python-data-4-pandas-sol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 5 - pandas\n",
    "[pandas](http://pandas.pydata.org) provides high-level data structures and functions designed to make working with structured or tabular data fast, easy and expressive. The primary objects in pandas that we will be using are the `DataFrame`, a tabular, column-oriented data structure with both row and column labels, and the `Series`, a one-dimensional labeled array object.\n",
    "\n",
    "pandas blends the high-performance, array-computing ideas of NumPy with the flexible data manipulation capabilities of spreadsheets and relational databases. It provides sophisticated indexing functionality to make it easy to reshape, slice and perform aggregations.\n",
    "\n",
    "While pandas adopts many coding idioms from NumPy, the most significant difference is that pandas is designed for working with tabular or heterogeneous data. NumPy, by contrast, is best suited for working with homogeneous numerical array data.\n",
    "<br>\n",
    "\n",
    "## Table of Contents:\n",
    "- [Data Structures](#structures)\n",
    "    - [Series](#series)\n",
    "    - [DataFrame](#dataframe)\n",
    "- [Essential Functionality](#ess_func)\n",
    "    - [Reindexing](#reindexing)\n",
    "    - [Dropping Entries](#removing)\n",
    "    - [Indexing, Slicing and Filtering](#indexing)\n",
    "    - [Arithmetic Operations](#arithmetic)\n",
    "- [Summarizing and Computing Descriptive Statistics](#sums)\n",
    "- [Loading and storing data](#loading)\n",
    "    - [Text Format](#text) \n",
    "    - [Web Scraping](#web)\n",
    "- [Data Cleaning and preperation](#cleaning)\n",
    "    - [Handling missing data](#missing)\n",
    "    - [Data transformation](#transformation)\n",
    "\n",
    "The common pandas import statment is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common pandas import statement\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Structures <a name=\"structures\"></a>\n",
    "## Series <a name=\"series\"></a>\n",
    "A Series is a one-dimensional array-like object containing a sequence of values and an associated array of data labels called its index.\n",
    "\n",
    "The easiest way to make a Series is from an array of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([4, 7, -5, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try printing out data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The string representation of a Series displayed interactively shows the index on the left and the values on the right. Because we didn't specify an index, the default on is simply integers 0 through N-1.\n",
    "\n",
    "You can output only the values of a Series using \n",
    "```python\n",
    "data.values\n",
    "```\n",
    "or you can get only the indices using\n",
    "```python\n",
    "data.index\n",
    "```\n",
    "Try it out below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can specify custom indeces when intialising the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.Series([4, 7, -5, 3], index=[\"a\", \"b\", \"c\", \"d\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can use these labels to access the data similar to a normal array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[\"a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to think about Series is as a fixed-length ordered dictionary. Furthermore, you can actually define a Series in a similar manner to a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = {\"Glasgow\" : 599650, \"Edinburgh\" : 464990, \"Abardeen\" : 196670, \"Dundee\" : 147710}\n",
    "data3 = pd.Series(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glasgow      599650\n",
       "Edinburgh    464990\n",
       "Abardeen     196670\n",
       "Dundee       147710\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do arithmetic operations between Series similar to NumPy arrays. Even if you have 2 datasets with different data, arithmetic operations will be aligned according to their indices.\n",
    "\n",
    "Let's look at an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities_uk = {\"Birmingham\" : 1092330, \"Leeds\": 751485, \"Glasgow\" : 599650,\n",
    "             \"Manchester\" : 503127, \"Edinburgh\" : 464990}\n",
    "data4 = pd.Series(cities_uk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Abardeen            NaN\n",
       "Birmingham          NaN\n",
       "Dundee              NaN\n",
       "Edinburgh      929980.0\n",
       "Glasgow       1199300.0\n",
       "Leeds               NaN\n",
       "Manchester          NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 + data4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how some of the results are NaN? Well, that is because there were no instances of those cities within both of the datasets. You can usually extract NaNs from a Series with\n",
    "```python\n",
    "data4.isnull()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame <a name=\"dataframe\"></a>\n",
    "A DataFrame represents a rectangular table of data and contains an ordered collection of columns, each of which can be a different value type. The DataFrame has both row and column index and can be thought of as a dict of Series all sharing the same index.\n",
    "\n",
    "The most common way to create a DataFrame is with dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"cities\" : [\"Glasgow\", \"Edinburgh\", \"Abardeen\", \"Dundee\"],\n",
    "        \"population\" : [599650, 464990, 196670, 147710],\n",
    "        \"year\" : [2011, 2013, 2013, 2013]}\n",
    "frame = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try printing it out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>cities</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Glasgow</td>\n",
       "      <td>599650</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>464990</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abardeen</td>\n",
       "      <td>196670</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dundee</td>\n",
       "      <td>147710</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cities  population  year\n",
       "0    Glasgow      599650  2011\n",
       "1  Edinburgh      464990  2013\n",
       "2   Abardeen      196670  2013\n",
       "3     Dundee      147710  2013"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter Notebooks prints it out in a nice table but the basic version of this is also just as readable!\n",
    "\n",
    "Additionally you can also specify the order of columns during initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2 = pd.DataFrame(data, columns=[\"year\", \"cities\", \"population\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can retrieve a particular column from a DataFrame with\n",
    "```python\n",
    "frame[\"cities\"]\n",
    "```\n",
