{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eea2a235-0f27-48f1-a2cd-7f1e3cdec9f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18f60cb-4f9c-42c5-91e1-c7cb5c68fa78",
   "metadata": {},
   "source": [
    "# Selecting and Modifying Rows/Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6b7794-bea3-4d98-9f28-dfec37ef9223",
   "metadata": {},
   "source": [
    "This subchapter will go into greater detail on how you can select/modify rows and columns in `pandas`. \n",
    "\n",
    "First, let us load in the same dataset as the first subchapter in the `Pandas` chapter. As a reminder, this dataset has beer sales across 50 continental states in the US. It is sourced from [_Salience and Taxation: Theory and Evidence_](https://www.aeaweb.org/articles?id=10.1257/aer.99.4.1145) by Chetty, Looney, and Kroft (AER 2009), and it includes 7 columns:\n",
    "- `st_name`: the state abbreviation\n",
    "- `year`: the year the data was recorded\n",
    "- `c_beer`: the quantity of beer consumed, in thousands of gallons\n",
    "- `beer_tax`: the ad valorem tax, as a percentage\n",
    "- `btax_dollars`: the excise tax, represented in dollars per case (24 cans) of beer \n",
    "- `population`: the population of the state, in thousands\n",
    "- `salestax`: the sales tax percentage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "28c73197-835e-4786-90e0-7a29a79f2d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
       "      <th>population</th>\n",
       "      <th>salestax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>1970</td>\n",
       "      <td>33098</td>\n",
       "      <td>72.341130</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3450</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>1971</td>\n",
       "      <td>37598</td>\n",
       "      <td>69.304600</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3497</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>1972</td>\n",
       "      <td>42719</td>\n",
       "      <td>67.149190</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3539</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>1973</td>\n",
       "      <td>46203</td>\n",
       "      <td>63.217026</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3580</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>1974</td>\n",
       "      <td>49769</td>\n",
       "      <td>56.933796</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3627</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>WY</td>\n",
       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "0         AL  1970   33098  72.341130         2.370        3450       4.0\n",
       "1         AL  1971   37598  69.304600         2.370        3497       4.0\n",
       "2         AL  1972   42719  67.149190         2.370        3539       4.0\n",
       "3         AL  1973   46203  63.217026         2.370        3580       4.0\n",
       "4         AL  1974   49769  56.933796         2.370        3627       4.0\n",
       "...      ...   ...     ...        ...           ...         ...       ...\n",
       "1703      WY  1999   12423   0.319894         0.045         492       4.0\n",
       "1704      WY  2000   12595   0.309491         0.045         494       4.0\n",
       "1705      WY  2001   12808   0.300928         0.045         494       4.0\n",
       "1706      WY  2002   13191   0.296244         0.045         499       4.0\n",
       "1707      WY  2003   15535   0.289643         0.045         501       4.0\n",
       "\n",
       "[1708 rows x 7 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/beer_tax.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3babea0-c486-4707-8fc2-654a8ef49ad8",
   "metadata": {},
   "source": [
    "## Extracting Rows/Columns that Satisfy a Given Condition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a3d028-84b1-4344-9c26-0c621c2ff071",
   "metadata": {},
   "source": [
    "An important task we want to accomplish when looking at data is to filter out all rows that satisfy a certain condition. In other words, we want to extract all the rows **where** a certain thing is accomplished. Conditional selection helps us achieve exactly this, extracting all rows that satisfy a certain condition.\n",
    "\n",
    "In the [previous subchapter](loading-looking.ipynb), we briefly gave an example of how we can use `.loc` to extract all rows that match certain values. To be more precise, when we pass in certain values for `.loc` and `iloc` to use, they generate an array of True/False values and use that to determine which values to include. This is very similar to our prior discussion on [NumPy slicing](../02-prereqs/numpy.ipynb).\n",
    "\n",
    "For example, let's say we want all the data for the state of Alabama. Then, we would want all rows where `st_name` is `CA`. We can generate a Series of True/False values corresponding to whether each row has a `st_name` of `AL` using the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0076d360-cf23-4db8-81e5-13175babd330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "        ...  \n",
       "1703    False\n",
       "1704    False\n",
       "1705    False\n",
       "1706    False\n",
       "1707    False\n",
       "Name: st_name, Length: 1708, dtype: bool"
      ]
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     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['st_name'] == 'AL'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e91ccb-d608-4fa3-acc8-2e8599c078bb",
   "metadata": {},
   "source": [
    "Now, to extract all rows where the state is Alabama, we can simply pass in this boolean series into the `.loc` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6cc48f37-123a-4dd2-b07e-059531963054",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "0      AL  1970   33098  72.341130          2.37        3450       4.0\n",
       "1      AL  1971   37598  69.304600          2.37        3497       4.0\n",
       "2      AL  1972   42719  67.149190          2.37        3539       4.0\n",
       "3      AL  1973   46203  63.217026          2.37        3580       4.0\n",
       "4      AL  1974   49769  56.933796          2.37        3627       4.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
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    }
   ],
   "source": [
    "df.loc[df['st_name'] == 'AL',:].head(5) # only showing the first 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d43d1a-2694-4044-a59e-35f8ebc3af87",
   "metadata": {},
   "source": [
    "Similarly, we can also use the context dependent `[]` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ded1e819-8528-40ec-95ca-e8e0333e3575",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>AL</td>\n",
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       "      <td>94530</td>\n",
       "      <td>16.299862</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4452</td>\n",
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       "    <tr>\n",
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       "      <td>AL</td>\n",
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       "      <td>4466</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>AL</td>\n",
       "      <td>2002</td>\n",
       "      <td>94732</td>\n",
       "      <td>15.602203</td>\n",
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       "      <td>4479</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>AL</td>\n",
       "      <td>2003</td>\n",
       "      <td>96188</td>\n",
       "      <td>15.254544</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4501</td>\n",
       "      <td>4.0</td>\n",
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      ],
      "text/plain": [
       "   st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "29      AL  1999   93828  16.847755          2.37        4430       4.0\n",
       "30      AL  2000   94530  16.299862          2.37        4452       4.0\n",
       "31      AL  2001   92893  15.848877          2.37        4466       4.0\n",
       "32      AL  2002   94732  15.602203          2.37        4479       4.0\n",
       "33      AL  2003   96188  15.254544          2.37        4501       4.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['st_name'] == 'AL'].tail(5) # only showing the last 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a90d4b-da5d-48d9-88c1-27805d82a454",
   "metadata": {},
   "source": [
    "We could also use the same technique to extract all columns that are integers (but not floats, so they do not contain decimals).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "596a6119-5595-4258-ab04-325fdf73d5a0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1706</th>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  c_beer  population\n",
       "0     1970   33098        3450\n",
       "1     1971   37598        3497\n",
       "2     1972   42719        3539\n",
       "3     1973   46203        3580\n",
