{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5ff7131",
   "metadata": {},
   "source": [
    "# pandas: Create DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1236cf26",
   "metadata": {},
   "source": [
    "## Package Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d08380d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-02T18:59:30.443023Z",
     "iopub.status.busy": "2025-09-02T18:59:30.442784Z",
     "iopub.status.idle": "2025-09-02T18:59:30.661256Z",
     "shell.execute_reply": "2025-09-02T18:59:30.660733Z"
    },
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0637d85b",
   "metadata": {},
   "source": [
    "## Dataset Import"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b67820",
   "metadata": {},
   "source": [
    "The dataset used in this notebook is from [Kaggle - Pokemon](https://www.kaggle.com/datasets/abcsds/pokemon)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3332eedd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-02T18:59:30.662883Z",
     "iopub.status.busy": "2025-09-02T18:59:30.662738Z",
     "iopub.status.idle": "2025-09-02T18:59:30.667277Z",
     "shell.execute_reply": "2025-09-02T18:59:30.666711Z"
    },
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/Pokemon.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b6ef7ad",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2025-09-02T18:59:30.677097Z",
     "shell.execute_reply": "2025-09-02T18:59:30.676363Z"
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>VenusaurMega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>719</td>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>600</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>719</td>\n",
       "      <td>DiancieMega Diancie</td>\n",
       "      <td>Rock</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>700</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>160</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Confined</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Ghost</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>150</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>720</td>\n",
       "      <td>HoopaHoopa Unbound</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>Dark</td>\n",
       "      <td>680</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>170</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>721</td>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "      <td>Water</td>\n",
       "      <td>600</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       #                   Name   Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0      1              Bulbasaur    Grass  Poison    318  45      49       49   \n",
       "1      2                Ivysaur    Grass  Poison    405  60      62       63   \n",
       "2      3               Venusaur    Grass  Poison    525  80      82       83   \n",
       "3      3  VenusaurMega Venusaur    Grass  Poison    625  80     100      123   \n",
       "4      4             Charmander     Fire     NaN    309  39      52       43   \n",
       "..   ...                    ...      ...     ...    ...  ..     ...      ...   \n",
       "795  719                Diancie     Rock   Fairy    600  50     100      150   \n",
       "796  719    DiancieMega Diancie     Rock   Fairy    700  50     160      110   \n",
       "797  720    HoopaHoopa Confined  Psychic   Ghost    600  80     110       60   \n",
       "798  720     HoopaHoopa Unbound  Psychic    Dark    680  80     160       60   \n",
       "799  721              Volcanion     Fire   Water    600  80     110      120   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0         65       65     45           1      False  \n",
       "1         80       80     60           1      False  \n",
       "2        100      100     80           1      False  \n",
       "3        122      120     80           1      False  \n",
       "4         60       50     65           1      False  \n",
       "..       ...      ...    ...         ...        ...  \n",
       "795      100      150     50           6       True  \n",
       "796      160      110    110           6       True  \n",
       "797      150      130     70           6       True  \n",
       "798      170      130     80           6       True  \n",
       "799      130       90     70           6       True  \n",
       "\n",
       "[800 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb7929a",
   "metadata": {},
   "source": [
    "## Manually Create a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fe735d",
   "metadata": {},
   "source": [
    "From a Dictionary\\\n",
    "The columns order is the order of keys insertion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16659cf2",
   "metadata": {
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     "hide-output",
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   "outputs": [
    {
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       "   Column 1  Column 2\n",
       "0       100       300\n",
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   "source": [
    "df = pd.DataFrame({'Column 1': [100,200], 'Column 2': [300,400]})\n",
    "df"
   ]
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  {
   "cell_type": "markdown",
   "id": "7548cdfa",
   "metadata": {},
   "source": [
    "From a list of random values w/ column names:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d096ed3b",
   "metadata": {
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    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
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       "      <td>0.155808</td>\n",
       "      <td>0.426032</td>\n",
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       "      <td>0.220790</td>\n",
       "      <td>0.990389</td>\n",
