diff --git a/icecream.csv b/icecream.csv
index dddc114..8f9ec87 100644
--- a/icecream.csv
+++ b/icecream.csv
@@ -1,93 +1,94 @@
-Github,Favourite Ice Cream,Favourite Colour,Height (cm)
-jdding,pistachio,green,160  # please use lower case letters for ice cream and colour
-abel4433291
-alecasat
-AnnavanHerwijnen
-Antonie11
-bakerman27
-bartligtenberg
-Bas-82
-beishuizen27-bot
-ben-rider
-bentedegen
-bogdanbuzatu04
-brammigamer
-brettbijland-commits
-charlottekoning10
-chenneman
-cleovos5
-daanfhb
-elvinli2697-star
-emelineneuteboom-rgb
-etyurkay
-Ewan2024
-franciscoacrei
-Giannis26J
-Gijsbezem
-Gijsjjmeijers
-gkosmidis4
-gretasch4-commits
-HuubKav
-HVerbaan
-hylkebleeker
-inesbmp
-IoanBirgaoanu
-irismobius
-JalissaRattan
-JarrikAlgera
-jasperijsselstein
-Jessegotthat
-jessevanderlaan
-jialeidng
-jipposteenstra
-jordstub
-jpvermeer
-julianvanheijningen
-Julievdl
-kawater31
-Kevin-Verbakel
-knarfie1038
-kschmoutziguer
-lafeberfabienne
-LisaFranse
-LoisZhao-1
-LorenzoBouman
-lucvanham8
-lukarekhvia
-maartjevennegoor1
-Manasaht31
-maritvw
-martijnhofwegen18-ui
-Michiel58
-mikegeerts
-mmharis
-mtliem98
-Mulhamomar
-NBrodbelt
-Neillzt
-nhollnagel,chocolate,green,183
-Nynke0607
-OscardeBoer5
-palaceparis
-pieterceulen12-alt
-Pommetjemayo
-pranshusharma0599-cloud
-ricodejong-tudelft
-Satt09
-Saumitra-Delft-2025
-sheikharfahmibinsheikharzimi
-stepkruisinga
-Tguleij
-Theivaprakasham
-Tjerko2002
-TristanRoussou
-turi1122
-wendyqxc
-wessel890
-WJSchakel
-wwouter
-yme116
-Youri-art, lemon, blue, 186 
-zakejorge
-Zef-1999
-zero31415
+Github,Favourite Ice Cream,Favourite Colour,Height (cm),Favorite Ice Cream,Favorite Colour
+jdding,pistachio,green,160  # please use lower case letters for ice cream and colour,,
+abel4433291,,,,,
+alecasat,,,,,
+AnnavanHerwijnen,,,,,
+Antonie11,,,,,
+bakerman27,,,,,
+bartligtenberg,,,,,
+Bas-82,,,,,
+beishuizen27-bot,,,,,
+ben-rider,,,,,
+bentedegen,,,,,
+bogdanbuzatu04,,,,,
+brammigamer,,,,,
+brettbijland-commits,,,,,
+charlottekoning10,,,,,
+chenneman,,,,,
+cleovos5,,,,,
+daanfhb,,,,,
+elvinli2697-star,,,,,
+emelineneuteboom-rgb,,,,,
+etyurkay,,,,,
+Ewan2024,,,,,
+franciscoacrei,,,,,
+Giannis26J,,,,,
+Gijsbezem,,,,,
+Gijsjjmeijers,,,,,
+gkosmidis4,,,,,
+gretasch4-commits,,,,,
+HuubKav,,,,,
+HVerbaan,,,,,
+hylkebleeker,,,,,
+inesbmp,,,,,
+IoanBirgaoanu,,,,,
+irismobius,,,,,
+JalissaRattan,,,,,
+JarrikAlgera,,,,,
+jasperijsselstein,,,,,
+Jessegotthat,,,,,
+jessevanderlaan,,,,,
+jialeidng,,,,,
+jipposteenstra,,,,,
+jordstub,,,,,
+jpvermeer,,,,,
+julianvanheijningen,,,,,
+Julievdl,,,,,
+kawater31,,,,,
+Kevin-Verbakel,,,,,
+knarfie1038,,,,,
+kschmoutziguer,,,,,
+lafeberfabienne,,,,,
+LisaFranse,,,,,
+LoisZhao-1,,,,,
+LorenzoBouman,,,,,
+lucvanham8,,,,,
+lukarekhvia,,,,,
+maartjevennegoor1,,,,,
+Manasaht31,,,,,
+maritvw,,,,,
+martijnhofwegen18-ui,,,,,
+Michiel58,,,,,
+mikegeerts,,,,,
+mmharis,,,,,
+mtliem98,,,,,
+Mulhamomar,,,,,
+NBrodbelt,,,,,
+Neillzt,,,,,
+nhollnagel,chocolate,green,183,,
+Nynke0607,,,,,
+OscardeBoer5,,,,,
+palaceparis,,,,,
+pieterceulen12-alt,,,,,
+Pommetjemayo,,,,,
+pranshusharma0599-cloud,,,,,
+ricodejong-tudelft,,,,,
+Satt09,,,,,
+Saumitra-Delft-2025,,,,,
+sheikharfahmibinsheikharzimi,,,,,
+stepkruisinga,,,,,
+Tguleij,,,,,
+Theivaprakasham,,,,,
+Tjerko2002,,,,,
+TristanRoussou,,,,,
+turi1122,,,,,
+wendyqxc,,,,,
+wessel890,,,,,
+WJSchakel,,,,,
+wwouter,,,,,
+yme116,,,,,
+Youri-art, lemon, blue, 186 ,,
+zakejorge,,,,,
+Zef-1999,,,,,
+zero31415,,,,,
+Mairah27,matcha latte,pink,167,,
diff --git a/lab7_3.ipynb b/lab7_3.ipynb
index bd914ba..a958a34 100644
--- a/lab7_3.ipynb
