From 690f8b46a978611c913858db1700fc7ae29b21f2 Mon Sep 17 00:00:00 2001 From: Mulhamomar Date: Mon, 22 Sep 2025 12:56:58 +0200 Subject: [PATCH 1/2] assignemnt-3-done --- .vscode/settings.json | 5 +++++ icecream.csv | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..a8c2003 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "python-envs.defaultEnvManager": "ms-python.python:conda", + "python-envs.defaultPackageManager": "ms-python.python:conda", + "python-envs.pythonProjects": [] +} \ No newline at end of file diff --git a/icecream.csv b/icecream.csv index 380e6da..3510295 100644 --- a/icecream.csv +++ b/icecream.csv @@ -62,7 +62,7 @@ Michiel58 mikegeerts mmharis mtliem98 -Mulhamomar +Mulhamomar, pistachio, blue, 175 NBrodbelt Neillzt nhollnagel From 90b492bf54b5615e3d5aea249d9a781979f2c7dd Mon Sep 17 00:00:00 2001 From: Mulhamomar Date: Mon, 22 Sep 2025 13:04:24 +0200 Subject: [PATCH 2/2] assignment3 --- lab7_3.ipynb | 36 +++++++++++++++++++++++++++++------- 1 file changed, 29 insertions(+), 7 deletions(-) diff --git a/lab7_3.ipynb b/lab7_3.ipynb index 56fc1bb..54e96e5 100644 --- a/lab7_3.ipynb +++ b/lab7_3.ipynb @@ -22,13 +22,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "3742e4e6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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fdrpEE37JYaibdev5C/V0S/qlZ5ooVqyY0yUCOYrtzYBM0haZnrMvJiZG1q9fb4JOv/7666+gP+u6thRThmGdOnWkfv36UqJECadLA7IF4QdcIuiio6NlxYoV5rue8UG7IW1y/fXXmw2+9UvDUL8TiHADwg8QMV2TelJaPYu7fv/zzz+tC7rMqlSpkjRr1kxatGghLVu2NAEJBBvCD1Y6cuSIN+wiIyNlzZo1Qd916ZSKFSt6g1C/ypcv73RJwGURfrBGbGyshIeHyw8//GC6MmnZZY+qVavKPffcI+3bt5cmTZpI7ty5nS4JSIPwg2slJiaa1l1y4G3fvt3pkqxz9dVXy1133WWCsG3btmY5BhAICD+4LvDmzp0r06ZNkx9//NGsp0NgyJs3r+ke7dSpkzz44INStGhRp0uCxQg/BD39X3jx4sXy1VdfycyZMwNiXR0uLV++fNKuXTvp1q2baRlqMAI5ifBD0NKlBxp4U6ZMkbi4OKfLwRV0jXbs2FEefvhhady4sdnNBshuhB+Cbquw6dOny4QJE2TZsmVOlwM/q1Klijz++OPSp08f1hMiWxF+CArbtm2Tjz76SCZOnCiHDh1yuhzkQLeojg0++eSTEhYW5nQ5cCHCDwFLlyLo5JXx48dLREQESxMspbvK9OvXT7p27Wo29Ab8gfBDwDlz5oxMmjRJRo0aZVp8gCpevLhpCT799NNmL1LgShB+CBjHjx83rbz33nvPnPYHSE+BAgWkb9++MmTIEKlQoYLT5SBIEX5wXHx8vAk8DT7W5SGzQkNDTVfo0KFDpUaNGk6XgyBD+MExeq67N998Uz777DNzTjzAF7o0okOHDjJ8+HCpVauW0+UgSBB+yHHauhs5cqS8//77kpCQ4HQ5cAk9y32XLl3k9ddfN2eeAC6F8EOOTmQZO3asvP322+zCgmyju8U89thj8tJLL0np0qWdLgcBivBDtktKSjKzN1999VVz1nMgJxQqVEgGDRokzz33HBtqIw3CD9nqp59+kmeeeUbWr1/vdCmwVMmSJU1XqLYGtWsUUIQfssXOnTvNp+5Zs2Y5XQrgXSw/btw4doyBwccg+NW5c+dkxIgRZuo5wYdAEh0dLY0aNZJHHnlEDh486HQ5cBgtP/jNvHnzZODAgbJ582anSwEuu1uMfkjTTbTpCrUT4Ycrtm/fPunfvz8tPQSdevXqmXWmderUcboU5DA+8uCKfP3111KzZk2CD0EpJiZGGjZsKK+88ookJiY6XQ5yEC0/+Nzae+KJJ2T27NlOlwL4Re3atWXy5Mm0Ai1Byw8+t/YIPrjJ6tWraQVahJYfMk3PtKATBAg9uB2tQPej5YdML1bXTYMJPtjSCtT1gB988IHTpSCbEH64pPPnz8sLL7wgbdu2NaceAmxas6onztUzRhw5csTpcuBndHvikru06C75S5YscboUwFF60typU6fK7bff7nQp8BNafkiXdm/qeAfBB/zvg2Dz5s3lrbfeEtoL7kDLD2m6OQcPHsxYB5CBO++8U6ZMmSIlSpRwuhRcAcIPXrrfYceOHSUyMtLpUoCApifL1d6RW265xelS4CO6PWGsWbPGrHEi+IDLi4uLM+N/33//vdOlwEeEH8wfsP4h6x80gMw5efKk3H///eZcgQg+hJ/l9A9X/4D1DxlA1uio0csvvywPPfSQJCQkOF0OsoAxP0udPn1aevXqJTNmzHC6FMAV6tatK+Hh4VKuXDmnS0EmEH4W0gW799xzjyxevNjpUgBX0eCLiIiQm266yelScBl0e1rmr7/+kqZNmxJ8QDb9fTVp0oT1sUGA8LPI+vXrzcSWtWvXOl0K4OqeldatW8sPP/zgdCm4BMLPEsuWLTOfSHft2uV0KYAVY+r33XefTJw40elSkAHCzwJz5syRVq1ayeHDh50uBbBGUlKS9O3bV958802nS0E6CD+Xmz59utmVnmnYgDNefPFFef75550uAxdhtqeL6f6DPXr0MJ9AAThr0KBBMmbMGKfLwN9o+bnUF198Id27dyf4gADx7rvvysCBA50uA38j/Fzoyy+/lN69e8uFCxecLgVACmPHjiUAAwTh58KuTt25heADAjcAtQsUziL8XES3KtMxPoIPCGzvvfcek2AcxoQXl9AtlXTLssTERKdLAZBJugxi2LBhTpdhJcLPBaKioqRly5Zy6tQpp0sBkEWfffaZ9OnTx+kyrEP4BblNmzaZnVsOHDjgdCkAfJA7d2757rvvTM8Ncg7hF8T27t1r9urcvn2706UAuAL58+eXX375xfw9I2cQfkHq2LFj0qxZM4mNjXW6FAB+cPXVV8vChQs5HVIOYbZnEDp79qzce++9BB/gIrr3bps2bcxpkZD9CL8g9Nhjj8mCBQucLgOAn2nwtW/fnr14cwDhF2RGjx5tti4D4E4rV640G1UgexF+QWTevHkydOhQp8sAkM1mzpwpr7/+utNluBoTXoLExo0bJSwszEx0AeB+ISEhMmvWLHNKMvgf4RcENPAaNmxo1vQBsEehQoVk6dKlcvPNNztdiuvQ7RngdJ/Ozp07E3yAhU6ePGkmwBw6dMjpUlyH8Atww4cPN2N9AOwUFxcnXbt2FTrp/IvwC2C//vqrjBgxwukyADjsp59+kpEjRzpdhqsw5heg4uPjpXbt2rJv3z6nSwEQAPLkyWPW97IFmn/Q8gtA+nmke/fuBB8Ar/Pnz0uXLl3kyJEjTpfiCoRfANLuDe3mAICUdu7cKb1793a6DFeg2zPALFmyRJo3b24+5QFAej744AMZMGCA02UENcIvwNbz1apVy3y6A4CMXHXVVbJs2TKpU6eO06UELbo9A8izzz5L8AHI1JlddP/PxMREp0sJWoRfgPjxxx9l4sSJTpcBIEisXr2apVBXgG7PAOnu1O2LOI8XgKwIDQ2V5cuX0/3pA1p+AdLdSfAByCrt9qT70zeEn8Po7gRwJej+9A3dng6iuxOAP9D9mXW0/Bz0wgsvEHwArph2e/bt29ecBQaZQ/g5JDo6Wj755BOnywDgEjExMfLxxx87XUbQoNvTAfqSN2rUSP744w+nSwHgIsWLFzfn/ixZsqTTpQQ8Wn4O0AkuBB8Af9NNr4cNG+Z0GUGBlp8D/3NWq1ZNDh486HQpAFwoJCREli5dKmFhYU6XEtBo+eWwF198keADkG20PdO/f38mv1wG4ZeDGJAGkFMT6j799FOnywhodHvmoBYtWpgzMQNAdtNJL9u2bZPChQs7XUpAouWXgzu5EHwAcooOr4waNcrpMgIWLb8coC9x3bp1zTZEAJBTChUqJFu3bpXSpUs7XUrAoeWXA6ZOnUrwAchxJ0+elDfeeMPpMgISLb8c2HaoevXqpu8dAHJa3rx5ZcOGDVKpUiWnSwkotPyymW5hRvABcMq5c+fk5ZdfdrqMgEPLLxudOnVKqlSpIvv373e6FAAWy5Url6xcuVJq1arldCkBg5ZfNho/fjzBB8BxuuD91VdfdbqMgELLL5ucPXvW9LHv3bvX6VIAwGx7tm7dOjMHAbT8ss3kyZMJPgABQ9s5I0eOdLqMgEHLLxskJSWZzauZ6AIg0M74ruv+ypcvL7aj5ZcNZsyYQfABCMilV+z68j+0/LJB7dq1JTY21ukyACCNAgUKyI4dO6w/4S0tPz+bM2cOwQcgYCUkJMj7778vtqPl52ecuQFAoCtevLj89ddfphVoK1p+frR27VqCD0DAO3LkiNlz2GaEn58XtQNAMBhv+fsV3Z5+3D29bNmycuLECadLAYBMWbZsmYSFhYmNaPn5yZdffknwAQgq4y1u/dHy8xPdMHbNmjVOlwEAmZYvXz4z8aVEiRJiG1p+frBo0SKCD0DQOXPmjEycOFFsRPj5wYQJE5wuAQB88vHHH5t9P21Dt+cV0nG+MmXKyOnTp50uBQB8snDhQmnSpInYhJbfFZo1axbBByCoff3112Ibwu8KffXVV06XAABXvBl/YmKi2ITwuwJ6vr7ffvvN6TIA4IocPnxY5s6dKzYh/K7AlClT5MKFC06XAQBX7CvLerGY8HIF6tWrJytXrnS6DADwy5q//fv3S5EiRcQGtPx8tG7dOoIPgKvW/H3zzTdiC8LvCgaIAcBNZlj0vka3p4/o8gTgNnnz5pWDBw9K4cKFxe1o+flA98Ij+AC4zblz5yQiIkJsQPj5IDw83OkSACBbhFvy/kb4+cCW/zkA2Gfu3LmSlJQkbkf4+bCX5/z5850uAwCyxaFDh2Tx4sXidoRfFml/uPaLA4BbhVvQu0X4ZdEPP/zgdAkAkK1+sOB9jqUOWVSuXDnZvXu302UAQLbauXOnlC9fXtyKll8WbN68meADYIXIyEhxM8IvC5joAsAW813+fkf4ZYHbPwkBgC3vd4z5ZUHZsmXNOfwAwAY7duyQChUqiBvR8sukjRs3EnwArDLfxV2fhF8mub0LAABset8j/DLp999/d7oEAMhRv7v4fY/wy6SoqCinSwCAHLVt2zY5fPiwuBHhlwnHjx+XLVu2OF0GAOS46OhocSPCL5O/fCbFArDRihUrxI0IP4s/+QCAre9/hJ/Fv3wAsPX9j/Cz+JcPAJezfft2V056Ifwu49ixY0x2AWC1FS4c9yP8LmP16tVMdgFgtVWrVonbEH6Z2NYMAGy20YXvg4TfZWzatMnpEgDAUZtc+D5I+Fn4iQcAbH8fJPws/MQDAFlx4MABOXr0qLgJ4XcJSUlJZm87ALDdJpc1BAi/S4iLi5PExESnywAAx20i/Ozhtl82APhqk8veDwm/S6DLEwD+Z+vWreImhN8l7N692+kSACAg7NmzR8T28KtcubIcOnQozXGdDaTXucXevXudLgEAAsJel70f5vJ1o1OdCXmxs2fPuqq15LZPOgDgqz0uez/Mk5Ubh4eHe/8dEREhRYsW9V7WMPz111+lYsWK4hZu+6QDAL46ceKEnDp1SgoWLChuEOLJwq7NuXL9r6EYEhKSZrPn0NBQE3yjR4+Wdu3aiRuUKFHClafyAABfZ3xWrVpVrGv5XbhwwXyvVKmSREVFScmSJcWttAuX4AOA1L1hVoZfysXfbkeXJwC4d9zPp/BTOr6nX/Hx8d4WYbKJEyeKG/ayAwD8P32/tzr8Xn31VXnttdekfv36cu2115oxQLc5fvy40yUAQMBNerE6/D766COZPHmydO/eXdzKTb9kAPCHkydPitXr/M6dOye33367uJmbfskA4A8nXNQo8Cn8HnnkEZkyZYq4mZt+yQDgDyds7/Y8c+aMfPLJJ/LLL79IrVq1zBq/lMaMGSPBzk2/ZADwh5Mu6hHzKfxiY2OlTp065t9//vlnquvcMvmF8AMA974v+hR+8+fPF7dz0yccAPCHEy4KP05plIGEhASnSwCAgJLgovdFn1p+LVu2vGT35m+//SbB7vz5806XAAABJSmds/lYFX7J433JEhMTZdWqVWb8r2fPnuIGF+9aAwC2u+Ci90Wfwu/dd99N9/jw4cNdM1bmpl8yAPjDBRe9L2bplEaXs2XLFmnYsKErzoawceNGp0sAgIDr9rzpppvE6o2t07N06VLJly+fuMGNN97odAkAgGziU/jdf//9qS5r41FPAbRixQp56aWX/FUbAACBE35FixZNc4Z3bSnpmR7uvPNOf9UGAEDgj/kBAOD6Mb/o6GhZv369+XfNmjWlbt26/qoLAIDACj89m2/nzp0lMjJSihUrZo4dPXrULH6fNm2alCpVyt91AgDg7PZmAwYMMHu8rV271ixr0C9d4K5nPx84cKD/qgMAIFDG/HTCi57OqEGDBqmOL1++3Ex40VYgAACuavnpKv+Lz+Gn9JibdgAAALiTT+F3xx13yNNPPy179uzxHtu9e7cMGjRIWrVq5c/6AAAIjG7PXbt2Sfv27c2YX/ny5b3Hbr75ZgkPD5dy5cr5v1IAAJxe56d303G/DRs2mMs1atSQ1q1b+6suAAACI/z0PH1PPfWULFu2TIoUKZLqumPHjsntt98uH330kTRt2jQ7agUAIOfH/N577z159NFH0wRf8gzQxx9/XMaMGeOfygAACITwW716tbRt2zbD63WZg+76AgCAa8Jv//796S5xSJYnTx45cOCAP+oCACAwwu+6664zO7lkJDY2Vq699lp/1AUAQGCE31133WXO13fmzJk0150+fVpeeeUVadeunT/rAwDA2dme2u1Zr149yZ07t5n1mXy2c13uMG7cOHOK+5iYGClTpoz/KwUAwKl1fjt27JB+/fpJRESEWetnHiQkRNq0aWMCsFKlSv6qDQCAwFrkfuTIEdmyZYsJwKpVq0rx4sX9Xx0AANmAM7kDAKzj08bWAAAEM8IPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwhSkydPlmLFil3yNsOHD5c6derkWE1AsCD8AADWIfwAANYh/IAg9/3330vVqlUlX7580qZNG9m1a1eGt23RooU888wzqY516NBBevXq5b189uxZGTJkiFx33XVSsGBBCQsLk8jIyGx9DkBOI/yAIJaQkCAjRoyQL774QhYvXixHjx6Vzp07X9FjPvXUU7J06VKZNm2axMbGSseOHaVt27ayefNmv9UNOC2P0wUA8F1iYqJ8+OGHpnWmPv/8c6lRo4YsX75cGjZsmOXH27lzp0yaNMl8L1u2rDmmrcB58+aZ42+++abfnwPgBMIPCGJ58uSRBg0aeC9Xr17dzABdv369T+G3Zs0aSUpKkmrVqqU6rl2hJUqU8EvNQCAg/ACL5MqVSzweT5rWY7KTJ09K7ty5JTo62nxPqVChQjlWJ5DdGPMDgtj58+dlxYoV3ssbN240437a9ZmeUqVKyd69e72XtZX3559/ei/XrVvXHIuPj5cbbrgh1dc111yTzc8GyDmEHxDEQkNDZcCAAfLHH3+Y1prO2rztttsy7PK84447ZM6cOeZrw4YN0q9fPxOWybS7s1u3btKjRw+ZNWuWxMXFmfHDt956y9wHcAvCDwhiBQoUkKFDh0rXrl2lcePGpmty+vTpGd6+T58+0rNnTxNuzZs3l8qVK0vLli1T3UYntuj1gwcPlhtvvNEshYiKipIKFSrkwDMCckaI5+IBAAAAXI6WHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfEICGDx8uderUcboMwLUIPyAb7Nu3TwYMGCCVK1eWq666SsqXLy/33HOP/Prrr06XBkBE8jhdAOA227dvl8aNG0uxYsXknXfekVtuuUUSExMlIiJC+vfvLxs2bJBAkJSUJCEhIZIrF5+BYR/+rwf87MknnzShsnz5cnnggQekWrVqUrNmTXn22Wdl2bJl5jY7d+6Ue++9VwoVKiRFihSRTp06yf79+zN8zAsXLshrr70m5cqVMy1J7RKdN2+e9/rIyEjzM48ePeo9tmrVKnNMw1hNnjzZBHJ4eLjcdNNN5nG0DsBGhB/gR4cPHzahpC28ggULprlew0eDTINPb7tgwQL5+eefZdu2bfLQQw9l+Ljvv/++jB49WkaNGiWxsbHSpk0bad++vWzevDlL9SUkJMjIkSPlP//5j6xdu1ZKly7t0/MEgh3dnoAfbdmyRTwej1SvXj3D2+i435o1ayQuLs6MBaovvvjCtA6joqKkQYMGae6joTd06FDp3LmzuawBNn/+fHnvvfdk3Lhxma5Pu1/Hjx8vtWvX9un5AW5Byw/wIw2+y1m/fr0JveTgU9oNqa1Cve5ix48flz179phxxJT0cnq3v5S8efNKrVq1snQfwI0IP8CPqlatasbZcnpSS/KklZThq628i+XPn9/UB9iO8AP86OqrrzbjcdoVeerUqTTX64SUGjVqyK5du8xXsnXr1pnrtAV4MZ0QU7ZsWVm8eHGq43o5+falSpUy3/fu3ZtqwguA9BF+gJ9p8OkygoYNG8q3335rJqVo9+QHH3wgjRo1ktatW5vlD926dZOYmBgzK7RHjx7SvHlzqV+/frqP+dxzz5lxvunTp8vGjRvlhRdeMOH29NNPm+tvuOEG042qi+P1582ZM8dMkAGQPia8AH6mC9s11EaMGCGDBw82rTFtmd16660yYcIE0+04e/Zsswi+WbNmpsuybdu2Mnbs2Awfc+DAgXLs2DHzePHx8abFp0sWtJtVhYaGytSpU6Vfv35mTE8nzbzxxhvSsWPHHHzmQPAI8WRmhB4AABeh2xMAYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPAGAdwg8AYB3CDwBgHcIPACC2+T+HZ5Ym8UmymwAAAABJRU5ErkJggg==", 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" ] @@ -46,9 +46,31 @@ "metadata": {}, "output_type": "display_data" }, + { + "ename": "TypeError", + "evalue": "'value' must be an instance of str or bytes, not a float", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 29\u001b[39m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# Plot heights\u001b[39;00m\n\u001b[32m 28\u001b[39m plt.figure()\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m \u001b[43mplt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mHeight (cm)\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mblack\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 30\u001b[39m plt.title(\u001b[33m\"\u001b[39m\u001b[33mHeight Distribution\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 31\u001b[39m plt.xlabel(\u001b[33m\"\u001b[39m\u001b[33mHeight (cm)\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > name_idx:\n\u001b[32m 448\u001b[39m warn_deprecated(\n\u001b[32m 449\u001b[39m since, message=\u001b[33m\"\u001b[39m\u001b[33mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 450\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[33m; the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 451\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 452\u001b[39m name=name, obj_type=\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m()\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\pyplot.py:3478\u001b[39m, in \u001b[36mhist\u001b[39m\u001b[34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[39m\n\u001b[32m 3453\u001b[39m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes.hist)\n\u001b[32m 3454\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mhist\u001b[39m(\n\u001b[32m 3455\u001b[39m x: ArrayLike | Sequence[ArrayLike],\n\u001b[32m (...)