JupyterNotebookMastery.ipynb

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     "start_time": "2024-02-25T19:45:14.475879Z"
    },
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Keyboard Shortcuts\n",
    "\n",
    "## Command Mode (press `Esc` to enable)\n",
    "\n",
    "- `Enter`: enter edit mode\n",
    "- `Shift` + `Enter`: run the cell, select below\n",
    "- `Ctrl` + `Enter`: run the cell\n",
    "- `Alt` + `Enter`: run the cell, insert below\n",
    "- `Y`: change cell to code\n",
    "- `M`: change cell to markdown\n",
    "- `A`: insert cell above\n",
    "- `B`: insert cell below\n",
    "- `D`, `D` (press the key twice): delete selected cell\n",
    "- `Z`: undo cell deletion\n",
    "- `Shift` + `M`: merge selected cells, or current cell with cell below if only one cell is selected\n",
    "- `Ctrl` + `S`: save and checkpoint\n",
    "- `L`: toggle line numbers\n",
    "- `Shift` + `L`: toggle line numbers in all cells (when in command mode)\n",
    "- `Up` or `K`: select cell above\n",
    "- `Down` or `J`: select cell below\n",
    "\n",
    "## Edit Mode (press `Enter` to enable)\n",
    "\n",
    "- `Esc`: enter command mode\n",
    "- `Shift` + `Enter`: run the cell, select below\n",
    "- `Ctrl` + `Enter`: run the cell\n",
    "- `Alt` + `Enter`: run the cell, insert below\n",
    "- `Ctrl` + `Shift` + `-`: split the cell at cursor\n",
    "- `Ctrl` + `Z`: undo last text edit\n",
    "- `Ctrl` + `Shift` + `Z` or `Ctrl` + `Y`: redo last text edit\n",
    "- `Ctrl` + `Home`: go to cell start\n",
    "- `Ctrl` + `End`: go to cell end\n",
    "- `Ctrl` + `Left`: go one word left\n",
    "- `Ctrl` + `Right`: go one word right\n",
    "- `Ctrl` + `Backspace`: delete word before\n",
    "- `Ctrl` + `Delete`: delete word after\n",
    "- `Tab`: code completion or indent\n",
    "- `Shift` + `Tab`: tooltip\n",
    "- `Ctrl` + `]`: indent\n",
    "- `Ctrl` + `[`: dedent\n",
    "- `Ctrl` + `A`: select all\n",
    "- `Ctrl` + `Shift` + `K`: comment out the selected lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b60d718-3ff0-437b-aa6c-c40b7042ab81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\n"
     ]
    }
   ],
   "source": [
    "# Utilities\n",
    "!cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "882baa8f-95ba-4ced-823c-b4e1bffde671",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-25T19:45:14.482925Z",
     "start_time": "2024-02-25T19:45:14.475879Z"
    }
   },
   "outputs": [],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f506e92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\admin\\anaconda3\\lib\\site-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f7cd023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reinitialized existing Git repository in C:/Users/admin/.git/\n"
     ]
    }
   ],
   "source": [
    "!git init"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d7bf004",
   "metadata": {},
   "source": [
    "## Display all the rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a58cdaf",
   "metadata": {},
   "outputs": [
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     "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",
       "        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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "279e33b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a833d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#You will get all row\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f9f2664",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c022bf02",
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
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       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
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       "..           ...       ...     ...   \n",
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       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
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    }
   ],
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      "text/plain": [
       "      c0  c1  c2  c3  c4  c5  c6  c7  c8  c9  ...  c55  c56  c57  c58  c59  \\\n",
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       "\n",
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       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/prasertcbs/basic-dataset/master/MNIST.csv')\n",
    "df"
   ]
  },
  {
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   "id": "1c77cfe7",
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   "outputs": [],
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       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>1792</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      c0  c1  c2  c3  c4  c5  c6  c7  c8  c9  c10  c11  c12  c13  c14  c15  \\\n",
       "0      0   0   5  13   9   1   0   0   0   0   13   15   10   15    5    0   \n",
       "1      0   0   0  12  13   5   0   0   0   0    0   11   16    9    0    0   \n",
       "2      0   0   0   4  15  12   0   0   0   0    3   16   15   14    0    0   \n",