+++ b/lab7_3.ipynb
@@ -22,13 +22,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "3742e4e6",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your details were added succesfully!\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -38,7 +45,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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JQ4YMMZwMgDtQgsDPzJ49W7t371ZQUJBuueUWrV27VrVq1TIdC4AbMB0KALAtDowBANgWJQgAsC1KEABgW5QgAMC2KEEAgG1RggAA26IEAQC2RQkCAGyLM8YAFejSpUvKzMzUsWPHdO7cOZ0/f17Z2dmFlyvXL1y4oNzcXBUUFKigoECWZcnhcMjPz09+fn7y9/eX0+lU1apVVbVqVVWpUqXI36tVq6bw8HDVq1evyHcWAiiKEgRcJD8/XxkZGdqzZ4/27duno0ePKisrS5mZmYV/nj59usJzValSRXXr1lW9evUK/6xXr54aNGig2NhYNW7c+LrfkgH4Mk6bBpTSuXPntGXLFu3atUt79uwpvOzfv1+XL182Ha/U/Pz8FB0drdjYWMXFxRX+2aJFC9WuXdt0PMCtKEHgOrKzs7V582Z9/fXXSk1NVWpqqtLT02WXfzaRkZG65ZZbCi9t2rShGOFTKEHgJ9LT07VmzRp9/vnn2rhxo60Kr6QiIyPVpk0bde3aVT169FCrVq3k58cxdvBOlCBsbd++fVqzZo1SUlL02Wef6ciRI6YjeZ0aNWoUFmLPnj3VokULShFegxKErVy4cEGffPKJli1bpk8//VSHDx82HcnnhIaGqkePHho4cKAGDhyoiIgI05GAa6IE4fMyMzOVlJSkpKQkpaSk6IcffjAdyTb8/PzUrl07DR48WIMHD1azZs1MRwKKoAThk3bt2qUlS5bov//9rzZv3sx+PQ/RqFEjDRo0SL/4xS/UuXNnORwO05Fgc5QgfMZ3332nxYsX691331VqaqrpOLiBmJgYjRo1Svfcc4/i4+NNx4FNUYLwaufPn9fSpUv17rvvKjk5Wfn5+aYjoQxat26t0aNHa9SoUapTp47pOLARShBead26dfrLX/6ipUuXKicnx3QcuIi/v7969+6thx56SEOGDFFAACe1gntRgvAa58+f16JFi/Tmm28qLS3NdBy4WWRkpB588EE99NBDqlu3ruk48FGUIDzejh07NG/ePP3jH//QuXPnTMdBBQsICNDQoUP18MMPq2fPnqbjwMdQgvBIlmVpxYoVeuWVV5SSkmI6DjxEQkKCJk+erPvuu0/BwcGm48AHUILwKPn5+Xr//ff14osvauvWrabjwEPVrVtXU6ZM0a9+9Su+KgrlQgnCI1y6dEnz58/XH//4R+3fv990HHiJ0NBQTZgwQZMmTVKtWrVMx4EXogRh1Pnz5zVv3jzNmTNH3333nek48FJOp1MPPPCApk6dqqioKNNx4EUoQRhx+fJl/fnPf9bMmTN14sQJ03HgIypVqqRHHnlETz31lMLCwkzHgRegBFGhCgoK9O677+rZZ5/VgQMHTMeBj6pevbqmTp2qRx99VE6n03QceDBKEBVm+fLleuqppzjgBRWmTp06euaZZ/Tggw8qMDDQdBx4IEoQbrdx40Y99thjWrdunekosKlGjRrphRde0LBhw0xHgYehBOE233//vX7729/q73//O9/iAI/Qu3dvvf7665ywG4X4+me4XEFBgd58803Fxsbq7bffpgDhMZKTk9WiRQtNnTpV58+fNx0HHoCRIFzqq6++0iOPPKLNmzebjgJcV2RkpGbPnq2RI0eajgKDGAnCJU6ePKnx48erU6dOFCC8wtGjR3X33XerV69e2rlzp+k4MIQSRLktXbpUCQkJ7PuDV0pJSVGrVq00a9Ysvo/ShpgORZmdPHlSEyZM0HvvvWc6CuAS7dq104IFCzhwxkYYCaJMroz+KED4ko0bNzIqtBlGgigVRn+wC0aF9sBIECW2atUqNWvWjAKELVwZFc6dO9d0FLgRJYgbysvL01NPPaW+ffvq2LFjpuMAFebSpUuaPHmy7rzzTp0+fdp0HLgB06G4rsOHD+vuu+/W+vXrTUcBjKpfv74WL16sjh07mo4CF2IkiGtKSkpSy5YtKUBA0sGDB9WtWzfNmjWLjwL5EEaCuMrly5f1xBNP6LXXXjMdBfBIffv21TvvvKOIiAjTUVBOlCCKOHbsmO6880598cUXpqMAHi06OloffvihWrdubToKyoHpUBRKTU1VmzZtKECgBA4fPqwuXbpwtLSXowQhSXrvvffUtWtXHTlyxHQUwGtcvHhRd999t5566in2E3oppkNtrqCgQE8//bReeOEF01EArzZo0CC9++67qlq1qukoKAVK0Mays7M1atQoLV++3HQUwCckJCQoKSlJjRo1Mh0FJUQJ2lRmZqb69eunrVu3mo4C+JSaNWtq+fLl6tChg+koKAH2CdrQ7t271alTJwoQcIOTJ0+qd+/e+uijj0xHQQlQgjazceNGdenSRQcPHjQdBfBZOTk5GjJkiN555x3TUXADlKCNfPzxx+rVq5e+//5701EAn5eXl6exY8fqpZdeMh0F10EJ2sSiRYs0ePBgXbhwwXQUwDYsy9KTTz6pxx57jI9QeCgOjLGBV199VVOmTOEfIWDQPffcowULFsjf3990FPwEI0EfN2vWLD366KMUIGDYokWLdPfddysvL890FPwEJejDZs6cqd/+9remYwD4nyVLlmjEiBHKzc01HQX/w3Soj5oxY4amTZtmOgaAYgwePFj//ve/FRgYaDqK7TES9EHPP/88BQh4sKSkJI0cOZKpUQ/ASNDHvPTSS3ryySdNxwBQAsOGDdPixYs5WMYgStCHvP7665o0aZLpGABK4Z577tE777wjh8NhOootMR3qI95//31NnjzZdAwApbRo0SI9/vjjpmPYFiNBH5CcnKwBAwbo8uXLpqMAKKOXXnpJU6dONR3DdihBL7d582b16NFD2dnZpqMAKAeHw6EFCxZozJgxpqPYCiXoxfbu3avOnTvr+PHjpqMAcIGAgAD997//1YABA0xHsQ1K0EsdO3ZMnTp10v79+01HAeBCTqdTq1evVvv27U1HsQVK0Avl5OSoa9eu2rx5s+koANygZs2a2rBhA99QXwE4OtQLjR07lgIEfNjJkyc1ePBg9vVXAErQy8ycOVNLliwxHQOAm+3YsUOjR49WQUGB6Sg+jRL0Ih9++KGeffZZ0zEAVJBly5bp6aefNh3Dp7FP0Ets27ZNHTt21Pnz501HAVDBFi9erJEjR5qO4ZMoQS9w8uRJtW3bVhkZGaajADCgcuXKWrdunVq3bm06is9hOtTD5efna9iwYRQgYGMXL17UkCFDdOLECdNRfA4l6OGee+