\u001b[39m\u001b[32m 3476\u001b[39m BarContainer | Polygon | \u001b[38;5;28mlist\u001b[39m[BarContainer | Polygon],\n\u001b[32m 3477\u001b[39m ]:\n\u001b[32m-> \u001b[39m\u001b[32m3478\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3479\u001b[39m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3480\u001b[39m \u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3481\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3482\u001b[39m \u001b[43m \u001b[49m\u001b[43mdensity\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdensity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3483\u001b[39m \u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3484\u001b[39m \u001b[43m \u001b[49m\u001b[43mcumulative\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcumulative\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3485\u001b[39m \u001b[43m \u001b[49m\u001b[43mbottom\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbottom\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3486\u001b[39m \u001b[43m \u001b[49m\u001b[43mhisttype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhisttype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3487\u001b[39m \u001b[43m \u001b[49m\u001b[43malign\u001b[49m\u001b[43m=\u001b[49m\u001b[43malign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3488\u001b[39m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[43m=\u001b[49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3489\u001b[39m \u001b[43m \u001b[49m\u001b[43mrwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3490\u001b[39m \u001b[43m \u001b[49m\u001b[43mlog\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3491\u001b[39m \u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3492\u001b[39m \u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3493\u001b[39m \u001b[43m \u001b[49m\u001b[43mstacked\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstacked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3494\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3495\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3496\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > name_idx:\n\u001b[32m 448\u001b[39m warn_deprecated(\n\u001b[32m 449\u001b[39m since, message=\u001b[33m\"\u001b[39m\u001b[33mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 450\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[33m; the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 451\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 452\u001b[39m name=name, obj_type=\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m()\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001b[39m, in \u001b[36m_preprocess_data..inner\u001b[39m\u001b[34m(ax, data, *args, **kwargs)\u001b[39m\n\u001b[32m 1521\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 1522\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minner\u001b[39m(ax, *args, data=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 1523\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1524\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1525\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1526\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcbook\u001b[49m\u001b[43m.