├── Stage_A_Quiz
    ├── FoodBalanceSheets_E_Africa_NOFLAG.csv
    ├── .ipynb_checkpoints
    │   ├── Course_Practice-checkpoint.ipynb
    │   └── Hamoye_Stage_A_Quiz-checkpoint.ipynb
    └── Stage_A_Quiz.ipynb
└── README.md


/Stage_A_Quiz/FoodBalanceSheets_E_Africa_NOFLAG.csv:
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https://raw.githubusercontent.com/Chisomnwa/Hamoye_Data_Engineering_Internship/main/Stage_A_Quiz/FoodBalanceSheets_E_Africa_NOFLAG.csv


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/README.md:
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 1 | # This is a repository for my Hamoye Data Engineering Internship Stage Quizzes
 2 | 
 3 | ## For Stage A:
 4 | 
 5 | ### Food Balance Sheet Analysis
 6 | 
 7 | This [notebook](https://github.com/Chisomnwa/Hamoye_Data_Engineering_Stage_Quizzes/blob/main/Stage_A_Quiz.ipynb) file contains my Tag along code for the stage A assessment quiz.
 8 | 
 9 | This quiz was an assessment of our understanding of the **Introduction to Python for Machine Learning Course.** The 60-minute quiz had sections numbered 1–20 where we imported, wrangled, and answered some questions on the African Food Production Dataset which was provided by the FAO website.
10 | 
11 | This food Balance Sheet compiled by the Food and Agriculture Organization of the United Nations contains a selection of African countries food supply between 2014 to 2018.
12 | 
13 | This food balance sheet shows the food items for human consumption, along with how much was produced, used, imported, or exported, and how it benefits society (per capita supply).
14 | 
15 | The dataset can be obtained via this link: [FoodBalanceSheets_E_Africa_NOFLAG.csv](https://github.com/HamoyeHQ/HDSC-Introduction-to-Python-for-machine-learning/files/7768140/FoodBalanceSheets_E_Africa_NOFLAG.csv)
16 | 
17 | **Please note!:** For some reason, GitHub imposes a 5-second timeout for rich rendering. If the notebook cannot be rendered within the timeout period of 5 seconds, GitHub does not render it. It happens to big and small files as well. If you could not view the codes on the Stage_A_Quiz.ipynb, you can easily view it on Jupyter nbviewer [here](https://nbviewer.org/github/Chisomnwa/Hamoye_Data_Engineering_Stage_Quizzes/blob/main/Stage_A_Quiz.ipynb).
18 | 
19 | ## For Stage B:
20 | 
21 | ### Appliances Energy Prediction Data
22 | 
23 | The dataset for the remainder of this quiz (from question 18) is the Appliances Energy Prediction data. The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters).
24 | 
25 | In this course, I developed a multivariate multiple regression model to study the effect of eight input variables on two output.
26 | 
27 | 
28 | If you could not view the codes on the Stage_B_Quiz.ipynb, you can easily view it on Jupyter nbviewer [here](https://nbviewer.org/github/Chisomnwa/Hamoye_Data_Engineering_Stage_Quizzes/blob/main/Stage_B_Quiz/Machine%20Learning%20Regression%20-%20Predicting%20Energy%20Efficiency.ipynb)
29 | 


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/Stage_A_Quiz/.ipynb_checkpoints/Course_Practice-checkpoint.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "4a946a91",
  7 |    "metadata": {},
  8 |    "outputs": [
  9 |     {
 10 |      "name": "stdout",
 11 |      "output_type": "stream",
 12 |      "text": [
 13 |       "The Zen of Python, by Tim Peters\n",
 14 |       "\n",
 15 |       "Beautiful is better than ugly.\n",
 16 |       "Explicit is better than implicit.\n",
 17 |       "Simple is better than complex.\n",
 18 |       "Complex is better than complicated.\n",
 19 |       "Flat is better than nested.\n",
 20 |       "Sparse is better than dense.\n",
 21 |       "Readability counts.\n",
 22 |       "Special cases aren't special enough to break the rules.\n",
 23 |       "Although practicality beats purity.\n",
 24 |       "Errors should never pass silently.\n",
 25 |       "Unless explicitly silenced.\n",
 26 |       "In the face of ambiguity, refuse the temptation to guess.\n",
 27 |       "There should be one-- and preferably only one --obvious way to do it.\n",
 28 |       "Although that way may not be obvious at first unless you're Dutch.\n",
 29 |       "Now is better than never.\n",
 30 |       "Although never is often better than *right* now.\n",
 31 |       "If the implementation is hard to explain, it's a bad idea.\n",
 32 |       "If the implementation is easy to explain, it may be a good idea.\n",
 33 |       "Namespaces are one honking great idea -- let's do more of those!\n"
 34 |      ]
 35 |     }
 36 |    ],
 37 |    "source": [
 38 |     "import this"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 2,
 44 |    "id": "4b3d123d",
 45 |    "metadata": {},
 46 |    "outputs": [
 47 |     {
 48 |      "data": {
 49 |       "application/json": {
 50 |        "cell": {
 51 |         "!": "OSMagics",
 52 |         "HTML": "Other",
 53 |         "SVG": "Other",
 54 |         "bash": "Other",
 55 |         "capture": "ExecutionMagics",
 56 |         "cmd": "Other",
 57 |         "debug": "ExecutionMagics",
 58 |         "file": "Other",
 59 |         "html": "DisplayMagics",
 60 |         "javascript": "DisplayMagics",
 61 |         "js": "DisplayMagics",
 62 |         "latex": "DisplayMagics",
 63 |         "markdown": "DisplayMagics",
 64 |         "perl": "Other",
 65 |         "prun": "ExecutionMagics",
 66 |         "pypy": "Other",
 67 |         "python": "Other",
 68 |         "python2": "Other",
 69 |         "python3": "Other",
 70 |         "ruby": "Other",
 71 |         "script": "ScriptMagics",
 72 |         "sh": "Other",
 73 |         "svg": "DisplayMagics",
 74 |         "sx": "OSMagics",
 75 |         "system": "OSMagics",
 76 |         "time": "ExecutionMagics",
 77 |         "timeit": "ExecutionMagics",
 78 |         "writefile": "OSMagics"
 79 |        },
 80 |        "line": {
 81 |         "alias": "OSMagics",
 82 |         "alias_magic": "BasicMagics",
 83 |         "autoawait": "AsyncMagics",
 84 |         "autocall": "AutoMagics",
 85 |         "automagic": "AutoMagics",
 86 |         "autosave": "KernelMagics",
 87 |         "bookmark": "OSMagics",
 88 |         "cd": "OSMagics",
 89 |         "clear": "KernelMagics",
 90 |         "cls": "KernelMagics",
 91 |         "colors": "BasicMagics",
 92 |         "conda": "PackagingMagics",
 93 |         "config": "ConfigMagics",
 94 |         "connect_info": "KernelMagics",
 95 |         "copy": "Other",
 96 |         "ddir": "Other",
 97 |         "debug": "ExecutionMagics",
 98 |         "dhist": "OSMagics",
 99 |         "dirs": "OSMagics",
100 |         "doctest_mode": "BasicMagics",
101 |         "echo": "Other",
102 |         "ed": "Other",
103 |         "edit": "KernelMagics",
104 |         "env": "OSMagics",
105 |         "gui": "BasicMagics",
106 |         "hist": "Other",
107 |         "history": "HistoryMagics",
108 |         "killbgscripts": "ScriptMagics",
109 |         "ldir": "Other",
110 |         "less": "KernelMagics",
111 |         "load": "CodeMagics",
112 |         "load_ext": "ExtensionMagics",
113 |         "loadpy": "CodeMagics",
114 |         "logoff": "LoggingMagics",
115 |         "logon": "LoggingMagics",
116 |         "logstart": "LoggingMagics",
117 |         "logstate": "LoggingMagics",
118 |         "logstop": "LoggingMagics",
119 |         "ls": "Other",
120 |         "lsmagic": "BasicMagics",
121 |         "macro": "ExecutionMagics",
122 |         "magic": "BasicMagics",
123 |         "matplotlib": "PylabMagics",
124 |         "mkdir": "Other",
125 |         "more": "KernelMagics",
126 |         "notebook": "BasicMagics",
127 |         "page": "BasicMagics",
128 |         "pastebin": "CodeMagics",
129 |         "pdb": "ExecutionMagics",
130 |         "pdef": "NamespaceMagics",
131 |         "pdoc": "NamespaceMagics",
132 |         "pfile": "NamespaceMagics",
133 |         "pinfo": "NamespaceMagics",
134 |         "pinfo2": "NamespaceMagics",
135 |         "pip": "PackagingMagics",
136 |         "popd": "OSMagics",
137 |         "pprint": "BasicMagics",
138 |         "precision": "BasicMagics",
139 |         "prun": "ExecutionMagics",
140 |         "psearch": "NamespaceMagics",
141 |         "psource": "NamespaceMagics",
142 |         "pushd": "OSMagics",
143 |         "pwd": "OSMagics",
144 |         "pycat": "OSMagics",
145 |         "pylab": "PylabMagics",
146 |         "qtconsole": "KernelMagics",
147 |         "quickref": "BasicMagics",
148 |         "recall": "HistoryMagics",
149 |         "rehashx": "OSMagics",
150 |         "reload_ext": "ExtensionMagics",
151 |         "ren": "Other",
152 |         "rep": "Other",
153 |         "rerun": "HistoryMagics",
154 |         "reset": "NamespaceMagics",
155 |         "reset_selective": "NamespaceMagics",
156 |         "rmdir": "Other",
157 |         "run": "ExecutionMagics",
158 |         "save": "CodeMagics",
159 |         "sc": "OSMagics",
160 |         "set_env": "OSMagics",
161 |         "store": "StoreMagics",
162 |         "sx": "OSMagics",
163 |         "system": "OSMagics",
164 |         "tb": "ExecutionMagics",
165 |         "time": "ExecutionMagics",
166 |         "timeit": "ExecutionMagics",
167 |         "unalias": "OSMagics",
168 |         "unload_ext": "ExtensionMagics",
169 |         "who": "NamespaceMagics",
170 |         "who_ls": "NamespaceMagics",
171 |         "whos": "NamespaceMagics",
172 |         "xdel": "NamespaceMagics",
173 |         "xmode": "BasicMagics"
174 |        }
175 |       },
176 |       "text/plain": [
177 |        "Available line magics:\n",
178 |        "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %conda  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
179 |        "\n",
180 |        "Available cell magics:\n",
181 |        "%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
182 |        "\n",
183 |        "Automagic is ON, % prefix IS NOT needed for line magics."
