6b. Introducing Scikit-Learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>target_names</td>\n",
       "      <td>[setosa, versicolor, virginica]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data</td>\n",
       "      <td>[[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>target</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DESCR</td>\n",
       "      <td>Iris Plants Database\\n\\nNotes\\n-----\\nData Set...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>feature_names</td>\n",
       "      <td>[sepal length (cm), sepal width (cm), petal le...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0                                                  1\n",
       "0   target_names                    [setosa, versicolor, virginica]\n",
       "1           data  [[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [...\n",
       "2         target  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
       "3          DESCR  Iris Plants Database\\n\\nNotes\\n-----\\nData Set...\n",
       "4  feature_names  [sepal length (cm), sepal width (cm), petal le..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(data['data'], columns=data['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(data['target'], columns=['Flower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "db = {0:'setosa',1:'versicolor',2:'virginical'}\n",
    "def f(num):\n",
    "    return db[num]\n",
    "    \n",
    "df2['name'] = df2['Flower'].map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df1,df2[['name']]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(100)\n",
    "x = 10 * rng.rand(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = 2 * x -1 + rng.randn(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9eeb0b8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LXXQkNc9g1ymdLmAtnUi9xxOUNKdGVnU6l1wqB3vs80CjPXF77FLvsceuWdV63OcxvScO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d7add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa323198>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VgFHJZic8Z4jYPQHk83r2zrp1WeNckwKsl4GBXFcMYhKSRCjmERHGwGg7U+qt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\nCCE9AMWcEEJ6gP8PRoAf960fpwQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa05a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], s=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>name</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 (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "     name  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:gl-env]",
   "language": "python",
   "name": "conda-env-gl-env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
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 "nbformat_minor": 1
}