\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1527\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbook\u001b[49m\u001b[43m.\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1529\u001b[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001b[32m 1530\u001b[39m auto_label = (bound.arguments.get(label_namer)\n\u001b[32m 1531\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m bound.kwargs.get(label_namer))\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:7053\u001b[39m, in \u001b[36mAxes.hist\u001b[39m\u001b[34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[39m\n\u001b[32m 7051\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m orientation == \u001b[33m\"\u001b[39m\u001b[33mvertical\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 7052\u001b[39m convert_units = \u001b[38;5;28mself\u001b[39m.convert_xunits\n\u001b[32m-> \u001b[39m\u001b[32m7053\u001b[39m x = [*\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mx\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m 7054\u001b[39m *\u001b[38;5;28mmap\u001b[39m(convert_units, x[\u001b[32m1\u001b[39m:])]\n\u001b[32m 7055\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# horizontal\u001b[39;00m\n\u001b[32m 7056\u001b[39m convert_units = \u001b[38;5;28mself\u001b[39m.convert_yunits\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2663\u001b[39m, in \u001b[36m_AxesBase._process_unit_info\u001b[39m\u001b[34m(self, datasets, kwargs, convert)\u001b[39m\n\u001b[32m 2661\u001b[39m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[32m 2662\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis.have_units():\n\u001b[32m-> \u001b[39m\u001b[32m2663\u001b[39m \u001b[43maxis\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2664\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map.items():\n\u001b[32m 2665\u001b[39m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[32m 2666\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\axis.py:1754\u001b[39m, in \u001b[36mAxis.update_units\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m 1752\u001b[39m neednew = \u001b[38;5;28mself\u001b[39m._converter != converter\n\u001b[32m 1753\u001b[39m \u001b[38;5;28mself\u001b[39m._set_converter(converter)\n\u001b[32m-> \u001b[39m\u001b[32m1754\u001b[39m default = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_converter\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1755\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1756\u001b[39m \u001b[38;5;28mself\u001b[39m.set_units(default)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\category.py:106\u001b[39m, in \u001b[36mStrCategoryConverter.default_units\u001b[39m\u001b[34m(data, axis)\u001b[39m\n\u001b[32m 104\u001b[39m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m axis.set_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 107\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 108\u001b[39m axis.units.update(data)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\category.py:182\u001b[39m, in \u001b[36mUnitData.