pydataset_practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pydataset 설치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:08:35.428822Z",
     "start_time": "2020-07-23T09:08:14.863375Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
      "Collecting pydataset\n",
      "  Downloading pydataset-0.2.0.tar.gz (15.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 15.9 MB 6.2 MB/s eta 0:00:01    |████████████████▋               | 8.3 MB 6.2 MB/s eta 0:00:02     |█████████████████████████▎      | 12.6 MB 6.2 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from pydataset) (0.25.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/lib/python3.7/site-packages (from pandas->pydataset) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.7/site-packages (from pandas->pydataset) (1.17.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas->pydataset) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->pydataset) (1.14.0)\n",
      "Building wheels for collected packages: pydataset\n",
      "  Building wheel for pydataset (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pydataset: filename=pydataset-0.2.0-py3-none-any.whl size=15939429 sha256=5530a2e30c8b8a251c3dbb1bf82d2b20823c4ab4f71258c9933d14698465e41b\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-iblutrw9/wheels/32/26/30/d71562a19eed948eaada9a61b4d722fa358657a3bfb5d151e2\n",
      "Successfully built pydataset\n",
      "Installing collected packages: pydataset\n",
      "Successfully installed pydataset-0.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install pydataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:45:32.600851Z",
     "start_time": "2020-07-23T09:45:32.596218Z"
    }
   },
   "source": [
    "## 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:08:43.972598Z",
     "start_time": "2020-07-23T09:08:37.614867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initiated datasets repo at: /home/jovyan/.pydataset/\n"
     ]
    }
   ],
   "source": [
    "from pydataset import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 종류 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:19:17.543459Z",
     "start_time": "2020-07-23T09:19:17.519628Z"
    }
   },
   "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>dataset_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AirPassengers</td>\n",
       "      <td>Monthly Airline Passenger Numbers 1949-1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BJsales</td>\n",
       "      <td>Sales Data with Leading Indicator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BOD</td>\n",
       "      <td>Biochemical Oxygen Demand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Formaldehyde</td>\n",
       "      <td>Determination of Formaldehyde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HairEyeColor</td>\n",
       "      <td>Hair and Eye Color of Statistics Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>VerbAgg</td>\n",
       "      <td>Verbal Aggression item responses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>cake</td>\n",
       "      <td>Breakage Angle of Chocolate Cakes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>cbpp</td>\n",
       "      <td>Contagious bovine pleuropneumonia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>grouseticks</td>\n",
       "      <td>Data on red grouse ticks from Elston et al. 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>sleepstudy</td>\n",
       "      <td>Reaction times in a sleep deprivation study</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        dataset_id                                             title\n",
       "0    AirPassengers       Monthly Airline Passenger Numbers 1949-1960\n",
       "1          BJsales                 Sales Data with Leading Indicator\n",
       "2              BOD                         Biochemical Oxygen Demand\n",
       "3     Formaldehyde                     Determination of Formaldehyde\n",
       "4     HairEyeColor         Hair and Eye Color of Statistics Students\n",
       "..             ...                                               ...\n",
       "752        VerbAgg                  Verbal Aggression item responses\n",
       "753           cake                 Breakage Angle of Chocolate Cakes\n",
       "754           cbpp                 Contagious bovine pleuropneumonia\n",
       "755    grouseticks  Data on red grouse ticks from Elston et al. 2001\n",
       "756     sleepstudy       Reaction times in a sleep deprivation study\n",
       "\n",
       "[757 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kind = data()\n",
    "kind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터셋 설명 보기 & 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:16:33.490199Z",
     "start_time": "2020-07-23T09:16:33.471671Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iris\n",
      "\n",
      "PyDataset Documentation (adopted from R Documentation. The displayed examples are in R)\n",
      "\n",
      "## Edgar Anderson's Iris Data\n",
      "\n",
      "### Description\n",
      "\n",
      "This famous (Fisher's or Anderson's) iris data set gives the measurements in\n",
      "centimeters of the variables sepal length and width and petal length and\n",
      "width, respectively, for 50 flowers from each of 3 species of iris. The\n",
      "species are _Iris setosa_, _versicolor_, and _virginica_.\n",
      "\n",
      "### Usage\n",
      "\n",
      "    iris\n",
      "    iris3\n",
      "\n",
      "### Format\n",
      "\n",
      "`iris` is a data frame with 150 cases (rows) and 5 variables (columns) named\n",
      "`Sepal.Length`, `Sepal.Width`, `Petal.Length`, `Petal.Width`, and `Species`.\n",
      "\n",
      "`iris3` gives the same data arranged as a 3-dimensional array of size 50 by 4\n",
      "by 3, as represented by S-PLUS. The first dimension gives the case number\n",
      "within the species subsample, the second the measurements with names `Sepal\n",
      "L.`, `Sepal W.`, `Petal L.`, and `Petal W.`, and the third the species.\n",
      "\n",
      "### Source\n",
      "\n",
      "Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems.\n",
      "_Annals of Eugenics_, **7**, Part II, 179–188.\n",
      "\n",
      "The data were collected by Anderson, Edgar (1935). The irises of the Gaspe\n",
      "Peninsula, _Bulletin of the American Iris Society_, **59**, 2–5.\n",
      "\n",
      "### References\n",
      "\n",
      "Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S Language_.\n",
      "Wadsworth & Brooks/Cole. (has `iris3` as `iris`.)\n",
      "\n",
      "### See Also\n",
      "\n",
      "`matplot` some examples of which use `iris`.\n",
      "\n",
      "### Examples\n",
      "\n",
      "    dni3 <- dimnames(iris3)\n",
      "    ii <- data.frame(matrix(aperm(iris3, c(1,3,2)), ncol = 4,\n",
      "                            dimnames = list(NULL, sub(\" L.\",\".Length\",\n",
      "                                            sub(\" W.\",\".Width\", dni3[[2]])))),\n",
      "        Species = gl(3, 50, labels = sub(\"S\", \"s\", sub(\"V\", \"v\", dni3[[3]]))))\n",
