python - 如何绘制来自具有相同列名的两个数据框的数据

Question

假设我有两个 DataFrame（示例和控件），如下所示：

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)

我有一个具有许多特征（列）的许多药物（化合物）的数据集。我想创建一个循环，从 df_Sample 的每一列为每种药物生成直方图，并排在 facetgrid 中。此外，为了比较，我需要从 df_Control 中的相同列中获取数据，并将其合并到 df_Sample 的相应直方图上。当我只想拥有 df_Sample 直方图时，我可以从下面的代码中获得所需的结果：

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)

但是，不能使脚本用于将样本与同一图表上的相关控件合并。我想也许有人可以修改我想到的脚本：

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)

尽管如此，我不知道是否可以，例如，为此目的使用“fig, ax = plt.subplots()”，或者是否可以使“sns.FacetGrid”工作。

我非常感谢你的好意建议。

标签： pythonpandasmatplotlibseabornhistogram

Answer 1

• 直接使用seaborn.FacetGrid已弃用。在这种情况下，最好使用seaborn.displot，这是一个图形级别的图。

• 遍历 的列名df1_sample，并使用列名来获取df1_Sample[col]和df1_Control[col]，这假设两个数据帧具有相同的列名，如 OP 中所示。

  • 如果列名不同，则使用for c1, c2 in zip(df1_Sample.columns[:-1], df1_Control.columns[:-1]):withdf1_Sample[c1]和，但是需要对两个数据框的列进行排序。df1_Control[c2]

• 在python 3.8.11, pandas 1.3.2, matplotlib 3.4.3,中测试seaborn 0.11.2

• 对于每对列，要分别绘制每种化合物的直方图，将数据组合成一个长数据框会更容易，然后用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)

• 对评论的回应ValueError: arrays must all be same length
  • 当两个数据帧之间的行数不相同时，这是组合来自两个数据帧的数据的另一种方法。
  • 问题变成了仅包含的'Compound'列，因此无法将行与from相关联。因此，所有控制数据都将绘制在单独的直方图中。df1_Control'Ctrl''Compound'df1_Sample
  • 因此 ,'Compound'列中的数据df1_Control需要正确标注。

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

• 由于未标记控制数据，因此对于每个化合物，请创建一个控制数据框，其中所有数据都使用给定化合物进行标记。这将允许将每种化合物与每列的所有对照数据的分布进行比较。

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)