{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0259174d-803e-4a49-b1c3-3afb66b8342b",
   "metadata": {},
   "source": [
    "# Creating a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef08f74f-d6f0-4359-817c-ba1f032c3d5c",
   "metadata": {},
   "source": [
    "## The library\n",
    "\n",
    "Like the NumPy `array` – and _unlike_ the Python `dict` and `list` – the pandas `DataFrame` is _not_ built into Python.\n",
    "\n",
    "And so, first, we might have to ensure that the <a href=\"https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html\" target=\"_blank\" rel=\"noopener\">pandas library is installed</a>.\n",
    "\n",
    "Only then can we tell Python to make the `pandas` module available to our code, using the `import` statement. For example:\n",
    "\n",
    "    import pandas\n",
    "\n",
    "Having done so, the `DataFrame` type would be available as: `pandas.DataFrame`.\n",
    "\n",
    "That is, unlike with the built-in `list`, we would refer to it as \"under\" the name `pandas`, with a dot between the two names.\n",
    "\n",
    "Or, we could import just `DataFrame`, such that it's available as just `DataFrame`, without the rigmarole:\n",
    "\n",
    "    from pandas import DataFrame\n",
    "\n",
    "However, we'll be using `pandas` a lot! And not *just* `DataFrame`. Following a common convention, we'll tell Python to assign the library module the name `pd`. This way, we'll be able to refer to the elements of the pandas interface, such as `DataFrame`, as _e.g._: `pd.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5efcf03f-2bf4-461f-9d8e-01b553fabdc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde2c831-fbf3-4488-aad1-1c71d161c705",
   "metadata": {},
   "source": [
    "## The data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21c0622-a4be-450c-8874-d0fe3fbfa1d9",
   "metadata": {},
   "source": [
    "We began to consider tabular or two-dimensional data in [Lists](../../04/1/Lists.html#other-lists), with the distances of planets from our sun. Let's expand on this example with the below data, adding the planets' masses, densities and gravities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5f7d27f-af8d-44ea-a2bf-7c646a8811c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Mercury', 57.9, 0.33, 5427.0, 3.7],\n",
       " ['Venus', 108.2, 4.87, 5243.0, 8.9],\n",
       " ['Earth', 149.6, 5.97, 5514.0, 9.8],\n",
       " ['Mars', 227.9, 0.642, 3933.0, 3.7],\n",
       " ['Jupiter', 778.6, 1898.0, 1326.0, 23.1],\n",
       " ['Saturn', 1433.5, 568.0, 687.0, 9.0],\n",
       " ['Uranus', 2872.5, 86.8, 1271.0, 8.7],\n",
       " ['Neptune', 4495.1, 102.0, 1638.0, 11.0]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_features = [\n",
    "    'name',                # familiar name\n",
    "    'solar_distance_km_6', # distance from sun: 10**6 km\n",
    "    'mass_kg_24',          # absolute mass: 10**24 kg\n",
    "    'density_kg_m3',       # density: kg/m**3\n",
    "    'gravity_m_s2',        # gravity: m/s**2\n",
    "]\n",
    "\n",
    "planets_data = [\n",
    "    ['Mercury', 57.9, 0.33, 5427.0, 3.7],\n",
    "    ['Venus', 108.2, 4.87, 5243.0, 8.9],\n",
    "    ['Earth', 149.6, 5.97, 5514.0, 9.8],\n",
    "    ['Mars', 227.9, 0.642, 3933.0, 3.7],\n",
    "    ['Jupiter', 778.6, 1898.0, 1326.0, 23.1],\n",
    "    ['Saturn', 1433.5, 568.0, 687.0, 9.0],\n",
    "    ['Uranus', 2872.5, 86.8, 1271.0, 8.7],\n",
    "    ['Neptune', 4495.1, 102.0, 1638.0, 11.0]\n",
    "]\n",
    "\n",
    "planets_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c565f02c-beb8-419b-88ce-15dd2b6dca80",
   "metadata": {},
   "source": [
    "Now let's *construct* a `DataFrame` for these data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01bbbeaf-8766-43ef-869e-3ea3846cc9e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Venus</td>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Earth</td>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mars</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0       1         2       3     4\n",
       "0  Mercury    57.9     0.330  5427.0   3.7\n",
       "1    Venus   108.2     4.870  5243.0   8.9\n",
       "2    Earth   149.6     5.970  5514.0   9.8\n",
       "3     Mars   227.9     0.642  3933.0   3.7\n",
       "4  Jupiter   778.6  1898.000  1326.0  23.1\n",
       "5   Saturn  1433.5   568.000   687.0   9.0\n",
       "6   Uranus  2872.5    86.800  1271.0   8.7\n",
       "7  Neptune  4495.1   102.000  1638.0  11.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets = pd.DataFrame(planets_data)\n",
    "\n",
    "planets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdcf7e2-b474-4c3a-a083-3de292ff0585",
   "metadata": {},
   "source": [
    "This presentation of our data already looks more like a spreadsheet!\n",
    "\n",
    "However, there's something odd about the above. We're accustomed now to numbering elements of a sequence by their *index* (or \"offset\") – 0, 1, 2, 3, … – and this works in this case for numbering our rows. But this isn't as useful a scheme for labeling our columns.\n",
    "\n",
    "We'll make manipulation of this data easier, and avoid confusion about what these values represent, by defining useful column labels.\n",
