practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s= pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2024-03-07', '2024-03-08', '2024-03-09', '2024-03-10',\n",
       "               '2024-03-11', '2024-03-12', '2024-03-13'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates=pd.date_range(\"20240307\",periods=7)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>1.114937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>-0.673103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  0.170617  0.192331\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774\n",
       "2024-03-12  0.499984 -0.973359  0.364871  1.393975  1.114937\n",
       "2024-03-13  1.140393  1.303040  1.477635 -1.315741 -0.673103"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.randn(7,5),index=dates,columns=list(\"ABCDE\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2024-03-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2024-03-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2024-03-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2024-03-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2024-03-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  2.0 2024-03-07  1.0  3   test  foo\n",
       "1  2.0 2024-03-07  1.0  3  train  foo\n",
       "2  2.0 2024-03-07  1.0  3   test  foo\n",
       "3  2.0 2024-03-07  1.0  3  train  foo\n",
       "4  2.0 2024-03-07  1.0  3   test  foo"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame({\n",
    "    \"A\":2.0,\n",
    "    \"B\":pd.Timestamp(\"20240307\"),\n",
    "    \"C\":pd.Series(1,index=list(range(5)),dtype=\"float32\"),\n",
    "    \"D\":np.array([3]*5,dtype=\"int32\"),\n",
    "    \"E\":pd.Categorical([\"test\",\"train\",\"test\",\"train\",\"test\"]),\n",
    "    \"F\":\"foo\",\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.applymap of      A          B    C  D      E    F\n",
       "0  2.0 2024-03-07  1.0  3   test  foo\n",
       "1  2.0 2024-03-07  1.0  3  train  foo\n",
       "2  2.0 2024-03-07  1.0  3   test  foo\n",
       "3  2.0 2024-03-07  1.0  3  train  foo\n",
       "4  2.0 2024-03-07  1.0  3   test  foo>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.applymap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  0.170617  0.192331\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>1.114937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>-0.673103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774\n",
       "2024-03-12  0.499984 -0.973359  0.364871  1.393975  1.114937\n",
       "2024-03-13  1.140393  1.303040  1.477635 -1.315741 -0.673103"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2024-03-07', '2024-03-08', '2024-03-09', '2024-03-10',\n",
       "               '2024-03-11', '2024-03-12', '2024-03-13'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D', 'E'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.to_numpy of                    A         B         C         D         E\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  0.170617  0.192331\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774\n",
       "2024-03-12  0.499984 -0.973359  0.364871  1.393975  1.114937\n",
       "2024-03-13  1.140393  1.303040  1.477635 -1.315741 -0.673103>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.0, Timestamp('2024-03-07 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
       "       [2.0, Timestamp('2024-03-07 00:00:00'), 1.0, 3, 'train', 'foo'],\n",
       "       [2.0, Timestamp('2024-03-07 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
       "       [2.0, Timestamp('2024-03-07 00:00:00'), 1.0, 3, 'train', 'foo'],\n",
       "       [2.0, Timestamp('2024-03-07 00:00:00'), 1.0, 3, 'test', 'foo']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007707</td>\n",
       "      <td>-0.054233</td>\n",
       "      <td>0.237889</td>\n",
       "      <td>0.159593</td>\n",
       "      <td>1.232340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.884461</td>\n",
       "      <td>0.946010</td>\n",
       "      <td>1.008041</td>\n",
       "      <td>1.141576</td>\n",
       "      <td>1.270347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>-0.673103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.169943</td>\n",
       "      <td>-0.743862</td>\n",
       "      <td>0.014050</td>\n",
       "      <td>-0.541270</td>\n",
       "      <td>0.653634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.431648</td>\n",
       "      <td>0.516720</td>\n",
       "      <td>0.742389</td>\n",
       "      <td>0.782296</td>\n",
       "      <td>1.771358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D         E\n",
       "count  7.000000  7.000000  7.000000  7.000000  7.000000\n",
       "mean   0.007707 -0.054233  0.237889  0.159593  1.232340\n",
       "std    0.884461  0.946010  1.008041  1.141576  1.270347\n",
       "min   -1.697719 -1.343420 -1.645207 -1.315741 -0.673103\n",
       "25%   -0.169943 -0.743862  0.014050 -0.541270  0.653634\n",
       "50%    0.087865  0.115033  0.319919  0.096213  1.128448\n",
       "75%    0.431648  0.516720  0.742389  0.782296  1.771358\n",
       "max    1.140393  1.303040  1.477635  1.854627  3.321050"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>2024-03-07</th>\n",
       "      <th>2024-03-08</th>\n",
       "      <th>2024-03-09</th>\n",
       "      <th>2024-03-10</th>\n",
       "      <th>2024-03-11</th>\n",
       "      <th>2024-03-12</th>\n",
       "      <th>2024-03-13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.008708</td>\n",
       "      <td>-1.697719</td>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.087865</td>\n",
       "      <td>0.499984</td>\n",
       "      <td>1.140393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.263466</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>1.303040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.477635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.096213</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>-1.315741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>1.802941</td>\n",
       "      <td>3.321050</td>\n",
       "      <td>1.128448</td>\n",
       "      <td>0.192331</td>\n",
       "      <td>1.739774</td>\n",
       "      <td>1.114937</td>\n",
       "      <td>-0.673103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2024-03-07  2024-03-08  2024-03-09  2024-03-10  2024-03-11  2024-03-12  \\\n",
       "A    0.363312    0.008708   -1.697719   -0.348594    0.087865    0.499984   \n",
       "B    0.263466   -0.514365    0.115033    0.769973   -1.343420   -0.973359   \n",
       "C   -1.645207    0.319919   -0.223490    1.119906    0.251591    0.364871   \n",
       "D    0.096213   -0.901252    1.854627    0.170617   -0.181289    1.393975   \n",
       "E    1.802941    3.321050    1.128448    0.192331    1.739774    1.114937   \n",
       "\n",
       "   2024-03-13  \n",
       "A    1.140393  \n",
       "B    1.303040  \n",
       "C    1.477635  \n",
       "D   -1.315741  \n",
       "E   -0.673103  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>E</th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>1.802941</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>0.363312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>3.321050</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.008708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>1.128448</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-1.697719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>0.192331</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>-0.348594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>1.739774</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.087865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>1.114937</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.499984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>-0.673103</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.140393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   E         D         C         B         A\n",
       "2024-03-07  1.802941  0.096213 -1.645207  0.263466  0.363312\n",
       "2024-03-08  3.321050 -0.901252  0.319919 -0.514365  0.008708\n",
       "2024-03-09  1.128448  1.854627 -0.223490  0.115033 -1.697719\n",
       "2024-03-10  0.192331  0.170617  1.119906  0.769973 -0.348594\n",
       "2024-03-11  1.739774 -0.181289  0.251591 -1.343420  0.087865\n",
       "2024-03-12  1.114937  1.393975  0.364871 -0.973359  0.499984\n",
       "2024-03-13 -0.673103 -1.315741  1.477635  1.303040  1.140393"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>1.114937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>-0.673103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774\n",
       "2024-03-12  0.499984 -0.973359  0.364871  1.393975  1.114937\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941\n",
       "2024-03-10 -0.348594  0.769973  1.119906  0.170617  0.192331\n",
       "2024-03-13  1.140393  1.303040  1.477635 -1.315741 -0.673103"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024-03-07    0.363312\n",
       "2024-03-08    0.008708\n",
       "2024-03-09   -1.697719\n",
       "2024-03-10   -0.348594\n",
       "2024-03-11    0.087865\n",
       "2024-03-12    0.499984\n",
       "2024-03-13    1.140393\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>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>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>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>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  0.170617  0.192331\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"20240308\":\"20240311\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.363312\n",
       "B    0.263466\n",
       "C   -1.645207\n",
       "D    0.096213\n",
       "E    1.802941\n",
       "Name: 2024-03-07 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2024-03-07  0.363312  0.263466\n",
       "2024-03-08  0.008708 -0.514365\n",
       "2024-03-09 -1.697719  0.115033\n",
       "2024-03-10 -0.348594  0.769973\n",
       "2024-03-11  0.087865 -1.343420"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"20240307\":\"20240311\",[\"A\",\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6977189354643087"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[2], \"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.348594\n",
       "B    0.769973\n",
       "C    1.119906\n",
       "D    0.170617\n",
       "E    0.192331\n",
       "Name: 2024-03-10 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2024-03-10  0.769973  1.119906\n",
       "2024-03-11 -1.343420  0.251591"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>0.319919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>-0.223490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>0.251591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2024-03-08  0.008708  0.319919\n",
       "2024-03-09 -1.697719 -0.223490\n",
       "2024-03-11  0.087865  0.251591"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2024-03-07  0.263466 -1.645207\n",
       "2024-03-08 -0.514365  0.319919\n",
       "2024-03-09  0.115033 -0.223490\n",
       "2024-03-10  0.769973  1.119906\n",
       "2024-03-11 -1.343420  0.251591\n",
       "2024-03-12 -0.973359  0.364871\n",
       "2024-03-13  1.303040  1.477635"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5143645368728167"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>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>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>1.114937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>-0.673103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774\n",
       "2024-03-12  0.499984 -0.973359  0.364871  1.393975  1.114937\n",
       "2024-03-13  1.140393  1.303040  1.477635 -1.315741 -0.673103"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"A\"]>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.739774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>1.114937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2024-03-07  0.363312  0.263466       NaN  0.096213  1.802941\n",
