ass5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "05dd013d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9258edc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"month\": [1, 2, 3, 4, 5],\n",
    "    \"weather\": [\"rainy\", \"sunny\", \"cloudy\", \"windy\", \"cloudy\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d33149de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'month': [1, 2, 3, 4, 5], 'weather': ['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']}\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "82eaa057",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "de89791d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ade71b37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "m = list(df['month'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e77a6924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df['weather'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9586f88",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'other'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'other'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mother\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3763\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'other'"
     ]
    }
   ],
   "source": [
    "m = list(df['other'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f7017b04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'rainy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[0])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d6540837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 'sunny']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[1])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9e3b9324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[4])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0e91c372",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "5",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:345\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 345\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    346\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[1;31mValueError\u001b[0m: 5 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[45], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m5\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1103\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1100\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   1102\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1343\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1341\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[0;32m   1342\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_label(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1293\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: AxisInt):\n\u001b[0;32m   1292\u001b[0m     \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1293\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mxs(label, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4095\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   4093\u001b[0m             new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[0;32m   4094\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4095\u001b[0m     loc \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   4097\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m   4098\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:347\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    346\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 347\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m    349\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 5"
     ]
    }
   ],
   "source": [
    "m = list(df.loc[5])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e11b6e78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month          1\n",
      "weather    rainy\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[0]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "61ef9249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month           5\n",
      "weather    cloudy\n",
      "Name: 4, dtype: object\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[4]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f967bbee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "1      2   sunny\n",