       "3      0   0   7  15  13   1   0   0   0   8   13    6   15    4    0    0   \n",
       "4      0   0   0   1  11   0   0   0   0   0    0    7    8    0    0    0   \n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ...  ...  ...  ...  ...   \n",
       "1792   0   0   4  10  13   6   0   0   0   1   16   14   12   16    3    0   \n",
       "1793   0   0   6  16  13  11   1   0   0   0   16   15   12   16    1    0   \n",
       "1794   0   0   1  11  15   1   0   0   0   0   13   16    8    2    1    0   \n",
       "1795   0   0   2  10   7   0   0   0   0   0   14   16   16   15    1    0   \n",
       "1796   0   0  10  14   8   1   0   0   0   2   16   14    6    1    0    0   \n",
       "\n",
       "      c16  c17  c18  c19  c20  c21  c22  c23  c24  c25  c26  c27  c28  c29  \\\n",
       "0       0    3   15    2    0   11    8    0    0    4   12    0    0    8   \n",
       "1       0    0    3   15   16    6    0    0    0    7   15   16   16    2   \n",
       "2       0    0    8   13    8   16    0    0    0    0    1    6   15   11   \n",
       "3       0    2    1   13   13    0    0    0    0    0    2   15   11    1   \n",
       "4       0    0    1   13    6    2    2    0    0    0    7   15    0    9   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "1792    0    4   16    6    3   16    4    0    0    0   12   16   16   16   \n",
       "1793    0    3   16    7    0   13    6    0    0    4   16    0    0   10   \n",
       "1794    0    0   16   15   10   16    5    0    0    0    8   16   16    7   \n",
       "1795    0    4   16    7    3   16    7    0    0    5   16   10    7   16   \n",
       "1796    0    0   15   15    8   15    0    0    0    0    5   16   16   10   \n",
       "\n",
       "      c30  c31  c32  c33  c34  c35  c36  c37  c38  c39  c40  c41  c42  c43  \\\n",
       "0       8    0    0    5    8    0    0    9    8    0    0    4   11    0   \n",
       "1       0    0    0    0    1   16   16    3    0    0    0    0    1   16   \n",
       "2       0    0    0    1    8   13   15    1    0    0    0    9   16   16   \n",
       "3       0    0    0    0    0    1   12   12    1    0    0    0    0    0   \n",
       "4       8    0    0    5   16   10    0   16    6    0    0    4   15   16   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "1792    5    0    0    0    0    4    4   16    8    0    0    0    0    0   \n",
       "1793    8    0    0    8   16    0    0   14    6    0    0    5   16    7   \n",
       "1794    0    0    0    0    9   16   16    4    0    0    0    0   16   14   \n",
       "1795    4    0    0    0    5   14   14   16    4    0    0    0    0    0   \n",
       "1796    0    0    0    0   12   15   15   12    0    0    0    4   16    6   \n",
       "\n",
       "      c44  c45  c46  c47  c48  c49  c50  c51  c52  c53  c54  c55  c56  c57  \\\n",
       "0       1   12    7    0    0    2   14    5   10   12    0    0    0    0   \n",
       "1      16    6    0    0    0    0    1   16   16    6    0    0    0    0   \n",
       "2       5    0    0    0    0    3   13   16   16   11    5    0    0    0   \n",
       "3       1   10    8    0    0    0    8    4    5   14    9    0    0    0   \n",
       "4      13   16    1    0    0    0    0    3   15   10    0    0    0    0   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "1792    0   15    5    0    0    0    5    7    7   16    4    0    0    0   \n",
       "1793    9   16    5    0    0    1   15   16   16   16    1    0    0    0   \n",
       "1794   16   15    0    0    0    0   15   15   15   16    0    0    0    0   \n",
       "1795    0   16    2    0    0    0    4    7    7   16    2    0    0    0   \n",
       "1796    4   16    6    0    0    8   16   10    8   16    8    0    0    1   \n",
       "\n",
       "      c58  c59  c60  c61  c62  c63  target  \n",
       "0       6   13   10    0    0    0       0  \n",
       "1       0   11   16   10    0    0       1  \n",
       "2       0    3   11   16    9    0       2  \n",
       "3       7   13   13    9    0    0       3  \n",
       "4       0    2   16    4    0    0       4  \n",
       "...   ...  ...  ...  ...  ...  ...     ...  \n",
       "1792    2   14   15    9    0    0       9  \n",
       "1793    6   16   14    6    0    0       0  \n",
       "1794    2    9   13    6    0    0       8  \n",
       "1795    5   12   16   12    0    0       9  \n",
       "1796    8   12   14   12    1    0       8  \n",
       "\n",
       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f56c29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7ee8559",
   "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>c0</th>\n",
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       "      <th>c4</th>\n",
       "      <th>...</th>\n",
       "      <th>c60</th>\n",
       "      <th>c61</th>\n",
       "      <th>c62</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      c0  c1  c2  c3  c4  ...  c60  c61  c62  c63  target\n",
       "0      0   0   5  13   9  ...   10    0    0    0       0\n",