__init__\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m 180\u001b[39m \u001b[38;5;28mself\u001b[39m._counter = itertools.count()\n\u001b[32m 181\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\category.py:217\u001b[39m, in \u001b[36mUnitData.update\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m 214\u001b[39m convertible = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 215\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m OrderedDict.fromkeys(data):\n\u001b[32m 216\u001b[39m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m \u001b[43m_api\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcheck_isinstance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m=\u001b[49m\u001b[43mval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 218\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[32m 219\u001b[39m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[32m 220\u001b[39m convertible = \u001b[38;5;28mself\u001b[39m._str_is_convertible(val)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\mulha\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\__init__.py:92\u001b[39m, in \u001b[36mcheck_isinstance\u001b[39m\u001b[34m(types, **kwargs)\u001b[39m\n\u001b[32m 90\u001b[39m names.remove(\u001b[33m\"\u001b[39m\u001b[33mNone\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 91\u001b[39m names.append(\u001b[33m\"\u001b[39m\u001b[33mNone\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m92\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 93\u001b[39m \u001b[33m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[33m must be an instance of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m\"\u001b[39m.format(\n\u001b[32m 94\u001b[39m k,\n\u001b[32m 95\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m, \u001b[39m\u001b[33m\"\u001b[39m.join(names[:-\u001b[32m1\u001b[39m]) + \u001b[33m\"\u001b[39m\u001b[33m or \u001b[39m\u001b[33m\"\u001b[39m + names[-\u001b[32m1\u001b[39m]\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) > \u001b[32m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m names[\u001b[32m0\u001b[39m],\n\u001b[32m 97\u001b[39m type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n", + "\u001b[31mTypeError\u001b[39m: 'value' must be an instance of str or bytes, not a float" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -62,7 +84,7 @@ "import matplotlib.pyplot as plt\n", "\n", "# Load dataset\n", - "data = pd.read_csv(\"expanded_dataset.csv\")\n", + "data = pd.read_csv(\"icecream.csv\")\n", "\n", "# Plot icecream\n", "plt.figure()\n", @@ -97,7 +119,7 @@ ], "metadata": { "kernelspec": { - "display_name": "mude-base", + "display_name": "TIL6022-25", "language": "python", "name": "python3" }, @@ -111,7 +133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.13.5" } }, "nbformat": 4,