184 |       ]
185 |      },
186 |      "execution_count": 2,
187 |      "metadata": {},
188 |      "output_type": "execute_result"
189 |     }
190 |    ],
191 |    "source": [
192 |     "%lsmagic"
193 |    ]
194 |   },
195 |   {
196 |    "cell_type": "code",
197 |    "execution_count": null,
198 |    "id": "baeeeab5",
199 |    "metadata": {},
200 |    "outputs": [],
201 |    "source": [
202 |     "## Introduction to Python for Machine Learning"
203 |    ]
204 |   },
205 |   {
206 |    "cell_type": "code",
207 |    "execution_count": null,
208 |    "id": "1e8b51d2",
209 |    "metadata": {},
210 |    "outputs": [],
211 |    "source": []
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": null,
216 |    "id": "e8cb7532",
217 |    "metadata": {},
218 |    "outputs": [],
219 |    "source": []
220 |   },
221 |   {
222 |    "cell_type": "code",
223 |    "execution_count": null,
224 |    "id": "77c3f93d",
225 |    "metadata": {},
226 |    "outputs": [],
227 |    "source": []
228 |   },
229 |   {
230 |    "cell_type": "code",
231 |    "execution_count": null,
232 |    "id": "f1425b5c",
233 |    "metadata": {},
234 |    "outputs": [],
235 |    "source": []
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": null,
240 |    "id": "80329667",
241 |    "metadata": {},
242 |    "outputs": [],
243 |    "source": []
244 |   },
245 |   {
246 |    "cell_type": "code",
247 |    "execution_count": null,
248 |    "id": "4e588e05",
249 |    "metadata": {},
250 |    "outputs": [],
251 |    "source": []
252 |   }
253 |  ],
254 |  "metadata": {
255 |   "kernelspec": {
256 |    "display_name": "Python 3 (ipykernel)",
257 |    "language": "python",
258 |    "name": "python3"
259 |   },
260 |   "language_info": {
261 |    "codemirror_mode": {
262 |     "name": "ipython",
263 |     "version": 3
264 |    },
265 |    "file_extension": ".py",
266 |    "mimetype": "text/x-python",
267 |    "name": "python",
268 |    "nbconvert_exporter": "python",
269 |    "pygments_lexer": "ipython3",
270 |    "version": "3.11.4"
271 |   }
272 |  },
273 |  "nbformat": 4,
274 |  "nbformat_minor": 5
275 | }
276 | 


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/Stage_A_Quiz/.ipynb_checkpoints/Hamoye_Stage_A_Quiz-checkpoint.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "629963e4",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "## Food Balance Sheet Analysis"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "code",
  13 |    "execution_count": 1,
  14 |    "id": "2f535948",
  15 |    "metadata": {},
  16 |    "outputs": [],
  17 |    "source": [
  18 |     "# import all packages and set plots to be embedded inline\n",
  19 |     "import numpy as np\n",
  20 |     "import pandas as pd\n",
  21 |     "import matplotlib.pyplot as plt\n",
  22 |     "import seaborn as sb\n",
  23 |     "\n",
  24 |     "%matplotlib inline\n",
  25 |     "\n",
  26 |     "# suppress warnings from final output\n",
  27 |     "import warnings\n",
  28 |     "warnings.simplefilter(\"ignore\")"
  29 |    ]
  30 |   },
  31 |   {
  32 |    "cell_type": "code",
  33 |    "execution_count": 2,
  34 |    "id": "ef43ca9c",
  35 |    "metadata": {},
  36 |    "outputs": [
  37 |     {
  38 |      "name": "stdout",
  39 |      "output_type": "stream",
  40 |      "text": [
  41 |       "(60943, 12)\n"
  42 |      ]
  43 |     },
  44 |     {
  45 |      "data": {
  46 |       "text/html": [
  47 |        "<div>\n",
  48 |        "<style scoped>\n",
  49 |        "    .dataframe tbody tr th:only-of-type {\n",
  50 |        "        vertical-align: middle;\n",
  51 |        "    }\n",
  52 |        "\n",
  53 |        "    .dataframe tbody tr th {\n",
  54 |        "        vertical-align: top;\n",
  55 |        "    }\n",
  56 |        "\n",
  57 |        "    .dataframe thead th {\n",
  58 |        "        text-align: right;\n",
  59 |        "    }\n",
  60 |        "</style>\n",
  61 |        "<table border=\"1\" class=\"dataframe\">\n",
  62 |        "  <thead>\n",
  63 |        "    <tr style=\"text-align: right;\">\n",
  64 |        "      <th></th>\n",
  65 |        "      <th>Area Code</th>\n",
  66 |        "      <th>Area</th>\n",
  67 |        "      <th>Item Code</th>\n",
  68 |        "      <th>Item</th>\n",
  69 |        "      <th>Element Code</th>\n",
  70 |        "      <th>Element</th>\n",
  71 |        "      <th>Unit</th>\n",
  72 |        "      <th>Y2014</th>\n",
  73 |        "      <th>Y2015</th>\n",
  74 |        "      <th>Y2016</th>\n",
  75 |        "      <th>Y2017</th>\n",
  76 |        "      <th>Y2018</th>\n",
  77 |        "    </tr>\n",
  78 |        "  </thead>\n",
  79 |        "  <tbody>\n",
  80 |        "    <tr>\n",
  81 |        "      <th>0</th>\n",
  82 |        "      <td>4</td>\n",
  83 |        "      <td>Algeria</td>\n",
  84 |        "      <td>2501</td>\n",
  85 |        "      <td>Population</td>\n",
  86 |        "      <td>511</td>\n",
  87 |        "      <td>Total Population - Both sexes</td>\n",
  88 |        "      <td>1000 persons</td>\n",
  89 |        "      <td>38924.00</td>\n",
  90 |        "      <td>39728.00</td>\n",
  91 |        "      <td>40551.00</td>\n",
  92 |        "      <td>41389.00</td>\n",
  93 |        "      <td>42228.00</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>1</th>\n",
  97 |        "      <td>4</td>\n",
  98 |        "      <td>Algeria</td>\n",
  99 |        "      <td>2501</td>\n",
 100 |        "      <td>Population</td>\n",
 101 |        "      <td>5301</td>\n",
 102 |        "      <td>Domestic supply quantity</td>\n",
 103 |        "      <td>1000 tonnes</td>\n",
 104 |        "      <td>0.00</td>\n",
 105 |        "      <td>0.00</td>\n",
 106 |        "      <td>0.00</td>\n",
 107 |        "      <td>0.00</td>\n",
 108 |        "      <td>0.00</td>\n",
 109 |        "    </tr>\n",
 110 |        "    <tr>\n",
 111 |        "      <th>2</th>\n",
 112 |        "      <td>4</td>\n",
 113 |        "      <td>Algeria</td>\n",
 114 |        "      <td>2901</td>\n",
 115 |        "      <td>Grand Total</td>\n",
 116 |        "      <td>664</td>\n",
 117 |        "      <td>Food supply (kcal/capita/day)</td>\n",
 118 |        "      <td>kcal/capita/day</td>\n",
 119 |        "      <td>3377.00</td>\n",
 120 |        "      <td>3379.00</td>\n",
 121 |        "      <td>3372.00</td>\n",
 122 |        "      <td>3341.00</td>\n",
 123 |        "      <td>3322.00</td>\n",
 124 |        "    </tr>\n",
 125 |        "    <tr>\n",
 126 |        "      <th>3</th>\n",
 127 |        "      <td>4</td>\n",
 128 |        "      <td>Algeria</td>\n",
 129 |        "      <td>2901</td>\n",
 130 |        "      <td>Grand Total</td>\n",
 131 |        "      <td>674</td>\n",
 132 |        "      <td>Protein supply quantity (g/capita/day)</td>\n",
 133 |        "      <td>g/capita/day</td>\n",
 134 |        "      <td>94.90</td>\n",
 135 |        "      <td>94.35</td>\n",
 136 |        "      <td>94.72</td>\n",
 137 |        "      <td>92.82</td>\n",
 138 |        "      <td>91.83</td>\n",
 139 |        "    </tr>\n",
 140 |        "    <tr>\n",
 141 |        "      <th>4</th>\n",
 142 |        "      <td>4</td>\n",
 143 |        "      <td>Algeria</td>\n",
 144 |        "      <td>2901</td>\n",
 145 |        "      <td>Grand Total</td>\n",
 146 |        "      <td>684</td>\n",
 147 |        "      <td>Fat supply quantity (g/capita/day)</td>\n",
 148 |        "      <td>g/capita/day</td>\n",
 149 |        "      <td>80.06</td>\n",
 150 |        "      <td>79.36</td>\n",
 151 |        "      <td>77.40</td>\n",
 152 |        "      <td>80.19</td>\n",
 153 |        "      <td>77.28</td>\n",
 154 |        "    </tr>\n",
 155 |        "  </tbody>\n",
 156 |        "</table>\n",
 157 |        "</div>"
 158 |       ],
 159 |       "text/plain": [
 160 |        "   Area Code     Area  Item Code         Item  Element Code  \\\n",
