{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AViPBBjjeAXZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C9-x4OGle_tV"
      },
      "source": [
        "#Python code that generated the magic square\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OpnZcPFe2QO",
        "outputId": "752caa61-af2f-438f-c770-2d3c059afa26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "# Create an N x N magic square. N must be odd.\n",
        "import numpy as np\n",
        "N = 5\n",
        "\n",
        "magic_square = np.zeros((N, N), dtype=int)\n",
        "\n",
        "n = 1\n",
        "i, j = 0, N // 2\n",
        "\n",
        "while n <= N**2:\n",
        "    magic_square[i, j] = n\n",
        "    n += 1\n",
        "    newi, newj = (i-1) % N, (j+1)% N\n",
        "    if magic_square[newi, newj]:\n",
        "        i += 1\n",
        "    else:\n",
        "        i, j = newi, newj\n",
        "\n",
        "print(type(magic_square))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "EqV7cqkarKqn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "hmuwrLtjqCqf"
      },
      "outputs": [],
      "source": [
        "angelone_matrix = pd.read_excel(\"/content/drive/MyDrive/CaRLOS/PP_Ankit_17.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "uMET18VTlOfa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "x = pd.DataFrame(angelone_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXdtL5Pwxp8E"
      },
      "source": [
        "#Verify for distinct primes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPTRA5lIe3FQ",
        "outputId": "088f61b5-ca1d-49eb-f6af-29db5d92149b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             0            1            2              3              4    \\\n",
            "0    10217828443  10216935853  10216043263  10216674733.0  10216163833.0   \n",
            "1    10216041793  10217831383  10216934383  10215652273.0  10216676053.0   \n",
            "2    10217710873  10217231083  10218158623  10216936153.0  10216024303.0   \n",
            "3    10218147943  10217700193  10217252443  10217839123.0  10216933933.0   \n",
            "4    10217241763  10218169303  10217689513  10216026523.0  10217843563.0   \n",
            "..           ...          ...          ...            ...            ...   \n",
            "723   9306028453   9305667673   9305306893   9305876023.0   9305564953.0   \n",
            "724   9305123953   9306394333   9305484733   9304978993.0   9306425803.0   \n",
            "725   9305484883   9304613293   9305690113   9306152413.0   9304351333.0   \n",
            "726   9305467993   9305262763   9305057533   9307610293.0   9306066613.0   \n",
            "727   9304835413   9305912233   9305040643   9304437133.0   9307781893.0   \n",
            "\n",
            "               5              6              7              8    \\\n",
            "0    10215652933.0  10218446353.0  10217702113.0  10216957873.0   \n",
            "1    10216163173.0  10216957783.0  10218446533.0  10217702023.0   \n",
            "2    10217841343.0  10216168993.0  10216137793.0  10216196233.0   \n",
            "3    10216028743.0  10216194913.0  10216167673.0  10216140433.0   \n",
            "4    10216931713.0  10216139113.0  10216197553.0  10216166353.0   \n",
            "..             ...            ...            ...            ...   \n",
            "723   9305253883.0   9306393253.0   9305462233.0   9304531213.0   \n",
            "724   9305290063.0   9304527313.0   9306401053.0   9305458333.0   \n",
            "725   9307696093.0   9305371813.0   9304565113.0   9306159523.0   \n",
            "726   9304522933.0   9306153193.0   9305365483.0   9304577773.0   \n",
            "727   9305980813.0   9304571443.0   9306165853.0   9305359153.0   \n",
            "\n",
            "               9    ...            718            719            720  \\\n",
            "0    10216375363.0  ...  10689803833.0  10689431083.0  10690410253.0   \n",
            "1    10216191133.0  ...  10690195063.0  10689794593.0  10689842773.0   \n",
            "2    10216949053.0  ...  10686848443.0  10690622083.0  10691103433.0   \n",
            "3    10217680093.0  ...  10688782693.0  10687038163.0  10691216113.0   \n",
            "4    10216215853.0  ...  10690716943.0  10688687833.0  10690959793.0   \n",
            "..             ...  ...            ...            ...            ...   \n",
            "723   9306491893.0  ...   9789563233.0   9789330553.0   9789110953.0   \n",
            "724   9305718133.0  ...   9790600333.0   9789161023.0   9775699273.0   \n",
            "725   9305605363.0  ...   9787894573.0   9790034983.0   9778732903.0   \n",