    "The result is going to be a Series\n",
    "\n",
    "Additionally, you can retrieve a row from the dataset using\n",
    "```python\n",
    "frame[1]\n",
    "```\n",
    "Try it out below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to add and modify the columns of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2[\"size\"] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>year</th>\n",
       "      <th>cities</th>\n",
       "      <th>population</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>599650</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>464990</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>Abardeen</td>\n",
       "      <td>196670</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>Dundee</td>\n",
       "      <td>147710</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     cities  population  size\n",
       "0  2011    Glasgow      599650   100\n",
       "1  2013  Edinburgh      464990   100\n",
       "2  2013   Abardeen      196670   100\n",
       "3  2013     Dundee      147710   100"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2[\"size\"] = [175, 264, 65.1, 60]  # in km^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to dicts, columns can be deleted using\n",
    "```python\n",
    "del frame2[\"size\"]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common way of creating DataFrames is from a nested dict of dicts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>cities</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Glasgow</td>\n",
       "      <td>599650</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>464990</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abardeen</td>\n",
       "      <td>196670</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dundee</td>\n",
       "      <td>147710</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cities  population  year\n",
       "0    Glasgow      599650  2011\n",
       "1  Edinburgh      464990  2013\n",
       "2   Abardeen      196670  2013\n",
       "3     Dundee      147710  2013"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = {\"Glasgow\": {2011: 599650},\n",
    "        \"Edinburgh\": {2013:464990},\n",
    "        \"Abardeen\": {2013: 196670}}\n",
    "\n",
    "frame3 = pd.DataFrame(data)\n",
    "frame3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a table of different ways of initialising a DataFrame for your reference\n",
    "\n",
    "| Type | Notes |\n",
    "| --- | --- |\n",
    "| 2D ndarray | A matrix of data; passing optional row and column labels |\n",
    "| dict of arrays, lists, or tuples | Each sequence becomes a column in the DataFrame; all sequences must be the same length |\n",
    "| dict of Series | Each value becomes a column; indexes from each Series are unioned together to<br>form the result's row index if not explicit index is passed |\n",
    "| dict of dicts | Each inner dict becomes a column; keys are unioned to form the row<br>index as in the \"dict of Series\" case |\n",
    "| List of dicts or Series | Each item becomes a row in the DataFrame; union of dict keys or<br>Series indices becomes the DataFrame's column labels |\n",
    "| List of lists or tuples | Treated as the \"2D ndarray\" case |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Essential Functionality <a name=\"ess_func\"></a>\n",
    "In this section, we will go through the fundamental mechanics of interacting with the data contained in a Series or DaraFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reindexing <a name=\"reindexing\"></a>\n",
    "With pandas it is easy to restructure the order of your columns and rows using the `reindex` function. Let's have a look at an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first define a new Series\n",
    "s = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    2\n",
       "a    1\n",
       "c    3\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now you can reshuffle the indices\n",
    "s = s.reindex(['d', 'b', 'a', 'c', 'e'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Easy as that! This can also be extended for DataFrames, where you can reorder both the columns and indices at the same time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>Edinburgh</th>\n",
       "      <th>Glasgow</th>\n",
       "      <th>Aberdeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Edinburgh  Glasgow  Aberdeen\n",
       "a          0        1         2\n",
       "b          3        4         5\n",
       "c          6        7         8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first define a new Dataframe\n",
    "data = np.reshape(np.arange(9), (3,3))\n",
    "df = pd.DataFrame(data, index=[\"a\", \"b\", \"c\"],\n",
    "                  columns=[\"Edinburgh\", \"Glasgow\", \"Aberdeen\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we can restructure it with reindex\n",
    "df = df.reindex(index=[\"a\", \"d\", \"c\", \"b\"],\n",
    "          columns=[\"Aberdeen\", \"Glasgow\", \"Edinburgh\", \"Dundee\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice something interesting? We can actually add new indices and columns using the `reindex` method. This results in the new slots in our table to be filled in with `NaN` values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing columns/indices <a name=\"removing\"></a>\n",
    "Similar to above, it is easy to remove entries. This is done with the `drop()` method and can be applied to both columns and indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edinburgh</th>\n",
       "      <th>Glasgow</th>\n",
       "      <th>Aberdeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Edinburgh  Glasgow  Aberdeen\n",
       "a          0        1         2\n",
       "c          6        7         8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define new DataFrame\n",
    "data = np.reshape(np.arange(9), (3,3))\n",
    "df = pd.DataFrame(data, index=[\"a\", \"b\", \"c\"],\n",
    "                  columns=[\"Edinburgh\", \"Glasgow\", \"Aberdeen\"])\n",
    "\n",
    "df.drop(\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glasgow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glasgow\n",
       "a        1\n",
       "b        4\n",
       "c        7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also drop from a column\n",
    "df.drop([\"Aberdeen\", \"Edinburgh\"], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing, slicing and filtering <a name=\"indexing\"></a>\n",
    "\n",
    "### Indexing\n",
    "\n",
    "Series indexing works analogously to NumPy array indexing (i.e. `data[...]`). You can also use the Series' index values instead of only integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.arange(4), index=['a', 'b', 'c', 'd'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [