Pandas_class1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14e6563a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16fa0869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "947fe117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.3.4)\n",
      "Collecting pandas\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'c:\\\\programdata\\\\anaconda3\\\\lib\\\\site-packages\\\\pandas-1.3.4.dist-info\\\\direct_url.json'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Downloading pandas-1.4.3-cp39-cp39-win_amd64.whl (10.6 MB)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.20.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Installing collected packages: pandas\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 1.3.4\n",
      "    Uninstalling pandas-1.3.4:\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cf670ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd04126d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas==1.4.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e33682",
   "metadata": {},
   "source": [
    "# Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36bcf460",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnic = [1,2,3,4]\n",
    "print(type(cnic))\n",
    "cnic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bafedb35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnic = pd.Series([1,2,3,4])\n",
    "print(type(cnic))\n",
    "cnic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "830bff03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-08-19', '2022-08-20', '2022-08-21', '2022-08-22',\n",
       "               '2022-08-23', '2022-08-24', '2022-08-25', '2022-08-26',\n",
       "               '2022-08-27', '2022-08-28',\n",
       "               ...\n",
       "               '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',\n",
       "               '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',\n",
       "               '2022-12-30', '2022-12-31'],\n",
       "              dtype='datetime64[ns]', length=135, freq='D')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range(\"2022-08-19\",\"2022-12-31\")\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e6299bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-08-19 00:00:00', '2022-08-19 00:01:00',\n",
       "               '2022-08-19 00:02:00', '2022-08-19 00:03:00',\n",
       "               '2022-08-19 00:04:00', '2022-08-19 00:05:00',\n",
       "               '2022-08-19 00:06:00', '2022-08-19 00:07:00',\n",
       "               '2022-08-19 00:08:00', '2022-08-19 00:09:00'],\n",
       "              dtype='datetime64[ns]', freq='T')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2022-08-19\",freq='min', periods=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5163aa5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-12-31', '2023-12-31', '2024-12-31', '2025-12-31',\n",
       "               '2026-12-31', '2027-12-31', '2028-12-31', '2029-12-31',\n",
       "               '2030-12-31', '2031-12-31'],\n",
       "              dtype='datetime64[ns]', freq='A-DEC')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2022-08-19\",freq='Y', periods=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fc0afa",
   "metadata": {},
   "source": [
    "# DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "129030f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.704537</td>\n",
       "      <td>-1.345460</td>\n",
       "      <td>1.381005</td>\n",
       "      <td>-0.587416</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176843</td>\n",
       "      <td>-0.729959</td>\n",
       "      <td>-0.057957</td>\n",
       "      <td>0.733902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.784994</td>\n",
       "      <td>-0.753315</td>\n",
       "      <td>0.482396</td>\n",
       "      <td>0.400130</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.399831</td>\n",
       "      <td>-0.792228</td>\n",
       "      <td>0.322202</td>\n",
       "      <td>-2.204300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.145672</td>\n",
       "      <td>-0.166504</td>\n",
       "      <td>0.533493</td>\n",
       "      <td>1.644104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.163829</td>\n",
       "      <td>-2.029823</td>\n",
       "      <td>-0.581694</td>\n",
       "      <td>-1.146002</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.704537 -1.345460  1.381005 -0.587416\n",
       "1 -1.176843 -0.729959 -0.057957  0.733902\n",
       "2  1.784994 -0.753315  0.482396  0.400130\n",
       "3 -0.399831 -0.792228  0.322202 -2.204300\n",
       "4 -1.145672 -0.166504  0.533493  1.644104\n",
       "5 -2.163829 -2.029823 -0.581694 -1.146002"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.randn(6, 4))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ea26cec7",
   "metadata": {},
   "source": [
    "np.random.randint(1,100, size=(6,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e04e8f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>contact</th>\n",
       "      <th>cnic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.521454</td>\n",
       "      <td>-0.232436</td>\n",
       "      <td>1.292814</td>\n",
       "      <td>0.191671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.950448</td>\n",
       "      <td>2.071522</td>\n",
       "      <td>-0.650411</td>\n",
       "      <td>-1.032165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.264723</td>\n",
       "      <td>-1.434084</td>\n",
       "      <td>1.641433</td>\n",
       "      <td>-0.251579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.721577</td>\n",
       "      <td>0.465493</td>\n",
       "      <td>1.788820</td>\n",
       "      <td>-0.826306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.058510</td>\n",
       "      <td>-0.282706</td>\n",
       "      <td>-0.163968</td>\n",
       "      <td>-1.304708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.573106</td>\n",
       "      <td>-1.157909</td>\n",
       "      <td>-0.955213</td>\n",
       "      <td>-0.739025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id      name   contact      cnic\n",
       "0 -0.521454 -0.232436  1.292814  0.191671\n",
       "1 -0.950448  2.071522 -0.650411 -1.032165\n",
       "2  0.264723 -1.434084  1.641433 -0.251579\n",
       "3  0.721577  0.465493  1.788820 -0.826306\n",
       "4  1.058510 -0.282706 -0.163968 -1.304708\n",
       "5 -0.573106 -1.157909 -0.955213 -0.739025"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.randn(6, 4), columns=[\"id\",'name','contact','cnic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fcdc80a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-19</th>\n",