       "4     1974   49769        3627\n",
       "...    ...     ...         ...\n",
       "1703  1999   12423         492\n",
       "1704  2000   12595         494\n",
       "1705  2001   12808         494\n",
       "1706  2002   13191         499\n",
       "1707  2003   15535         501\n",
       "\n",
       "[1708 rows x 3 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,df.dtypes == int]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c69cb89b-3b4b-487a-855e-9bca6cdd386f",
   "metadata": {},
   "source": [
    "Alternatively, if we wanted to extract all the rows where the index is less than 30, we could similarly use the `.iloc` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "881ee775-0213-462f-972c-b06653555be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index < 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fb2c183f-4bd2-443e-81fb-4443ba279579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
       "      <th>population</th>\n",
       "      <th>salestax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AL</td>\n",
       "      <td>1995</td>\n",
       "      <td>86991</td>\n",
       "      <td>18.417560</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4297</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AL</td>\n",
       "      <td>1996</td>\n",
       "      <td>89534</td>\n",
       "      <td>17.889332</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4331</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AL</td>\n",
       "      <td>1997</td>\n",
       "      <td>88807</td>\n",
       "      <td>17.488075</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4368</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AL</td>\n",
       "      <td>1998</td>\n",
       "      <td>92082</td>\n",
       "      <td>17.219852</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4405</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>AL</td>\n",
       "      <td>1999</td>\n",
       "      <td>93828</td>\n",
       "      <td>16.847755</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4430</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "25      AL  1995   86991  18.417560          2.37        4297       4.0\n",
       "26      AL  1996   89534  17.889332          2.37        4331       4.0\n",
       "27      AL  1997   88807  17.488075          2.37        4368       4.0\n",
       "28      AL  1998   92082  17.219852          2.37        4405       4.0\n",
       "29      AL  1999   93828  16.847755          2.37        4430       4.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[df.index < 30,:].tail(5) # only showing the last 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becd185c-66f0-4645-8ce0-e2c1562da4ce",
   "metadata": {},
   "source": [
    "As you can imagine, quickly and easily generating these boolean arrays/Series is very important for accessing our elements!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "531350fd-f08d-4b10-9c45-22bea0dfdbb0",
   "metadata": {},
   "source": [
    "### Logical Operators\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68874d06-aa17-45b6-bb22-0ab06848d943",
   "metadata": {},
   "source": [
    "\n",
    "What if we have multiple conditions we want to filter on? Let's say we want to look at all the rows where `beer_tax` is less than 3 and `btax_dollars` is less than 0.5. We can generate boolean filters for both of those conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ad6af1f-cb90-41d6-8f5b-6d31004a7169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1703     True\n",
       "1704     True\n",
       "1705     True\n",
       "1706     True\n",
       "1707     True\n",
       "Name: beer_tax, Length: 1708, dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['beer_tax'] < 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d0b98e9-e814-4123-a6b2-48b7cd20d357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1703     True\n",
       "1704     True\n",
       "1705     True\n",
       "1706     True\n",
       "1707     True\n",
       "Name: btax_dollars, Length: 1708, dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['btax_dollars'] < 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e941078f-b6b2-465a-9751-8b6f08e3864c",
   "metadata": {},
   "source": [
    "How can we combine both of these boolean arrays? Well, since we're interested in cases where both of these are true, we can use the `&` operator. The `&` operator is the logical way of expressing you only want the cases where elements from both the arrays are true.\n",
    "\n",
    "The code is shown below. Note the parenthesis around both the boolean arrays - this is an absolutely crucial piece of syntax!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c1222be-2bcd-453d-97db-916dcc6a88aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
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       "  <tbody>\n",
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       "      <th>77</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1979</td>\n",
       "      <td>77684</td>\n",
       "      <td>2.936328</td>\n",
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       "      <td>2636</td>\n",
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       "      <th>78</th>\n",
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       "      <th>79</th>\n",
       "      <td>AZ</td>\n",
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       "      <td>2.345187</td>\n",
       "      <td>0.180</td>\n",
       "      <td>2810</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1982</td>\n",
       "      <td>86249</td>\n",
       "      <td>2.209093</td>\n",
       "      <td>0.180</td>\n",
       "      <td>2890</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1983</td>\n",
       "      <td>83067</td>\n",
       "      <td>2.140336</td>\n",
       "      <td>0.180</td>\n",
       "      <td>2969</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1703</th>\n",
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       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>628 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer  beer_tax  btax_dollars  population  salestax\n",
       "77        AZ  1979   77684  2.936328         0.180        2636       4.0\n",
       "78        AZ  1980   79660  2.587105         0.180        2736       4.0\n",
       "79        AZ  1981   86283  2.345187         0.180        2810       4.0\n",
       "80        AZ  1982   86249  2.209093         0.180        2890       4.0\n",
       "81        AZ  1983   83067  2.140336         0.180        2969       4.0\n",
       "...      ...   ...     ...       ...           ...         ...       ...\n",
       "1703      WY  1999   12423  0.319894         0.045         492       4.0\n",
       "1704      WY  2000   12595  0.309491         0.045         494       4.0\n",
       "1705      WY  2001   12808  0.300928         0.045         494       4.0\n",
       "1706      WY  2002   13191  0.296244         0.045         499       4.0\n",
       "1707      WY  2003   15535  0.289643         0.045         501       4.0\n",
       "\n",
       "[628 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['beer_tax'] < 3) & (df['btax_dollars'] < 0.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1015594b-fac5-486a-b710-3974368e5614",
   "metadata": {},
   "source": [
    "Alternatively, if we were looking for the states where either `beer_tax` is less than 3 or `btax_dollars` is less than 0.5, we could use the logical `|` operator. The `|` operator is the logical way of expressing you only want the cases where an element from either array is true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3c69325-fcd5-4135-a86a-ab9b7419ec6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>69</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1971</td>\n",
       "      <td>41837</td>\n",
       "      <td>5.263641</td>\n",
       "      <td>0.180</td>\n",
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       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
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       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
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       "<p>1144 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer  beer_tax  btax_dollars  population  salestax\n",
       "68        AZ  1970   38604  5.494264         0.180        1795       3.0\n",
       "69        AZ  1971   41837  5.263641         0.180        1896       3.0\n",
       "70        AZ  1972   47949  5.099939         0.180        2008       3.0\n",
       "71        AZ  1973   53380  4.801294         0.180        2124       3.0\n",
       "72        AZ  1974   58188  4.324086         0.180        2223       3.0\n",
       "...      ...   ...     ...       ...           ...         ...       ...\n",
       "1703      WY  1999   12423  0.319894         0.045         492       4.0\n",
