week2_importing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 2 - Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import h2o\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: openjdk version \"1.8.0_232\"; OpenJDK Runtime Environment (build 1.8.0_232-8u232-b09-0ubuntu1~19.04.1-b09); OpenJDK 64-Bit Server VM (build 25.232-b09, mixed mode)\n",
      "  Starting server from /home/megan/Projects/h2oclass/lib/python3.7/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpacucw4il\n",
      "  JVM stdout: /tmp/tmpacucw4il/h2o_megan_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpacucw4il/h2o_megan_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/Chicago</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.28.0.3</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>9 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_megan_h7oogv</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>1.520 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>3</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>3</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>{'http': None, 'https': None}</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.7.3 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/Chicago\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.28.0.3\n",
       "H2O cluster version age:    9 days\n",
       "H2O cluster name:           H2O_from_python_megan_h7oogv\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.520 Gb\n",
       "H2O cluster total cores:    3\n",
       "H2O cluster allowed cores:  3\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:       {'http': None, 'https': None}\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python version:             3.7.3 final\n",
       "--------------------------  ------------------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create our x of 1001\n",
    "x = [z*0.01 for z in range(1001)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.01, 0.02, 0.03, 0.04]\n",
      "[9.96, 9.97, 9.98, 9.99, 10.0]\n"
     ]
    }
   ],
   "source": [
    "print(x[:5])\n",
    "print(x[996:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create y as the sine of x, with some noise\n",
    "y = np.sin(x)\n",
    "y += np.random.normal(0, 0.1, len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# view our data\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe\n",
    "sine_wave = pd.DataFrame({\n",
    "    'a':x,\n",
    "    'b':y\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# convert the pandas dataframe to an H2O frame\n",
    "sine_wave_h2o = h2o.H2OFrame(\n",
    "    sine_wave\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>a                </th><th>b                    </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>real             </td><td>real                 </td></tr>\n",
       "<tr><td>mins   </td><td>0.0              </td><td>-1.283143861317382   </td></tr>\n",
       "<tr><td>mean   </td><td>4.999999999999934</td><td>0.175136572532225    </td></tr>\n",
       "<tr><td>maxs   </td><td>10.0             </td><td>1.322688852460212    </td></tr>\n",
       "<tr><td>sigma  </td><td>2.891081112663593</td><td>0.6753314450861596   </td></tr>\n",
       "<tr><td>zeros  </td><td>1                </td><td>0                    </td></tr>\n",
       "<tr><td>missing</td><td>0                </td><td>0                    </td></tr>\n",
       "<tr><td>0      </td><td>0.0              </td><td>-0.07905426489402469 </td></tr>\n",
       "<tr><td>1      </td><td>0.01             </td><td>0.018131174945459728 </td></tr>\n",
       "<tr><td>2      </td><td>0.02             </td><td>-0.006897379047226929</td></tr>\n",
       "<tr><td>3      </td><td>0.03             </td><td>-0.08871406656716235 </td></tr>\n",
       "<tr><td>4      </td><td>0.04             </td><td>-0.0865333760452629  </td></tr>\n",
       "<tr><td>5      </td><td>0.05             </td><td>-0.020589078711240508</td></tr>\n",
       "<tr><td>6      </td><td>0.06             </td><td>0.08308445378073151  </td></tr>\n",
       "<tr><td>7      </td><td>0.07             </td><td>0.056094331122770714 </td></tr>\n",
       "<tr><td>8      </td><td>0.08             </td><td>0.04765373518304185  </td></tr>\n",
       "<tr><td>9      </td><td>0.09             </td><td>0.25445191713674775  </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# view a summary of the H2O frame\n",
    "sine_wave_h2o.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# if look in H2O Flow, the above frame wasn't named nicely - let's name it\n",
    "sine_wave_h2o = h2o.H2OFrame(\n",
    "    sine_wave,\n",
    "    destination_frame = 'sine_wave'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>a</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>-0.079054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.018131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.006897</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.088714</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.086533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>9.96</td>\n",
       "      <td>-0.379995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>9.97</td>\n",
       "      <td>-0.492576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>9.98</td>\n",
       "      <td>-0.575166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>9.99</td>\n",
       "      <td>-0.391441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>10.00</td>\n",
       "      <td>-0.450663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b\n",
       "0      0.00 -0.079054\n",
       "1      0.01  0.018131\n",
       "2      0.02 -0.006897\n",
       "3      0.03 -0.088714\n",
       "4      0.04 -0.086533\n",
       "...     ...       ...\n",
       "996    9.96 -0.379995\n",
       "997    9.97 -0.492576\n",
       "998    9.98 -0.575166\n",
       "999    9.99 -0.391441\n",
       "1000  10.00 -0.450663\n",
       "\n",
       "[1001 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert an H2O frame to a pandas dataframe\n",
    "sine_wave_h2o.as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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