c4t4r/Session7.ipynb

## Session7.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "763ea150-80e1-4097-a87c-5d93f7c2f4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bab 1 \n",
    "\n",
    "# 6 types of metadata\n",
    "# Structural Metadata\n",
    "# Descriptive Metadata\n",
    "# Preservation Metadata\n",
    "# Administrative Metadata\n",
    "# Provenance Metadata\n",
    "# Definitional Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f91e155-1e36-4c26-81ff-81791c411456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bab 2\n",
    "\n",
    "# the four foundational aspects of data science :\n",
    "#   Mathematics\n",
    "#   Technology\n",
    "#   Visualization\n",
    "#   Communication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6be4f98-2abd-44b8-a1ab-634ee9ac0da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#fff\n"
     ]
    }
   ],
   "source": [
    "# Bab 3\n",
    "\n",
    "x = [1, 'Hello', True, 1.093E-3, '#fff']\n",
    "print(x[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d7a63d0-5e14-4111-a62d-3e4d0c36284e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "1\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "x = [9, 3, 4, 1, 4, 5, 6, 3, 7, 8]\n",
    "for i in x [2:6]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa0c2bac-dbdd-482b-9711-3594a18714f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "   2\n",
      "     3\n",
      "   4\n",
      "     5\n",
      "       6\n",
      "   7\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "x = [1, [2, [3]], [4, [5, [6]]], [7], 8]\n",
    "for i in x:\n",
    "    if isinstance (i, list):\n",
    "        for j in i:\n",
    "            if isinstance (j, list):\n",
    "                for k in j:\n",
    "                    if isinstance (k, list):\n",
    "                        for l in k:\n",
    "                            print('      ', l)\n",
    "                    else:\n",
    "                        print('    ', k)\n",
    "            else:\n",
    "                print('  ', j)\n",
    "    else:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b026bee-3e80-447f-837b-d148ce49f5a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 4, 5]\n",
      "['a', 2, 'b', 'c']\n",
      "[1, 3, 4, 5, 'a', 2, 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = ['a', 'b', 'c']\n",
    "z = x.pop(1)\n",
    "y.insert(1, z)\n",
    "z = x + y\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f78077a5-ae5d-428f-918f-80a75dbb43f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 2000, 'i', 'o', 'u']\n",
      "['a', 'e', 'i', 'o', 'u']\n"
     ]
    }
   ],
   "source": [
    "a = 'aeiou'\n",
    "b = tuple(a)\n",
    "c = list(b)\n",
    "d = c\n",
    "e = list(d)\n",
    "\n",
    "c[1] = 2000\n",
    "print(d)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "047c8d39-2388-44c5-bf50-b6ade2307d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5]\n",
      "[2, 4, 3, 1, 0, 5]\n",
      "[5, 4, 3, 2, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "x = [5, 0, 1, 3, 4, 2]\n",
    "a = x.copy()\n",
    "a.sort()\n",
    "print(a)\n",
    "b = x.copy()\n",
    "b.reverse()\n",
    "print(b)\n",
    "c = x.copy()\n",
    "c.sort(reverse=True)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95b33dd4-d4ed-4ebc-b55c-0505c30a1ff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of 1:  4\n",
      "\n",
      "index of 1:  1\n",
      "index of 1:  4\n",
      "index of 1:  8\n",
      "index of 1:  11\n"
     ]
    }
   ],
   "source": [
    "x = [0, 1, 2, 3, 1, 4, 5, 6, 1, 9, 9, 1]\n",
