{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting and Manipulating Data Frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting ready\n",
    "\n",
    "1. Import both `pandas` and `numpy` libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Create a range of dates using `date_range`. We will use this as the index of our `DataFrame`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range(start='1/1/2020', periods=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Create a `numpy` of size (8, 4) with random numbers. We will use these numbers as the content of our `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.randn(8, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Create a `DataFrame` with index and contex given by `dates` and `data`, respectively. We will specify the names of the columns via the input `columns`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Product_A</th>\n",
       "      <th>Product_B</th>\n",
       "      <th>Product_C</th>\n",
       "      <th>Product_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.881663</td>\n",
       "      <td>0.987620</td>\n",
       "      <td>0.515382</td>\n",
       "      <td>-0.244335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.367481</td>\n",
       "      <td>-1.071615</td>\n",
       "      <td>0.216773</td>\n",
       "      <td>1.318869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>1.174202</td>\n",
       "      <td>1.252871</td>\n",
       "      <td>-0.545009</td>\n",
       "      <td>0.077535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.007827</td>\n",
       "      <td>-1.093999</td>\n",
       "      <td>1.472493</td>\n",
       "      <td>-0.493387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.972515</td>\n",
       "      <td>0.119187</td>\n",
       "      <td>-0.563892</td>\n",
       "      <td>0.389504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>2.905071</td>\n",
       "      <td>0.168398</td>\n",
       "      <td>-0.679490</td>\n",
       "      <td>-0.571970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.070295</td>\n",
       "      <td>0.269492</td>\n",
       "      <td>0.107710</td>\n",
       "      <td>0.998263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.657587</td>\n",
       "      <td>1.262432</td>\n",
       "      <td>-0.284617</td>\n",
       "      <td>0.465781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product_A  Product_B  Product_C  Product_D\n",
       "2020-01-01   0.881663   0.987620   0.515382  -0.244335\n",
       "2020-01-02  -0.367481  -1.071615   0.216773   1.318869\n",
       "2020-01-03   1.174202   1.252871  -0.545009   0.077535\n",
       "2020-01-04  -1.007827  -1.093999   1.472493  -0.493387\n",
       "2020-01-05  -0.972515   0.119187  -0.563892   0.389504\n",
       "2020-01-06   2.905071   0.168398  -0.679490  -0.571970\n",
       "2020-01-07  -0.070295   0.269492   0.107710   0.998263\n",
       "2020-01-08   0.657587   1.262432  -0.284617   0.465781"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data, index=dates, columns=['Product_A', 'Product_B', 'Product_C', 'Product_D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to do it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Select a column of the `DataFrame` by simply specifying its name as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.881663\n",
       "2020-01-02   -0.367481\n",
       "2020-01-03    1.174202\n",
       "2020-01-04   -1.007827\n",
       "2020-01-05   -0.972515\n",
       "2020-01-06    2.905071\n",
       "2020-01-07   -0.070295\n",
       "2020-01-08    0.657587\n",
       "Freq: D, Name: Product_A, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.881663\n",
       "2020-01-02   -0.367481\n",
       "2020-01-03    1.174202\n",
       "2020-01-04   -1.007827\n",
       "2020-01-05   -0.972515\n",
       "2020-01-06    2.905071\n",
       "2020-01-07   -0.070295\n",
       "2020-01-08    0.657587\n",
       "Freq: D, Name: Product_A, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Product_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Select a column by using the method `loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.881663\n",
       "2020-01-02   -0.367481\n",
       "2020-01-03    1.174202\n",
       "2020-01-04   -1.007827\n",
       "2020-01-05   -0.972515\n",
       "2020-01-06    2.905071\n",
       "2020-01-07   -0.070295\n",
       "2020-01-08    0.657587\n",
       "Freq: D, Name: Product_A, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'Product_A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Select several columns using the method `loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_A</th>\n",
       "      <th>Product_B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.881663</td>\n",
       "      <td>0.987620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.367481</td>\n",
       "      <td>-1.071615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>1.174202</td>\n",
       "      <td>1.252871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.007827</td>\n",
       "      <td>-1.093999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.972515</td>\n",
       "      <td>0.119187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>2.905071</td>\n",
       "      <td>0.168398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.070295</td>\n",
       "      <td>0.269492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.657587</td>\n",
       "      <td>1.262432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product_A  Product_B\n",