 161 |        "0          4  Algeria       2501   Population           511   \n",
 162 |        "1          4  Algeria       2501   Population          5301   \n",
 163 |        "2          4  Algeria       2901  Grand Total           664   \n",
 164 |        "3          4  Algeria       2901  Grand Total           674   \n",
 165 |        "4          4  Algeria       2901  Grand Total           684   \n",
 166 |        "\n",
 167 |        "                                  Element             Unit     Y2014  \\\n",
 168 |        "0           Total Population - Both sexes     1000 persons  38924.00   \n",
 169 |        "1                Domestic supply quantity      1000 tonnes      0.00   \n",
 170 |        "2           Food supply (kcal/capita/day)  kcal/capita/day   3377.00   \n",
 171 |        "3  Protein supply quantity (g/capita/day)     g/capita/day     94.90   \n",
 172 |        "4      Fat supply quantity (g/capita/day)     g/capita/day     80.06   \n",
 173 |        "\n",
 174 |        "      Y2015     Y2016     Y2017     Y2018  \n",
 175 |        "0  39728.00  40551.00  41389.00  42228.00  \n",
 176 |        "1      0.00      0.00      0.00      0.00  \n",
 177 |        "2   3379.00   3372.00   3341.00   3322.00  \n",
 178 |        "3     94.35     94.72     92.82     91.83  \n",
 179 |        "4     79.36     77.40     80.19     77.28  "
 180 |       ]
 181 |      },
 182 |      "execution_count": 2,
 183 |      "metadata": {},
 184 |      "output_type": "execute_result"
 185 |     }
 186 |    ],
 187 |    "source": [
 188 |     "# import dataset to pandas dataframe\n",
 189 |     "df_FBS = pd.read_csv('FoodBalanceSheets_E_Africa_NOFLAG.csv', encoding='Latin-1')\n",
 190 |     "\n",
 191 |     "# Checking the total number of rows and columns in our dataframe\n",
 192 |     "print(df_FBS.shape)\n",
 193 |     "\n",
 194 |     "#Looking at the first five rows in our dataframe\n",
 195 |     "df_FBS.head()"
 196 |    ]
 197 |   },
 198 |   {
 199 |    "cell_type": "code",
 200 |    "execution_count": 3,
 201 |    "id": "c3a2e817",
 202 |    "metadata": {},
 203 |    "outputs": [
 204 |     {
 205 |      "data": {
 206 |       "text/html": [
 207 |        "<div>\n",
 208 |        "<style scoped>\n",
 209 |        "    .dataframe tbody tr th:only-of-type {\n",
 210 |        "        vertical-align: middle;\n",
 211 |        "    }\n",
 212 |        "\n",
 213 |        "    .dataframe tbody tr th {\n",
 214 |        "        vertical-align: top;\n",
 215 |        "    }\n",
 216 |        "\n",
 217 |        "    .dataframe thead th {\n",
 218 |        "        text-align: right;\n",
 219 |        "    }\n",
 220 |        "</style>\n",
 221 |        "<table border=\"1\" class=\"dataframe\">\n",
 222 |        "  <thead>\n",
 223 |        "    <tr style=\"text-align: right;\">\n",
 224 |        "      <th></th>\n",
 225 |        "      <th>Area Code</th>\n",
 226 |        "      <th>Area</th>\n",
 227 |        "      <th>Item Code</th>\n",
 228 |        "      <th>Item</th>\n",
 229 |        "      <th>Element Code</th>\n",
 230 |        "      <th>Element</th>\n",
 231 |        "      <th>Unit</th>\n",
 232 |        "      <th>Y2014</th>\n",
 233 |        "      <th>Y2015</th>\n",
 234 |        "      <th>Y2016</th>\n",
 235 |        "      <th>Y2017</th>\n",
 236 |        "      <th>Y2018</th>\n",
 237 |        "    </tr>\n",
 238 |        "  </thead>\n",
 239 |        "  <tbody>\n",
 240 |        "    <tr>\n",
 241 |        "      <th>60938</th>\n",
 242 |        "      <td>181</td>\n",
 243 |        "      <td>Zimbabwe</td>\n",
 244 |        "      <td>2899</td>\n",
 245 |        "      <td>Miscellaneous</td>\n",
 246 |        "      <td>5142</td>\n",
 247 |        "      <td>Food</td>\n",
 248 |        "      <td>1000 tonnes</td>\n",
 249 |        "      <td>42.00</td>\n",
 250 |        "      <td>46.00</td>\n",
 251 |        "      <td>33.00</td>\n",
 252 |        "      <td>19.00</td>\n",
 253 |        "      <td>16.00</td>\n",
 254 |        "    </tr>\n",
 255 |        "    <tr>\n",
 256 |        "      <th>60939</th>\n",
 257 |        "      <td>181</td>\n",
 258 |        "      <td>Zimbabwe</td>\n",
 259 |        "      <td>2899</td>\n",
 260 |        "      <td>Miscellaneous</td>\n",
 261 |        "      <td>645</td>\n",
 262 |        "      <td>Food supply quantity (kg/capita/yr)</td>\n",
 263 |        "      <td>kg</td>\n",
 264 |        "      <td>3.06</td>\n",
 265 |        "      <td>3.33</td>\n",
 266 |        "      <td>2.35</td>\n",
 267 |        "      <td>1.33</td>\n",
 268 |        "      <td>1.08</td>\n",
 269 |        "    </tr>\n",
 270 |        "    <tr>\n",
 271 |        "      <th>60940</th>\n",
 272 |        "      <td>181</td>\n",
 273 |        "      <td>Zimbabwe</td>\n",
 274 |        "      <td>2899</td>\n",
 275 |        "      <td>Miscellaneous</td>\n",
 276 |        "      <td>664</td>\n",
 277 |        "      <td>Food supply (kcal/capita/day)</td>\n",
 278 |        "      <td>kcal/capita/day</td>\n",
 279 |        "      <td>3.00</td>\n",
 280 |        "      <td>4.00</td>\n",
 281 |        "      <td>3.00</td>\n",
 282 |        "      <td>1.00</td>\n",
 283 |        "      <td>1.00</td>\n",
 284 |        "    </tr>\n",
 285 |        "    <tr>\n",
 286 |        "      <th>60941</th>\n",
 287 |        "      <td>181</td>\n",
 288 |        "      <td>Zimbabwe</td>\n",
 289 |        "      <td>2899</td>\n",
 290 |        "      <td>Miscellaneous</td>\n",
 291 |        "      <td>674</td>\n",
 292 |        "      <td>Protein supply quantity (g/capita/day)</td>\n",
 293 |        "      <td>g/capita/day</td>\n",
 294 |        "      <td>0.10</td>\n",
 295 |        "      <td>0.11</td>\n",
 296 |        "      <td>0.08</td>\n",
 297 |        "      <td>0.04</td>\n",
 298 |        "      <td>0.04</td>\n",
 299 |        "    </tr>\n",
 300 |        "    <tr>\n",
 301 |        "      <th>60942</th>\n",
 302 |        "      <td>181</td>\n",
 303 |        "      <td>Zimbabwe</td>\n",
 304 |        "      <td>2899</td>\n",
 305 |        "      <td>Miscellaneous</td>\n",
 306 |        "      <td>684</td>\n",
 307 |        "      <td>Fat supply quantity (g/capita/day)</td>\n",
 308 |        "      <td>g/capita/day</td>\n",
 309 |        "      <td>0.04</td>\n",
 310 |        "      <td>0.05</td>\n",
 311 |        "      <td>0.03</td>\n",
 312 |        "      <td>0.02</td>\n",
 313 |        "      <td>0.01</td>\n",
 314 |        "    </tr>\n",
 315 |        "  </tbody>\n",
 316 |        "</table>\n",
 317 |        "</div>"
 318 |       ],
 319 |       "text/plain": [
 320 |        "       Area Code      Area  Item Code           Item  Element Code  \\\n",
 321 |        "60938        181  Zimbabwe       2899  Miscellaneous          5142   \n",
 322 |        "60939        181  Zimbabwe       2899  Miscellaneous           645   \n",
 323 |        "60940        181  Zimbabwe       2899  Miscellaneous           664   \n",
 324 |        "60941        181  Zimbabwe       2899  Miscellaneous           674   \n",
 325 |        "60942        181  Zimbabwe       2899  Miscellaneous           684   \n",
 326 |        "\n",
 327 |        "                                      Element             Unit  Y2014  Y2015  \\\n",
 328 |        "60938                                    Food      1000 tonnes  42.00  46.00   \n",
 329 |        "60939     Food supply quantity (kg/capita/yr)               kg   3.06   3.33   \n",
 330 |        "60940           Food supply (kcal/capita/day)  kcal/capita/day   3.00   4.00   \n",
 331 |        "60941  Protein supply quantity (g/capita/day)     g/capita/day   0.10   0.11   \n",
 332 |        "60942      Fat supply quantity (g/capita/day)     g/capita/day   0.04   0.05   \n",
 333 |        "\n",
 334 |        "       Y2016  Y2017  Y2018  \n",
 335 |        "60938  33.00  19.00  16.00  \n",
 336 |        "60939   2.35   1.33   1.08  \n",
