basic_functions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rc('font', family='Verdana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(f'x:\\n{x}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eye:\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "eye = np.eye(4)\n",
    "print(f'eye:\\n{eye}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sparse_matrix:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n",
      "<class 'scipy.sparse.csr.csr_matrix'>\n"
     ]
    }
   ],
   "source": [
    "sparse_matrix = sparse.csr_matrix(eye)\n",
    "print(f'sparse_matrix:\\n{sparse_matrix}')\n",
    "print(type(sparse_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Format COO:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n",
      "<class 'scipy.sparse.coo.coo_matrix'>\n"
     ]
    }
   ],
   "source": [
    "data = np.ones(4)\n",
    "row_indices = np.arange(4)\n",
    "col_indices = np.arange(4)\n",
    "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
    "print(\"Format COO:\\n{}\".format(eye_coo))\n",
    "print(type(eye_coo))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5641cce908>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 100)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, marker=\"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>New York</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>Paris</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linda</td>\n",
       "      <td>London</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Location  Age\n",
       "0   John  New York   24\n",
       "1   Anna     Paris   13\n",
       "2  Peter    Berlin   53\n",
       "3  Linda    London   33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linda</td>\n",
       "      <td>London</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name Location  Age\n",
       "2  Peter   Berlin   53\n",
       "3  Linda   London   33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
    "'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
    "'Age' : [24, 13, 53, 33]\n",
    "}\n",
    "data_pandas = pd.DataFrame(data)\n",
    "display(data_pandas)\n",
    "display(data_pandas[data_pandas.Age > 24])"
   ]
  }
 ],
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