       "      <td>0.747130</td>\n",
       "      <td>0.048883</td>\n",
       "      <td>0.967204</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.459662</td>\n",
       "      <td>0.909124</td>\n",
       "      <td>0.942143</td>\n",
       "      <td>0.585818</td>\n",
       "      <td>0.165209</td>\n",
       "      <td>0.396012</td>\n",
       "      <td>0.585996</td>\n",
       "      <td>0.012020</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.451281</td>\n",
       "      <td>0.454347</td>\n",
       "      <td>0.902485</td>\n",
       "      <td>0.801921</td>\n",
       "      <td>0.007451</td>\n",
       "      <td>0.954774</td>\n",
       "      <td>0.510324</td>\n",
       "      <td>0.733164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.027769</td>\n",
       "      <td>0.490229</td>\n",
       "      <td>0.210495</td>\n",
       "      <td>0.879985</td>\n",
       "      <td>0.370400</td>\n",
       "      <td>0.412179</td>\n",
       "      <td>0.689901</td>\n",
       "      <td>0.277350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e         f         g  \\\n",
       "0  0.155808  0.426032  0.828060  0.220790  0.990389  0.747130  0.048883   \n",
       "1  0.459662  0.909124  0.942143  0.585818  0.165209  0.396012  0.585996   \n",
       "2  0.451281  0.454347  0.902485  0.801921  0.007451  0.954774  0.510324   \n",
       "3  0.027769  0.490229  0.210495  0.879985  0.370400  0.412179  0.689901   \n",
       "\n",
       "          h  \n",
       "0  0.967204  \n",
       "1  0.012020  \n",
       "2  0.733164  \n",
       "3  0.277350  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.rand(4, 8), columns=list('abcdefgh'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe398e7",
   "metadata": {},
   "source": [
    "From a dictionary including Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6aff393b",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-09-02T18:59:30.693559Z"
    },
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     "hide-output",
     "scroll-output"
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    {
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       "<div>\n",
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       "   col1  col2\n",
       "0     0   NaN\n",
       "1     1   NaN\n",
       "2     2   2.0\n",
       "3     3   3.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'col1': [0,1,2,3], 'col2': pd.Series([2,3], index=[2,3])}, index=[0,1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571d74e9",
   "metadata": {},
   "source": [
    "From numpy ndarray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5a4f1cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-02T18:59:30.695111Z",
     "iopub.status.busy": "2025-09-02T18:59:30.695001Z",
     "iopub.status.idle": "2025-09-02T18:59:30.698608Z",
     "shell.execute_reply": "2025-09-02T18:59:30.698199Z"
    },
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  1  2  3\n",
       "1  4  5  6\n",
       "2  7  8  9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
    "                   columns=['a', 'b', 'c'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f53fc4",
   "metadata": {},
   "source": [
    "From a numpy ndarray that has labeled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca2cdf3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-02T18:59:30.699746Z",
     "iopub.status.busy": "2025-09-02T18:59:30.699641Z",
     "iopub.status.idle": "2025-09-02T18:59:30.703576Z",
     "shell.execute_reply": "2025-09-02T18:59:30.703168Z"
    },
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "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>c</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c  a\n",
       "0  3  1\n",
       "1  6  4\n",
       "2  9  7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array([(1,2,3), (4,5,6), (7,8,9)], dtype=[(\"a\", \"i4\"), (\"b\", \"i4\"), (\"c\", \"i4\")])\n",
    "df = pd.DataFrame(data=d, columns=['c', 'a'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654b5879",
   "metadata": {},
   "source": [
    "From Series/DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7d10002",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-02T18:59:30.704762Z",
     "iopub.status.busy": "2025-09-02T18:59:30.704652Z",
     "iopub.status.idle": "2025-09-02T18:59:30.708449Z",
     "shell.execute_reply": "2025-09-02T18:59:30.708072Z"
    },
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "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>hehe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hehe\n",
       "c     3\n",
       "a     1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series([1,2,3], index=['a','b','c'])\n",
    "df = pd.DataFrame(data=ser, index=['c', 'a'], columns=['hehe'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e125321a",
   "metadata": {},
   "source": [
    "If we construct from DataFrame, then the columns in the new DataFrame must be a subset of the original columns. If not, the new columns will be filled with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87bc4465",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-02T18:59:30.709768Z",
     "iopub.status.busy": "2025-09-02T18:59:30.709658Z",
     "iopub.status.idle": "2025-09-02T18:59:30.713634Z",
     "shell.execute_reply": "2025-09-02T18:59:30.713248Z"
    },
    "tags": [
     "hide-output",
     "scroll-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x\n",
      "c  3\n",
      "a  1 \n",
      "     z\n",
      "c NaN\n",
      "a NaN\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame([1,2,3], index=['a','b','c'], columns=['x'])\n",
    "df2 = pd.DataFrame(data=df1, index=['c', 'a'])\n",
    "df3 = pd.DataFrame(data=df1, index=['c', 'a'], columns=['z'])\n",
    "print(df2, '\\n',df3)"
   ]
  }
 ],
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