45paSkmI4BwLAjR47o3nvvFZN3rsV0qAdbvXq1+vTpw9FhAAq9+OKLfF2aC1GCHur48eNq2bKlsrKyTEcB4EECAgL0+eefq2PHjqaj+ASmQz2QZVkaM2YMBQjgKnl5ebr77rt1+vRp01F8AiXogV566SWtXLnSdAwAHurgwYMaP3686Rg+gelQD/Pll1+qW7duysvLMx0FgIebO3euJk6caDqGV6MEPciFCxd0880383EIACUSHBysb775RvHx8aajeC2mQz3Ik08+SQECKLFLly5p7Nixys/PNx3Fa1GCHiIlJUXz5s0zHQOAl9m4caNmz55tOobXYjrUA5w/f16JiYmMAgGUCdOiZcdI0AMwDQqgPJgWLTtK0LCUlBS9+eabpmMA8HJMi5YN06EGcTQoAFdiWrT0GAkaNHPmTAoQgMtcunRJjzzyiOkYXoWRoCF79uzRzTffrMuXL5uOAsDH8CW8JUcJGtK3b1998sknpmMA8EGRkZHatWuXqlSpYjqKx2M61IClS5dSgADc5ujRo5oxY4bpGF6BkWAFy8nJUXx8vA4dOmQ6CgAfFhgYqC1btnCQzA0wEqxgzz//PAUIwO1yc3M5uXYJMBKsQPv27VOzZs106dIl01EA2MSSJUt01113mY7hsRgJVqDf//73FCCACvW73/1Oubm5pmN4LEqwgmzevFn/+te/TMcAYDN79+7VW2+9ZTqGx2I6tILcdtttWrVqlekYAGyoTp062rdvn5xOp+koHoeRYAVYvXo1BQjAmO+++06vvvqq6RgeiZFgBWjXrp02bdpkOgYAG6tevbr279+vsLAw01E8CiNBN/vggw8oQADGnT17Vi+88ILpGB6HkaAb5efnq1mzZtq9e7fpKACgSpUqKT09XVFRUaajeAxGgm60ZMkSChCAx/jhhx/4zsGfYSToRi1bttSWLVtMxwCAQk6nUwcPHlStWrVMR/EIjATd5KOPPqIAAXicnJwczZ0713QMj8FI0E26deumtWvXmo4BAFcJDQ3VoUOH+KolMRJ0i/Xr11OAADzW6dOn9Ze//MV0DI/ASNANBg4cqBUrVpiOAQDXVK9ePWVkZCgoKMh0FKMYCbpYWloaBQjA42VmZmrhwoWmYxhHCboYpyYC4C14v2I61KVOnz6tyMhIXbx40XQUACiRlJQU9ejRw3QMYxgJutD8+fMpQABeZd68eaYjGMVI0EUsy1JcXJzS09NNRwGAEgsMDNTBgwdVt25d01GMYCToIqtWraIAAXid3Nxc/e1vfzMdwxhK0EXsPqUAwHv99a9/VV5enukYRlCCLnD48GEtX77cdAwAKJOjR48qKSnJdAwjKEEXePvtt5Wfn286BgCU2V//+lfTEYzgwBgXaNy4sfbt22c6BgCUmb+/v44cOaI6deqYjlKhGAmW05dffkkBAvB6+fn5Wrx4sekYFY4SLKdFixaZjgAALmHH9zOmQ8shNzdX9erV0/fff286CgC4xM6dO9W0aVPTMSoMI8Fy+PjjjylAAD7FbqNBSrAc7PbLAsD3/fOf/5SdJgiZDi2j7Oxs1a5dm3OFAvA569atU+fOnU3HqBCMBMvoo48+ogAB+KQPPvjAdIQKQwmWkV3PrgDA9y1btsx0hArDdGgZ5OXlKSIiQqdPnzYdBQDcYvv27UpISDAdw+0YCZbBunXrKEAAPs0us12UYBnY5ZcDgH3Z5X2O6dAyaNKkifbu3Ws6BgC4jZ+fn7KyshQREWE6ilsxEiylHTt2UIAAfF5BQYFWrFhhOobbUYKlZIdfCgCQ7HGUKCVYSsnJyaYjAECFWLNmjQoKCkzHcCtKsBTy8vK0fv160zEAoEKcPn1aaWlppmO4FSVYCps2bdL58+dNxwCACpOSkmI6gltRgqWwZs0a0xEAoEL5+vseJVgKvv7LAAA/9/nnn/v0fkFKsIRyc3PZHwjAds6cOaNvv/3WdAy3oQRLaNOmTbpw4YLpGABQ4Xx5FowSLKG1a9eajgAARnz++eemI7gNJVhCX3/9tekIAGBEamqq6QhuQwmWkC//EgDA9Rw5ckTHjx83HcMtKMESOHXqlDIyMkzHAABjfHU2jBIsAUaBAOzOV98HKcES8NUfPgCUlK++D1KCJeCrP3wAKClffR+kBEvAV+fCAaCkjhw5omPHjpmO4XKU4A2cO3dOBw4cMB0DAIzbsmWL6QguRwnewJ49e0xHAACP4Ivvh5TgDezevdt0BADwCJSgDfniDx0AysIXBwWU4A1QggDwI198P6QEb8AX/+cDAGVx6NAhXbp0yXQMl6IEbyA9Pd10BADwCAUFBdq7d6/pGC5FCV5HZmamzp8/bzoGAHgMX5sSpQSvg5NmA0BR+/fvNx3BpSjB68jMzDQdAQA8SlZWlukILlWmEmzYsKFOnjx51fIzZ86oYcOG5Q7lKXzthw0A5eVrg4MyleCBAweUn59/1fJLly7p6NGj5Q7lKXzthw0A5eVrg4OA0qyclJRU+PeVK1eqevXqhdfz8/OVnJysmJgYl4Uzzdd+2ABQXr42OChVCQ4dOlSS5HA4dN999xW5LTAwUDExMXr55ZddFs40X/thA0B5+drgoFQlWFBQIElq0KCBNm3apFq1arkllKfwtR82AJRXdna2zp8/rypVqpiO4hJl2ieYkZHh8wUoUYIAUBxfem8s1Ujwp5KTk5WcnKzjx48XjhCv+Pvf/17uYKbl5+fr1KlTpmMAgMc5fvy4mjRpYjqGS5SpBJ977jnNmDFDbdq0Ud26deVwOFydy7js7GzTEQDAI/nS+2OZSvDPf/6zFixYoHvvvdfVeTwGp0sDgOL50vtjmfYJXr58WZ06dXJ1Fo/iS//TAQBX8qX3xzKV4AMPPKB//vOfrs7iUXzphwwAruRL749lmg794Ycf9Ne//lWffvqpEhMTFRgYWOT2V155xSXhTPKl4T4AuJIvvT+WqQTT0tLUsmVLSdK2bduK3OYrB8n40v90AMCVfOn9sUwlmJKS4uocHseXfsgA4Eq+9P7IVyldQ05OjukIAOCRfOn9sUwjwZ49e1532nP16tVlDuQpivuWDACAb70/lqkEr+wPvCI3N1fffvuttm3bdtWJtb3Vz8+CAwD4kS+9P5apBOfMmVPs8unTp/vMUUO+9EMGAFfypfdHh2VZlqs2tnfvXrVr184nzrm5evVqpaammo4BSJJiYmKUmJhoOgYgSbIsS02bNjUdwyXKfALt4nz55ZeqVKmSKzdpTEhIiE/t/IV3a9KkieLi4kzHAHxOmUrwzjvvLHLdsixlZWXp66+/1jPPPOOSYKb5yucdAQDXVqYSrF69epHrfn5+iouL04wZM3Tbbbe5JJhplCAA+L4yleD8+fNdncPj+Pv7m44AAHCzcu0TTE1N1c6dO+VwOJSQkKBWrVq5KpdxPz8fKgDA95SpBI8fP66RI0dqzZo1qlGjhizL0tmzZ9WzZ0+99957Cg8Pd3XOChccHGw6AgDAzcp02rSJEyfq3Llz2r59u06dOqXTp09r27ZtOnfunCZNmuTqjEYEBQWZjgAAcLMyjQQ//vhjffrpp4qPjy9clpCQoDfeeMNnDoyhBAHA95VpJFhQUFDsPrPAwECfOZMA06EA4PvKVIK9evXS5MmTlZmZWbjs6NGjevTRR9W7d2+XhTOJkSAA+L4yleCf/vQnZWdnKyYmRo0aNVLjxo3VoEEDZWdn6/XXX3d1RiMoQQDwfWXaJxgdHa3Nmzdr1apV2rVrlyzLUkJCgm699VZX5zMmKChIDodDLjy1KgDAw5RqJLh69WolJCTo3LlzkqQ+ffpo4sSJmjRpktq2batmzZpp7dq1bgla0RwOh5xOp+kYAAA3KlUJvvrqq3rwwQdVrVq1q26rXr26fvnLX+qVV15xWTjTqlatajoCAMCNSlWCW7ZsUb9+/a55+2233eZTXz9ECQKAbytVCR47duy6pxMLCAjQiRMnyh3KU1SpUsV0BACAG5WqBCMjI7V169Zr3p6Wlqa6deuWO5SnYCQIAL6tVCU4YMAAPfvss/rhhx+uuu3ixYuaNm2aBg4c6LJwpjESBADfVqqPSDz99NNaunSpYmNjNWHCBMXFxcnhcGjnzp164403lJ+fr9///vfuylrhGAkCgG8rVQnWrl1bX3zxhX7961/rd7/7XeFn6BwOh/r27at58+apdu3abglqAiUIAL6t1B+Wr1+/vj766COdPn1ae/fulWVZatKkiUJDQ92Rz6iwsDDTEQAAblTmL9UNDQ1V27ZtXZnF41SuXFlOp1M5OTmmowAA3KBM5w61k5o1a5qOAABwE0rwBihBAPBdlOANUIIA4LsowRugBAHAd1GCN0AJAoDvogRvICwsTA6Hw3QMAIAbUII3EBAQoPDwcNMxAABuQAmWgC+dFBwA8P9RgiVACQKAb6IES6BevXqmIwAA3IASLIE6depwcAwA+CBKsAQCAwM5OAYAfBAlWELsFwQA30MJllBkZKTpCAAAF6MES6h+/fqmIwAAXIwSLKHw8HA5nU7TMQAALkQJlpDD4VBMTIzpGAAAF6IES4EpUQDwLZRgKTRo0MB0BACAC1GCpRAeHq6QkBDTMQAALkIJlhJTogDgOyjBUmrYsKHpCAAAF6EESyk2NtZ0BACAi1CCpVS1alW+VQIAfAQlWAaMBgHAN1CCZRAXF2c6AgDABSjBMqhTp46qV69uOgYAoJwowTJiShQAvB8lWEZMiQKA96MEy6hBgwZ8qwQAeDlKsIz8/PzUrFkz0zEAAOVACZZDYmKi6QgAgHKgBMshKipKYWFhpmMAAMqIEiyn5s2bm44AACgjSrCcmBIFAO9FCZZTzZo1FRkZaToGAKAMKEEXaNGihekIAIAyoARdIDExUUFBQaZjAABKiRJ0geDgYN18882mYwAASokSdJG2bduajgAAKCVK0EVq166t6Oho0zEAAKVACboQo0EA8C6UoAslJCQoJCTEdAwAQAlRgi7k7++vVq1amY4BACghStDF2rVrJ39/f9MxAAAlQAm6WNWqVfnwPAB4CUrQDTp37iyHw2E6BgDgBihBNwgLC1NCQoLpGACAG6AE3aRLly6mIwAAboASdJM6deqocePGpmMAAK6DEnQjRoMA4NkoQTeqX7++GjRoYDoGAOAaKEE36927t+kIAIBroATdLDIyUvHx8aZjAACKQQlWgF69evG5QQDwQJRgBahVq5ZatmxpOgYA4GcowQrSo0cPBQQEmI4BAPgJSrCCVKtWTe3atTMdAwDwE5RgBerataucTqfpGACA/6EEK1ClSpV06623mo4BAPgfSrCCtWzZUlFRUaZjAABECVY4h8Oh/v3785EJAPAAlKAB9erVU+vWrU3HAADbowQN6d27typXrmw6BgDYGiVoSOXKlTmvKAAYRgka1Lp1a910002mYwCAbVGCBjkcDg0ZMkSBgYGmowCALVGChoWFhalXr16mYwCALVGCHqB9+/ZMiwKAAZSgB2BaFADMoAQ9RFhYGEeLAkAFowQ9SLt27VS/fn3TMQDANihBD+JwOHTHHXfwIXoAqCCUoIepXr26hgwZYjoGANgCJeiB4uLi1L59e9MxAMDnUYIeqk+fPqpbt67pGADg0yhBD+Xv76+77rpLwcHBpqMAgM+iBD1YWFiYBg4caDoGAPgsStDDNW/eXG3btjUdAwB8EiXoBfr166eYmBjTMQDA51CCXsDPz0/Dhg1TaGio6SgA4FMoQS/hdDo1cuRIBQUFmY4CAD6DEvQiERERuuOOO0zHAACfQQl6maZNm6pnz56mYwCAT6AEvVC3bt108803m44BAF6PEvRSQ4YMUcOGDU3HAACvRgl6KX9/fw0fPpxTqwFAOVCCXiw4OFijR49WWFiY6SgA4JUoQS8XEhKie+65R1WqVDEdBQC8DiXoA0JDQzV69GhOtg0ApUQJ+og6depo5MiRCggIMB0FALwGJehDYmJiKEIAKAVK0Mc0atRIw4cPl7+/v+koAODxKEEf1KRJE4oQAEqAEvRRsbGxGjFiBFOjAHAdlKAPa9KkCfsIAeA6KEEf16hRIz4+AQDXQAnaQExMjMaOHcsH6gHgZyjBMlqwYIFq1Khx3XWmT5+uli1bVkieG6lTp47GjRvHKdYAHxUTE6NXX321xOuvWbNGDodDZ86ccVsmb0AJ2khoaKjGjRvHSbcBH7Rp0yY99NBDpmN4HUrQZkJCQjR27Fi+hgnwMeHh4XI6naZjeB1KsJw+/PBDxcbGqlKlSurTp48OHz58zXV79Oih3/zmN0WWDR06VGPHji28fvnyZT3xxBOKjIxUSEiI2rdvrzVr1rg0c1BQkEaNGsUX8wJepEePHpowYYImTJigGjVqqGbNmnr66adlWZakq6dDHQ6H3nrrLd1xxx1yOp1q0qSJkpKSrrn9ixcv6vbbb1eHDh106tQpdz8dj0EJlkNOTo7+8Ic/aOHChVq/fr3OnTunkSNHlmub999/v9avX6/33ntPaWlpGjZsmPr166f09HQXpf6Rv7+/7rzzTvXs2dOl2wXgPgsXLlRAQIA2bNiguXPnas6cOXrrrbeuuf5zzz2n4cOHKy0tTQMGDNDo0aOLLbizZ8/qtttu0+XLl5WcnGyrYwcowXLIzc3Vn/70J3Xs2FG33HKLFi5cqC+++EIbN24s0/b27dunxYsXa8mSJeratasaNWqkxx9/XF26dNH8+fNdnP5H3bp104gRIxQUFOSW7QNwnejoaM2ZM0dxcXEaPXq0Jk6cqDlz5lxz/bFjx+ruu+9W48aN9fzzz+vChQtXvT8dO3ZM3bt3V0REhFasWKGQkBB3Pw2PQgmWQ0BAgNq0aVN4vWnTpqpRo4Z27txZpu1t3rxZlmUpNjZWVapUKbx89tln2rdvn6tiX6Vp06YaP368QkND3fYYAMqvQ4cOcjgchdc7duyo9PR05efnF7t+YmJi4d9DQkJUtWpVHT9+vMg6t956qxo2bKh//etftvzPMKcSKaef/kJeb5kk+fn5Fc7fX5Gbm1v494KCAvn7+ys1NfWq8366+zN+EREReuCBB7RkyRIdOHDArY8FoGIEBgYWue5wOFRQUFBk2e23364PPvhAO3bssOVxAowEyyEvL09ff/114fXdu3frzJkzatq0abHrh4eHKysrq/B6fn6+tm3bVni9VatWys/P1/Hjx9W4ceMilzp16rjvifyP0+nUvffeq7Zt27r9sQCU3ldffXXV9SZNmpTrZPkvvvii7rvvPvXu3Vs7duwob0SvQwmWQ2BgoCZOnKgNGzZo8+bNuv/++9WhQwe1a9eu2PV79eqlFStWaMWKFdq1a5cefvjhIh9UjY2N1ejRozVmzBgtXbpUGRkZ2rRpk2bNmqWPPvqoQp6Tn5+fBgwYoF/84hecag3wMIcPH9aUKVO0e/duLV68WK+//romT55c7u3Onj1bo0ePVq9evbRr1y4XJPUeTIeWg9Pp1JNPPqlRo0bpyJEj6tKli/7+979fc/1x48Zpy5YtGjNmjAICAvToo49edXTm/PnzNXPmTD322GM6evSoatasqY4dO2rAgAHufjpFNG/eXPXq1dO///3vIqNXAOaMGTNGFy9eVLt27eTv76+JEye67APyc+bMUX5+vnr16qU1a9YoNjbWJdv1dA7r5zupgJ/Iz8/XqlWrtGHDBtNRbG3IkCEecwo+mNGjRw+1bNmyVKdGw40xHYrr8vf3V79+/TRy5EhVrlzZdBwAcClKECUSFxenX/3qV6pfv77pKADgMkyHolQsy9LGjRuVnJxc5OMdcC+mQwH3YCSIUnE4HGrfvj2jQgA+gRJEmYSFhem+++5Tv379rvpALgB4C0oQZcaoEIC3owRRbldGhYMGDeL7zAB4FT4sD5dwOBxq3bq14uPjtXr1aqWmpl51nlQA8DSMBOFSlStX1u23364HHnhAUVFRpuMAwHVRgnCLevXqady4cRo8eDBTpAA8FtOhcBuHw6FWrVopPj5ea9eu1caNG5WXl2c6FgAUYiQIt6tUqZL69OmjiRMnqnXr1tf8vkUAqGiUICpMtWrVNGjQID388MOKj483HQcAmA5FxatVq5aGDx+uo0ePKjk5WRkZGaYjAbApShDGREZGasyYMTp06JDWrVun9PR005EA2AwlCONuuukmjRo1St99953Wr1+v7du38xlDABWCb5GAxzl16pTWr1+vLVu2KD8/33Qcj8C3SADuQQnCY2VnZ2vjxo365ptvdOHCBdNxjKIEAfegBOHx8vPztWPHDn399dc6dOiQ6ThGUIKAe7BPEB7P399fN998s26++WYdO3ZMmzZt0tatW3X58mXT0QB4OUaC8EqXLl1SWlqatmzZoqNHj5qO43aMBAH3YCQIrxQcHKy2bduqbdu2OnXqlNLS0rR161adOnXKdDQAXoSRIHzKkSNHlJaWpu3btysnJ8d0HJdhJAi4ByNB+JSoqChFRUWpX79+ysjI0O7du7Vnzx6dPXvWdDQAHoiRIGzh2LFj2r17t3bv3q3MzEzTcUqNkSDgHowEYQu1a9dW7dq11a1bN2VnZys9PV379+/XgQMHbP8ZRMDOKEHYTtWqVdW6dWu1bt1aknTixAkdOHCg8OJL+xIBXB8lCNsLDw9XeHi42rZtK0k6fvy4Dh48qMzMTGVmZurEiROcyxTwUZQg8DMRERGKiIgovJ6bm6vvvvtOWVlZysrKohgBH0IJAjcQGBio6OhoRUdHFy7Ly8vTqVOndPLkyasuTKcC3oMSBMogICDgqhHjFRcvXtTp06eVnZ1d5HL+/HmdP39e2dnZunDhAiNJwANQgoCLVa5cWZUrV77uOpZlKTc3V5cvX9alS5eK/HnlUlBQIMuyZFmWatasWUHpAXvhc4IAANvyMx0AAABTKEEAgG1RggAA26IEAQC2RQkCAGyLEgQA2BYlCACwLUoQAGBblCAAwLYoQQCAbVGCAADbogQBALZFCQIAbIsSBADYFiUIALAtShAAYFuUIADAtihBAIBtUYIAANuiBAEAtkUJAgBsixIEANgWJQgAsC1KEABgW5QgAMC2KEEAgG1RggAA26IEAQC2RQkCAGyLEgQA2BYlCACwLUoQAGBblCAAwLYoQQCAbVGCAADbogQBALZFCQIAbIsSBADYFiUIALAtShAAYFuUIADAtihBAIBtUYIAANuiBAEAtkUJAgBsixIEPNT06dPVsmVL0zEAn0YJAm7y3XffaeLEiWrYsKGCg4MVHR2tQYMGKTk52XQ0AP8TYDoA4IsOHDigzp07q0aNGnrppZeUmJio3NxcrVy5Uo888oh27dplOqIkKT8/Xw6HQ35+/H8Y9sRvPuAGDz/8sBwOhzZu3Ki77rpLsbGxatasmaZMmaKvvvpKknTo0CENGTJEVapUUbVq1TR8+HAdO3bsmtssKCjQjBkzFBUVpeDgYLVs2VIff/xx4e1r1qyRw+HQmTNnCpd9++23cjgcOnDggCRpwYIFqlGjhpYvX66EhAQFBwfr4MGDbnkNAG9ACQIudurUKX388cd65JFHFBISctXtNWrUkGVZGjp0qE6dOqXPPvtMq1at0r59+zRixIhrbve1117Tyy+/rNmzZystLU19+/bV4MGDlZ6eXqp8OTk5euGFF/TWW29p+/btioiIKPVzBHwF06GAi+3du1eWZalp06bXXOfTTz9VWlqaMjIyFB0dLUn6xz/+oWbNmmnTpk1q27btVfeZPXu2nnzySY0cOVKSNGvWLKWkpOjVV1/VG2+8UeJ8ubm5mjdvnlq0aFHKZwb4HkaCgItZliVJcjgc11xn586dio6OLixASUpISFCNGjW0c+fOq9Y/d+6cMjMz1blz5yLLO3fuXOz61xMUFKTExMRS3QfwVZQg4GJNmjSRw+G4bjlZllVsSV5r+RU/v+2n6185uOVKCUs/jvp+rnLlytd9DMBOKEHAxcLCwtS3b1+98cYbunDhwlW3nzlzRgkJCTp06JAOHz5cuHzHjh06e/as4uPjr7pPtWrVVK9ePa1bt67I8i+++KJw/fDwcElSVlZW4e3ffvutK54S4LMoQcAN5s2bp/z8fLVr104ffPCB0tPTtXPnTs2dO1cdO3bUrbfeqsTERI0ePVqbN2/Wxo0bNWbMGHXv3l1t2rQpdptTp07VrFmz9P7772v37t367W9/q2+//VaTJ0+WJDVu3FjR0dGaPn269uzZoxUrVujll1+uyKcNeB0OjAHcoEGDBtq8ebP+8Ic/6LHHHlNWVpbCw8N1yy236M0335TD4dCHH36oiRMnqlu3bvLz81O/fv30+uuvX3ObkyZN0rlz5/TYY4/p+PHjSkhIUFJSkpo0aSJJCgwM1OLFi/XrX/9aLVq0UNu2bTVz5kwNGzasop424HUc1k93IAAAYCNMhwIAbIsSBADYFiUIALAtShAAYFuUIADAtihBAIBtUYIAANuiBAEAtkUJAgBsixIEANgWJQgAsC1KEABgW/8PaEwQ7+85yEUAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -51,21 +58,21 @@