    "y = x.copy()\n",
    "print('count of 1: ', y.count(1))\n",
    "print()\n",
    "for i in range(y.count(1)):\n",
    "    print('index of 1: ', (y.index(1) + i))\n",
    "    y.pop(y.index(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f130260-75f3-466a-bb74-fa43ed81b1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 4]\n",
      "[1, 3, 5]\n",
      "[1, 3, 5, 3, 5, 7, 5, 7, 9]\n"
     ]
    }
   ],
   "source": [
    "x = [i for i in range (0, 5, 2)]\n",
    "y = [i for i in range (1, 6, 2)]\n",
    "z = [i+j for i in x for j in y]\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c4fedec4-65fd-48df-b8f7-5d824827d95b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original\n",
      "[1, 5, 2, 4, 3] \n",
      "\n",
      "after bubble sort\n",
      "[5, 4, 3, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 5, 2, 4, 3]\n",
    "print('original'); print(x, '\\n')\n",
    "N = len(x)\n",
    "for i in range(N):\n",
    "    for j in range(i+1, N):\n",
    "        if x[i] < x[j]:\n",
    "            x[i], x[j] = x[j], x[i]\n",
    "print('after bubble sort'); print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa36c161-35dd-4fda-a85c-d358246049af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 1, 'b', 2, 'c', 3] \n",
      "\n",
      "['a', 'b', 'c'] \n",
      "\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "def sep_odd_even_index(z):\n",
    "    N = len(z)\n",
    "    r = range(N)\n",
    "    x = [z[i] for i in r if i % 2 == 0]\n",
    "    y = [z[i] for i in r if i % 2 == 1]\n",
    "    return[x,y]\n",
    "\n",
    "mix = ['a', 1, 'b', 2, 'c', 3]\n",
    "x, y = sep_odd_even_index(mix)\n",
    "print(mix, '\\n')\n",
    "print(x, '\\n')\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8444c7-1877-4f35-af76-212dd912f280",
   "metadata": {},
   "source": [
    "\"sep_odd_even_index\" is use to use separate the odd or even elements from the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b6558933-47cd-4ccd-8b45-bf6185c159cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 1, 10)\n",
    "y = np.linspace(0, 1, 10)\n",
    "\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = np.sqrt(X**2 + Y**2)\n",
    "\n",
    "plt.figure(figsize=(4.8, 4.8))\n",
    "plt.contourf(X, Y, Z)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ab62fc6-a001-43ff-a3c1-9a7537b0756c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 5, 10) # [0, 0.55, 1.11, 1.66, 2.22, 2.77, 3.33, 3.88, 4.44, 5]\n",
    "y = np.exp(x)  # [1, 1.74, 3.03, 5.29, 9.22, 16.08, 28.03, 48.85, 85.15, 148.41]\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "30001ce9-c35c-43fb-83bb-7b5cc67682f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x =[5, 7, 8, 7, 2, 17, 2, 9,\n",
    "    4, 11, 12, 9, 6] \n",
    " \n",
    "y =[99, 86, 87, 88, 100, 86, \n",
    "    103, 87, 94, 78, 77, 85, 86]\n",
    " \n",
    "plt.scatter(x, y, c =\"blue\")\n",
    " \n",
    "# To show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ddbff75b-84be-4c40-bcba-c8b44906081d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "x = np.linspace(1, 4, 20)\n",
    "y = np.linspace(1, 4, 20)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = np.sqrt((X - 2.5)**2 + (Y - 2.5)**2)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "plt.title('cm.jet')\n",
    "plt.contourf(X, Y, Z, cmap=cm.jet)\n",
    "\n",
    "plt.subplot(2, 3, 2)\n",
    "plt.grid()\n",
    "plt.title('cm.gray')\n",
    "plt.contourf(X, Y, Z, cmap=cm.gray)\n",
    "\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.grid()\n",
    "plt.title('cm.viridis')\n",