       "      <td>-0.089898</td>\n",
       "      <td>0.376738</td>\n",
       "      <td>0.024094</td>\n",
       "      <td>-1.089194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-20</th>\n",
       "      <td>-0.913717</td>\n",
       "      <td>0.260547</td>\n",
       "      <td>1.376271</td>\n",
       "      <td>0.256325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-21</th>\n",
       "      <td>-1.640352</td>\n",
       "      <td>1.057048</td>\n",
       "      <td>-1.436458</td>\n",
       "      <td>1.354261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-22</th>\n",
       "      <td>1.122673</td>\n",
       "      <td>-0.370800</td>\n",
       "      <td>0.358803</td>\n",
       "      <td>0.700202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23</th>\n",
       "      <td>0.166491</td>\n",
       "      <td>0.313519</td>\n",
       "      <td>3.180983</td>\n",
       "      <td>0.547978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-24</th>\n",
       "      <td>0.318518</td>\n",
       "      <td>0.587981</td>\n",
       "      <td>0.162551</td>\n",
       "      <td>-1.386533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-08-19 -0.089898  0.376738  0.024094 -1.089194\n",
       "2022-08-20 -0.913717  0.260547  1.376271  0.256325\n",
       "2022-08-21 -1.640352  1.057048 -1.436458  1.354261\n",
       "2022-08-22  1.122673 -0.370800  0.358803  0.700202\n",
       "2022-08-23  0.166491  0.313519  3.180983  0.547978\n",
       "2022-08-24  0.318518  0.587981  0.162551 -1.386533"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20220819', periods=6)\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6,4), \n",
    "                  index=dates,\n",
    "                 columns=list(\"ABCD\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5d52c4c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-08-19', '2022-08-20', '2022-08-21', '2022-08-22',\n",
       "               '2022-08-23', '2022-08-24'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad4efcb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': [1, 2, 3, 4],\n",
       " 'name': ['A', 'B', 'C', nan],\n",
       " 'course': ['ai', 'ai', 'ai', 'ai']}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"id\":[1,2,3,4],\n",
    "       \"name\":['A',\"B\",\"C\",np.nan],\n",
    "       'course':['ai','ai','ai','ai']}\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2b6ca19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>course</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>ai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>ai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>ai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ai</td>\n",
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      "text/plain": [
       "   id name course\n",
       "0   1    A     ai\n",
       "1   2    B     ai\n",
       "2   3    C     ai\n",
       "3   4  NaN     ai"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d80356b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id         int64\n",
       "name      object\n",
       "course    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "64bc66a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      4 non-null      int64 \n",
      " 1   name    3 non-null      object\n",
      " 2   course  4 non-null      object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 224.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bbc6a3f8",
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>course</th>\n",
       "      <th>fee</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.020897</td>\n",
       "      <td>-0.008004</td>\n",
       "      <td>0.035131</td>\n",
       "      <td>0.016440</td>\n",
       "      <td>-0.004307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.009259</td>\n",
       "      <td>0.975446</td>\n",
       "      <td>0.992343</td>\n",
       "      <td>1.008654</td>\n",
       "      <td>1.003551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.107984</td>\n",
       "      <td>-3.324405</td>\n",
       "      <td>-2.751341</td>\n",
       "      <td>-3.703084</td>\n",
       "      <td>-2.881385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.620666</td>\n",
       "      <td>-0.645343</td>\n",
       "      <td>-0.657843</td>\n",
       "      <td>-0.648753</td>\n",
       "      <td>-0.722361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.020929</td>\n",
       "      <td>-0.008035</td>\n",
       "      <td>0.012826</td>\n",
       "      <td>0.014489</td>\n",
       "      <td>-0.019191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.698245</td>\n",
       "      <td>0.648941</td>\n",
       "      <td>0.702314</td>\n",
       "      <td>0.710845</td>\n",
       "      <td>0.668833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.713492</td>\n",
       "      <td>2.995384</td>\n",
       "      <td>3.481271</td>\n",
       "      <td>3.098351</td>\n",
       "      <td>3.122432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id         name       course          fee       timing\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.020897    -0.008004     0.035131     0.016440    -0.004307\n",
       "std       1.009259     0.975446     0.992343     1.008654     1.003551\n",
       "min      -3.107984    -3.324405    -2.751341    -3.703084    -2.881385\n",
       "25%      -0.620666    -0.645343    -0.657843    -0.648753    -0.722361\n",
       "50%       0.020929    -0.008035     0.012826     0.014489    -0.019191\n",
       "75%       0.698245     0.648941     0.702314     0.710845     0.668833\n",
       "max       2.713492     2.995384     3.481271     3.098351     3.122432"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()# only return numeric data type column details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed932e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ai\n",
       "1    ai\n",
       "2    ai\n",
       "3    ai\n",
       "Name: course, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.course # column name must be written withouspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "51bde6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8e1806be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      A\n",
       "1      B\n",
       "2      C\n",