      "2      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[1:2]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9f8a0e29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[0:4]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b411f096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[3:21]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "03904db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[-1:3]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "45a717e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "a      1   rainy\n",
      "b      2   sunny\n",
      "c      3  cloudy\n",
      "d      4   windy\n",
      "e      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "idx = ['a', 'b', 'c', 'd', 'e']\n",
    "df = pd.DataFrame(data, index=idx)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a98a3513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month          1\n",
      "weather    rainy\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bd970187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month           5\n",
      "weather    cloudy\n",
      "Name: e, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a620ead0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "a      1   rainy\n",
      "b      2   sunny\n",
      "c      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['a':'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "24be41e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a']['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ddf93173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'windy'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['d']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7027aab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cloudy'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['c']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "21df1707",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_no_idx_sep_comma.csv\", sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0a2d1c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_def_options.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ae75e951",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_semicolon.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6d92d8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_tab.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "01c87654",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_space.csv\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e2dda369",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"data_no_idx.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eb5eda5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_excel(\"data_no_idx.xlsx\")\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a6a1c3a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.read_csv(\"data_no_idx_sep_comma.csv\")\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "83156047",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       t         x         y\n",
      "0    0.0  2.000000  0.000000\n",
      "1    0.2  1.984229  0.250666\n",
      "2    0.4  1.937166  0.497380\n",
      "3    0.6  1.859553  0.736249\n",
      "4    0.8  1.752613  0.963507\n",
      "..   ...       ...       ...\n",
      "95  19.0  1.618034 -1.175571\n",
      "96  19.2  1.752613 -0.963507\n",
      "97  19.4  1.859553 -0.736249\n",
      "98  19.6  1.937166 -0.497380\n",
      "99  19.8  1.984229 -0.250666\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "lissa = pd.read_excel(r\"C:\\Users\\BOCAH\\Downloads\\lissajous_circle.xlsx\")\n",
    "print(lissa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0310cba5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.2</td>\n",
       "      <td>1.984229</td>\n",
       "      <td>0.250666</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.937166</td>\n",
       "      <td>0.497380</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.859553</td>\n",
       "      <td>0.736249</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.752613</td>\n",
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      "text/plain": [
       "     t         x         y\n",
       "0  0.0  2.000000  0.000000\n",
       "1  0.2  1.984229  0.250666\n",
       "2  0.4  1.937166  0.497380\n",
       "3  0.6  1.859553  0.736249\n",
       "4  0.8  1.752613  0.963507"
      ]
