src/notebook/iris_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('/home/cloud/summer_school/my_practical_exrcise/data/raw/iris.csv')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = np.array(['setosa', 'versicolor', 'virginica'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = []\n",
    "for specy in iris[['species']].values:\n",
    "    if specy[0] == 'setosa':\n",
    "        target.append(0)\n",
    "    elif specy[0] == 'versicolor':\n",
    "        target.append(1)\n",
    "    else:\n",
    "        target.append(2)\n",
    "target = np.array(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# import some data to play with\n",
    "X = data[:, :2]  # we only take the first two features.\n",
    "y = target\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "\n",
    "plt.figure(2, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "# Plot the training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n",
    "            edgecolor='k')\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}