      "evalue": "'value' must be an instance of str or bytes, not a float",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 29\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;66;03m# Plot heights\u001b[39;00m\n\u001b[1;32m     28\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m---> 29\u001b[0m plt\u001b[38;5;241m.\u001b[39mhist(data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHeight (cm)\u001b[39m\u001b[38;5;124m\"\u001b[39m], bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     30\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHeight Distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     31\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHeight (cm)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/_api/deprecation.py:453\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m    448\u001b[0m     warn_deprecated(\n\u001b[1;32m    449\u001b[0m         since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    450\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    451\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    452\u001b[0m         name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/pyplot.py:3478\u001b[0m, in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[1;32m   3453\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mhist)\n\u001b[1;32m   3454\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mhist\u001b[39m(\n\u001b[1;32m   3455\u001b[0m     x: ArrayLike \u001b[38;5;241m|\u001b[39m Sequence[ArrayLike],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3476\u001b[0m     BarContainer \u001b[38;5;241m|\u001b[39m Polygon \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m[BarContainer \u001b[38;5;241m|\u001b[39m Polygon],\n\u001b[1;32m   3477\u001b[0m ]:\n\u001b[0;32m-> 3478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m gca()\u001b[38;5;241m.\u001b[39mhist(\n\u001b[1;32m   3479\u001b[0m         x,\n\u001b[1;32m   3480\u001b[0m         bins\u001b[38;5;241m=\u001b[39mbins,\n\u001b[1;32m   3481\u001b[0m         \u001b[38;5;28mrange\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mrange\u001b[39m,\n\u001b[1;32m   3482\u001b[0m         density\u001b[38;5;241m=\u001b[39mdensity,\n\u001b[1;32m   3483\u001b[0m         weights\u001b[38;5;241m=\u001b[39mweights,\n\u001b[1;32m   3484\u001b[0m         cumulative\u001b[38;5;241m=\u001b[39mcumulative,\n\u001b[1;32m   3485\u001b[0m         bottom\u001b[38;5;241m=\u001b[39mbottom,\n\u001b[1;32m   3486\u001b[0m         histtype\u001b[38;5;241m=\u001b[39mhisttype,\n\u001b[1;32m   3487\u001b[0m         align\u001b[38;5;241m=\u001b[39malign,\n\u001b[1;32m   3488\u001b[0m         orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[1;32m   3489\u001b[0m         rwidth\u001b[38;5;241m=\u001b[39mrwidth,\n\u001b[1;32m   3490\u001b[0m         log\u001b[38;5;241m=\u001b[39mlog,\n\u001b[1;32m   3491\u001b[0m         color\u001b[38;5;241m=\u001b[39mcolor,\n\u001b[1;32m   3492\u001b[0m         label\u001b[38;5;241m=\u001b[39mlabel,\n\u001b[1;32m   3493\u001b[0m         stacked\u001b[38;5;241m=\u001b[39mstacked,\n\u001b[1;32m   3494\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \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 \u001b[38;5;28;01melse\u001b[39;00m {}),\n\u001b[1;32m   3495\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3496\u001b[0m     )\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/_api/deprecation.py:453\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m    448\u001b[0m     warn_deprecated(\n\u001b[1;32m    449\u001b[0m         since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    450\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    451\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    452\u001b[0m         name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/__init__.py:1524\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1521\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1523\u001b[0m     \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[0;32m-> 1524\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\n\u001b[1;32m   1525\u001b[0m             ax,\n\u001b[1;32m   1526\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(cbook\u001b[38;5;241m.