       "2020-01-01   0.881663   0.987620\n",
       "2020-01-02  -0.367481  -1.071615\n",
       "2020-01-03   1.174202   1.252871\n",
       "2020-01-04  -1.007827  -1.093999\n",
       "2020-01-05  -0.972515   0.119187\n",
       "2020-01-06   2.905071   0.168398\n",
       "2020-01-07  -0.070295   0.269492\n",
       "2020-01-08   0.657587   1.262432"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['Product_A', 'Product_B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Select a set of rows and columns using the method `loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_A    2.905071\n",
       "Product_B    0.168398\n",
       "Name: 2020-01-06 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[5],['Product_A', 'Product_B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_B</th>\n",
       "      <th>Product_C</th>\n",
       "      <th>Product_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.168398</td>\n",
       "      <td>-0.679490</td>\n",
       "      <td>-0.571970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.269492</td>\n",
       "      <td>0.107710</td>\n",
       "      <td>0.998263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>1.262432</td>\n",
       "      <td>-0.284617</td>\n",
       "      <td>0.465781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product_B  Product_C  Product_D\n",
       "2020-01-06   0.168398  -0.679490  -0.571970\n",
       "2020-01-07   0.269492   0.107710   0.998263\n",
       "2020-01-08   1.262432  -0.284617   0.465781"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[5] :,'Product_B': ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Select a column by using `.iloc`. In this case, we have to pass the position of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01    0.987620\n",
       "2020-01-02   -1.071615\n",
       "2020-01-03    1.252871\n",
       "2020-01-04   -1.093999\n",
       "2020-01-05    0.119187\n",
       "2020-01-06    0.168398\n",
       "2020-01-07    0.269492\n",
       "2020-01-08    1.262432\n",
       "Freq: D, Name: Product_B, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Select several columns by using the method `iloc`"
   ]
  },
  {
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_B</th>\n",
       "      <th>Product_C</th>\n",
       "      <th>Product_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.987620</td>\n",
       "      <td>0.515382</td>\n",
       "      <td>-0.244335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-1.071615</td>\n",
       "      <td>0.216773</td>\n",
       "      <td>1.318869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>1.252871</td>\n",
       "      <td>-0.545009</td>\n",
       "      <td>0.077535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.093999</td>\n",
       "      <td>1.472493</td>\n",
       "      <td>-0.493387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0.119187</td>\n",
       "      <td>-0.563892</td>\n",
       "      <td>0.389504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>0.168398</td>\n",
       "      <td>-0.679490</td>\n",
       "      <td>-0.571970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.269492</td>\n",
       "      <td>0.107710</td>\n",
       "      <td>0.998263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>1.262432</td>\n",
       "      <td>-0.284617</td>\n",
       "      <td>0.465781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product_B  Product_C  Product_D\n",
       "2020-01-01   0.987620   0.515382  -0.244335\n",
       "2020-01-02  -1.071615   0.216773   1.318869\n",
       "2020-01-03   1.252871  -0.545009   0.077535\n",
       "2020-01-04  -1.093999   1.472493  -0.493387\n",
       "2020-01-05   0.119187  -0.563892   0.389504\n",
       "2020-01-06   0.168398  -0.679490  -0.571970\n",
       "2020-01-07   0.269492   0.107710   0.998263\n",
       "2020-01-08   1.262432  -0.284617   0.465781"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Select a row by using the method `iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_A    0.881663\n",
       "Product_B    0.987620\n",
       "Product_C    0.515382\n",
       "Product_D   -0.244335\n",
       "Name: 2020-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "8. Reset the index of the `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Product_A</th>\n",
       "      <th>Product_B</th>\n",
       "      <th>Product_C</th>\n",
       "      <th>Product_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.881663</td>\n",
       "      <td>0.987620</td>\n",
       "      <td>0.515382</td>\n",
       "      <td>-0.244335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>-0.367481</td>\n",
       "      <td>-1.071615</td>\n",
       "      <td>0.216773</td>\n",
       "      <td>1.318869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1.174202</td>\n",
       "      <td>1.252871</td>\n",
       "      <td>-0.545009</td>\n",
       "      <td>0.077535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>-1.007827</td>\n",
       "      <td>-1.093999</td>\n",
       "      <td>1.472493</td>\n",
       "      <td>-0.493387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>-0.972515</td>\n",
       "      <td>0.119187</td>\n",
       "      <td>-0.563892</td>\n",
       "      <td>0.389504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>2.905071</td>\n",
       "      <td>0.168398</td>\n",
       "      <td>-0.679490</td>\n",
       "      <td>-0.571970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>-0.070295</td>\n",
       "      <td>0.269492</td>\n",
