diff --git a/pandas.ipynb b/pandas.ipynb
index f0a8a33..295d912 100644
--- a/pandas.ipynb
+++ b/pandas.ipynb
@@ -45,7 +45,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
    "source": [
     "We can access the elements in an array using multi-index notation, familiar in small variations in many computing environments and languages - with the usual Pythonic conventions, e.g., counting starts from 0, slicing a:b is inclusive:exclusive, negative indices, etc\n",
     "\n",
@@ -336,7 +340,8 @@
     "\n",
     "Series4 = Series1 > Series2 \n",
     "\n",
-    "# Take a look at the different Series objects!"
+    "# Take a look at the different Series objects!\n",
+    "Series4"
    ]
   },
   {
@@ -374,7 +379,8 @@
    "source": [
     "Series1 = pd.Series([1,10],index=[\"om\",\"iros\"])\n",
     "Series2 = pd.Series([4,-1],index=[\"pap\",\"as\"])\n",
-    "Series3 = Series1 + Series2"
+    "Series3 = Series1 + Series2\n",
+    "Series3"
    ]
   },
   {
@@ -421,7 +427,8 @@
     "# accesing by list of index labels\n",
     "\n",
     "my_series.index = [\"om\",\"ir\",\"os\",\"pap\",\"pas\",\"pil\",\"io\",\"po\",\"ulos\",\"is\",\"best\"]\n",
-    "x = my_series[[\"om\",\"pap\"]]"
+    "x = my_series[[\"om\",\"pap\"]]\n",
+    "x"
    ]
   },
   {
@@ -445,7 +452,8 @@
     "# Notice the index of x is a SUBSET of the index of \"my_series\"\n",
     "# This can be useful when needing to relate values back to the original \"my_series\"!\n",
     "\n",
-    "x = my_series[choose]"
+    "x = my_series[choose]\n",
+    "x"
    ]
   },
   {
@@ -477,7 +485,9 @@
     "# Filter \"my_series\" to be all the elements that are NOT\n",
     "# equal to 0, using the \"choose\" boolean mask below: \n",
     "\n",
-    "choose = my_series == 0.0\n"
+    "choose = my_series == 0.0\n",
+    "my_series[my_series != 0]\n",
+    "my_series[~choose]"
    ]
   },
   {
@@ -554,7 +564,8 @@
     "# 1. Create a boolean mask by using the .notna() method.\n",
     "# 2. Use the mask to subset the Series.\n",
     "\n",
-    "names = pd.Series(['foo','bar',None,'baz','qux',None])\n"
+    "names = pd.Series(['foo','bar',None,'baz','qux',None])\n",
+    "names[names.notna()]"
    ]
   },
   {
@@ -598,12 +609,22 @@
     "    # Your code here\n",
     "    # HINT: delete the \"pass\" when your done\n",
     "    # HINT2: handle None values!\n",
-    "    pass\n",
+    "    try:\n",
+    "        return s.lower()\n",
+    "    except AttributeError:\n",
+    "        return None\n",
     "\n",
-    "\n",
-    "names = pd.Series(['Foo', 'BAR', None, 'foo', None, 'bar', 'bAR', 'foo', None])"
+    "names = pd.Series(['Foo', 'BAR', None, 'foo', None, 'bar', 'bAR', 'foo', None])\n",
+    "names_lower = names.map(lower)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -614,7 +635,8 @@
     "\n",
     "# Using the series from above, now lowercased, count the occurences of each name\n",
     "# Hint: It's simple, just use .value_counts()!\n",
-    "\n"
+    "\n",
+    "names_lower.value_counts()"
    ]
   },
   {
@@ -652,6 +674,7 @@
    "source": [
     "# the other important attribute: name of rows and columns\n",
     "tips.index\n",
+    "\n",
     "tips.columns"
    ]
   },
@@ -765,7 +788,9 @@
     "\n",
     "# Using the tips dataframe, create a new one that contains the \n",
     "# information contained in all rows between the 20th (inclusive) \n",
-    "# and the 45th (exclusive) and only the columns: tip, sex, day"
+    "# and the 45th (exclusive) and only the columns: tip, sex, day\n",
+    "\n",
+    "tips.loc[20:45,[\"tip\",\"sex\",\"day\"]]"
    ]
   },
   {
@@ -844,7 +869,8 @@
     "# tip and size for only Male clients during Dinner. \n",
     "\n",
     "# HINT: Remember that \"size\" cannot be accessed via dot notation, as it's an \n",
-    "# attribute of the series!"
