s2n10-grouping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping\n",
    "\n",
    "A common need is bound to arise where you will need to look at an aggregate view of a `DataFrame` by a certain value.  This is where grouping comes in.\n",
    "\n",
    "CashBox has asked that we produce a list of the top 10 users who have been on the receiving side of transactions the most. They would like to see the user's first and last name, their email, and the total number of transactions where the user was the receiver.\n",
    "\n",
    "We can acheive this by grouping our data. We need to take a look at the **`transactions`** `DataFrame` and group it by the **`receiver`** field, which is the username.\n",
    "\n",
    "Grouping on a value returns a new type of object called the [`GroupBy`](https://pandas.pydata.org/pandas-docs/stable/api.html#groupby).\n",
    "\n",
    "Let's first explore how to create one of these, and then how to wield it's power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(475, 998)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "pd.options.display.max_rows = 10\n",
    "users = pd.read_csv(os.path.join('data', 'users.csv'), index_col=0)\n",
    "transactions = pd.read_csv(os.path.join('data', 'transactions.csv'), index_col=0)\n",
    "# Sanity check\n",
    "(len(users), len(transactions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remind ourselves about the types of data we have in the **`transactions`** `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sender        object\n",
       "receiver      object\n",
       "amount       float64\n",
       "sent_date     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by a specific column is pretty straight forward. We want to group by the receiver, so we use the [`DataFrame.groupby`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.groupby.DataFrameGroupBy"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_receiver = transactions.groupby('receiver')\n",
    "\n",
    "# Let's see what type of object we got back\n",
    "type(grouped_by_receiver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We received a [`DataFrameGroupBy`](https://pandas.pydata.org/pandas-docs/stable/api.html#groupby) object. There are quite a few methods here.\n",
    "\n",
    "Let's take a look first at [`GroupBy.size`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.size.html). This will return a `Series` of how many members are in each of the groups. In our case this is the number of transactions that each user received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "receiver\n",
       "aaron            6\n",
       "acook            1\n",
       "adam.saunders    2\n",
       "adrian           3\n",
       "adrian.blair     7\n",
       "                ..\n",
       "wilson           2\n",
       "wking            2\n",
       "wright3590       4\n",
       "young            2\n",
       "zachary.neal     4\n",
       "Length: 410, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a Series of total number of rows\n",
    "grouped_by_receiver.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can use the `DataFrameGroupBy.count` method to see counts of how many non missing data points we have across each column in our group across the columns of our `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>sender</th>\n",
       "      <th>amount</th>\n",
       "      <th>sent_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>receiver</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaron</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acook</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adam.saunders</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian.blair</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wilson</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wking</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wright3590</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zachary.neal</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               sender  amount  sent_date\n",
       "receiver                                \n",
       "aaron               6       6          6\n",
       "acook               1       1          1\n",
       "adam.saunders       2       2          2\n",
       "adrian              3       3          3\n",
       "adrian.blair        7       7          7\n",
       "...               ...     ...        ...\n",
       "wilson              2       2          2\n",
       "wking               2       2          2\n",
       "wright3590          4       4          4\n",
       "young               2       2          2\n",
       "zachary.neal        4       4          4\n",
       "\n",
       "[410 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_receiver.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `GroupBy` object provides aggregate functions that makes getting calculations quick and seamless. For instance, if we use the `GroupBy.sum` method we can see each numeric column summed up for each grouping. In our case there is only one numeric column **`amount`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>receiver</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaron</th>\n",
       "      <td>366.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acook</th>\n",
       "      <td>94.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adam.saunders</th>\n",
       "      <td>101.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian</th>\n",
       "      <td>124.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian.blair</th>\n",
       "      <td>462.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wilson</th>\n",
       "      <td>44.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wking</th>\n",
       "      <td>74.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wright3590</th>\n",