 337 |        "60940   3.00   1.00   1.00  \n",
 338 |        "60941   0.08   0.04   0.04  \n",
 339 |        "60942   0.03   0.02   0.01  "
 340 |       ]
 341 |      },
 342 |      "execution_count": 3,
 343 |      "metadata": {},
 344 |      "output_type": "execute_result"
 345 |     }
 346 |    ],
 347 |    "source": [
 348 |     "# Looking at the last five rows in our dataframe\n",
 349 |     "df_FBS.tail()"
 350 |    ]
 351 |   },
 352 |   {
 353 |    "cell_type": "code",
 354 |    "execution_count": 4,
 355 |    "id": "79025bfc",
 356 |    "metadata": {},
 357 |    "outputs": [
 358 |     {
 359 |      "name": "stdout",
 360 |      "output_type": "stream",
 361 |      "text": [
 362 |       "<class 'pandas.core.frame.DataFrame'>\n",
 363 |       "RangeIndex: 60943 entries, 0 to 60942\n",
 364 |       "Data columns (total 12 columns):\n",
 365 |       " #   Column        Non-Null Count  Dtype  \n",
 366 |       "---  ------        --------------  -----  \n",
 367 |       " 0   Area Code     60943 non-null  int64  \n",
 368 |       " 1   Area          60943 non-null  object \n",
 369 |       " 2   Item Code     60943 non-null  int64  \n",
 370 |       " 3   Item          60943 non-null  object \n",
 371 |       " 4   Element Code  60943 non-null  int64  \n",
 372 |       " 5   Element       60943 non-null  object \n",
 373 |       " 6   Unit          60943 non-null  object \n",
 374 |       " 7   Y2014         59354 non-null  float64\n",
 375 |       " 8   Y2015         59395 non-null  float64\n",
 376 |       " 9   Y2016         59408 non-null  float64\n",
 377 |       " 10  Y2017         59437 non-null  float64\n",
 378 |       " 11  Y2018         59507 non-null  float64\n",
 379 |       "dtypes: float64(5), int64(3), object(4)\n",
 380 |       "memory usage: 5.6+ MB\n"
 381 |      ]
 382 |     }
 383 |    ],
 384 |    "source": [
 385 |     "# Looking at the information of our data\n",
 386 |     "df_FBS.info()"
 387 |    ]
 388 |   },
 389 |   {
 390 |    "cell_type": "code",
 391 |    "execution_count": 5,
 392 |    "id": "2c26aebf",
 393 |    "metadata": {},
 394 |    "outputs": [
 395 |     {
 396 |      "data": {
 397 |       "text/html": [
 398 |        "<div>\n",
 399 |        "<style scoped>\n",
 400 |        "    .dataframe tbody tr th:only-of-type {\n",
 401 |        "        vertical-align: middle;\n",
 402 |        "    }\n",
 403 |        "\n",
 404 |        "    .dataframe tbody tr th {\n",
 405 |        "        vertical-align: top;\n",
 406 |        "    }\n",
 407 |        "\n",
 408 |        "    .dataframe thead th {\n",
 409 |        "        text-align: right;\n",
 410 |        "    }\n",
 411 |        "</style>\n",
 412 |        "<table border=\"1\" class=\"dataframe\">\n",
 413 |        "  <thead>\n",
 414 |        "    <tr style=\"text-align: right;\">\n",
 415 |        "      <th></th>\n",
 416 |        "      <th>Area Code</th>\n",
 417 |        "      <th>Item Code</th>\n",
 418 |        "      <th>Element Code</th>\n",
 419 |        "      <th>Y2014</th>\n",
 420 |        "      <th>Y2015</th>\n",
 421 |        "      <th>Y2016</th>\n",
 422 |        "      <th>Y2017</th>\n",
 423 |        "      <th>Y2018</th>\n",
 424 |        "    </tr>\n",
 425 |        "  </thead>\n",
 426 |        "  <tbody>\n",
 427 |        "    <tr>\n",
 428 |        "      <th>count</th>\n",
 429 |        "      <td>60943.000000</td>\n",
 430 |        "      <td>60943.000000</td>\n",
 431 |        "      <td>60943.000000</td>\n",
 432 |        "      <td>59354.000000</td>\n",
 433 |        "      <td>59395.000000</td>\n",
 434 |        "      <td>59408.000000</td>\n",
 435 |        "      <td>59437.000000</td>\n",
 436 |        "      <td>59507.000000</td>\n",
 437 |        "    </tr>\n",
 438 |        "    <tr>\n",
 439 |        "      <th>mean</th>\n",
 440 |        "      <td>134.265576</td>\n",
 441 |        "      <td>2687.176706</td>\n",
 442 |        "      <td>3814.856456</td>\n",
 443 |        "      <td>134.196282</td>\n",
 444 |        "      <td>135.235966</td>\n",
 445 |        "      <td>136.555222</td>\n",
 446 |        "      <td>140.917765</td>\n",
 447 |        "      <td>143.758381</td>\n",
 448 |        "    </tr>\n",
 449 |        "    <tr>\n",
 450 |        "      <th>std</th>\n",
 451 |        "      <td>72.605709</td>\n",
 452 |        "      <td>146.055739</td>\n",
 453 |        "      <td>2212.007033</td>\n",
 454 |        "      <td>1567.663696</td>\n",
 455 |        "      <td>1603.403984</td>\n",
 456 |        "      <td>1640.007194</td>\n",
 457 |        "      <td>1671.862359</td>\n",
 458 |        "      <td>1710.782658</td>\n",
 459 |        "    </tr>\n",
 460 |        "    <tr>\n",
 461 |        "      <th>min</th>\n",
 462 |        "      <td>4.000000</td>\n",
 463 |        "      <td>2501.000000</td>\n",
 464 |        "      <td>511.000000</td>\n",
 465 |        "      <td>-1796.000000</td>\n",
 466 |        "      <td>-3161.000000</td>\n",
 467 |        "      <td>-3225.000000</td>\n",
 468 |        "      <td>-1582.000000</td>\n",
 469 |        "      <td>-3396.000000</td>\n",
 470 |        "    </tr>\n",
 471 |        "    <tr>\n",
 472 |        "      <th>25%</th>\n",
 473 |        "      <td>74.000000</td>\n",
 474 |        "      <td>2562.000000</td>\n",
 475 |        "      <td>684.000000</td>\n",
 476 |        "      <td>0.000000</td>\n",
 477 |        "      <td>0.000000</td>\n",
 478 |        "      <td>0.000000</td>\n",
 479 |        "      <td>0.000000</td>\n",
 480 |        "      <td>0.000000</td>\n",
 481 |        "    </tr>\n",
 482 |        "    <tr>\n",
 483 |        "      <th>50%</th>\n",
 484 |        "      <td>136.000000</td>\n",
 485 |        "      <td>2630.000000</td>\n",
 486 |        "      <td>5142.000000</td>\n",
 487 |        "      <td>0.090000</td>\n",
 488 |        "      <td>0.080000</td>\n",
 489 |        "      <td>0.080000</td>\n",
 490 |        "      <td>0.100000</td>\n",
 491 |        "      <td>0.070000</td>\n",
 492 |        "    </tr>\n",
 493 |        "    <tr>\n",
 494 |        "      <th>75%</th>\n",
 495 |        "      <td>195.000000</td>\n",
 496 |        "      <td>2775.000000</td>\n",
 497 |        "      <td>5511.000000</td>\n",
 498 |        "      <td>8.340000</td>\n",
 499 |        "      <td>8.460000</td>\n",
 500 |        "      <td>8.430000</td>\n",
 501 |        "      <td>9.000000</td>\n",
 502 |        "      <td>9.000000</td>\n",
 503 |        "    </tr>\n",
 504 |        "    <tr>\n",
 505 |        "      <th>max</th>\n",
 506 |        "      <td>276.000000</td>\n",
 507 |        "      <td>2961.000000</td>\n",
 508 |        "      <td>5911.000000</td>\n",
 509 |        "      <td>176405.000000</td>\n",
 510 |        "      <td>181137.000000</td>\n",
 511 |        "      <td>185960.000000</td>\n",
 512 |        "      <td>190873.000000</td>\n",
 513 |        "      <td>195875.000000</td>\n",
 514 |        "    </tr>\n",
 515 |        "  </tbody>\n",
 516 |        "</table>\n",
 517 |        "</div>"
 518 |       ],
 519 |       "text/plain": [
 520 |        "          Area Code     Item Code  Element Code          Y2014          Y2015  \\\n",
 521 |        "count  60943.000000  60943.000000  60943.000000   59354.000000   59395.000000   \n",
 522 |        "mean     134.265576   2687.176706   3814.856456     134.196282     135.235966   \n",
 523 |        "std       72.605709    146.055739   2212.007033    1567.663696    1603.403984   \n",
 524 |        "min        4.000000   2501.000000    511.000000   -1796.000000   -3161.000000   \n",
 525 |        "25%       74.000000   2562.000000    684.000000       0.000000       0.000000   \n",
 526 |        "50%      136.000000   2630.000000   5142.000000       0.090000       0.080000   \n",
 527 |        "75%      195.000000   2775.000000   5511.000000       8.340000       8.460000   \n",
 528 |        "max      276.000000   2961.000000   5911.000000  176405.000000  181137.000000   \n",
 529 |        "\n",
 530 |        "               Y2016          Y2017          Y2018  \n",
 531 |        "count   59408.000000   59437.000000   59507.000000  \n",
 532 |        "mean      136.555222     140.917765     143.758381  \n",
 533 |        "std      1640.007194    1671.862359    1710.782658  \n",
 534 |        "min     -3225.000000   -1582.000000   -3396.000000  \n",
 535 |        "25%         0.000000       0.000000       0.000000  \n",
 536 |        "50%         0.080000       0.100000       0.070000  \n",
 537 |        "75%         8.430000       9.000000       9.000000  \n",
 538 |        "max    185960.000000  190873.000000  195875.000000  "
 539 |       ]
 540 |      },
 541 |      "execution_count": 5,
 542 |      "metadata": {},
 543 |      "output_type": "execute_result"
 544 |     }
 545 |    ],
 546 |    "source": [
 547 |     "# Looking at the statistical description of our data\n",
 548 |     "df_FBS.describe()"
 549 |    ]
 550 |   },
 551 |   {
 552 |    "cell_type": "code",
 553 |    "execution_count": 6,
 554 |    "id": "02f2f527",
 555 |    "metadata": {},
 556 |    "outputs": [
 557 |     {
 558 |      "data": {
 559 |       "text/plain": [
 560 |        "Area Code          0\n",
 561 |        "Area               0\n",
 562 |        "Item Code          0\n",
 563 |        "Item               0\n",
 564 |        "Element Code       0\n",
 565 |        "Element            0\n",
 566 |        "Unit               0\n",
 567 |        "Y2014           1589\n",
 568 |        "Y2015           1548\n",
 569 |        "Y2016           1535\n",
 570 |        "Y2017           1506\n",
 571 |        "Y2018           1436\n",
 572 |        "dtype: int64"
 573 |       ]
 574 |      },
 575 |      "execution_count": 6,
 576 |      "metadata": {},
 577 |      "output_type": "execute_result"
 578 |     }
 579 |    ],
 580 |    "source": [
 581 |     "# Checking for missing values in our dataframe\n",
 582 |     "df_FBS.isnull().sum()"
 583 |    ]
 584 |   },
 585 |   {
 586 |    "cell_type": "code",
 587 |    "execution_count": 7,
 588 |    "id": "a02277ed",
 589 |    "metadata": {},
 590 |    "outputs": [
 591 |     {
 592 |      "data": {
 593 |       "text/plain": [
 594 |        "False"
 595 |       ]
 596 |      },
 597 |      "execution_count": 7,
 598 |      "metadata": {},
 599 |      "output_type": "execute_result"
 600 |     }
 601 |    ],
 602 |    "source": [
 603 |     "# Checking if we have any duplicated rows\n",
 604 |     "df_FBS.duplicated().any()"
 605 |    ]
 606 |   },
 607 |   {
 608 |    "cell_type": "code",
 609 |    "execution_count": null,
 610 |    "id": "adca0331",
 611 |    "metadata": {},
 612 |    "outputs": [],
 613 |    "source": []
 614 |   },
 615 |   {
 616 |    "cell_type": "markdown",
 617 |    "id": "eea55dee",
 618 |    "metadata": {},
 619 |    "source": [
 620 |     "## Answering the Quiz Questions"
 621 |    ]
 622 |   },
 623 |   {
 624 |    "cell_type": "markdown",
 625 |    "id": "75509885",
 626 |    "metadata": {},
 627 |    "source": [
 628 |     "#### Question 1\n",
 629 |     "\n",
 630 |     "What is the total Protein supply quantity in Madagascar in 2015?\n",
 631 |     "\n",
 632 |     "#### Ans:\n",
 633 |     "    \n",
 634 |     "B - 173.05"
 635 |    ]
 636 |   },
 637 |   {
 638 |    "cell_type": "code",
 639 |    "execution_count": 8,
 640 |    "id": "2b67fe96",
 641 |    "metadata": {},
 642 |    "outputs": [
 643 |     {
 644 |      "name": "stdout",
 645 |      "output_type": "stream",
 646 |      "text": [
 647 |       "Total Protein supply quantity in Madagascar in 2015: 173.04999999999998 g/capita/day\n"
 648 |      ]
 649 |     }
 650 |    ],
 651 |    "source": [
 652 |     "# Filter the dataframe to select rows for Madagascar and the year 2015\n",
 653 |     "madagascar_2015 = df_FBS[(df_FBS['Area'] == 'Madagascar') & (df_FBS['Element'] == 'Protein supply quantity (g/capita/day)') & (df_FBS['Y2015'] != 0)]\n",
 654 |     "\n",
 655 |     "# Calculate the total protein supply quantity in Madagascar for 2015\n",
 656 |     "total_protein_supply_2015 = madagascar_2015['Y2015'].sum()\n",
 657 |     "\n",
 658 |     "# Print the result\n",
 659 |     "print(\"Total Protein supply quantity in Madagascar in 2015:\", total_protein_supply_2015, \"g/capita/day\")\n"
 660 |    ]
 661 |   },
 662 |   {
 663 |    "cell_type": "code",
 664 |    "execution_count": null,
 665 |    "id": "0ab458db",
 666 |    "metadata": {},
 667 |    "outputs": [],
 668 |    "source": []
 669 |   },
 670 |   {
 671 |    "cell_type": "markdown",
 672 |    "id": "ed6981a3",
 673 |    "metadata": {},
 674 |    "source": [
 675 |     "#### Question 2\n",
 676 |     "\n",
 677 |     "What is the mean and standard deviation across the whole dataset for the year 2017 to 2 decimal places?\n",
 678 |     "\n",
 679 |     "#### Ans:\n",
 680 |     "    \n",
 681 |     "E - 140.92 and 1671.86"
 682 |    ]
 683 |   },
 684 |   {
 685 |    "cell_type": "code",
 686 |    "execution_count": 9,
 687 |    "id": "49c627f0",
 688 |    "metadata": {},
 689 |    "outputs": [
 690 |     {
 691 |      "data": {
 692 |       "text/html": [
 693 |        "<div>\n",
 694 |        "<style scoped>\n",
 695 |        "    .dataframe tbody tr th:only-of-type {\n",
 696 |        "        vertical-align: middle;\n",
 697 |        "    }\n",
 698 |        "\n",
 699 |        "    .dataframe tbody tr th {\n",
 700 |        "        vertical-align: top;\n",
 701 |        "    }\n",
 702 |        "\n",
 703 |        "    .dataframe thead th {\n",
 704 |        "        text-align: right;\n",
 705 |        "    }\n",
 706 |        "</style>\n",
 707 |        "<table border=\"1\" class=\"dataframe\">\n",
 708 |        "  <thead>\n",
 709 |        "    <tr style=\"text-align: right;\">\n",
 710 |        "      <th></th>\n",
 711 |        "      <th>Area Code</th>\n",
 712 |        "      <th>Item Code</th>\n",
 713 |        "      <th>Element Code</th>\n",
 714 |        "      <th>Y2014</th>\n",
 715 |        "      <th>Y2015</th>\n",
 716 |        "      <th>Y2016</th>\n",
 717 |        "      <th>Y2017</th>\n",
 718 |        "      <th>Y2018</th>\n",
 719 |        "    </tr>\n",
 720 |        "  </thead>\n",
 721 |        "  <tbody>\n",
 722 |        "    <tr>\n",
 723 |        "      <th>count</th>\n",
 724 |        "      <td>60943.000000</td>\n",
 725 |        "      <td>60943.000000</td>\n",
 726 |        "      <td>60943.000000</td>\n",
 727 |        "      <td>59354.000000</td>\n",
 728 |        "      <td>59395.000000</td>\n",
 729 |        "      <td>59408.000000</td>\n",
 730 |        "      <td>59437.000000</td>\n",
 731 |        "      <td>59507.000000</td>\n",
 732 |        "    </tr>\n",
 733 |        "    <tr>\n",
 734 |        "      <th>mean</th>\n",
 735 |        "      <td>134.265576</td>\n",
 736 |        "      <td>2687.176706</td>\n",
 737 |        "      <td>3814.856456</td>\n",
 738 |        "      <td>134.196282</td>\n",
 739 |        "      <td>135.235966</td>\n",
 740 |        "      <td>136.555222</td>\n",
 741 |        "      <td>140.917765</td>\n",
 742 |        "      <td>143.758381</td>\n",
 743 |        "    </tr>\n",
 744 |        "    <tr>\n",
 745 |        "      <th>std</th>\n",
 746 |        "      <td>72.605709</td>\n",
 747 |        "      <td>146.055739</td>\n",
 748 |        "      <td>2212.007033</td>\n",
 749 |        "      <td>1567.663696</td>\n",
 750 |        "      <td>1603.403984</td>\n",
 751 |        "      <td>1640.007194</td>\n",
 752 |        "      <td>1671.862359</td>\n",
 753 |        "      <td>1710.782658</td>\n",
 754 |        "    </tr>\n",
 755 |        "    <tr>\n",
 756 |        "      <th>min</th>\n",
 757 |        "      <td>4.000000</td>\n",
 758 |        "      <td>2501.000000</td>\n",
 759 |        "      <td>511.000000</td>\n",
