Issue5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d5baca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "248cac58",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \"month\": [1,2,3,4,5],\n",
    "    \"weather\": [\"rainy\", \"sunny\", \"cloudy\", \"windy\", \"cloudy\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f6311c4",
   "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": 4,
   "id": "96392ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "572e7b77",
   "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": 6,
   "id": "909136c8",
   "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": 7,
   "id": "49c8cba2",
   "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": 8,
   "id": "0031084c",
   "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;32mC:\\konda\\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;32mC:\\konda\\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;32mC:\\konda\\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[8], 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;32mC:\\konda\\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;32mC:\\konda\\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": 9,
   "id": "9f3e2a46",
   "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": 10,
   "id": "08de5699",
   "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": 11,
   "id": "6c5a795d",
   "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": 12,
   "id": "687daf56",
   "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;32mC:\\konda\\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[12], 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;32mC:\\konda\\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;32mC:\\konda\\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;32mC:\\konda\\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;32mC:\\konda\\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;32mC:\\konda\\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": 12,
   "id": "d1ee325f",
   "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": 13,
   "id": "2e79a774",
   "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": 14,
   "id": "ba59708f",
   "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": 15,
   "id": "36f5b595",
   "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": 16,
   "id": "b52bdde7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[3:20]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1e0fdd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "0      1   rainy\n",
      "1      2   sunny\n",
      "2      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[-1:2]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ae0924b",
   "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": 19,
   "id": "3d42acd7",
   "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": 20,
   "id": "52804a42",
   "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": 21,
   "id": "ccaf2177",
   "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": 22,
   "id": "5c8d6b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a']['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d0ff584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'windy'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['d']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e8e80d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"datarasha.csv\", sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d04d260f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"datarasha2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e0422c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"datarasha3.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9079a40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"datarasha4.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45ca9273",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"datarasha5.csv\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8963dbbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"datarasha1.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "25e8f3ee",
   "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(\"datarasha1.xlsx\")\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f7429448",
   "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(\"datarasha.csv\")\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6825fab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. What would be the best separation characters for CSV file?\n",
    "#2. Explain why you choose that answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cd5aca0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. The best choice for the separation character in a CSV (Comma-Separated Values) file is the comma (,). \n",
    "#2. This is because the comma is widely accepted as the standard delimiter for CSV files due to several reasons:\n",
    "#           Universally Recognized: The comma is universally recognized and supported by most software applications and programming languages for parsing CSV files. It is a widely accepted standard, making it a reliable choice for data interchange.\n",
    "\n",
    "#           Minimal Ambiguity: The comma is a character that is rarely used within textual data fields. This reduces the likelihood of ambiguity or confusion when parsing the CSV file.\n",
    "\n",
    "#           Ease of Use: Using the comma as the separator is simple and straightforward. It requires minimal effort for both data producers and consumers to work with CSV files delimited by commas.\n",
    "\n",
    "#           Compatibility: Many applications, libraries, and programming languages have built-in support for parsing CSV files with commas as delimiters. This compatibility ensures seamless integration and interoperability across different platforms and systems.\n",
    "\n",
    "#           While other characters such as semicolons (;), tabs (\\t), or pipes (|) can also be used as delimiters, they may not be as universally supported or as intuitive as the comma. Therefore, the comma remains the preferred choice for separating values in CSV files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7dec8b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df_circle = pd.read_excel(\"lissajous_circle.xlsx\")\n",
    "df_butterfly = pd.read_excel(\"lissajous_butterfly.xlsx\")\n",
    "df_distribution = pd.read_excel(\"distribution.xlsx\")\n",
    "df_fruit = pd.read_excel(\"fruit_pie_chart.xlsx\")\n",
    "df_contour = pd.read_excel(\"contour.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7dcc0d68",
   "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": [
    "df4 = pd.read_excel(\"lissajous_circle.xlsx\")\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "99810d1e",
   "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)) \n",
    "\n",
    "plt.scatter(df_circle[\"x\"], df_circle[\"y\"], s=5, alpha=0.8, label=\"Lissajous_Circle\")\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": 38,
   "id": "41bd877a",
   "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": [
    "df5 = pd.read_excel(\"lissajous_butterfly.xlsx\")\n",
    "print(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fa6b9251",
   "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))\n",
    "plt.scatter(df_butterfly[\"x\"], df_butterfly[\"y\"], s=5, alpha=0.8, label=\"Lissajous_Butterfly\")\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": 40,
   "id": "b26a6879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          x\n",
      "0        36\n",
      "1        31\n",
      "2        34\n",
      "3        29\n",
      "4         5\n",
      "...     ...\n",
      "249995  997\n",
      "249996  997\n",
      "249997  998\n",
      "249998  998\n",
      "249999  999\n",
      "\n",
      "[250000 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "df6 = pd.read_excel(\"distribution.xlsx\")\n",
    "print(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1062e6a3",
   "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='red')\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": 42,
   "id": "3b80db1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        fruit  amount\n",
      "0      tomato      18\n",
      "1      orange      12\n",
      "2       apple      13\n",
      "3      banana      20\n",
      "4        kiwi      14\n",
      "5      durian      15\n",
      "6      manggo       5\n",
      "7  strawberry      15\n"
     ]
    }
   ],
   "source": [
    "df7 = pd.read_excel(\"fruit_pie_chart.xlsx\")\n",
    "print(df7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6274b386",
   "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": 45,
   "id": "d982cff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       x    y          z\n",
      "0      0    0  82.462113\n",
      "1      5    0  77.620873\n",
      "2     10    0  72.801099\n",
      "3     15    0  68.007353\n",
      "4     20    0  63.245553\n",
      "..   ...  ...        ...\n",
      "436   80  100  80.000000\n",
      "437   85  100  80.156098\n",
      "438   90  100  80.622577\n",
      "439   95  100  81.394103\n",
      "440  100  100  82.462113\n",
      "\n",
      "[441 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "df8 = pd.read_excel(\"contour.xlsx\")\n",
    "print(df8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "78645dc4",
   "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='jet')\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": "2345f38e",
   "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
}