Pandas takes all columns of a dataframe even when some columns are specified

Question

I am trying to train KMeans model using Scikit-Learn. I am stuck on this issue for 2 days. Pandas is selecting all columns of a dataframe even though I specified 2 columns. Here is the dataframe in the form of .to_dict():

{'area': {0: 15.26, 1: 14.88, 2: 14.29, 3: 13.84, 4: 16.14, 5: 14.38, 6: 14.69, 7: 16.63, 8: 16.44, 9: 15.26, 10: 14.03, 11: 13.89, 12: 13.78, 13: 13.74, 14: 14.59, 15: 13.99, 16: 15.69, 17: 14.7, 18: 12.72, 19: 14.16, 20: 14.11, 21: 15.88, 22: 12.08, 23: 15.01, 24: 16.19, 25: 13.02, 26: 12.74, 27: 14.11, 28: 13.45, 29: 13.16, 30: 15.49, 31: 14.09, 32: 13.94, 33: 15.05, 34: 16.2, 35: 17.08, 36: 14.8, 37: 14.28, 38: 13.54, 39: 13.5, 40: 13.16, 41: 15.5, 42: 15.11, 43: 13.8, 44: 15.36, 45: 14.99, 46: 14.79, 47: 14.86, 48: 14.43, 49: 15.78, 50: 14.49, 51: 14.33, 52: 14.52, 53: 15.03, 54: 14.46, 55: 14.92, 56: 15.38, 57: 12.11, 58: 11.23, 59: 12.36, 60: 13.22, 61: 12.78, 62: 12.88, 63: 14.34, 64: 14.01, 65: 12.73, 66: 17.63, 67: 16.84, 68: 17.26, 69: 19.11, 70: 16.82, 71: 16.77, 72: 17.32, 73: 20.71, 74: 18.94, 75: 17.12, 76: 16.53, 77: 18.72, 78: 20.2, 79: 19.57, 80: 19.51, 81: 18.27, 82: 18.88, 83: 18.98, 84: 21.18, 85: 20.88, 86: 20.1, 87: 18.76, 88: 18.81, 89: 18.59, 90: 18.36, 91: 16.87, 92: 19.31, 93: 18.98, 94: 18.17, 95: 18.72, 96: 16.41, 97: 17.99, 98: 19.46, 99: 19.18, 100: 18.95, 101: 18.83, 102: 17.63, 103: 19.94, 104: 18.55, 105: 18.45, 106: 19.38, 107: 19.13, 108: 19.14, 109: 20.97, 110: 19.06, 111: 18.96, 112: 19.15, 113: 18.89, 114: 20.03, 115: 20.24, 116: 18.14, 117: 16.17, 118: 18.43, 119: 15.99, 120: 18.75, 121: 18.65, 122: 17.98, 123: 20.16, 124: 17.55, 125: 18.3, 126: 18.94, 127: 15.38, 128: 16.16, 129: 15.56, 130: 17.36, 131: 15.57, 132: 15.6, 133: 16.23, 134: 13.07, 135: 13.32, 136: 13.34, 137: 12.22, 138: 11.82, 139: 11.21, 140: 11.43, 141: 12.49, 142: 12.7, 143: 10.79, 144: 11.83, 145: 12.01, 146: 12.26, 147: 11.18, 148: 11.36, 149: 11.19, 150: 11.34, 151: 12.13, 152: 11.75, 153: 11.49, 154: 12.54, 155: 12.02, 156: 12.05, 157: 12.55, 158: 11.14, 159: 12.1, 160: 12.44, 161: 12.15, 162: 11.35, 163: 11.55, 164: 11.4, 165: 10.83, 166: 10.8, 167: 11.26, 168: 10.74, 169: 11.48, 170: 12.21, 171: 11.41, 172: 12.46, 173: 12.19, 174: 11.65, 175: 12.89, 176: 11.56, 177: 11.81, 178: 10.91, 179: 11.23, 180: 10.59, 181: 10.93, 182: 11.27, 183: 11.87, 184: 10.82, 185: 12.11, 186: 12.8, 187: 12.79, 188: 13.37, 189: 12.62, 190: 12.76, 191: 12.38, 192: 11.18, 193: 12.37, 194: 12.19, 195: 11.23, 196: 13.2, 197: 11.84, 198: 12.3}, 'perimeter': {0: 14.84, 1: 14.57, 2: 14.09, 3: 13.94, 4: 14.99, 5: 14.21, 6: 14.49, 7: 15.46, 8: 15.25, 9: 14.85, 10: 14.16, 11: 14.02, 12: 14.06, 13: 14.05, 14: 14.28, 15: 13.83, 16: 14.75, 17: 14.21, 18: 13.57, 19: 14.4, 20: 14.26, 21: 14.9, 22: 13.23, 23: 14.76, 24: 15.16, 25: 13.76, 26: 13.67, 27: 14.18, 28: 14.02, 29: 13.82, 30: 14.94, 31: 14.41, 32: 14.17, 33: 14.68, 34: 15.27, 35: 15.38, 36: 14.52, 37: 14.17, 38: 13.85, 