    "plt.contourf(X, Y, Z, cmap=cm.viridis)\n",
    "\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "plt.title('cm.winter')\n",
    "plt.contourf(X, Y, Z, cmap=cm.winter)\n",
    "\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.xlabel('x')\n",
    "plt.grid()\n",
    "plt.title('cm.seismic')\n",
    "plt.contourf(X, Y, Z, cmap=cm.seismic)\n",
    "\n",
    "plt.subplot(2, 3, 6)\n",
    "plt.xlabel('x')\n",
    "plt.grid()\n",
    "plt.title('cm.Set3')\n",
    "plt.contourf(X, Y, Z, cmap=cm.Set3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a451e67-57fe-4e57-989f-809678f697ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_29480\\4080736814.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4baea5fd-619f-4b26-a053-eb7c41750ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'month': [1, 2, 3, 4, 5], 'weather': ['rainy', 'sunny', 'cloudy', 'windy', 'cloudy']}\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    \"month\": [1, 2, 3, 4, 5],\n",
    "    \"weather\": [\"rainy\", \"sunny\", \"cloudy\", \"windy\", \"cloudy\"]\n",
    "}\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e53a5f40-4589-4923-a016-016812207f96",
   "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": [
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a84428a4-5c76-41c4-a0f3-2fcf2a856460",
   "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": 36,
   "id": "b54dea83-e06a-49e2-8908-a37e0c13e8c2",
   "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": 37,
   "id": "8a0d0125-775b-4145-a7f0-89a8482656f6",
   "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": 38,
   "id": "4ca9ad1e-858b-4db9-bffd-05b94ddb8c94",
   "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": 39,
   "id": "7fa5e34c-5397-4545-9cbb-df8c3c5cb40a",
   "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": 41,
   "id": "ecc649d6-3cde-40e4-a161-dcc776bc6475",
   "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": 42,
   "id": "7ffccfc2-b718-4134-a568-83cdd94a25de",
   "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": 43,
   "id": "cdd327a9-b9e1-4190-bdab-29b0dd8fc535",
   "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": 44,
   "id": "ab13d813-4bc2-4a00-906a-73f35d0ed392",
   "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": 45,
   "id": "3ee762ef-9fee-4805-9e36-aff7895b5cc8",
   "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": 46,
   "id": "ae88d0b7-907d-4df5-8f6f-a5464b9b0da2",
   "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": 47,
   "id": "1ebc0266-61dd-4abb-ab11-899555a68a74",
   "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": 48,
   "id": "03fa440f-06d9-421b-9b11-0f3f895dd905",
   "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": 49,
   "id": "bb3236dd-fc86-4bfa-a4f5-337a97ea2251",
   "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": 50,
   "id": "42f296ac-9176-4199-9146-bbf3be0db627",
   "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": 51,
   "id": "781046ca-a1d8-4842-a770-5065f9fcbbd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a']['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7ab88fdd-6621-4bfa-9d78-08097594761e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'windy'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['d']['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e3cd64d5-b756-4cfd-b8c0-e88a81791752",
   "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": [
    "data = pd.read_excel(\"lissajous_circle.xlsx\")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e99a2ed1-e320-4d46-869e-b1243d81a3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = list(data['t'])\n",
