birongzah/pandas_dataframe.ipynb

## pandas_dataframe.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1a3a2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a0851bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = { \n",
    "    \"month\": [1,2,3,4,5],\n",
    "    \"weather\": [\"rainy\",\"sunny\",\"cloudy\",\"windy\",\"cloudy\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7fd24cc",
   "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": 6,
   "id": "afcca3b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create DataFrame\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09397a2f",
   "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": 9,
   "id": "4ad87e17",
   "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": 17,
   "id": "b35552a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "#ACCES AS LIST\n",
    "#Get 2nd Column\n",
    "m = list(df['weather'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5535181",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'other'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\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[1;32m   3803\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[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'other'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#get non-existing column\u001b[39;00m\n\u001b[0;32m----> 2\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[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\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[1;32m   3806\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[0;32m-> 3807\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[1;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\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[1;32m   3803\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[0;32m-> 3804\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[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'other'"
     ]
    }
   ],
   "source": [
    "#get non-existing column\n",
    "m = list(df['other'])\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9fc2deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'rainy']\n"
     ]
    }
   ],
   "source": [
    "#get 1st row based on index\n",
    "m = list(df.loc[0])\n",
    "print (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5878d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 'sunny']\n"
     ]
    }
   ],
   "source": [
    "#get 2nd row based on index\n",
    "m = list(df.loc[1])\n",
    "print (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c361ed50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 'cloudy']\n"
     ]
    }
   ],
   "source": [
    "#get 5th row based on index\n",
    "m = list(df.loc[4])\n",
    "print (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "96a4c8f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month          1\n",
      "weather    rainy\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#ACCES AS DATAFRAME\n",
    "#get 1st row based on index\n",
    "m = df.loc[0]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0d7dbdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month           5\n",
      "weather    cloudy\n",
      "Name: 4, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#get 5th row based on index\n",
    "m = df.loc[4]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7415651",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "1      2   sunny\n",
      "2      3  cloudy\n"
     ]
    }
   ],
   "source": [
    "#get range of index\n",
    "m = df.loc[1:2]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "acc7724a",
   "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": 23,
   "id": "8a96e78c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month weather\n",
      "3      4   windy\n",
      "4      5  cloudy\n"
     ]
    }
   ],
   "source": [
    "m = df.loc[3:25]\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3571ad63",
   "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": 25,
   "id": "0d8922e3",
   "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": [
    "# CHANGE INDEX\n",
    "#use letter as index\n",
    "idx = ['a','b','c','d','e']\n",
    "df = pd.DataFrame(data, index=idx)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4740477d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month          1\n",
      "weather    rainy\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Print Row with index \n",
    "print(df.loc['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b05f634",
   "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": 29,
   "id": "dd8999bb",
   "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 rows from a until c\n",
    "print(df.loc['a':'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5ec7acda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#acces a cell\n",
    "# row 'a'column month\n",
    "df.loc['a']['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8c62261c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'windy'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row 'd'column weather\n",
    "df.loc['d']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "63e0063f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SAVE DATAFRAME TO CSV\n",
    "df.to_csv(\"data_no_idx_sep_comma.csv\",sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "560a5197",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"dat_def_options.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "38ea4d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_semicolon.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fddb6fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_tab.csv\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "428a82dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data_sep_space.csv\",sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c0c8a58e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SAVE DATAFRAME TO XLSX\n",
    "df.to_excel(\"data_no_idx.xlsx\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b87cfc4",
   "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",
      "5      6       storm\n",
      "6      7    ice cold\n",
      "7      8  raning ice\n"
     ]
    }
   ],
   "source": [
    "#you can edit the data inside the excel \n",
    "df2 = pd.read_excel(\"data_no_idx.xlsx\")\n",
    "print (df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4fd4dfe8",
   "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": [
    "#read data_no_idx_sep_comma.xslx\n",
    "df3 = pd.read_csv(\"data_no_idx_sep_comma.csv\")\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ab68717b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saya memilih koma (,) sebagai separator terbaik. dengan alasan:\n",
    "#Mudah dibaca dan dipahami\n",
    "#Kompatibel dengan banyak aplikasi spreadsheet\n",
    "#Merupakan format default Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3ad902d3",
   "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": 88,
   "id": "644acf29",
   "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": [
    "print(df_circle)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e1e00a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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": 90,
   "id": "8375c6ca",
   "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": 91,
   "id": "2890cefa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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": 97,
   "id": "7f537c23",
   "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": 99,
   "id": "3059cdba",
   "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": 127,
   "id": "5b540db5",
   "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()"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}