       "1      0   0   0  12  13  ...   16   10    0    0       1\n",
       "2      0   0   0   4  15  ...   11   16    9    0       2\n",
       "3      0   0   7  15  13  ...   13    9    0    0       3\n",
       "4      0   0   0   1  11  ...   16    4    0    0       4\n",
       "...   ..  ..  ..  ..  ..  ...  ...  ...  ...  ...     ...\n",
       "1792   0   0   4  10  13  ...   15    9    0    0       9\n",
       "1793   0   0   6  16  13  ...   14    6    0    0       0\n",
       "1794   0   0   1  11  15  ...   13    6    0    0       8\n",
       "1795   0   0   2  10   7  ...   16   12    0    0       9\n",
       "1796   0   0  10  14   8  ...   14   12    1    0       8\n",
       "\n",
       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563eb658",
   "metadata": {},
   "source": [
    "## Supress Warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "15653cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n",
    "\n",
    "# Trying to modify a slice of the DataFrame directly\n",
    "subset = df[df['A'] > 1]\n",
    "subset['B'] = subset['B'] + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47fc3b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29274f9d",
   "metadata": {},
   "source": [
    "## Print all variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb900288",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2fd57f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.describe()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "95df40d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a22127a2",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "df.info()\n",
    "a=2\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcee60f8",
   "metadata": {},
   "source": [
    "## Print output of any other cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a587da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Out[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a70116c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e414c610",
   "metadata": {},
   "source": [
    "## Close long output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da2f42ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(max_row, 20)\n",
    "Out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db164c8-50c5-40d7-a035-cb276378967e",
   "metadata": {},
   "source": [
    " ### Remove extra output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "63e3c763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25abafc2810>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1, 2, 3], [4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1ef3459f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3], [4, 5, 6]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107f2cb1",
   "metadata": {},
   "source": [
    "# Magic Commands\n",
    "\n",
    "**%** is used for **line magics**. Line magic commands apply only to the rest of the line on which they are used.\n",
    "\n",
    "**%%** is used for **cell magics**. Cell magic commands apply to the entire cell in which they are the first line. This means that everything below the %% command (up to the end of the cell) is considered part of the command. Cell magics allow you to write more complex or multi-line commands, affecting or transforming the entire cell's content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a46ad671",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "076b04ed",
   "metadata": {},
   "source": [
    "## %time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7c3264e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 93.8 ms\n",
      "Wall time: 105 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "499999500000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of using %time for a single line of code\n",
    "%time sum(range(1000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2ab09877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "499999500000\n",
      "CPU times: total: 312 ms\n",
      "Wall time: 730 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Example of using %%time for an entire cell\n",
    "total = 0\n",
    "for i in range(1000000):\n",
    "    total += i\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3be9ad34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.8 ms ± 2.96 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(range(1000000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c126913a",
   "metadata": {},
   "source": [
    "## %run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16bad994",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run sum.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360e19da",
   "metadata": {},
   "source": [
    "## %% writefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9488783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing example.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile example.txt\n",
    "\n",
    "Hello, this is the first line of the file.\n",