    "x1 = list(data['x'])\n",
    "y1 = list(data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2dc81a44-ca87-4e92-829c-c3fe85b33af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t1, '-', color='tab:red', label='t1')\n",
    "plt.plot(x1, '-.', color='tab:green', label='x1')\n",
    "plt.plot(y1, ':', color='tab:blue', label='y1')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "01d77c28-b617-4312-a840-756d87953860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2d8c6cb0b10>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "\n",
    "plt.scatter(x1, y1, marker='o', color='tab:blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b2e51723-1b58-484b-b2e1-b7006caa2972",
   "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": [
    "data2 = pd.read_excel(\"lissajous_butterfly.xlsx\")\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "40ebbf35-4958-4a4a-855e-4d72f1f66dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "t2 = list(data2['t'])\n",
    "x2 = list(data2['x'])\n",
    "y2 = list(data2['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "980cb3b1-8899-4147-b598-0be6d427734d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2d8c6c4e9d0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "\n",
    "plt.scatter(x2, y2, marker='o', color='tab:blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b4d15672-f1a0-4aa7-812d-87efeba31f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics as stat\n",
    "import random as rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c49a6d1d-e7e4-4edf-a242-537547e8c987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[60, 30, 20, 90, 10, 90, 30, 70, 10, 30, 90, 80, 20, 90, 70, 60, 30, 70, 90, 60, 90, 50, 30, 50, 20, 70, 60, 40, 30]\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "N = 29\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(1, 9) * 10\n",
    "    x.append(xi)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c939a8cb-62c6-4488-a9bc-6658c8f0b743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 73, 29, 49, 47, 48, 32, 16, 42, 53, 30, 34, 30, 43, 17, 18, 44, 66, 30, 77, 54, 13, 20, 78, 30, 23, 36, 51, 54, 39, 76, 40, 10, 14, 69, 10, 33, 39, 37, 66, 14, 70, 41, 72, 78, 64, 45, 31, 22, 67, 13, 14, 56, 38, 19, 58, 60, 52, 15, 52, 14, 53, 52, 11, 74, 62, 60, 63, 44, 54, 25, 73, 67, 43, 67, 58, 59, 15, 42, 41, 66, 77, 41, 71, 58, 11, 16, 13, 23, 74, 33, 59, 40, 16, 36, 74, 36, 37, 16, 80]\n"
     ]
    }
   ],
   "source": [
    "x = [\n",
    "    rnd.randint(10, 80)\n",
    "    for i in range(0,100)\n",
    "]\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e46f524b-4332-4c1c-a0fc-3eb103aa10f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x, bins=20)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dde57d85-beb1-4764-acc4-212c4bb27b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGeCAYAAACKDztsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAqw0lEQVR4nO3dfXSU9Z3//9fkhgmJJBBccqMJZreeQhWBEokRV8FEAqIFYUUWSllkwWqCYnoUsgcQEIuwVFgQRd0Wj7vgDacLorvGxoBQawgkgHflcGPTwhGS9BSSkUSGIfP5/eEv83VIgAQmTD6Xz8c5c3Suueaa94vMh7y4MpNxGWOMAAAALBIR7gEAAADaiwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFgnKtwDXAq/369jx46pW7ducrlc4R4HAAC0gTFGX3/9tVJTUxURcZnnUEw7bd++3dxzzz0mJSXFSDKbNm06774PPfSQkWRWrFgRtP1vf/ubmThxounWrZtJSEgwDz74oPn666/bPMPRo0eNJC5cuHDhwoWLhZejR4+2t3600O4zMA0NDerfv78efPBBjR079rz7bdq0STt37lRqamqL2yZNmqTjx4+rpKREPp9PU6dO1YwZM7Rhw4Y2zdCtWzdJUlVVlRITE9sbodPw+Xz63e9+p+HDhys6Ojrc41wSJ2SQnJHDCRkkcnQmTsggOSOHEzJI0okTJ5SRkRH4Pn452l1gRo4cqZEjR15wn6+++kozZ87U+++/r1GjRgXdtn//fhUXF2v37t3KzMyUJK1evVp33323li9f3mrhOVfzj426deum+Pj49kboNHw+n2JjYxUfH2/tE9IJGSRn5HBCBokcnYkTMkjOyOGEDNK3OSSF5OUfIX8NjN/v1+TJk/XEE0/ohhtuaHF7WVmZunfvHigvkpSbm6uIiAiVl5frvvvua3Efr9crr9cbuO7xeCR9+wfR/Idho+bZyRB+TsjhhAwSOToTJ2SQnJHDCRmk0M4f8gKzdOlSRUVF6dFHH2319urqavXq1St4iKgoJSYmqrq6utX7LFmyRAsXLmyxfdu2bYqNjb38ocOspKQk3CNcNidkkJyRwwkZJHJ0Jk7IIDkjh+0ZGhsbQ3askBaYyspK/cd//If27NkT0ncHFRUVqbCwMHDd4/EoLS1Nw4YNU8+ePUP2OFeaz+dTSUmJ7rrrLmtPCTohg+SMHE7IIJGjM3FCBskZOZyQQZL+9re/hexYIS0wv//971VbW6v09PTAtqamJv3iF7/QypUr9ec//1nJycmqra0Nut/Zs2d14sQJJScnt3pct9stt9vdYnt0dLTVX8hmTsjhhAySM3I4IYNEjs7ECRkkZ+SwPUMoZw9pgZk8ebJyc3ODtuXl5Wny5MmaOnWqJCk7O1t1dXWqrKzUoEGDJElbt26V3+9XVlZWKMcBAAAO1e4Cc+rUKR0+fDhwvaqqSvv27VNiYqLS09Nb/EgnOjpaycnJ+uEPfyhJ6tu3r0aMGKHp06dr7dq18vl8Kigo0IQJE9r0DiQAAIB2/xq8iooKDRw4UAMHDpQkFRYWauDAgZo/f36bj7F+/Xr16dNHOTk5uvvuu3Xbbbfp5Zdfbu8oAADge6rdZ2CGDh0qY0yb9//zn//cYltiYmKbf2kdAADAufgwRwAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA64T8wxwBAEDHuHHB+/I2he6zBq+0qLMNITsWZ2AAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDrtLjA7duzQvffeq9TUVLlcLm3evDlwm8/n0+zZs9WvXz/FxcUpNTVVP/vZz3Ts2LGgY5w4cUKTJk1SfHy8unfvrmnTpunUqVOXHQYAAHw/tLvANDQ0qH///lqzZk2L2xobG7Vnzx7NmzdPe/bs0f/8z//owIED+slPfhK036RJk/TFF1+opKRE7777rnbs2KEZM2ZcegoAAPC9EtXeO4wcOVIjR45s9baEhASVlJQEbXv++ec1ePBgHTlyROnp6dq/f7+Ki4u1e/duZWZmSpJWr16tu+++W8uXL1dqauolxAAAAN8n7S4w7VVfXy+Xy6Xu3btLksrKytS9e/dAeZGk3NxcRUREqLy8XPfdd1+LY3i9Xnm93sB1j8cj6dsfWfl8vo4N0IGaZydD+DkhhxMySOToTJyQQXJGjubZ3REmzJNcnih/6Obv0AJz+vRpzZ49W//8z/+s+Ph4SVJ1dbV69eoVPERUlBITE1VdXd3qcZYsWaKFCxe22L5t2zbFxsaGfvAr7NyzVjZyQgbJGTmckEEiR2fihAySM3I8nekP9wiXpbHRr4khOlaHFRifz6fx48fLGKMXX3zxso5VVFSkwsLCwHWPx6O0tDQNGzZMPXv2vNxRw8bn86mkpER33XWXoqOjwz3OJXFCBskZOZyQQSJHZ+KEDJIzcjRnmFcRIa/fFe5xLlmUL3Rvfu6QAtNcXv7yl79o69atgbMvkpScnKza2tqg/c+ePasTJ04oOTm51eO53W653e4W26Ojo619Mn6XE3I4IYPkjBxOyCCRozNxQgbJGTm8fpe8TfYWmKYQlq+Q/x6Y5vJy6NAhffDBBy3OkGRnZ6uurk6VlZWBbVu3bpXf71dWVlaoxwEAAA7U7jMwp06d0uHDhwPXq6qqtG/fPiUmJiolJUX/9E//pD179ujdd99VU1NT4HUtiYmJ6tKli/r27asRI0Zo+vTpWrt2rXw+nwoKCjRhwgTegQQAANqk3QWmoqJCw4YNC1xvfm3KlClTtGDBAm3ZskWSNGDAgKD7bdu2TUOHDpUkrV+/XgUFBcrJyVFERITGjRunVatWXWIEAADwfdPuAjN06FAZc/63QV3otmaJiYnasGFDex8aAABAEp+FBAAALESBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsExXuAQDAyW5c8L68Ta5wj3FJ3JFGywaHewqgdZyBAQAA1qHAAAAA61BgAACAdSgwAADAOryIFyFh8wsVJV6s2BnxnAJwIZyBAQAA1qHAAAAA67S7wOzYsUP33nuvUlNT5XK5tHnz5qDbjTGaP3++UlJS1LVrV+Xm5urQoUNB+5w4cUKTJk1SfHy8unfvrmnTpunUqVOXFQQAAHx/tLvANDQ0qH///lqzZk2rty9btkyrVq3S2rVrVV5erri4OOXl5en06dOBfSZNmqQvvvhCJSUlevfdd7Vjxw7NmDHj0lMAAIDvlXa/iHfkyJEaOXJkq7cZY7Ry5UrNnTtXo0ePliS99tprSkpK0ubNmzVhwgTt379fxcXF2r17tzIzMyVJq1ev1t13363ly5crNTX1MuIAAIDvg5C+BqaqqkrV1dXKzc0NbEtISFBWVpbKysokSWVlZerevXugvEhSbm6uIiIiVF5eHspxAACAQ4X0bdTV1dWSpKSkpKDtSUlJgduqq6vVq1ev4CGiopSYmBjY51xer1derzdw3ePxSJJ8Pp98Pl/I5r/Smmd3QgZ3hAnzJJeneX4nfC1sziA57zllcw4nrAvJGWvDKesiyh+6+a34PTBLlizRwoULW2zftm2bYmNjwzBRaJWUlIR7hMv2dKY/3COEhBO+Fk7IIDnnOeWEHE55Tjkhh+3Pp8ZGvyaG6FghLTDJycmSpJqaGqWkpAS219TUaMCAAYF9amtrg+539uxZnThxInD/cxUVFamwsDBw3ePxKC0tTcOGDVPPnj1DGeGK8vl8Kikp0V133aXo6Ohwj3NJmjPMq4iQ12/xLx2LMHo60++Ir4XNGSTnPadszuGEdSE5Y204ZV1E+UL3ypWQFpiMjAwlJyertLQ0UFg8Ho/Ky8v18MMPS5Kys7NVV1enyspKDRo0SJK0detW+f1+ZWVltXpct9stt9vdYnt0dLS1T8bvckIOr99l9W9NbeaEr4UTMkjOeU45IYdTnlNOyGH786kphOWr3QXm1KlTOnz4cOB6VVWV9u3bp8TERKWnp2vWrFlavHixrr/+emVkZGjevHlKTU3VmDFjJEl9+/bViBEjNH36dK1du1Y+n08FBQWaMGEC70ACAABt0u4CU1FRoWHDhgWuN/9oZ8qUKXr11Vf15JNPqqGhQTNmzFBdXZ1uu+02FRcXKyYmJnCf9evXq6CgQDk5OYqIiNC4ceO0atWqEMQBAADfB+0uMEOHDpUx538Vscvl0qJFi7Ro0aLz7pOYmKgNGza096EBAAAk8VlIAADAQhQYAABgHQoMAACwDgUGAABYx4rfxOt0Ny5439r39bsjjZYNDvcUAIDvG87AAAAA61BgAACAdfgREgDggmz+MbfEj7qdijMwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWsfpt1FlLSnU2Ki7cY1wy3tqHjsBbXoHW2bw2WBctcQYGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtY/WGOQKjxYW8AYAfOwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwTsgLTFNTk+bNm6eMjAx17dpV//AP/6Cnn35axpjAPsYYzZ8/XykpKeratatyc3N16NChUI8CAAAcKuQFZunSpXrxxRf1/PPPa//+/Vq6dKmWLVum1atXB/ZZtmyZVq1apbVr16q8vFxxcXHKy8vT6dOnQz0OAABwoKhQH/Djjz/W6NGjNWrUKEnSddddp9dff127du2S9O3Zl5UrV2ru3LkaPXq0JOm1115TUlKSNm/erAkTJoR6JAAA4DAhPwNz6623qrS0VAcPHpQkffLJJ/roo480cuRISVJVVZWqq6uVm5sbuE9CQoKysrJUVlYW6nEAAIADhfwMzJw5c+TxeNSnTx9FRkaqqalJzzzzjCZNmiRJqq6uliQlJSUF3S8pKSlw27m8Xq+8Xm/gusfjkSS5I4wiI02r97GBO8IE/ddGTsggOSOHEzJI5OhMnJBBckYOJ2SQpCh/6OYPeYF56623tH79em3YsEE33HCD9u3bp1mzZik1NVVTpky5pGMuWbJECxcubLF97kC/YmObLnfksHs60x/uES6bEzJIzsjhhAwSOToTJ2SQnJHD9gyNjX5NDNGxQl5gnnjiCc2ZMyfwWpZ+/frpL3/5i5YsWaIpU6YoOTlZklRTU6OUlJTA/WpqajRgwIBWj1lUVKTCwsLAdY/Ho7S0NC3eG6Gz0ZGhjnDFuCOMns70a15FhLx+V7jHuSROyCA5I4cTMkjk6EyckEFyRg4nZJCkKF/oXrkS8gLT2NioiIjgASMjI+X3f9saMzIylJycrNLS0kBh8Xg8Ki8v18MPP9zqMd1ut9xud4vtXr9LZ5vs/UI28/pd8lqewwkZJGfkcEIGiRydiRMySM7IYXuGphCWr5AXmHvvvVfPPPOM0tPTdcMNN2jv3r167rnn9OCDD0qSXC6XZs2apcWLF+v6669XRkaG5s2bp9TUVI0ZMybU4wAAAAcKeYFZvXq15s2bp0ceeUS1tbVKTU3VQw89pPnz5wf2efLJJ9XQ0KAZM2aorq5Ot912m4qLixUTExPqcQAAgAOFvMB069ZNK1eu1MqVK8+7j8vl0qJFi7Ro0aJQPzwAAPge4LOQAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDodUmC++uor/fSnP1XPnj3VtWtX9evXTxUVFYHbjTGaP3++UlJS1LVrV+Xm5urQoUMdMQoAAHCgkBeYkydPasiQIYqOjtZ7772nP/7xj/rVr36lHj16BPZZtmyZVq1apbVr16q8vFxxcXHKy8vT6dOnQz0OAABwoKhQH3Dp0qVKS0vTunXrAtsyMjIC/2+M0cqVKzV37lyNHj1akvTaa68pKSlJmzdv1oQJE0I9EgAAcJiQF5gtW7YoLy9P999/v7Zv365rrrlGjzzyiKZPny5JqqqqUnV1tXJzcwP3SUhIUFZWlsrKylotMF6vV16vN3Dd4/FIktwRRpGRJtQRrhh3hAn6r42ckEFyRg4nZJDI0Zk4IYPkjBxOyCBJUf7Qze8yxoT0TyMmJkaSVFhYqPvvv1+7d+/WY489prVr12rKlCn6+OOPNWTIEB07dkwpKSmB+40fP14ul0tvvvlmi2MuWLBACxcubLF9w4YNio2NDeX4AACggzQ2NmrixImqr69XfHz8ZR0r5Gdg/H6/MjMz9ctf/lKSNHDgQH3++eeBAnMpioqKVFhYGLju8XiUlpamxXsjdDY6MiRzh4M7wujpTL/mVUTI63eFe5xL4oQMkjNyOCGDRI7OxAkZJGfkcEIGSYryhe6ltyEvMCkpKfrRj34UtK1v37767W9/K0lKTk6WJNXU1ASdgampqdGAAQNaPabb7Zbb7W6x3et36WyTvV/IZl6/S17Lczghg+SMHE7IIJGjM3FCBskZOWzP0BTC8hXydyENGTJEBw4cCNp28OBB9e7dW9K3L+hNTk5WaWlp4HaPx6Py8nJlZ2eHehwAAOBAIT8D8/jjj+vWW2/VL3/5S40fP167du3Syy+/rJdfflmS5HK5NGvWLC1evFjXX3+9MjIyNG/ePKWmpmrMmDGhHgcAADhQyAvMzTffrE2bNqmoqEiLFi1SRkaGVq5cqUmTJgX2efLJJ9XQ0KAZM2aorq5Ot912m4qLiwMvAAYAALiQkBcYSbrnnnt0zz33nPd2l8ulRYsWadGiRR3x8AAAwOH4LCQAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgnQ4vMM8++6xcLpdmzZoV2Hb69Gnl5+erZ8+euuqqqzRu3DjV1NR09CgAAMAhOrTA7N69Wy+99JJuuummoO2PP/643nnnHW3cuFHbt2/XsWPHNHbs2I4cBQAAOEiHFZhTp05p0qRJeuWVV9SjR4/A9vr6ev3617/Wc889pzvvvFODBg3SunXr9PHHH2vnzp0dNQ4AAHCQqI46cH5+vkaNGqXc3FwtXrw4sL2yslI+n0+5ubmBbX369FF6errKysp0yy23tDiW1+uV1+sNXPd4PJIkd4RRZKTpqAgdzh1hgv5rIydkkJyRwwkZJHJ0Jk7IIDkjhxMySFKUP3Tzd0iBeeONN7Rnzx7t3r27xW3V1dXq0qWLunfvHrQ9KSlJ1dXVrR5vyZIlWrhwYYvtcwf6FRvbFJKZw+npTH+4R7hsTsggOSOHEzJI5OhMnJBBckYO2zM0Nvo1MUTHCnmBOXr0qB577DGVlJQoJiYmJMcsKipSYWFh4LrH41FaWpoW743Q2ejIkDxGOLgjjJ7O9GteRYS8fle4x7kkTsggOSOHEzJI5OhMnJBBckYOJ2SQpChf6F65EvICU1lZqdraWv34xz8ObGtqatKOHTv0/PPP6/3339eZM2dUV1cXdBampqZGycnJrR7T7XbL7Xa32O71u3S2yd4vZDOv3yWv5TmckEFyRg4nZJDI0Zk4IYPkjBy2Z2gKYfkKeYHJycnRZ599FrRt6tSp6tOnj2bPnq20tDRFR0ertLRU48aNkyQdOHBAR44cUXZ2dqjHAQAADhTyAtOtWzfdeOONQdvi4uLUs2fPwPZp06apsLBQiYmJio+P18yZM5Wdnd3qC3gBAADO1WHvQrqQFStWKCIiQuPGjZPX61VeXp5eeOGFcIwCAAAsdEUKzIcffhh0PSYmRmvWrNGaNWuuxMMDAACH4bOQAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHVCXmCWLFmim2++Wd26dVOvXr00ZswYHThwIGif06dPKz8/Xz179tRVV12lcePGqaamJtSjAAAAhwp5gdm+fbvy8/O1c+dOlZSUyOfzafjw4WpoaAjs8/jjj+udd97Rxo0btX37dh07dkxjx44N9SgAAMChokJ9wOLi4qDrr776qnr