30 - DataFrames ข้อมูลตาราง.ipynb

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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![dataframe.png](attachment:dataframe.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) `dataframe` ชุดข้อมูลตาราง"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dataframe` เป็นชุดแบบตารางที่สร้างขึ้นด้วย `library` `pandas`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ข้อมูลของ `series` ประกอบด้วย 3 ส่วน คือ `data` `index` และ `column`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) `import` `pandas` นำ `pandas` มาใช้บน Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dataframe` เป็น `object` ที่ถูกสร้างขึ้นด้วย `library` `pandas` ดังนั้นจะใช้งานได้จะต้องเรียก `pandas` มาไว้บน Jupyter Notebook ก่อน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) สร้าง `dataframe` ผ่าน `pd.DataFrame()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สร้าง `dataframe` เปล่า"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) กำหนด `data` ด้วย `list` `object`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [1,2,3,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) กำหนด `index` ด้วย `list` `object`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = ['a','b','c','d']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) กำหนด `column` ด้วย `list` `object`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "column = ['column']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# DataFrames\n",
    "\n",
    "Dataframe เป็นเหมือนตารางที่เกิดจากการรวม series เข้าด้วยกันด้วย index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column\n",
       "a       1\n",
       "b       2\n",
       "c       3\n",
       "d       4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.DataFrame(data=data,index=index,columns=column)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7) การดึงข้อมูลด้วย `column`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ใช้สัญลักษณ์ `[]` เช่นเดียวกับการดึงข้อมูลจากทุก `object` ที่เราได้เรียนไปแล้ว พอดึงแล้วเราจะได้ `object` `series`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "Name: column, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table['column']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8) การดึงข้อมูลด้วย `column` และ `index`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ใช้สัญลักษณ์ `[]` เช่นเดียวกับการดึงข้อมูลจากทุก `object` ที่เราได้เรียนไปแล้ว พอดึงแล้วเราจะได้ `object` `series`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table['column']['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9) การดึงข้อมูลด้วย `index`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ใช้คำสั่ง `.loc[]` โดยใส่ `index` ที่ต้องการดึงไปใน `[]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "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>column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column\n",
       "a       1\n",
       "b       2\n",
       "c       3\n",
       "d       4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "column    1\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.loc['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10) การ filter ใน `dataframe`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "การ filter ใช้ `list` ของ `boolean` เป็นการกำหนดการโชว์หรือไม่โชว์บรรทัดนั้น"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column\n",
       "a       1\n",
       "b       2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[[True,True,False,False]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "โดยปกติเราจะสร้าง `list` ของ `boolean` ด้วยการเขียนเงื่อนไข"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     True\n",
       "b     True\n",
       "c    False\n",
       "d    False\n",
       "Name: column, dtype: bool"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table['column'] < 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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>column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column\n",
       "a       1\n",
       "b       2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[table['column'] < 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ติดต่อเราได้ที่\n",
    "\n",
    "Facebook  : www.facebook.com/ultimatepython\n",
    "\n",
    "Website   : www.ultimatepython.com\n",
    "\n",
    "Messenger : www.m.me/ultimatepython"
   ]
  }
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