       "1704      WY  2000   12595  0.309491         0.045         494       4.0\n",
       "1705      WY  2001   12808  0.300928         0.045         494       4.0\n",
       "1706      WY  2002   13191  0.296244         0.045         499       4.0\n",
       "1707      WY  2003   15535  0.289643         0.045         501       4.0\n",
       "\n",
       "[1144 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['beer_tax'] < 3) | (df['btax_dollars'] < 0.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b160e0f1-64c5-4501-8ba1-eeaf2b781728",
   "metadata": {},
   "source": [
    "What about cases where either `beer_tax` is less than 3 or `btax_dollars` is less than 0.5, but not both? Well, here we could use the `^` operator, which is the logical way of expressing you only want the cases where an element from either of the arrays is true, but both the elements are not true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6f28bfb3-006c-483f-96c4-b1277bc9b29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
       "      <th>population</th>\n",
       "      <th>salestax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1970</td>\n",
       "      <td>38604</td>\n",
       "      <td>5.494264</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>1795</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1971</td>\n",
       "      <td>41837</td>\n",
       "      <td>5.263641</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>1896</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1972</td>\n",
       "      <td>47949</td>\n",
       "      <td>5.099939</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1973</td>\n",
       "      <td>53380</td>\n",
       "      <td>4.801294</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>2124</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1974</td>\n",
       "      <td>58188</td>\n",
       "      <td>4.324086</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>2223</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>WI</td>\n",
       "      <td>1972</td>\n",
       "      <td>128725</td>\n",
       "      <td>4.113951</td>\n",
       "      <td>0.1452</td>\n",
       "      <td>4502</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>WI</td>\n",
       "      <td>1973</td>\n",
       "      <td>133792</td>\n",
       "      <td>3.873043</td>\n",
       "      <td>0.1452</td>\n",
       "      <td>4524</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>WI</td>\n",
       "      <td>1974</td>\n",
       "      <td>136807</td>\n",
       "      <td>3.488096</td>\n",
       "      <td>0.1452</td>\n",
       "      <td>4546</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>WI</td>\n",
       "      <td>1975</td>\n",
       "      <td>146350</td>\n",
       "      <td>3.196341</td>\n",
       "      <td>0.1452</td>\n",
       "      <td>4579</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>WI</td>\n",
       "      <td>1976</td>\n",
       "      <td>142811</td>\n",
       "      <td>3.022199</td>\n",
       "      <td>0.1452</td>\n",
       "      <td>4596</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer  beer_tax  btax_dollars  population  salestax\n",
       "68        AZ  1970   38604  5.494264        0.1800        1795       3.0\n",
       "69        AZ  1971   41837  5.263641        0.1800        1896       3.0\n",
       "70        AZ  1972   47949  5.099939        0.1800        2008       3.0\n",
       "71        AZ  1973   53380  4.801294        0.1800        2124       3.0\n",
       "72        AZ  1974   58188  4.324086        0.1800        2223       3.0\n",
       "...      ...   ...     ...       ...           ...         ...       ...\n",
       "1644      WI  1972  128725  4.113951        0.1452        4502       4.0\n",
       "1645      WI  1973  133792  3.873043        0.1452        4524       4.0\n",
       "1646      WI  1974  136807  3.488096        0.1452        4546       4.0\n",
       "1647      WI  1975  146350  3.196341        0.1452        4579       4.0\n",
       "1648      WI  1976  142811  3.022199        0.1452        4596       4.0\n",
       "\n",
       "[516 rows x 7 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['beer_tax'] < 3) ^ (df['btax_dollars'] < 0.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5f596b-1892-47e1-b19e-8259dc43b39d",
   "metadata": {},
   "source": [
    "Notice how the number of columns with the `&` operator plus the number of columns with the `^` operator sum to the number of columns with the `|` operator (628 + 516 = 1144)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea047b82-1ba3-41a1-9d78-24683b9d98e7",
   "metadata": {},
   "source": [
    "Finally, what if you're interested in the cases where something isn't true? For example, what if you're interested in all the cases where the `st_name` isn't `AL`? Well, then you can use the `~` operator, which logically represents the inverse of a boolean array (so it converts all true values into false and all false values intro true). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "09575326-d347-46f2-9f37-25e4f2c73dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "        ...  \n",
       "1703    False\n",
       "1704    False\n",
       "1705    False\n",
       "1706    False\n",
       "1707    False\n",
       "Name: st_name, Length: 1708, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['st_name'] == 'AL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3f6fbd6-45aa-4483-a77c-2760ad4fb58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1703     True\n",
       "1704     True\n",
       "1705     True\n",
       "1706     True\n",
       "1707     True\n",
       "Name: st_name, Length: 1708, dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~(df['st_name'] == 'AL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6912de3e-1ecb-4448-a5e5-eba751659318",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>c_beer</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>AK</td>\n",
       "      <td>1970</td>\n",
       "      <td>5372</td>\n",
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       "      <td>304</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>AK</td>\n",
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       "      <td>6336</td>\n",
       "      <td>13.159102</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>316</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>AK</td>\n",
       "      <td>1972</td>\n",
       "      <td>6038</td>\n",
       "      <td>12.749847</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>324</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>AK</td>\n",
       "      <td>1973</td>\n",
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       "      <td>12.003234</td>\n",
       "      <td>0.5625</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>AK</td>\n",
       "      <td>1974</td>\n",
       "      <td>7598</td>\n",
       "      <td>10.810215</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>341</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>1703</th>\n",
       "      <td>WY</td>\n",
       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1674 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "34        AK  1970    5372  13.735660        0.5625         304       0.0\n",
       "35        AK  1971    6336  13.159102        0.5625         316       0.0\n",
       "36        AK  1972    6038  12.749847        0.5625         324       0.0\n",
       "37        AK  1973    6453  12.003234        0.5625         331       0.0\n",
       "38        AK  1974    7598  10.810215        0.5625         341       0.0\n",
       "...      ...   ...     ...        ...           ...         ...       ...\n",
       "1703      WY  1999   12423   0.319894        0.0450         492       4.0\n",
       "1704      WY  2000   12595   0.309491        0.0450         494       4.0\n",
       "1705      WY  2001   12808   0.300928        0.0450         494       4.0\n",
       "1706      WY  2002   13191   0.296244        0.0450         499       4.0\n",
       "1707      WY  2003   15535   0.289643        0.0450         501       4.0\n",
       "\n",
       "[1674 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~(df['st_name'] == 'AL')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fb7f82-73ed-4099-89ad-d28d099ad2d0",
   "metadata": {},
   "source": [
    "Here's a table summarizing all the different logical operators to combine boolean arrays.\n",
    "\n",
    "Symbol | Usage      | Meaning \n",
    "------ | ---------- | -------------------------------------\n",
    "~    | ~p       | Returns the negation of p\n",
    "&#124; | p &#124; q | p OR q\n",
    "&    | p & q    | p AND q\n",
    "^  | p ^ q | p XOR q (exclusive or)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fa0843-25f4-43b0-b9c0-8e74afdc7c68",
   "metadata": {},
   "source": [
