{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAB 04.01 - Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/ai4eng.v1/main/content/init.py\n",
    "import init; init.init(force_download=False); init.get_weblink()\n",
    "\n",
    "init.endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from local.lib.rlxmoocapi import submit, session\n",
    "session.LoginSequence(endpoint=init.endpoint, course_id=init.course_id, lab_id=\"L04.01\", varname=\"student\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observe the following synthetic example with missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from IPython.display import Image\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 20\n",
    "place = np.r_[[\"Medellin\", \"Bogota\", \"Madrid\"]][(np.random.randint(3, size=n))]\n",
    "age = np.random.randint(50, size=n)+10\n",
    "children = np.r_[[(np.random.randint(2) if i<30 else (np.random.randint(4))) for i in age]]\n",
    "risk = np.r_[[np.random.random()*(.2 if i==\"Medellin\" else .8) for i in place]].round(3)\n",
    "risk[np.random.permutation(len(risk))[:5]]=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>age</th>\n",
       "      <th>risk</th>\n",
       "      <th>children</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0.112</td>\n",
       "      <td>2</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>0.093</td>\n",
       "      <td>1</td>\n",
       "      <td>Medellin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0.638</td>\n",
       "      <td>1</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>0.111</td>\n",
       "      <td>3</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>58</td>\n",
       "      <td>0.633</td>\n",
       "      <td>2</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0</td>\n",
       "      <td>Medellin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>58</td>\n",
       "      <td>0.299</td>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>52</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1</td>\n",
       "      <td>Medellin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>47</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44</td>\n",
       "      <td>0.742</td>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>54</td>\n",
       "      <td>0.567</td>\n",
       "      <td>2</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0</td>\n",
       "      <td>Medellin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>0.299</td>\n",
       "      <td>1</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Medellin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>0.081</td>\n",
       "      <td>2</td>\n",
       "      <td>Medellin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   risk children     place\n",
       "0   59  0.112        2    Bogota\n",
       "1   36  0.093        1  Medellin\n",
       "2   25  0.638        1    Madrid\n",
       "3   57    NaN        1    Madrid\n",
       "4   59  0.641        0    Madrid\n",
       "5   50  0.111        3    Bogota\n",
       "6   58  0.633        2    Bogota\n",
       "7   13  0.025        0  Medellin\n",
       "8   10    NaN        1    Bogota\n",
       "9   58  0.299        3    Madrid\n",
       "10  52  0.004        1  Medellin\n",
       "11  47    0.2        0    Madrid\n",
       "12  55    NaN        3    Bogota\n",
       "13  44  0.742        3    Madrid\n",
       "14  27    NaN        0    Madrid\n",
       "15  54  0.567        2    Bogota\n",
       "16  28  0.028        0  Medellin\n",
       "17  24  0.299        1    Bogota\n",
       "18  57    NaN        0  Medellin\n",
       "19  40  0.081        2  Medellin"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d01 = pd.DataFrame([age, risk, children, place], index=[\"age\", \"risk\", \"children\", \"place\"]).T\n",
    "d01.to_csv(\"risk.csv\", index=False)\n",
    "d01\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observe, in particular, that risk in Medellín is usually lower than in Bogotá, so we will try to fix missing data using this fact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f25b2ddf7d0>"
      ]
     },
     "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": [
    "\n",
    "k = d01[d01.place==\"Bogota\"][\"risk\"].dropna()\n",
    "plt.scatter(k, [0]*len(k), label=\"Bogota\")\n",
    "k = d01[d01.place==\"Medellin\"][\"risk\"].dropna()\n",
    "plt.scatter(k, [1]*len(k), label=\"Medellin\")\n",
    "k = d01[d01.place==\"Madrid\"][\"risk\"].dropna()\n",
    "plt.scatter(k, [2]*len(k), label=\"Madrid\")\n",
    "plt.grid();\n",
    "plt.xlabel(\"risk level\")\n",
    "plt.ylabel(\"city\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">**NOTE**</font>: If you solve this lab using Python, make sure to **NOT MODIFY** dataframe `d01`. You can make make a copy and work on that copy:\n",
    "\n",
    "       my_d01 = d01.copy()\n",
    "       my_d01['risk'] = ... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1. FillNA in `risk` with corresponding city average\n",
    "\n",
    "Observe that the above dataframe has been stored in the file `risk.csv`. You will have to fill in the missing values in the **risk** column with the related city mean in the following way:\n",