            "726   9305842723.0  ...   9789270853.0   9789118873.0   9778961293.0   \n",
            "727   9305169103.0  ...   9790647133.0   9788658703.0   9777540793.0   \n",
            "\n",
            "               721            722            723            724  \\\n",
            "0    10690129033.0  10689847813.0  10689152653.0  10689128833.0   \n",
            "1    10690420333.0  10690123993.0  10689076963.0  10689208753.0   \n",
            "2    10690949473.0  10691226433.0  10690133143.0  10689290383.0   \n",
            "3    10691093113.0  10690970113.0  10690923343.0  10690120003.0   \n",
            "4    10691236753.0  10691082793.0  10689303523.0  10690949623.0   \n",
            "..             ...            ...            ...            ...   \n",
            "723   9782656693.0   9776202433.0   9793811173.0   9782417233.0   \n",
            "724   9790117273.0   9782153533.0   9771010663.0   9793836433.0   \n",
            "725   9777219553.0   9779282533.0   9791036503.0   9788440663.0   \n",
            "726   9778411663.0   9777862033.0   9790241983.0   9790188913.0   \n",
            "727   9779603773.0   9778090423.0   9789288253.0   9791937163.0   \n",
            "\n",
            "               725            726            727  \n",
            "0    10689105013.0  10691315863.0  10691102143.0  \n",
            "1    10689100783.0  10690876663.0  10691339383.0  \n",
            "2    10690936483.0  10689150883.0  10688732083.0  \n",
            "3    10689316663.0  10689553723.0  10689146893.0  \n",
            "4    10690106863.0  10688736073.0  10689561703.0  \n",
            "..             ...            ...            ...  \n",
            "723   9771023293.0   9797698903.0   9782177773.0  \n",
            "724   9782404603.0   9766611313.0   9797789563.0  \n",
            "725   9791089573.0   9779560933.0   9774800593.0  \n",
            "726   9790135843.0   9780682033.0   9778651123.0  \n",
            "727   9789341323.0   9775710403.0   9782501653.0  \n",
            "\n",
            "[728 rows x 728 columns]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "x = pd.DataFrame(angelone_matrix)\n",
        "#arr_2d = np.array(magic_square)\n",
        "arr_2d = np.array(angelone_matrix)\n",
        "x = pd.DataFrame(arr_2d)\n",
        "x = x.iloc[0:728, 0:728]\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmQaH1bCPjWo",
        "outputId": "570a372f-372b-40c4-a8b3-46a6bf9060d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10217828443\n"
          ]
        }
      ],
      "source": [
        "print(x[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZhmuq58PjSZ",
        "outputId": "72a9a788-b233-4488-bdf3-6e8025d32686"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This number is distinct prime, run full code for actual results\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "arr_2d = np.array(x)\n",
        "max_prime =  0\n",
        "min_prime = x[0][0]\n",
        "for element in arr_2d.flat:\n",
        "    if element % 2 == 0:\n",
        "       print(\"This number is not distinct prime\")\n",
        "    else:\n",
        "      if element>max_prime:\n",
        "        max_prime = element\n",
        "      if element<min_prime:\n",
        "        min_prime = element\n",
        "      print(\"This number is distinct prime, run full code for actual results\")\n",
        "      break\n",
        "      #Remove break when running for actual results as the code runs and goes unresponsive\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJmJk99DxjvM"
      },
      "source": [
        "#Print the smallest and largest prime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6rxpM3OuyXQ",
        "outputId": "97668ee1-40a4-457e-f55a-a4b69b390e5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Smallest prime\n",
            "10217828443\n",
            "Large Prime\n",
            "10217828443\n"
          ]
        }
      ],
      "source": [
        "print(\"Smallest prime\")\n",
        "print(min_prime)\n",
        "print(\"Large Prime\")\n",
        "print(max_prime)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7yuHCYaxhZd"
      },
      "source": [
        "#Central element"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "434O2PyZvns9",
        "outputId": "f788ceab-06a8-4fca-b7bf-b17c0a4496f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10218158623\n"
          ]
        }
      ],
      "source": [
        "print(x[N//2][N//2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3oAXyj_GxfgD"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}