    "\n",
    "Luckily, we already defined our features above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebbc04e6-884b-4622-93de-2e546da4b241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name', 'solar_distance_km_6', 'mass_kg_24', 'density_kg_m3', 'gravity_m_s2']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5140ea86-2e1a-4a72-b79a-8540bd2bbe40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>solar_distance_km_6</th>\n",
       "      <th>mass_kg_24</th>\n",
       "      <th>density_kg_m3</th>\n",
       "      <th>gravity_m_s2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Venus</td>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Earth</td>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mars</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "0  Mercury                 57.9       0.330         5427.0           3.7\n",
       "1    Venus                108.2       4.870         5243.0           8.9\n",
       "2    Earth                149.6       5.970         5514.0           9.8\n",
       "3     Mars                227.9       0.642         3933.0           3.7\n",
       "4  Jupiter                778.6    1898.000         1326.0          23.1\n",
       "5   Saturn               1433.5     568.000          687.0           9.0\n",
       "6   Uranus               2872.5      86.800         1271.0           8.7\n",
       "7  Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets = pd.DataFrame(planets_data, columns=planets_features)\n",
    "\n",
    "planets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b630ef-3c5d-4b4c-90d3-e3cb794ca690",
   "metadata": {},
   "source": [
    "That's better!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234a2fda-adaf-4461-94b7-e91331403dd4",
   "metadata": {},
   "source": [
    "Indeed, <a href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html\" target=\"_blank\" rel=\"noopener\">there are _many_ ways to construct a `DataFrame`</a>.\n",
    "\n",
    "For another example, we might have specified our data as a single dictionary of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd808ba2-803c-4a6d-b998-da66305481f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>solar_distance_km_6</th>\n",
       "      <th>mass_kg_24</th>\n",
       "      <th>density_kg_m3</th>\n",
       "      <th>gravity_m_s2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Venus</td>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Earth</td>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mars</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "0  Mercury                 57.9       0.330         5427.0           3.7\n",
       "1    Venus                108.2       4.870         5243.0           8.9\n",
       "2    Earth                149.6       5.970         5514.0           9.8\n",
       "3     Mars                227.9       0.642         3933.0           3.7\n",
       "4  Jupiter                778.6    1898.000         1326.0          23.1\n",
       "5   Saturn               1433.5     568.000          687.0           9.0\n",
       "6   Uranus               2872.5      86.800         1271.0           8.7\n",
       "7  Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_dict = {\n",
    "    'name': ['Mercury', 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Uranus', 'Neptune'],\n",
    "    'solar_distance_km_6': [57.9, 108.2, 149.6, 227.9, 778.6, 1433.5, 2872.5, 4495.1],\n",
    "    'mass_kg_24': [0.33, 4.87, 5.97, 0.642, 1898.0, 568.0, 86.8, 102.0],\n",
    "    'density_kg_m3': [5427.0, 5243.0, 5514.0, 3933.0, 1326.0, 687.0, 1271.0, 1638.0],\n",
    "    'gravity_m_s2': [3.7, 8.9, 9.8, 3.7, 23.1, 9.0, 8.7, 11.0],\n",
    "}\n",
    "\n",
    "pd.DataFrame(planets_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c736b2-debb-430c-8269-c96b70d2dc34",
   "metadata": {},
   "source": [
    "### Data formats\n",
    "\n",
    "Of course, it is _very_ common to store data in a file format, such as CSV. <a href=\"https://pandas.pydata.org/docs/user_guide/io.html#io\" target=\"_blank\" rel=\"noopener\">pandas supports a great many common data encoding formats</a>, and makes it easy to construct `DataFrames` from them.\n",
    "\n",
    "For example, if we had a CSV file in our \"Documents\" folder, we might construct a `DataFrame` from it using the pandas `read_csv` function, like so:\n",
    "\n",
    "```py\n",
    "data = pd.read_csv('/Users/MySelf/Documents/my-data.csv')\n",
    "```\n",
    "\n",
    "Above, we simply gave pandas the file system path to our CSV data. The `read_csv` function also supports <a href=\"https://docs.python.org/3/glossary.html#term-file-object\" target=\"_blank\" rel=\"noopener\">file objects</a>, such as those returned by Python's `open` function.\n",
    "\n",
    "Our planetary data, encoded as CSV, takes the following form:\n",
    "\n",
    "```\n",
    "name,solar_distance_km_6,mass_kg_24,density_kg_m3,gravity_m_s2\n",
    "Mercury,57.9,0.33,5427.0,3.7\n",
    "Venus,108.2,4.87,5243.0,8.9\n",
    "Earth,149.6,5.97,5514.0,9.8\n",
    "Mars,227.9,0.642,3933.0,3.7\n",
    "Jupiter,778.6,1898.0,1326.0,23.1\n",
