09_PandasBasics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "- **pandas** の基本について学ぶ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import everything I nead :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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>0</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>0</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>0</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",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "data = iris.data\n",
    "feat_names = iris.feature_names\n",
    "df_iris = pd.DataFrame(data, columns=feat_names)\n",
    "df_iris['target'] = iris.target\n",
    "df_iris.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>num</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
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       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>a</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>c</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  id1 id2  num\n",
       "0   A   a    1\n",
       "1   A   a    2\n",
       "2   A   b    3\n",
       "3   A   b    4\n",
       "4   B   a    5\n",
       "5   B   c    6\n",
       "6   B   c    7\n",
       "7   C   a    8\n",
       "8   C   c    9"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id1 = ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C']\n",
    "id2 = ['a', 'a', 'b', 'b', 'a', 'c', 'c', 'a', 'c']\n",
    "num = [ 1,   2,   3,   4,   5,   6,   7,   8,   9]\n",
    "\n",
    "df_smp = pd.DataFrame()\n",
    "df_smp['id1'] = id1\n",
    "df_smp['id2'] = id2\n",
    "df_smp['num'] = num\n",
    "df_smp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics of Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>a</td>\n",
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       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>a</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
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       "      <td>9</td>\n",
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      "text/plain": [
       "  id1 id2  num\n",
       "0   A   a    1\n",
       "1   A   a    2\n",
       "2   A   b    3\n",
       "3   A   b    4\n",
       "4   B   a    5\n",
       "5   B   c    6\n",
       "6   B   c    7\n",
       "7   C   a    8\n",
       "8   C   c    9"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_smp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id2</th>\n",
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       "      <th>id1</th>\n",
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       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <th>B</th>\n",
       "      <td>3</td>\n",
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       "      <th>C</th>\n",
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      "text/plain": [
       "     id2  num\n",
       "id1          \n",
       "A      4    4\n",
       "B      3    3\n",
       "C      2    2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# id1 について平均\n",
    "g1 = df_smp.groupby(['id1']).count()\n",
    "pd.DataFrame(g1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <th>g1</th>\n",
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       "      <th>2</th>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>a</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>c</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  id1 id2  num  g1\n",
       "0   A   a    1   4\n",
       "1   A   a    2   4\n",
       "2   A   b    3   4\n",
       "3   A   b    4   4\n",
       "4   B   a    5   3\n",
       "5   B   c    6   3\n",
       "6   B   c    7   3\n",
       "7   C   a    8   2\n",
       "8   C   c    9   2"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# g1: id1 についてカウント\n",
    "g1 = df_smp.groupby(['id1'])['num'].transform('count').values\n",
    "df_smp['g1'] = g1\n",
    "df_smp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---> g1 には、一つ前のセルの通り、A には 4、Bには3、Cには2 が入っている"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>A</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>A</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>a</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>c</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  id1 id2  num  g1  g2\n",
       "0   A   a    1   4   2\n",
       "1   A   a    2   4   2\n",
       "2   A   b    3   4   2\n",
       "3   A   b    4   4   2\n",
       "4   B   a    5   3   1\n",
       "5   B   c    6   3   2\n",
       "6   B   c    7   3   2\n",
       "7   C   a    8   2   1\n",
       "8   C   c    9   2   1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# g2: [id1, id2] についてカウント\n",
    "g2 = df_smp.groupby(['id1', 'id2'])['g1'].transform('count').values\n",
    "df_smp['g2'] = g2\n",
    "df_smp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---> 例) Aかつa は 2種類、Bかつaは1種類 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>num</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>a</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>c</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id1 id2  num  g1  g2  g3\n",
       "0   A   a    1   4   2   2\n",
       "1   A   a    2   4   2   2\n",
       "2   A   b    3   4   2   4\n",
       "3   A   b    4   4   2   4\n",
       "4   B   a    5   3   1   5\n",
       "5   B   c    6   3   2   7\n",
       "6   B   c    7   3   2   7\n",
       "7   C   a    8   2   1   8\n",
       "8   C   c    9   2   1   9"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max\n",
    "g3 = df_smp.groupby(['id1', 'id2'])['num'].transform('max').values\n",
    "df_smp['g3'] = g3\n",
    "df_smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>num</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "      <th>g4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>c</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>a</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>c</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id1 id2  num  g1  g2  g3   g4\n",
       "0   A   a    1   4   2   2  1.5\n",
       "1   A   a    2   4   2   2  1.5\n",
       "2   A   b    3   4   2   4  3.5\n",
       "3   A   b    4   4   2   4  3.5\n",
       "4   B   a    5   3   1   5  5.0\n",
       "5   B   c    6   3   2   7  6.5\n",
       "6   B   c    7   3   2   7  6.5\n",
       "7   C   a    8   2   1   8  8.0\n",
       "8   C   c    9   2   1   9  9.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean\n",
    "g4 = df_smp.groupby(['id1', 'id2'])['num'].transform('mean').values\n",
    "df_smp['g4'] = g4\n",
    "df_smp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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>0</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>0</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>0</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>0</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>0</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",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# == について\n",
    "# 以下は同じ意味\n",
    "\n",
    "_ = df_iris[df_iris['target']==0]\n",
    "_ = df_iris[df_iris['target'].isin([0])]\n",
    "\n",
    "_ = df_iris.query('target==0')\n",
    "_ = df_iris.query('target in [0]')\n",
    "_.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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>0</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>0</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>0</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>0</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>0</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",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以下は同じ意味\n",
    "_ = df_iris[(df_iris['target']==0) | (df_iris['target']==1)]\n",
    "_ = df_iris[df_iris['target'].isin([0, 1])]\n",
    "\n",
    "_ = df_iris.query('target in [0, 1]')\n",
    "_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Usefull Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reduce_mem_usage\n",
    "- DataFrame のメモリの使用領域の最適化\n",
    "- ref: https://qiita.com/nannoki/items/2a8934de31ad2258439d\n",
    "- 例えば、データフレーム内のある列のデータは、本当にint32型が好ましいのかなどをチェックしている。そこまでのビット数が必要でないのであれば、int16型にするなどの変更が行われている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is      : {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def do_it(dict_):\n",
    "    df = pd.DataFrame(dict_.data)\n",
    "    df = reduce_mem_usage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is      : 0.05 MB\n",
      "Memory usage after optimization is: 0.01 MB\n",
      "Decreased by 74.9%\n",
      "--------------------------------------------------------------------------------\n",
      "Memory usage of dataframe is      : 0.13 MB\n",
      "Memory usage after optimization is: 0.03 MB\n",
      "Decreased by 75.0%\n",
      "--------------------------------------------------------------------------------\n",
      "Memory usage of dataframe is      : 0.88 MB\n",
      "Memory usage after optimization is: 0.22 MB\n",
      "Decreased by 75.0%\n",
      "--------------------------------------------------------------------------------\n",
      "Memory usage of dataframe is      : 0.00 MB\n",
      "Memory usage after optimization is: 0.00 MB\n",
      "Decreased by 73.8%\n"
     ]
    }
   ],
   "source": [
    "# sklearn のデータ・セットで確認\n",
    "do_it(load_boston())\n",
    "print('-' * 80)\n",
    "do_it(load_breast_cancer())\n",
    "print('-' * 80)\n",
    "do_it(load_digits())\n",
    "print('-' * 80)\n",
    "do_it(load_iris())"
   ]
  },
  {
   "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": []
  }
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