       "3    NaN\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c1b7c073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['id'] # we can call column name with and without space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a75e9110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      A\n",
       "1      B\n",
       "2      C\n",
       "3    NaN\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c532863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ai\n",
       "1    ai\n",
       "2    ai\n",
       "3    ai\n",
       "Name: course, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['course']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a6b8c1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>course</th>\n",
       "      <th>fee</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.842016</td>\n",
       "      <td>-0.579071</td>\n",
       "      <td>-0.303341</td>\n",
       "      <td>1.104890</td>\n",
       "      <td>0.459728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.706828</td>\n",
       "      <td>-1.019872</td>\n",
       "      <td>-0.552877</td>\n",
       "      <td>-0.497479</td>\n",
       "      <td>0.238497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.309599</td>\n",
       "      <td>1.008439</td>\n",
       "      <td>0.258514</td>\n",
       "      <td>0.325916</td>\n",
       "      <td>-0.262803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.264834</td>\n",
       "      <td>0.410003</td>\n",
       "      <td>1.185528</td>\n",
       "      <td>0.728187</td>\n",
       "      <td>0.995847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.014250</td>\n",
       "      <td>1.190572</td>\n",
       "      <td>-0.197618</td>\n",
       "      <td>-1.882747</td>\n",
       "      <td>-1.150723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.692439</td>\n",
       "      <td>0.384439</td>\n",
       "      <td>-1.025914</td>\n",
       "      <td>0.878569</td>\n",
       "      <td>-1.196510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.071553</td>\n",
       "      <td>1.586133</td>\n",
       "      <td>1.189026</td>\n",
       "      <td>0.830562</td>\n",
       "      <td>1.389735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.006909</td>\n",
       "      <td>0.429845</td>\n",
       "      <td>0.885719</td>\n",
       "      <td>-0.748293</td>\n",
       "      <td>-1.421737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>-0.761227</td>\n",
       "      <td>-1.015376</td>\n",
       "      <td>-1.599916</td>\n",
       "      <td>0.175126</td>\n",
       "      <td>-0.547677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.708633</td>\n",
       "      <td>0.272250</td>\n",
       "      <td>-1.284934</td>\n",
       "      <td>0.742597</td>\n",
       "      <td>0.306220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id      name    course       fee    timing\n",
       "0    0.842016 -0.579071 -0.303341  1.104890  0.459728\n",
       "1   -1.706828 -1.019872 -0.552877 -0.497479  0.238497\n",
       "2    0.309599  1.008439  0.258514  0.325916 -0.262803\n",
       "3   -0.264834  0.410003  1.185528  0.728187  0.995847\n",
       "4    1.014250  1.190572 -0.197618 -1.882747 -1.150723\n",
       "..        ...       ...       ...       ...       ...\n",
       "995  0.692439  0.384439 -1.025914  0.878569 -1.196510\n",
       "996  0.071553  1.586133  1.189026  0.830562  1.389735\n",
       "997  1.006909  0.429845  0.885719 -0.748293 -1.421737\n",
       "998 -0.761227 -1.015376 -1.599916  0.175126 -0.547677\n",
       "999  0.708633  0.272250 -1.284934  0.742597  0.306220\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000,5), \n",
    "                  columns=['id','name','course','fee','timing'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1e718ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.303341</td>\n",
       "      <td>1.104890</td>\n",
       "      <td>0.459728</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.706828</td>\n",
       "      <td>-1.019872</td>\n",
       "      <td>-0.552877</td>\n",
       "      <td>-0.497479</td>\n",
       "      <td>0.238497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.309599</td>\n",
       "      <td>1.008439</td>\n",
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       "      <td>0.325916</td>\n",
       "      <td>-0.262803</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.185528</td>\n",
       "      <td>0.728187</td>\n",
       "      <td>0.995847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.014250</td>\n",
       "      <td>1.190572</td>\n",
       "      <td>-0.197618</td>\n",
       "      <td>-1.882747</td>\n",
       "      <td>-1.150723</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id      name    course       fee    timing\n",
       "0  0.842016 -0.579071 -0.303341  1.104890  0.459728\n",
       "1 -1.706828 -1.019872 -0.552877 -0.497479  0.238497\n",
       "2  0.309599  1.008439  0.258514  0.325916 -0.262803\n",
       "3 -0.264834  0.410003  1.185528  0.728187  0.995847\n",
       "4  1.014250  1.190572 -0.197618 -1.882747 -1.150723"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()# top 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ca9e7c9f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.175126</td>\n",
       "      <td>-0.547677</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.708633</td>\n",
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       "      <td>-1.284934</td>\n",
       "      <td>0.742597</td>\n",
       "      <td>0.306220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id      name    course       fee    timing\n",
       "995  0.692439  0.384439 -1.025914  0.878569 -1.196510\n",
       "996  0.071553  1.586133  1.189026  0.830562  1.389735\n",
       "997  1.006909  0.429845  0.885719 -0.748293 -1.421737\n",
       "998 -0.761227 -1.015376 -1.599916  0.175126 -0.547677\n",
       "999  0.708633  0.272250 -1.284934  0.742597  0.306220"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7e56b00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>course</th>\n",
       "      <th>fee</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.842016</td>\n",
       "      <td>-0.579071</td>\n",
       "      <td>-0.303341</td>\n",
       "      <td>1.104890</td>\n",
       "      <td>0.459728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.706828</td>\n",
       "      <td>-1.019872</td>\n",
       "      <td>-0.552877</td>\n",
       "      <td>-0.497479</td>\n",
       "      <td>0.238497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.309599</td>\n",
       "      <td>1.008439</td>\n",
       "      <td>0.258514</td>\n",