\u001b[39msanitize_sequence, args),\n\u001b[1;32m   1527\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{k: cbook\u001b[38;5;241m.\u001b[39msanitize_sequence(v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems()})\n\u001b[1;32m   1529\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1530\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1531\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/axes/_axes.py:7053\u001b[0m, in \u001b[0;36mAxes.hist\u001b[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m   7051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   7052\u001b[0m     convert_units \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits\n\u001b[0;32m-> 7053\u001b[0m     x \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_unit_info([(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, x[\u001b[38;5;241m0\u001b[39m])], kwargs),\n\u001b[1;32m   7054\u001b[0m          \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(convert_units, x[\u001b[38;5;241m1\u001b[39m:])]\n\u001b[1;32m   7055\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# horizontal\u001b[39;00m\n\u001b[1;32m   7056\u001b[0m     convert_units \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/axes/_base.py:2663\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[0;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[1;32m   2661\u001b[0m     \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[1;32m   2662\u001b[0m     \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\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[0;32m-> 2663\u001b[0m         axis\u001b[38;5;241m.\u001b[39mupdate_units(data)\n\u001b[1;32m   2664\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   2665\u001b[0m     \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[1;32m   2666\u001b[0m     \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",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/axis.py:1754\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1752\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[1;32m   1753\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_converter(converter)\n\u001b[0;32m-> 1754\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter\u001b[38;5;241m.\u001b[39mdefault_units(data, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1755\u001b[0m \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\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1756\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/category.py:106\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 106\u001b[0m     axis\u001b[38;5;241m.\u001b[39mset_units(UnitData(data))\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    108\u001b[0m     axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/category.py:182\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[1;32m    181\u001b[0m \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[0;32m--> 182\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate(data)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/category.py:217\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    214\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m OrderedDict\u001b[38;5;241m.\u001b[39mfromkeys(data):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m--> 217\u001b[0m     _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m    219\u001b[0m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[1;32m    220\u001b[0m         convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_str_is_convertible(val)\n",