39: 13.85, 40: 13.55, 41: 14.86, 42: 14.54, 43: 14.04, 44: 14.76, 45: 14.56, 46: 14.52, 47: 14.67, 48: 14.4, 49: 14.91, 50: 14.61, 51: 14.28, 52: 14.6, 53: 14.77, 54: 14.35, 55: 14.43, 56: 14.77, 57: 13.47, 58: 12.63, 59: 13.19, 60: 13.84, 61: 13.57, 62: 13.5, 63: 14.37, 64: 14.29, 65: 13.75, 66: 15.98, 67: 15.67, 68: 15.73, 69: 16.26, 70: 15.51, 71: 15.62, 72: 15.91, 73: 17.23, 74: 16.49, 75: 15.55, 76: 15.34, 77: 16.19, 78: 16.89, 79: 16.74, 80: 16.71, 81: 16.09, 82: 16.26, 83: 16.66, 84: 17.21, 85: 17.05, 86: 16.99, 87: 16.2, 88: 16.29, 89: 16.05, 90: 16.52, 91: 15.65, 92: 16.59, 93: 16.57, 94: 16.26, 95: 16.34, 96: 15.25, 97: 15.86, 98: 16.5, 99: 16.63, 100: 16.42, 101: 16.29, 102: 15.86, 103: 16.92, 104: 16.22, 105: 16.12, 106: 16.72, 107: 16.31, 108: 16.61, 109: 17.25, 110: 16.45, 111: 16.2, 112: 16.45, 113: 16.23, 114: 16.9, 115: 16.91, 116: 16.12, 117: 15.38, 118: 15.97, 119: 14.89, 120: 16.18, 121: 16.41, 122: 15.85, 123: 17.03, 124: 15.66, 125: 15.89, 126: 16.32, 127: 14.9, 128: 15.33, 129: 14.89, 130: 15.76, 131: 15.15, 132: 15.11, 133: 15.18, 134: 13.92, 135: 13.94, 136: 13.95, 137: 13.32, 138: 13.4, 139: 13.13, 140: 13.13, 141: 13.46, 142: 13.71, 143: 12.93, 144: 13.23, 145: 13.52, 146: 13.6, 147: 13.04, 148: 13.05, 149: 13.05, 150: 12.87, 151: 13.73, 152: 13.52, 153: 13.22, 154: 13.67, 155: 13.33, 156: 13.41, 157: 13.57, 158: 12.79, 159: 13.15, 160: 13.59, 161: 13.45, 162: 13.12, 163: 13.1, 164: 13.08, 165: 12.96, 166: 12.57, 167: 13.01, 168: 12.73, 169: 13.05, 170: 13.47, 171: 12.95, 172: 13.41, 173: 13.36, 174: 13.07, 175: 13.77, 176: 13.31, 177: 13.45, 178: 12.8, 179: 12.82, 180: 12.41, 181: 12.8, 182: 12.86, 183: 13.02, 184: 12.83, 185: 13.27, 186: 13.47, 187: 13.53, 188: 13.78, 189: 13.67, 190: 13.38, 191: 13.44, 192: 12.72, 193: 13.47, 194: 13.2, 195: 12.88, 196: 13.66, 197: 13.21, 198: 13.34}, 'compactness': {0: 0.871, 1: 0.8811, 2: 0.905, 3: 0.8955, 4: 0.9034, 5: 0.8951, 6: 0.8799, 7: 0.8747, 8: 0.888, 9: 0.8696, 10: 0.8796, 11: 0.888, 12: 0.8759, 13: 0.8744, 14: 0.8993, 15: 0.9183, 16: 0.9058, 17: 0.9153, 18: 0.8686, 19: 0.8584, 20: 0.8722, 21: 0.8988, 22: 0.8664, 23: 0.8657, 24: 0.8849, 25: 0.8641, 26: 0.8564, 27: 0.882, 28: 0.8604, 29: 0.8662, 30: 0.8724, 31: 0.8529, 32: 0.8728, 33: 0.8779, 34: 0.8734, 35: 0.9079, 36: 0.8823, 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6.55, 104: 5.894, 105: 5.794, 106: 5.965, 107: 5.924, 108: 6.053, 109: 6.316, 110: 6.163, 111: 5.75, 112: 6.185, 113: 5.966, 114: 6.32, 115: 6.188, 116: 6.011, 117: 5.703, 118: 5.905, 119: 5.144, 120: 5.992, 121: 6.102, 122: 5.919, 123: 6.185, 124: 5.661, 125: 5.962, 126: 5.949, 127: 5.795, 128: 5.795, 129: 5.847, 130: 5.971, 131: 5.879, 132: 5.752, 133: 5.922, 134: 5.395, 135: 5.44, 136: 5.307, 137: 5.221, 138: 5.178, 139: 5.275, 140: 5.132, 141: 5.002, 142: 5.316, 143: 5.194, 144: 5.307, 145: 5.27, 146: 5.36, 147: 5.001, 148: 5.263, 149: 5.219, 150: 5.003, 151: 5.22, 152: 5.31, 153: 5.31, 154: 5.491, 155: 5.308, 156: 5.046, 157: 5.176, 158: 5.049, 159: 5.056, 