       "      <td>0.325916</td>\n",
       "      <td>-0.262803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.264834</td>\n",
       "      <td>0.410003</td>\n",
       "      <td>1.185528</td>\n",
       "      <td>0.728187</td>\n",
       "      <td>0.995847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.014250</td>\n",
       "      <td>1.190572</td>\n",
       "      <td>-0.197618</td>\n",
       "      <td>-1.882747</td>\n",
       "      <td>-1.150723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.558091</td>\n",
       "      <td>0.876454</td>\n",
       "      <td>0.884595</td>\n",
       "      <td>0.408703</td>\n",
       "      <td>1.236471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.705080</td>\n",
       "      <td>0.056720</td>\n",
       "      <td>-0.730886</td>\n",
       "      <td>-0.144292</td>\n",
       "      <td>1.058086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.111384</td>\n",
       "      <td>-1.004318</td>\n",
       "      <td>-1.095346</td>\n",
       "      <td>-0.118461</td>\n",
       "      <td>1.861170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.121031</td>\n",
       "      <td>-0.425330</td>\n",
       "      <td>0.223732</td>\n",
       "      <td>-0.781417</td>\n",
       "      <td>0.979076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.832034</td>\n",
       "      <td>-0.703051</td>\n",
       "      <td>0.551663</td>\n",
       "      <td>-0.576635</td>\n",
       "      <td>-1.369211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id      name    course       fee    timing\n",
       "0  0.842016 -0.579071 -0.303341  1.104890  0.459728\n",
       "1 -1.706828 -1.019872 -0.552877 -0.497479  0.238497\n",
       "2  0.309599  1.008439  0.258514  0.325916 -0.262803\n",
       "3 -0.264834  0.410003  1.185528  0.728187  0.995847\n",
       "4  1.014250  1.190572 -0.197618 -1.882747 -1.150723\n",
       "5 -0.558091  0.876454  0.884595  0.408703  1.236471\n",
       "6 -0.705080  0.056720 -0.730886 -0.144292  1.058086\n",
       "7  1.111384 -1.004318 -1.095346 -0.118461  1.861170\n",
       "8  0.121031 -0.425330  0.223732 -0.781417  0.979076\n",
       "9  0.832034 -0.703051  0.551663 -0.576635 -1.369211"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "99123ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>timing</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.842016</td>\n",
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       "      <td>-0.303341</td>\n",
       "      <td>1.10489</td>\n",
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       "         id      name    course      fee    timing\n",
       "0  0.842016 -0.579071 -0.303341  1.10489  0.459728"
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     "execution_count": 45,
     "metadata": {},
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   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6ea9770b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'name', 'course', 'fee', 'timing'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6fb482b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1000, step=1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8f24de02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.84201644, -0.57907112, -0.30334061,  1.10489047,  0.4597284 ],\n",
       "       [-1.70682772, -1.01987235, -0.55287654, -0.49747895,  0.23849726],\n",
       "       [ 0.30959938,  1.00843911,  0.25851361,  0.3259156 , -0.26280307],\n",
       "       ...,\n",
       "       [ 1.00690926,  0.42984535,  0.88571935, -0.74829293, -1.42173723],\n",
       "       [-0.76122714, -1.01537628, -1.59991564,  0.17512571, -0.54767724],\n",
       "       [ 0.70863298,  0.27224986, -1.28493358,  0.74259731,  0.30621986]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_numpy()# convert dataframe into numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "03a77e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.84201644, -0.57907112, -0.30334061,  1.10489047,  0.4597284 ],\n",
       "       [-1.70682772, -1.01987235, -0.55287654, -0.49747895,  0.23849726],\n",
       "       [ 0.30959938,  1.00843911,  0.25851361,  0.3259156 , -0.26280307],\n",
       "       ...,\n",
       "       [ 1.00690926,  0.42984535,  0.88571935, -0.74829293, -1.42173723],\n",
       "       [-0.76122714, -1.01537628, -1.59991564,  0.17512571, -0.54767724],\n",
       "       [ 0.70863298,  0.27224986, -1.28493358,  0.74259731,  0.30621986]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "90d79287",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "    <tr>\n",
       "      <th>2022-08-19</th>\n",
       "      <td>0.048407</td>\n",
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       "      <td>-0.238528</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-20</th>\n",
       "      <td>0.499567</td>\n",
       "      <td>0.180788</td>\n",
       "      <td>-0.556364</td>\n",
       "      <td>0.739215</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-21</th>\n",
       "      <td>-0.403139</td>\n",
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       "      <th>2022-08-22</th>\n",
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       "      <td>-2.149116</td>\n",
       "      <td>-0.026479</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-23</th>\n",
       "      <td>1.493505</td>\n",
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       "      <th>2022-08-24</th>\n",
       "      <td>-1.056522</td>\n",
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       "      <td>1.721627</td>\n",
       "      <td>1.005990</td>\n",
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       "                   A         B         C         D\n",
       "2022-08-19  0.048407 -0.172112  0.277748 -0.238528\n",
       "2022-08-20  0.499567  0.180788 -0.556364  0.739215\n",
       "2022-08-21 -0.403139 -0.097741  1.184154  0.330514\n",
       "2022-08-22  1.863836  0.239431 -2.149116 -0.026479\n",
       "2022-08-23  1.493505  0.795696  1.681031  0.854054\n",
       "2022-08-24 -1.056522  0.366321  1.721627  1.005990"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dates = pd.date_range('20220819', periods=6)\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6,4), \n",
    "                  index=dates,\n",
    "                 columns=list(\"ABCD\"))\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d2165b9",
   "metadata": {},
   "outputs": [
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       "   2022-08-19  2022-08-20  2022-08-21  2022-08-22  2022-08-23  2022-08-24\n",