       "2024-03-08  0.008708       NaN  0.319919       NaN  3.321050\n",
       "2024-03-09       NaN  0.115033       NaN  1.854627  1.128448\n",
       "2024-03-10       NaN  0.769973  1.119906  0.170617  0.192331\n",
       "2024-03-11  0.087865       NaN  0.251591       NaN  1.739774\n",
       "2024-03-12  0.499984       NaN  0.364871  1.393975  1.114937\n",
       "2024-03-13  1.140393  1.303040  1.477635       NaN       NaN"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[\"F\"] = [\"one\",\"two\",\"three\",\"four\",\"five\",\"six\",\"seven\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.363312</td>\n",
       "      <td>0.263466</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>0.096213</td>\n",
       "      <td>1.802941</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>-0.901252</td>\n",
       "      <td>3.321050</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>1.854627</td>\n",
       "      <td>1.128448</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>0.170617</td>\n",
       "      <td>0.192331</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>-0.181289</td>\n",
       "      <td>1.739774</td>\n",
       "      <td>five</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>1.393975</td>\n",
       "      <td>1.114937</td>\n",
       "      <td>six</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>-1.315741</td>\n",
       "      <td>-0.673103</td>\n",
       "      <td>seven</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E      F\n",
       "2024-03-07  0.363312  0.263466 -1.645207  0.096213  1.802941    one\n",
       "2024-03-08  0.008708 -0.514365  0.319919 -0.901252  3.321050    two\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  1.854627  1.128448  three\n",
       "2024-03-10 -0.348594  0.769973  1.119906  0.170617  0.192331   four\n",
       "2024-03-11  0.087865 -1.343420  0.251591 -0.181289  1.739774   five\n",
       "2024-03-12  0.499984 -0.973359  0.364871  1.393975  1.114937    six\n",
       "2024-03-13  1.140393  1.303040  1.477635 -1.315741 -0.673103  seven"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C, D, E, F]\n",
       "Index: []"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2[\"E\"].isin([\"two\",\"four\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024-03-07    1\n",
       "2024-03-08    2\n",
       "2024-03-09    3\n",
       "2024-03-10    4\n",
       "2024-03-11    5\n",
       "2024-03-12    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range(\"20240307\", periods=6))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"F\"]=s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.at[dates[0],\"A\"]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iat[0,1]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,\"D\"] = np.array([5] * len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.802941</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.321050</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.128448</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.192331</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>0.251591</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.739774</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>0.364871</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.114937</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>1.140393</td>\n",
       "      <td>1.303040</td>\n",
       "      <td>1.477635</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.673103</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D         E    F\n",
       "2024-03-07  0.000000  0.000000 -1.645207  5.0  1.802941  1.0\n",
       "2024-03-08  0.008708 -0.514365  0.319919  5.0  3.321050  2.0\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  5.0  1.128448  3.0\n",
       "2024-03-10 -0.348594  0.769973  1.119906  5.0  0.192331  4.0\n",
       "2024-03-11  0.087865 -1.343420  0.251591  5.0  1.739774  5.0\n",
       "2024-03-12  0.499984 -0.973359  0.364871  5.0  1.114937  6.0\n",
       "2024-03-13  1.140393  1.303040  1.477635  5.0 -0.673103  NaN"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.802941</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>-0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>-0.319919</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-3.321050</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>-0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.128448</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>-0.769973</td>\n",
       "      <td>-1.119906</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-0.192331</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>-0.087865</td>\n",
       "      <td>-1.343420</td>\n",
       "      <td>-0.251591</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.739774</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>-0.499984</td>\n",
       "      <td>-0.973359</td>\n",
       "      <td>-0.364871</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.114937</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>-1.140393</td>\n",
       "      <td>-1.303040</td>\n",
       "      <td>-1.477635</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-0.673103</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D         E    F\n",
       "2024-03-07  0.000000  0.000000 -1.645207 -5.0 -1.802941 -1.0\n",
       "2024-03-08 -0.008708 -0.514365 -0.319919 -5.0 -3.321050 -2.0\n",
       "2024-03-09 -1.697719 -0.115033 -0.223490 -5.0 -1.128448 -3.0\n",
       "2024-03-10 -0.348594 -0.769973 -1.119906 -5.0 -0.192331 -4.0\n",
       "2024-03-11 -0.087865 -1.343420 -0.251591 -5.0 -1.739774 -5.0\n",
       "2024-03-12 -0.499984 -0.973359 -0.364871 -5.0 -1.114937 -6.0\n",
       "2024-03-13 -1.140393 -1.303040 -1.477635 -5.0 -0.673103  NaN"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2>0]=-df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.802941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.321050</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.128448</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.192331</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D         E    F         E\n",
       "2024-03-07  0.000000  0.000000 -1.645207  5.0  1.802941  1.0  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919  5.0  3.321050  2.0  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  5.0  1.128448  3.0  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  5.0  0.192331  4.0  0.192331"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + [\"E\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.802941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.321050</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.128448</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.192331</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D         E    F         E\n",
       "2024-03-07  0.000000  0.000000 -1.645207  5.0  1.802941  1.0  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919  5.0  3.321050  2.0  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  5.0  1.128448  3.0  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  5.0  0.192331  4.0  0.192331"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how=\"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.645207</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.802941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.008708</td>\n",
       "      <td>-0.514365</td>\n",
       "      <td>0.319919</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.321050</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.321050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-1.697719</td>\n",
       "      <td>0.115033</td>\n",
       "      <td>-0.223490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.128448</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.128448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-0.348594</td>\n",
       "      <td>0.769973</td>\n",
       "      <td>1.119906</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.192331</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.192331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D         E    F         E\n",
       "2024-03-07  0.000000  0.000000 -1.645207  5.0  1.802941  1.0  1.802941\n",
       "2024-03-08  0.008708 -0.514365  0.319919  5.0  3.321050  2.0  3.321050\n",
       "2024-03-09 -1.697719  0.115033 -0.223490  5.0  1.128448  3.0  1.128448\n",
       "2024-03-10 -0.348594  0.769973  1.119906  5.0  0.192331  4.0  0.192331"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      E      F      E\n",
       "2024-03-07  False  False  False  False  False  False  False\n",
       "2024-03-08  False  False  False  False  False  False  False\n",
       "2024-03-09  False  False  False  False  False  False  False\n",
       "2024-03-10  False  False  False  False  False  False  False"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.044195\n",
       "B   -0.091871\n",
       "C    0.237889\n",
       "D    5.000000\n",
       "E    1.232340\n",
       "F    3.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024-03-07    1.026289\n",
       "2024-03-08    1.689219\n",
       "2024-03-09    1.220379\n",
       "2024-03-10    1.788936\n",
       "2024-03-11    1.789302\n",
       "2024-03-12    2.001072\n",
       "2024-03-13    1.649593\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8,9], index=dates).shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-2.697719</td>\n",
       "      <td>-0.884967</td>\n",
       "      <td>-1.223490</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.128448</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-3.348594</td>\n",
       "      <td>-2.230027</td>\n",
       "      <td>-1.880094</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.807669</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>-4.912135</td>\n",
       "      <td>-6.343420</td>\n",
       "      <td>-4.748409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.260226</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>-4.859607</td>\n",
       "      <td>-4.696960</td>\n",
       "      <td>-4.522365</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-6.673103</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D         E    F\n",
       "2024-03-07       NaN       NaN       NaN  NaN       NaN  NaN\n",
       "2024-03-08       NaN       NaN       NaN  NaN       NaN  NaN\n",
       "2024-03-09 -2.697719 -0.884967 -1.223490  4.0  0.128448  2.0\n",
       "2024-03-10 -3.348594 -2.230027 -1.880094  2.0 -2.807669  1.0\n",
       "2024-03-11 -4.912135 -6.343420 -4.748409  0.0 -3.260226  0.0\n",
       "2024-03-12       NaN       NaN       NaN  NaN       NaN  NaN\n",
       "2024-03-13 -4.859607 -4.696960 -4.522365 -1.0 -6.673103  NaN"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    -0.247491\n",
       "B    -0.514478\n",
       "C     1.332181\n",
       "D    28.000000\n",
       "E     6.901103\n",
       "F    19.600000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg(lambda x : np.mean(x) * 5.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-03-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-166.494905</td>\n",
       "      <td>506.0</td>\n",
       "      <td>182.457664</td>\n",
       "      <td>101.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-08</th>\n",
       "      <td>0.881210</td>\n",
       "      <td>-52.053691</td>\n",
       "      <td>32.375847</td>\n",
       "      <td>506.0</td>\n",
       "      <td>336.090310</td>\n",
       "      <td>202.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-09</th>\n",
       "      <td>-171.809156</td>\n",
       "      <td>11.641388</td>\n",
       "      <td>-22.617224</td>\n",
       "      <td>506.0</td>\n",
       "      <td>114.198937</td>\n",
       "      <td>303.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-10</th>\n",
       "      <td>-35.277734</td>\n",
