431319, 1: 0.0207483, 2: 0.0272823, 3: 0.0526484, 4: 0.0874202, 5: 0.0260405, 6: 0.0325056, 7: 0.0588061, 8: 0.0335587, 9: 0.0496844, 10: 0.0273733, 11: 0.0306711, 12: 0.014466, 13: 0.0147694, 14: 0.0207914, 15: 0.0134007, 16: 0.0534635, 17: 0.0133466, 18: 0.113961, 19: 0.00055431},
'Nuclei in individual cell region Selected - Number of Spots per Area of Individual Cell Region resized': {0: 0.000228885, 1: 0.000299427, 2: 0.000460529, 3: 0.000898473, 4: 0.00112151, 5: 0.000575225, 6: 0.000618595, 7: 0.00144611, 8: 0.000720351, 9: 0.000163049, 10: 0.000361593, 11: 0.000511068, 12: 0.000329205, 13: 0.000375027, 14: 0.000536769, 15: 0.000270167, 16: 0.000831255, 17: 0.000344429, 18: 0.00138465, 19: 2.2077e-05},
'Compound': {0: 'Ciprofloxacin-Low', 1: 'Flunisolide-Medium', 2: 'Famprofazone-Medium', 3: 'Alprenolol-High', 4: 'Dyclonine-Low', 5: 'Flunisolide-Medium', 6: 'Zaleplon-Medium', 7: 'Hexetidine-Low', 8: 'Hexetidine-High', 9: 'Amprolium-Medium', 10: 'Pindolol-Low', 11: 'Zaleplon-High', 12: 'Famprofazone-Low', 13: 'Dyclonine-High', 14: 'Montensin-Medium', 15: 'Pindolol-Medium', 16: 'Hexetidine-Medium', 17: 'Flunisolide-Medium', 18: 'Dyclonine-Medium', 19: 'Hexetidine-Low'}}
df1_Sample = pd.DataFrame(df_Sample)

