{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Series\n",
    "There are couple of ways to create a new [`Series`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html) from scratch. \n",
    "\n",
    "Let's explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just like how NumPy is almost always abbreviated as np...\n",
    "import numpy as np\n",
    "#  pandas is usually shortened to pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating from a dictionary\n",
    "\n",
    "Let's use this sample data here. In our example, `test_balance_data` is just a standard Python dictionary the key is username, and the value is that user's current account balance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_balance_data = {\n",
    "    'pasan': 20.00,\n",
    "    'treasure': 20.18,\n",
    "    'ashley': 1.05,\n",
    "    'craig': 42.42,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Series` constructor accepts any dict-like object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "balances = pd.Series(test_balance_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that labels have been set from the `test_balance_data.keys()`  and the values are set from `test_balance_data.values()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan       20.00\n",
       "treasure    20.18\n",
       "ashley       1.05\n",
       "craig       42.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating from an Iterable\n",
    "\n",
    "You can pass any iterable as the first argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "unlabeled_balances = pd.Series([20.00, 20.18, 1.05, 42.42])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*NOTE*: When labels are not present they're defaulted to incremental integers starting at 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20.00\n",
       "1    20.18\n",
       "2     1.05\n",
       "3    42.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unlabeled_balances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also provide the `index` argument which requires an iterable the same size as your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_balances = pd.Series(\n",
    "    [20.00, 20.18, 1.05, 42.42],\n",
    "    index=['pasan', 'treasure', 'ashley', 'craig']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, the order of the labels is guaranteed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan       20.00\n",
       "treasure    20.18\n",
       "ashley       1.05\n",
       "craig       42.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_balances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing to remember is that a NumPy array is also iterable. In fact, you'll find NumPy and Pandas get along really well together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20.00\n",
       "1    20.18\n",
       "2     1.05\n",
       "3    42.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndbalances = np.array([20.00, 20.18, 1.05, 42.42])\n",
    "pd.Series(ndbalances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating from a scalar and an index\n",
    "\n",
    "If you pass in a scalar that value will be broadcasted to the keys specified in the index argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "guil     20.0\n",
       "jay      20.0\n",
       "james    20.0\n",
       "ben      20.0\n",
       "nick     20.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(20.00, index=[\"guil\", \"jay\", \"james\", \"ben\", \"nick\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learn More\n",
    "* [Introduction to Data Structures - Series (pandas documentation)](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#series)"
   ]
  }
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