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     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lissa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ddcc6441",
   "metadata": {},
   "outputs": [
    {
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       "      <th>95</th>\n",
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       "      <td>1.618034</td>\n",
       "      <td>-1.175571</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>19.2</td>\n",
       "      <td>1.752613</td>\n",
       "      <td>-0.963507</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>19.4</td>\n",
       "      <td>1.859553</td>\n",
       "      <td>-0.736249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>19.6</td>\n",
       "      <td>1.937166</td>\n",
       "      <td>-0.497380</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19.8</td>\n",
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      ],
      "text/plain": [
       "       t         x         y\n",
       "95  19.0  1.618034 -1.175571\n",
       "96  19.2  1.752613 -0.963507\n",
       "97  19.4  1.859553 -0.736249\n",
       "98  19.6  1.937166 -0.497380\n",
       "99  19.8  1.984229 -0.250666"
      ]
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     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lissa.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0d9ae0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "df_circle = pd.read_excel(r\"C:\\Users\\BOCAH\\Downloads\\lissajous_circle.xlsx\")\n",
    "df_butterfly = pd.read_excel(r\"C:\\Users\\BOCAH\\Downloads\\lissajous_butterfly.xlsx\")\n",
    "df_distribution = pd.read_excel(r\"C:\\Users\\BOCAH\\Downloads\\distribution.xlsx\")\n",
    "df_fruit = pd.read_excel(r\"C:\\Users\\BOCAH\\Downloads\\fruit_pie_chart.xlsx\")\n",
    "df_contour = pd.read_excel(r\"C:\\Users\\BOCAH\\Downloads\\contour.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5a52c74a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vv14JCQk6ceKE0tPT9cc//lFdunRxPf633367VqxYoUmTJikrK0uDBw9WRUWF/vWvf6lLly667bbb6vT4d+/eXWvXrtWSJUvUu3dvNWnSxBXi27dvr7feekvXXXedWrZsqejoaHXo0EF//OMfde2116p///66//771aFDBx0/flzffPON3nnnnTqNr60vDz30kFatWqUbbrhBTzzxhNq3b68NGzZo8eLFuv/++9WxY0dJqna/JKmiokKffPKJ7rrrLp/vAxBU/PVJOwCBq/JqD95+srOzq1ztobi42EyaNMn06NHDNG/e3ERERJhOnTqZuXPnmpMnTxpjjNm3b5+5/fbbzSWXXGIiIiJMVFSUueqqq8zKlSvdtv/OO++YK664woSHh5v4+Hjz29/+1rz//vtVrl4wYcIE06FDB7d1T506ZWbOnGnat29v7Ha7iYuLM/fff7/56aef3JYrKSkxM2bMMAkJCSYiIsIMHDjQZGZmevwkfp8+fcyFF15oHA6Hufjii81DDz1kfvjhh1r1cv/+/Wby5Mnm0ksvNQ6Hw0RERJjLL7/cTJ8+3WRnZ7vti6erPUyZMsXj/X755Zfm1ltvNa1atTJhYWGmXbt2ZuLEiaa4uNgY4/lqD8YY8/nnn5tf//rXJiYmxtjtdhMbG2uGDBlili5dWuO+LF++3ISEhJjDhw+75pWUlJhnn33WjBgxwrRr1844HA4THh5uunTpYmbMmGGOHTvmdh+nTp0yjz/+uLnssstMWFiYadWqlRkyZIjZtm2ba5naPv4//vij+dWvfmVatGhhbDabOfMl7e9//7vp1auXcTgcRpLbY5qdnW1+85vfmPj4eGO3281FF11k+vXrZ5588knXMpX9+9vf/lZjXypV93id6exjzBhjDh48aMaNG2datWpl7Ha76dSpk/m///f/VrkCR3X79eGHHxpJ5tNPP611zYAV2Yz5z8dwASDI3HLLLTp06JB27tzp71Isobi4WO3atdPDDz+smTNn+rscnGX8+PH69ttv9c9//tPfpQABjTG/AIJOTk6O3nzzTW3evFlJSUn+LscywsPDNW/ePNe31yFw7N+/X2vWrNH8+fP9XQoQ8BjzCyDovPrqq1q4cKGGDBlS5Ru50LDuvfde/fzzz/r222/VvXt3f5eD/8jJydGiRYt07bXX+rsUIOAx7AEAAACWwbAHAAAAWAbhFwAAAJZB+AUAAIBl8IG3GlRUVOjw4cNq1qxZnb/mEgAAAA3PGKPjx4+rTZs2Xr8+vRLhtwaHDx9WQkKCv8sAAABADQ4dOqS2bdtWuwzhtwaVXxN66NAhNW/evMG353Q6tWnTJiUnJ8tutzf49oIJvfGMvnhHbzyjL57RF+/ojWf0xTtf96awsFAJCQlev979TITfGlQOdWjevLnPwm9kZKSaN2/OL9JZ6I1n9MU7euMZffGMvnhHbzyjL975qze1GaLKB94AAABgGYRfAAAAWAbhFwAAAJbBmF8AAIB6YoxRWVmZysvL/V2KXzmdToWGhqq4uLheehESEqLQ0NB6uews4RcAAKAelJaWKi8vT0VFRf4uxe+MMYqNjdWhQ4fq7XsSIiMjFRcXp7CwsPO6H8IvAADAeaqoqFB2drZCQkLUpk0bhYWFWfrLsSoqKnTixAk1bdq0xi+dqIkxRqWlpTp69Kiys7N12WWXndd9En4BAADOU2lpqSoqKpSQkKDIyEh/l+N3FRUVKi0tVXh4+HmHX0mKiIiQ3W7XwYMHXfd7rvjAGwAAQD2pj6AHz+qrtzxCAAAAsAzCLwAAACyD8AsAAIBq2Ww2rV+/3u/3UR8IvwAAABaXn5+vBx54QBdffLEcDocSEhI0atQoffjhh5KkvLw8jRgxws9V1g+u9gAAAGBhBw4c0DXXXKMWLVpowYIF6tGjh5xOpz744ANNmTJF+/btU2xsbLX34XQ6ZbfbfVTx+QmaM7/PPPOMrrzySjVr1kwxMTG6+eablZWVVeN6W7ZsUe/evRUeHq6LL75YS5cu9UG1AAAAwWHy5Mmy2Wz65JNP9Ktf/UodO3ZU165dNX36dG3fvl2S+5CFAwcOyGaz6a9//asGDRqk8PBwvfHGG5KkV199VV27dlVERIQ6d+6sBx54wOt2c3NzNXbsWF144YVq1aqVbrrpJh04cKChdzd4wu+WLVs0ZcoUbd++XWlpaSorK1NycrJOnjzpdZ3s7GyNHDlS/fv3165duzRnzhw9+OCDSk1N9WHlAAAAgenHH3/Uxo0bNWXKFF1wwQVVbm/RooXXdWfOnKkHH3xQX331lYYNG6YlS5ZoypQpuvfee/X5559r9erVuvTSSz2uW1RUpMGDB6tp06baunWr/vGPf6hp06YaPny4SktL62v3PAqaYQ8bN250m16xYoViYmL06aefasCAAR7XWbp0qdq1a6eFCxdKkrp06aKdO3fq2Wef1ZgxYxq6ZAAAgDoprzD6685D+iK3QF3jo/TrPgkKadJw3xT3zTffyBijzp0713ndadOmafTo0a7pJ598Ug8//LD+z//5P6qoqFBsbKwGDRrkcd0333xTTZo00SuvvOL6JrwVK1aoRYsWSk9PV3Jy8jntT20ETfg9W0FBgSSpZcuWXpfJyMio0rxhw4Zp+fLlXsemlJSUqKSkxDVdWFgo6fRYFqfTWR+lV6tyG77YVrChN57RF+8aU29Kyyo09+0v9OXhQl3eprnm/bKrwkLP7Y939dGX+qwnUDSm46W+0RvPzuxLeXm5jDGqqKhQRUXFOd/nmh2H9McPv5azvEJpX30vY4xuuzKhvkquory8XJJctVenct8ql/vFL37h+v+RI0d0+PBhDR48WBUVFTLGeLzfyvV37typb775Rs2aNXPbRnFxsb7++mtdf/31HrdvjJHT6VRISIjbbXU5NoMy/BpjNH36dF177bXq1q2b1+Xy8/PVunVrt3mtW7dWWVmZfvjhB8XFxVVZ55lnntG8efOqzN+0aZNPv64wLS3NZ9sKNvTGM/riXWPpTf9wqf/FklSgv286dN73d759qe96AkVjOV4aAr3xLC0tTaGhoYqNjdWJEyfO68/2uw78oNKycsU1dyivsES7DvygkZ2i6rFad7GxsbLZbMrMzNSQIUOqXfbUqVMqLCzUiRMnXPPOPEkonR7OUDlPko4fP+7xPoqLi9WzZ08tW7asynZatWrldh+VSktLderUKW3dulVlZWVutxUVFdWwp/8VlOF36tSp2r17t/7xj3/UuGzlqfRKle9Ezp5fafbs2Zo+fbprurCwUAkJCUpOTlbz5s3Po+racTqdSktL09ChQ4PmU5O+Qm88oy/eNabe3LTon/r2hxMKDw1RcVm5Lo5uqremXnNO91UffanPegJFYzpe6hu98ezMvpSXl+vQoUNq2rSpwsPDz/k+e3WI1tb9Pyn/eKnCQkPUq0N0g+aP5s2bKzk5Wa+++qp++9vfVhn3+/PPP7vG/UZERKh58+Zq2rSpJOmCCy5w1da8eXN16NBB27dv1w033CBjjI4fP65mzZq5Za7K+7j66qu1fv16XXzxxbXev+LiYkVERGjAgAFVeuwpLHsTdOH3gQce0Ntvv62tW7eqbdu21S4bGxur/Px8t3lHjhxRaGioWrVq5XEdh8Mhh8NRZb7dbvfpL7yvtxdM6I1n9MW7c+1NaVmFZqbu1t7cAnWLj9L8MT389qf9Tm1aaN+RkyopqZDNZlOnNi3O+/E+n2OmIeo5V/X9OPG75B298cxut6tJkyay2Wxq0qSJmjQ59+Nv7JXtZLPZ3Mb8NmnAMb+StGTJEvXr1099+/bVE088oR49eqisrExpaWlasmSJvvrqK0ly7Vvl/p29rykpKZo0aZJat26tYcOGKT8/X59//rkefPBB1zKV64wfP17PPfecbrnlFj3xxBNq27atcnJytHbtWv32t7/1mPEqe+zpOKzLcRk04dcYowceeEDr1q1Tenq6EhMTa1wnKSlJ77zzjtu8TZs2qU+fPvzyAqjRzNTdeiszV8ZI+4+e/jPfC2N7+qWW+WN6SJJbwPOnQKonkB4n4HyFNLHp9qva+XSbiYmJ+uyzz/TUU0/p4YcfVl5eni666CL17t1bS5YsqfX9TJgwQcXFxXrhhRf0yCOPqFWrVvrVr37lcdnIyEht3bpVM2fO1OjRo3X8+HHFx8fruuuua/C/tAdN+J0yZYpWr16tt956S82aNXOd0Y2KilJERISk00MWcnNztWrVKknSpEmTtGjRIk2fPl333HOPMjIytHz5cv3lL3/x234ACB57cwtkjBRuD1Gxs1x7cwv8VktYaJOACnSBVE8gPU5AsIqLi9OiRYu0aNEij7dXDhuVpA4dOrhNn+m+++7Tfffdp4qKChUWFroF2bPXiY2N1WuvvVYP1ddN0Hw0d8mSJSooKNCgQYMUFxfn+lmzZo1rmby8POXk5LimExMT9d577yk9PV09e/bU73//e7344otc5gxArXSLj5LNJhU7y2WznZ5G4OFxAlAXQXPm19s7jDOtXLmyyryBAwfqs88+a4CKADR2gfSnfXjH4wSgLoIm/AKArwXSn/bhHY8TgLoImmEPAAAAwPnizC+AgBNIlxgDaovjFlLthmni3NRXbwm/AAIOl65CMOK4tbbKS6gWFRW5rkKF+lX5LW7ne7lawi+AgMOlqxCMOG6tLSQkRC1atNCRI0cknb6Orbdvk7WCiooKlZaWqri4+Ly+9EM6fca3qKhIR44cUYsWLRQSEnJe90f4BRBwusVHaf/RE1y6CkGF4xaxsbGS5ArAVmaM0alTpxQREVFvbwJatGjh6vH5IPwCCDhcugrBiOMWNptNcXFxiomJkdPp9Hc5fuV0OrV161YNGDCgXr5V1263n/cZ30qEXwABh0tXIRhx3KJSSEhIvQW1YBUSEqKysjKFh4fXS/itT3wMFQAAAJZB+AUAAIBlEH4BAABgGYz5BeCGC/UDwYvfX6BmhF8AbrhQPxC8+P0FasbbQQBuzrxQvzHiQv1AEOH3F6gZ4ReAm27xUbLZxIX6gSDE7y9QM4Y9AHDDhfqB4MXvL1Azwi8AN1yoHwhe/P4CNWPYAwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMrjOLxCASssqNDN1t9uF6sNCea8KIHDxvIVgQfgFAtDM1N16KzNXxkj7j56QJC5cDyCg8byFYMFbMiAA7c0tkDFSuD1ExpyeBoBAxvMWggXhFwhA3eKjZLNJxc5y2WynpwEgkPG8hWDBsAcgAM0f00OS3MbOAUAg43kLwYLwCwSgsNAmjJUDEFR43kKwYNgDAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMvg642BGpSWVWhm6m6376sPC+V9IwD4G8/POBeEX6AGM1N3663MXBkj7T96QpL4/noACAA8P+NcBNXbo61bt2rUqFFq06aNbDab1q9fX+3y6enpstlsVX727dvnm4LRKOzNLZAxUrg9RMacngYA+B/PzzgXQRV+T548qSuuuEKLFi2q03pZWVnKy8tz/Vx22WUNVCEao27xUbLZpGJnuWy209MAAP/j+RnnIqiGPYwYMUIjRoyo83oxMTFq0aJF/RcES5g/pockuY0pAwD4H8/POBdBFX7PVa9evVRcXKzLL79cv/vd7zR48GCvy5aUlKikpMQ1XVhYKElyOp1yOp0NXmvlNnyxrWDjr97YJC0Y3fW/M0y5nM5yn9ZQHY4Z7+iNZ/TFM/riXaD2xt/Pz4Hal0Dg697UZTs2Y4xpwFoajM1m07p163TzzTd7XSYrK0tbt25V7969VVJSotdff11Lly5Venq6BgwY4HGdlJQUzZs3r8r81atXKzIysr7KBwAAQD0pKirSuHHjVFBQoObNm1e7bKMOv56MGjVKNptNb7/9tsfbPZ35TUhI0A8//FBjM+uD0+lUWlqahg4dKrvd3uDbCyb0xjP64h298Yy+eEZfvKM3ntEX73zdm8LCQkVHR9cq/Fpi2MOZ+vbtqzfeeMPr7Q6HQw6Ho8p8u93u0wPb19sLJvTGM/riHb3xjL54Rl+8ozee0RfvfNWbumwjqK72UB927dqluLg4f5cBAAAAPwiqM78nTpzQN99845rOzs5WZmamWrZsqXbt2mn27NnKzc3VqlWrJEkLFy5Uhw4d1LVrV5WWluqNN95QamqqUlNT/bULAAAA8KOgCr87d+50u1LD9OnTJUkTJkzQypUrlZeXp5ycHNftpaWleuSRR5Sbm6uIiAh17dpVGzZs0MiRI31eOwAAAPwvqMLvoEGDVN3n81auXOk2PWPGDM2YMaOBqwIAAECwsNyYXwAAAFgX4RcAAACWQfgFAACAZRB+AQAAYBmEXwAAAFhGUF3tAThbaVmFZqbu1t7cAnWLj9L8MT0UFsp7OgBA/eB1pvEh/CKozUzdrbcyc2WMtP/oCUnSC2N7+rcoAECjwetM48NbFwS1vbkFMkYKt4fImNPTAADUF15nGh/CL4Jat/go2WxSsbNcNtvpaQAA6guvM40Pwx4Q1OaP6SFJbmOxAACoL7zOND6EXwS1sNAmjL0CADQYXmcaH4Y9AAAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALCMUH8XgMavtKxCM1N3a29ugbrFR2n+mB4KC+V9FwDA2nh99A/CLxrczNTdeiszV8ZI+4+ekCS9MLanf4sCAMDPeH30D95eoMHtzS2QMVK4PUTGnJ4GAMDqeH30D8IvGly3+CjZbFKxs1w22+lpAACsjtdH/2DYAxrc/DE9JMltTBMAAFbH66N/EH7R4MJCmzCGCQCAs/D66B8MewAAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWEZQhd+tW7dq1KhRatOmjWw2m9avX1/jOlu2bFHv3r0VHh6uiy++WEuXLm34QgEAABCQgir8njx5UldccYUWLVpUq+Wzs7M1cuRI9e/fX7t27dKcOXP04IMPKjU1tYErBQAAQCAK9XcBdTFixAiNGDGi1ssvXbpU7dq108KFCyVJXbp00c6dO/Xss89qzJgxDVQlAAAAAlVQhd+6ysjIUHJystu8YcOGafny5XI6nbLb7VXWKSkpUUlJiWu6sLBQkuR0OuV0Ohu24P9s58x/8V/0xjP64h298Yy+eEZfvKM3ntEX73zdm7psx2aMMQ1YS4Ox2Wxat26dbr75Zq/LdOzYURMnTtScOXNc87Zt26ZrrrlGhw8fVlxcXJV1UlJSNG/evCrzV69ercjIyHqpHQAAAPWnqKhI48aNU0FBgZo3b17tso36zK90OiSfqTLrnz2/0uzZszV9+nTXdGFhoRISEpScnFxjM+uD0+lUWlqahg4d6vHMtK+UllVo7ttf6MvDhbq8TXPN+2VXhYX6d4h4oPQm0NAX7+iNZ/TFM/riHb3xLJD6Emiv277uTeVf6mujUYff2NhY5efnu807cuSIQkND1apVK4/rOBwOORyOKvPtdrtPD2xfb+9sM9Zm6q3MPBkj7TtyUuVqohfG9vRbPWfyd28CFX3xjt54Rl88oy/e0RvPAqEvgfq67ave1GUbQXW1h7pKSkpSWlqa27xNmzapT58+fj9IA93e3AIZI4XbQ2TM6WkAABCYeN2uvaAKvydOnFBmZqYyMzMlnb6UWWZmpnJyciSdHrJw5513upafNGmSDh48qOnTp+urr77Sq6++quXLl+uRRx7xR/lBpVt8lGw2qdhZLpvt9DQAAAhMvG7XXlANe9i5c6cGDx7smq4cmzthwgStXLlSeXl5riAsSYmJiXrvvff00EMP6aWXXlKbNm304osvcpmzWpg/poek0+8cu8VHuaYBAEDg4XW79oIq/A4aNEjVXZxi5cqVVeYNHDhQn332WQNW1TiFhQbGWCEAAFAzXrdrL6iGPQAAAADng/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAywj1dwFoOKVlFZqZult7cwvULT5K88f0UFgo73cAAMB/WS0vEH4bsZmpu/VWZq6MkfYfPSFJemFsT/8WBQAAAorV8kLjjfXQ3twCGSOF20NkzOlpAACAM1ktLxB+G7Fu8VGy2aRiZ7lsttPTAAAAZ7JaXmDYQyM2f0wPSXIbwwMAAHAmq+UFwm8jFhbapFGP2QEAAOfPanmBYQ8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwjKALv4sXL1ZiYqLCw8PVu3dvffzxx16XTU9Pl81mq/Kzb98+H1YMAACAQBFU4XfNmjWaNm2aHn30Ue3atUv9+/fXiBEjlJOTU+16WVlZysvLc/1cdtllPqoYAAAAgSSowu/zzz+vu+66S3fffbe6dOmihQsXKiEhQUuWLKl2vZiYGMXGxrp+QkJCfFRx/Sstq9BDazI19PktemhNpkrLKvxdEgAAsIjGkENC/V1AbZWWlurTTz/VrFmz3OYnJydr27Zt1a7bq1cvFRcX6/LLL9fvfvc7DR482OuyJSUlKikpcU0XFhZKkpxOp5xO53nsQe1UbsPbtuas3aONew7LGOm7H48rRBV6ZnT3Bq8rENTUG6uiL97RG8/oi2f0xTt645kV+1LbHOLr3tRlOzZjjGnAWurN4cOHFR8fr3/+85/q16+fa/7TTz+t1157TVlZWVXWycrK0tatW9W7d2+VlJTo9ddf19KlS5Wenq4BAwZ43E5KSormzZtXZf7q1asVGRlZfzsEAACAelFUVKRx48apoKBAzZs3r3bZoDnzW8lms7lNG2OqzKvUqVMnderUyTWdlJSkQ4cO6dlnn/UafmfPnq3p06e7pgsLC5WQkKDk5OQam1kfnE6n0tLSNHToUNnt9qr1rd2jDf95x2WzSTd0b2OpM7/V9caq6It39MYz+uIZffGO3nhmxb7UNof4ujeVf6mvjaAJv9HR0QoJCVF+fr7b/CNHjqh169a1vp++ffvqjTfe8Hq7w+GQw+GoMt9ut/v0wPa2vafH9FS5mmhvboG6xUfp6TE9ZA8NqqHb583Xj0WwoC/e0RvP6Itn9MU7euOZlfpS1xziq97UZRtBE37DwsLUu3dvpaWl6ZZbbnHNT0tL00033VTr+9m1a5fi4uIaokSfCAttohfG9vR3GQAAwIIaQw4JmvArSdOnT9f48ePVp08fJSUladmyZcrJydGkSZMknR6ykJubq1WrVkmSFi5cqA4dOqhr164qLS3VG2+8odTUVKWmpvpzNwAAAOAnQRV+x44dq2PHjumJJ55QXl6eunXrpvfee0/t27eXJOXl5bld87e0tFSPPPKIcnNzFRERoa5du2rDhg0aOXKkv3YBAAAAfhRU4VeSJk+erMmTJ3u8beXKlW7TM2bM0IwZM3xQFQAAAIKBtT4pBQAAAEsj/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsI9XcB8Gz22j36PPe4usVHaf6YHgoL5X0KAAAIXKVlFZqZult7cwt0RXwz9Q/3d0WeEX4D1IY9h1VcZtP+oyckSS+M7enfggAAAKoxM3W33srMlTHSdz8eV/8r/V2RZ5xODFDGSOH2EBkj7c0t8Hc5AAAA1dqbW+CWXwIV4TdA2WxSsbNcNpvULT7K3+UAAABUq1t8lFt+CVQMewhQN3Rv4zbmFwAAIJBV5pXKMb/SIf8W5AXhN0A9M7q77Ha7v8sAAAColbDQJq7PKDmdTr33XmCGX4Y9AAAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAso9bh97vvvmvIOmpt8eLFSkxMVHh4uHr37q2PP/642uW3bNmi3r17Kzw8XBdffLGWLl3qo0oBAAAQaGodfrt166bXX3+9IWup0Zo1azRt2jQ9+uij2rVrl/r3768RI0YoJyfH4/LZ2dkaOXKk+vfvr127dmnOnDl68MEHlZqa6uPKAQAAEAhqHX6ffvppTZkyRWPGjNGxY8casiavnn/+ed111126++671aVLFy1cuFAJCQlasmSJx+WXLl2qdu3aaeHCherSpYvuvvtu/eY3v9Gzzz7r48prp7SsQrPX7pEkzV67R6VlFX6uCAAAoG4CPc+E1nbByZMna8SIEbrrrrvUtWtXLVu2TL/85S8bsjY3paWl+vTTTzVr1iy3+cnJydq2bZvHdTIyMpScnOw2b9iwYVq+fLmcTqfsdnuVdUpKSlRSUuKaLiwslCQ5nU45nc7z3Y1qzVm7R3//Ilf9e0t//yJXkvTM6O4Nus1gUtn/hn4cgg198Y7eeEZfPKMv3tEbz+iLZ/7IM3V5DGzGGFPXDSxatEgPPfSQunTpotBQ9/z82Wef1fXuauXw4cOKj4/XP//5T/Xr1881/+mnn9Zrr72mrKysKut07NhREydO1Jw5c1zztm3bpmuuuUaHDx9WXFxclXVSUlI0b968KvNXr16tyMjIetobAAAA1JeioiKNGzdOBQUFat68ebXL1vrMb6WDBw8qNTVVLVu21E033VQl/DY0m83mNm2MqTKvpuU9za80e/ZsTZ8+3TVdWFiohIQEJScn19jM8zX7P++U5vWu0NxPm+j6rvGc+T2D0+lUWlqahg4d6vGsvVXRF+/ojWf0xTP64h298Yy+eOaPPFP5l/raqFNy/fOf/6yHH35Y119/vfbu3auLLrqozsWdq+joaIWEhCg/P99t/pEjR9S6dWuP68TGxnpcPjQ0VK1atfK4jsPhkMPhqDLfbrc3+IH99Jie//nfIV3fNV5Pj+kpeyhXozubLx6LYERfvKM3ntEXz+iLd/TGM/rizh95pi79r3Ulw4cP18yZM7Vo0SKtXbvWp8FXksLCwtS7d2+lpaW5zU9LS3MbBnGmpKSkKstv2rRJffr0CciDNCy0ieud0TOjuyuM4AsAAIJMoOeZWp/5LS8v1+7du9W2bduGrKda06dP1/jx49WnTx8lJSVp2bJlysnJ0aRJkySdHrKQm5urVatWSZImTZqkRYsWafr06brnnnuUkZGh5cuX6y9/+Yvf9gEAAAD+U+vwe/YZVH8YO3asjh07pieeeEJ5eXnq1q2b3nvvPbVv316SlJeX53bN38TERL333nt66KGH9NJLL6lNmzZ68cUXNWbMGH/tAgAAAPzIt59WqweTJ0/W5MmTPd62cuXKKvMGDhzYYFegAAAAQHAJrEEYAAAAQAMi/