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   "cell_type": "markdown",
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   "source": [
    "# Lab 04\n",
    "\n",
    "\n",
    "The file https://raw.githubusercontent.com/masterfloss/data/main/migracoes2015.csv has information related to the migratory stock in 2015. It shows the number of people initially from a country (origin) living in another country (destination).\n",
    "1.\tcomplete comments \n",
    "2.\timprove outputs so that users may know the results' meaning\n",
    "3.\tinterpret the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c8e115e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# inorder to use pyvis library, you need to install:\n",
    "# !pip install pyvis \n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "from pyvis.network import Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93897c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "df =pd.read_csv('https://raw.githubusercontent.com/masterfloss/data/main/migracoes2015.csv',sep=\";\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3f33694",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "newdf=df[df['population']>500000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7ec7bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "G=nx.Graph()\n",
    "G=nx.from_pandas_edgelist(newdf, 'origin', 'destination',['population'])\n",
    "\n",
    "net=Network(notebook=True)\n",
    "net.from_nx(G)\n",
    "net.width=1000\n",
    "net.height=1000\n",
    "net.show('example.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1db229ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "print(G['Portugal']['France']['population'])\n",
    "print(G['France']['Portugal']['population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b1ba1e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "G1 = nx.from_pandas_edgelist(newdf,'origin', target='destination',edge_attr=['population'],create_using=nx.DiGraph())\n",
    "\n",
    "net=Network(notebook=True,directed=True)\n",
    "net.from_nx(G1)\n",
    "net.width=1000\n",
    "net.height=1000\n",
    "net.show('example.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16c56674",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "dc=nx.degree_centrality(G1)\n",
    "print(max(dc, key=dc.get))\n",
    "#\n",
    "idc=nx.in_degree_centrality(G1)\n",
    "print(max(idc, key=idc.get))\n",
    "#\n",
    "odc=nx.out_degree_centrality(G1)\n",
    "print(max(odc, key=odc.get))\n",
    "#\n",
    "print(nx.degree(G1)['United States of America'])\n",
    "#\n",
    "print(G1.in_degree()['United States of America'])\n",
    "#\n",
    "print(G1.out_degree()['United States of America'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3bf5c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "eig=nx.eigenvector_centrality(G1)\n",
    "print(max(eig, key=eig.get))\n",
    "#\n",
    "bitCent=nx.betweenness_centrality(G1)\n",
    "print(max(bitCent, key=bitCent.get))\n",
    "#\n",
    "bitCent=nx.closeness_centrality(G1)\n",
    "print(max(bitCent, key=bitCent.get))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae0dbd1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "print(nx.clustering(G1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef3401f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "badd4ce1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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