16qXKykrdfvvtqq+v169//Wtt2LBBd955pyRp3bp16tu3r3bu3Klbbrkl1CMBAACHCXmBOVd9fb0kKTExUZJUWVkpn8+n3NzcwD59+vRRenq6ysrKWi0wXq9XXq83cN3j8UiS3BFGkZGmI8fvUO4IE/RfGzkhg+SMHE7IIJGjM3FCBskZOZyQQZKi/KGb32WM6bA/Db/fr5/85Ceqq6vTRx99JEnasGGDpk6dGlRIJGnw4MEaNmyYli5d2uI4CxYs0MKFC1ts37Bhg2JjYztmeAAAEFKNjY2aOHGi6uvrFR8ff1nH6tAzMPn5+fr8888D5eVSFRUVqbCwMHDd4/EoLS1Ni/dG6Gx05OWOGTbuCKOnM/2aVxEhr98V7nEuiRMySM7I4YQMEjk6EydkkJyRwwkZJCnKF7qX3nZYgSkoKNC7776rHTt26Nprrw1sT05O1pkzZ1RXV6fu3bsHttfU1Cg5ObnVY7ndbrnd7hbbvX6XzjbZ+4Vs5vW75LU8hxMySM7I4YQMEjk6EydkkJyRw/YMTSEsXyF/F5IxRgUFBdq0aZO2bt2qjIyMoNsHDRqk6OholZaWBrYdOHBAR44cUXZ2dqjHAQAADhTyMzD5+fnasGGD3n77bXXr1k3V1dWSpISEBHXt2lUJCQmaNm2aCgsLlZiYqPj4eM2cOVPZ2dm8AwkAALRJyAvMiy++KEkaOnRo0PZ169bpX/7lXyRJK1asUEREhMaNGyev16u8vDy98MILoR4FAAA4VMgLTFve1BQTE6M1a9ZozZo1oX54AADwPcBnIQEAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrhLXArFmzRtddd51iYmKUlZWlXbt2hXMcAABgibAVmDfffFOFhYV66qmntGfPHvXv3195eXmqra0N10gAAMASYSswzz33nKZPn66pU6fqRz/6kdauXavY2Fj95je/CddIAADAElHheNAzZ86osrJSRUVFgW0RERHKzc1VWVlZi/29Xq+8Xm/gen19vSQpytfQ8cN2oCi/UWOjX1G+CDX5XeEe55I4IYPkjBxOyCCRozNxQgbJGTmckEH6f9+3jTGXfzATBl999ZWRZD7++OOg7U888YQZPHhwi/2feuopI4kLFy5cuHDh4oDLl19+edldIixnYNqrqKhIhYWFget1dXXq3bu3jhw5ooSEhDBOdnk8Ho/S0tJ09OhRxcfHh3ucS+KEDJIzcjghg0SOzsQJGSRn5HBCBunbn6Ckp6crMTHxso8VlgJz9dVXKzIyUjU1NUHba2pqlJyc3GJ/t9stt9vdYntCQoLVX8hm8fHx1udwQgbJGTmckEEiR2fihAySM3I4IYP07ctGLvsYIZij3bp06aJBgwaptLQ0sM3v96u0tFTZ2dnhGAkAAFgkbD9CKiws1JQpU5SZmanBgwdr5cqVamho0NSpU8M1EgAAsETYCswDDzygv/71r5o/f76qq6s1YMAAFRcXKykp6aL3dbvdeuqpp1r9sZJNnJDDCRkkZ+RwQgaJHJ2JEzJIzsjhhAxSaHO4jAnFe5kAAACuHD4LCQAAWIcCAwAArEOBAQAA1qHAAAAA63TaArNkyRLdfPPN6tatm3r16qUxY8bowIEDQfucPn1a+fn56tmzp6666iqNGzeuxS/HC7e25GhmjNHIkSPlcrm0efPmKzvoBbQlQ3V1tSZPnqzk5GTFxcXpxz/+sX7729+GaeLWvfjii7rpppsCvwgqOztb7733niTpxIkTmjlzpn74wx+qa9euSk9P16OPPhr43K3O5EI5mpWVlenOO+9UXFyc4uPjdfvtt+ubb74J08QX9+yzz8rlcmnWrFmBbTas73O1lqNZZ13f52otgw3re8GCBXK5XEGXPn36SLJrfV8oRzMb1vdXX32ln/70p+rZs6e6du2qfv36qaKiInC7MUbz589XSkqKunbtqtzcXB06dKhdj9FpC8z27duVn5+vnTt3qqSkRD6fT8OHD1dDw//7AMfHH39c77zzjjZu3Kjt27fr2LFjGjt2bBinbqktOZqtXLlSLlfn+5CutmT42c9+pgMHDmjLli367LPPNHbsWI0fP1579+4N4+TBrr32Wj377LOqrKxURUWF7rzzTo0ePVpffPGFjh07pmPHjmn58uX6/PPP9eqrr6q4uFjTpk0L99gtXCiH9O1fbiNGjNDw4cO1a9cu7d69WwUFBSH5zZcdYffu3XrppZd00003BW23YX1/1/lyNOus6/u7zpfBhvUtSTfccIOOHz8euHz00UeSZNX6ls6fQ7JjfZ88eVJDhgxRdHS03nvvPf3xj3/Ur371K/Xo0SOwz7Jly7Rq1SqtXbtW5eXliouLU15enk6fPt32B7rsT1O6Qmpra40ks337dmOMMXV1dSY6Otps3LgxsM/+/fuNJFNWVhauMS/q3BzN9u7da6655hpz/PhxI8ls2rQpPAO2QWsZ4uLizGuvvRa0X2JionnllVeu9Hjt0qNHD/Of//mfrd721ltvmS5duhifz3eFp2q/7+bIysoyc+fODfNEbfP111+b66+/3pSUlJg77rjDPPbYY8YY+9b3+XI0s2F9XyiDDev7qaeeMv3792/z/p11fV8shw3re/bs2ea222477+1+v98kJyebf//3fw9sq6urM26327z++uttfpzOU9kuovlUX/MHQFVWVsrn8yk3NzewT58+fZSenq6ysrKwzNgW5+aQpMbGRk2cOFFr1qxp9bOgOpvWMtx666168803deLECfn9fr3xxhs6ffq0hg4dGqYpL6ypqUlvvPGGGhoazvvxFfX19YqPj1dUVOf9zNNzc9TW1qq8vFy9evXSrbfeqqSkJN1xxx1B/4LrTPLz8zVq1KigdSzZt77Pl0OyZ31fKIMt6/vQoUNKTU3V3//932vSpEk6cuTIefftzOv7fDlsWd9btmxRZmam7r//fvXq1UsDBw7UK6+8Eri9qqpK1dXVQc+1hIQEZWVltW99X1bNukKamprMqFGjzJAhQwLb1q9fb7p06dJi35tvvtk8+eSTV3K8NmsthzHGzJgxw0ybNi1wXZ30X2jGnD/DyZMnzfDhw40kExUVZeLj4837778fpinP79NPPzVxcXEmMjLSJCQkmP/93/9tdb+//vWvJj093fzbv/3bFZ6wbc6Xo6yszEgyiYmJ5je/+Y3Zs2ePmTVrlunSpYs5ePBgmKcO9vrrr5sbb7zRfPPNN8YYE/SvfpvW94VyGGPH+r5YBhvW9//93/+Zt956y3zyySemuLjYZGdnm/T0dOPxeFrs25nX94Vy2LK+3W63cbvdpqioyOzZs8e89NJLJiYmxrz66qvGGGP+8Ic/GEnm2LFjQfe7//77zfjx49v8OFYUmJ///Oemd+/e5ujRo4FtNv0F16y1HG+//bb5wQ9+YL7++uvAts74F1yz1jIYY0xBQYEZPHiw+eCDD8y+ffvMggULTEJCgvn000/DNGnrvF6vOXTokKmoqDBz5swxV199tfniiy+C9qmvrzeDBw82I0aMMGfOnAnTpBd2vhzNfzEUFRUF7d+vXz8zZ86cME3b0pEjR0yvXr3MJ598EthmY4G5WA4b1vfFMhhjz/r+rpMnT5r4+PgWPyK2YX1/13dz2LK+o6OjTXZ2dtC2mTNnmltuucUY8z0qMPn5+ebaa681f/rTn4K2l5aWGknm5MmTQdvT09PNc889dwUnbJvz5XjssceMy+UykZGRgYskExERYe64447wDHse58tw+PBhI8l8/vnnQdtzcnLMQw89dCVHbLecnBwzY8aMwHWPx2Oys7NNTk5O4F+jNmjO8ac//clIMv/1X/8VdPv48ePNxIkTwzRdS5s2bTKSWjzvm9fCBx98YMX6vliOgoKCTr++L5bB5vWdmZkZ9I3d1vXdnMOW9Z2enh501tEYY1544QWTmppqjDHmyy+/NJLM3r17g/a5/fbbzaOPPtrmx+l8P/z7/xljNHPmTG3atEkffvihMjIygm4fNGiQoqOjVVpaqnHjxkmSDhw4oCNHjpz3NQ3hcLEcc+bM0b/+678GbevXr59WrFihe++990qOel4Xy9DY2ChJLV4FHxkZKb/ff8XmvBR+v19er1eS5PF4lJeXJ7fbrS1btigmJibM07Vdc47rrrtOqampLd7mfvDgQY0cOTJM07WUk5Ojzz77LGjb1KlT1adPH82ePVtpaWlWrO+L5bj66qv10EMPBd3e2db3xTLYur5PnTqlL7/8UpMnT5Zk7/r+bg5b1veQIUNanbF3796SpIyMDCUnJ6u0tFQDBgyQ9O3Xp7y8XA8//HDbH+jyelbHefjhh01CQoL58MMPzfHjxwOXxsbGwD4///nPTXp6utm6daupqKgw2dnZLU5bhVtbcpxLnewU88UynDlzxvzgBz8w//iP/2jKy8vN4cOHzfLly43L5Trva0zCYc6cOWb79u2mqqrKfPrpp2bOnDnG5XKZ3/3ud6a+vt5kZWWZfv36mcOHDwflPHv2bLhHD3KhHMYYs2LFChMfH282btxoDh06ZObOnWtiYmLM4cOHwzz5hZ37Ywsb1ndrWnsX0nd1tvXdmu9msGV9/+IXvzAffvihqaqqMn/4wx9Mbm6uufrqq01tba1V6/tCOYyxY33v2rXLREVFmWeeecYcOnTIrF+/3sTGxpr//u//Duzz7LPPmu7du5u3337bfPrpp2b06NEmIyOjXWfGOm2BkdTqZd26dYF9vvnmG/PII4+YHj16mNjYWHPfffeZ48ePh2/oVrQlR2v36Ux/wbUlw8GDB83YsWNNr169TGxsrLnppptavO0y3B588EHTu3dv06VLF/N3f/d3JicnJ/BNf9u2befNWVVVFd7Bz3GhHM2WLFlirr32WhMbG2uys7PN73//+zBN23bnfuO3YX23xmkFxhg71vcDDzxgUlJSTJcuXcw111xjHnjggcA3dZvW94VyNLNhfb/zzjvmxhtvNG632/Tp08e8/PLLQbf7/X4zb948k5SUZNxut8nJyTEHDhxo12O4jDGm7edrAAAAws+a3wMDAADQjAIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOv8f+SzCLAtItYVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000; a = 20; b = 60; x = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    x.append(xi)\n",
    "plt.hist(x, bins=10)\n",
    "plt.xticks(np.arange(20, 64, 4))\n",
    "plt.xlim([20, 60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c65942ea-8ecf-4577-977e-f54bc94482bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 6000; a=1; b=6; dice = []\n",
    "for i in range(N):\n",
    "    xi = rnd.randint(a, b)\n",
    "    dice.append(xi)\n",
    "plt.hist(dice, bins=6)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0c4adc6b-42ba-44cc-91f8-876e6ced946f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 100\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100 si = 5\")\n",
    "\n",
    "mu = 200\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,2)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200 si = 5\")\n",
    "\n",
    "mu = 300\n",
    "si = 5\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300 si = 5\")\n",
    "\n",
    "mu = 100\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100 si = 10\")\n",
    "\n",
    "mu = 200\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,5)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200 si = 10\")\n",
    "\n",
    "mu = 300\n",
    "si = 10\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300 si = 10\")\n",
    "\n",
    "mu = 100\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 100 si = 20\")\n",
    "\n",
    "mu = 200\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,8)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 200 si = 20\")\n",
    "\n",
    "mu = 300\n",
    "si = 20\n",
    "N = 100000\n",
    "values = np.random.normal(mu, si, N)\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "plt.hist(values, 100)\n",
    "plt.axvline(values.mean(), color='r')\n",
    "plt.xlim([60,340])\n",
    "plt.title(\"mu = 300 si = 20\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e7bd68-caf3-42b6-a389-7994e40d8a4b",
   "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}