    "### Multiple Labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af6af74-3152-4067-8fdc-94a3a309e67c",
   "metadata": {},
   "source": [
    "If you want to keep all rows where a certain column is in a list of given values, you can use the `.isin()` method. This is logically equivalent to using the `|` operator multiple times, but it's easier. For example, below we get all rows corresponding to the states of California, Florida, Texas and Nebraska."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1e2ff2ae-9cb2-4dd6-a81e-98f2dfe2c90d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1707    False\n",
       "1706    False\n",
       "1705    False\n",
       "1704    False\n",
       "1703    False\n",
       "        ...  \n",
       "4       False\n",
       "3       False\n",
       "2       False\n",
       "1       False\n",
       "0       False\n",
       "Name: st_name, Length: 1708, dtype: bool"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['st_name'].isin(['CA','FL','TX','NE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7c97c42a-00e3-4b90-8e5f-3ba2c419096f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>20585</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>CA</td>\n",
       "      <td>1973</td>\n",
       "      <td>417463</td>\n",
       "      <td>2.400647</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>20869</td>\n",
       "      <td>5.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>CA</td>\n",
       "      <td>1974</td>\n",
       "      <td>464237</td>\n",
       "      <td>2.162043</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>21174</td>\n",
       "      <td>5.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>TX</td>\n",
       "      <td>1999</td>\n",
       "      <td>546353</td>\n",
       "      <td>3.166952</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>20558</td>\n",
       "      <td>6.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>TX</td>\n",
       "      <td>2000</td>\n",
       "      <td>556051</td>\n",
       "      <td>3.063962</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>20949</td>\n",
       "      <td>6.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>TX</td>\n",
       "      <td>2001</td>\n",
       "      <td>557425</td>\n",
       "      <td>2.979188</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>21341</td>\n",
       "      <td>6.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>TX</td>\n",
       "      <td>2002</td>\n",
       "      <td>563150</td>\n",
       "      <td>2.932819</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>21737</td>\n",
       "      <td>6.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>TX</td>\n",
       "      <td>2003</td>\n",
       "      <td>558837</td>\n",
       "      <td>2.867468</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>22119</td>\n",
       "      <td>6.250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer  beer_tax  btax_dollars  population  salestax\n",
       "1571      CA  1970  363645  2.747132        0.0900       20023     5.000\n",
       "1570      CA  1971  380397  2.631820        0.0900       20346     5.000\n",
       "1569      CA  1972  401928  2.549970        0.0900       20585     5.000\n",
       "1568      CA  1973  417463  2.400647        0.0900       20869     5.167\n",
       "1567      CA  1974  464237  2.162043        0.0900       21174     5.250\n",
       "...      ...   ...     ...       ...           ...         ...       ...\n",
       "240       TX  1999  546353  3.166952        0.4455       20558     6.250\n",
       "239       TX  2000  556051  3.063962        0.4455       20949     6.250\n",
       "238       TX  2001  557425  2.979188        0.4455       21341     6.250\n",
       "237       TX  2002  563150  2.932819        0.4455       21737     6.250\n",
       "236       TX  2003  558837  2.867468        0.4455       22119     6.250\n",
       "\n",
       "[136 rows x 7 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['st_name'].isin(['CA','FL','TX','NE'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6487216-d34a-4665-857a-04b7bb4d514e",
   "metadata": {},
   "source": [
    "### Editing Rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ab7829-6fb4-4ae2-a64f-7267a1882e5c",
   "metadata": {},
   "source": [
    "We can also use conditional selection to edit certain rows. For example, let's say we found out we made an error with measuring `beer_tax` for the state of Alabama, and the true beer tax is actually 0.1 higher than what we've measured. We can easily use boolean filtering to select the columns where we've made this mistake and fix them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbc80134-aa0d-4a2e-96d9-c5d674d2f8b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     72.341130\n",
       "1     69.304600\n",
       "2     67.149190\n",
       "3     63.217026\n",
       "4     56.933796\n",
       "5     52.171677\n",
       "6     49.329280\n",
       "7     46.317430\n",
       "8     43.049637\n",
       "9     38.661655\n",
       "10    34.063545\n",
       "11    30.878284\n",
       "12    29.086386\n",
       "13    28.181087\n",
       "14    27.014784\n",
       "15    26.085838\n",
       "16    25.609820\n",
       "17    24.708065\n",
       "18    23.726425\n",
       "19    22.635775\n",
       "20    21.475412\n",
       "21    20.608194\n",
       "22    20.005960\n",
       "23    19.424470\n",
       "24    18.939516\n",
       "25    18.417560\n",
       "26    17.889332\n",
       "27    17.488075\n",
       "28    17.219852\n",
       "29    16.847755\n",
       "30    16.299862\n",
       "31    15.848877\n",
       "32    15.602203\n",
       "33    15.254544\n",
       "Name: beer_tax, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['st_name']=='AL', 'beer_tax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30c7bbca-0058-41c9-9475-d967099a0376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     72.441130\n",
       "1     69.404600\n",
       "2     67.249190\n",
       "3     63.317026\n",
       "4     57.033796\n",
       "5     52.271677\n",
       "6     49.429280\n",
       "7     46.417430\n",
       "8     43.149637\n",
       "9     38.761655\n",
       "10    34.163545\n",
       "11    30.978284\n",
       "12    29.186386\n",
       "13    28.281087\n",
       "14    27.114784\n",
       "15    26.185838\n",
       "16    25.709820\n",
       "17    24.808065\n",
       "18    23.826425\n",
       "19    22.735775\n",
       "20    21.575412\n",
       "21    20.708194\n",
       "22    20.105960\n",
       "23    19.524470\n",
       "24    19.039516\n",
       "25    18.517560\n",
       "26    17.989332\n",
       "27    17.588075\n",
       "28    17.319852\n",
       "29    16.947755\n",
       "30    16.399862\n",
       "31    15.948877\n",
       "32    15.702203\n",
       "33    15.354544\n",
       "Name: beer_tax, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['st_name']=='AL', 'beer_tax'] + 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd414b35-37b4-4ad1-912a-73f3215b2c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>st_name</th>\n",
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       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
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       "      <td>33098</td>\n",
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       "      <th>1</th>\n",
       "      <td>AL</td>\n",
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       "      <td>2.370</td>\n",
       "      <td>3539</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>1973</td>\n",
       "      <td>46203</td>\n",
       "      <td>63.317026</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3580</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>1974</td>\n",
       "      <td>49769</td>\n",
       "      <td>57.033796</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3627</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>1703</th>\n",
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       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>1704</th>\n",
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       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "0         AL  1970   33098  72.441130         2.370        3450       4.0\n",
       "1         AL  1971   37598  69.404600         2.370        3497       4.0\n",
       "2         AL  1972   42719  67.249190         2.370        3539       4.0\n",
       "3         AL  1973   46203  63.317026         2.370        3580       4.0\n",
       "4         AL  1974   49769  57.033796         2.370        3627       4.0\n",
       "...      ...   ...     ...        ...           ...         ...       ...\n",
       "1703      WY  1999   12423   0.319894         0.045         492       4.0\n",
       "1704      WY  2000   12595   0.309491         0.045         494       4.0\n",