    "\n",
    "1. Download the file `risk.csv`\n",
    "1. Compute the mean risk **per city** \n",
    "1. Substitute any missing value in the risk column by the corresponding city mean\n",
    "1. Create a new csv file named `risk_fixed.csv`, with the **exact** same structure but with the missing values replaced\n",
    "1. Upload your `risk_fixed.csv` file to the notebook environment\n",
    "1. Run the evaluation cell below\n",
    "\n",
    "### Use the tool of your choice \n",
    "(Excel, Orange, your programming language, or even this notebook if you can program python)\n",
    "\n",
    "**For Python**, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable `r01`\n",
    "\n",
    "**use three decimal places for precision**\n",
    "\n",
    "### Example\n",
    "for the following data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "width": 200
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/cities.png\", width=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you must create a file with the following content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "image/png": {
       "width": 200
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/cities-riskfree.png\", width=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**your solution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "r01 = pd.read_csv(\"risk_fixed.csv\")\n",
    "r01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### submit your answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_01\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2. Standardize `age` so that min=0, max=1\n",
    "\n",
    "Standardizing values is, in certain cases, a necesity for ML models, providing stability and increased performance.\n",
    "\n",
    "In this task you will have to standardize the column `age` so that all values stay in the [0,1] interval. Given any value $x_i$, its corresponsing stardardized value $s_i$ will be:\n",
    "\n",
    "$$s_i = \\frac{x_i-min}{max-min}$$\n",
    "\n",
    "where $min$, $max$ is the minimum and maximum ages respectively\n",
    "\n",
    "You must use again the file `risk.csv` and create and upload a file named `age_minmax.csv` with your answer. You should **only modify** the `age` column, leaving the rest as you find them in the csv file.\n",
    "\n",
    "**For Python**, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable `r02`\n",
    "\n",
    "For the previous example, the correct answer would be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/cities-ageminmax.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**load your file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "r02 = pd.read_csv(\"age_minmax.csv\")\n",
    "r02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### submit your answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_02\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3. Standardize `age` so that $\\mu=0$ and $\\sigma=1$\n",
    "\n",
    "\n",
    "In this task you will have to standardize the column `age` so that all values stay have zero mean and standard deviation of 1. Given any value $x_i$, its corresponsing stardardized value $s_i$ will be:\n",
    "\n",
    "$$s_i = \\frac{s_i-\\mu}{\\sigma}$$\n",
    "\n",
    "where $\\mu$ is the mean of all age values, and $\\sigma$ is the standard deviation.\n",
    "\n",
    "You must use again the file `risk.csv` and create and upload a file named `age_meanstd.csv` with your answer. You should **only modify** the `age` column, leaving the rest as you find them in the csv file.\n",
    "\n",
    "**For Python**, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable `r03`\n",
    "\n",
    "For the previous example, the correct answer would be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/cities-agemeanstd.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**load your file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "r03 = pd.read_csv(\"age_meanstd.csv\")\n",
    "r03"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### submit your answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_03\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4. Create a one-hot encoding for  `place`\n",
    "\n",
    "substitute the column `place` for three new columns with **onehot** encoding. You must use again the file `risk.csv` and create and upload a file named `place_onehot.csv` with your answer.\n",
    "\n",
    "**For Python**, you do not have to download and upload anything, just use Pandas and store the resulting dataset in the variable `r04`\n",
    "\n",
    "The solution for the example above should look like this. Observe that **you must name the columns** as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/cities_onehot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**load your file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "r04 = pd.read_csv(\"place_onehot.csv\")\n",
    "r04"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### submit your answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_04\");"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}