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsI9XcB8Gz22j36PPe4usVHaf6YHgoL5X0KAAAIXKVlFZqZult7cwt0RXwz9Q/3d0WeEX4D1IY9h1VcZtP+oyckSS+M7enfggAAAKoxM3W33srMlTHSdz8eV/8r/V2RZ5xODFDGSOH2EBkj7c0t8Hc5AAAA1dqbW+CWXwIV4TdA2WxSsbNcNpvULT7K3+UAAABUq1t8lFt+CVQMewhQN3Rv4zbmFwAAIJBV5pXKMb/SIf8W5AXhN0A9M7q77Ha7v8sAAAColbDQJq7PKDmdTr33XmCGX4Y9AAAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALAMwi8AAAAsg/ALAAAAyyD8AgAAwDIIvwAAALCMoAm/P/30k8aPH6+oqChFRUVp/Pjx+vnnn6tdZ+LEibLZbG4/ffv29U3BAAAACDih/i6gtsaNG6fvvvtOGzdulCTde++9Gj9+vN55551q1xs+fLhWrFjhmg4LC2vQOgEAABC4giL8fvXVV9q4caO2b9+uq6++WpL05z//WUlJScrKylKnTp28rutwOBQbG+urUhtcaVmFZqbu1t7cAnWLj9L8MT0UFho0J/ABAEAQaww5JCjCb0ZGhqKiolzBV5L69u2rqKgobdu2rdrwm56erpiYGLVo0UIDBw7UU089pZiYGK/Ll5SUqKSkxDVdWFgoSXI6nXI6nfWwN9Wr3Ia3bc1Zu0cb9xyWMdJ3Px5XiCr0zOjuDV5XIKipN1ZFX7yjN57RF8/oi3f0xjMr9qW2OcTXvanLdmzGGNOAtdSLp59+WitXrtS///1vt/kdO3bU//7f/1uzZ8/2uN6aNWvUtGlTtW/fXtnZ2XrsscdUVlamTz/9VA6Hw+M6KSkpmjdvXpX5q1evVmRk5PnvDAAAAOpVUVGRxo0bp4KCAjVv3rzaZf165tdb0DzTjh07JEk2m63KbcYYj/MrjR071vX/bt26qU+fPmrfvr02bNig0aNHe1xn9uzZmj59umu6sLBQCQkJSk5OrrGZ9cHpdCotLU1Dhw6V3W6vWt/aPdrwn3dcNpt0Q/c2ljrzW11vrIq+eEdvPKMvntEX7+iNZ1bsS21ziK97U/mX+trwa/idOnWqbrvttmqX6dChg3bv3q3vv/++ym1Hjx5V69ata729uLg4tW/fXl9//bXXZRwOh8ezwna73acHtrftPT2mp8rVxDXW5ukxPWQPsrE258vXj0WwoC/e0RvP6Itn9MU7euOZlfpS1xziq97UZRt+Db/R0dGKjo6ucbmkpCQVFBTok08+0VVXXSVJ+te//qWCggL169ev1ts7duyYDh06pLi4uHOu2d/CQpvohbE9/V0GAACwoMaQQ4LilGGXLl00fPhw3XPPPdq+fbu2b9+ue+65RzfeeKPbh906d+6sdevWSZJOnDihRx55RBkZGTpw4IDS09M1atQoRUdH65ZbbvHXrgAAAMCPgiL8StL//M//qHv37kpOTlZycrJ69Oih119/3W2ZrKwsFRQUSJJCQkK0Z88e3XTTTerYsaMmTJigjh07KiMjQ82aNfPHLgAAAMDPguJSZ5LUsmVLvfHGG9Uuc+aFKyIiIvTBBx80dFkAAAAIIkFz5hcAAAA4X4RfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlhPq7ADSc0rIKzUzdrb25BeoWH6X5Y3ooLJT3OwAA4L+slhcIv43YzNTdeiszV8ZI+4+ekCS9MLanf4sCAAABxWp5ofHGemhvboGMkcLtITLm9DQAAMCZrJYXCL+NWLf4KNlsUrGzXDbb6WkAAIAzWS0vMOyhEZs/pockuY3hAQAAOJPV8gLhtxELC23SqMfsAACA82e1vMCwBwAAAFgG4RcAAACWQfgFAACAZRB+AQAAYBmEXwAAAFgG4RcAAACWQfgFAACAZRB+AQAAYBmEXwAAAFgG4RcAAACWQfgFAACAZRB+AQAAYBmEXwAAAFgG4RcAAACWQfgFAACAZRB+AQAAYBmEXwAAAFgG4RcAAACWEervAhCYSssqNDN1t/bmFqhbfJTmj+mhsFDeKwEAEIh43a49wi88mpm6W29l5soYaf/RE5KkF8b29G9RAADAI163ay9o3hI89dRT6tevnyIjI9WiRYtarWOMUUpKitq0aaOIiAgNGjRIX3zxRcMW2kjszS2QMVK4PUTGnJ4GAACBidft2gua8FtaWqpbb71V999/f63XWbBggZ5//nktWrRIO3bsUGxsrIYOHarjx483YKWNQ7f4KNlsUrGzXDbb6WkAABCYeN2uvaAZ9jBv3jxJ0sqVK2u1vDFGCxcu1KOPPqrRo0dLkl577TW1bt1aq1ev1n333ddQpTYK88f0kCS3sUMAACAw8bpde0ETfusqOztb+fn5Sk5Ods1zOBwaOHCgtm3b5jX8lpSUqKSkxDVdWFgoSXI6nXI6nQ1b9H+2c+a//mKTtGB01//OMOVyOsv9Vo8UOL0JNPTFO3rjGX3xjL54R288C6S+BNrrtq97U5ftNNrwm5+fL0lq3bq12/zWrVvr4MGDXtd75plnXGeZz7Rp0yZFRkbWb5HVSEtL89m2gg298Yy+eEdvPKMvntEX7+iNZ/TFO1/1pqioqNbL+jX8pqSkeAyaZ9qxY4f69Olzztuw2Wxu08aYKvPONHv2bE2fPt01XVhYqISEBCUnJ6t58+bnXEdtOZ1OpaWlaejQobLb7Q2+vWBCbzyjL97RG8/oi2f0xTt64xl98c7Xvan8S31t+DX8Tp06Vbfddlu1y3To0OGc7js2NlbS6TPAcXFxrvlHjhypcjb4TA6HQw6Ho8p8u93u0wPb19sLJvTGM/riHb3xjL54Rl+8ozee0RfvfNWbumzDr+E3Ojpa0dHRDXLfiYmJio2NVVpamnr16iXp9BUjtmzZovnz5zfINgEAABDYguZSZzk5OcrMzFROTo7Ky8uVmZmpzMxMnThxwrVM586dtW7dOkmnhztMmzZNTz/9tNatW6e9e/dq4sSJioyM1Lhx4/y1GwAAAPCjoPnA2+OPP67XXnvNNV15Nnfz5s0aNGiQJCkrK0sFBf+9qPOMGTN06tQpTZ48WT/99JOuvvpqbdq0Sc2aNfNp7QAAAAgMQRN+V65cWeM1fo0xbtM2m00pKSlKSUlpuMIAAAAQNIJm2AMAAABwvgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMoLmG94QvErLKjQzdbf25haoW3yU5o/pobBQ3ncBAKyN10f/IPyiwc1M3a23MnNljLT/6AlJ0gtje/q3KAAA/IzXR//g7QUa3N7cAhkjhdtDZMzpaQAArI7XR/8g/KLBdYuPks0mFTvLZbOdngYAwOp4ffQPhj2gwc0f00OS3MY0AQBgdbw++gfhFw0uLLQJY5gAADgLr4/+wbAHAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlhPq7AOB8lJZVaGbqbu3NLVC3+CjNH9NDYaG8pwMA1A9eZxofwi+C2szU3XorM1fGSPuPnpAkvTC2p3+LAgA0GrzOND68dUFQ25tbIGOkcHuIjDk9DQBAfeF1pvEh/CKodYuPks0mFTvLZbOdngYAoL7wOtP4MOwBQW3+mB6S5DYWCwCA+sLrTOND+EVQCwttwtgrAECD4XWm8WHYAwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLCJrw+9RTT6lfv36KjIxUixYtarXOxIkTZbPZ3H769u3bsIUCAAAgYAVN+C0tLdWtt96q+++/v07rDR8+XHl5ea6f9957r4EqBAAAQKALmi+5mDdvniRp5cqVdVrP4XAoNja2ASoCAABAsAma8Huu0tPTFRMToxYtWmjgwIF66qmnFBMT43X5kpISlZSUuKYLCwslSU6nU06ns8HrrdyGL7YVbOiNZ/TFO3rjGX3xjL54R288oy/e+bo3ddmOzRhjGrCWerdy5UpNmzZNP//8c43LrlmzRk2bNlX79u2VnZ2txx57TGVlZfr000/lcDg8rpOSkuI6y3ym1atXKzIy8nzLBwAAQD0rKirSuHHjVFBQoObNm1e7rF/Dr7egeaYdO3aoT58+rum6hN+z5eXlqX379nrzzTc1evRoj8t4OvObkJCgH374ocZm1gen06m0tDQNHTpUdru9wbcXTOiNZ/TFO3rjGX3xjL54R288oy/e+bo3hYWFio6OrlX49euwh6lTp+q2226rdpkOHTrU2/bi4uLUvn17ff31116XcTgcHs8K2+12nx7Yvt5eMPF1b0rLKjQzdbf25haoW3yU5o/pobDQwPusKMeMd/TGM/riGX3xLtB6EyjPz4HWl0Diq97UZRt+Db/R0dGKjo722faOHTumQ4cOKS4uzmfbRPCbmbpbb2Xmyhhp/9ETkqQXxvb0b1EAAJ6fcU4C7/SVFzk5OcrMzFROTo7Ky8uVmZmpzMxMnThxwrVM586dtW7dOknSiRMn9MgjjygjI0MHDhxQenq6Ro0apejoaN1yyy3+2g0Eob25BTJGCreHyJjT0wAA/+P5GeciaK728Pjjj+u1115zTffq1UuStHnzZg0aNEiSlJWVpYKC0wd+SEiI9uzZo1WrVunnn39WXFycBg8erDVr1qhZs2Y+rx/Bq1t8lPYfPaFiZ7lsttPTAAD/4/kZ5yJowu/KlStrvMbvmZ/di4iI0AcffNDAVcEK5o/pIUluY8oAAP7H8zPORdCEX8BfwkKbMIYMAAIQz884F0Ez5hcAAAA4X4RfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGXzDGxCASssqNDN1t9tXdoaF8l4VQODieQvBgvALBKCZqbv1VmaujJH2Hz0hSXyFJ4CAxvMWggVvyYAAtDe3QMZI4fYQGXN6GgACGc9bCBaEXyAAdYuPks0mFTvLZbOdngaAQMbzFoIFwx6AADR/TA9Jchs7BwCBjOctBAvCLxCAwkKbMFYOQFDheQvBgmEPAAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADL4Dq/ANyUllVoZuputwvVh4XyPhkIBvz+AjUj/AJwMzN1t97KzJUx0v6jJySJC9cDQYLfX6BmvB0E4GZvboGMkcLtITLm9DSA4MDvL1Azwi8AN93io2SzScXOctlsp6cBBAd+f4GaMewBgJv5Y3pIktuYQQDBgd9foGaEXwBuwkKbMEYQCFL8/gI1Y9gDAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDD7wBiDg8C1VCEYct0BwIPwCCDh8SxWCEcctEBx4Swog4PAtVQhGHLdAcCD8Agg4fEsVghHHLRAcGPYAIODwLVUIRhy3QHAg/AIIOHxLFYIRxy0QHAi/AOAFn94PDjxOAOqC8AsAXvDp/eDA4wSgLoLirfGBAwd01113KTExUREREbrkkks0d+5clZaWVrueMUYpKSlq06aNIiIiNGjQIH3xxRc+qhpAsOPT+8GBxwlAXQRF+N23b58qKir08ssv64svvtALL7ygpUuXas6cOdWut2DBAj3//PNatGiRduzYodjYWA0dOlTHjx/3UeUAghmf3g8OPE4A6iIohj0MHz5cw4cPd01ffPHFysrK0pIlS/Tss896XMcYo4ULF+rRRx/V6NGjJUmvvfaaWrdurdWrV+u+++7zSe0AglcgfXo/0Ma1BlI9gfQ4AQh8QRF+PSkoKFDLli293p6dna38/HwlJye75jkcDg0cOFDbtm3zGn5LSkpUUlLimi4sLJQkOZ1OOZ3Oeqreu8pt+GJbwYbeeEZfvDvf3tgkLRjd9b8zTLmczvJ6qKzu5qzdo417DssY6bsfjytEFXpmdPdzuq/6OGbqs57zVV+PE79L3tEbz+iLd77uTV22YzPGmAaspUHs379fv/jFL/Tcc8/p7rvv9rjMtm3bdM011yg3N1dt2rRxzb/33nt18OBBffDBBx7XS0lJ0bx586rMX716tSIjI+tnBwAAAFBvioqKNG7cOBUUFKh58+bVLuvXM7/eguaZduzYoT59+rimDx8+rOHDh+vWW2/1GnzPZLPZ3KaNMVXmnWn27NmaPn26a7qwsFAJCQlKTk6usZn1wel0Ki0tTUOHDpXdbm/w7QUTeuMZffGuMfVm9to92vCfM602m3RD9zbndeb3fPtSn/UEisZ0vNQ3euMZffHO172p/Et9bfg1/E6dOlW33XZbtct06NDB9f/Dhw9r8ODBSkpK0rJly6pdLzY2VpKUn5+vuLg41/wjR46odevWXtdzOBxyOBxV5tvtdp8e2L7eXjChN57RF+8aQ2+eHtNT5WriGtf69Jgesp/nGNvz6UtD1BMoGsPx0lDojWf0xTtf9aYu2/Br+I2OjlZ0dHStls3NzdXgwYPVu3dvrVixQk2aVP8km5iYqNjYWKWlpalXr16SpNLSUm3ZskXz588/79oBwJcC7dvDAq0eAKitoHibfvjwYQ0aNEgJCQl69tlndfToUeXn5ys/P99tuc6dO2vdunWSTg93mDZtmp5++mmtW7dOe/fu1cSJExUZGalx48b5YzcAAADgZ0FxtYdNmzbpm2++0TfffKO2bdu63Xbm5/WysrJUUPDfi5vPmDFDp06d0uTJk/XTTz/p6quv1qZNm9SsWTOf1Q4AAIDAERThd+LEiZo4cWKNy5194QqbzaaUlBSlpKQ0TGEAAAAIKkEx7AEAAACoD4RfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGYRfAAAAWAbhFwAAAJZB+AUAAIBlEH4BAABgGaH+LiDQGWMkSYWFhT7ZntPpVFFRkQoLC2W3232yzWBBbzyjL97RG8/oi2f0xTt64xl98c7XvanMaZW5rTqE3xocP35ckpSQkODnSgAAAFCd48ePKyoqqtplbKY2EdnCKioqdPjwYTVr1kw2m63Bt1dYWKiEhAQdOnRIzZs3b/DtBRN64xl98Y7eeEZfPKMv3tEbz+iLd77ujTFGx48fV5s2bdSkSfWjejnzW4MmTZqobdu2Pt9u8+bN+UXygt54Rl+8ozee0RfP6It39MYz+uKdL3tT0xnfSnzgDQAAAJZB+AUAAIBlEH4DjMPh0Ny5c+VwOPxdSsChN57RF+/ojWf0xTP64h298Yy+eBfIveEDbwAAALAMzvwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPz62YEDB3TXXXcpMTFRERERuuSSSzR37lyVlpZWu54xRikpKWrTpo0iIiI0aNAgffHFFz6q2jeeeuop9evXT5GRkWrRokWt1pk4caJsNpvbT9++fRu2UD84l95Y4Zj56aefNH78eEVFRSkqKkrjx4/Xzz//XO06jfWYWbx4sRITExUeHq7evXvr448/rnb5LVu2qHfv3goPD9fFF1+spUuX+qhS36pLX9LT06scGzabTfv27fNhxQ1v69atGjVqlNq0aSObzab169fXuI5Vjpe69sYqx8wzzzyjK6+8Us2aNVNMTIxuvvlmZWVl1bheoBw3hF8/27dvnyoqKvTyyy/riy++0AsvvKClS5dqzpw51a63YMECPf/881q0aJF27Nih2NhYDR06VMePH/dR5Q2vtLRUt956q+6///46rTd8+HDl5eW5ft57770GqtB/zqU3Vjhmxo0bp8zMTG3cuFEbN25UZmamxo8fX+N6je2YWbNmjaZNm6ZHH31Uu3btUv/+/TVixAjl5OR4XD47O1sjR45U//79tWvXLs2ZM0cPPvigUlNTfVx5w6prXyplZWW5HR+XXXaZjyr2jZMnT+qKK67QokWLarW8VY4Xqe69qdTYj5ktW7ZoypQp2r59u9LS0lRWVqbk5GSdPHnS6zoBddwYBJwFCxaYxMREr7dXVFSY2NhY84c//ME1r7i42ERFRZmlS5f6okSfWrFihYmKiqrVshMmTDA33XRTg9YTSGrbGyscM19++aWRZLZv3+6al5GRYSSZffv2eV2vMR4zV111lZk0aZLbvM6dO5tZs2Z5XH7GjBmmc+fObvPuu+8+07dv3war0R/q2pfNmzcbSeann37yQXWBQZJZt25dtctY5Xg5W216Y8Vjxhhjjhw5YiSZLVu2eF0mkI4bzvwGoIKCArVs2dLr7dnZ2crPz1dycrJrnsPh0MCBA7Vt2zZflBjQ0tPTFRMTo44dO+qee+7RkSNH/F2S31nhmMnIyFBUVJSuvvpq17y+ffsqKiqqxn1sTMdMaWmpPv30U7fHWpKSk5O99iEjI6PK8sOGDdPOnTvldDobrFZfOpe+VOrVq5fi4uJ03XXXafPmzQ1ZZlCwwvFyvqx2zBQUFEhStdklkI4bwm+A2b9/v/70pz9p0qRJXpfJz8+XJLVu3dptfuvWrV23WdWIESP0P//zP/roo4/03HPPaceOHRoyZIhKSkr8XZpfWeGYyc/PV0xMTJX5MTEx1e5jYztmfvjhB5WXl9fpsc7Pz/e4fFlZmX744YcGq9WXzqUvcXFxWrZsmVJTU7V27Vp16tRJ1113nbZu3eqLkgOWFY6Xc2XFY8YYo+nTp+vaa69Vt27dvC4XSMcN4beBpKSkeBz0fubPzp073dY5fPiwhg8frltvvVV33313jduw