df_Control =\
{'Nuclei in individual cell region Selected - Nucleus Area [µm²]': {106695: 205.185, 106696: 160.008, 106697: 329.227, 106698: 264.521, 106699: 242.867, 106700: 225.598, 106701: 53.7438, 106702: 63.8908, 106703: 208.244, 106704: 195.48, 106705: 218.51, 106706: 160.262, 106707: 190.568, 106708: 254.697, 106709: 239.399, 106710: 59.5907, 106711: 228.267, 106712: 164.512, 106713: 125.691, 106714: 177.412},
'Nuclei in individual cell region Selected - Nucleus Roundness': {106695: 0.985695, 106696: 0.679483, 106697: 0.980048, 106698: 0.918674, 106699: 0.882368, 106700: 0.910482, 106701: 0.833087, 106702: 0.915233, 106703: 0.981635, 106704: 0.944526, 106705: 0.949615, 106706: 0.757661, 106707: 0.939818, 106708: 0.950865, 106709: 0.941393, 106710: 0.817561, 106711: 0.919093, 106712: 0.973769, 106713: 0.944191, 106714: 0.956228},
'Nuclei in individual cell region Selected - Nucleus Width [µm]': {106695: 12.7764, 106696: 10.5496, 106697: 18.2818, 106698: 14.348, 106699: 10.9667, 106700: 11.5818, 106701: 5.76001, 106702: 7.3426, 106703: 14.0801, 106704: 12.031, 106705: 13.4403, 106706: 11.6433, 106707: 12.6239, 106708: 13.4706, 106709: 13.9272, 106710: 6.47673, 106711: 12.4858, 106712: 12.6239, 106713: 10.9543, 106714: 12.5293},
'Nuclei in individual cell region Selected - Nucleus Length [µm]': {106695: 19.4166, 106696: 16.8765, 106697: 22.8452, 106698: 23.532, 106699: 24.0351, 106700: 22.2779, 106701: 9.97151, 106702: 10.0935, 106703: 18.1891, 106704: 19.4324, 106705: 19.2288, 106706: 15.9256, 106707: 17.6098, 106708: 24.0853, 106709: 20.7766, 106710: 10.9706, 106711: 19.783, 106712: 15.9821, 106713: 14.4354, 106714: 17.575},
'Nuclei in individual cell region Selected - Nucleus Ratio Width to Length': {106695: 0.658015, 106696: 0.62511, 106697: 0.800247, 106698: 0.609723, 106699: 0.45628, 106700: 0.519879, 106701: 0.577646, 106702: 0.727458, 106703: 0.774099, 106704: 0.61912, 106705: 0.698966, 106706: 0.731104, 106707: 0.716864, 106708: 0.559289, 106709: 0.670332, 106710: 0.590371, 106711: 0.631136, 106712: 0.789875, 106713: 0.758852, 106714: 0.7129},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Correlation 1 px': {106695: 0.973546, 106696: 0.970546, 106697: 0.967139, 106698: 0.974698, 106699: 0.968529, 106700: 0.972811, 106701: 0.978456, 106702: 0.972309, 106703: 0.975749, 106704: 0.97255, 106705: 0.977455, 106706: 0.965869, 106707: 0.977174, 106708: 0.969181, 106709: 0.977156, 106710: 0.979732, 106711: 0.975186, 106712: 0.97187, 106713: 0.978189, 106714: 0.975682},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Contrast 1 px': {106695: 0.00425443, 106696: 0.00819948, 106697: 0.00291286, 106698: 0.00296901, 106699: 0.00336917, 106700: 0.00358292, 106701: 0.00548305, 106702: 0.00543524, 106703: 0.00346719, 106704: 0.00445449, 106705: 0.00386494, 106706: 0.00941484, 106707: 0.00300193, 106708: 0.00308412, 106709: 0.00300024, 106710: 0.0049655, 106711: 0.00337084, 106712: 0.00346975, 106713: 0.00513168, 106714: 0.00352557},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Sum Variance 1 px': {106695: 0.0793487, 106696: 0.137136, 106697: 0.0435847, 106698: 0.0579307, 106699: 0.0526702, 106700: 0.0649955, 106701: 0.125886, 106702: 0.0967257, 106703: 0.0706206, 106704: 0.0799989, 106705: 0.0847513, 106706: 0.135571, 106707: 0.0649855, 106708: 0.0492589, 106709: 0.0649172, 106710: 0.121263, 106711: 0.0670809, 106712: 0.0608073, 106713: 0.116288, 106714: 0.071609},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 Haralick Homogeneity 1 px': {106695: 0.657532, 106696: 0.546708, 106697: 0.72884, 106698: 0.721774, 106699: 0.700476, 106700: 0.696009, 106701: 0.618728, 106702: 0.59469, 106703: 0.693487, 106704: 0.647874, 106705: 0.678351, 106706: 0.528893, 106707: 0.706147, 106708: 0.72233, 106709: 0.714676, 106710: 0.605918, 106711: 0.700766, 106712: 0.691383, 106713: 0.646318, 106714: 0.70725},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Spot 0 px': {106695: 