-      "File \u001b[0;32m/opt/anaconda3/envs/TIL6022-25/lib/python3.12/site-packages/matplotlib/_api/__init__.py:92\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(types, **kwargs)\u001b[0m\n\u001b[1;32m     90\u001b[0m     names\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     91\u001b[0m     names\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 92\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m must be an instance of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m     94\u001b[0m         k,\n\u001b[1;32m     95\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(names[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m names[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     96\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m names[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m     97\u001b[0m         type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a float"
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 43\u001b[39m\n\u001b[32m     41\u001b[39m \u001b[38;5;66;03m# Plot heights\u001b[39;00m\n\u001b[32m     42\u001b[39m plt.figure()\n\u001b[32m---> \u001b[39m\u001b[32m43\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     44\u001b[39m plt.title(\u001b[33m\"\u001b[39m\u001b[33mHeight Distribution\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     45\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\\maira\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only.<locals>.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\\maira\\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\\maira\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only.<locals>.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\\maira\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001b[39m, in \u001b[36m_preprocess_data.<locals>.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\\maira\\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\\maira\\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\\maira\\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\\maira\\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\\maira\\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\\maira\\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\\maira\\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"
      ]
     },
     {
@@ -86,6 +93,19 @@
     "# Load dataset\n",
     "data = pd.read_csv(\"icecream.csv\")\n",
     "\n",
+    "# Create new row with details\n",
+    "data = data[data[\"Github\"] != \"Mairah27\"]\n",
+    "new_row = {\n",
+    "    \"Github\": \"Mairah27\",\n",
+    "    \"Favourite Ice Cream\": \"matcha latte\",\n",
+    "    \"Favourite Colour\": \"pink\",\n",
+    "    \"Height (cm)\": 167\n",
+    "}\n",
+    "\n",
+    "data = pd.concat([data, pd.DataFrame([new_row])], ignore_index=True)\n",
+    "\n",
+    "data.to_csv(\"icecream.csv\", index=False)\n",
+    "\n",
     "# Plot icecream\n",
     "plt.figure()\n",
     "data[\"Favourite Ice Cream\"].value_counts().plot(kind=\"pie\")\n",