       "A    0.048407    0.499567   -0.403139    1.863836    1.493505   -1.056522\n",
       "B   -0.172112    0.180788   -0.097741    0.239431    0.795696    0.366321\n",
       "C    0.277748   -0.556364    1.184154   -2.149116    1.681031    1.721627\n",
       "D   -0.238528    0.739215    0.330514   -0.026479    0.854054    1.005990"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.T"
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  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fa7d1e43",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2022-08-21</th>\n",
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       "      <th>2022-08-23</th>\n",
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       "      <th>2022-08-24</th>\n",
       "      <td>-1.056522</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2022-08-19  0.048407 -0.172112  0.277748 -0.238528\n",
       "2022-08-20  0.499567  0.180788 -0.556364  0.739215\n",
       "2022-08-21 -0.403139 -0.097741  1.184154  0.330514\n",
       "2022-08-22  1.863836  0.239431 -2.149116 -0.026479\n",
       "2022-08-23  1.493505  0.795696  1.681031  0.854054\n",
       "2022-08-24 -1.056522  0.366321  1.721627  1.005990"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "748ef84d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2022-08-22</th>\n",
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       "                   D         C         B         A\n",
       "2022-08-19 -0.238528  0.277748 -0.172112  0.048407\n",
       "2022-08-20  0.739215 -0.556364  0.180788  0.499567\n",
       "2022-08-21  0.330514  1.184154 -0.097741 -0.403139\n",
       "2022-08-22 -0.026479 -2.149116  0.239431  1.863836\n",
       "2022-08-23  0.854054  1.681031  0.795696  1.493505\n",
       "2022-08-24  1.005990  1.721627  0.366321 -1.056522"
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     "execution_count": 56,
     "metadata": {},
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   "source": [
    "df.sort_index(axis=1, ascending=False)"
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   "execution_count": 57,
   "id": "9db58fad",
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       "      <th>2022-08-22</th>\n",
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       "      <td>-2.149116</td>\n",
       "      <td>-0.026479</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-21</th>\n",
       "      <td>-0.403139</td>\n",
       "      <td>-0.097741</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-20</th>\n",
       "      <td>0.499567</td>\n",
       "      <td>0.180788</td>\n",
       "      <td>-0.556364</td>\n",
       "      <td>0.739215</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-19</th>\n",
       "      <td>0.048407</td>\n",
       "      <td>-0.172112</td>\n",
       "      <td>0.277748</td>\n",
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       "                   A         B         C         D\n",
       "2022-08-24 -1.056522  0.366321  1.721627  1.005990\n",
       "2022-08-23  1.493505  0.795696  1.681031  0.854054\n",
       "2022-08-22  1.863836  0.239431 -2.149116 -0.026479\n",
       "2022-08-21 -0.403139 -0.097741  1.184154  0.330514\n",
       "2022-08-20  0.499567  0.180788 -0.556364  0.739215\n",
       "2022-08-19  0.048407 -0.172112  0.277748 -0.238528"
      ]
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.sort_index(axis=0, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "98f19dcc",
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   "outputs": [
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       "      <th>2022-08-20</th>\n",
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       "      <td>-0.556364</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-22</th>\n",
       "      <td>1.863836</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-24</th>\n",
       "      <td>-1.056522</td>\n",
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       "      <td>1.493505</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2022-08-19  0.048407 -0.172112  0.277748 -0.238528\n",
       "2022-08-21 -0.403139 -0.097741  1.184154  0.330514\n",
       "2022-08-20  0.499567  0.180788 -0.556364  0.739215\n",
       "2022-08-22  1.863836  0.239431 -2.149116 -0.026479\n",
       "2022-08-24 -1.056522  0.366321  1.721627  1.005990\n",
       "2022-08-23  1.493505  0.795696  1.681031  0.854054"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"B\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ef5530c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2022-01-01</th>\n",
       "      <td>-1.866937</td>\n",
       "      <td>1.409070</td>\n",
       "      <td>0.341600</td>\n",
       "      <td>0.679900</td>\n",
       "      <td>1.027986</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>0.523291</td>\n",
       "      <td>-0.480144</td>\n",
       "      <td>0.423636</td>\n",
       "      <td>-0.143711</td>\n",
       "      <td>0.868866</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>-0.681438</td>\n",
       "      <td>-0.966202</td>\n",
       "      <td>0.974627</td>\n",
       "      <td>0.664628</td>\n",
       "      <td>-0.813955</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>-1.133385</td>\n",
       "      <td>-0.391024</td>\n",
       "      <td>1.968381</td>\n",
       "      <td>-2.668945</td>\n",
       "      <td>0.090983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>0.948157</td>\n",
       "      <td>-0.150922</td>\n",
       "      <td>-1.672643</td>\n",
       "      <td>0.308564</td>\n",
       "      <td>-0.668931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-22</th>\n",
       "      <td>-0.136483</td>\n",
       "      <td>-1.682927</td>\n",
       "      <td>-0.715902</td>\n",
       "      <td>1.099216</td>\n",
       "      <td>-2.153809</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-23</th>\n",
       "      <td>-1.158356</td>\n",
       "      <td>1.007871</td>\n",
       "      <td>-0.041899</td>\n",
       "      <td>0.886728</td>\n",
       "      <td>-0.283480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-24</th>\n",
       "      <td>1.587424</td>\n",
       "      <td>0.537881</td>\n",
       "      <td>1.025222</td>\n",
       "      <td>0.924478</td>\n",
       "      <td>-0.905626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>-0.616551</td>\n",
       "      <td>0.357774</td>\n",
       "      <td>1.679738</td>\n",
       "      <td>-1.464398</td>\n",
       "      <td>0.264212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26</th>\n",