    "And this is the second line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ba31f12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing hello_world.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile hello_world.py\n",
    "\n",
    "def hello_world():\n",
    "    print(\"Hello, world!\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    hello_world()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6dc7ea7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, world!\n"
     ]
    }
   ],
   "source": [
    "%run hello_world.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17161834",
   "metadata": {},
   "source": [
    "## %%html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd0c3d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"color: red; font-size: 20px;\">Hello, world!</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<div style=\"color: red; font-size: 20px;\">Hello, world!</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "414a7fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static.remove.bg/sample-gallery/graphics/bird-thumbnail.jpg\" alt=\"Sample Image\" width=\"300\">\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src=\"https://static.remove.bg/sample-gallery/graphics/bird-thumbnail.jpg\" alt=\"Sample Image\" width=\"300\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "298a761c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\">\n",
       "  <tr>\n",
       "    <th>Firstname</th>\n",
       "    <th>Lastname</th>\n",
       "    <th>Age</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>John</td>\n",
       "    <td>Doe</td>\n",
       "    <td>50</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>Jane</td>\n",
       "    <td>Doe</td>\n",
       "    <td>45</td>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<table border=\"1\">\n",
    "  <tr>\n",
    "    <th>Firstname</th>\n",
    "    <th>Lastname</th>\n",
    "    <th>Age</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>John</td>\n",
    "    <td>Doe</td>\n",
    "    <td>50</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>Jane</td>\n",
    "    <td>Doe</td>\n",
    "    <td>45</td>\n",
    "  </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "08f9fc4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<button onclick=\"alert('Hello, world!')\">Click Me!</button>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<button onclick=\"alert('Hello, world!')\">Click Me!</button>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1a5a2e",
   "metadata": {},
   "source": [
    "## %%javascript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bfd18832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "alert('Hello, Jupyter!');\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "alert('Hello, Jupyter!');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8eb720ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<select id=\"dataSelection\">\n",
       "    <option value=\"data1\">Dataset 1</option>\n",
       "    <option value=\"data2\">Dataset 2</option>\n",
       "    <option value=\"data3\">Dataset 3</option>\n",
       "</select>\n",
       "<div id=\"displayArea\">Selected Dataset Information will appear here.</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<select id=\"dataSelection\">\n",
    "    <option value=\"data1\">Dataset 1</option>\n",
    "    <option value=\"data2\">Dataset 2</option>\n",
    "    <option value=\"data3\">Dataset 3</option>\n",
    "</select>\n",
    "<div id=\"displayArea\">Selected Dataset Information will appear here.</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ad50aea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "// Function to update display based on selection\n",
       "function updateDisplay() {\n",
       "    var selection = document.getElementById('dataSelection').value;\n",
       "    var displayText = 'You selected ' + selection + ', displaying results...';\n",
       "    // This could be a place to dynamically update a chart or data visualization\n",
       "    document.getElementById('displayArea').innerText = displayText;\n",
       "}\n",
       "\n",
       "// Add event listener to dropdown\n",
       "document.getElementById('dataSelection').addEventListener('change', updateDisplay);\n",
       "\n",
       "// Initialize display\n",
       "updateDisplay();\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "\n",
    "// Function to update display based on selection\n",
    "function updateDisplay() {\n",
    "    var selection = document.getElementById('dataSelection').value;\n",
    "    var displayText = 'You selected ' + selection + ', displaying results...';\n",
    "    // This could be a place to dynamically update a chart or data visualization\n",
    "    document.getElementById('displayArea').innerText = displayText;\n",
    "}\n",
    "\n",
    "// Add event listener to dropdown\n",
    "document.getElementById('dataSelection').addEventListener('change', updateDisplay);\n",
    "\n",
    "// Initialize display\n",
    "updateDisplay();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ed9179",
   "metadata": {},
   "source": [