       "      <td>77.921295</td>\n",
       "      <td>113.334465</td>\n",
       "      <td>506.0</td>\n",
       "      <td>19.463867</td>\n",
       "      <td>404.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-11</th>\n",
       "      <td>8.891926</td>\n",
       "      <td>-135.954105</td>\n",
       "      <td>25.461003</td>\n",
       "      <td>506.0</td>\n",
       "      <td>176.065167</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-12</th>\n",
       "      <td>50.598404</td>\n",
       "      <td>-98.503929</td>\n",
       "      <td>36.924981</td>\n",
       "      <td>506.0</td>\n",
       "      <td>112.831593</td>\n",
       "      <td>607.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-13</th>\n",
       "      <td>115.407738</td>\n",
       "      <td>131.867630</td>\n",
       "      <td>149.536704</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-68.117996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A           B           C      D           E      F\n",
       "2024-03-07    0.000000    0.000000 -166.494905  506.0  182.457664  101.2\n",
       "2024-03-08    0.881210  -52.053691   32.375847  506.0  336.090310  202.4\n",
       "2024-03-09 -171.809156   11.641388  -22.617224  506.0  114.198937  303.6\n",
       "2024-03-10  -35.277734   77.921295  113.334465  506.0   19.463867  404.8\n",
       "2024-03-11    8.891926 -135.954105   25.461003  506.0  176.065167  506.0\n",
       "2024-03-12   50.598404  -98.503929   36.924981  506.0  112.831593  607.2\n",
       "2024-03-13  115.407738  131.867630  149.536704  506.0  -68.117996    NaN"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.transform(lambda x:x*101.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    6\n",
       "2    4\n",
       "3    1\n",
       "4    0\n",
       "5    0\n",
       "6    3\n",
       "7    2\n",
       "8    6\n",
       "9    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.random.randint(0,7,size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2\n",
       "6    2\n",
       "0    2\n",
       "4    1\n",
       "3    1\n",
       "2    1\n",
       "5    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      a\n",
       "1      b\n",
       "2      c\n",
       "3    abc\n",
       "4    bcd\n",
       "5    cde\n",
       "6    NaN\n",
       "7    dog\n",
       "8    cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([\"A\",\"B\",\"C\",\"Abc\",\"Bcd\",\"Cde\",np.nan,\"Dog\",\"Cat\"])\n",
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.569124</td>\n",
       "      <td>-0.008036</td>\n",
       "      <td>0.711494</td>\n",
       "      <td>-1.993419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.143293</td>\n",
       "      <td>0.954190</td>\n",
       "      <td>-1.121366</td>\n",
       "      <td>-0.314025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.350547</td>\n",
       "      <td>-0.249527</td>\n",
       "      <td>0.067780</td>\n",
       "      <td>0.131967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.124611</td>\n",
       "      <td>-0.181622</td>\n",
       "      <td>-1.554929</td>\n",
       "      <td>0.526672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.214642</td>\n",
       "      <td>-0.681040</td>\n",
       "      <td>-0.229344</td>\n",
       "      <td>-0.540240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.408896</td>\n",
       "      <td>-1.658094</td>\n",
       "      <td>0.306651</td>\n",
       "      <td>0.175936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.416287</td>\n",
       "      <td>0.106926</td>\n",
       "      <td>-0.257119</td>\n",
       "      <td>-0.144769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.924534</td>\n",
       "      <td>-0.665664</td>\n",
       "      <td>1.289644</td>\n",
       "      <td>0.395429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.198424</td>\n",
       "      <td>-1.432365</td>\n",
       "      <td>0.647087</td>\n",
       "      <td>-0.935510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.234712</td>\n",
       "      <td>-0.517360</td>\n",
       "      <td>0.866225</td>\n",
       "      <td>-0.611668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.569124 -0.008036  0.711494 -1.993419\n",
       "1 -0.143293  0.954190 -1.121366 -0.314025\n",
       "2 -0.350547 -0.249527  0.067780  0.131967\n",
       "3  0.124611 -0.181622 -1.554929  0.526672\n",
       "4 -0.214642 -0.681040 -0.229344 -0.540240\n",
       "5 -0.408896 -1.658094  0.306651  0.175936\n",
       "6 -0.416287  0.106926 -0.257119 -0.144769\n",
       "7  1.924534 -0.665664  1.289644  0.395429\n",
       "8  0.198424 -1.432365  0.647087 -0.935510\n",
       "9 -1.234712 -0.517360  0.866225 -0.611668"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.randn(10,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = [df[:3], df[3:7], df[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.569124</td>\n",
       "      <td>-0.008036</td>\n",
       "      <td>0.711494</td>\n",
       "      <td>-1.993419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.143293</td>\n",
       "      <td>0.954190</td>\n",
       "      <td>-1.121366</td>\n",
       "      <td>-0.314025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.350547</td>\n",
       "      <td>-0.249527</td>\n",
       "      <td>0.067780</td>\n",
       "      <td>0.131967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.124611</td>\n",
       "      <td>-0.181622</td>\n",
       "      <td>-1.554929</td>\n",
       "      <td>0.526672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.214642</td>\n",
       "      <td>-0.681040</td>\n",
       "      <td>-0.229344</td>\n",
       "      <td>-0.540240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.408896</td>\n",
       "      <td>-1.658094</td>\n",
       "      <td>0.306651</td>\n",
       "      <td>0.175936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.416287</td>\n",
       "      <td>0.106926</td>\n",
       "      <td>-0.257119</td>\n",
       "      <td>-0.144769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.924534</td>\n",
       "      <td>-0.665664</td>\n",
       "      <td>1.289644</td>\n",
       "      <td>0.395429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.198424</td>\n",
       "      <td>-1.432365</td>\n",
       "      <td>0.647087</td>\n",
       "      <td>-0.935510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.234712</td>\n",
       "      <td>-0.517360</td>\n",
       "      <td>0.866225</td>\n",
       "      <td>-0.611668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.569124 -0.008036  0.711494 -1.993419\n",
       "1 -0.143293  0.954190 -1.121366 -0.314025\n",
       "2 -0.350547 -0.249527  0.067780  0.131967\n",
       "3  0.124611 -0.181622 -1.554929  0.526672\n",
       "4 -0.214642 -0.681040 -0.229344 -0.540240\n",
       "5 -0.408896 -1.658094  0.306651  0.175936\n",
       "6 -0.416287  0.106926 -0.257119 -0.144769\n",
       "7  1.924534 -0.665664  1.289644  0.395429\n",
       "8  0.198424 -1.432365  0.647087 -0.935510\n",
       "9 -1.234712 -0.517360  0.866225 -0.611668"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key':['foo','foo'],\"lval\":[1,2]})\n",
    "right = pd.DataFrame({\"key\":[\"foo\",\"foo\"],'rval':[4,5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right,on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key':['foo','bar'],\"lval\":[1,2]})\n",
    "right = pd.DataFrame({\"key\":[\"foo\",\"bar\"],'rval':[4,5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  bar     2     5"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right,on=\"key\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.761991</td>\n",
       "      <td>-0.566572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.505625</td>\n",
       "      <td>-1.390620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.658856</td>\n",
       "      <td>-1.400854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.179396</td>\n",
       "      <td>1.046674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.186022</td>\n",
       "      <td>0.556663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>1.232340</td>\n",
       "      <td>-0.054266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.038531</td>\n",
       "      <td>-0.961333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.203021</td>\n",
       "      <td>-1.332257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -0.761991 -0.566572\n",
       "1  bar    one -0.505625 -1.390620\n",
       "2  foo    two -1.658856 -1.400854\n",
       "3  bar  three  0.179396  1.046674\n",
       "4  foo    two  0.186022  0.556663\n",
       "5  bar    two  1.232340 -0.054266\n",
       "6  foo    one -0.038531 -0.961333\n",
       "7  foo  three -0.203021 -1.332257"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"A\": [\"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"foo\"],\n",
    "        \"B\": [\"one\", \"one\", \"two\", \"three\", \"two\", \"two\", \"one\", \"three\"],\n",
    "        \"C\": np.random.randn(8),\n",
    "        \"D\": np.random.randn(8),\n",
    "    }\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>0.906110</td>\n",
       "      <td>-0.398212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-2.476377</td>\n",
       "      <td>-3.704353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  0.906110 -0.398212\n",
       "foo -2.476377 -3.704353"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"A\")[[\"C\", \"D\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.505625</td>\n",
       "      <td>-1.390620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.179396</td>\n",
       "      <td>1.046674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.232340</td>\n",
       "      <td>-0.054266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.800522</td>\n",
       "      <td>-1.527905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.203021</td>\n",
       "      <td>-1.332257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.472834</td>\n",
       "      <td>-0.844191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one   -0.505625 -1.390620\n",
       "    three  0.179396  1.046674\n",
       "    two    1.232340 -0.054266\n",
       "foo one   -0.800522 -1.527905\n",
       "    three -0.203021 -1.332257\n",
       "    two   -1.472834 -0.844191"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"A\", \"B\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrays = [\n",
    "   [\"bar\", \"bar\", \"baz\", \"baz\", \"foo\", \"foo\", \"qux\", \"qux\"],\n",
    "   [\"one\", \"two\", \"one\", \"two\", \"one\", \"two\", \"one\", \"two\"],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.495318</td>\n",
       "      <td>-0.092093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.227312</td>\n",
       "      <td>-1.300807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.224033</td>\n",
       "      <td>-2.601734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.488627</td>\n",
       "      <td>-0.509089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.495318 -0.092093\n",
       "      two    -0.227312 -1.300807\n",
       "baz   one    -1.224033 -2.601734\n",
       "      two     0.488627 -0.509089"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_arrays(arrays, names=[\"first\", \"second\"])\n",
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=[\"A\", \"B\"])\n",
    "df2 = df[:4]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.495318\n",
       "               B   -0.092093\n",
       "       two     A   -0.227312\n",
       "               B   -1.300807\n",
       "baz    one     A   -1.224033\n",
       "               B   -2.601734\n",
       "       two     A    0.488627\n",
       "               B   -0.509089\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = df2.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.495318</td>\n",
       "      <td>-0.227312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.092093</td>\n",
