D11 pandas 類別資料與缺失值處理 作業題目.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FkeqfrdQEuUu"
   },
   "source": [
    "作業目標:<br>\n",
    "1. 運用編碼處理類別資料<br>\n",
    "2. 補缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KQnEzEUWSn-6"
   },
   "source": [
    "作業重點: <br>\n",
    "1. 類別編碼有多種方法，需分辨使用方法與時機<br>\n",
    "2. 補缺失值須因應情境決定如何補值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QqR61iXKExtj"
   },
   "source": [
    "題目 : \n",
    "將以下問卷資料的職業(Profession)欄位缺失值填入字串'others'，更進一步將字串做編碼。\n",
    "此時用什麼方式做編碼比較適合?為什麼?\n",
    "\n",
    "import pandas as pd <br>\n",
    "q_df = pd.DataFrame([['male', 'teacher'], \n",
    "              ['male', 'engineer'],\n",
    "              ['female', None],\n",
    "              ['female', 'engineer']],columns=['Sex','Profession'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "executionInfo": {
     "elapsed": 845,
     "status": "ok",
     "timestamp": 1606568962588,
     "user": {
      "displayName": "献竤黃",
      "photoUrl": "",
      "userId": "07529243043474362942"
     },
     "user_tz": -480
    },
    "id": "31UzVh-FEvyu",
    "outputId": "66c3c15b-0453-47b9-e878-d66430365402"
   },
   "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>Sex</th>\n",
       "      <th>Profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>teacher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex Profession\n",
       "0    male    teacher\n",
       "1    male   engineer\n",
       "2  female       None\n",
       "3  female   engineer"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "q_df = pd.DataFrame([['male', 'teacher'], \n",
    "              ['male', 'engineer'],\n",
    "              ['female', None],\n",
    "              ['female', 'engineer']],columns=['Sex','Profession'])\n",
    "q_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "-FiQRlTM56hL"
   },
   "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>Sex</th>\n",
       "      <th>Profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>teacher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex Profession\n",
       "0    male    teacher\n",
       "1    male   engineer\n",
       "2  female     others\n",
       "3  female   engineer"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值填入字串'others'\n",
    "new_df = q_df.fillna(\"others\")\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "fqxavNQV7MiJ"
   },
   "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>Sex</th>\n",
       "      <th>Profession</th>\n",
       "      <th>engineer</th>\n",
       "      <th>others</th>\n",
       "      <th>teacher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>teacher</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>others</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex Profession  engineer  others  teacher\n",
       "0    male    teacher         0       0        1\n",
       "1    male   engineer         1       0        0\n",
       "2  female     others         0       1        0\n",
       "3  female   engineer         1       0        0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#更進一步將字串做編碼。 此時用什麼方式做編碼比較適合?為什麼?\n",
    "# 無順序 --> 一般性: one-hot encoding\n",
    "\n",
    "one_hot_df = pd.get_dummies(new_df[\"Profession\"])\n",
    "final_df = pd.concat([new_df, one_hot_df], axis=1)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AORv7fdP71kC"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOjMmDZf/vVbf+1vJaHFaKg",
   "collapsed_sections": [],
   "name": "類別資料與缺值處理作業題目.ipynb",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}