       "1705      WY  2001   12808   0.300928         0.045         494       4.0\n",
       "1706      WY  2002   13191   0.296244         0.045         499       4.0\n",
       "1707      WY  2003   15535   0.289643         0.045         501       4.0\n",
       "\n",
       "[1708 rows x 7 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['st_name']=='AL', 'beer_tax'] = df.loc[df['st_name']=='AL', 'beer_tax'] + 0.1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b037833f-09e5-4479-b6e2-f2e7054692b8",
   "metadata": {},
   "source": [
    "It's that simple! However, since our data was (presumably) correctly measured, let us revert this change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "504d089f-bfe0-4160-b86a-59965390a643",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1706</th>\n",
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       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
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       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
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      ],
      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "0         AL  1970   33098  72.341130         2.370        3450       4.0\n",
       "1         AL  1971   37598  69.304600         2.370        3497       4.0\n",
       "2         AL  1972   42719  67.149190         2.370        3539       4.0\n",
       "3         AL  1973   46203  63.217026         2.370        3580       4.0\n",
       "4         AL  1974   49769  56.933796         2.370        3627       4.0\n",
       "...      ...   ...     ...        ...           ...         ...       ...\n",
       "1703      WY  1999   12423   0.319894         0.045         492       4.0\n",
       "1704      WY  2000   12595   0.309491         0.045         494       4.0\n",
       "1705      WY  2001   12808   0.300928         0.045         494       4.0\n",
       "1706      WY  2002   13191   0.296244         0.045         499       4.0\n",
       "1707      WY  2003   15535   0.289643         0.045         501       4.0\n",
       "\n",
       "[1708 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['st_name']=='AL', 'beer_tax'] = df.loc[df['st_name']=='AL', 'beer_tax'] - 0.1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2d4b89-61aa-4e52-a737-21ff0a62a7df",
   "metadata": {},
   "source": [
    "## Modifying Columns and Indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9cfa207-9411-4396-b2c6-7dba79f518ed",
   "metadata": {},
   "source": [
    "Now, we move on to modifying the columns in our DataFrames. The first task we will consider is how to add a new column.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ac5fd4-8658-42e1-8d41-aa4881fcf067",
   "metadata": {},
   "source": [
    "### Adding Columns\n",
    "\n",
    "Let's say we want to add a new column where we calculate the total percentage taxed by combining the `beer_tax` and `salestax`. Both of these % taxes compound, so we will need to be careful with the math! Below, we calculate a series with the sum of these two taxes, as a percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2abe63f4-e00b-4b3e-9736-c26d59821dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       79.234775\n",
       "1       76.076784\n",
       "2       73.835158\n",
       "3       69.745707\n",
       "4       63.211148\n",
       "          ...    \n",
       "1703     4.332690\n",
       "1704     4.321871\n",
       "1705     4.312965\n",
       "1706     4.308094\n",
       "1707     4.301229\n",
       "Length: 1708, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df['beer_tax'] / 100 + 1) * (df['salestax'] / 100 + 1) - 1) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d9d3c7-9a23-42c3-9f0a-45049a972a5f",
   "metadata": {},
   "source": [
    "Now, we want to set this series equal to a new column named `total_percent_tax` in our dataframe. The syntax for doing this follows `df[New_Column_Name] = Series/Array_Name`, and is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a28e0a02-9f31-45cf-b6d9-d55596bf5aa6",
   "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>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
       "      <th>population</th>\n",
       "      <th>salestax</th>\n",
       "      <th>total_percent_tax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>1970</td>\n",
       "      <td>33098</td>\n",
       "      <td>72.341130</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3450</td>\n",
       "      <td>4.0</td>\n",
       "      <td>79.234775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>1971</td>\n",
       "      <td>37598</td>\n",
       "      <td>69.304600</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3497</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.076784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>1972</td>\n",
       "      <td>42719</td>\n",
       "      <td>67.149190</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3539</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73.835158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>1973</td>\n",
       "      <td>46203</td>\n",
       "      <td>63.217026</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3580</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.745707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>1974</td>\n",
       "      <td>49769</td>\n",
       "      <td>56.933796</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3627</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.211148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  st_name  year  c_beer   beer_tax  btax_dollars  population  salestax  \\\n",
       "0      AL  1970   33098  72.341130          2.37        3450       4.0   \n",
       "1      AL  1971   37598  69.304600          2.37        3497       4.0   \n",
       "2      AL  1972   42719  67.149190          2.37        3539       4.0   \n",
       "3      AL  1973   46203  63.217026          2.37        3580       4.0   \n",
       "4      AL  1974   49769  56.933796          2.37        3627       4.0   \n",
       "\n",
       "   total_percent_tax  \n",
       "0          79.234775  \n",
       "1          76.076784  \n",
       "2          73.835158  \n",
       "3          69.745707  \n",
       "4          63.211148  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_percent_tax'] = ((df['beer_tax'] / 100 + 1) * (df['salestax'] / 100 + 1) - 1) * 100\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5021012b-2c15-4818-965d-77b38f24c2a3",
   "metadata": {},
   "source": [
    "Voila, our new column has been added!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07017740-ca0d-469e-bae1-e156bcfa3fbb",
   "metadata": {},
   "source": [
    "### Renaming Columns\n",
    "\n",
    "Looking at the table above, `c_beer` feels like a confusing name. Instead, let us change to `beer_consumption`, a more informative name! To do so, we can use the DataFrame [`rename` method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rename.html), as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e5f00e7-a1e7-409e-ae03-efc10627a8b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st_name</th>\n",
       "      <th>year</th>\n",
       "      <th>beer_consumption</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
       "      <th>population</th>\n",
       "      <th>salestax</th>\n",
       "      <th>total_percent_tax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
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       "      <td>2.370</td>\n",
       "      <td>3450</td>\n",
       "      <td>4.0</td>\n",
       "      <td>79.234775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>1971</td>\n",
       "      <td>37598</td>\n",
       "      <td>69.304600</td>\n",
       "      <td>2.370</td>\n",
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       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>1972</td>\n",
       "      <td>42719</td>\n",
       "      <td>67.149190</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3539</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73.835158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>1973</td>\n",
       "      <td>46203</td>\n",
       "      <td>63.217026</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3580</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.745707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>1974</td>\n",
       "      <td>49769</td>\n",
       "      <td>56.933796</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3627</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.211148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>WY</td>\n",
       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.332690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.321871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.312965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.308094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.301229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  beer_consumption   beer_tax  btax_dollars  population  \\\n",