      "    all.equal(ii, iris) # TRUE\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 데이터셋 설명보기\n",
    "data('iris', show_doc=True)\n",
    "\n",
    "# 데이터셋 불러오기\n",
    "iris = data('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:48:35.005601Z",
     "start_time": "2020-07-23T09:48:34.987533Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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>1</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>2</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>3</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>4</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>5</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",
       "1           5.1          3.5           1.4          0.2  setosa\n",
       "2           4.9          3.0           1.4          0.2  setosa\n",
       "3           4.7          3.2           1.3          0.2  setosa\n",
       "4           4.6          3.1           1.5          0.2  setosa\n",
       "5           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## titanic 예시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:14:35.515138Z",
     "start_time": "2020-07-23T09:14:35.502368Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "titanic\n",
      "\n",
      "PyDataset Documentation (adopted from R Documentation. The displayed examples are in R)\n",
      "\n",
      "## titanic\n",
      "\n",
      "### Description\n",
      "\n",
      "The data is an observation-based version of the 1912 Titanic passenger\n",
      "survival log,\n",
      "\n",
      "### Usage\n",
      "\n",
      "    data(titanic)\n",
      "\n",
      "### Format\n",
      "\n",
      "A data frame with 1316 observations on the following 4 variables.\n",
      "\n",
      "`class`\n",
      "\n",
      "a factor with levels `1st class` `2nd class` `3rd class` `crew`\n",
      "\n",
      "`age`\n",
      "\n",
      "a factor with levels `child` `adults`\n",
      "\n",
      "`sex`\n",
      "\n",
      "a factor with levels `women` `man`\n",
      "\n",
      "`survived`\n",
      "\n",
      "a factor with levels `no` `yes`\n",
      "\n",
      "### Details\n",
      "\n",
      "titanic is saved as a data frame. Used to assess risk ratios\n",
      "\n",
      "### Source\n",
      "\n",
      "Found in many other texts\n",
      "\n",
      "### References\n",
      "\n",
      "Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press Hilbe,\n",
      "Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University\n",
      "Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC\n",
      "\n",
      "### Examples\n",
      "\n",
      "    data(titanic)\n",
      "    titanic$survival <- titanic$survived == \"yes\"\n",
      "    glmlr <- glm(survival ~ age + sex + factor(class), family=binomial, data=titanic)\n",
      "    summary(glmlr)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data('titanic', show_doc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-23T09:20:51.555771Z",
     "start_time": "2020-07-23T09:20:51.534995Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>class</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st class</td>\n",
       "      <td>adults</td>\n",
       "      <td>man</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st class</td>\n",
       "      <td>adults</td>\n",
       "      <td>man</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st class</td>\n",
       "      <td>adults</td>\n",
       "      <td>man</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st class</td>\n",
       "      <td>adults</td>\n",
       "      <td>man</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1st class</td>\n",
       "      <td>adults</td>\n",
       "      <td>man</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>3rd class</td>\n",
       "      <td>child</td>\n",
       "      <td>women</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>3rd class</td>\n",
       "      <td>child</td>\n",
       "      <td>women</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>3rd class</td>\n",
       "      <td>child</td>\n",
       "      <td>women</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>3rd class</td>\n",
       "      <td>child</td>\n",
       "      <td>women</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>3rd class</td>\n",
       "      <td>child</td>\n",
       "      <td>women</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1316 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          class     age    sex survived\n",
       "1     1st class  adults    man      yes\n",
       "2     1st class  adults    man      yes\n",
       "3     1st class  adults    man      yes\n",
       "4     1st class  adults    man      yes\n",
       "5     1st class  adults    man      yes\n",
       "...         ...     ...    ...      ...\n",
       "1312  3rd class   child  women       no\n",
       "1313  3rd class   child  women       no\n",
       "1314  3rd class   child  women       no\n",
       "1315  3rd class   child  women       no\n",
       "1316  3rd class   child  women       no\n",
       "\n",
       "[1316 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = data('titanic')\n",
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cake 예시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>replicate</th>\n",
       "      <th>recipe</th>\n",
       "      <th>temperature</th>\n",
       "      <th>angle</th>\n",
       "      <th>temp</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>A</td>\n",
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       "      <td>42</td>\n",
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       "      <td>185</td>\n",
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       "      <td>A</td>\n",
       "      <td>215</td>\n",
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       "      <td>195</td>\n",
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       "</table>\n",
       "<p>270 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "     replicate recipe  temperature  angle  temp\n",
       "1            1      A          175     42   175\n",
       "2            1      A          185     46   185\n",
       "3            1      A          195     47   195\n",
       "4            1      A          205     39   205\n",
       "5            1      A          215     53   215\n",
       "..         ...    ...          ...    ...   ...\n",
       "266         15      C          185     28   185\n",
       "267         15      C          195     25   195\n",
       "268         15      C          205     25   205\n",
       "269         15      C          215     31   215\n",
       "270         15      C          225     25   225\n",
       "\n",
       "[270 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
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   ],
   "source": [
    "cake = data('cake')\n",
    "cake"
   ]
  }
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