xgao32/untitled0.ipynb

## untitled0.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMqCb4wUUHAak8Pqhx1i4nt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/xgao32/837c6284cf5716fc01f36b43510fc98f/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyUIGXODt5K_"
      },
      "source": [
        "import networkx as nx\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "np.random.seed(1)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSTEZ8Z6uB_0"
      },
      "source": [
        "edge_list = []\n",
        "for i in range(5):\n",
        "    for j in range(i+1,5):\n",
        "        edge_list.append((i,j,np.random.randint(10)))"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "SkspnHyPuCDH",
        "outputId": "d38dcbbd-413b-43be-d965-48d79b034246"
      },
      "source": [
        "df = pd.DataFrame(edge_list, columns = [\"I\",\"J\",\"V\"])\n",
        "df"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>I</th>\n",
              "      <th>J</th>\n",
              "      <th>V</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   I  J  V\n",
              "0  0  1  1\n",
              "1  0  2  9\n",
              "2  0  3  8\n",
              "3  0  4  2\n",
              "4  1  2  3\n",
              "5  1  3  1\n",
              "6  1  4  2\n",
              "7  2  3  7\n",
              "8  2  4  2\n",
              "9  3  4  6"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "pHPPS-c8uCFo",
        "outputId": "9f2575d6-852d-43cc-e2b2-09fc642e8e94"
      },
      "source": [
        "G = nx.from_pandas_edgelist(df, source = \"I\", target = \"J\", edge_attr = \"V\")\n",
        "nx.draw(G)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjhYfAKluCIX",
        "outputId": "67240982-6429-48cb-faa9-44b032193ddf"
      },
      "source": [
        "nx.to_edgelist(G)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EdgeDataView([(0, 1, {'V': 1}), (0, 2, {'V': 9}), (0, 3, {'V': 8}), (0, 4, {'V': 2}), (1, 2, {'V': 3}), (1, 3, {'V': 1}), (1, 4, {'V': 2}), (2, 3, {'V': 7}), (2, 4, {'V': 2}), (3, 4, {'V': 6})])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "md6QLB99uCK7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYskrk4-uCNn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otjpzIK7uCQc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaGmw6MfuCSZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMOPZqNMuCVJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}