       "0         AL  1970             33098  72.341130         2.370        3450   \n",
       "1         AL  1971             37598  69.304600         2.370        3497   \n",
       "2         AL  1972             42719  67.149190         2.370        3539   \n",
       "3         AL  1973             46203  63.217026         2.370        3580   \n",
       "4         AL  1974             49769  56.933796         2.370        3627   \n",
       "...      ...   ...               ...        ...           ...         ...   \n",
       "1703      WY  1999             12423   0.319894         0.045         492   \n",
       "1704      WY  2000             12595   0.309491         0.045         494   \n",
       "1705      WY  2001             12808   0.300928         0.045         494   \n",
       "1706      WY  2002             13191   0.296244         0.045         499   \n",
       "1707      WY  2003             15535   0.289643         0.045         501   \n",
       "\n",
       "      salestax  total_percent_tax  \n",
       "0          4.0          79.234775  \n",
       "1          4.0          76.076784  \n",
       "2          4.0          73.835158  \n",
       "3          4.0          69.745707  \n",
       "4          4.0          63.211148  \n",
       "...        ...                ...  \n",
       "1703       4.0           4.332690  \n",
       "1704       4.0           4.321871  \n",
       "1705       4.0           4.312965  \n",
       "1706       4.0           4.308094  \n",
       "1707       4.0           4.301229  \n",
       "\n",
       "[1708 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"c_beer\":\"beer_consumption\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a4b1dc-a155-4376-9bda-c6657d9eed52",
   "metadata": {},
   "source": [
    "As we can see, the `columns` parameter of the `rename` method takes in a dictionary where all the keys are the old column names and all the values are the new column names.\n",
    "\n",
    "Note, however, that the original DataFrame `df` has not changed yet. Instead, `pandas` methods just return a copy of the modified DataFrame for you to see. To change the original dataframe, you can either manually assign the new DataFrame to `df` or set the `inplace` parameter of the `rename` method to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c14c73e9-f624-4bd9-86ef-bbbc88670882",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>st_name</th>\n",
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       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
       "      <th>btax_dollars</th>\n",
       "      <th>population</th>\n",
       "      <th>salestax</th>\n",
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       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.321871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.312965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.308094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.301229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  salestax  \\\n",
       "0         AL  1970   33098  72.341130         2.370        3450       4.0   \n",
       "1         AL  1971   37598  69.304600         2.370        3497       4.0   \n",
       "2         AL  1972   42719  67.149190         2.370        3539       4.0   \n",
       "3         AL  1973   46203  63.217026         2.370        3580       4.0   \n",
       "4         AL  1974   49769  56.933796         2.370        3627       4.0   \n",
       "...      ...   ...     ...        ...           ...         ...       ...   \n",
       "1703      WY  1999   12423   0.319894         0.045         492       4.0   \n",
       "1704      WY  2000   12595   0.309491         0.045         494       4.0   \n",
       "1705      WY  2001   12808   0.300928         0.045         494       4.0   \n",
       "1706      WY  2002   13191   0.296244         0.045         499       4.0   \n",
       "1707      WY  2003   15535   0.289643         0.045         501       4.0   \n",
       "\n",
       "      total_percent_tax  \n",
       "0             79.234775  \n",
       "1             76.076784  \n",
       "2             73.835158  \n",
       "3             69.745707  \n",
       "4             63.211148  \n",
       "...                 ...  \n",
       "1703           4.332690  \n",
       "1704           4.321871  \n",
       "1705           4.312965  \n",
       "1706           4.308094  \n",
       "1707           4.301229  \n",
       "\n",
       "[1708 rows x 8 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # original dataframe has not changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "44d5e9b3-ba34-4d28-a7ad-02c663e77722",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>67.149190</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3539</td>\n",
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       "      <td>AL</td>\n",
       "      <td>1974</td>\n",
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       "      <td>56.933796</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3627</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.321871</td>\n",
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       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
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       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.301229</td>\n",
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       "  </tbody>\n",
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       "<p>1708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  beer_consumption   beer_tax  btax_dollars  population  \\\n",
       "0         AL  1970             33098  72.341130         2.370        3450   \n",
       "1         AL  1971             37598  69.304600         2.370        3497   \n",
       "2         AL  1972             42719  67.149190         2.370        3539   \n",
       "3         AL  1973             46203  63.217026         2.370        3580   \n",
       "4         AL  1974             49769  56.933796         2.370        3627   \n",
       "...      ...   ...               ...        ...           ...         ...   \n",
       "1703      WY  1999             12423   0.319894         0.045         492   \n",
       "1704      WY  2000             12595   0.309491         0.045         494   \n",
       "1705      WY  2001             12808   0.300928         0.045         494   \n",
       "1706      WY  2002             13191   0.296244         0.045         499   \n",
       "1707      WY  2003             15535   0.289643         0.045         501   \n",
       "\n",
       "      salestax  total_percent_tax  \n",
       "0          4.0          79.234775  \n",
       "1          4.0          76.076784  \n",
       "2          4.0          73.835158  \n",
       "3          4.0          69.745707  \n",
       "4          4.0          63.211148  \n",
       "...        ...                ...  \n",
       "1703       4.0           4.332690  \n",
       "1704       4.0           4.321871  \n",
       "1705       4.0           4.312965  \n",
       "1706       4.0           4.308094  \n",
       "1707       4.0           4.301229  \n",
       "\n",
       "[1708 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={\"c_beer\":\"beer_consumption\"}) # you can manually reassign the `df` variable\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b673ec53-76d9-48e3-b50c-123635c167b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.321871</td>\n",
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       "    <tr>\n",
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       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
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       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.301229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  beer_consumption   beer_tax  btax_dollars  population  \\\n",
       "0         AL  1970             33098  72.341130         2.370        3450   \n",
       "1         AL  1971             37598  69.304600         2.370        3497   \n",
       "2         AL  1972             42719  67.149190         2.370        3539   \n",
       "3         AL  1973             46203  63.217026         2.370        3580   \n",
       "4         AL  1974             49769  56.933796         2.370        3627   \n",
       "...      ...   ...               ...        ...           ...         ...   \n",
       "1703      WY  1999             12423   0.319894         0.045         492   \n",
       "1704      WY  2000             12595   0.309491         0.045         494   \n",
       "1705      WY  2001             12808   0.300928         0.045         494   \n",
       "1706      WY  2002             13191   0.296244         0.045         499   \n",
