week1/Day2-HandsOn1.ipynb

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    "\n",
    "<img src=\"../assets/logo3.png\" width=\"200\" height=\"200\" >\n",
    "<div style=\"display:block\"><br><br>\n",
    "    <div style=\"display:block\" align=left display=block> \n",
    "        <font size=5><b>Day 2 Hands-On 1 - Numpy Basics</b></font><br>\n",
    "        <hr/>\n",
    "</div>"
   ]
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   "source": [
    "**Execute the cell below. By running this cell, a dataset will be loaded from `patents_small.csv` file. In this notebook, you are asked to analyze this data in several ways. There are three numpy arrays in this dataset:**\n",
    "- `patent_number`: a unique identifier for each patetnt\n",
    "- `patent features`: a vector of 16 features describing several properties of each patent\n",
    "- `category`: the category to which a patent belongs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('patents.csv')\n",
    "df.head()\n",
    "patent_number = df['publication_number'].to_numpy()\n",
    "patent_features = df['patent_embedding'].to_numpy()\n",
    "temp = []\n",
    "for i in range(patent_features.size):\n",
    "    s = str(patent_features[i])\n",
    "    s1 = s.replace(r'\\n', '')\n",
    "    temp.append(\n",
    "        np.array(s.split()[1:-1], dtype='float')[:16]\n",
    "    )\n",
    "\n",
    "patent_features = np.stack(temp)\n",
    "patent_category = df['category']"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />"
   ]
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   "cell_type": "markdown",
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   "source": [
    "#### 1- Which patent has the highest norm? (Eucledian distance from origin)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2- Find the two patents that are the farthest from eachother."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3- Write a function that, given a patent number, finds its nearest neighbour.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4- For each patent category, find the cluster center. This quantity is computed by taking average of all patents associated with each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5- How many patents have a nearest neighbour that is in the same category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6- What is the average and std of distances between every pair of patents?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7- What is the average and std of distances between every pair of patents within a category? Using these calculated quantities, which cluster do you think is more condensed? Which one is more scattered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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