+    "# attribute of the series!\n",
+    "tips[(tips.sex==\"Male\")&(tips.time==\"Dinner\")].corr().loc[\"size\",\"tip\"]"
    ]
   },
   {
@@ -942,7 +968,10 @@
     "# Challenge: \n",
     "\n",
     "# Get the second largest bill by gender!\n",
-    "# HINT: use sort_values and iloc!"
+    "# HINT: use sort_values and iloc!\n",
+    "def sec_max_bill(df):\n",
+    "    return df.total_bill.sort_values(ascending=False).iloc[1]\n",
+    "tips.groupby(\"sex\").apply(sec_max_bill)"
    ]
   },
   {
@@ -972,7 +1001,7 @@
     "def day_mean(df):\n",
     "    # Hint: you will need to group by \"day\"\n",
     "    # in this function, then get the mean tip. \n",
-    "    pass\n",
+    "    return df.groupby(\"day\").tip.mean()\n",
     "\n",
     "\n",
     "tips.groupby(\"sex\").apply(day_mean)"
@@ -1218,6 +1247,634 @@
     "\n",
     "*Needless to say that eyeballing is OK for making sure your code makes sense, but will not result in full credits for the project. We want a fully automated code. To carry out the project successfully you need to use most the attributes and methods described earlier. The last one is a little tricky*"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Buyer</th>\n",
+       "      <th>Product</th>\n",
+       "      <th>Quantity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Jackson</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Jackson</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>John</td>\n",
+       "      <td>orange</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>John</td>\n",
+       "      <td>potato</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Tom</td>\n",
+       "      <td>tomato</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Buyer Product  Quantity\n",
+       "0  Jackson   apple         4\n",
+       "1  Jackson   apple         9\n",
+       "2     John  orange         9\n",
+       "3     John  potato        10\n",
+       "4      Tom  tomato         4"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "supermarket_prices = pd.read_csv(\"supermarket_prices.csv\")\n",
+    "supermarket_transactions = pd.read_csv(\"supermarket_transactions.csv\")\n",
+    "supermarket_transactions.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Quantity</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Buyer</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>Emma</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Jackson</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>John</td>\n",
+       "      <td>122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Liam</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Lucas</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Sandra</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Sophia</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Tom</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Quantity\n",
+       "Buyer            \n",
+       "Emma           81\n",
+       "Jackson        70\n",
+       "John          122\n",
+       "Liam           81\n",
+       "Lucas          62\n",
+       "Sandra         78\n",
+       "Sophia         61\n",
+       "Tom            49"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#How many items each client has purchased\n",
+    "supermarket_transactions.groupby('Buyer').sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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></th>\n",
+       "      <th>Quantity</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Buyer</th>\n",
+       "      <th>Product</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"4\" valign=\"top\">Emma</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>banana</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"4\" valign=\"top\">Jackson</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>orange</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"5\" valign=\"top\">John</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>banana</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>orange</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"5\" valign=\"top\">Liam</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>banana</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>orange</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"5\" valign=\"top\">Lucas</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>banana</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>orange</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"4\" valign=\"top\">Sandra</td>\n",
+       "      <td>banana</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>orange</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"5\" valign=\"top\">Sophia</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>banana</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>orange</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td rowspan=\"4\" valign=\"top\">Tom</td>\n",
+       "      <td>apple</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>banana</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>potato</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>tomato</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Quantity\n",
+       "Buyer   Product          \n",
+       "Emma    apple          25\n",
+       "        banana         26\n",
+       "        potato         14\n",
+       "        tomato         16\n",
+       "Jackson apple          18\n",
+       "        orange         28\n",
+       "        potato          8\n",
+       "        tomato         16\n",
+       "John    apple           7\n",