0.00861118, 106696: 0.00931817, 106697: 0.00761309, 106698: 0.00837558, 106699: 0.0082335, 106700: 0.00795943, 106701: 0.00823312, 106702: 0.00783509, 106703: 0.00730663, 106704: 0.00735734, 106705: 0.00698037, 106706: 0.00857095, 106707: 0.007307, 106708: 0.00651859, 106709: 0.00674888, 106710: 0.00777671, 106711: 0.00729998, 106712: 0.00619496, 106713: 0.00603798, 106714: 0.0066989},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Hole 0 px': {106695: 0.00781875, 106696: 0.00746205, 106697: 0.00702571, 106698: 0.00721342, 106699: 0.00711043, 106700: 0.00706697, 106701: 0.00467969, 106702: 0.00478292, 106703: 0.00639713, 106704: 0.00709484, 106705: 0.00655664, 106706: 0.00715089, 106707: 0.00645719, 106708: 0.00597439, 106709: 0.00616917, 106710: 0.00496998, 106711: 0.00638658, 106712: 0.00532789, 106713: 0.00529905, 106714: 0.00612883},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Edge 0 px': {106695: 0.0729417, 106696: 0.110059, 106697: 0.0569585, 106698: 0.0598186, 106699: 0.0642045, 106700: 0.0669293, 106701: 0.0972561, 106702: 0.0924378, 106703: 0.0656912, 106704: 0.0757634, 106705: 0.069061, 106706: 0.111463, 106707: 0.063571, 106708: 0.0612379, 106709: 0.0594756, 106710: 0.09432, 106711: 0.065867, 106712: 0.0676253, 106713: 0.0765422, 106714: 0.0634227},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Ridge 0 px': {106695: 0.0149807, 106696: 0.0148023, 106697: 0.0134511, 106698: 0.013989, 106699: 0.0136802, 106700: 0.0135172, 106701: 0.0128792, 106702: 0.0118276, 106703: 0.0124749, 106704: 0.0131911, 106705: 0.0119413, 106706: 0.0147721, 106707: 0.012416, 106708: 0.0114262, 106709: 0.0113361, 106710: 0.0129007, 106711: 0.0124422, 106712: 0.010958, 106713: 0.0110026, 106714: 0.0118087},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Valley 0 px': {106695: 0.0161717, 106696: 0.0220035, 106697: 0.0138008, 106698: 0.0147323, 106699: 0.0145775, 106700: 0.0143745, 106701: 0.0137458, 106702: 0.0146674, 106703: 0.0141544, 106704: 0.0154375, 106705: 0.013253, 106706: 0.0246233, 106707: 0.0128277, 106708: 0.012231, 106709: 0.0126041, 106710: 0.013144, 106711: 0.0138948, 106712: 0.0126162, 106713: 0.0149189, 106714: 0.0139237},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Saddle 0 px': {106695: 0.0164057, 106696: 0.0177186, 106697: 0.0141956, 106698: 0.0141493, 106699: 0.0148899, 106700: 0.0142285, 106701: 0.0138838, 106702: 0.0152582, 106703: 0.013782, 106704: 0.0151764, 106705: 0.0132659, 106706: 0.0180964, 106707: 0.013406, 106708: 0.0118983, 106709: 0.0130469, 106710: 0.0133371, 106711: 0.0139153, 106712: 0.0121744, 106713: 0.0139629, 106714: 0.013405},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Bright 0 px': {106695: 0.0205846, 106696: 0.0209992, 106697: 0.0183712, 106698: 0.019501, 106699: 0.0190916, 106700: 0.0187158, 106701: 0.0183655, 106702: 0.0170604, 106703: 0.01726, 106704: 0.0179373, 106705: 0.0164909, 106706: 0.0203458, 106707: 0.0171765, 106708: 0.0156539, 106709: 0.0157577, 106710: 0.0180485, 106711: 0.0172014, 106712: 0.0149629, 106713: 0.0148986, 106714: 0.0161332},
'Nuclei in individual cell region Selected - Nucleus HOECHST 33342 SER Dark 0 px': {106695: 0.0209977, 106696: 0.0260261, 106697: 0.0182116, 106698: 0.0192058, 106699: 0.0189864, 106700: 0.0187439, 106701: 0.0162118, 106702: 0.017106, 106703: 0.0180129, 106704: 0.0197488, 106705: 0.0173244, 106706: 0.0281423, 106707: 0.0168369, 106708: 0.0159467, 106709: 0.0164385, 106710: 0.0159221, 106711: 0.0177785, 106712: 0.0157466, 106713: 0.0177876, 106714: 0.0176109},
'Nuclei in individual cell region Selected - Intensity Nucleus HOECHST 33342 Mean': {106695: 11255.9, 106696: 26866.3, 106697: 10728.4, 106698: 9493.33, 106699: 11029.7, 106700: 10894.4, 106701: 40807.4, 106702: 33047.2, 106703: 11523.0, 106704: 14381.5, 106705: 11346.3, 106706: 26054.4, 106707: 12701.5, 106708: 9906.32, 106709: 14341.3, 106710: 26745.4, 106711: 11314.5, 106712: 12563.2, 106713: 13698.5, 106714: 11522.0},