       "      <td>-1.985317</td>\n",
       "      <td>0.680967</td>\n",
       "      <td>0.669273</td>\n",
       "      <td>-1.021693</td>\n",
       "      <td>-1.148795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-01-01 -1.866937  1.409070  0.341600  0.679900  1.027986\n",
       "2022-01-02  0.523291 -0.480144  0.423636 -0.143711  0.868866\n",
       "2022-01-03 -0.681438 -0.966202  0.974627  0.664628 -0.813955\n",
       "2022-01-04 -1.133385 -0.391024  1.968381 -2.668945  0.090983\n",
       "2022-01-05  0.948157 -0.150922 -1.672643  0.308564 -0.668931\n",
       "...              ...       ...       ...       ...       ...\n",
       "2024-09-22 -0.136483 -1.682927 -0.715902  1.099216 -2.153809\n",
       "2024-09-23 -1.158356  1.007871 -0.041899  0.886728 -0.283480\n",
       "2024-09-24  1.587424  0.537881  1.025222  0.924478 -0.905626\n",
       "2024-09-25 -0.616551  0.357774  1.679738 -1.464398  0.264212\n",
       "2024-09-26 -1.985317  0.680967  0.669273 -1.021693 -1.148795\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000,5),\n",
    "                  columns=list('ABCDE'),\n",
    "                 index=pd.date_range('2022-01-01', periods=1000))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "75768dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-01-01   -1.866937\n",
       "2022-01-02    0.523291\n",
       "2022-01-03   -0.681438\n",
       "2022-01-04   -1.133385\n",
       "2022-01-05    0.948157\n",
       "                ...   \n",
       "2024-09-22   -0.136483\n",
       "2024-09-23   -1.158356\n",
       "2024-09-24    1.587424\n",
       "2024-09-25   -0.616551\n",
       "2024-09-26   -1.985317\n",
       "Freq: D, Name: A, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0140df94",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.672643</td>\n",
       "      <td>0.308564</td>\n",
       "      <td>-0.668931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-22</th>\n",
       "      <td>-0.136483</td>\n",
       "      <td>-1.682927</td>\n",
       "      <td>-0.715902</td>\n",
       "      <td>1.099216</td>\n",
       "      <td>-2.153809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-23</th>\n",
       "      <td>-1.158356</td>\n",
       "      <td>1.007871</td>\n",
       "      <td>-0.041899</td>\n",
       "      <td>0.886728</td>\n",
       "      <td>-0.283480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-24</th>\n",
       "      <td>1.587424</td>\n",
       "      <td>0.537881</td>\n",
       "      <td>1.025222</td>\n",
       "      <td>0.924478</td>\n",
       "      <td>-0.905626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>-0.616551</td>\n",
       "      <td>0.357774</td>\n",
       "      <td>1.679738</td>\n",
       "      <td>-1.464398</td>\n",
       "      <td>0.264212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26</th>\n",
       "      <td>-1.985317</td>\n",
       "      <td>0.680967</td>\n",
       "      <td>0.669273</td>\n",
       "      <td>-1.021693</td>\n",
       "      <td>-1.148795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-01-01 -1.866937  1.409070  0.341600  0.679900  1.027986\n",
       "2022-01-02  0.523291 -0.480144  0.423636 -0.143711  0.868866\n",
       "2022-01-03 -0.681438 -0.966202  0.974627  0.664628 -0.813955\n",
       "2022-01-04 -1.133385 -0.391024  1.968381 -2.668945  0.090983\n",
       "2022-01-05  0.948157 -0.150922 -1.672643  0.308564 -0.668931\n",
       "...              ...       ...       ...       ...       ...\n",
       "2024-09-22 -0.136483 -1.682927 -0.715902  1.099216 -2.153809\n",
       "2024-09-23 -1.158356  1.007871 -0.041899  0.886728 -0.283480\n",
       "2024-09-24  1.587424  0.537881  1.025222  0.924478 -0.905626\n",
       "2024-09-25 -0.616551  0.357774  1.679738 -1.464398  0.264212\n",
       "2024-09-26 -1.985317  0.680967  0.669273 -1.021693 -1.148795\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e9a1f53d",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'(slice(None, None, 2), slice(None, None, None))' is an invalid key",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\LAB-01~1\\AppData\\Local\\Temp/ipykernel_13564/2503224747.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3359\u001b[0m             \u001b[0mcasted_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3363\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '(slice(None, None, 2), slice(None, None, None))' is an invalid key"
     ]
    }
   ],
   "source": [
    "df[::2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c1307f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-1.866937</td>\n",
       "      <td>0.341600</td>\n",
       "      <td>1.027986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>-0.681438</td>\n",
       "      <td>0.974627</td>\n",
       "      <td>-0.813955</td>\n",
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       "      <th>2022-01-05</th>\n",
       "      <td>0.948157</td>\n",
       "      <td>-1.672643</td>\n",
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       "      <th>2022-01-07</th>\n",
       "      <td>1.065384</td>\n",
       "      <td>-0.792763</td>\n",
       "      <td>-0.212378</td>\n",
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       "      <th>2022-01-09</th>\n",
       "      <td>-0.358231</td>\n",
       "      <td>-0.009009</td>\n",
       "      <td>-1.859979</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>2024-09-17</th>\n",
       "      <td>-0.334280</td>\n",
       "      <td>-0.005040</td>\n",
       "      <td>0.146837</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-19</th>\n",
       "      <td>-3.146439</td>\n",
       "      <td>1.151376</td>\n",
       "      <td>-0.013285</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-21</th>\n",
       "      <td>0.191642</td>\n",
       "      <td>0.425952</td>\n",
       "      <td>-0.104534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-23</th>\n",
       "      <td>-1.158356</td>\n",
       "      <td>-0.041899</td>\n",
       "      <td>-0.283480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>-0.616551</td>\n",
       "      <td>1.679738</td>\n",
       "      <td>0.264212</td>\n",
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       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
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       "                   A         C         E\n",
       "2022-01-01 -1.866937  0.341600  1.027986\n",
       "2022-01-03 -0.681438  0.974627 -0.813955\n",
       "2022-01-05  0.948157 -1.672643 -0.668931\n",
