s1n03-vectorization-and-broadcasting.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Series Vectorization and Broadcasting\n",
    "\n",
    "Just like NumPy, pandas offers powerful vectorized methods. It also leans on broadcasting.\n",
    "\n",
    "Let's explore!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "test_balance_data = {\n",
    "    'pasan': 20.00,\n",
    "    'treasure': 20.18,\n",
    "    'ashley': 1.05,\n",
    "    'craig': 42.42,\n",
    "}\n",
    "\n",
    "test_deposit_data = {\n",
    "    'pasan': 20,\n",
    "    'treasure': 10,\n",
    "    'ashley': 100,\n",
    "    'craig': 55,   \n",
    "}\n",
    "\n",
    "balances = pd.Series(test_balance_data)\n",
    "deposits = pd.Series(test_deposit_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorization\n",
    "While it is indeed possible to loop through each item and apply it to another..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan        40.00\n",
       "treasure     30.18\n",
       "ashley      101.05\n",
       "craig        97.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for label, value in deposits.iteritems():\n",
    "    balances[label] += value\n",
    "balances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...it's important to remember to lean on vectorization and skip the loops altogether.  Vectorization is faster and as you can see, easier to read and write."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan        40.00\n",
       "treasure     30.18\n",
       "ashley      101.05\n",
       "craig        97.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Undo the change using inplace subtraction\n",
    "balances -= deposits\n",
    "\n",
    "# This is the same as the loop above using inplace addition\n",
    "balances += deposits\n",
    "balances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting a Scalar\n",
    "Also just like NumPy arrays, the mathematical operators have been overridden to use the vectorized versions of the same operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pasan        45.00\n",
       "treasure     35.18\n",
       "ashley      106.05\n",
       "craig       102.42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 is brodacsted and added to each and every value. This returns a new Series.\n",
    "balances + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting a Series\n",
    "Labels are used to line up entries. When the label only exists in one side, a `np.nan` (<b>n</b>ot <b>a</b> <b>n</b>umber ) is put in place.\n",
    "\n",
    "CashBox is giving out free coupons that user's can scan into the app to get $1 added to their accounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "craig     1\n",
       "ashley    1\n",
       "james     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coupons = pd.Series(1, ['craig', 'ashley', 'james'])\n",
    "coupons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to add the coupons to people who cashed them in. This addition will return a new `Series`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ashley      102.05\n",
       "craig        98.42\n",
       "james          NaN\n",
       "pasan          NaN\n",
       "treasure       NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a new Series\n",
    "balances + coupons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how values that are not in both `Series` are set to `np.nan`. This isn't what we want! Pasan had $45.00 and now he has nothing. He is going to be so bummed!\n",
    "\n",
    "Also take note that James is not in the **`balances`** `Series` but he is in the **`coupons`** `Series`. Note how he is now added to the new `Series`, but his value is also set to `np.nan`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the `fill_value` parameter\n",
    "It is possible to fill missing values so that everything aligns. The concept is to use the `add` method directly along with the the keyword argument `fill_value`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ashley      102.05\n",
       "craig        98.42\n",
       "james         1.00\n",
       "pasan        40.00\n",
       "treasure     30.18\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a new Series\n",
    "balances.add(coupons, fill_value=0)"
   ]
  }
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