'Nuclei in individual cell region Selected - Intensity Nucleus HOECHST 33342 StdDev': {106695: 3365.55, 106696: 10684.8, 106697: 2379.63, 106698: 2439.61, 106699: 2738.63, 106700: 2988.91, 106701: 15826.6, 106702: 11332.9, 106703: 3275.83, 106704: 4358.66, 106705: 3512.88, 106706: 10206.0, 106707: 3486.11, 106708: 2382.69, 106709: 3903.72, 106710: 10256.8, 106711: 3153.22, 106712: 3386.94, 106713: 4945.58, 106714: 3311.51},
'Nuclei in individual cell region Selected - Individual Cell Region resized Area [µm²]': {106695: 473.462, 106696: 774.458, 106697: 1080.01, 106698: 775.12, 106699: 734.379, 106700: 491.614, 106701: 129.6, 106702: 211.134, 106703: 549.947, 106704: 359.89, 106705: 548.911, 106706: 564.429, 106707: 409.792, 106708: 637.515, 106709: 525.013, 106710: 146.614, 106711: 479.139, 106712: 357.323, 106713: 302.253, 106714: 377.957},
'Nuclei in individual cell region Selected - Individual Cell Region resized Roundness': {106695: 0.808892, 106696: 0.804004, 106697: 0.913968, 106698: 0.86118, 106699: 0.89257, 106700: 0.882859, 106701: 0.559616, 106702: 0.84152, 106703: 0.860951, 106704: 0.939933, 106705: 0.912447, 106706: 0.778607, 106707: 0.892991, 106708: 0.855366, 106709: 0.729886, 106710: 0.782424, 106711: 0.901534, 106712: 0.907393, 106713: 0.855864, 106714: 0.821204},
'Nuclei in individual cell region Selected - Individual Cell Region resized Width [µm]': {106695: 17.961, 106696: 24.8965, 106697: 30.1663, 106698: 25.6286, 106699: 23.994, 106700: 20.1043, 106701: 6.52379, 106702: 13.5987, 106703: 21.1186, 106704: 16.9182, 106705: 22.8623, 106706: 21.0169, 106707: 19.2897, 106708: 23.6516, 106709: 16.6386, 106710: 10.5238, 106711: 21.0928, 106712: 19.1464, 106713: 15.8732, 106714: 13.3183},
'Nuclei in individual cell region Selected - Individual Cell Region resized Length [µm]': {106695: 33.1711, 106696: 38.3933, 106697: 43.0947, 106698: 39.4182, 106699: 37.0375, 106700: 31.0506, 106701: 20.972, 106702: 20.0319, 106703: 33.7673, 106704: 28.7935, 106705: 28.5463, 106706: 36.9713, 106707: 29.5297, 106708: 32.7235, 106709: 38.1401, 106710: 16.6881, 106711: 30.8736, 106712: 24.0524, 106713: 24.5909, 106714: 32.0091},
'Nuclei in individual cell region Selected - Individual Cell Region resized Ratio Width to Length': {106695: 0.541467, 106696: 0.648461, 106697: 0.700001, 106698: 0.650171, 106699: 0.647832, 106700: 0.647469, 106701: 0.311071, 106702: 0.678853, 106703: 0.625416, 106704: 0.587569, 106705: 0.800884, 106706: 0.568466, 106707: 0.653231, 106708: 0.72277, 106709: 0.43625, 106710: 0.630619, 106711: 0.6832, 106712: 0.79603, 106713: 0.645494, 106714: 0.41608},
'Nuclei in individual cell region Selected - Relative Spot Intensity': {106695: 0.053115, 106696: 0.030453, 106697: 0.0528771, 106698: 0.0706828, 106699: 0.0553709, 106700: 0.0548624, 106701: 0.0991606, 106702: 0.0846535, 106703: 0.0676428, 106704: 0.138471, 106705: 0.0741397, 106706: 0.0459002, 106707: 0.0422811, 106708: 0.0763994, 106709: 0.0122011, 106710: 0.020017, 106711: 0.0777289, 106712: 0.0340526, 106713: 0.0368442, 106714: 0.0485223},
'Nuclei in individual cell region Selected - Number of Spots per Area of Individual Cell Region resized': {106695: 0.00107697, 106696: 0.00052672, 106697: 0.000865569, 106698: 0.0009429, 106699: 0.000833198, 106700: 0.000898907, 106701: 0.00170492, 106702: 0.000885526, 106703: 0.00108172, 106704: 0.00207802, 106705: 0.00105279, 106706: 0.000451698, 106707: 0.000829531, 106708: 0.000906473, 106709: 0.000258992, 106710: 0.000231857, 106711: 0.00106421, 106712: 0.000570803, 106713: 0.000843502, 106714: 0.000629581},
'Compound': {106695: 'Ctrl', 106696: 'Ctrl', 106697: 'Ctrl', 106698: 'Ctrl', 106699: 'Ctrl', 106700: 'Ctrl', 106701: 'Ctrl', 106702: 'Ctrl', 106703: 'Ctrl', 106704: 'Ctrl', 106705: 'Ctrl', 106706: 'Ctrl', 106707: 'Ctrl', 106708: 'Ctrl', 106709: 'Ctrl', 106710: 'Ctrl', 106711: 'Ctrl', 106712: 'Ctrl', 106713: 'Ctrl', 106714: 'Ctrl'}}
df1_Control = pd.DataFrame(df_Control)