 760 |        "      <td>-1796.000000</td>\n",
 761 |        "      <td>-3161.000000</td>\n",
 762 |        "      <td>-3225.000000</td>\n",
 763 |        "      <td>-1582.000000</td>\n",
 764 |        "      <td>-3396.000000</td>\n",
 765 |        "    </tr>\n",
 766 |        "    <tr>\n",
 767 |        "      <th>25%</th>\n",
 768 |        "      <td>74.000000</td>\n",
 769 |        "      <td>2562.000000</td>\n",
 770 |        "      <td>684.000000</td>\n",
 771 |        "      <td>0.000000</td>\n",
 772 |        "      <td>0.000000</td>\n",
 773 |        "      <td>0.000000</td>\n",
 774 |        "      <td>0.000000</td>\n",
 775 |        "      <td>0.000000</td>\n",
 776 |        "    </tr>\n",
 777 |        "    <tr>\n",
 778 |        "      <th>50%</th>\n",
 779 |        "      <td>136.000000</td>\n",
 780 |        "      <td>2630.000000</td>\n",
 781 |        "      <td>5142.000000</td>\n",
 782 |        "      <td>0.090000</td>\n",
 783 |        "      <td>0.080000</td>\n",
 784 |        "      <td>0.080000</td>\n",
 785 |        "      <td>0.100000</td>\n",
 786 |        "      <td>0.070000</td>\n",
 787 |        "    </tr>\n",
 788 |        "    <tr>\n",
 789 |        "      <th>75%</th>\n",
 790 |        "      <td>195.000000</td>\n",
 791 |        "      <td>2775.000000</td>\n",
 792 |        "      <td>5511.000000</td>\n",
 793 |        "      <td>8.340000</td>\n",
 794 |        "      <td>8.460000</td>\n",
 795 |        "      <td>8.430000</td>\n",
 796 |        "      <td>9.000000</td>\n",
 797 |        "      <td>9.000000</td>\n",
 798 |        "    </tr>\n",
 799 |        "    <tr>\n",
 800 |        "      <th>max</th>\n",
 801 |        "      <td>276.000000</td>\n",
 802 |        "      <td>2961.000000</td>\n",
 803 |        "      <td>5911.000000</td>\n",
 804 |        "      <td>176405.000000</td>\n",
 805 |        "      <td>181137.000000</td>\n",
 806 |        "      <td>185960.000000</td>\n",
 807 |        "      <td>190873.000000</td>\n",
 808 |        "      <td>195875.000000</td>\n",
 809 |        "    </tr>\n",
 810 |        "  </tbody>\n",
 811 |        "</table>\n",
 812 |        "</div>"
 813 |       ],
 814 |       "text/plain": [
 815 |        "          Area Code     Item Code  Element Code          Y2014          Y2015  \\\n",
 816 |        "count  60943.000000  60943.000000  60943.000000   59354.000000   59395.000000   \n",
 817 |        "mean     134.265576   2687.176706   3814.856456     134.196282     135.235966   \n",
 818 |        "std       72.605709    146.055739   2212.007033    1567.663696    1603.403984   \n",
 819 |        "min        4.000000   2501.000000    511.000000   -1796.000000   -3161.000000   \n",
 820 |        "25%       74.000000   2562.000000    684.000000       0.000000       0.000000   \n",
 821 |        "50%      136.000000   2630.000000   5142.000000       0.090000       0.080000   \n",
 822 |        "75%      195.000000   2775.000000   5511.000000       8.340000       8.460000   \n",
 823 |        "max      276.000000   2961.000000   5911.000000  176405.000000  181137.000000   \n",
 824 |        "\n",
 825 |        "               Y2016          Y2017          Y2018  \n",
 826 |        "count   59408.000000   59437.000000   59507.000000  \n",
 827 |        "mean      136.555222     140.917765     143.758381  \n",
 828 |        "std      1640.007194    1671.862359    1710.782658  \n",
 829 |        "min     -3225.000000   -1582.000000   -3396.000000  \n",
 830 |        "25%         0.000000       0.000000       0.000000  \n",
 831 |        "50%         0.080000       0.100000       0.070000  \n",
 832 |        "75%         8.430000       9.000000       9.000000  \n",
 833 |        "max    185960.000000  190873.000000  195875.000000  "
 834 |       ]
 835 |      },
 836 |      "execution_count": 9,
 837 |      "metadata": {},
 838 |      "output_type": "execute_result"
 839 |     }
 840 |    ],
 841 |    "source": [
 842 |     "# Get the descriptive statistcs of the columns\n",
 843 |     "df_FBS.describe()"
 844 |    ]
 845 |   },
 846 |   {
 847 |    "cell_type": "code",
 848 |    "execution_count": null,
 849 |    "id": "e043e507",
 850 |    "metadata": {},
 851 |    "outputs": [],
 852 |    "source": []
 853 |   },
 854 |   {
 855 |    "cell_type": "markdown",
 856 |    "id": "faa62caa",
 857 |    "metadata": {},
 858 |    "source": [
 859 |     "#### Question 3\n",
 860 |     "\n",
 861 |     "Perform a groupby operation on ‘Element’.  What year has the highest sum of Stock Variation?\n",
 862 |     "\n",
 863 |     "#### Ans:\n",
 864 |     "    \n",
 865 |     "A - 2014"
 866 |    ]
 867 |   },
 868 |   {
 869 |    "cell_type": "code",
 870 |    "execution_count": 10,
 871 |    "id": "f3b782b1",
 872 |    "metadata": {},
 873 |    "outputs": [
 874 |     {
 875 |      "data": {
 876 |       "text/html": [
 877 |        "<div>\n",
 878 |        "<style scoped>\n",
 879 |        "    .dataframe tbody tr th:only-of-type {\n",
 880 |        "        vertical-align: middle;\n",
 881 |        "    }\n",
 882 |        "\n",
 883 |        "    .dataframe tbody tr th {\n",
 884 |        "        vertical-align: top;\n",
 885 |        "    }\n",
 886 |        "\n",
 887 |        "    .dataframe thead th {\n",
 888 |        "        text-align: right;\n",
 889 |        "    }\n",
 890 |        "</style>\n",
 891 |        "<table border=\"1\" class=\"dataframe\">\n",
 892 |        "  <thead>\n",
 893 |        "    <tr style=\"text-align: right;\">\n",
 894 |        "      <th></th>\n",
 895 |        "      <th>Y2014</th>\n",
 896 |        "      <th>Y2015</th>\n",
 897 |        "      <th>Y2016</th>\n",
 898 |        "      <th>Y2017</th>\n",
 899 |        "      <th>Y2018</th>\n",
 900 |        "    </tr>\n",
 901 |        "    <tr>\n",
 902 |        "      <th>Element</th>\n",
 903 |        "      <th></th>\n",
 904 |        "      <th></th>\n",
 905 |        "      <th></th>\n",
 906 |        "      <th></th>\n",
 907 |        "      <th></th>\n",
 908 |        "    </tr>\n",
 909 |        "  </thead>\n",
 910 |        "  <tbody>\n",
 911 |        "    <tr>\n",
 912 |        "      <th>Domestic supply quantity</th>\n",
 913 |        "      <td>1996716.35</td>\n",
 914 |        "      <td>2021493.55</td>\n",
 915 |        "      <td>2044842.70</td>\n",
 916 |        "      <td>2088198.10</td>\n",
 917 |        "      <td>2161192.10</td>\n",
 918 |        "    </tr>\n",
 919 |        "    <tr>\n",
 920 |        "      <th>Export Quantity</th>\n",
 921 |        "      <td>150020.64</td>\n",
 922 |        "      <td>157614.47</td>\n",
 923 |        "      <td>151920.46</td>\n",
 924 |        "      <td>182338.80</td>\n",
 925 |        "      <td>181594.80</td>\n",
 926 |        "    </tr>\n",
 927 |        "    <tr>\n",
 928 |        "      <th>Fat supply quantity (g/capita/day)</th>\n",
 929 |        "      <td>10225.56</td>\n",
 930 |        "      <td>10235.74</td>\n",
 931 |        "      <td>10102.77</td>\n",
 932 |        "      <td>10253.84</td>\n",
 933 |        "      <td>10258.69</td>\n",
 934 |        "    </tr>\n",
 935 |        "    <tr>\n",
 936 |        "      <th>Feed</th>\n",
 937 |        "      <td>216927.89</td>\n",
 938 |        "      <td>225050.22</td>\n",
 939 |        "      <td>228958.65</td>\n",
 940 |        "      <td>223705.68</td>\n",
 941 |        "      <td>233489.68</td>\n",
 942 |        "    </tr>\n",
 943 |        "    <tr>\n",
 944 |        "      <th>Food</th>\n",
 945 |        "      <td>1212332.49</td>\n",
 946 |        "      <td>1232361.10</td>\n",
 947 |        "      <td>1247022.17</td>\n",
 948 |        "      <td>1258888.28</td>\n",
 949 |        "      <td>1303841.28</td>\n",
 950 |        "    </tr>\n",
 951 |        "    <tr>\n",
 952 |        "      <th>Food supply (kcal/capita/day)</th>\n",
 953 |        "      <td>454257.00</td>\n",