       "      <td>195.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>83.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zachary.neal</th>\n",
       "      <td>186.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               amount\n",
       "receiver             \n",
       "aaron          366.15\n",
       "acook           94.65\n",
       "adam.saunders  101.15\n",
       "adrian         124.36\n",
       "adrian.blair   462.88\n",
       "...               ...\n",
       "wilson          44.39\n",
       "wking           74.07\n",
       "wright3590     195.45\n",
       "young           83.57\n",
       "zachary.neal   186.01\n",
       "\n",
       "[410 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_receiver.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now where were we? Oh right, we're trying to figure out the people who was received the most transactions.  So why don't we use this group to create a new column on our **`users`** `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column in users called transaction count, and set the values to the size of the matching group\n",
    "users['transaction_count'] = grouped_by_receiver.size()\n",
    "\n",
    "# Not every user has made a transaction, let's see what kind of missing data we are dealing with\n",
    "len(users[users.transaction_count.isna()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we don't have a transaction record for everyone, not every user will be in our grouping.  So when we created the new column, we ended up adding some `np.nan`.  Let's fix that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>email_verified</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>referral_count</th>\n",
       "      <th>balance</th>\n",
       "      <th>transaction_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaron</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Davis</td>\n",
       "      <td>aaron6348@gmail.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>6</td>\n",
       "      <td>18.14</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acook</th>\n",
       "      <td>Anthony</td>\n",
       "      <td>Cook</td>\n",
       "      <td>cook@gmail.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-05-12</td>\n",
       "      <td>2</td>\n",
       "      <td>55.45</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adam.saunders</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Saunders</td>\n",
       "      <td>adam@gmail.com</td>\n",
       "      <td>False</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>3</td>\n",
       "      <td>72.12</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian</th>\n",
       "      <td>Adrian</td>\n",
       "      <td>Fang</td>\n",
       "      <td>adrian.fang@teamtreehouse.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-04-28</td>\n",
       "      <td>3</td>\n",
       "      <td>30.01</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian.blair</th>\n",
       "      <td>Adrian</td>\n",
       "      <td>Blair</td>\n",
       "      <td>adrian9335@gmail.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-06-16</td>\n",
       "      <td>7</td>\n",
       "      <td>25.85</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wilson</th>\n",
       "      <td>Robert</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>robert@yahoo.com</td>\n",
       "      <td>False</td>\n",
       "      <td>2018-05-16</td>\n",
       "      <td>5</td>\n",
       "      <td>59.75</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wking</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>King</td>\n",
       "      <td>wanda.king@holt.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2</td>\n",
       "      <td>67.08</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wright3590</th>\n",
       "      <td>Jacqueline</td>\n",
       "      <td>Wright</td>\n",
       "      <td>jacqueline.wright@gonzalez.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>6</td>\n",
       "      <td>18.48</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Young</td>\n",
       "      <td>jessica4028@yahoo.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-07-17</td>\n",
       "      <td>4</td>\n",
       "      <td>75.39</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zachary.neal</th>\n",
       "      <td>Zachary</td>\n",
       "      <td>Neal</td>\n",
       "      <td>zneal@gmail.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>1</td>\n",
       "      <td>39.90</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>475 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               first_name last_name                           email  \\\n",
       "aaron               Aaron     Davis             aaron6348@gmail.com   \n",
       "acook             Anthony      Cook                  cook@gmail.com   \n",
       "adam.saunders        Adam  Saunders                  adam@gmail.com   \n",
       "adrian             Adrian      Fang   adrian.fang@teamtreehouse.com   \n",
       "adrian.blair       Adrian     Blair            adrian9335@gmail.com   \n",
       "...                   ...       ...                             ...   \n",
       "wilson             Robert    Wilson                robert@yahoo.com   \n",
       "wking               Wanda      King             wanda.king@holt.com   \n",
       "wright3590     Jacqueline    Wright  jacqueline.wright@gonzalez.com   \n",
       "young             Jessica     Young           jessica4028@yahoo.com   \n",
       "zachary.neal      Zachary      Neal                 zneal@gmail.com   \n",
       "\n",
       "               email_verified signup_date  referral_count  balance  \\\n",
       "aaron                    True  2018-08-31               6    18.14   \n",
       "acook                    True  2018-05-12               2    55.45   \n",
       "adam.saunders           False  2018-05-29               3    72.12   \n",