    "Saturn,1433.5,568.0,687.0,9.0\n",
    "Uranus,2872.5,86.8,1271.0,8.7\n",
    "Neptune,4495.1,102.0,1638.0,11.0\n",
    "```\n",
    "\n",
    "Note that we've included our feature names as the first row of our data. (This is optional – but useful!)\n",
    "\n",
    "And below we'll reload our planets `DataFrame`, similarly to the above – (but from a file buffer of that data, `planets_csv`, the details of which are hidden below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca877460-cec6-42bb-b9c4-7410902ee76d",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Hello!\n",
    "#\n",
    "# This code allows you to download and execute this notebook as-is –\n",
    "# without a separate CSV file.\n",
    "#\n",
    "# We can just *pretend* that `planets_csv` is a path to a file –\n",
    "# or, more apt, a file object opened with the Python `open` function.\n",
    "#\n",
    "# (Really it's an in-memory file object … but that's not important here!)\n",
    "#\n",
    "import io\n",
    "\n",
    "\n",
    "planets_encoded = '''\\\n",
    "name,solar_distance_km_6,mass_kg_24,density_kg_m3,gravity_m_s2\n",
    "Mercury,57.9,0.33,5427.0,3.7\n",
    "Venus,108.2,4.87,5243.0,8.9\n",
    "Earth,149.6,5.97,5514.0,9.8\n",
    "Mars,227.9,0.642,3933.0,3.7\n",
    "Jupiter,778.6,1898.0,1326.0,23.1\n",
    "Saturn,1433.5,568.0,687.0,9.0\n",
    "Uranus,2872.5,86.8,1271.0,8.7\n",
    "Neptune,4495.1,102.0,1638.0,11.0\n",
    "'''\n",
    "\n",
    "planets_csv = io.StringIO(planets_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be1c8683-1dcc-4e38-b957-3290ab10e8b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name</th>\n",
       "      <th>solar_distance_km_6</th>\n",
       "      <th>mass_kg_24</th>\n",
       "      <th>density_kg_m3</th>\n",
       "      <th>gravity_m_s2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Venus</td>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Earth</td>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mars</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "0  Mercury                 57.9       0.330         5427.0           3.7\n",
       "1    Venus                108.2       4.870         5243.0           8.9\n",
       "2    Earth                149.6       5.970         5514.0           9.8\n",
       "3     Mars                227.9       0.642         3933.0           3.7\n",
       "4  Jupiter                778.6    1898.000         1326.0          23.1\n",
       "5   Saturn               1433.5     568.000          687.0           9.0\n",
       "6   Uranus               2872.5      86.800         1271.0           8.7\n",
       "7  Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets = pd.read_csv(planets_csv)\n",
    "\n",
    "planets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056e60fe-ca84-446b-a50d-67fff7427b72",
   "metadata": {},
   "source": [
    "Note that pandas automatically inferred that the first row of our CSV data specified the feature names."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdc7693-b407-423d-924a-290efd8abee2",
   "metadata": {},
   "source": [
    "## The index\n",
    "\n",
    "pandas's default index – the familiar range of integers starting with `0` – is most often sensible for computational data.\n",
    "\n",
    "This is represented by the `RangeIndex` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "abe10a4b-a1a9-43ca-a073-319170c26020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=8, step=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a6131a-b083-4e9f-b20f-e0ceaee80c41",
   "metadata": {},
   "source": [
    "Of course, that's _not_ how we think about the planets!\n",
    "\n",
    "We can tell pandas to use a more familiar index instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a502cfa7-4b43-47eb-bcdf-bce3e4f7bf67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=1, stop=9, step=1, name='number')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.RangeIndex(1, 9, name='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3de389f-da2e-481c-b2a0-1db73b95aab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>solar_distance_km_6</th>\n",
       "      <th>mass_kg_24</th>\n",
       "      <th>density_kg_m3</th>\n",
       "      <th>gravity_m_s2</th>\n",
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       "    <tr>\n",
       "      <th>number</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venus</td>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
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       "      <th>3</th>\n",
       "      <td>Earth</td>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mars</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "number                                                                       \n",
       "1       Mercury                 57.9       0.330         5427.0           3.7\n",
       "2         Venus                108.2       4.870         5243.0           8.9\n",
       "3         Earth                149.6       5.970         5514.0           9.8\n",
       "4          Mars                227.9       0.642         3933.0           3.7\n",