160: 5.27, 161: 5.338, 162: 5.132, 163: 4.956, 164: 5.089, 165: 5.185, 166: 5.063, 167: 5.092, 168: 4.963, 169: 5.002, 170: 5.178, 171: 4.825, 172: 5.147, 173: 5.158, 174: 5.135, 175: 5.316, 176: 5.182, 177: 5.352, 178: 4.956, 179: 4.957, 180: 4.794, 181: 5.045, 182: 5.001, 183: 5.132, 184: 5.089, 185: 5.012, 186: 4.914, 187: 4.958, 188: 5.091, 189: 5.231, 190: 4.83, 191: 5.045, 192: 4.828, 193: 5.001, 194: 4.87, 195: 5.003, 196: 5.056, 197: 5.044, 198: 5.063}, 'class': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1, 12: 1, 13: 1, 14: 1, 15: 1, 16: 1, 17: 1, 18: 1, 19: 1, 20: 1, 21: 1, 22: 1, 23: 1, 24: 1, 25: 1, 26: 1, 27: 1, 28: 1, 29: 1, 30: 1, 31: 1, 32: 1, 33: 1, 34: 1, 35: 1, 36: 1, 37: 1, 38: 1, 39: 1, 40: 1, 41: 1, 42: 1, 43: 1, 44: 1, 45: 1, 46: 1, 47: 1, 48: 1, 49: 1, 50: 1, 51: 1, 52: 1, 53: 1, 54: 1, 55: 1, 56: 1, 57: 1, 58: 1, 59: 1, 60: 1, 61: 1, 62: 1, 63: 1, 64: 1, 65: 1, 66: 2, 67: 2, 68: 2, 69: 2, 70: 2, 71: 2, 72: 2, 73: 2, 74: 2, 75: 2, 76: 2, 77: 2, 78: 2, 79: 2, 80: 2, 81: 2, 82: 2, 83: 2, 84: 2, 85: 2, 86: 2, 87: 2, 88: 2, 89: 2, 90: 2, 91: 2, 92: 2, 93: 2, 94: 2, 95: 2, 96: 2, 97: 2, 98: 2, 99: 2, 100: 2, 101: 2, 102: 2, 103: 2, 104: 2, 105: 2, 106: 2, 107: 2, 108: 2, 109: 2, 110: 2, 111: 2, 112: 2, 113: 2, 114: 2, 115: 2, 116: 2, 117: 2, 118: 2, 119: 2, 120: 2, 121: 2, 122: 2, 123: 2, 124: 2, 125: 2, 126: 2, 127: 2, 128: 2, 129: 2, 130: 2, 131: 2, 132: 2, 133: 2, 134: 3, 135: 3, 136: 3, 137: 3, 138: 3, 139: 3, 140: 3, 141: 3, 142: 3, 143: 3, 144: 3, 145: 3, 146: 3, 147: 3, 148: 3, 149: 3, 150: 3, 151: 3, 152: 3, 153: 3, 154: 3, 155: 3, 156: 3, 157: 3, 158: 3, 159: 3, 160: 3, 161: 3, 162: 3, 163: 3, 164: 3, 165: 3, 166: 3, 167: 3, 168: 3, 169: 3, 170: 3, 171: 3, 172: 3, 173: 3, 174: 3, 175: 3, 176: 3, 177: 3, 178: 3, 179: 3, 180: 3, 181: 3, 182: 3, 183: 3, 184: 3, 185: 3, 186: 3, 187: 3, 188: 3, 189: 3, 190: 3, 191: 3, 192: 3, 193: 3, 194: 3, 195: 3, 196: 3, 197: 3, 198: 3}}

Here is the code:

cols=['area', 'perimeter', 'compactness', 'length', 'width', 'asymmetry', 'groove', 'class']

x = 'perimeter'
y = 'asymmetry'
z = df[[x, y]].values

kmeans = KMeans(n_clusters=3, n_init=10).fit(z)

clusters = kmeans.labels_

print(clusters)

cluster_df = pd.DataFrame(np.hstack((z, clusters.reshape(-1, 1))), columns=[x, y, "class"])

sns.scatterplot(x=x, y=y, hue='class', data=cluster_df)
plt.show()

sns.scatterplot(x=x, y=y, hue='class', data=df)
plt.show()

This is the plot of the original dataset

This is the predicted plot

I was expecting the predicted plot to look like the original dataset plot.

The predicted plot looks like sns plotted all the different columns together.

PS : I am a beginner so I might not know stuff so please don't dislike.

EDIT: I think it is a problem with the term 'cluster_df', but I don't know what is wrong with it or what should I change.

Answer 1

It turns out that it was the line:

kmeans = KMeans(n_clusters=3, n_init=10).fit(z)

It should be:

kmeans = KMeans(n_clusters=3).fit(z)

without the n_init=10