Assignment+1.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-social-network-analysis/resources/yPcBs) course resource._\n",
    "\n",
    "---"
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    "# Assignment 1 - Creating and Manipulating Graphs\n",
    "\n",
    "Eight employees at a small company were asked to choose 3 movies that they would most enjoy watching for the upcoming company movie night. These choices are stored in the file `Employee_Movie_Choices.txt`.\n",
    "\n",
    "A second file, `Employee_Relationships.txt`, has data on the relationships between different coworkers. \n",
    "\n",
    "The relationship score has value of `-100` (Enemies) to `+100` (Best Friends). A value of zero means the two employees haven't interacted or are indifferent.\n",
    "\n",
    "Both files are tab delimited."
   ]
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    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from networkx.algorithms import bipartite\n",
    "\n",
    "\n",
    "# This is the set of employees\n",
    "employees = set(['Pablo',\n",
    "                 'Lee',\n",
    "                 'Georgia',\n",
    "                 'Vincent',\n",
    "                 'Andy',\n",
    "                 'Frida',\n",
    "                 'Joan',\n",
    "                 'Claude'])\n",
    "\n",
    "# This is the set of movies\n",
    "movies = set(['The Shawshank Redemption',\n",
    "              'Forrest Gump',\n",
    "              'The Matrix',\n",
    "              'Anaconda',\n",
    "              'The Social Network',\n",
    "              'The Godfather',\n",
    "              'Monty Python and the Holy Grail',\n",
    "              'Snakes on a Plane',\n",
    "              'Kung Fu Panda',\n",
    "              'The Dark Knight',\n",
    "              'Mean Girls'])\n",
    "\n",
    "\n",
    "# you can use the following function to plot graphs\n",
    "# make sure to comment it out before submitting to the autograder\n",
    "#def plot_graph(G, weight_name=None):\n",
    "  #  '''\n",
    "  #  G: a networkx G\n",
    "  #  weight_name: name of the attribute for plotting edge weights (if G is weighted)\n",
    "  #  '''\n",
    "  #  %matplotlib notebook\n",
    "  #  import matplotlib.pyplot as plt\n",
    "    \n",
    "  #  plt.figure()\n",
    "  #  pos = nx.spring_layout(G)\n",
    "  #  edges = G.edges()\n",
    "  #  weights = None\n",
    "    \n",
    "  #  if weight_name:\n",
    "  #      weights = [int(G[u][v][weight_name]) for u,v in edges]\n",
    "  #      labels = nx.get_edge_attributes(G,weight_name)\n",
    "  #      nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)\n",
    "  #      nx.draw_networkx(G, pos, edges=edges, width=weights);\n",
    "  #  else:\n",
    "  #      nx.draw_networkx(G, pos, edges=edges);"
   ]
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   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "Using NetworkX, load in the bipartite graph from `Employee_Movie_Choices.txt` and return that graph.\n",
    "\n",
    "*This function should return a networkx graph with 19 nodes and 24 edges*"
   ]
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  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
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   "source": [
    "def answer_one():\n",
    "        \n",
    "    # Your Code Here\n",
    "    data=pd.read_csv(\"Employee_Movie_Choices.txt\",sep=\"\\t\")\n",
    "    #print(data)\n",
    "    G = nx.from_pandas_dataframe(data, '#Employee', 'Movie')\n",
    "    #print(G.edges(data=True))\n",
    "    #plot_graph(G)\n",
    "    return G # Your Answer Here\n",
    "#answer_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Using the graph from the previous question, add nodes attributes named `'type'` where movies have the value `'movie'` and employees have the value `'employee'` and return that graph.\n",
    "\n",
    "*This function should return a networkx graph with node attributes `{'type': 'movie'}` or `{'type': 'employee'}`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
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   "source": [
    "def answer_two():\n",
    "    \n",
    "    # Your Code Here\n",
    "    G = answer_one()\n",
    "    for i in G.edges():\n",
    "        G.add_node(i[0],role=\"employee\")\n",
    "        G.add_node(i[1],role=\"movie\")\n",
    "    #plot_graph(G)\n",
    "    return G# Your Answer Here\n",
    "#answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
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   "source": [
    "### Question 3\n",
    "\n",
    "Find a weighted projection of the graph from `answer_two` which tells us how many movies different pairs of employees have in common.\n",
    "\n",
    "*This function should return a weighted projected graph.*"
   ]
  },
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   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
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   "source": [
    "def answer_three():\n",
    "        \n",
    "    # Your Code Here\n",
    "    G=answer_two()\n",
    "    P= bipartite.weighted_projected_graph(G,employees)\n",
    "    #plot_graph(P,'weight')\n",
    "    #print(P.edges(data=True))\n",
    "    return P# Your Answer Here\n",
    "#answer_three()"
   ]
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   "metadata": {
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   "source": [
    "### Question 4\n",
    "\n",
    "Suppose you'd like to find out if people that have a high relationship score also like the same types of movies.\n",
    "\n",
    "Find the Pearson correlation ( using `DataFrame.corr()` ) between employee relationship scores and the number of movies they have in common. If two employees have no movies in common it should be treated as a 0, not a missing value, and should be included in the correlation calculation.\n",
    "\n",
    "*This function should return a float.*"
   ]
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   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
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   "source": [
    "def answer_four():\n",
    "        \n",
    "    # Your Code Here\n",
    "    G_df = pd.read_csv('Employee_Relationships.txt',sep=\"\\t\", header=None, names=['n1', 'n2', 'relationship'])\n",
    "    G1 = nx.from_pandas_dataframe(G_df, 'n1', 'n2', edge_attr='relationship')\n",
    "    #print(G1.edges(data=True))\n",
    "    G = answer_three()\n",
    "    F = nx.compose(G1,G)\n",
    "    #print(F.edges(data=True))\n",
    "    d={}\n",
    "    d[\"relationship\"] = [x[2][\"relationship\"] for x in F.edges(data=True)]\n",
    "    d[\"weight\"]=[x[2][\"weight\"] if \"weight\" in x[2] else 0 for x in F.edges(data=True) ]\n",
    "    d[\"employee\"] = [x[0] for x in F.edges(data=True)]\n",
    "    d[\"other_employee\"] = [x[1] for x in F.edges(data=True)]\n",
    "    df1 = pd.DataFrame(d)\n",
    "    #print(df1)\n",
    "    corr = df1[\"relationship\"].corr(df1[\"weight\"])\n",
    "    #print(corr)\n",
    "    return corr # Your Answer Here\n",
    "#answer_four()"
   ]
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