    "## %%latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "51cafcfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-25T19:36:02.092487Z",
     "start_time": "2024-02-25T19:36:02.084615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{align}\n",
       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1{c} \\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1{c} \\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
       "\\end{align}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\\begin{align}\n",
    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1{c} \\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1{c} \\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9011fba",
   "metadata": {},
   "source": [
    "# Jupyter Widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "213a85ca-eb79-4fee-900b-c21eb698ec64",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade jupyterlab ipywidgets\n",
    "!jupyter lab clean\n",
    "!jupyter lab build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd396663-7f1b-4d70-973d-2a19ca6fdecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "! jupyter labextension install @jupyter-widgets/jupyterlab-manager@2.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "defae234",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-25T19:28:20.142475Z",
     "start_time": "2024-02-25T19:28:20.116304Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "slider = widgets.IntSlider(\n",
    "    value=7,\n",
    "    min=0,\n",
    "    max=10,\n",
    "    step=1,\n",
    "    description='Test Slider:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    ")\n",
    "display(slider)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23179100",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-25T19:37:06.215974Z",
     "start_time": "2024-02-25T19:37:06.042608Z"
    },
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interactive\n",
    "\n",
    "def plot_sine_wave(frequency=1.0):\n",
    "    # Generate x values\n",
    "    x = np.linspace(0, 2 * np.pi, 100)\n",
    "    # Calculate y values based on the current frequency\n",
    "    y = np.sin(x * frequency)\n",
    "    \n",
    "    # Plotting the sine wave\n",
    "    plt.figure(figsize=(10,4))\n",
    "    plt.plot(x, y)\n",
    "    plt.ylim(-1.1, 1.1)\n",
    "    plt.title('Sine Wave')\n",
    "    plt.show()\n",
    "\n",
    "# Create an interactive widget for the sine wave frequency\n",
    "interactive_plot = interactive(plot_sine_wave, frequency=(1, 10, 0.1))\n",
    "display(interactive_plot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f7b5ebe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-25T19:30:26.036932Z",
     "start_time": "2024-02-25T19:30:25.913133Z"
    },
    "code_folding": [
     7,
     14
    ],
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc4bc131131e4b108cefcb54d294c24c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description='Marker Size:', max=30, min=5), FloatSlider(value=99.005…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from ipywidgets import interactive, IntSlider, FloatSlider\n",
    "\n",
    "# Generate a random dataset\n",
    "np.random.seed(42)\n",
    "df = pd.DataFrame({\n",
    "    'x': np.random.rand(100),\n",
    "    'y': np.random.rand(100),\n",
    "    'data_value': np.random.rand(100) * 100  # A column to filter by\n",
    "})\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot(marker_size, filter_value):\n",
    "    filtered_df = df[df['data_value'] < filter_value]\n",
    "    \n",
    "    fig = px.scatter(\n",
    "        filtered_df, x='x', y='y', size=np.full(len(filtered_df), marker_size),\n",
    "        size_max=marker_size, title='Interactive Scatter Plot'\n",
    "    )\n",
    "    fig.update_traces(marker=dict(opacity=0.8))\n",
    "    fig.update_layout(transition_duration=500)\n",
    "    fig.show()\n",
    "\n",
    "# Create widgets\n",
    "marker_size_slider = IntSlider(min=5, max=30, value=10, description='Marker Size:')\n",
    "filter_slider = FloatSlider(min=df['data_value'].min(), max=df['data_value'].max(), value=df['data_value'].max(), description='Filter Value:')\n",
    "\n",
    "# Display interactive plot\n",
    "interactive_plot = interactive(update_plot, marker_size=marker_size_slider, filter_value=filter_slider)\n",
    "display(interactive_plot)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd45f1ab",
   "metadata": {},
   "source": [
    "# Jupyter Extensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9859c76a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Enabling: jupyter_nbextensions_configurator\n",
      "- Writing config: C:\\Users\\Nitish\\.jupyter\n",
      "    - Validating...\n",
      "      jupyter_nbextensions_configurator 0.6.3 ok\n",
      "Enabling notebook nbextension nbextensions_configurator/config_menu/main...\n",
      "Enabling tree nbextension nbextensions_configurator/tree_tab/main...\n"
     ]
    }
   ],
   "source": [
    "!pip install jupyter_contrib_nbextensions\n",
    "!jupyter contrib nbextension install --user\n",