       "5       Jupiter                778.6    1898.000         1326.0          23.1\n",
       "6        Saturn               1433.5     568.000          687.0           9.0\n",
       "7        Uranus               2872.5      86.800         1271.0           8.7\n",
       "8       Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(planets_data,\n",
    "             columns=planets_features,\n",
    "             index=pd.RangeIndex(1, 9, name='number'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dfa5acf-660c-4bcc-ba63-2746eaa38193",
   "metadata": {},
   "source": [
    "We don't even have to use ranges … or numbers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ada95ab-61fe-4344-9a45-f5373a203e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>solar_distance_km_6</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <td>Venus</td>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>third</th>\n",
       "      <td>Earth</td>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fourth</th>\n",
       "      <td>Mars</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fifth</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sixth</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seventh</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eighth</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name  solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "ordinal                                                                       \n",
       "first    Mercury                 57.9       0.330         5427.0           3.7\n",
       "second     Venus                108.2       4.870         5243.0           8.9\n",
       "third      Earth                149.6       5.970         5514.0           9.8\n",
       "fourth      Mars                227.9       0.642         3933.0           3.7\n",
       "fifth    Jupiter                778.6    1898.000         1326.0          23.1\n",
       "sixth     Saturn               1433.5     568.000          687.0           9.0\n",
       "seventh   Uranus               2872.5      86.800         1271.0           8.7\n",
       "eighth   Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinals = ['first', 'second', 'third', 'fourth', 'fifth', 'sixth', 'seventh', 'eighth']\n",
    "\n",
    "planet_ordinals = pd.DataFrame(planets_data,\n",
    "                               columns=planets_features,\n",
    "                               index=pd.Index(ordinals, name='ordinal'))\n",
    "\n",
    "planet_ordinals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9576ebce-205b-45f3-9888-743cceb202fc",
   "metadata": {},
   "source": [
    "But, in the end, perhaps we'd prefer not to count the planets at all.\n",
    "\n",
    "Whenever a data feature makes sense to use as the data index – that is, it's sufficient to _always_ **uniquely identify** individuals, we can just tell pandas to use that column as the index, instead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5b25fc-6435-4cf3-b0a1-dc5fbb928d04",
   "metadata": {},
   "source": [
    "We'll learn more about manipulating `DataFrames` in subsequent sections. But, for now, here's how we would set the `name` feature as our index, (at least when constructing a `DataFrame` from `lists` or `dicts`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9dff830-cbc3-4b40-b04d-9fe4e4b42dc9",
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jupiter</th>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturn</th>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uranus</th>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neptune</th>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "name                                                                 \n",
       "Mercury                 57.9       0.330         5427.0           3.7\n",
       "Venus                  108.2       4.870         5243.0           8.9\n",
       "Earth                  149.6       5.970         5514.0           9.8\n",
       "Mars                   227.9       0.642         3933.0           3.7\n",
       "Jupiter                778.6    1898.000         1326.0          23.1\n",
       "Saturn                1433.5     568.000          687.0           9.0\n",
       "Uranus                2872.5      86.800         1271.0           8.7\n",
       "Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.set_index('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33709da9-56b8-4163-8666-68b03614908b",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# Hello!\n",
    "#\n",
    "# This cell has been hidden – it's just an implementation concern.\n",
    "#\n",
    "# Generally, when working with files, you won't need to worry about this.\n",
    "#\n",
    "planets_csv.seek(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddcfe281-6fbd-4d7c-a71b-79520be3d967",
   "metadata": {},
   "source": [
    "The `read_csv` function, on the other hand, supports this case specifically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "073652e2-bb44-4121-a03a-758d7c9aaf0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>solar_distance_km_6</th>\n",
       "      <th>mass_kg_24</th>\n",
       "      <th>density_kg_m3</th>\n",