       "2022-01-07  1.065384 -0.792763 -0.212378\n",
       "2022-01-09 -0.358231 -0.009009 -1.859979\n",
       "...              ...       ...       ...\n",
       "2024-09-17 -0.334280 -0.005040  0.146837\n",
       "2024-09-19 -3.146439  1.151376 -0.013285\n",
       "2024-09-21  0.191642  0.425952 -0.104534\n",
       "2024-09-23 -1.158356 -0.041899 -0.283480\n",
       "2024-09-25 -0.616551  1.679738  0.264212\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[::2,::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e7b3b63a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2022-08-19</th>\n",
       "      <td>1.786041</td>\n",
       "      <td>0.427430</td>\n",
       "      <td>-1.566723</td>\n",
       "      <td>1.539848</td>\n",
       "      <td>0.632830</td>\n",
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       "      <th>2022-08-20</th>\n",
       "      <td>-0.387742</td>\n",
       "      <td>0.506238</td>\n",
       "      <td>-0.271490</td>\n",
       "      <td>1.588526</td>\n",
       "      <td>-2.047650</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-21</th>\n",
       "      <td>-2.093724</td>\n",
       "      <td>0.545696</td>\n",
       "      <td>1.638837</td>\n",
       "      <td>-0.555082</td>\n",
       "      <td>-0.156712</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-22</th>\n",
       "      <td>-1.192459</td>\n",
       "      <td>0.560238</td>\n",
       "      <td>0.237532</td>\n",
       "      <td>0.085022</td>\n",
       "      <td>1.640753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23</th>\n",
       "      <td>0.249335</td>\n",
       "      <td>0.350450</td>\n",
       "      <td>-1.742840</td>\n",
       "      <td>-1.211795</td>\n",
       "      <td>-2.117827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-24</th>\n",
       "      <td>-1.838433</td>\n",
       "      <td>0.106027</td>\n",
       "      <td>-0.411439</td>\n",
       "      <td>0.041878</td>\n",
       "      <td>1.173537</td>\n",
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       "    <tr>\n",
       "      <th>2022-08-25</th>\n",
       "      <td>-1.768996</td>\n",
       "      <td>-1.061819</td>\n",
       "      <td>0.793424</td>\n",
       "      <td>1.270446</td>\n",
       "      <td>-0.651629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-26</th>\n",
       "      <td>-1.204922</td>\n",
       "      <td>-0.593493</td>\n",
       "      <td>0.717683</td>\n",
       "      <td>0.661442</td>\n",
       "      <td>0.974534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-27</th>\n",
       "      <td>-0.688686</td>\n",
       "      <td>-0.981076</td>\n",
       "      <td>-0.299250</td>\n",
       "      <td>0.802117</td>\n",
       "      <td>-1.083810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-28</th>\n",
       "      <td>-0.980351</td>\n",
       "      <td>-0.695836</td>\n",
       "      <td>0.964842</td>\n",
       "      <td>-0.752760</td>\n",
       "      <td>-0.668254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-29</th>\n",
       "      <td>0.843503</td>\n",
       "      <td>1.037039</td>\n",
       "      <td>0.653401</td>\n",
       "      <td>-0.780882</td>\n",
       "      <td>1.687726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-30</th>\n",
       "      <td>0.721826</td>\n",
       "      <td>-1.024535</td>\n",
       "      <td>-0.066744</td>\n",
       "      <td>1.429505</td>\n",
       "      <td>0.979305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-31</th>\n",
       "      <td>-1.206242</td>\n",
       "      <td>1.581320</td>\n",
       "      <td>2.905882</td>\n",
       "      <td>-0.975631</td>\n",
       "      <td>-0.552423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-08-19  1.786041  0.427430 -1.566723  1.539848  0.632830\n",
       "2022-08-20 -0.387742  0.506238 -0.271490  1.588526 -2.047650\n",
       "2022-08-21 -2.093724  0.545696  1.638837 -0.555082 -0.156712\n",
       "2022-08-22 -1.192459  0.560238  0.237532  0.085022  1.640753\n",
       "2022-08-23  0.249335  0.350450 -1.742840 -1.211795 -2.117827\n",
       "2022-08-24 -1.838433  0.106027 -0.411439  0.041878  1.173537\n",
       "2022-08-25 -1.768996 -1.061819  0.793424  1.270446 -0.651629\n",
       "2022-08-26 -1.204922 -0.593493  0.717683  0.661442  0.974534\n",
       "2022-08-27 -0.688686 -0.981076 -0.299250  0.802117 -1.083810\n",
       "2022-08-28 -0.980351 -0.695836  0.964842 -0.752760 -0.668254\n",
       "2022-08-29  0.843503  1.037039  0.653401 -0.780882  1.687726\n",
       "2022-08-30  0.721826 -1.024535 -0.066744  1.429505  0.979305\n",
       "2022-08-31 -1.206242  1.581320  2.905882 -0.975631 -0.552423"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"20220819\":\"20220831\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a3098fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "\n",
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       "<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>2022-08-19</th>\n",
       "      <td>1.786041</td>\n",
       "      <td>0.427430</td>\n",
       "      <td>-1.566723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-20</th>\n",
       "      <td>-0.387742</td>\n",
       "      <td>0.506238</td>\n",
       "      <td>-0.271490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-21</th>\n",
       "      <td>-2.093724</td>\n",
       "      <td>0.545696</td>\n",
       "      <td>1.638837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-22</th>\n",
       "      <td>-1.192459</td>\n",
       "      <td>0.560238</td>\n",
       "      <td>0.237532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-23</th>\n",
       "      <td>0.249335</td>\n",
       "      <td>0.350450</td>\n",
       "      <td>-1.742840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-24</th>\n",
       "      <td>-1.838433</td>\n",
       "      <td>0.106027</td>\n",
       "      <td>-0.411439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-25</th>\n",
       "      <td>-1.768996</td>\n",
       "      <td>-1.061819</td>\n",
       "      <td>0.793424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-26</th>\n",
       "      <td>-1.204922</td>\n",
       "      <td>-0.593493</td>\n",
       "      <td>0.717683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-27</th>\n",
       "      <td>-0.688686</td>\n",
       "      <td>-0.981076</td>\n",
       "      <td>-0.299250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-28</th>\n",
       "      <td>-0.980351</td>\n",
       "      <td>-0.695836</td>\n",