I have a data set for many drugs (Compounds) with many features (columns). I want to make a loop to generate histograms from each column of df_Sample for each drug, side by side in a facetgrid. In addition, for comparison, I need to take the data from same columns in the df_Control and merge it on the appropriate histogram of df_Sample. When I just want to have df_Sample histograms, I get the desired results from the code below:

i = 0
for i, column, in enumerate(df1_Sample.columns):
    sns.FacetGrid(data=df1_Control, col='Compound', col_wrap= 6).map(plt.hist, column) 
    file_name = 'plot_' + column + '.png'
    plt.savefig(file_name)

However, cannot make the script work for merging the samples with the relevant control on the same graph. I thought maybe someone can revise the script that came to my mind:

i1 = 0
i2 = 0
for (i1, column1), (i2, column2) in zip(enumerate(df1_Sample.columns), enumerate(df1_Sample.columns)):
    sns.FacetGrid(data=[df1_Sample, df1_Control], col='Compound', col_wrap= 6).map(plt.hist, column) #In FaceGrid, use col for determining the identifier, which is the name of the compounds.
    sns.FacetGrid(data=df1_Control, col='Compound', col_wrap= 6).map(plt.hist, column)
    plt.xlabel("Data", size=14)
    plt.ylabel("Count", size=14)
    plt.legend(loc='upper right')
    file_name = 'plot_' + column + '.png'
    plt.savefig(file_name, dpi=1200)

Nevertheless, I don't know if can, for example, use 'fig, ax = plt.subplots()' somehow for this purpose or can make the 'sns.FacetGrid' work.

I very much appreciate your kind suggestion.

pandas seaborn histogram matplotlib python
1 Answers

  • Direct use of seaborn.FacetGrid is deprecate. In this case it's better to use seaborn.displot, which is a figure-level plot.

  • Iterate through the column names for df1_sample, and use the column name to get df1_Sample[col] and df1_Control[col], which assumes both dataframes have the same column names, as shown in the OP.

    • Use for c1, c2 in zip(df1_Sample.columns[:-1], df1_Control.columns[:-1]): with df1_Sample[c1] and df1_Control[c2] if the column names are not the same, however the columns of both dataframes need to be ordered.

  • Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2

  • For each column pair, to plot the histogram for each compound separately, it will be easier to combine the data into a long dataframe, and then plot with seaborn.displot.

# assumes both dataframes have same number of columns and the have the same name
for col in df1_Sample.columns[:-1]:
    
    # combine the data from sample and control
    compound = df1_Sample['Compound']
    sample = df1_Sample[col].tolist()
    control = df1_Control[col].tolist()
    data = pd.DataFrame({'sample': sample, 'control': control, 'compound': compound})
    data = data.melt(id_vars='compound')  # convert data to a long form 
    
    # plot the data
    p = sns.displot(data=data, x='value', hue='variable', col='compound', col_wrap=4, height=3.5)
    p.fig.subplots_adjust(top=0.94) # adjust the Figure in p
    p.fig.suptitle(col)
    file_name = f'plot {col}.png'
    p.savefig(file_name, dpi=1200)

enter image description here

  • Response to comment about ValueError: arrays must all be same length
    • Here is an alternate way to combine the data from the two dataframes, when the number of rows between the two dataframes is not the same.
    • The issue becomes that the 'Compound' column in df1_Control only contains 'Ctrl', so there's no way to associate the rows with the 'Compound' from df1_Sample. So, all of the control data would be plotted in a separate histogram.
    • Therefore, the data in the 'Compound' column of df1_Control, needs to be properly labeled.
for col in df1_Sample.columns[:1]:
    