+       "        banana         28\n",
+       "        orange         46\n",
+       "        potato         18\n",
+       "        tomato         23\n",
+       "Liam    apple          21\n",
+       "        banana         16\n",
+       "        orange         16\n",
+       "        potato         21\n",
+       "        tomato          7\n",
+       "Lucas   apple          14\n",
+       "        banana          3\n",
+       "        orange         17\n",
+       "        potato          9\n",
+       "        tomato         19\n",
+       "Sandra  banana          2\n",
+       "        orange         37\n",
+       "        potato         38\n",
+       "        tomato          1\n",
+       "Sophia  apple          14\n",
+       "        banana         13\n",
+       "        orange          7\n",
+       "        potato         14\n",
+       "        tomato         13\n",
+       "Tom     apple          18\n",
+       "        banana          6\n",
+       "        potato         16\n",
+       "        tomato          9"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#How many items of each type each client has purchased\n",
+    "by_client_type = supermarket_transactions.groupby(['Buyer','Product']).sum()\n",
+    "by_client_type"
+   ]
+  },
+  {
+   "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>total_spent</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Buyer</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>Emma</td>\n",
+       "      <td>246.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Jackson</td>\n",
+       "      <td>202.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>John</td>\n",
+       "      <td>461.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Liam</td>\n",
+       "      <td>263.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Lucas</td>\n",
+       "      <td>176.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Sandra</td>\n",
+       "      <td>300.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Sophia</td>\n",
+       "      <td>189.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Tom</td>\n",
+       "      <td>126.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         total_spent\n",
+       "Buyer               \n",
+       "Emma           246.4\n",
+       "Jackson        202.8\n",
+       "John           461.3\n",
+       "Liam           263.3\n",
+       "Lucas          176.0\n",
+       "Sandra         300.8\n",
+       "Sophia         189.4\n",
+       "Tom            126.1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Calculate the total amount spent by each client\n",
+    "merged_tables = pd.merge(supermarket_transactions, supermarket_prices, on = 'Product', how = 'outer')\n",
+    "merged_tables['total_spent'] = merged_tables.Quantity*merged_tables.Price\n",
+    "client_spent = merged_tables[['Buyer','total_spent']].groupby(['Buyer']).sum()\n",
+    "client_spent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Emma'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#The company that provides the supermarket with bananas wishes to give a prize to the client that \n",
+    "#has spent the largest proportion of their spending on bananas. Who should win the prize?\n",
+    "\n",
+    "merged_banana = merged_tables[merged_tables.Product=='banana']\n",
+    "spent_banana = merged_banana[['Buyer','total_spent']].groupby('Buyer').sum()\n",
+    "spent_banana_total = pd.merge(spent_banana,client_spent, on = 'Buyer', how = 'inner')\n",
+    "spent_banana_total['prop_banana'] = spent_banana_total['total_spent_x']/spent_banana_total['total_spent_y']\n",
+    "spent_banana_total.prop_banana.idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>Buyer</th>\n",
+       "      <th>Product</th>\n",
+       "      <th>prop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>John</td>\n",
+       "      <td>orange</td>\n",
+       "      <td>0.428788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>Liam</td>\n",
+       "      <td>potato</td>\n",
+       "      <td>0.271174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>Sophia</td>\n",
+       "      <td>potato</td>\n",
+       "      <td>0.251320</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Buyer Product      prop\n",
+       "2     John  orange  0.428788\n",
+       "8     Liam  potato  0.271174\n",
+       "13  Sophia  potato  0.251320"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#A marketing company that works with the supermarket is interested to understand better the \n",
+    "#characteristics of the three people that have spent most of their spending on bananas. \n",
+    "#For each one of them report the other product that they have spent most of their remaining income on.\n",
+    "top_three = spent_banana_total.prop_banana.sort_values()[-4:-1].index.values\n",
+    "spending = pd.DataFrame()\n",
+    "for person in top_three:\n",
+    "    spending = pd.concat([spending, merged_tables[merged_tables.Buyer == person]],axis=0)    \n",
+    "spent_buyer_prod = spending.groupby(['Buyer','Product']).sum().reset_index()[['Buyer','Product','total_spent']]\n",
+    "total_buyer_prod = pd.merge(spent_buyer_prod,client_spent,on='Buyer',how='left')\n",
+    "total_buyer_prod['prop'] = total_buyer_prod.total_spent_x/total_buyer_prod.total_spent_y\n",
+    "prop_no_banana = total_buyer_prod[['Buyer','Product','prop']][total_buyer_prod.Product!='banana']\n",
+    "prop_no_banana.loc[prop_no_banana.groupby('Buyer').idxmax().iloc[:,0],:]"
+   ]
   }
  ],
  "metadata": {
@@ -1237,7 +1894,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,