 954 |        "      <td>453383.00</td>\n",
 955 |        "      <td>451810.00</td>\n",
 956 |        "      <td>454681.00</td>\n",
 957 |        "      <td>455261.00</td>\n",
 958 |        "    </tr>\n",
 959 |        "    <tr>\n",
 960 |        "      <th>Food supply quantity (kg/capita/yr)</th>\n",
 961 |        "      <td>49650.63</td>\n",
 962 |        "      <td>49345.13</td>\n",
 963 |        "      <td>48985.28</td>\n",
 964 |        "      <td>48690.04</td>\n",
 965 |        "      <td>49056.85</td>\n",
 966 |        "    </tr>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>Import Quantity</th>\n",
 969 |        "      <td>274144.48</td>\n",
 970 |        "      <td>267018.46</td>\n",
 971 |        "      <td>286582.78</td>\n",
 972 |        "      <td>294559.09</td>\n",
 973 |        "      <td>287997.09</td>\n",
 974 |        "    </tr>\n",
 975 |        "    <tr>\n",
 976 |        "      <th>Losses</th>\n",
 977 |        "      <td>153223.00</td>\n",
 978 |        "      <td>155439.00</td>\n",
 979 |        "      <td>157787.00</td>\n",
 980 |        "      <td>160614.00</td>\n",
 981 |        "      <td>163902.00</td>\n",
 982 |        "    </tr>\n",
 983 |        "    <tr>\n",
 984 |        "      <th>Other uses (non-food)</th>\n",
 985 |        "      <td>78718.13</td>\n",
 986 |        "      <td>66254.41</td>\n",
 987 |        "      <td>69563.68</td>\n",
 988 |        "      <td>91645.97</td>\n",
 989 |        "      <td>91300.97</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>Processing</th>\n",
 993 |        "      <td>282923.00</td>\n",
 994 |        "      <td>287929.00</td>\n",
 995 |        "      <td>280631.00</td>\n",
 996 |        "      <td>292836.00</td>\n",
 997 |        "      <td>308429.00</td>\n",
 998 |        "    </tr>\n",
 999 |        "    <tr>\n",
1000 |        "      <th>Production</th>\n",
1001 |        "      <td>1931287.75</td>\n",
1002 |        "      <td>1947019.39</td>\n",
1003 |        "      <td>1943537.15</td>\n",
1004 |        "      <td>2030056.89</td>\n",
1005 |        "      <td>2075072.89</td>\n",
1006 |        "    </tr>\n",
1007 |        "    <tr>\n",
1008 |        "      <th>Protein supply quantity (g/capita/day)</th>\n",
1009 |        "      <td>11836.46</td>\n",
1010 |        "      <td>11833.95</td>\n",
1011 |        "      <td>11779.69</td>\n",
1012 |        "      <td>11842.45</td>\n",
1013 |        "      <td>11833.56</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>Residuals</th>\n",
1017 |        "      <td>30149.00</td>\n",
1018 |        "      <td>30045.00</td>\n",
1019 |        "      <td>37224.00</td>\n",
1020 |        "      <td>35500.00</td>\n",
1021 |        "      <td>34864.00</td>\n",
1022 |        "    </tr>\n",
1023 |        "    <tr>\n",
1024 |        "      <th>Seed</th>\n",
1025 |        "      <td>21922.92</td>\n",
1026 |        "      <td>23976.82</td>\n",
1027 |        "      <td>23389.20</td>\n",
1028 |        "      <td>24870.14</td>\n",
1029 |        "      <td>25263.14</td>\n",
1030 |        "    </tr>\n",
1031 |        "    <tr>\n",
1032 |        "      <th>Stock Variation</th>\n",
1033 |        "      <td>58749.83</td>\n",
1034 |        "      <td>34910.99</td>\n",
1035 |        "      <td>33140.12</td>\n",
1036 |        "      <td>54316.91</td>\n",
1037 |        "      <td>20577.91</td>\n",
1038 |        "    </tr>\n",
1039 |        "    <tr>\n",
1040 |        "      <th>Total Population - Both sexes</th>\n",
1041 |        "      <td>1031585.00</td>\n",
1042 |        "      <td>1058081.00</td>\n",
1043 |        "      <td>1085107.00</td>\n",
1044 |        "      <td>1112641.00</td>\n",
1045 |        "      <td>1140605.00</td>\n",
1046 |        "    </tr>\n",
1047 |        "    <tr>\n",
1048 |        "      <th>Tourist consumption</th>\n",
1049 |        "      <td>416.00</td>\n",
1050 |        "      <td>349.00</td>\n",
1051 |        "      <td>89.00</td>\n",
1052 |        "      <td>91.00</td>\n",
1053 |        "      <td>90.00</td>\n",
1054 |        "    </tr>\n",
1055 |        "  </tbody>\n",
1056 |        "</table>\n",
1057 |        "</div>"
1058 |       ],
1059 |       "text/plain": [
1060 |        "                                             Y2014       Y2015       Y2016  \\\n",
1061 |        "Element                                                                      \n",
1062 |        "Domestic supply quantity                1996716.35  2021493.55  2044842.70   \n",
1063 |        "Export Quantity                          150020.64   157614.47   151920.46   \n",
1064 |        "Fat supply quantity (g/capita/day)        10225.56    10235.74    10102.77   \n",
1065 |        "Feed                                     216927.89   225050.22   228958.65   \n",
1066 |        "Food                                    1212332.49  1232361.10  1247022.17   \n",
1067 |        "Food supply (kcal/capita/day)            454257.00   453383.00   451810.00   \n",
1068 |        "Food supply quantity (kg/capita/yr)       49650.63    49345.13    48985.28   \n",
1069 |        "Import Quantity                          274144.48   267018.46   286582.78   \n",
1070 |        "Losses                                   153223.00   155439.00   157787.00   \n",
1071 |        "Other uses (non-food)                     78718.13    66254.41    69563.68   \n",
1072 |        "Processing                               282923.00   287929.00   280631.00   \n",
1073 |        "Production                              1931287.75  1947019.39  1943537.15   \n",
1074 |        "Protein supply quantity (g/capita/day)    11836.46    11833.95    11779.69   \n",
1075 |        "Residuals                                 30149.00    30045.00    37224.00   \n",
1076 |        "Seed                                      21922.92    23976.82    23389.20   \n",
1077 |        "Stock Variation                           58749.83    34910.99    33140.12   \n",
1078 |        "Total Population - Both sexes           1031585.00  1058081.00  1085107.00   \n",
1079 |        "Tourist consumption                         416.00      349.00       89.00   \n",
1080 |        "\n",
1081 |        "                                             Y2017       Y2018  \n",
1082 |        "Element                                                         \n",
1083 |        "Domestic supply quantity                2088198.10  2161192.10  \n",
1084 |        "Export Quantity                          182338.80   181594.80  \n",
1085 |        "Fat supply quantity (g/capita/day)        10253.84    10258.69  \n",
1086 |        "Feed                                     223705.68   233489.68  \n",
1087 |        "Food                                    1258888.28  1303841.28  \n",
1088 |        "Food supply (kcal/capita/day)            454681.00   455261.00  \n",
1089 |        "Food supply quantity (kg/capita/yr)       48690.04    49056.85  \n",
1090 |        "Import Quantity                          294559.09   287997.09  \n",
1091 |        "Losses                                   160614.00   163902.00  \n",
1092 |        "Other uses (non-food)                     91645.97    91300.97  \n",
1093 |        "Processing                               292836.00   308429.00  \n",
1094 |        "Production                              2030056.89  2075072.89  \n",
1095 |        "Protein supply quantity (g/capita/day)    11842.45    11833.56  \n",
1096 |        "Residuals                                 35500.00    34864.00  \n",
1097 |        "Seed                                      24870.14    25263.14  \n",
1098 |        "Stock Variation                           54316.91    20577.91  \n",
1099 |        "Total Population - Both sexes           1112641.00  1140605.00  \n",
1100 |        "Tourist consumption                          91.00       90.00  "
1101 |       ]
1102 |      },
1103 |      "execution_count": 10,
1104 |      "metadata": {},
1105 |      "output_type": "execute_result"