2Wxu08aYKvMCzbn0pS7Gjh2rG264Qd26ddOoUaP0/vvv69///rc2bNhQj3vRMBq6N1LjP2Y87UtN+xjMx0x16vpYe1re0/xgV5e+dOrUSffcc49+8YtfKCkpSYsXL9YNN9ygZ5991helBjSrHC91ZcVjZurUqdq9e7f+8pe/1LhsoBw3oT7dmoVMnTpVt912W7XLdOjQwfX/w4cPa/DgwUpKStKyZcuqXS82NlbS6XdRcXFxrvlHjhyp8q4q0NS1L+crLi5O7du319dff11v99lQGrI3Vjhmdu/ere+//77KbUePHq3TPgbTMeNJdHS0QkJCqpzNrO6xjo2N9bh8aGioWrVq1WC1+tK59MWTvn376o033qjv8oKKFY6X+tSYj5kHHnhAb7/9trZu3aq2bdtWu2wgHTeE3wYSHR2t6OjoWi2bm5urwYMHq3fv3lqxYoWaNKn+hHxiYqJiY2OVlpamXr16STo9nm3Lli2aP3/+edfekOrSl/pw7NgxHTp0yC3wBaqG7I0VjpmkpCQVFBTok08+0VVXXSVJ+te//qWCggL169ev1tsLpmPGk7CwMPXu3VtpaWm65ZZbXPPT0tJ00003eVwnKSlJ77zzjtu8TZs2qU+fPrLb7Q1ar6+cS1882bVrV9AeG/XFCsdLfWqMx4wxRg888IDWrVun9PR0JSYm1rhOQB03Pv+IHdzk5uaaSy+91AwZMsR89913Ji8vz/Vzpk6dOpm1a9e6pv/whz+YqKgos3btWrNnzx5z++23m7i4OFNYWOjrXWgwBw8eNLt27TLz5s0zTZs2Nbt27TK7du0yx48fdy1zZl+OHz9uHn74YbNt2zaTnZ1tNm/ebJKSkkx8fHyj6osxde+NMdY4ZoYPH2569OhhMjIyTEZGhunevbu58cYb3ZaxwjHz5ptvGrvdbpYvX26+/PJLM23aNHPBBReYAwcOGGOMmTVrlhk/frxr+W+//dZERkaahx56yHz55Zdm+fLlxm63m//3//6fv3ahQdS1Ly+88IJZt26d+fe//2327t1rZs2aZSSZ1NRUf+1Cgzh+/LjrOUSSef75582uXbvMwYMHjTHWPV6MqXtvrHLM3H///SYqKsqkp6e75ZaioiLXMoF83BB+/WzFihVGksefM0kyK1ascE1XVFSYuXPnmtjYWONwOMyAAQPMnj17fFx9w5owYYLHvmzevNm1zJl9KSoqMsnJyeaiiy4ydrvdtGvXzkyYMMHk5OT4ZwcaUF17Y4w1jpljx46ZO+64wzRr1sw0a9bM3HHHHVUuOWSVY+all14y7du3N2FhYeYXv/iF2yWIJkyYYAYOHOi2fHp6uunVq5cJCwszHTp0MEuWLPFxxb5Rl77Mnz/fXHLJJSY8PNxceOGF5tprrzUbNmzwQ9UNq/LyXGf/TJgwwRhj7eOlrr2xyjHjLbec+ZoTyMeNzZj/jDYGAAAAGjmu9gAAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCAADAMgi/AAAAsAzCLwAAACyD8AsAAADLIPwCgAWUl5erX79+GjNmjNv8goICJSQk6He/+52fKgMA3+LrjQHAIr7++mv17NlTy5Yt0x133CFJuvPOO/X5559rx44dCgsL83OFANDwCL8AYCEvvviiUlJStHfvXu3YsUO33nqrPvnkE/Xs2dPfpQGATxB+AcBCjDEaMmSIQkJCtGfPHj3wwAMMeQBgKYRfALCYffv2qUuXLurevbs+++wzhYaG+rskAPAZPvAGABbz6quvKjIyUtnZ2fruu+/8XQ4A+BRnfgHAQjIyMjRgwAC9//77WrBggcrLy/X3v/9dNpvN36UBgE9w5hcALOLUqVOaMGGC7rvvPl1//fV65ZVXtGPHDr388sv+Lg0AfIbwCwAWMWvWLFVUVGj+/PmSpHbt2um5557Tb3/7Wx04cMC/xQGAjzDsAQAsYMuWLbruuuuUnp6ua6+91u22YcOGqaysjOEPACyB8AsAAADLYNgDAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAyCL8AAACwDMIvAAAALIPwCwAAAMsg/AIAAMAy/j/Zq2/5rLRnYwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Adjust figure size as needed\n",
    "\n",
    "# Assuming columns are x and y\n",
    "plt.scatter(df_circle[\"x\"], df_circle[\"y\"], s=5, alpha=0.8, label=\"Circle\")  # Adjust marker size and opacity\n",
    "\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"Lissajous Circle (Scatter Plot)\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c06dabdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       t         x         y\n",
      "0    0.0  2.000000  0.000000\n",
      "1    0.2  1.996053  0.250666\n",
      "2    0.4  1.984229  0.497380\n",
      "3    0.6  1.964575  0.736249\n",
      "4    0.8  1.937166  0.963507\n",
      "..   ...       ...       ...\n",
      "95  19.0  1.902113 -1.175571\n",
      "96  19.2  1.937166 -0.963507\n",
      "97  19.4  1.964575 -0.736249\n",
      "98  19.6  1.984229 -0.497380\n",
      "99  19.8  1.996053 -0.250666\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_butterfly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "71110af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Adjust figure size as needed\n",
    "\n",
    "# Assuming columns are x and y\n",
    "plt.scatter(df_butterfly[\"x\"], df_butterfly[\"y\"], s=5, alpha=0.8, label=\"Circle\")  # Adjust marker size and opacity\n",
    "\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"Lissajous Butterfly (Scatter Plot)\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c3300706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='x', data=df_distribution, color='skyblue')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Count Plot of x Values')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b16cdaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(df_fruit['amount'], labels=df_fruit['fruit'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Pie Chart of Fruit Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ba2119cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.tricontourf(df_contour['x'], df_contour['y'], df_contour['z'], cmap='magma')\n",
    "plt.colorbar(label='z')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Contour Plot')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fe27cad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}