       "adrian                   True  2018-04-28               3    30.01   \n",
       "adrian.blair             True  2018-06-16               7    25.85   \n",
       "...                       ...         ...             ...      ...   \n",
       "wilson                  False  2018-05-16               5    59.75   \n",
       "wking                    True  2018-06-01               2    67.08   \n",
       "wright3590               True  2018-02-08               6    18.48   \n",
       "young                    True  2018-07-17               4    75.39   \n",
       "zachary.neal             True  2018-07-26               1    39.90   \n",
       "\n",
       "               transaction_count  \n",
       "aaron                        6.0  \n",
       "acook                        1.0  \n",
       "adam.saunders                2.0  \n",
       "adrian                       3.0  \n",
       "adrian.blair                 7.0  \n",
       "...                          ...  \n",
       "wilson                       2.0  \n",
       "wking                        2.0  \n",
       "wright3590                   4.0  \n",
       "young                        2.0  \n",
       "zachary.neal                 4.0  \n",
       "\n",
       "[475 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set all missing data to 0, since in reality, there have been 0 received transactions for this user\n",
    "users.transaction_count.fillna(0, inplace=True)\n",
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check it out! There's our column, but it's a floating point number, we don't need that.  Let's convert it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert from the default type of float64 to int64 (no precision needed)\n",
    "users.transaction_count = users.transaction_count.astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we want to get the user with the highest transaction count, so let's sort by that descending."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>transaction_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>scott3928</th>\n",
       "      <td>Scott</td>\n",
       "      <td>NaN</td>\n",
       "      <td>scott@yahoo.com</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfinley</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>Finley</td>\n",
       "      <td>samuel@gmail.com</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adrian.blair</th>\n",
       "      <td>Adrian</td>\n",
       "      <td>Blair</td>\n",
       "      <td>adrian9335@gmail.com</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdeleon</th>\n",
       "      <td>Hannah</td>\n",
       "      <td>Deleon</td>\n",
       "      <td>hannah@yahoo.com</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miranda6426</th>\n",
       "      <td>Miranda</td>\n",
       "      <td>Rogers</td>\n",
       "      <td>miranda.rogers@gmail.com</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaron</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Davis</td>\n",
       "      <td>aaron6348@gmail.com</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corey</th>\n",
       "      <td>Corey</td>\n",
       "      <td>Fuller</td>\n",
       "      <td>fuller8100@yahoo.com</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heather</th>\n",
       "      <td>Heather</td>\n",
       "      <td>Ray</td>\n",
       "      <td>hray@yahoo.com</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jennifer.hebert</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Hebert</td>\n",
       "      <td>jennifer.hebert@yahoo.com</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edwards</th>\n",
       "      <td>Michael</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>edwards5456@gmail.com</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                first_name last_name                      email  \\\n",
       "scott3928            Scott       NaN            scott@yahoo.com   \n",
       "sfinley             Samuel    Finley           samuel@gmail.com   \n",
       "adrian.blair        Adrian     Blair       adrian9335@gmail.com   \n",
       "hdeleon             Hannah    Deleon           hannah@yahoo.com   \n",
       "miranda6426        Miranda    Rogers   miranda.rogers@gmail.com   \n",
       "aaron                Aaron     Davis        aaron6348@gmail.com   \n",
       "corey                Corey    Fuller       fuller8100@yahoo.com   \n",
       "heather            Heather       Ray             hray@yahoo.com   \n",
       "jennifer.hebert   Jennifer    Hebert  jennifer.hebert@yahoo.com   \n",
       "edwards            Michael   Edwards      edwards5456@gmail.com   \n",
       "\n",
       "                 transaction_count  \n",
       "scott3928                        9  \n",
       "sfinley                          8  \n",
       "adrian.blair                     7  \n",
       "hdeleon                          7  \n",
       "miranda6426                      7  \n",
       "aaron                            6  \n",
       "corey                            6  \n",
       "heather                          6  \n",
       "jennifer.hebert                  6  \n",
       "edwards                          6  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort our values by the new field descending (so the largest comes first), and then by first name ascending\n",
    "users.sort_values(\n",
    "    ['transaction_count', 'first_name'],\n",
    "    ascending=[False, True],\n",
    "    inplace=True\n",
    ")\n",
    "# Take a look at our top 10 receivers, showing only the columns we want\n",
    "users.loc[:, ['first_name', 'last_name', 'email', 'transaction_count']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here they are, the Top 10 Receivers!  Nice work putting all those skills together!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}