       "      <td>-1.300807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-1.224033</td>\n",
       "      <td>0.488627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-2.601734</td>\n",
       "      <td>-0.509089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.495318 -0.227312\n",
       "      B -0.092093 -1.300807\n",
       "baz   A -1.224033  0.488627\n",
       "      B -2.601734 -0.509089"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.495318</td>\n",
       "      <td>-0.092093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.227312</td>\n",
       "      <td>-1.300807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.224033</td>\n",
       "      <td>-2.601734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.488627</td>\n",
       "      <td>-0.509089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.495318 -0.092093\n",
       "      two    -0.227312 -1.300807\n",
       "baz   one    -1.224033 -2.601734\n",
       "      two     0.488627 -0.509089"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.495318</td>\n",
       "      <td>-1.224033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.092093</td>\n",
       "      <td>-2.601734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.227312</td>\n",
       "      <td>0.488627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.300807</td>\n",
       "      <td>-0.509089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.495318 -1.224033\n",
       "       B -0.092093 -2.601734\n",
       "two    A -0.227312  0.488627\n",
       "       B -1.300807 -0.509089"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>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>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.330700</td>\n",
       "      <td>-0.495464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.322953</td>\n",
       "      <td>0.292899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.200314</td>\n",
       "      <td>0.795770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.820346</td>\n",
       "      <td>1.591201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-1.862959</td>\n",
       "      <td>0.691159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.561255</td>\n",
       "      <td>-0.651644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.163182</td>\n",
       "      <td>-0.048222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.131541</td>\n",
       "      <td>-1.209428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.536604</td>\n",
       "      <td>1.319557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.167862</td>\n",
       "      <td>0.273349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-1.908593</td>\n",
       "      <td>0.168079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.331298</td>\n",
       "      <td>0.057425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo -1.330700 -0.495464\n",
       "1     one  B  foo -1.322953  0.292899\n",
       "2     two  C  foo -0.200314  0.795770\n",
       "3   three  A  bar  1.820346  1.591201\n",
       "4     one  B  bar -1.862959  0.691159\n",
       "5     one  C  bar  1.561255 -0.651644\n",
       "6     two  A  foo  1.163182 -0.048222\n",
       "7   three  B  foo -1.131541 -1.209428\n",
       "8     one  C  foo -0.536604  1.319557\n",
       "9     one  A  bar  0.167862  0.273349\n",
       "10    two  B  bar -1.908593  0.168079\n",
       "11  three  C  bar  1.331298  0.057425"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({\n",
    "    \"A\":[\"one\",\"one\",\"two\",\"three\"] *3,\n",
    "    'B':[\"A\",\"B\",\"C\"] * 4,\n",
    "    \"C\": [\"foo\",\"foo\",\"foo\",\"bar\",\"bar\",\"bar\"]*2,\n",
    "    \"D\":np.random.randn(12),\n",
    "    \"E\":np.random.randn(12),\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>0.167862</td>\n",
       "      <td>-1.330700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.862959</td>\n",
       "      <td>-1.322953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.561255</td>\n",
       "      <td>-0.536604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>1.820346</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.131541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.331298</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.163182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.908593</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.200314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A  0.167862 -1.330700\n",
       "      B -1.862959 -1.322953\n",
       "      C  1.561255 -0.536604\n",
       "three A  1.820346       NaN\n",
       "      B       NaN -1.131541\n",
       "      C  1.331298       NaN\n",
       "two   A       NaN  1.163182\n",
       "      B -1.908593       NaN\n",
       "      C       NaN -0.200314"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values =\"D\",index=[\"A\",\"B\"],columns=[\"C\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024-01-01    26024\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(\"1/1/2024\",periods=100,freq=\"s\")\n",
    "ts=pd.Series(np.random.randint(0,500,len(rng)),index=rng)\n",
    "ts.resample(\"5Min\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06    0.415026\n",
       "2012-03-07    0.810846\n",
       "2012-03-08    1.160131\n",
       "2012-03-09   -0.363298\n",
       "2012-03-10   -1.186952\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(\"3/6/2012 00:00\", periods=5, freq=\"D\")\n",
    "ts = pd.Series(np.random.randn(len(rng)), rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    0.415026\n",
       "2012-03-07 00:00:00+00:00    0.810846\n",
       "2012-03-08 00:00:00+00:00    1.160131\n",
       "2012-03-09 00:00:00+00:00   -0.363298\n",
       "2012-03-10 00:00:00+00:00   -1.186952\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize(\"UTC\")\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 05:30:00+05:30    0.415026\n",
       "2012-03-07 05:30:00+05:30    0.810846\n",
       "2012-03-08 05:30:00+05:30    1.160131\n",
       "2012-03-09 05:30:00+05:30   -0.363298\n",
       "2012-03-10 05:30:00+05:30   -1.186952\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert(\"Asia/kolkata\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\n",
       "               '2012-03-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-13', '2012-03-14', '2012-03-15', '2012-03-16',\n",
       "               '2012-03-16'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng + pd.offsets.BusinessDay(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    e\n",
       "5    f\n",
       "Name: grade, dtype: category\n",
       "Categories (6, object): ['a', 'b', 'c', 'd', 'e', 'f']"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"id\" : [1,2,3,4,5,6],\"raw_grade\":[\"a\",\"b\",\"c\",\"d\",'e',\"f\"]})\n",
    "df[\"grade\"]=df[\"raw_grade\"].astype(\"category\")\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2     very bad\n",
       "3       medium\n",
       "4          bad\n",
       "5         fine\n",
       "Name: grade, dtype: category\n",
       "Categories (6, object): ['very good', 'good', 'medium', 'fine', 'bad', 'very bad']"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_categories = [\"very good\",\"good\",\"very bad\",\"medium\",\"bad\",\"fine\"]\n",
    "df[\"grade\"] = df[\"grade\"].cat.rename_categories(new_categories)\n",
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very good\",\"good\",\"medium\",\"fine\",\"bad\",\"very bad\"])\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>d</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>fine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>e</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>very bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "0   1         a  very good\n",
       "1   2         b       good\n",
       "3   4         d     medium\n",
       "5   6         f       fine\n",
       "4   5         e        bad\n",
       "2   3         c   very bad"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"grade\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very good    1\n",
       "good         1\n",
       "medium       1\n",
       "fine         1\n",
       "bad          1\n",
       "very bad     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\",observed=False).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.close(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range(\"1/1/2000\", periods=1000))\n",
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGtCAYAAAA4bV9OAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2SElEQVR4nO3deXhU5dkG8Hv2ySSZ7BsQAmHfQVQMIkpF0KKtVq11qUttaxXbulapilptbWurttbaqlVa69Jat09rFQQVURZFQHbZA4EsZJtss5/vj5lz5pyZM8lMktnv33XlMnPmzMybeMg887zP+7waQRAEEBERESUhbaIHQERERBQOAxUiIiJKWgxUiIiIKGkxUCEiIqKkxUCFiIiIkhYDFSIiIkpaDFSIiIgoaekTPYCB8nq9OHr0KHJzc6HRaBI9HCIiIoqAIAjo6OjAkCFDoNWGz5ukfKBy9OhRVFZWJnoYRERE1A+HDx/GsGHDwt6f8oFKbm4uAN8ParVaEzwaIiIiioTNZkNlZaX0Ph5Oygcq4nSP1WploEJERJRi+irbYDEtERERJS0GKkRERJS0GKgQERFR0mKgQkREREmLgQoRERElLQYqRERElLQYqBAREVHSYqBCRERESYuBChERESUtBipERESUtBioEBERUdJioEJERERJi4EKERERJa2YBioPPfQQTjrpJOTm5qK0tBTnn38+du/erTjHbrdj8eLFKCoqQk5ODi688EI0NDTEclhEREQUoefXHsTPX98Kr1dIyOvHNFD56KOPsHjxYqxbtw4rVqyAy+XCggUL0NXVJZ1z880346233sIrr7yCjz76CEePHsW3vvWtWA6LiIiIIuD1Crjnze14cX0tNhxsScgY9LF88nfffVdxe9myZSgtLcXGjRsxd+5ctLe3429/+xtefPFFfO1rXwMAPPfcc5gwYQLWrVuHU045JZbDIyIiol7UtfVI3/e4PAkZQ1xrVNrb2wEAhYWFAICNGzfC5XJh/vz50jnjx4/H8OHDsXbtWtXncDgcsNlsii8iIiIafHsbO6Xv27tdCRlD3AIVr9eLm266CaeeeiomT54MAKivr4fRaER+fr7i3LKyMtTX16s+z0MPPYS8vDzpq7KyMtZDJyIiykiHW7ul75u7nAkZQ9wClcWLF2Pbtm14+eWXB/Q8S5YsQXt7u/R1+PDhQRohERERyXU5AtM9LV2OhIwhpjUqohtvvBFvv/02Vq9ejWHDhknHy8vL4XQ60dbWpsiqNDQ0oLy8XPW5TCYTTCZTrIdMRESU8Xqcbun7lnTMqAiCgBtvvBGvv/46Vq1ahZEjRyrunzlzJgwGA1auXCkd2717N2pra1FTUxPLoREREVEfup2BjIq8XiWeYppRWbx4MV588UW8+eabyM3NlepO8vLykJWVhby8PFx77bW45ZZbUFhYCKvVih//+Meoqanhih8iIqIE65at9NlU24YOuwu5ZkNcxxDTjMqTTz6J9vZ2nHHGGaioqJC+/vWvf0nnPProozj33HNx4YUXYu7cuSgvL8drr70Wy2ERERGRire2HMUDb++Qmrv1yDIqbq+QkKxKTDMqgtB3Fzuz2YwnnngCTzzxRCyHQkRERH348UubAAAnjyzEwknl6HK4FffXtfVgxvCCuI6Je/0QERGRQnOnr3A2uMnbjS9uiigJMZgYqBAREZGCRuP7r1hMW5htlO7b19Sl9pCYYaBCREREcLq90vf+OEUKVC48Yah0X3tPfJcpM1AhIiIidMt6pogZFbGPysJJ5Siz+nqY2ezukMfGEgMVIiIiUvRMcftX/YjHsow6VBfnAAA6GKgQERFRvMkDFXFZshiUWM0G5Jp9C4VtPfHdnJCBChERESmmfuwuD5xur7Tqxxeo+Bq9MaNCREREg87p9uLPH+7Ftrp21fvlGZXfLf8KGw+1AvDVq+Sa9VJGpcPOjAoRERENsufXHcJv392Ncx9fE3Lf4ZZufOepdYpjlz7tu51r0kOr1cDqD1S21rXD441fLxUGKkRERBlgd70t7H2/X7477H15Ft+Ujzj18/Ge47jr9a2DO7heMFAhIiLKAFkGnfT939YcwOXPrJNa5Lt7yZBYzWKgEth15+XPDsdolKEYqBAREWUAszEQqDzw9g58srcZf197EABgzQq/I3Ke/77ezoklBipEREQJtqehA/Xt9pi+hlmvCzlW29wNIBCMqCmw+NrnyzMq8ZSYVyUiIiIAQH27HWc9uhoAcPDXi2L2OmZDaKDS0uVrh68JuSegstACIFCjIhIEARpNb48cHMyoEBERJdCOY+rLhQebWkzR5m/e5pDt8xNs3rgSAKEZFfly5lhiRoWIiCiBPLIYwen2wqiPTQ7BpRKMiAGK3d/Y7cZ5o/HX1fvg8gj4w3emQ6PRYFZ1EYDQQKWpw4FsU+zDCAYqRERECSTvSdLj9MQuUPGEBiriqh8xYMk26fHJnV/D8Q4nJg6xKs61Bk39HGjuwoji7JiMVY5TP0RERAnkcAemULqcsWtP71JZgtztD1TEjIpJr0VprjkkSBHvk9vf1BWDUYZioEJERJRAnY5AcBLLug+1qZ8u/+vZXb771ApuRRqNBjOG50u3DxzvHNwBhsFAhYiIKIG6ZIFKTywDlTBTP4IgSFkds6H3sOA/P5qNu74+AQDQ3Okc/EGqYKBCRESUQJ12eUYldlM/To9v6ueWs8Ziy70LAPg60n7V0ImP9xwHAJhUeq3I6bQaFOf6+qrEaxdlBipEREQJ1CGf+nHFPqNi0GmRLetS+/1/fCZ931dGBQgU1cZrF2UGKkRERAkkz6jEcurHLQUqGuh1Whh1vhDgcEuPdI5O23cDt1wpUGFGhYiIKO31yLIo8nqVwebyT/0Y/AGKU7Vmpe9ASeynYmOgQkRElP7kXWF7Yjj145RN/YQzb3xJn88jBiqc+iEiIsoA8kAlltMpLtnUDwAsOWe84v77zpsIi7HvPrDi1I/D7YWzl9b7g4WBChERUQI5ZFkUWwyzFGKgIna+ve70UXhj8anS/fn+XZL7kiNrmx+PrAoDFSIiogSSZ1RsPTEMVNzKGhUAGFOaI33vFUI716rRaTWw+qd/Wrtj30uFgQoREVECyQOV9lgGKl7f6+hlK3vkmwpWFUW+b8+Q/CwAwJHWnj7OHDgGKkRERAkkn/rZcdSGZz7eL63+ESLMckRCqlEJ2rPnvZvm4k+XzcDMqoKIn2tYgS9QqWvrwfPrDuHxlXsGbZzBuHsyERFRAni8Aq5+bgP2Hw9s7newuRsP/ncnjrT2YFhBFv7w/h78+0c1mFARuklgtLr9S4/NQd1nx5XnYlx5blTPNdSfUdl4qBWvfVEHALjoxGGoyMsa8DiDMaNCRESUAFuOtEmt64O9s/UYHvzvTnQ43HhpQ+2AX2vjoVYpIKrIMw/4+cSpHzFIAQI9WOwuDx58ewdO++0qfLpP/eeLBgMVIiKiJNPY4ZC+zzL2vv9OJC588lPp+/JBCFTysgwhx+z+Kaw/rdqLZ9YcwOGWHlz5tw0Dfi0GKkRERAlgD2rupg/Tvr61a3BX1pgNAw98xF4qcmKzun99flg65vYOvMaGgQoREVECBLerv2zWcNXzWgY5UBkM1qzQEldxn6KRQauH3Cqt+qPBQIWIiCgBgvf1EVfSBGsexEDlJ2eOGZTn6S2j0uVU/lxNnY6Qc6PBQIWIiCgBOoMClXCdYaPJqGw81Iqdx2zSUmRAmdG4evaI6AYZhtjwTU6cymqwKQOTpg4GKkRERCknOKMyWtYlVq6lM7JAZeOhVlz45Kc45w8f40fPb5SOd8tqYSyDUJgLhMmoOD3Y39SJ4/4MiriEecdR24Bei4EKERFRAgQHKicML8BdX58Qcl6Hww2Hu+9dldfKlgKv3NUY8jp6rQYm/eC87eeqZFSe/eQA3tpyDABw2phijCi2AADufG3rgDYvZKBCRESUAJ2yYtoHzp8MAPjB3GqsuHkurju9Gh/cdgZ0/pVArV29t9Zv7XLid8u/Ur1PLNq1GHXQaNRXFkVLvnKozGoCAHzV0IlnPt4PwBd07W3slM5p6+l/nQ0DFSIiogQQd0q+bcFYfPeUKun4mLJcLDlnAkYWZ6PAX7fSV53KiypN4bbVtaO9xyVlVOS7Hg+GJy47Afd/YxLGlwe65nb4X6sox4jzpg6RjtudzKgQERGllG117QCA6hL12hQgULT6/LqDvT6XR6VfybmPr8FNL2+SVuFYBjlQWTS1AlfNHqFoTicqzDbi5rPGSrd7XH1PXYXDQIWIiCjO2rqd2FXfAQA4eWRh2PPEtvcvbTgc9hwAMOjU384/P9iKBpsdwOBnVETBjesAX6CSbdJLBbUMVIiIiFLIhgMtAHwrfYpzTGHPO3dqRUTPZ9Cp1550ONz460e+upHZo4qiHGVkfnXBlJBjRdm+n0ls/y82g+sPBipERERx9tlBX6DSWzYFAJaeOxEAoNEA3l7a0Tt6WVUjZm7mjCmOdpgRqRlVhD2/PEdxrMDiW76c5S+67XG5Qx4XKQYqREREcbbjmK+3yPRh+b2el+2frhEEwN7LEuVuZ9+BgFWl98lgMei0kG9VJGZSAhkVFtMSERGljK8afEt3x5bn9npelmwZcHcv0yfB+wapiVWNihpx+XIgo8KpHyIiopTQ1u2U2sqPCdONVqTVaqRusm3d4XupyJvH1VSr16JkxzFQEXeCZqBCRESUYmpbugEApbmmiIIH8U1+4WOrwxalitmW+86bKHWEDabWTTZWxMZygakf1qgQERElPUEQcPnT6wEAQ8Pslhz6GN9/PV4B722vVz1H7JWSbdKr1qLoBrF9fjRYo0JERJRCDjV3S91btf1oZ1/v74kSrNtfo5Jt0isCkqoiX3Ylx6QftPb50RCnfg41d/X7ORioEBERxUlbT6DOpD8b9bWGaaUvdZ816uCSLWM+scq3/DkehbRqgdDYMl8Nzn+3HkOXw40dR2248tkN+KqhI+LnZaBCREQUJ/JA475vTIz68eH2/BFrVLJNerg9gQBovH9VUTwCFUEI7fNy0cxK5Jj0cLi92NfUiR/843Os/qoJV/5tQ8TPG9NAZfXq1TjvvPMwZMgQaDQavPHGG4r7BUHA0qVLUVFRgaysLMyfPx979uyJ5ZCIiIgSprXbF2icNqYYM6t6b/Ymumn+mJDHB+t0BDIqY8sCS55P8jeUE6eA4k2n1WDSEN+mhfuaOlHX1gMg/BSWmpgGKl1dXZg2bRqeeOIJ1ft/+9vf4o9//CP+8pe/YP369cjOzsbChQtht0f+AxAREaUKMSOS798VORI3zR+Lxy6Zrnh8sG5/oJJt1ONbJwzDHWePx+s3zMb0ynz89ydz8Ij/8bEUrgZmlH8J9pbD7Yrjda3dET1vTHNB55xzDs455xzV+wRBwGOPPYa7774b3/zmNwEA//jHP1BWVoY33ngD3/nOd2I5NCIiorgTe6EUWqLrEjvEv7lfs0qg4vUK6HYFpn50Wg2uP2OUdP+kIXn9He6gmD2qCC+ur8WL62sVxzceao3o8QmrUTlw4ADq6+sxf/586VheXh5mzZqFtWvXhn2cw+GAzWZTfBEREaUCceommowKAAzzL2U+0toTslux3e2RljBnm3TBD024+RPKAABOj7J4eE9TZ0SPT1igUl/vWwteVlamOF5WVibdp+ahhx5CXl6e9FVZWRnTcRIREQ0WMVApiDKjUpFnRmG2ER6vgN31gRUzTR0O3P36Num2WZ98gYrZoFP9ee0R7qiccqt+lixZgvb2dunr8OHDiR4SERFRRFq7fFM/BdnRZVQ0Gg0mVPiKZPc0dqK9x4UH3t6BUx5aidc21UnnabXx75Ui0vXy2sU5ppBjkexPBCQwUCkvLwcANDQ0KI43NDRI96kxmUywWq2KLyIiolQQyKhEF6gAgTf7tm4nLn1qHf625gA83tAlwYny3NUnIdekxx++Mz3kPvleRKIeV2Rt9RMWqIwcORLl5eVYuXKldMxms2H9+vWoqalJ1LCIiIhiRgxUCqPMqABAXpZv+qSt24Udx5KvPvPU0cXYcu8CfHP60JD7Gv2bMMr1thu0XExX/XR2dmLv3r3S7QMHDmDz5s0oLCzE8OHDcdNNN+HBBx/EmDFjMHLkSNxzzz0YMmQIzj///FgOi4iIKO4EQZCmfvKjrFEBAoFKe4/6LsoLJpapHo+ncFNPY8tyQ4Krwy2RLU+OaUbl888/x4wZMzBjxgwAwC233IIZM2Zg6dKlAICf/exn+PGPf4wf/vCHOOmkk9DZ2Yl3330XZrM5lsMiIiKKm2117bjhhY3YftQmrXwZSEYlXKDywPmT+z/IGHtMZTqotqUnosfGNKNyxhlnqLbUFWk0GvziF7/AL37xi1gOg4iIKGHOfXwNAOCzg76+IRajTtqsLxp9BSrZcWiT31/ybrnRSrlVP0RERKmoyV+nUV2S3a+djMVApbkrtN4DACz9CH5SAQMVIiKiGHG4QwtGq4tz+vVcYqBypFV9yiSRS5Mj8dR3Z8Jq1uPuRROiehwDFSIiohhRKxgdUZzdr+fKswRW/aSiBZPKseXeBVg0tSKqxzFQISIiGoA1e47jw92NIcddHi/mP7I65Hh+VvQrfoBARkXOYvRN94wbQA1IPGk0GhRlhzZ/6w0DFSIion5qtNlxxd/W4+rnPkO3U9nA7ODxLtXH5Jj7V/QaHKiMLs3BR7fPwz3nTsQjl0zr13MmglGvxZo75kV8PgMVIiKiflq+I9BdvdOuDFRqw/QJye3n6pwsgw5GnVZxuyTXhGvnjEz4DsnRGpqfhV9GuJyagQoREVE/yTcItLu8sNld+M/GI+iwu3CoWT1Q6W9GRaPRwCrLqvRniXOy0Gg0+OaM0A62apJ30TUREVGSO9Zul753uD24982v8PqmOry7rRTDCiyqj8kZQL+TvCw9jnf6liebjakbqESDGRUiIqJ+arAFAhW7y4vX/TsZv7+zEYeafTUqwctxc/uZUQGUdSpZhsx4C8+Mn5KIiNKGIAhYvr0eexo6+j45xuQZFbvbg3JrYAuYQ/4alfHlVsXxHFP/Vv0Ayl2XU3nqJxoMVIiIKKVsOdKOHz6/EWc9uhqbaluxdl9zQsbh8ngVXWLtLg/K8wIBidhDparIgiH5gePZpv4HGCW5gaW9WZz6ISIiSj717YHOrBf8+VNc+vQ6fHmkLe7jaOxwQL6d3VOr90O+u53L47tVkWdW7HWTbez/1I88UDFnSEaFxbRERJRSOh2hbel31Xdg6rD8uI5DHjABwMd7joecYzHqoNdpcfvCcSjOMWHiEOuAWt2XyjMqDFSIiIiST5fDHXLM4xVUzoyt+nb1zQHlxB2Ni3JMuG3huAG/ZkluYAopUwIVTv0QEVFK6VQJVIKbrcXDsXb1zQHl+tvcLZwKWQ1Mpkz9MFAhIqKUopZRUQteYq25y9nnOf1t7hbOuPJArUtjh72XM9MHAxUiIkopyRKodEfwmgMpnFUjz6LIC2vTGWtUiIgopagV0yZi6qfLGTqOYIOdUQGAt26cg3e3H8Pls6oG/bmTEQMVIiJKKUmTUfHvlmzSa+Fwe1XPGewaFQCYMiwPU4al1iaEA8GpHyIiSildztCgpCMBgUqXP7Mzpiwn7DltPa54DSdtMVAhIqKUoppRscc/IBAzKoumDAl7Tm9BDEWGgQoREaWULpUale4I6kViNY4JFbm497yJquf84LTqeA4pLTFQISKilCLWo1x6ciWMet/bWCICFTGjkm3S45pTR6KqyKK4/7q51SjOyYyVObHEQIWIiFKKWKPyvVNH4o0bTgWQoIyK/zUt/s0BWzoDfVX+78ZTcfsgdKIlrvohIqIUI9aoZJv0cPpX2/SoFNjGmthHReyVIi/ojfe+Q+mMGRUiIkoZDrdH2pU426SXshndLg8EIX77/QiCgG6XMqNyzakjAAAXzxwWt3FkAmZUBtHv3tuN/9tyFK/dMJvzkkREMSAvpM026iBuRCwIgMPtjdv+N11OD8S4KNdsAADccfZ4zB1TglOqi+IyhkzBjMog+tMHe1Hb0o0/f7Av0UMhIkpL4rSPSa+FXqeFRdai/h9rD+L7f/9cKnKNpQ7/cmi9VgOzwfdWajboMG98KbKMmbFZYLwwUImBo21976hJRETRE1f85Pg7vuq0Gmnlz6/e2YX3dzbgxfW1MR9Hh79lf65ZD41GE/PXy2QMVGLgeKdjQI+3u+JfvU5ElArkhbQiS1AGQ63PymATMyritA/FDgOVCDXa7FJ1eV+aBhCorNlzHJPvfQ/PrjnQ7+cgIkpXdf6MdWG2UTpmCapL0cXhnc0my6hQbDFQicD+pk6c/KuV+O7f1oc9R15tfqzdjpYuZ9hze3P1cxvg9gr4xds7+vV4IqJ0tm5/MwBgZlWBdCy4JqSlK/bt9DsYqMQNA5UIvL6pDgCw/kBL2HPE5XIA4HR7MfPBFTjc0h31a7m98VteR0SUShpsdrz2he/v8amjAytrCixGxXnNXQObfo8Ep37ih4FKBHTaQKFUuGpyu1s5JyoIwOOr9uB/W4/16zWD51yJiDLd9qPtcLi9GF5owRljS6Xj3z6pUnFeuDpBu8uD+nb7oIyFGZX4ybhA5e0vj2K9P3UYqR5Za2b5ip4epwe3v7IFlz+zDg0qF/+/Pz+C61/4At/80xqc/dhq1R0/5Vpl00WVBZZeziQiyjxt3b4sRlWRBVrZB8iFk8oV57X3qE/9nPv4Gpzy0EocPN414LEc7/AFQ8HZHBp8GRWobKtrx40vbsIlT63Dtrr2iB9XbwsEIUdaA4HKql2NeGXjEXyytxlr9h4P+/gtR9qxq74Db24+2uvrfNXQIX0v/0dIRESBAMSapZxuycsyIN8SOCZmO4LtbewEACzfUT/gsez3Bzsji7MH/FzUu4wKVP63LTAN01tgEazRFkgjyotktx9tVz0eTl+xxx7/PyKAS5SJiIKJGZX8rNC6kHd/Ohf3nTcRANAZJlARyWsK+2tfk+/v9aiSnAE/F/UuowKV3fWBQOBIazc+P9gCbwTFqzZ7II3Y3uPCp/uOo66tBzuP2aTjzf5AZWh+Fr4TNF8qCtetUBAEfHmkDQ2yzA0DFSIiJTGjkqcSqJTnmXGWfwpILaMi/1sfaauJcLxeQVoswYxK7GVMFdCR1m68v7NBuv3PdbX457paPHj+ZFw+a3ivnQXlF/0ne4/j/rd8S4flRVRifYnJoJU6JgYLF8X/b1s9bnjhC8WxHgYqREQKtl4CFSDwN9np8WLZJwdw1ewR0t/2LtlCCJdnYIFKl9MNMe6RTzlRbGRMRuXKZzeoHr/7jW24/p9fqN4n6pBlVN7f2Sg7HrjwxYxKlkGH604fpfo84bYhf2lDaLtneQEvEREBrd2+v7PhgoNs2b4/9721Ayt2BD6cyrvVdg/w76v4eJ1WA5M+Y95GEyZjfsP7m8JXeb+7vR53vvolnvl4Pz4Jql0RBCFsYZacWKSVY9KjJNeE926aG3JOuH8c48tzQ4453N6IpqWIiDKBze7C54daAQDleVmq5+i0GkVGe29TYLq/U7bqUgx4+kt8rmyjjvv8xEHGBCp9efmzw3jwvztx+TPK7rN2lzeiJmxiMe0Z43xr+8eV52L3g2fjlOpC6ZxwgUq4KSFH0DyqIAiKDrhERJniy8Pt6LC7UW41Y87o4rDnGXSBwEEetOyRrarsb+dwkdp+QxQ7DFRUyDMZ8mmfvpj0WnzrhKGy2zo8deWJOHO8L3hRqzt57P2vsOzTg6rPd/VzGxT/oL637DOc+/gauAc4v0pElGrEv8XDCrIUTTiDyT/g6bQaCIKAdfubcb2sDlBcPdRfnQxU4iojApVosxDVP38Hj674CgDQ0UeTNrmzJ5ejzGpWHLOaDZg0NA+Aelfbx97fE/b51h9owYP+PX88XgEf7G7C9qM27KrvCPsYIqJ0JP4tzumjE2xxjkn6vsfpwRub6/Cdp9Ypzhno1E+3v96FgUp8ZESgIs9kPH/tybjm1BF9PuYPK30BhFifUpxjwsUzh/X6mBNlm2TJie3wg6d+HO6+C7pe8+8zJD+XU6JElGnE3ijhVlWKLj15uPR9l8OD1zeFNtocaEZFXEGUY+JWJ/GQEYHK8Q5f9GzUaTFndDHuOHt8RMEKEEg3FucY8fDF07BgYhkAYP6EUswaWYgLZgSmek4eWaT6HGKgErySJ9w86d+/dzLK/ZmZvCwDWrucsLsC6UwNGKkQUWYRp1v62gTwurnV0jLlbqcbHm/oVHmnwz2gXiri4gz5KiOKnYwIVDYfaQMAjCnLgUajgdmgw7Rh+dL9y645KWy2JHjjqT9ffgI2/PxMPHPVSfjXdTWKoq6xZeodCi3+i7krKFBp7lQPVLyCgH9ddwoAX4OjUx5aqdhka6A9AIiIUk0gUOk9ONBqNbh2zkjpMe4wixXaevo3/fPm5jop495XdocGR0b8lt/50tc6f5Ys42HQBWK0GZUFmD2qGE2dDny4u0k67nR7Q7by1uu0KJXVoSyaWoGtde04c0Jp2GVqVv8/rOCNsprDZFS0Gg3K8wKv4XB78YV/WZ54m4gok3REOPUDBDId3U4PPEGrNq1mPWx2N1q7XCjNNas9vFc/fXmz9H0em73FRdoHKg02O97d7tuA6vwZQ6TjAgIXr8Wkg0GnxYKJ5YpApcfp6XMrb7NBh/u+ManXMRRk+3bXbA8q4GrpUm5FPn9CKTrsbsweVaQIpADgmGx35khqW4iI0omYUYkkULH4a0e6HG5FewmjXouiHBNsdnfYHZZ7E7ww48zxZVE/B0Uv7QMVcXqlOMeEqbLpHnmQLQYFF80chs8OtuB1fwFrt8sNWx+BSiTEDbTagv5hHG2zK24/feWJYbMy+2XbkjtczKgQUWbp9Ge3+1r1AwQyKl1OtyKjYjHqpAx3NK0nWrucyMsyKFaBXjZrOGaPUq9LpMGV1jUqbo8Xv353F4DQ6my1am2jXotHL5kuXchdDo+s0rz/KT4xPdje41L8o6lt7pa+P2tiWUiQ8uOvjZa+3y/rsMipHyLKNFKNSgQZFTHr0unwKGr6LAYdrP4PjrYIA5UPdjVixgMr8Mya/dKebtlGHX51wRRoe+nnQoMnrTMqf1i5B6u/8k3lWIKqs08fW4rzpw/BZH+PE7lsk28O0zf1I9aoDCSj4pv6EQRfFL+vqRNvbTmGf31+GADw/TkjcdvCcSGPu2n+WGw/asOqXY3YfjSwUzOnfogo00g1KhH8LRaDkY4el6I5nMmgk/6WL3ltK8aU5qq+B8jd8u/NAIBfvbMLJ43wdRoXp/MpPtI6o/LlkXbpe71OGfnqtBo89p0Z+P5p1SGPE5cTdznd0j8O6wACFaNei2z/c7Z1u3D7K18qutGeM6UCZkNohken1eDe8yaGHGdGhYgyTTQ1KtaswAIGeRZbq/E14QR826Oc+/iaPp9LK8t0i6UEBRYGKvGU1oGKvOwp3H46asTsS4/Tgw6HctVPf+X7L+zWbidaZEW10yvzMW1Y+Ii+qig75B+mQ6UVPxFROot0eTLg6z8F+KZ35HUlWo1GyrZEyisroP3+Pz4HwIxKvCVFoPLEE09gxIgRMJvNmDVrFjZs2BD1cxxt6wk51iW7QKPZH0ctozKQqR8gsC15W49Lavz2j++djFd+VAO9rvf/DSGBCjMqRJRBBEGIql5QzJq4PAKaOgKrK+XFtJFwebxoVeliW8BlyXGV8EDlX//6F2655Rbce++9+OKLLzBt2jQsXLgQjY2NUT3PLf/eEnJMXtUdyQ7IIjEosfXIA5WBZlT8gUq3Uwo0JlRYQ5Yhq8kOKvxloEJEmcThDuxiH0mNisWoU924sNRqjvhvuccr4A3/CtBgQ/OzInoOGhwJD1QeeeQR/OAHP8A111yDiRMn4i9/+QssFgueffbZqJ5nW117yDExAgei6+Za6E/r/fz1rahr9WVqBp5R8T1ngy0Q3WcZI9snIifoHxaLaYkok4grdDQa38qdvmg0GtXMSbnVjOGFlohe86cvb8Lt//lS9b4pfRTg0uBKaKDidDqxceNGzJ8/Xzqm1Woxf/58rF27VvUxDocDNptN8aXmg12NOCprkhaujbKaItnum05/gDPgQMU/L1ovG5NZH9mvP3g5HvuoEFEmkaZ9jPqIlwSrZV5mVRdizphilbNDve3vaK7mhDAb0FJsJDRQOX78ODweD8rKlN39ysrKUF9fr/qYhx56CHl5edJXZWWl6nnXLPtMcXtKLwWrwYpUCqVyB9BHBQhM/Rxr92Vo9FpNn7UpouCpn+A9g4iI0pm04ieKD4wXz6yETqvBxTOH4ZUf1eCecydi0ZQKGHRaPHPliQACRbfBesvA//TMMSizRt96n/ov5fqoLFmyBLfccot022azhQ1WRN8+cRhuXzg+4tdQW/4WzT8QNeJytve2NwAAsiJIXwbGo/zHtKm2NcyZRETpR9zrrDTX1MeZAT85cwwWzxsNrcY3FST2QAGA8RW5AAB7mBWU8rKBYKdUsxttvCU0UCkuLoZOp0NDQ4PieENDA8rLy1UfYzKZYDKFXqxhOs8DAH5z4dSwrenV9KhcvGqFWdEoDYrATVEEKsHTTrvqO9De7eKGWESUEd7Z5suwXzBjaFSPC/d3W+xb5XB74fUKIdNJHb0EKvn8uxt3CZ36MRqNmDlzJlauXCkd83q9WLlyJWpqaqJ6ruDLcfJQKwBg0hBrVEEKAHx9SgWMEdaPRGrBxDJFpsZsiPz51TI8/dlQi4goFR3w73U2WLUh8oy22ipKsX+WSC8LZNjsLf4SvurnlltuwdNPP42///3v2LlzJ66//np0dXXhmmuuiep5goORbn8dxz3nhnZ27UuZ1YzP7pqPNxafCsAX7AyU2aDDkq8Hpp+imfoZW+5LU1rNeqnDbZczfMRPRJQuup1uqRdKVWH2oDynvBO4WgZdnlHRaTWoKgqsFCqzRj79RIMj4TUql1xyCZqamrB06VLU19dj+vTpePfdd0MKbPsSnOHriqLdspq8LAOmV+Zjxc1zUZY3OIVTc0YHqs29QuSrkM6dUoGxZTmosGbhvD+tQVdLN7oZqBBRBjjk37y1wGIYtOlunVYDo04Lp8erGqiINSqjS3Pw8g9Pwa/+uxP7mnxZnWgz9DRwCQ9UAODGG2/EjTfeOMBnUV48gS6GA/sRx5TlDujxcvL1++JFHwmtVoPx5b6sjtQ118GVP0SU/sReVsMKIut/EimTwReoXPDEJ7j5rLE4eWQhRpXkAAhM/ZRbzSjOMeFnZ4+H2ajDd0+pGtQxUGSSIlAZDIIsQ+H1CtIS3uwBBiqDSaPRwKjXwjmAzrLiz8OMChFlgnqbr/dU+SBltkVZBh067G40djiw5LWtAICPfzYPlYWWkK1TyvPM+NUFUwb19SlyCa9RGSxuryAFAN2yVN5AG7UNtscumQ4AuHr2iH49nhkVIsokYpPMikEOVEYUhda7rN7TBMC3yz0Qvs8KxVdyvYsPUI/TA6Neiz0NHQB8ldqmQV69M1Bfn1KBj24/o997RWT7d3Y+3No9mMMiIkpKx9pjk1GZOMSKDQdbFMdsPb5MSkuXb4f7Qu6SnBSS6118gLpdvovs1S+OAABOH1uSlIVPVUXZEXelDSYua37s/T1hmxUREaUL8UPZYGdUJlSE1h/WtvhqBxmoJJe0ClTE6ZBG/8Z/Z4wvTeRwYkK+f9Ex2fdEROnG4xWw3b/h7MSKwd0IcGRxTsgxcZqJgUpySatApdvpRnuPC4f9VeIFadhBcH9Tp/Q9m74RUTp4c3MdNh9uCzm+r6kTXU4PLEYdRpeGBhYDMaIodBWR+De1mYFKUkmrQKW504kzf/8hdh7z7aicjh0E7zg70DSu1f+PiYgoVW0+3IafvrwZ5z/xSch9ext9H8zGluUOeBuTYCWyfYPE527vcUEQBDT4VxoxUEkOaRWo7Dhmw/HOwJt3OlZsXzRzGCoLfYW4rd0MVIgotYnBiBqxdf7I4sHpSCun0WjwkzPHYM7oYrx6/WwAQHuPGx/sbkRLlxNGvXbQOuHSwKTVqp99TcoLPh03j9JoNJheWYDDLT1o7ebUDxGlD0EQFAsgDvoDFbWlxIPhlrPGAgDq2nzlArYeF3Ye860aXTipnBu/Jom0yqgEd3tNx6kfACj0/+Ph1A8RpTp5s87gDQJrW3wrfqpU6kkGk5h9d3q8aPRP+5Tmck+fZJFWgcqWoGIssTlausn3B2Cc+iGiVCff9azHqWy50BCjrrTBso06qU7lkD84spqZTUkWaRWoiGqqi/Dq9bOTsofKYBBXM2V6oNLe48ITH+yV0rZElHrkW4rINwgUBEFqnz/YPVSCaTQalFt9r/Hhbl93WmtWWlVGpLS0DFRuWzgWM6sKEj2MmCnwV6K3dmV2jcptr2zBw+/txg0vfJHooRBRP8n3LeuWZVRsPW7YXb4gpswa20AFAL5bo9xwMJcZlaSRloHK1GH5iR5CTBVw6gcAsGJHA4DQKT8iSh3y4EQ+9XPM5suU5lsMMBtiP41/+tgSxW1rku0Tl8nSMlAx9LM9faoQ1/ZneqAiMibZfk5EFDl5oCLPrnyytxkAMLY0tNV9LFSXZEPeqsWahu0tUhX/wqegfKlGxaWomM8kXm/g5+7vBo9ElHiKqR9ZjcrH/p2MF04uj8s4THodKvICf0tYTJs8GKikIHHqx+n2osuZmRsTdtgDf9zSdXUXUSbodqhP/Yh7tlWXxK/pmrwTbXFOera3SEVpE6iM8e8Dcca4kj7OTH0Wow45Jt/8aX2GbkzYIpv28ngzM6tElOqOdzrw2qY66bZ8Guh4py9QKc6OXz8TryxDXcI+KkkjbaqFHr90Bv73VTuumj0i0UOJOY1Gg6H5Wdjd0IG6tp5B36wrFcjrc+RLGokodVzwZ+X+Pl0OX6b0mY/3o7HDF6gUxTGzIV8qna7tLVJR2mRUhhVacOuCcSjOyYwoeGiBby61rjUze4jIu/IGN4kiouT3xAd7cbhF+fdL/ADy4H93SsfiuTFgOm67kg7SJlDJNMP8gcqR1u4EjyQxWrqYUSFKZQ+/tzvkWFOHQ9qIUBSPpcmiB86fjOribDx2yfS4vSb1LW2mfjJNkX/eNlU2JnS4PTDpB/YHZ+XOBhxt68F3a0bgsCyTxIwKUXp4YX0tXlhfm7DXH19uxarbzkjY65M6BiopSlzp0iNb2pes9jd1YtEf1+CyWcNxz7kTQ+5v7LCjod2BKcPyen2ea//+OQDggbd3wukJzCW7vQJcHm/a988hyjR/vvyERA+BkgD/sqcoi8kXqHSnQDbhT6v2osflwd/WHFC9f9avVuK8P63B9qPtYZ9DnjWRBymiVPg9EFF0xpfHp9kbJTcGKilKyqikQX2GuCLwU38nSjXiUsVw7GnweyAipWwTk/7EqZ+UlWXw/a9LhUyCVtaX2usVFLfl1DIloiaVQEWrAbIMOnQ5PaxTIUoRn+w9jqdW7w85nmvWKxo5AgxUyIcZlRQlZlRSIVCRhyU2e/jiX1cvgcq+xs6QY9kmPSym1AnYiDLJvqZO3PvmNpzzh4/x89e3Sscvf2Y9PvqqSXGuXqvBH78zI+Q5LHFc8UPJi4FKigoEKslfTNslG+OiP66RmjoFUwtU2nt8+xnd/p8vQ+7LMemRZUifKTCidHL/Wzvw97WHsPOYDS+ur+0167nhrvmYN74Uf7vqRMUuxuGyr5RZGKikqKwUyqi0dgWyKHVtPfj98q/wwa5GeL0C3LLgxOVRtsJ/b3s9TnhgBf60aq/q8yoClRT4PRBlktVBWZNDLV2q5/32wqlSU7czJ5ThtDHFMR8bpRYGKikq2+ib8kiFN+i2HuV0z7OfHMA1yz7D46v2KupS1u5rxovra6Udoa97fiM8XgG/X/GVdM5/fzJH+t5i0ksBGzMqRMmj0Ra6B9nB46GBSmVhFr59UqXi2CUnVaK6JBtX1VTFbHyUWliplKLkUz+CICT1vhTher28v7MBV8r+GG2ta8fW17dCr9Uo/njlZRnQ7g925BuF6TSQmsgxUCFKHrUtoR2za1u6Q6Z3r5s7KuS8XLMBq249I1ZDoxTEjEqKEjMJXsE3RZLM7C71ItkOuwsOd+h9P3v1S+xvChTPyv+4FVgC+364vUJKNb4jyhSHVbb2aO9xoUGWaXn+2pNxxSnMmlDfGKikKIsxkAxbvr0hgSPpW7hsR2u3S7Fbqdy7suBL7JFi0Glg0GkxNN+3z9H0ynyYjaxRIUo2wZsNAoCtx41XPj8CABhXlovTxpSEnEOkhoFKitJpNbh2zkgAkLZDT1ZioPH+LXPx8EVT8foNswH4lip3u9QzIQ3tgU9eXn+Nrdk/zfPOT07DLy+YjBu/Nloqpu3m1A9R0jjW7gtUrjhlOEYUWQAAz687hC9qWwEAF84cmrCxUephoJLCzhxfCiDwRyEZeb2CNL2TbzHi4hMrMXmob08fQfDtlqrmWHtoMZ6YPcmzGHD5rCqU5pqlqR87MypECfWnVXvwrH+bDJu/cdvokhz86PRAHcrHe44DCGyqShQJFtOmsAr/FMixdnvSFtTKa1DE7doNOq3UhbJeJSAB1IvxzIbQuJp9VIgSr66tB79b7ludd8UpVej0Byo5ZoP0YUIu28RGbhQ5ZlRSWLnVDMDXS6UjTBO1RJPvwWPWBy43sW+CWuYEAHbVd4Qcy1LpUplK/WSI0lWHrON0l8Mt3c4165FrDv08zNb4FA0GKiksy6iDQefLooTr9ppoPbJCWL0ucLmV+YOsR/w9UiYNseLkEYW9PpdZLVBhRoUo4eQfFDodbnT6/x7lmvTIUQlK5IsBiPrCQCXFif/gkzVQETMqwUHGMP+0lcik18IrKDvTBlMNVMQaFQYqRAljkzV1tNldsqkfPfTa0LcZTv1QNBiopDjx00qnIznfqHvCBCpDQgIVHXoPU3rPqHDqhyhx2mWBSqfdLe2CnGs2YNIQKxZMLFOcn82MCkWBgUqKEz+ZdCdtRsVXTBtcXzK0QBmoWIw6nDiioNfnylIrpmUfFaKEE1f5AMDdb2xDp78BY45JD61Wg6euPBFl1sBKH9aoUDQYqKQ4ceqnM0kDlQ93NwIAPF5lvuTM8aUYX54r3S7PM+OnZ47B7QvHYcXNc1Wfa1iBJeSYhXv9ECWcfOpnT2MnxFlceSGtUVZMr7YSiCgcBiopTpz66UrSFvKP+3c+rmtT9noptZrx9o8DGwzmmg2wGPVYPG80xpTl4uSRoYW1d5w9PuSYmbsnEyWcze4KOWbSa2GSBSder/I+okjxaklx4tRPV5LWqIjOGBfaLlu+Cij4E9bz156MJy8/ATptoDeMUeWPG1f9ECXW3sZO/PWj/SHHx5blKno7leeZpe+TsecTJS8GKikuO8lX/Yi9Xm5bME71/hnD8wEA35w+RHHcpNfhnCkVKMvtvYOlOPXFjApRYtzzxjbV4+NkU7sAcMMZozB1WB5evb4mHsOiNMKKphQnFqUla6AiZjqywsxJv/SDU9De45L6qgRbet5E/OifX+Dq2SNU72dGhSixjneqb4Nx6ugixe0zJ5ThzAllqucS9YaBSoqTApUkzShIgYrK0mLAV2OituxYdPbkCqy+fV7IKiFRlqyYNlm3ESBKZ6NKcrCnsROjS3Owt7FTOn7BjGEJHBWlE079pLhso1ijknwZFY9XgNOtvjw5GsOLLIpaFTkxUBEE5b5CRBQf4s7lPzp9FHL9H5ysKm3zifqLgUqKyzYl7/JkebfYcFM/AyUPgFinQhR/Yg+nbKMOf71yJqZV5uOF75+S4FFROmHYm+JykrhGRd4tNlbLEXVaDSxGHbqdHrT1uFDg3+yQiOJDnHbONukxe1Qx3lxcnOARUbphRiXFWcTlyUmYTbDL6lNiWTsiLnusD7MTMxHFTre/hxP376FYYaCS4pJ51U9fK34GS4UYqNh6+jiTiAab2MOJOyJTrDBQSXHJPPUj1owMpJA2EuLS5mPMqBDF1faj7dLyZG40SLHCQCXFiR1dw039bD/ajj0NHfEckiSwc3JsLzMxo3KsjYEKUTz97D9fSt9bOPVDMRKzd5Bf/vKXmD17NiwWC/Lz81XPqa2txaJFi2CxWFBaWorbb78dbnfyZQaSWW8Zlb2NnVj0xzX41pOfwhu0KWA8xGvqZ0RRNgBgX1NnH2cS0WBq6w7s8cONBilWYhaoOJ1OXHzxxbj++utV7/d4PFi0aBGcTic+/fRT/P3vf8eyZcuwdOnSWA0pLYk1Kt1OT0gw8tKGWgBAh92NDnv8AsBupxuCIKDBPxVTlN17G/yBGlPma9W9p5GBClE8TRpiBQDotRrWqFDMxOzKuv/++wEAy5YtU71/+fLl2LFjB95//32UlZVh+vTpeOCBB3DHHXfgvvvug9HIZaaRkM8Ld7s88Pr3V7eaDfhKNuXT3uNCnsUQ8/HUNndjwWMf4ZzJFSj179Mzsjg7pq85pjQHANDU4UCDzR62HT8RDS4xa/rwxVMTPBJKZwmrUVm7di2mTJmCsrLA3g8LFy6EzWbD9u3bwz7O4XDAZrMpvjKZ2aCF2LS1tcuJafcvx6m/XgWPVwgJVOJh+Y562F1evL6pTnr9WAcq2SY9TvBvbvjWlqMxfS2iTNftdON/W4+h2+mWGk2ykJZiKWGBSn19vSJIASDdrq+vD/u4hx56CHl5edJXZWVlTMeZ7DQajTT9s62uHYLgm+ppsNnRYAtsFhavQCXfEsiEfbC7CQBQWai+T89gOmtiOQBga117ROdvOdyGa57bkLBCY6JUddsrW3D9C1/gsff3SLVxYq0cUSxEFajceeed0Gg0vX7t2rUrVmMFACxZsgTt7e3S1+HDh2P6eqlA/DQjr9E43NKtOCdegUqPM7QWJh5z18P8mxZGsvLH7vLgm098gg92N+GnL2+O8ciI0ss7W30fJF9aXxvoocJAhWIoqqvr1ltvxdVXX93rOdXV1RE9V3l5OTZs2KA41tDQIN0XjslkgskU2+LMVCN2hNx5LDANVtembH4Wr0Cl0xG6TLq33ZEHy5B8X13K0fa+m779+n+BYLqxg0uaiSJlswf+jkyrzJcymDlcmkwxFFWgUlJSgpKSkkF54ZqaGvzyl79EY2MjSktLAQArVqyA1WrFxIkTB+U1MoWYdpUHKkcTFKioLZOOdR8VAKjI82VU6tvt8HgFxW7LXQ43LMZAG/9lnx6U7ivOYdBLFKn9TV3S9webu6S/K9nMqFAMxewdpLa2Fps3b0ZtbS08Hg82b96MzZs3o7PTNz2xYMECTJw4Ed/97nexZcsWvPfee7j77ruxePFiZkyiJE6tHGwOTPckLqOiEqjoY/9pqzTXBK0GcHsFNHcFanPe3FyHSfe+hzc210nHjLINEotyuLqMKFL7Zb2KjrQG/sYwUKFYilmgsnTpUsyYMQP33nsvOjs7MWPGDMyYMQOff/45AECn0+Htt9+GTqdDTU0NrrjiClx55ZX4xS9+EashpS21PxJ1QbUa7T3OuIylW6VGJR5TP3qdFgX+Qt5b/71FyiiJNSg3/2uLdK7YyRYA8rMYqBBFKlxTxRyu+qEYitnVtWzZsrA9VERVVVV45513YjWEjKE2P5y4qR+1GpX4LC7LNevR3OXEx3uOY/avV+GF78/q8zGeBHTsJUpV+xq7Qo49e/WJ0Gpjtzs6Eff6SQMulTfbWv+qn+GFFgAJnvqJQ0YFAOwur+L2b95VrkATN0ls7Qpkl+xu9T2SiCiUWkZFrA8jihUGKmlADEYA4IxxvmJnp9v3pi22uE5koGLSx+cyC552+vKIsqfKnsYOHDjeBZtsOwFHUHBDROo8XgGHmrtDjg9hoEIxxkAlDVxxShW+d+pIfHDbGSgJWsUyeWgegPgEKoIg4MBxZWrYbNBKq21iTWznHc6u+g4s++SA4tja/c14ft2hWA6LKC10Od1wepSB/aIpFXHZmoMyGwOVNDA0PwtLz5uIkcXZIYW1Ukalu/dAZcWOBmw53DagcdTb7GjpUhbtxmvaBwCunePr4TOtMl/1/n2NnWjq9K0IKsoOFNHe88Y2uDzMrBD1plul/uw3F3GPH4o9lmqnmRFFFsXtceW+nYU7HL4djdWyG/ubOvGDf/hWYx389aJ+v/aehtD563gsTRbdfNYYzBpZiBOqCjDt/uXS8SyDDj0uD9p7XDje6QukzplSjn+uq5XO2dfUifHl1riNlSjVdPmnVq1mPZ656iQAbJ1P8cGMSpo5dXSx9H2uSS/9IREEhKRtRfKeK/LOk9ESH1tdEtiEUK+L32oAk16HeeNLkZdlwC1njZWOV/mDtwPHu7DhQAsAYEi+cl797Mc+hiBwBRBROGJGJdukx8kjC3HyyMIEj4gyBQOVNDOqJEf63isIMMkyGg63eqDilB0PXtYcjU5/kWqVrLi325mYVTU1o4qk70cU+QKn9f4gBfBNlwULXjVERAFiRsViZLt8ii8GKmlG3s/A6fHCIMtohFvh0iqrX6lrjTxQEQQB/7flqLQDsbjiJy8rUFznDBMcxZp8DFVB02FAYBNDubY4NcUjSkXiqjp2oaV4Y6CSxlweX02KuDw43NRPW3fgDTqajMrqPcfxk5c24axHV+NIazcabL5uuDlmvfSp67JZw/s7/AGRByrDVQKV0lxzyLE2lYLjwy3dePLDfegYwJQYUTqQdkpmRoXijKFxBjDptXC4vXCEWb7bKgtUxGLTSHwpWyU05zcfSN/nmg14/tqTsXZfM647fVT0Ax4EVnMgUKksUAYqf/jOdMX9IrUl3Bf8+RMc73TiwPFO/PaiaYM/UKIUIWVU2C6f4oxXXBqaP6EM7+9swEUzhwEATAYdYHeHrVGRT/3IN/Tri16nnpDLMekxs6oQM6sSV2yXZdTh3vMmwuXxKop7Ad/48iwG3HPuROg0wJtbjmJTbZtqRkUM3FZ/dTwu4yZKVlJGhVM/FGe84tLQI5dMw6qdjZg/sQwAYPQHFOECFXmPleA+KL0Jt71Hrjk5LqtrTh0JIHQlU64/m3LtHN/9q/f4gpDeNm50c08gynCBjAqnfii+WKOShqxmA86fMVRammzybwoYbupHXkQazdRPuBU9yRKoiHJNehTKGrwFjy/fX8+illERebl0mTLYl0fa8LvlXwEALJz6oThjoJIBxCXK4Ypp5bUZzZ2RT/2Ea8uvVv+RSBqNBhMqcqXbwYGKeLvDHrpPkYi7LFMm+96yz6Tvk+2DCKU/BioZQFz1E255sjzg2NfUFbJfTzjhmsOVWUNX1CTapCF50ve5QYGUuNzyTx/sxX82HpGawsl5GahQBpNnWq1ZyfVBhNIfA5UMYNRHVqMiLjt8Z+uxiJ7X1qOegajIS75A5Uenj8L0ynzMHVsCa9AnwhzZ7dte2YJv/3VtyN4/rFEh8mFGheKNgUoGkDIq7tCaEq9XQIe/Udv5M4YCAI60hm7lrqbept5zRV4PkiwKs414/YbZ+Mf3Tg7Z70htv5Kv/E3sRB7WqFAGk/+TCQ70iWKNgUoGEGtU1DIqHXY3xPfgKUN90yOHW/pu+uZ0e7G7vkP1PrWND5NBuHGp9YXYeqRdMd3DqR/KZDrZv53gqVOiWGOgkgF6W/Uj1qdkGXSoLvb1GzkcQUZlT2MHXJ7QN+/F8xLT4G0g1FqCN3Y40CP7fXHqhzKZTisPVJhRofjiFZcBemuhLy5NzssySK3m61p74PEKij9OwRptvtVBY8tyMK7ciprqIpw4ogBjy3LDPiZZqU39dDndIcuv3R5v2CZ3ROlMGagwo0LxxUAlA4iBitruwGJGJS/LgLJcM4w6LZweL46192CYrPW81yvgyY/2wen24kenj5ICnDKrGY9fOiMOP0Xs5Kh8Qux2eNDlUBYLdzrcyLckX/0NUazJp36yDGz4RvHFQCUDiA2axG3a5eSBilarwdCCLBw43oXDLcpAZfmOejz83m4AwJiyHKk5Wl4aLFWU7zAt6nK6pU0WRR12BiqUeXYctUkF92eMK0F5Eq7qo/TGPHYG6K2hmRioiL0RKgt9wcnhFmWdyltbAkuWW7tdUqCSb0n9QEWtQV23w4O6oJ2ke2sIR5SuXvviiPT9I9+enriBUMZioJIBxBqMzl4CFTEzMjTf92npWLsym9DUEehYa3d6pMflZ6V+hqGy0IL7zpuI+RNKpWNdTjfqWoMDlfAt9onSlXxpfjK2HqD0x0AlA4gZA7U32uBApTTXF6g0dARNe8jqNbqdHrR1+2pU0iGjAgBXnzoSz1x1Ev5yxQkAfD/jhoPKDrWdDmZUKPO4/av7fnLmmASPhDIVa1QygDj10+lww+7ywCwrhrP1KKdwxPnnhqCMSqcjEOT0uDxoC5oyShfiUuWNh1qlY3lZBrT3uDj1QxlJbBQpFuUTxRuvvAwgrmr57GArJt37HlbtapDuC86olFlNAEIzKvJpox6nGw3+5cmluabYDTwBgneGHZqfhZrqIgBAY9DvhCgTiI0iGahQovDKywDyvgcer4Alr22Vbgev3hE3FKxvD9SkCIKgyCZ0OjzYecwGwPdGnk7E/Y7kThxRAAB4ds1BtHY5Q+4nSmfiZqYmLkumBGGgkgGCO0nKswahGRVfoNLc5ZA25nO4vYrOrK/KVgFUpFmgEtxO/5cXTMbls6pQZjWh3mbHO9si27CRKF1w6ocSjTUqGSA4UCmQFcAGL08utBhh0Gng8gjY39SFDrsLVUXZYZ9bratrKhteZMEdZ49HmdWEM8eXIc//uzptTAn+s/FI2B2jidIVp34o0dLrXYZUFWcr60jkxbTBGRWtVoPSXDPq2nrw05c3YVd9By6bNVz1eU8eURijESfW9WeE7lckTgn1qOyXRJSuNtW24tN9zQACm5sSxRtD5AygDdqzRwxOPN5A7Ym8w6xYULvLvzvyi+trQ57TpNfixR/Misl4k5HYNrxHpbsvUTryegVc8OdPpdvi5qZE8cYrLwOJBbTyviryQGWorHV+OCeNKMyoDfqymFGhDCN+oBFx6ocShVdehhhZHKgzEf8AifUWWQYdjLI/QqeNLlZ9DvkfqsrC9Cqi7UsgoxK6sSNROmrpVq5w49QPJQoDlQzx7NUn4aKZwwD4Gr853V6p02p2UEHsrGr12pPZo4qk70+pLlI9J10FMiqc+qHM0BYSqPDtghKDV16GGFmcjd9cOBXibu3tPS50++stckzKT0riEuVgJbkmrL59Hn7xzUlYNKUipuNNNoGMCqd+KDO0dHHqh5IDr7wMotNqpH1/2nucUkYluBur2aBT3cMnL8uA4UUWXFkzIqPqUwDWqFBmcXm8qLcpOzFz6ocShcuTM0y+xbdvTVu3C93+7EC2KfQPUGmuSSq6FWUZM/dyYUaFMsl5j6+RVv2J9DpNmLOJYiuzPhYT8v2re9q6XegKU6MCBHZclnN7MreQlBkVyhQerxASpADps1M6pZ7M/YicofIsRgBAW48so6KSKSnLC9Sp/PnyE/DcJwdw9akj4jLGZCRlVBioUJpzupUfSJacMx7nTK4ImSImihdeeRkmkFFxSq2x1Tbiq5YtZ/76lAp8PcOKZ4OJGZVuBwMVSm/i3j6iMyeUYnhR372ViGKFUz8ZRkzfylf9qE39nDPZF5hkqwQxmSg/y5eJau9xwSvboJEo3TiCMiq97fVFFA/MqGQYeY2Kzt9aX62YduIQK16/YTZKwyxVzjSF2b5Axe0VYLO7kO+fQiNKNw6XMlAxZNgKP0o+DFQyjLxGxezvixBu7nnG8IK4jSvZGfVaWM162OxuHO90MlChtBU89UOUaAyVM4y8RqW5y9d5UswWUO+KcnybNTZ3OhI8EqLYkU/9DMljRpUSj4FKhpHXqNS3+xo6lXN6JyJF/oBODPCIUp0gCPiqoQMuWesBu2xl20s/PCURwyJSYKCSYcRApa3bhQZ/58lwLfNJqdifUTna1pPgkRANjsfe34MFj67G0je3S8fEjMq4slwW0lJSYKCSYfL8q1cabHYpM1DB9G5EpgzLAwBsPNSa4JGkJo9XkIJjSg5/WLkHAPDShloc909pijUqJgPfHig58ErMMKVWX1ZA/NRk0mvZcTJCs0b6dpX+6Ksm1DZ3J3g0qee2V7Zg1q9W4vJn1immFygxxM7UIjFTKK764SaElCx4JWYYq9mgCEzGledCo+EeHpGYMbwA1SXZ6HZ6cN0/NyZ6OCnn9U11AIBP9jZj2acHEzsYQnOnU/V24EMMeyhRcmCgkoGqCgNdJqf6pzOobzqtBkvOmQAAONTcleDRpLb9TZ2JHkLGawpavSZOBYtTP2ZO/VCS4JWYgYbLCuROHVWcwJGknhOG5wMAup0efLi7MbGDSSGuoA0t7S6v1BmZEiN4mX2zVKPCjAolFwYqGeiqmiqMKLJg3rgSLJxUnujhpJRc2a7SVz/3Gb6oZWFtbwRBwDMf78f/bT6qOP5/W47ippc3w+H2oLa5G10ON65d9hn+ue5QgkaaeY4HTf28u70eAPDvzw8DYI0KJY+YXYkHDx7Etddei5EjRyIrKwujRo3CvffeC6dT+Y/jyy+/xGmnnQaz2YzKykr89re/jdWQyO/EEYX48PZ5eO6ak6HVsj4lGsagP96batsSM5AUsWpXIx78707c+sqWkPuW72jAZU+vx9yHP8Cv/7cLK3c14u43tmEfp4XiIjijsqm2DfXtdmyrswEAcsxsXE7JIWaByq5du+D1evHXv/4V27dvx6OPPoq//OUv+PnPfy6dY7PZsGDBAlRVVWHjxo14+OGHcd999+Gpp56K1bCIBlV7N5u/9Wb7UVuv94tLvZ+XZVK2HmmP6ZgI2HHUht+v+AoA8J2TKqXj6w80S98vnjc67uMiUhOzkPnss8/G2WefLd2urq7G7t278eSTT+J3v/sdAOCFF16A0+nEs88+C6PRiEmTJmHz5s145JFH8MMf/jBWQyMaNO09rkQPIan19GMZMnutxN6S17dK348ty0VlYRYOt/Tgpy9vBgCcWFXARpCUNOI6Cdne3o7CwkLp9tq1azF37lwYjYG9ZhYuXIjdu3ejtVV97t/hcMBmsym+iBKFgUrv2vqRcWqwcS+lWKtvD3RXLs41IcugLJwdU5Yb7yERhRW3QGXv3r14/PHHcd1110nH6uvrUVZWpjhPvF1fX6/6PA899BDy8vKkr8rKStXziOKBgUrvjrQqtxsoimADzIYOZlRiTRAC3+dnGRS3AWBsWU58B0TUi6gDlTvvvBMajabXr127dikeU1dXh7PPPhsXX3wxfvCDHwxowEuWLEF7e7v0dfjw4QE9H1G0Fk4KBNdtDFR6VRcUqFTKeviE09DOQCXWnLLl4jlmPTxeZaQyppQZFUoeUdeo3Hrrrbj66qt7Pae6ulr6/ujRo5g3bx5mz54dUiRbXl6OhoYGxTHxdnm5+rJZk8kEk8kU7bCJBs0fL52BF9fX4v63djCj0gtBEFAXtIFjQQTbNbSwQDmmBEFAh93Xw2bR1ArMqMyHJyilwowKJZOoA5WSkhKUlJREdG5dXR3mzZuHmTNn4rnnnoNWq0zg1NTU4K677oLL5YLB4PsDtmLFCowbNw4FBQXRDo0oLkx6HeaNK8X9b+3A/qYuPL5yD3585phEDyvpNHU6pOZhomxT339yWrsYqMRSt9MjZVAevmgqNBpNSEalJJcfBil5xKxGpa6uDmeccQaGDx+O3/3ud2hqakJ9fb2i9uSyyy6D0WjEtddei+3bt+Nf//oX/vCHP+CWW26J1bCIBkVVUWAKQ1zmSUoN7aFFsTkRBCrtPS54g944afCI2RSdViMV0QYHKtz/i5JJzJYnr1ixAnv37sXevXsxbNgwxX2CP82Yl5eH5cuXY/HixZg5cyaKi4uxdOlSLk2mpKfRaLD03In4xds7Ej2UpKW2NDkvK/zUj16rgdsrwCsANrsL+Za+C28peja7b7rSatZLAYlbFqh8c/qQhIyLKJyYZVSuvvpqCIKg+iU3depUfPzxx7Db7Thy5AjuuOOOWA2JaFBdMGOo9H3wJ9JI7W3sQGOa9g0RN7eT++HcagwryFI9vyDbKGVcWjj9EzMd/kBFvh2EPIP1mwunxn1MRL3hZg5E/SSvt+h0RL/B3ra6dsx/ZDXm/OaDtNmNWf57cLi8IfcX5Zjw8c/m4bQxoZth5pr1yPcX27Z2s0g5Vmz+qZ9cWYv8P146A0adFg+cPxlmAzcjpOTCQIWon4x6rbRxW38ClU2H2wD4lor21Wo+Ffxx5R5Mvvc9fLL3OACEFNKKNBoNhuaHZlVyzQapz0rwPjQ0eMQaFasso3Lq6GJsu38hvntKVaKGRRQWAxWiARA/lXbaow9U5D1GbnjhC7z2xZFBG1ciPOIvKr7j1S8BBKZ+5owuxrVzRuLJy0+Qzl08bzTMBi0sxsCn9wKLARV5vgDmGHupxIytR5z6UZYoBm+4SZQseGUSDYBYU9HpiH6qIrjHyC3/Dt1hOBW1+adtxIxKllGHe86diHOmVEjnVBZa8MU9Z+HJK2ZKxwosRgzxZ1qCfzc0eMTeP70VNhMlEwYqRAOQ4/9U2tGPjMrRNH0zFqfBHP5VP6Ywn9QtRj1KZf068rIMGJLv2wiPgUrsiH1qCiLYzoAoGTBQIRoAMaPS3Bn9KpXgOgydVhOyKi6ViRmV3oozh+QFalXMBp1Uu3LMH6i4PV68vKEW+5s6YzjSzCIWKudH0CWYKBkwUCEagGEFvsZvr/ajviS4/b7HK6DLGbqkNxUJggC7f9VPuIwKAFizAnUSdpcHef43T3FlykufHcadr23F137/UQxHmzmOtvVI12oB+9RQimCgQjQA3/L3UtnTGN0nfq9XkAKVJy4LFJke70jN1S6vb1IGaja7WyqmNenDZ1TkHVAFQZAyVF3+6aMNB1oGe6gZ7ffLA12UI9l3iSgZMFAhGoDyPF9NhV2lC2swt8eLP67cg88PtqDT6YbYY+vMCaUY7t9V+HiKLsu9+V/KQuCmjsA+PyZD739mbjhjFPItBnz/tOpAcbI/o8JG7oOrvScwRWllMS2lCAYqRAMg1l+oNTcL9vJnh/HIiq9w0V/Wot1fJ2DSa2E26FBu9QU86bIs1xeo9F5MK/rZ2ePxxd1nobLQIhUndzrdIfU6t/57C1Z/1RSbASeJ5k4HemI4/VcoK6Dlqh9KFQxUiAZADFScHm+fbfS/auiQvg9eIiqudkmXlUBNnQ4peOtt6kek1fpyJ2JGRRCAL2pbsf5As3TOq18cwZXPbojBaCO38VALLnt6HXYeG/wGfXVtPaj59SpMWPpuzDoVi9fd8EILJg3Ji8lrEA02BipEA2CWTWv0Nf3jlWUIxDcMceWF2D8kXQKVA01dgamfKBqJZRl08McsuPDJtWiwJddU2IVPrsWn+5rxs/98OejP/cme43D6f2dX/G39oD8/ALR2+a67n509LibPTxQLDFSIBsAsyxb0HagEvq/3T/EUZfv6iIiBypYj7QNeory3sQPf/starNlzfEDPE40Kf62O6NH3v8KqXY0A+q5RkdNoNMg2xmxT90HT2j34mybKA9nDLbEJWFv84y7kih9KIQxUiAZAq9VIrce7nZ5eMyLyAOSgP7U/ojgbADCzqgAaDbD5cJu0B5D8cRsPtUorYfqy5LWt2HCwJWafytWIQdqD50+WjomN37Ki3OSuo4+f09vPnaoHSj61N0Rlr6JINXc68L+tx0J+jmbZjtG5ptgEa2Kzt8IcBiqUOhioEA2Q2R+o3Pnal5j961VhCz69snrbA8d9gUq1P1CZUGHFmeNLAQCfH1QuyX19Ux0ufPJTXP/CFxGNpz9dcgeq218AOr0yP+S+aBuLyff/UWOzJ2Zn5V31gbqU/izttdldcHm8+PZf1+L6F77A65vqAPj2RBIEQdk0MAbLnbxeQcoEMaNCqYSBCtEAiQW1n+z1FX6Km/IF86pkVKqKLNKxE6oKAPimf+Se/vgAAES84qVE1pb+032+6Z81e45j+i+W491tx8I+zuH2SCt1ouH2eKV6FLVdkfOjfFO8oo8dfFu6Bn/aJRLr9gcCyO4oV+Y0dTgw7f7l+MafPsG+Jt//+/9tq8fxTgdO/uVK3PjSJrR0BepxIllFFi2b3SVNP0b7/4QokRioEA1QcIv4Y+12dNhdWPjoajz0zk7puDzT3+gvEpUvF60uzgEQqF8R9TjDZ0g67C488cFexSqRItlz/sr/+lf8bT3aul1Y8tpW1edxebw49dcfYOGjq6OeWumW1eZkq0xZ5Ee5DPbnX5+AFTfPDXu/2AI+3nbLMiqRTsOJ3tteD0FA0GohASt2NKC9x4X/fnkMjbJmf5GsIouWGODlmvTcKZlSCq9WogEyqxSLfrL3OHY3dOCvq/dLPVPkGZUmf2O3XHPgTTzXrGx2BgBOt7fXDfp+v/wrPPzeblzw50+lY1pZt1er2YCNhwKZgDKrsuhVdKi5C8c7HTjY3K0IPCLR7fCdb9D56nWuO71acX9/Pr0Pl2WagnVGGSQMFvkKpGgzKmrTcfubuhQzPBsPtSru7092qzfitA83I6RUw0CFaIDUikXln1hf2XgYdpdHqkkAfH1CgMDuy0Cgh4j8jfh7yz6DyxMIcII/yYtTO/LpkB5ZoKHXabFyZ6N0O3h1DuArhH1z81HF7WgEF83eefZ4xf1Wc/SFob31XulOWKASyHRFGyy9tKE25NjxToeiE7E4fSbdHuTpH7EGhoEKpZrkXwdIlORMKoGK/E3m1S/qUBomk5ErexPPVglU1uxVLjE+3ulQTK9kqSzllQcaPU432mSbH3Y5QoOQ+9/aoXgjjbYzqljcKrZkl+/fA/iCpf64e9EE/G9bPYpzjHhve4N0PFEbN8oDlWimfvY2dqC2pTvkuM3uxrJPD4Z9nH2QMip2lwevfH5YGgP3+KFUw0CFaICCa1QA5c7ITR2OsHUfObJAQ5r6cfjax7tVHtNgc6CqKFu6bZG99oodDZgzuliRUel2eqSpJ/G5gwV/2o82oxLcvG6wfP+0anz/tGp8uve4IlDp7qVmJ1Ycbo+iNqatxwW3xxtRELatLnwX2+OdoYXBRr0WTrdX2n16oJ75eD9+J9uM8KQRhYPyvETxwqkfogEyqxQmtsgagrV1OxX1KXJi63ggkFHxeAXYXV6p54XcsXZlvYrLE3gz+8E/PscjK3ajR/YG1+30oE22Ed2OY7Y+G8r1RBuodCu3A5DLGYR+ILNHF+P1G2Zj4aQyAOpZoVgTi581Gt/PKQjAG7Lpst7slm2d0Jcckx5Wf91StAFjOOuDdqC+8IRhg/K8RPHCQIVogNQyKvIgw+0V8Nt3d/f5PBaDDuKsSafDrWgAJjrSqgxUgpfqrtvfAocio+JGW9AqmT9/uK/XcUQ79dPmD8ryswK1D+Kb4b3nTYzqucKZMbwAFXm+pc/RrrgZDI0dvmmfYQVZ+Ob0IQCUezf1Zm9jZ8gx+RLy8eW50vdFOUapOHuwApXinMBrmQ1alFlNvZxNlHwYqBANkFoxbUuXMjiot/W9K7JWq5GmgjodbikIyTLocMMZowCE7gUU3Mq9tdsZMvUTHKg8/F4gaFq7rxnBos6o9PgCB6sso/LLCybj/248FRfNHLxP72IjuK4ETP2IK37Kcs3Syqn2CJdJHw6qTxlfnqtouFYuK3CuKsqWAt/BmvqRT8nlmPQhNUREyY6BCtEAqS1P7m0vGL1/uueyWcND7pMKau2BjMrUYXkYVuBbrtsQFPAEL5M91m5XLG/2BSrhx/L0x/tDjkXzSV4QBDz6vq/+Qf6GaDboMHVY/qC+KYq/m+4ETP2IvW3K8sxSQCafUgtHEATU+bNgYmbj8lOqFNNkZbmBQGV0SU4gozJIxbRu2aqxWdVFg/KcRPHEYlqiAVKb+lGbtgGAq2ePwH3fmIRup1s1E2PN0qPe5itQ7bAHaj/khbYir1cIWdLq8QqK1/Z4hV5XyRhlxaBzRhdjzd7jUWVUth8NFIoa+rm6J1IJzaj4p35Kc01SA7vgTJWaT/c1S3sXvXvTadjb2ImTRxTio92BLsOlsqmY0aU52HKkDQBgH8Dqph6nB0+t3o+zJ5dLv69hBVm46+sT+v2cRInCjArRAKktT1YrhAWALP+brcWonoIXaxcaO+xSLUaOSY9sk+9x8gyK/BP3ny6boZrZkRvin2Iw6rRSQa0Y+Dx6yTRpbD3Ovqccdhy14e43tiqalJ01oazPxw2ElFFJwPLk4x2+/5/FOSYpcyRf2RXOC+sPSd8X55hwSnURtFqNIqMiLzg+dXQRiv0bBso71UbrTx/swaPvf4WFj62WMlDXza0e0GaKRInCQIVogFSnfvyByrVzRioanvW1k7A4DdDY4UCn/w3GYtLB4q9dkReSyotevz65QvEmNDQ/K+S13r/1dAC+9uzip3yxDqbAYpTOjySjctFfPsU/19Xi3v/bDgD41oyhmDIsr8/HDUS2rH7H7vKEbDUQK29tOYpXvzgCwDe9JRYNR5JRcbp9AeF1c5XdeuWByqmji2E2aHHSiAJUFWWj0j/NF1zbEo3PDwYCSLHTsEWl5w5RKmCgQjRAasFHh6xb6zTZjsJ9BSol/mmARptDCkqyTXrpTVqeTRADCpNeC61Wo2jHP2N4PoYXBtrQmw1aWIyBPV7Elu7SbrrZRmlqJZIaleCsRm8t7weLRcoqufGNP63BKQ+tVOxxFCs/fmmT9H1+llEKMsQalY2HWoP28AkQl4+PKctVHJcHt1VFFqz/+Xw8f+0sAIHf5cHm/gcq8mm4buk66v3aI0pWDFSIBkheo5Ib1C7eqNcqlp+ajb2/WZRKGRXZ1I9RL71JyzMqYkAhTtnIMzeluWbFzszip+lck3I/IWn/F4tR+jmiXZ4MAAsmlkf9mGhJwZrDg68afEt+V+xo6O0hg67AYkCeRexz4sWh5i5c8te1WPTHj/Glv7ZETgxUDDrlNJ98iwWLUY+8LIP0+xcb+r2/swGNEawWU6OT9ecR/x8zo0KpioEK0QDJPx1XF2cr7jPqtZg0JDAl0mdGxV+j0tThkOpHgjMqYn2JWEsiPqc8SMrLMmCEbCziOTlSUa4LdpdHWgKbbzEEalSiXJ588shCTBxijeox/SFmfBQFxX00rxuo4OxSnsWAXJMeYhzw5uajcHsFeAUoCmRFYqBiCmoKKM94yIMKAJg9qkjK2qzdH7p8PBJub6DO6MBxX9aJGRVKVQxUiAbILNtAb2RwoKLTomZUYEloX9MqYn+NHUdtUrFmjimQUXF7BTj9b35iQCEFISZ5oKLHWNl0gxhMied02N2KzInFqI+4RiW4UFgs/ow1sZhWvpFfmJ0J+mR3eSLaWLApqKA132JUFMOu2hXY8FFtObHTvzQ4eEWUsZcVUgadr14F6F/h8NG2HnyyNxDgiL+jvCxuRkipiYEK0QDJF++MUMmolFnNuHr2CGQbdTh9bEmvzyWuKOlwuPHxHt+GhNkmvWJPH3EVhxhQmKWMSqBGJc9iwDRZcau4G7F8h2bx8UadFjqtRgpU+loWe+NLXyhux2sVjpgRkAcn/d1h+Ot//BgnPLCizy63TZ1BgYo/QMn3B5S76wPdadVWS7nc4tSP8k/tsILeV9+IQVl/uvC+t71e9fiIONQREcUCJy2JBqhD1mAtePmnmLW497yJuOfciSFp/mAF2aGferNNOuh1WpgNWthdXnQ53SjINkrPLU7ZBE/9VJfkSLebuxyKczrtbim7Y/JnW8wRTv3IP60DwE/OHNPr+YMlW6XGIrjpWnOnQ1qhc+2catXft8Ptwf4m33TIl0faFRmvYMdlGZWrZ4+QAgix6Zv8d/XsJwfQ3uPC7789TToWqFFRBioLJ5Xjh3OrMUNWaC1nUSmejtTB4+oFxv3dxZoo0RioEA1QpWx1TfAmfOLqH41GA10ETVoLVHYgFp8z26iH3eWUNuWzB039lFsDHU7zsgxBBZUuxXP5lvj63kTFjEw0y5NFO39xthQoxZpafU/wEuGFj30sTQ15vMD1/q0H5OT9T/qa/hEzKvMnlOG+b0ySjuerbMAIAK9+cQS/u3iq1CNHDFSMeuX/fK1Wg5/30nwt2xhaPB2pAyqrhX79rSlRPw9RsmCITTRAs0YW4rcXTsXrN8yWPnGLrlBpk98btTdjcW8ZsS5C7H0SPPUzZ0yx9JjgcTj9UxBiMW2H3R1S45LVj1U/fTWZG0xarUYqqBUFb1Ugr1/57KBy12CRTRaoBG9JEExs9FaSq8x05asElCL5Hj3OMFM/fZGmfvrRhVfsv/L7i6dhQoUVv794Gr5zcnTXIVEyYUaFaIA0Gg2+fVIlAGBbXbt0/CdnjkGpLMsR6XMFEwOVUqsJ+493STv5im/ERf7pomEFFnz7xGE41m7HaNm0j1yOyfcG2+lwS7ssi8FGljG6XXuLso1x3+DOYtQrpkPkGRUhaAVQuJHJH9NX07imTt/98h2IAcBqDh+odDhcUpYpXDFtX6ROxP3Y10jc22nKsDz876enRf14omTDjArRIJpYEVim6/H2r9DzWzOGKm6LPTfEHiviSpQP/cthv3VC4PzfXjQNz187S6pHuMD/XKeO9tVhiDUq7+9swGXPrAcQyMiYI5j6cchWtrx389yof7aByglaYtvW7cSm2lbMfmgl/rbmgOI+V5glQfKpn+BVPcECGRVloGLpZalvlyy4CFej0heLrAtvNARBkGqmgnv6EKUqXslEg0ir1eCXF0zGi+trcVXNiH49x++/PQ1NnQ5p1Y+ozN+1VpyuEOsXetu/5cHzJ+OU6kKc5W/IJtaoHJLVMURTo9La5XuT12k10lLqeMq3GAHZ2Nt6XLjgz58CAB78707FuS63eqAoz6j0VY8jTiUFZ1TUCntFrd1O2I95ML48N1CjEmWgktPPfY3sLi/c/gCtt6wPUSphoEI0yC6fVYXLZ1X1+/EajQb3fWMSLn1qHX5wWmCPGDGjcuB4NwQhsHNyb8Ws2SY9LjkpUJ8QXOwLyAKVCDYlFFfZ5GUZoO1jBVMsFAWtiuptvx0xSAgmz6j0FQiIxbQhGRXZ79yo00q9bQDgnje2YftRG35z4ZRARkUf3e+qvztF2+yBQDK4nocoVTFQIUpCo0pysP7nZypqQE6o8jUBW7mrQTUjEokclekAs39qSeqj0kuWod0fGOSFWfUSa4Uqy7fDCTf1I++Novazvre9Hk63F+dNGyItTw7JqMgCvsrCLOxrCiwJ3n7Ut+/Pna9thVg20+9i2iinfjrsgdVd8a4fIooVBipESSr4jWZmVQGG5mehrq0H+493SsfN+sjfBHNVMioi+dSPIAiqb3RiNsKaqEAlii644aZ+5LsSB0/9HO904LrnNwIATqkuQpc/49JbRmV4oUURqIjktb39D1Sim/qx+etTrFn8007pg8W0RClEXBYrFoHqtZqoGnmpZVQO+ncgFhu+ebwCXJ7eC1ETlVGRb1fQl3BTP0dae6Tv5VM/a/c148QH35dui3vkmA1aqa+JSF6jIu+jE07wXj99ye5l6qfBZsene4+HrHICAs0Hc02sT6H0wUCFKIXkSPvd+GpFopn2kT9eTtyJWN7DJVyRqfiJPVGBinwly/2yBmxq3CpTP3sbO7D5cJt0Wz718/5O5U7Mh/wBXEmuKSS7JF/1UxqUbVET9aofsZhWJaMy5zercNkz6/FFbWvIfWKPGK74oXTCq5kohYhvQGJGJdqGa2oZFfEN36DTQq/VwO0VYHd5VIORQEYlMX86Lp9VhQPHu/CNaUOk/XaC5Zr16LC7pWZrotrmblz69HrFMXlzu+DmcbX+KaLg+hRAmVGZPboY8w+3oyTXiAUTy/H7Fbuxrc4m3a/VhO6Q3Jcc//M7PV443V5piXqH3SVlu3YctWFmVaHiceKKsOIIgieiVMFAhSiFiBkRsSDUFMVUCBA6JbDknPG4siawQinLoEOHwx22O60twVM/WUYdfnmBrx28vNZELi/LgA67O2TqZ9mnB9HU4UBFnhkPXzQNV/xtPeplnWnbg1YQiQXLaoGKvEalzGrGM1edKN2eN74Uf1tzAA+8vQNA9NkUQLmSq9vphlHvC8q2HA40FFSrE6pr801rDetlyTpRquHUD1EKETMi4mqUaPfZMRu0iizM2LJcxbRGXxsTJrpGRU5tGgsI9JWRBypfHmnDs5/4GsIt+foEVJcEdrle8tpWuD3ekIzKIX8gFFxICygzJMUqBb7TZZsNRttDBfA1+RMf979t9Wj0B1R7GuW7Nfv+H22ra8eL62shCALq/PU3Q/vYnZkolTCjQpRCxBb4YiOyaKd+NBoNKvKypELR4CLPvpq+JVOgEryfkaiywIINB1rg9gi46/Wt2HioFbvqA2/wp4wsVGQ5XtpQixyTLqQny94G32PKVbZBGFmcje+cVImiHKNqVmtcea70vXpZct/E3ixLXtuKklwTPrtrvrTrMxD4f3Tu42sAAHqdBst3+OpshjKjQmmEgQpRChFrVKRi2iinfgBfh1spUDGoByr2MFM/0vLkJOh6apQFWVecMhz/XFcLACjM9o2tw+HGC+trQx5XajWH9E95+uMD0s7V4hJwcWlyVVHoqh6NRoNfXzg17Njk2R5DJNtm96Gpw4FfvLUDz687JB3rdnrglmWN/vrRPun7E4YXDPg1iZIFp36IUoj4BigGDNGu+gGUGYLgbIB86sdmd+GlDbWKepVkyqjIjSgKTOVYemlvL1JbLiz+bEPylRkU+XP3R3/+H6kRp65Eu+o7MOne96Tb4oqsr08pR0EUjfGIkh0DFaIUMrJY+aYZ7dQPAJTnBaYFQqd+fLdf/uwwpt63HEte24q/yD6pJ7rhWzgzqwoglo2cNqY47HmvXl8DwJcROXN8qeI+cTVz8I7XwyPok6Lmp2eOAQA89K0p/Xp8X9bsaZK2UQCATn+gIm61QJQuOPVDlEKmD89X3B5WEP2b6AjZVEZwRkWc+lmxI9BT5H/bjuHMCaXIMemTLqOy6tbTUdvSjRnDC7B2yZlo6nBgRLF6BuSj289AlSw78sD5k7Hy16tCzpNvtmjUa6Ume9G6af4YXD17RL+zG//43sm48tkNYe9vDaqpEWtW+jteomTFjApRCrGaDVImYFhBlvSpPRryrExIjYrKKqKvGjrxjT99ggv+/KnUmyQvSd4Mq0tycMY43++jzGrG5KF50IfpWVKRpywwVds3KMugk2pVAKA429jvPXM0Gs2ApmDmji3BSz84JeT4d0/pfcPLggTsak0USwxUiFLMHy6dgQfPn4x/XVfTrzdCecYhuMdHliF8klXMpmg1gYZkyShc3xJj0DSX2aDD+dOHKI5lm3Q4dXRg6ihcU7l4qRlVhNGlOdLtuWNLMFa2okgNMyqUbmIaqHzjG9/A8OHDYTabUVFRge9+97s4evSo4pwvv/wSp512GsxmMyorK/Hb3/42lkMiSnk5Jj2uOKWq30tQS3NNuHjmMJw3bYgiewCEZljUWLMM0EbZaTWeoukC+9h3ZihqULKMOpw4ItDttbnLofawuJIXP583tUKx1YGaaHaYJkoFMQ1U5s2bh3//+9/YvXs3Xn31Vezbtw8XXXSRdL/NZsOCBQtQVVWFjRs34uGHH8Z9992Hp556KpbDIspoGo0GD188DY9fOiNkWuOMsSUozDbia+NL8d5Nc1Ufnyz1KYNF3o/FYtArAp0GW+IDFfny6LljSxRdcdXkJsHScaLBFNP87c033yx9X1VVhTvvvBPnn38+XC4XDAYDXnjhBTidTjz77LMwGo2YNGkSNm/ejEceeQQ//OEPYzk0IlKxYFI5Fkwql24b9dqQPXNSMVD557Wzwt6XI9tgUKzR+cnXRuOPq/ZiyTnjYz62vsiXR5dZzZhemS/tyaSmr0CGKNXEbaK5paUFL7zwAmbPng2DwfeHbu3atZg7dy6MxkCqcuHChfjNb36D1tZWFBSENi1yOBxwOAKfcmw2W8g5RDQ4rGa91FwucCy1ApXbF47DnF6WLCsyKv43+Z/OH4uzJ1dgfB/1IPHw3Zoq7G3sxDx/EfWQ/CzMGVOMD3c3qZ7PQIXSTcyLae+44w5kZ2ejqKgItbW1ePPNN6X76uvrUVZWpjhfvF1fX6/6fA899BDy8vKkr8rKytgNnijDqQUlqZBRefem06Tv++qDIt8JWXyT12k1mDjEmhS1OGaDDr+5aCrOnhzIdMn/H9x61ljF+ZE0vCNKJVEHKnfeeSc0Gk2vX7t27ZLOv/3227Fp0yYsX74cOp0OV155JQShv7tfAEuWLEF7e7v0dfjw4X4/FxH1LlclKEm2Zm9qxpdb8YPTRuLU0UVYMKms13OzFVM/qfEmP8e/Msmo1yI/qHiWGRVKN1H/q7z11ltx9dVX93pOdXW19H1xcTGKi4sxduxYTJgwAZWVlVi3bh1qampQXl6OhoYGxWPF2+Xl5VBjMplgMoXuZkpEg89qDv0TkQoZFQC4a9HEiM6T1+AMwrY8cXHRzGEQAJw0ohCfH2yRjms16tsDEKWyqAOVkpISlJSU9OvFvF7fHwSxxqSmpgZ33XWXVFwLACtWrMC4ceNU61OIKL7Upn6K0mz5q7y777hyawJHEjmNRoNvn+ib9t55LFCnZzHq+92gjihZxSzPuX79enz22WeYM2cOCgoKsG/fPtxzzz0YNWoUamp8+21cdtlluP/++3HttdfijjvuwLZt2/CHP/wBjz76aKyGRURRsGaF/omYVpkf/4HE0JWzq+Bwe3DC8ALFiqdUUSbrs6LWWZgo1cUsULFYLHjttddw7733oqurCxUVFTj77LNx9913S1M3eXl5WL58ORYvXoyZM2eiuLgYS5cu5dJkoiSh1jxs6rC8BIwkdkpzzRFPEyWjYQXhN5kkSgcxC1SmTJmCVatCN/wKNnXqVHz88cexGgYRDUBRtrIe7DcXToG5j86oFF8lOYH/Rx3+HZSJ0klqlLgTUULI943ZdM9ZA9pkj2JDvoRa3I+JKJ0wT0hEYeXImqExk5K8bl84DoBvNRBRumFGhYjCypEtT2b9Q/JaPG80vja+VLEvEFG6YKBCRGGNly3XTYYurRTehIrUWFpNFC0GKkQUVmG2EatuPZ3LXokoYRioEFGvqktyEj0EIspgnHQmIiKipMVAhYiIiJIWAxUiIiJKWgxUiIiIKGkxUCEiIqKkxUCFiIiIkhYDFSIiIkpaDFSIiIgoaTFQISIioqTFQIWIiIiSFgMVIiIiSloMVIiIiChpMVAhIiKipJXyuycLggAAsNlsCR4JERERRUp83xbfx8NJ+UClubkZAFBZWZngkRAREVG0mpubkZeXF/b+lA9UCgsLAQC1tbW9/qDhnHTSSfjss8/69dp8bGq8dio+1mazobKyEocPH4bVao3ba6fi7yqRr52Kj03Ua6fiNZ2Kr5vIx0b7+Pb2dgwfPlx6Hw8n5QMVrdZXZpOXl9evi1+n0/XrcXxs6rx2Kj5WZLVa43pdp+rvKhXHnYm/LyC1rulUfN1EPra/jxffx8Pe3+/RpInFixfzsXF4bCJfOxUfO1D9fe1U/V2l4rgz8fc1EIm4plPxdRP52MF4vBqN0FcVS5Kz2WzIy8tDe3v7gD+9EiULXteUbnhNU7BIr4mUz6iYTCbce++9MJlMiR4K0aDhdU3phtc0BYv0mkj5jAoRERGlr5TPqBAREVH6YqBCA6LRaPDGG28kehhEg4rXNaWbVL6mGaiQwtVXX43zzz8/0cMgGlS8rindZNI1zUCFiIiIklbSByqZFDUmmxEjRuCxxx5THJs+fTruu+++hIwnXfCaTixe17HB6zpx0v2aTvpAhYiIiDJXSgUq7777LubMmYP8/HwUFRXh3HPPxb59+6T7Dx48CI1Gg9deew3z5s2DxWLBtGnTsHbt2gSOmig8XtOUjnhd02BKqUClq6sLt9xyCz7//HOsXLkSWq0WF1xwAbxer+K8u+66C7fddhs2b96MsWPH4tJLL4Xb7U7QqInC4zVN6YjXNQ2mlNqU8MILL1TcfvbZZ1FSUoIdO3Zg8uTJ0vHbbrsNixYtAgDcf//9mDRpEvbu3Yvx48fHdbypTqvVIrgfoMvlStBo0hOv6fjjdR17vK7jK92v6ZTKqOzZsweXXnopqqurYbVaMWLECABAbW2t4rypU6dK31dUVAAAGhsb4zbOdFFSUoJjx45Jt202Gw4cOJDAEaUfXtPxx+s69nhdx1e6X9MplVE577zzUFVVhaeffhpDhgyB1+vF5MmT4XQ6FecZDAbpe41GAwAhKUfq29e+9jUsW7YM5513HvLz87F06VLodLpEDyut8JqOP17XscfrOr7S/ZpOmUClubkZu3fvxtNPP43TTjsNALBmzZoEjyr9eL1e6PW+y2LJkiU4cOAAzj33XOTl5eGBBx5Iqyg90XhNxw+v6/jhdR0fmXRNp0ygUlBQgKKiIjz11FOoqKhAbW0t7rzzzkQPK+00NjZi9OjRAACr1YqXX35Zcf9VV12luM09LfuP13T88LqOH17X8ZFJ13TS16iIUaNWq8XLL7+MjRs3YvLkybj55pvx8MMPJ3p4aaO1tRVvv/02PvzwQ8yfPz/Rw0lrvKbjh9d1/PC6jo9MvKaTPqMijxrnz5+PHTt2KO6XR4kjRowIiRrz8/NTOpKMl+9973v47LPPcOutt+Kb3/xmooeT1nhNxw+v6/jhdR0fmXhNJ22g0traik8++QQffvghfvSjHyV6OGnv9ddfT/QQ0h6v6fjjdR17vK7jKxOv6aQNVDIxaqT0xmua0hGva4o1jcBcGxERESWppC+mJSIioszFQIWIiIiSFgMVIiIiSloJD1QeeughnHTSScjNzUVpaSnOP/987N69W3GO3W7H4sWLUVRUhJycHFx44YVoaGhQnFNbW4tFixbBYrGgtLQUt99+e8gunB9++CFOOOEEmEwmjB49GsuWLYv1j0cZKl7X9bFjx3DZZZdh7Nix0Gq1uOmmm+Lx41EGitc1/dprr+Gss85CSUkJrFYrampq8N5778XlZ6TklPBA5aOPPsLixYuxbt06rFixAi6XCwsWLEBXV5d0zs0334y33noLr7zyCj766CMcPXoU3/rWt6T7PR4PFi1aBKfTiU8//RR///vfsWzZMixdulQ658CBA1i0aBHmzZuHzZs346abbsL3v/99/gOgmIjXde1wOFBSUoK7774b06ZNi+vPSJklXtf06tWrcdZZZ+Gdd97Bxo0bMW/ePJx33nnYtGlTXH9eSiJCkmlsbBQACB999JEgCILQ1tYmGAwG4ZVXXpHO2blzpwBAWLt2rSAIgvDOO+8IWq1WqK+vl8558sknBavVKjgcDkEQBOFnP/uZMGnSJMVrXXLJJcLChQtj/SMRxey6ljv99NOFn/70p7H9QYj84nFNiyZOnCjcf//9MfpJKNklPKMSrL29HQBQWFgIANi4cSNcLpeiVfD48eMxfPhwrF27FgCwdu1aTJkyBWVlZdI5CxcuhM1mw/bt26VzgtsNL1y4UHoOoliK1XVNlCjxuqa9Xi86Ojqk16HMk1SBitfrxU033YRTTz0VkydPBgDU19fDaDQiPz9fcW5ZWRnq6+ulc+QXvni/eF9v59hsNvT09MTixyECENvrmigR4nlN/+53v0NnZye+/e1vD/JPQakiqTrTLl68GNu2beOW4JRWeF1TuonXNf3iiy/i/vvvx5tvvonS0tKYvhYlr6TJqNx44414++238cEHH2DYsGHS8fLycjidTrS1tSnOb2hoQHl5uXROcGW5eLuvc6xWK7Kysgb7xyECEPvrmije4nVNv/zyy/j+97+Pf//73xmzSzCpS3igIggCbrzxRrz++utYtWoVRo4cqbh/5syZMBgMWLlypXRs9+7dqK2tRU1NDQCgpqYGW7duRWNjo3TOihUrYLVaMXHiROkc+XOI54jPQTSY4nVdE8VLPK/pl156Cddccw1eeuklLFq0KMY/GSW9BBfzCtdff72Ql5cnfPjhh8KxY8ekr+7ubumcH/3oR8Lw4cOFVatWCZ9//rlQU1Mj1NTUSPe73W5h8uTJwoIFC4TNmzcL7777rlBSUiIsWbJEOmf//v2CxWIRbr/9dmHnzp3CE088Ieh0OuHdd9+N689LmSFe17UgCMKmTZuETZs2CTNnzhQuu+wyYdOmTcL27dvj9rNSZojXNf3CCy8Ier1eeOKJJxSv09bWFtefl5JHwgMVAKpfzz33nHROT0+PcMMNNwgFBQWCxWIRLrjgAuHYsWOK5zl48KBwzjnnCFlZWUJxcbFw6623Ci6XS3HOBx98IEyfPl0wGo1CdXW14jWIBlM8r2u116mqqorDT0mZJF7X9Omnn676OldddVWcflJKNtw9mYiIiJJWwmtUiIiIiMJhoEJERERJi4EKERERJS0GKkRERJS0GKgQERFR0mKgQkREREmLgQoRERElLQYqRERElLQYqBAREVHSYqBCRERESYuBChERESWt/wfY0Ahsk+kaZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x187b8243210>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    np.random.randn(1000, 4), index=ts.index, columns=[\"A\", \"B\", \"C\", \"D\"]\n",