    # combine the data from sample and control

    sample = df1_Sample[[col, 'Compound']].copy()
    sample['variable'] = 'sample'
    control = df1_Control[[col, 'Compound']].copy()
    control['variable'] = 'control'
    data = pd.concat([sample, control]).reset_index(drop=True)
    data.columns = ['value', 'compound', 'variable']
    display(data)

    # plot the data
    p = sns.displot(data=data, x='value', hue='variable', col='compound', col_wrap=4, height=3.5)
    p.fig.subplots_adjust(top=0.94) # adjust the Figure in p
    p.fig.suptitle(col)
    file_name = f'plot {col}.png'
    p.savefig(file_name)

[out]:
       value             compound variable
0   189.4800    Ciprofloxacin-Low   sample
1   153.7360   Flunisolide-Medium   sample
2   199.2190  Famprofazone-Medium   sample
3   221.4000      Alprenolol-High   sample
4   261.6480        Dyclonine-Low   sample
5   304.0890   Flunisolide-Medium   sample
6   345.9350      Zaleplon-Medium   sample
7   218.9350       Hexetidine-Low   sample
8   232.6010      Hexetidine-High   sample
9   240.9120     Amprolium-Medium   sample
10  208.1250         Pindolol-Low   sample
11  260.7130        Zaleplon-High   sample
12  161.1120     Famprofazone-Low   sample
13  270.1810       Dyclonine-High   sample
14  165.8880     Montensin-Medium   sample
15  342.0770      Pindolol-Medium   sample
16  158.3760    Hexetidine-Medium   sample
17  557.0350   Flunisolide-Medium   sample
18  319.9130     Dyclonine-Medium   sample
19  257.2970       Hexetidine-Low   sample
20  205.1850                 Ctrl  control
21  160.0080                 Ctrl  control
22  329.2270                 Ctrl  control
23  264.5210                 Ctrl  control
24  242.8670                 Ctrl  control
25  225.5980                 Ctrl  control
26   53.7438                 Ctrl  control
27   63.8908                 Ctrl  control
28  208.2440                 Ctrl  control
29  195.4800                 Ctrl  control
30  218.5100                 Ctrl  control
31  160.2620                 Ctrl  control
32  190.5680                 Ctrl  control
33  254.6970                 Ctrl  control
34  239.3990                 Ctrl  control
35   59.5907                 Ctrl  control
36  228.2670                 Ctrl  control
37  164.5120                 Ctrl  control
38  125.6910                 Ctrl  control
39  177.4120                 Ctrl  control

enter image description here

  • Since the control data is not labeled, for each compound, crate a control dataframe where all the data is tagged with the given compound. This would allow for comparing each compound against the distribution of all the control data for each column.
for col in df1_Sample.columns[:1]:  # testing on first column; change to [:-1] for all but the last column
    
    # combine the data from sample and control
    sample = df1_Sample[[col, 'Compound']].copy()
    sample['variable'] = 'sample'
    
    control = df1_Control[[col]].copy()
    control['variable'] = 'control'
    
    compounds = df1_Sample['Compound'].unique()
    
    # for each compound, crate a control dataframe where all the data is tagged with the given compound
    control_list = list()
    for compound in compounds:
        ctrl = control.copy()
        ctrl['Compound'] = compound
        control_list.append(ctrl)
            
    data = pd.concat([sample] + control_list).reset_index(drop=True)
    data.columns = ['value', 'compound', 'variable']

    display(data.head())  # display works in a notebook, otherwise use print
    display(data.tail())  # remove or comment these display lines out

    # plot the data
    p = sns.displot(data=data, x='value', hue='variable', col='compound', col_wrap=4, height=3.5)
    p.fig.subplots_adjust(top=0.94) # adjust the Figure in p
    p.fig.suptitle(col)
    file_name = f'plot {col}.png'
    p.savefig(file_name)

enter image description here

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