       "1707      WY  2003             15535   0.289643         0.045         501   \n",
       "\n",
       "      salestax  total_percent_tax  \n",
       "0          4.0          79.234775  \n",
       "1          4.0          76.076784  \n",
       "2          4.0          73.835158  \n",
       "3          4.0          69.745707  \n",
       "4          4.0          63.211148  \n",
       "...        ...                ...  \n",
       "1703       4.0           4.332690  \n",
       "1704       4.0           4.321871  \n",
       "1705       4.0           4.312965  \n",
       "1706       4.0           4.308094  \n",
       "1707       4.0           4.301229  \n",
       "\n",
       "[1708 rows x 8 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"c_beer\":\"beer_consumption\"}, inplace = True) # or you can use the inplace method\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d3be649-6171-4a13-a539-4641b53bafb5",
   "metadata": {},
   "source": [
    "### Dropping Columns\n",
    "\n",
    "Finally, let's say we want to drop a column. For this, we can use the DataFrame [`drop` method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html). For example, we drop the `salestax` column below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "84e04299-fffa-442e-aeba-9e40d3a3f11c",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>1974</td>\n",
       "      <td>49769</td>\n",
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       "      <td>2.370</td>\n",
       "      <td>3627</td>\n",
       "      <td>63.211148</td>\n",
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       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>WY</td>\n",
       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.332690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
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       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.321871</td>\n",
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       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.312965</td>\n",
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       "    <tr>\n",
       "      <th>1706</th>\n",
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       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.308094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.301229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  beer_consumption   beer_tax  btax_dollars  population  \\\n",
       "0         AL  1970             33098  72.341130         2.370        3450   \n",
       "1         AL  1971             37598  69.304600         2.370        3497   \n",
       "2         AL  1972             42719  67.149190         2.370        3539   \n",
       "3         AL  1973             46203  63.217026         2.370        3580   \n",
       "4         AL  1974             49769  56.933796         2.370        3627   \n",
       "...      ...   ...               ...        ...           ...         ...   \n",
       "1703      WY  1999             12423   0.319894         0.045         492   \n",
       "1704      WY  2000             12595   0.309491         0.045         494   \n",
       "1705      WY  2001             12808   0.300928         0.045         494   \n",
       "1706      WY  2002             13191   0.296244         0.045         499   \n",
       "1707      WY  2003             15535   0.289643         0.045         501   \n",
       "\n",
       "      total_percent_tax  \n",
       "0             79.234775  \n",
       "1             76.076784  \n",
       "2             73.835158  \n",
       "3             69.745707  \n",
       "4             63.211148  \n",
       "...                 ...  \n",
       "1703           4.332690  \n",
       "1704           4.321871  \n",
       "1705           4.312965  \n",
       "1706           4.308094  \n",
       "1707           4.301229  \n",
       "\n",
       "[1708 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"salestax\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b38fa5a-a517-4dc5-9ca5-9a5639410264",
   "metadata": {},
   "source": [
    "Once again, the `drop` method has a `columns` parameter that takes in a list of the columns to drop (no need for a dictionary here as there are no key-value pairs). \n",
    "\n",
    "However, you can also simply pass in a list of the columns to drop and then use the `axis` parameter to specify they represent column names, as done below. We encourage you to read the documentation if you're interested in further detail!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f5eabc61-2a97-44e8-af65-5a4dbf095b59",
   "metadata": {},
   "outputs": [
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       "      <td>494</td>\n",
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       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.308094</td>\n",
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       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.301229</td>\n",
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       "<p>1708 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "     st_name  year  beer_consumption  btax_dollars  population  \\\n",
       "0         AL  1970             33098         2.370        3450   \n",
       "1         AL  1971             37598         2.370        3497   \n",
       "2         AL  1972             42719         2.370        3539   \n",
       "3         AL  1973             46203         2.370        3580   \n",
       "4         AL  1974             49769         2.370        3627   \n",
       "...      ...   ...               ...           ...         ...   \n",
       "1703      WY  1999             12423         0.045         492   \n",
       "1704      WY  2000             12595         0.045         494   \n",
       "1705      WY  2001             12808         0.045         494   \n",
       "1706      WY  2002             13191         0.045         499   \n",
       "1707      WY  2003             15535         0.045         501   \n",
       "\n",
       "      total_percent_tax  \n",
       "0             79.234775  \n",
       "1             76.076784  \n",
       "2             73.835158  \n",
       "3             69.745707  \n",
       "4             63.211148  \n",
       "...                 ...  \n",
       "1703           4.332690  \n",
       "1704           4.321871  \n",
       "1705           4.312965  \n",
       "1706           4.308094  \n",
       "1707           4.301229  \n",
       "\n",
       "[1708 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"salestax\",\"beer_tax\"], axis = 'columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c6e70c-e783-4e39-9b0c-45ec7dde814f",
   "metadata": {},
   "source": [
    "Finally, once again the `drop` parameter does not modify the original table. To modify the original table, you can once again reassign it to the original table or use the `inplace` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "62f72213-d7c8-469b-ac3d-c2ec4ecff473",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  \\\n",
       "0         AL  1970   33098  72.341130         2.370        3450   \n",
       "1         AL  1971   37598  69.304600         2.370        3497   \n",
       "2         AL  1972   42719  67.149190         2.370        3539   \n",
       "3         AL  1973   46203  63.217026         2.370        3580   \n",
       "4         AL  1974   49769  56.933796         2.370        3627   \n",
       "...      ...   ...     ...        ...           ...         ...   \n",
       "1703      WY  1999   12423   0.319894         0.045         492   \n",
       "1704      WY  2000   12595   0.309491         0.045         494   \n",
       "1705      WY  2001   12808   0.300928         0.045         494   \n",
       "1706      WY  2002   13191   0.296244         0.045         499   \n",
       "1707      WY  2003   15535   0.289643         0.045         501   \n",
       "\n",
       "      total_percent_tax  \n",
       "0             79.234775  \n",
       "1             76.076784  \n",
       "2             73.835158  \n",
       "3             69.745707  \n",
       "4             63.211148  \n",
       "...                 ...  \n",
       "1703           4.332690  \n",
       "1704           4.321871  \n",
       "1705           4.312965  \n",
       "1706           4.308094  \n",
       "1707           4.301229  \n",
       "\n",
       "[1708 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=[\"salestax\"]) # reassigning to df\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "41cd79f2-08cf-48e4-bd6f-42dff29502b4",
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   "outputs": [
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       "      <td>63.211148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>WY</td>\n",