    "!jupyter nbextensions_configurator enable --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "074d3645",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Table of content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc9d4f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code Folding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "95d0ce29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hinterland\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abe6de08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "275ed5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hide Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dcacd01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable Inspector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00669339",
   "metadata": {},
   "source": [
    "# ChatGPT Extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fa3e49b-721f-4030-8dd6-9e6da61c9b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gpt_jupyterlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a0045b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explain\n",
    "\n",
    "def bubble_sort(arr):\n",
    "    n = len(arr)\n",
    "    # Traverse through all array elements\n",
    "    for i in range(n):\n",
    "        # Last i elements are already in place\n",
    "        for j in range(0, n-i-1):\n",
    "            # Traverse the array from 0 to n-i-1. Swap if the element found is greater\n",
    "            # than the next element\n",
    "            if arr[j] > arr[j+1]:\n",
    "                arr[j], arr[j+1] = arr[j+1], arr[j]\n",
    "    return arr\n",
    "\n",
    "# Example usage\n",
    "arr = [64, 34, 25, 12, 22, 11, 90]\n",
    "sorted_arr = bubble_sort(arr)\n",
    "print(\"Sorted array is:\", sorted_arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35144db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# debug\n",
    "def buggy_bubble_sort(arr):\n",
    "    n = len(arr)\n",
    "    for i in range(n):\n",
    "        # Intentional bug: Should be 'range(n-i-1)'\n",
    "        for j in range(n):\n",
    "            # Intentional bug: Missing condition to avoid index out of range error\n",
    "            if arr[j] > arr[j+1]:\n",
    "                # Intentional bug: Incorrect swapping logic\n",
    "                temp = arr[j]\n",
    "                arr[j] = arr[j+1]\n",
    "                arr[j+1] = temp\n",
    "    return arr\n",
    "\n",
    "# Example usage\n",
    "arr = [64, 34, 25, 12, 22, 11, 90]\n",
    "sorted_arr = buggy_bubble_sort(arr)\n",
    "print(\"Sorted array is:\", sorted_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "07b94260",
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete\n",
    "\n",
    "def bubble_sort(arr):\n",
    "    # Sorting logic to be implemented\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5cd1a8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "055f7cba89fe4270bcdba0f7c163b762",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10, description='Marker Size:', max=30, min=5), FloatSlider(value=99.005…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# question\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from ipywidgets import interactive, IntSlider, FloatSlider\n",
    "\n",
    "# Generate a random dataset\n",
    "np.random.seed(42)\n",
    "df = pd.DataFrame({\n",
    "    'x': np.random.rand(100),\n",
    "    'y': np.random.rand(100),\n",
    "    'data_value': np.random.rand(100) * 100  # A column to filter by\n",
    "})\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot(marker_size, filter_value):\n",
    "    filtered_df = df[df['data_value'] < filter_value]\n",
    "    \n",
    "    fig = px.scatter(\n",
    "        filtered_df, x='x', y='y', size=np.full(len(filtered_df), marker_size),\n",
    "        size_max=marker_size, title='Interactive Scatter Plot'\n",
    "    )\n",
    "    fig.update_traces(marker=dict(opacity=0.8))\n",
    "    fig.update_layout(transition_duration=500)\n",
    "    fig.show()\n",
    "\n",
    "# Create widgets\n",
    "marker_size_slider = IntSlider(min=5, max=30, value=10, description='Marker Size:')\n",
    "filter_slider = FloatSlider(min=df['data_value'].min(), max=df['data_value'].max(), value=df['data_value'].max(), description='Filter Value:')\n",
    "\n",
    "# Display interactive plot\n",
    "interactive_plot = interactive(update_plot, marker_size=marker_size_slider, filter_value=filter_slider)\n",
    "display(interactive_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b29913-4267-422a-bbf9-2c656d82fdcf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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  "language_info": {
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   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
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    "python": {
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     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
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