       "      <td>0.107710</td>\n",
       "      <td>0.998263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>0.657587</td>\n",
       "      <td>1.262432</td>\n",
       "      <td>-0.284617</td>\n",
       "      <td>0.465781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Product_A  Product_B  Product_C  Product_D\n",
       "0 2020-01-01   0.881663   0.987620   0.515382  -0.244335\n",
       "1 2020-01-02  -0.367481  -1.071615   0.216773   1.318869\n",
       "2 2020-01-03   1.174202   1.252871  -0.545009   0.077535\n",
       "3 2020-01-04  -1.007827  -1.093999   1.472493  -0.493387\n",
       "4 2020-01-05  -0.972515   0.119187  -0.563892   0.389504\n",
       "5 2020-01-06   2.905071   0.168398  -0.679490  -0.571970\n",
       "6 2020-01-07  -0.070295   0.269492   0.107710   0.998263\n",
       "7 2020-01-08   0.657587   1.262432  -0.284617   0.465781"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this operation does not modify the original `DataFrame`. \n",
    "Verify this by inspecting it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_A</th>\n",
       "      <th>Product_B</th>\n",
       "      <th>Product_C</th>\n",
       "      <th>Product_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.881663</td>\n",
       "      <td>0.987620</td>\n",
       "      <td>0.515382</td>\n",
       "      <td>-0.244335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.367481</td>\n",
       "      <td>-1.071615</td>\n",
       "      <td>0.216773</td>\n",
       "      <td>1.318869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>1.174202</td>\n",
       "      <td>1.252871</td>\n",
       "      <td>-0.545009</td>\n",
       "      <td>0.077535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-1.007827</td>\n",
       "      <td>-1.093999</td>\n",
       "      <td>1.472493</td>\n",
       "      <td>-0.493387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-0.972515</td>\n",
       "      <td>0.119187</td>\n",
       "      <td>-0.563892</td>\n",
       "      <td>0.389504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>2.905071</td>\n",
       "      <td>0.168398</td>\n",
       "      <td>-0.679490</td>\n",
       "      <td>-0.571970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>-0.070295</td>\n",
       "      <td>0.269492</td>\n",
       "      <td>0.107710</td>\n",
       "      <td>0.998263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.657587</td>\n",
       "      <td>1.262432</td>\n",
       "      <td>-0.284617</td>\n",
       "      <td>0.465781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product_A  Product_B  Product_C  Product_D\n",
       "2020-01-01   0.881663   0.987620   0.515382  -0.244335\n",
       "2020-01-02  -0.367481  -1.071615   0.216773   1.318869\n",
       "2020-01-03   1.174202   1.252871  -0.545009   0.077535\n",
       "2020-01-04  -1.007827  -1.093999   1.472493  -0.493387\n",
       "2020-01-05  -0.972515   0.119187  -0.563892   0.389504\n",
       "2020-01-06   2.905071   0.168398  -0.679490  -0.571970\n",
       "2020-01-07  -0.070295   0.269492   0.107710   0.998263\n",
       "2020-01-08   0.657587   1.262432  -0.284617   0.465781"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to modify the original `DataFrame`, use the input `inplace=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Product_A</th>\n",
       "      <th>Product_B</th>\n",
       "      <th>Product_C</th>\n",
       "      <th>Product_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.881663</td>\n",
       "      <td>0.987620</td>\n",
       "      <td>0.515382</td>\n",
       "      <td>-0.244335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>-0.367481</td>\n",
       "      <td>-1.071615</td>\n",
       "      <td>0.216773</td>\n",
       "      <td>1.318869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1.174202</td>\n",
       "      <td>1.252871</td>\n",
       "      <td>-0.545009</td>\n",
       "      <td>0.077535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>-1.007827</td>\n",
       "      <td>-1.093999</td>\n",
       "      <td>1.472493</td>\n",
       "      <td>-0.493387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>-0.972515</td>\n",
       "      <td>0.119187</td>\n",
       "      <td>-0.563892</td>\n",
       "      <td>0.389504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>2.905071</td>\n",
       "      <td>0.168398</td>\n",
       "      <td>-0.679490</td>\n",
       "      <td>-0.571970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>-0.070295</td>\n",
       "      <td>0.269492</td>\n",
       "      <td>0.107710</td>\n",
       "      <td>0.998263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>0.657587</td>\n",
       "      <td>1.262432</td>\n",
       "      <td>-0.284617</td>\n",
       "      <td>0.465781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Product_A  Product_B  Product_C  Product_D\n",
       "0 2020-01-01   0.881663   0.987620   0.515382  -0.244335\n",
       "1 2020-01-02  -0.367481  -1.071615   0.216773   1.318869\n",
       "2 2020-01-03   1.174202   1.252871  -0.545009   0.077535\n",
       "3 2020-01-04  -1.007827  -1.093999   1.472493  -0.493387\n",
       "4 2020-01-05  -0.972515   0.119187  -0.563892   0.389504\n",
       "5 2020-01-06   2.905071   0.168398  -0.679490  -0.571970\n",
       "6 2020-01-07  -0.070295   0.269492   0.107710   0.998263\n",
       "7 2020-01-08   0.657587   1.262432  -0.284617   0.465781"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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