       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.332690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.321871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.312965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.308094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.301229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer  btax_dollars  population  total_percent_tax\n",
       "0         AL  1970   33098         2.370        3450          79.234775\n",
       "1         AL  1971   37598         2.370        3497          76.076784\n",
       "2         AL  1972   42719         2.370        3539          73.835158\n",
       "3         AL  1973   46203         2.370        3580          69.745707\n",
       "4         AL  1974   49769         2.370        3627          63.211148\n",
       "...      ...   ...     ...           ...         ...                ...\n",
       "1703      WY  1999   12423         0.045         492           4.332690\n",
       "1704      WY  2000   12595         0.045         494           4.321871\n",
       "1705      WY  2001   12808         0.045         494           4.312965\n",
       "1706      WY  2002   13191         0.045         499           4.308094\n",
       "1707      WY  2003   15535         0.045         501           4.301229\n",
       "\n",
       "[1708 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"salestax\",\"beer_tax\"], axis = 'columns', inplace = True) # using inplace parameter\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c5d307-1b4d-4689-88b5-f7db5c6ee9e3",
   "metadata": {},
   "source": [
    "### Modifying the Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7106188c-e900-46a1-a237-beac6720961a",
   "metadata": {},
   "source": [
    "To modify the index of a DataFrame, there are three main methods, as discussed below.\n",
    "\n",
    "`.set_index()` Sets the index of the DataFrame to a particular column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e565b2c9-3bca-4187-b66a-82bf455e32af",
   "metadata": {},
   "outputs": [
    {
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       "      <th>c_beer</th>\n",
       "      <th>beer_tax</th>\n",
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       "      <td>2.370</td>\n",
       "      <td>3450</td>\n",
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       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>1971</td>\n",
       "      <td>37598</td>\n",
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       "      <td>2.370</td>\n",
       "      <td>3497</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>1972</td>\n",
       "      <td>42719</td>\n",
       "      <td>67.149190</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3539</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>1973</td>\n",
       "      <td>46203</td>\n",
       "      <td>63.217026</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3580</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>1974</td>\n",
       "      <td>49769</td>\n",
       "      <td>56.933796</td>\n",
       "      <td>2.370</td>\n",
       "      <td>3627</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
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       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "st_name                                                             \n",
       "AL       1970   33098  72.341130         2.370        3450       4.0\n",
       "AL       1971   37598  69.304600         2.370        3497       4.0\n",
       "AL       1972   42719  67.149190         2.370        3539       4.0\n",
       "AL       1973   46203  63.217026         2.370        3580       4.0\n",
       "AL       1974   49769  56.933796         2.370        3627       4.0\n",
       "...       ...     ...        ...           ...         ...       ...\n",
       "WY       1999   12423   0.319894         0.045         492       4.0\n",
       "WY       2000   12595   0.309491         0.045         494       4.0\n",
       "WY       2001   12808   0.300928         0.045         494       4.0\n",
       "WY       2002   13191   0.296244         0.045         499       4.0\n",
       "WY       2003   15535   0.289643         0.045         501       4.0\n",
       "\n",
       "[1708 rows x 6 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('st_name', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0ffc7e-7553-4d60-8e7a-cac7611aed49",
   "metadata": {},
   "source": [
    "`.reset_index()` converts the current index into a column and makes the new index as a default numbering from 0 to the very last row in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "51648c41-a4a3-40bf-80fc-1644ec826798",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1706</th>\n",
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       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index st_name  year  c_beer   beer_tax  btax_dollars  population  \\\n",
       "0         0      AL  1970   33098  72.341130         2.370        3450   \n",
       "1         1      AL  1971   37598  69.304600         2.370        3497   \n",
       "2         2      AL  1972   42719  67.149190         2.370        3539   \n",
       "3         3      AL  1973   46203  63.217026         2.370        3580   \n",
       "4         4      AL  1974   49769  56.933796         2.370        3627   \n",
       "...     ...     ...   ...     ...        ...           ...         ...   \n",
       "1703   1703      WY  1999   12423   0.319894         0.045         492   \n",
       "1704   1704      WY  2000   12595   0.309491         0.045         494   \n",
       "1705   1705      WY  2001   12808   0.300928         0.045         494   \n",
       "1706   1706      WY  2002   13191   0.296244         0.045         499   \n",
       "1707   1707      WY  2003   15535   0.289643         0.045         501   \n",
       "\n",
       "      salestax  \n",
       "0          4.0  \n",
       "1          4.0  \n",
       "2          4.0  \n",
       "3          4.0  \n",
       "4          4.0  \n",
       "...        ...  \n",
       "1703       4.0  \n",
       "1704       4.0  \n",
       "1705       4.0  \n",
       "1706       4.0  \n",
       "1707       4.0  \n",
       "\n",
       "[1708 rows x 8 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cedb35c-56e5-4eaf-a8dd-52171fbf158b",
   "metadata": {},
   "source": [
    "Finally, you can manually set the index equal to something else by simply reassigning the DataFrame/Series attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ce4a9283-3a05-4e20-a9d2-7144912f12ab",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WY</td>\n",
       "      <td>1999</td>\n",
       "      <td>12423</td>\n",
       "      <td>0.319894</td>\n",
       "      <td>0.045</td>\n",
       "      <td>492</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WY</td>\n",
       "      <td>2000</td>\n",
       "      <td>12595</td>\n",
       "      <td>0.309491</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WY</td>\n",
       "      <td>2001</td>\n",
       "      <td>12808</td>\n",
       "      <td>0.300928</td>\n",
       "      <td>0.045</td>\n",
       "      <td>494</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WY</td>\n",
       "      <td>2002</td>\n",
       "      <td>13191</td>\n",
       "      <td>0.296244</td>\n",
       "      <td>0.045</td>\n",
       "      <td>499</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WY</td>\n",
       "      <td>2003</td>\n",
       "      <td>15535</td>\n",
       "      <td>0.289643</td>\n",
       "      <td>0.045</td>\n",
       "      <td>501</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1708 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     st_name  year  c_beer   beer_tax  btax_dollars  population  salestax\n",
       "1707      AL  1970   33098  72.341130         2.370        3450       4.0\n",
       "1706      AL  1971   37598  69.304600         2.370        3497       4.0\n",
       "1705      AL  1972   42719  67.149190         2.370        3539       4.0\n",
       "1704      AL  1973   46203  63.217026         2.370        3580       4.0\n",
       "1703      AL  1974   49769  56.933796         2.370        3627       4.0\n",
       "...      ...   ...     ...        ...           ...         ...       ...\n",
       "4         WY  1999   12423   0.319894         0.045         492       4.0\n",
       "3         WY  2000   12595   0.309491         0.045         494       4.0\n",
       "2         WY  2001   12808   0.300928         0.045         494       4.0\n",
       "1         WY  2002   13191   0.296244         0.045         499       4.0\n",
       "0         WY  2003   15535   0.289643         0.045         501       4.0\n",
       "\n",
       "[1708 rows x 7 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = np.arange(len(df)-1, -1, -1)\n",
    "df"
   ]
  }
 ],
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