s1n02-accessing-a-series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accessing a Series\n",
    "\n",
    "There are multiple ways to get to the data stored in your `Series`. Let's explore the **`balances`** `Series`. \n",
    "\n",
    "Remember, the `Series` is indexed by username. The label is the username, the value is that user's cash balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "import pandas as pd\n",
    "\n",
    "from utils import render\n",
    "\n",
    "\n",
    "# Standard Python dictionary\n",
    "test_balance_data = {\n",
    "    'pasan': 20.00,\n",
    "    'treasure': 20.18,\n",
    "    'ashley': 1.05,\n",
    "    'craig': 42.42,\n",
    "}\n",
    "\n",
    "balances = pd.Series(test_balance_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing by Index\n",
    "\n",
    "A `Series` is ordered and indexable. It is zero based and you can access it by index, just like you would a list or array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first user's balance\n",
    "balances[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a loook at the value returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(balances[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value is wrapped in a [`NumPy.Scalar`](https://docs.scipy.org/doc/numpy-1.15.0/reference/arrays.scalars.html) so that it keeps it's data type and will play well with other data types and NumPy data structures.\n",
    "\n",
    "The same positional indexing works just as it does with a standard list. The indices begin start with 0, and negative numbers can be used to access values from the end of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.42"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The last balance\n",
    "balances[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing by Label\n",
    "Since a series is labelled, you can also access it much like you would a standard `dict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balances['pasan']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Series` behave like dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "The label pasan has a value of 20.0"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "The label treasure has a value of 20.18"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "The label ashley has a value of 1.05"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "The label craig has a value of 42.42"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label, value in balances.items():\n",
    "    render(\"The label {} has a value of {}\".format(label, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Accessing a non-existent key raises a `KeyError`."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    balances['kermit']\n",
    "except KeyError:\n",
    "    render('Accessing a non-existent key raises a `KeyError`.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Use `get` to safely access keys. `None` is returned if key not present."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if balances.get('kermit') is None:\n",
    "    render('Use `get` to safely access keys. `None` is returned if key not present.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Use `in` to test the existence of a label."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if 'kermit' not in balances:\n",
    "    render('Use `in` to test the existence of a label.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing by Property\n",
    "As long as your label meets variable naming constraints, it will be available as a property via dot notation on the `Series`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.05"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balances.ashley"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing More Explicitly with `loc` and `iloc`\n",
    "\n",
    "So far we have used a label and a positional index to access the value. This can get confusing as to what is being used, a label or a position. Because of this ambiguity, it is possible to be more explicit, [which yes wise Pythonista](https://www.python.org/dev/peps/pep-0020/), is always better than implicit.\n",
    "\n",
    "A `Series` exposes a property named `loc` which can be used to explicitly lookup by label based indices only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balances.loc['pasan']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And to use the positional index explicitly, you can use the property `iloc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first value\n",
    "balances.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing by Slice\n",
    "Like a NumPy array, a `Series` also provides a way to use slices to get different portions of the data, returned as a `Series`.  \n",
    "\n",
    "*WARNING*: Slicing with indices vs. labels behaves differently. The latter is inclusive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing by Positional Index\n",
    "When using positional indices, the slice is exclusive. The last item **is not** included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan       20.00\n",
       "treasure    20.18\n",
       "ashley       1.05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Includes values from zero\n",
    "#  up until **and not** including 3\n",
    "balances.iloc[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing by Label\n",
    "When using labels, the slice is inclusive. The last item **is** included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan       20.00\n",
       "treasure    20.18\n",
       "ashley       1.05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Include the values starting at 'pasan' \n",
    "#  up until **and** including 'ashley'\n",
    "balances.loc['pasan':'ashley']"
   ]
  }
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