    ")\n",
    "df = df.cumsum()\n",
    "plt.figure();\n",
    "df.plot()\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.120066\n",
       "b   -1.395849\n",
       "c    1.301421\n",
       "d   -1.136224\n",
       "e    1.704160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.random.randn(5),index=[\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.142949\n",
       "1   -0.918961\n",
       "2    0.477319\n",
       "3    1.352349\n",
       "4   -0.434715\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.random.randn(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "a    0\n",
       "c    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={\"b\":1,\"a\":0,\"c\":2}\n",
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\"a\": 0.0, \"b\": 1.0, \"c\": 2.0}\n",
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "c    2.0\n",
       "d    NaN\n",
       "a    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d, index=[\"b\", \"c\", \"d\", \"a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.0\n",
       "b    5.0\n",
       "c    5.0\n",
       "d    5.0\n",
       "e    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(5.0, index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12006575684658594"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.120066\n",
       "b   -1.395849\n",
       "c    1.301421\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    1.301421\n",
       "e    1.704160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>s.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    1.704160\n",
       "d   -1.136224\n",
       "b   -1.395849\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[[4,3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.886862\n",
       "b    0.247623\n",
       "c    3.674514\n",
       "d    0.321029\n",
       "e    5.496764\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PandasArray>\n",
       "[-0.12006575684658594,  -1.3958486981227531,   1.3014207861734062,\n",
       "  -1.1362237272585187,    1.704159543597302]\n",
       "Length: 5, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.12006576, -1.3958487 ,  1.30142079, -1.13622373,  1.70415954])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12006575684658594"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(\"e\" in s)\n",
    "print(\"f\" in s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get(\"f\",np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.240132\n",
       "b   -2.791697\n",
       "c    2.602842\n",
       "d   -2.272447\n",
       "e    3.408319\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.240132\n",
       "b   -2.791697\n",
       "c    2.602842\n",
       "d   -2.272447\n",
       "e    3.408319\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.886862\n",
       "b    0.247623\n",
       "c    3.674514\n",
       "d    0.321029\n",
       "e    5.496764\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a         NaN\n",
       "b   -2.791697\n",
       "c    2.602842\n",
       "d   -2.272447\n",
       "e         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[1:] + s.iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.505627\n",
      "1    0.759416\n",
      "2   -2.410302\n",
      "3    1.348963\n",
      "4    2.470067\n",
      "Name: something, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'something'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.random.randn(5),name=\"something\")\n",
    "print(s)\n",
    "s.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "something\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'different'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2=s.rename(\"different\")\n",
    "print(s.name)\n",
    "s2.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\n",
    "    \"one\": pd.Series([1.0, 2.0, 3.0], index=[\"a\", \"b\", \"c\"]),\n",
    "    \"two\": pd.Series([1.0, 2.0, 3.0, 4.0], index=[\"a\", \"b\", \"c\", \"d\"]),\n",
    "}\n",
    "df =pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "b  2.0  2.0\n",
       "a  1.0  1.0\n",
       "c  3.0  3.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d,index=[\"b\",\"a\",\"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   two three\n",
       "d  4.0   NaN\n",
       "b  2.0   NaN\n",
       "a  1.0   NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=[\"d\", \"b\", \"a\"], columns=[\"two\", \"three\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "0  1.0  4.0\n",
       "1  2.0  3.0\n",
       "2  3.0  2.0\n",
       "3  4.0  1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\"one\": [1.0, 2.0, 3.0, 4.0], \"two\": [4.0, 3.0, 2.0, 1.0]}\n",
    "pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>b'Hello'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>b'World'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B         C\n",
       "0  1  2.0  b'Hello'\n",
       "1  2  3.0  b'World'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.zeros((2,), dtype=[(\"A\", \"i4\"), (\"B\", \"f4\"), (\"C\", \"a10\")])\n",
    "data[:] = [(1, 2.0, \"Hello\"), (2, 3.0, \"World\")]\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>b'Hello'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>b'World'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A    B         C\n",
       "first   1  2.0  b'Hello'\n",
       "second  2  3.0  b'World'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, index=[\"first\", \"second\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>C</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'Hello'</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'World'</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C  A    B\n",
       "0  b'Hello'  1  2.0\n",
       "1  b'World'  2  3.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=[\"C\", \"A\", \"B\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b     c\n",
       "0  1   2   NaN\n",
       "1  5  10  20.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = [{\"a\": 1, \"b\": 2}, {\"a\": 5, \"b\": 10, \"c\": 20}]\n",
    "pd.DataFrame(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        a   b     c\n",
       "first   1   2   NaN\n",
       "second  5  10  20.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data2, index=[\"first\", \"second\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b\n",
       "0  1   2\n",
       "1  5  10"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data2, columns=[\"a\", \"b\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">a</th>\n",
       "      <th colspan=\"2\" halign=\"left\">b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
       "      <th>B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a              b      \n",
       "       b    a    c    a     b\n",
       "A B  1.0  4.0  5.0  8.0  10.0\n",
       "  C  2.0  3.0  6.0  7.0   NaN\n",
       "  D  NaN  NaN  NaN  NaN   9.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        (\"a\", \"b\"): {(\"A\", \"B\"): 1, (\"A\", \"C\"): 2},\n",
    "        (\"a\", \"a\"): {(\"A\", \"C\"): 3, (\"A\", \"B\"): 4},\n",
    "        (\"a\", \"c\"): {(\"A\", \"B\"): 5, (\"A\", \"C\"): 6},\n",
    "        (\"b\", \"a\"): {(\"A\", \"C\"): 7, (\"A\", \"B\"): 8},\n",
    "        (\"b\", \"b\"): {(\"A\", \"D\"): 9, (\"A\", \"B\"): 10},\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>ser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ser\n",
       "a    0\n",
       "b    1\n",
       "c    2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series(range(3), index=list(\"abc\"), name=\"ser\")\n",
    "pd.DataFrame(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y\n",
       "0  0  0\n",
       "1  0  3\n",
       "2  2  3"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "Point = namedtuple(\"Point\", \"x y\")\n",
    "pd.DataFrame([Point(0, 0), Point(0, 3), (2, 3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y    z\n",
       "0  0  0  0.0\n",
       "1  0  3  5.0\n",
       "2  2  3  NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Point3D = namedtuple(\"Point3D\", \"x y z\")\n",
    "pd.DataFrame([Point3D(0, 0, 0), Point3D(0, 3, 5), Point(2, 3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y\n",
       "0  0  0\n",
       "1  0  3\n",
       "2  2  3"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dataclasses import make_dataclass\n",
    "Point = make_dataclass(\"Point\", [(\"x\", int), (\"y\", int)])\n",
    "pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(dict([(\"A\",[1,2,3]),(\"B\",[4,5,6])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two  three\n",
       "A    1    2      3\n",
       "B    4    5      6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(\n",
    "    dict([(\"A\", [1, 2, 3]), (\"B\", [4, 5, 6])]),\n",
    "    orient=\"index\",\n",
    "    columns=[\"one\", \"two\", \"three\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1, 2., b'Hello'), (2, 3., b'World')],\n",
       "      dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b'Hello'</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b'World'</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A    B\n",
       "C               \n",
       "b'Hello'  1  2.0\n",
       "b'World'  2  3.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_records(data, index=\"C\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two  three   flag\n",
       "a  1.0  1.0    1.0  False\n",
       "b  2.0  2.0    4.0  False\n",
       "c  3.0  3.0    9.0   True\n",
       "d  NaN  4.0    NaN  False"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"three\"] = df[\"one\"] * df[\"two\"]\n",
    "df[\"flag\"] = df[\"one\"]>2\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"two\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>one</th>\n",
       "      <th>three</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  three   flag\n",
       "a  1.0    1.0  False\n",
       "b  2.0    4.0  False\n",
       "c  3.0    9.0   True\n",
       "d  NaN    NaN  False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>one</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one   flag\n",
       "a  1.0  False\n",
       "b  2.0  False\n",
       "c  3.0   True\n",
       "d  NaN  False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pop(\"three\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>one</th>\n",
       "      <th>flag</th>\n",
       "      <th>one_trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one   flag  one_trunc\n",
       "a  1.0  False        1.0\n",
       "b  2.0  False        2.0\n",
       "c  3.0   True        NaN\n",
       "d  NaN  False        NaN"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"one_trunc\"] = df[\"one\"][:2]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>bar</th>\n",
       "      <th>flag</th>\n",
       "      <th>one_trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  bar   flag  one_trunc\n",
       "a  1.0  1.0  False        1.0\n",
       "b  2.0  2.0  False        2.0\n",
       "c  3.0  3.0   True        NaN\n",
       "d  NaN  NaN  False        NaN"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(1,\"bar\",df[\"one\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}