       "      <th>gravity_m_s2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jupiter</th>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturn</th>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uranus</th>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neptune</th>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "name                                                                 \n",
       "Mercury                 57.9       0.330         5427.0           3.7\n",
       "Venus                  108.2       4.870         5243.0           8.9\n",
       "Earth                  149.6       5.970         5514.0           9.8\n",
       "Mars                   227.9       0.642         3933.0           3.7\n",
       "Jupiter                778.6    1898.000         1326.0          23.1\n",
       "Saturn                1433.5     568.000          687.0           9.0\n",
       "Uranus                2872.5      86.800         1271.0           8.7\n",
       "Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(planets_csv, index_col='name')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc759e02-cf38-4a97-8eab-f71103146ae8",
   "metadata": {},
   "source": [
    "Now these `DataFrames` are looking great! Let's see what we can do with them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68a1d00-2b47-4c95-839d-b63acc602fc4",
   "metadata": {},
   "source": [
    "## Operations\n",
    "\n",
    "As we've seen with the `list`, (and the string), the `DataFrame` can be manipulated by functions and built-in operators. Moreover, these offer special-purpose functions which have been *bound* to their types – that is, *methods* – which are invoked with expressions of the form below:\n",
    "\n",
    "    name_of_dataframe.name_of_method(argument0, argument1, ..., keyword0=value0, ...)\n",
    "    \n",
    "For example, above we used the `set_index` method to construct a new `DataFrame` with the `name` column set as the data index. Here it is again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a0f096a-7d48-4a52-9895-0dbfc89c2342",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\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>solar_distance_km_6</th>\n",
       "      <th>mass_kg_24</th>\n",
       "      <th>density_kg_m3</th>\n",
       "      <th>gravity_m_s2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mercury</th>\n",
       "      <td>57.9</td>\n",
       "      <td>0.330</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venus</th>\n",
       "      <td>108.2</td>\n",
       "      <td>4.870</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>149.6</td>\n",
       "      <td>5.970</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>227.9</td>\n",
       "      <td>0.642</td>\n",
       "      <td>3933.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jupiter</th>\n",
       "      <td>778.6</td>\n",
       "      <td>1898.000</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturn</th>\n",
       "      <td>1433.5</td>\n",
       "      <td>568.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uranus</th>\n",
       "      <td>2872.5</td>\n",
       "      <td>86.800</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neptune</th>\n",
       "      <td>4495.1</td>\n",
       "      <td>102.000</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         solar_distance_km_6  mass_kg_24  density_kg_m3  gravity_m_s2\n",
       "name                                                                 \n",
       "Mercury                 57.9       0.330         5427.0           3.7\n",
       "Venus                  108.2       4.870         5243.0           8.9\n",
       "Earth                  149.6       5.970         5514.0           9.8\n",
       "Mars                   227.9       0.642         3933.0           3.7\n",
       "Jupiter                778.6    1898.000         1326.0          23.1\n",
       "Saturn                1433.5     568.000          687.0           9.0\n",
       "Uranus                2872.5      86.800         1271.0           8.7\n",
       "Neptune               4495.1     102.000         1638.0          11.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.set_index('name')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276c89e8-7dd4-4838-8f50-b62237ab1794",
   "metadata": {},
   "source": [
    "And, similar to methods, there are *attributes* and *properties*. These are values which are similarly bound to the `DataFrame`, but which need not be called:\n",
    "\n",
    "    name_of_dataframe.name_of_property\n",
    "    \n",
    "We made use of the `index` property above as well, to inspect our `DataFrame`'s currently-assigned index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "abb6b2d0-48eb-4dac-aead-5d4761a2b2e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=8, step=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0db98b6-ca01-4073-8614-debc59f46d1d",
   "metadata": {},
   "source": [
    "pandas offers us many functions, methods and properties to explore!\n",
    "\n",
    "And now we're ready to explore the dimensions our data."
   ]
  }
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