       "      <td>0.964842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-29</th>\n",
       "      <td>0.843503</td>\n",
       "      <td>1.037039</td>\n",
       "      <td>0.653401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-30</th>\n",
       "      <td>0.721826</td>\n",
       "      <td>-1.024535</td>\n",
       "      <td>-0.066744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-31</th>\n",
       "      <td>-1.206242</td>\n",
       "      <td>1.581320</td>\n",
       "      <td>2.905882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C\n",
       "2022-08-19  1.786041  0.427430 -1.566723\n",
       "2022-08-20 -0.387742  0.506238 -0.271490\n",
       "2022-08-21 -2.093724  0.545696  1.638837\n",
       "2022-08-22 -1.192459  0.560238  0.237532\n",
       "2022-08-23  0.249335  0.350450 -1.742840\n",
       "2022-08-24 -1.838433  0.106027 -0.411439\n",
       "2022-08-25 -1.768996 -1.061819  0.793424\n",
       "2022-08-26 -1.204922 -0.593493  0.717683\n",
       "2022-08-27 -0.688686 -0.981076 -0.299250\n",
       "2022-08-28 -0.980351 -0.695836  0.964842\n",
       "2022-08-29  0.843503  1.037039  0.653401\n",
       "2022-08-30  0.721826 -1.024535 -0.066744\n",
       "2022-08-31 -1.206242  1.581320  2.905882"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"20220819\":\"20220831\",\"A\":\"C\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ead16efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-0.011071</td>\n",
       "      <td>-1.533039</td>\n",
       "      <td>1.732452</td>\n",
       "      <td>-0.156739</td>\n",
       "      <td>-1.296019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>0.167118</td>\n",
       "      <td>-0.909854</td>\n",
       "      <td>-0.359810</td>\n",
       "      <td>0.938328</td>\n",
       "      <td>-1.586487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>0.505722</td>\n",
       "      <td>0.412334</td>\n",
       "      <td>1.423426</td>\n",
       "      <td>0.049415</td>\n",
       "      <td>-0.842096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>1.620874</td>\n",
       "      <td>0.210487</td>\n",
       "      <td>0.777044</td>\n",
       "      <td>-0.267907</td>\n",
       "      <td>-1.653465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>-0.011838</td>\n",
       "      <td>-0.221734</td>\n",
       "      <td>0.550236</td>\n",
       "      <td>0.234247</td>\n",
       "      <td>-0.005029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-22</th>\n",
       "      <td>-0.897426</td>\n",
       "      <td>-0.837589</td>\n",
       "      <td>0.223029</td>\n",
       "      <td>-0.610379</td>\n",
       "      <td>-0.529389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-23</th>\n",
       "      <td>0.123323</td>\n",
       "      <td>-0.084549</td>\n",
       "      <td>-0.159268</td>\n",
       "      <td>1.090736</td>\n",
       "      <td>-1.291929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-24</th>\n",
       "      <td>0.407201</td>\n",
       "      <td>-1.152260</td>\n",
       "      <td>1.839268</td>\n",
       "      <td>0.574859</td>\n",
       "      <td>0.277034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25</th>\n",
       "      <td>-0.027482</td>\n",
       "      <td>-0.920144</td>\n",
       "      <td>0.725855</td>\n",
       "      <td>1.084304</td>\n",
       "      <td>1.243414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26</th>\n",
       "      <td>-0.008828</td>\n",
       "      <td>0.672409</td>\n",
       "      <td>0.805895</td>\n",
       "      <td>1.139277</td>\n",
       "      <td>0.749138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-01-01 -0.011071 -1.533039  1.732452 -0.156739 -1.296019\n",
       "2022-01-02  0.167118 -0.909854 -0.359810  0.938328 -1.586487\n",
       "2022-01-03  0.505722  0.412334  1.423426  0.049415 -0.842096\n",
       "2022-01-04  1.620874  0.210487  0.777044 -0.267907 -1.653465\n",
       "2022-01-05 -0.011838 -0.221734  0.550236  0.234247 -0.005029\n",
       "...              ...       ...       ...       ...       ...\n",
       "2024-09-22 -0.897426 -0.837589  0.223029 -0.610379 -0.529389\n",
       "2024-09-23  0.123323 -0.084549 -0.159268  1.090736 -1.291929\n",
       "2024-09-24  0.407201 -1.152260  1.839268  0.574859  0.277034\n",
       "2024-09-25 -0.027482 -0.920144  0.725855  1.084304  1.243414\n",
       "2024-09-26 -0.008828  0.672409  0.805895  1.139277  0.749138\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2022-01-01', periods=1000)\n",
    "df = pd.DataFrame(np.random.randn(1000,5),\n",
    "                  columns=list('ABCDE'),\n",
    "                 index=dates)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "33bdf78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-2.018226</td>\n",
       "      <td>0.337000</td>\n",
       "      <td>-0.299606</td>\n",
       "      <td>-1.338330</td>\n",
       "      <td>0.011784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>-1.476907</td>\n",
       "      <td>1.084406</td>\n",
       "      <td>-1.526556</td>\n",
       "      <td>-1.631015</td>\n",
       "      <td>-0.260835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>-1.073067</td>\n",
       "      <td>-0.130388</td>\n",
       "      <td>0.796487</td>\n",
       "      <td>0.666989</td>\n",
       "      <td>-0.813438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>-0.945858</td>\n",
       "      <td>0.032115</td>\n",
       "      <td>-1.763085</td>\n",
       "      <td>1.020452</td>\n",
       "      <td>-0.202044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>-0.111355</td>\n",
       "      <td>-0.530954</td>\n",
       "      <td>-0.803937</td>\n",
       "      <td>-0.429307</td>\n",
       "      <td>0.182059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-01-01 -2.018226  0.337000 -0.299606 -1.338330  0.011784\n",
       "2022-01-02 -1.476907  1.084406 -1.526556 -1.631015 -0.260835\n",
       "2022-01-03 -1.073067 -0.130388  0.796487  0.666989 -0.813438\n",
       "2022-01-04 -0.945858  0.032115 -1.763085  1.020452 -0.202044\n",
       "2022-01-05 -0.111355 -0.530954 -0.803937 -0.429307  0.182059"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6ede2d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.011071\n",
       "B   -1.533039\n",
       "C    1.732452\n",
       "D   -0.156739\n",
       "E   -1.296019\n",
       "Name: 2022-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "927c0772",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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