1106 |     }
1107 |    ],
1108 |    "source": [
1109 |     "# Get the list of years\n",
1110 |     "year_list = ['Y2014', 'Y2015', 'Y2016', 'Y2017', 'Y2018']\n",
1111 |     "\n",
1112 |     "#groupby by Element to view the highst sum of Stock Varaiation\n",
1113 |     "df_FBS.groupby(['Element'])[year_list].agg(np.sum)"
1114 |    ]
1115 |   },
1116 |   {
1117 |    "cell_type": "code",
1118 |    "execution_count": null,
1119 |    "id": "92b90a86",
1120 |    "metadata": {},
1121 |    "outputs": [],
1122 |    "source": []
1123 |   },
1124 |   {
1125 |    "cell_type": "markdown",
1126 |    "id": "3a478719",
1127 |    "metadata": {},
1128 |    "source": [
1129 |     "#### Question 4\n",
1130 |     "\n",
1131 |     "Answer the following questions based on the African food production dataset provided by the FAO website already provided\n",
1132 |     "\n",
1133 |     "What is the total sum of Wine produced in 2015 and 2018 respectively?\n",
1134 |     "\n",
1135 |     "Hint:Perform a groupby sum aggregation on ‘Item’\n",
1136 |     "\n",
1137 |     "#### Ans:\n",
1138 |     "    \n",
1139 |     "B - 4251.81 and 4039.32"
1140 |    ]
1141 |   },
1142 |   {
1143 |    "cell_type": "code",
1144 |    "execution_count": 11,
1145 |    "id": "cb373e12",
1146 |    "metadata": {},
1147 |    "outputs": [
1148 |     {
1149 |      "data": {
1150 |       "text/html": [
1151 |        "<div>\n",
1152 |        "<style scoped>\n",
1153 |        "    .dataframe tbody tr th:only-of-type {\n",
1154 |        "        vertical-align: middle;\n",
1155 |        "    }\n",
1156 |        "\n",
1157 |        "    .dataframe tbody tr th {\n",
1158 |        "        vertical-align: top;\n",
1159 |        "    }\n",
1160 |        "\n",
1161 |        "    .dataframe thead th {\n",
1162 |        "        text-align: right;\n",
1163 |        "    }\n",
1164 |        "</style>\n",
1165 |        "<table border=\"1\" class=\"dataframe\">\n",
1166 |        "  <thead>\n",
1167 |        "    <tr style=\"text-align: right;\">\n",
1168 |        "      <th></th>\n",
1169 |        "      <th>Y2015</th>\n",
1170 |        "      <th>Y2018</th>\n",
1171 |        "    </tr>\n",
1172 |        "    <tr>\n",
1173 |        "      <th>Item</th>\n",
1174 |        "      <th></th>\n",
1175 |        "      <th></th>\n",
1176 |        "    </tr>\n",
1177 |        "  </thead>\n",
1178 |        "  <tbody>\n",
1179 |        "    <tr>\n",
1180 |        "      <th>Alcohol, Non-Food</th>\n",
1181 |        "      <td>2180.00</td>\n",
1182 |        "      <td>2293.00</td>\n",
1183 |        "    </tr>\n",
1184 |        "    <tr>\n",
1185 |        "      <th>Alcoholic Beverages</th>\n",
1186 |        "      <td>98783.72</td>\n",
1187 |        "      <td>97847.27</td>\n",
1188 |        "    </tr>\n",
1189 |        "    <tr>\n",
1190 |        "      <th>Animal Products</th>\n",
1191 |        "      <td>11811.73</td>\n",
1192 |        "      <td>11578.61</td>\n",
1193 |        "    </tr>\n",
1194 |        "    <tr>\n",
1195 |        "      <th>Animal fats</th>\n",
1196 |        "      <td>200675.72</td>\n",
1197 |        "      <td>269648.27</td>\n",
1198 |        "    </tr>\n",
1199 |        "    <tr>\n",
1200 |        "      <th>Apples and products</th>\n",
1201 |        "      <td>10559.15</td>\n",
1202 |        "      <td>9640.51</td>\n",
1203 |        "    </tr>\n",
1204 |        "    <tr>\n",
1205 |        "      <th>...</th>\n",
1206 |        "      <td>...</td>\n",
1207 |        "      <td>...</td>\n",
1208 |        "    </tr>\n",
1209 |        "    <tr>\n",
1210 |        "      <th>Vegetables, Other</th>\n",
1211 |        "      <td>158104.08</td>\n",
1212 |        "      <td>163987.21</td>\n",
1213 |        "    </tr>\n",
1214 |        "    <tr>\n",
1215 |        "      <th>Vegetal Products</th>\n",
1216 |        "      <td>107064.17</td>\n",
1217 |        "      <td>107775.39</td>\n",
1218 |        "    </tr>\n",
1219 |        "    <tr>\n",
1220 |        "      <th>Wheat and products</th>\n",
1221 |        "      <td>234710.51</td>\n",
1222 |        "      <td>242645.19</td>\n",
1223 |        "    </tr>\n",
1224 |        "    <tr>\n",
1225 |        "      <th>Wine</th>\n",
1226 |        "      <td>4251.81</td>\n",
1227 |        "      <td>4039.32</td>\n",
1228 |        "    </tr>\n",
1229 |        "    <tr>\n",
1230 |        "      <th>Yams</th>\n",
1231 |        "      <td>203151.78</td>\n",
1232 |        "      <td>221272.09</td>\n",
1233 |        "    </tr>\n",
1234 |        "  </tbody>\n",
1235 |        "</table>\n",
1236 |        "<p>119 rows × 2 columns</p>\n",
1237 |        "</div>"
1238 |       ],
1239 |       "text/plain": [
1240 |        "                         Y2015      Y2018\n",
1241 |        "Item                                     \n",
1242 |        "Alcohol, Non-Food      2180.00    2293.00\n",
1243 |        "Alcoholic Beverages   98783.72   97847.27\n",
1244 |        "Animal Products       11811.73   11578.61\n",
1245 |        "Animal fats          200675.72  269648.27\n",
1246 |        "Apples and products   10559.15    9640.51\n",
1247 |        "...                        ...        ...\n",
1248 |        "Vegetables, Other    158104.08  163987.21\n",
1249 |        "Vegetal Products     107064.17  107775.39\n",
1250 |        "Wheat and products   234710.51  242645.19\n",
1251 |        "Wine                   4251.81    4039.32\n",
1252 |        "Yams                 203151.78  221272.09\n",
1253 |        "\n",
1254 |        "[119 rows x 2 columns]"
1255 |       ]
1256 |      },
1257 |      "execution_count": 11,
1258 |      "metadata": {},
1259 |      "output_type": "execute_result"
1260 |     }
1261 |    ],
1262 |    "source": [
1263 |     "# Grouo by sum aggregation of Item for the year columns 2015 and 2018\n",
1264 |     "wine_2015_and_2018 = df_FBS.groupby('Item')[['Y2015', 'Y2018']].sum()\n",
1265 |     "wine_2015_and_2018"
1266 |    ]
1267 |   },
1268 |   {
1269 |    "cell_type": "code",
1270 |    "execution_count": null,
1271 |    "id": "1afa88a2",
1272 |    "metadata": {},
1273 |    "outputs": [],
1274 |    "source": []
1275 |   },
1276 |   {
1277 |    "cell_type": "markdown",
1278 |    "id": "fe6e9cc0",
1279 |    "metadata": {},
1280 |    "source": [
1281 |     "#### Question 5\n",
1282 |     "\n",
1283 |     "Given the following numpy array \n",
1284 |     "\n",
1285 |     "array  = ([[94, 89, 63],\n",
1286 |     "\n",
1287 |     "        [93, 92, 48],\n",
1288 |     "\n",
1289 |     "        [92, 94, 56]])\n",
1290 |     "\n",
1291 |     "How would you select  the elements in bold and italics from the array?\n",
1292 |     "\n",
1293 |     "#### Ans:\n",
1294 |     "    \n",
1295 |     "C - array[ : 2, 1 : ] "
1296 |    ]
1297 |   },
1298 |   {
1299 |    "cell_type": "code",
1300 |    "execution_count": null,
1301 |    "id": "27dc9ffc",
1302 |    "metadata": {},
1303 |    "outputs": [],
1304 |    "source": []
1305 |   },
1306 |   {
1307 |    "cell_type": "markdown",
1308 |    "id": "493a77cd",
1309 |    "metadata": {},
1310 |    "source": [
1311 |     "#### Question 6\n",
1312 |     "\n",
1313 |     "Given the following python code, what would the output of the code give?\n",
1314 |     "\n",
1315 |     "my_tuppy = (1,2,5,8)\n",
1316 |     "\n",
1317 |     "my_tuppy[2] = 6\n",
1318 |     "\n",
1319 |     "#### Ans:\n",
1320 |     "    \n",
1321 |     "B - Type Error"
1322 |    ]
1323 |   },
1324 |   {
1325 |    "cell_type": "code",
1326 |    "execution_count": null,
1327 |    "id": "fb11ec1f",
1328 |    "metadata": {},
1329 |    "outputs": [],
1330 |    "source": []
1331 |   },
1332 |   {
1333 |    "cell_type": "markdown",
1334 |    "id": "2db2a42a",
1335 |    "metadata": {},
1336 |    "source": [
1337 |     "#### Question 7\n",
1338 |     "\n",
1339 |     "Select columns ‘Y2017’ and ‘Area’, Perform a groupby operation on ‘Area’.  Which of these Areas had the 7th lowest\n",
1340 |     "sum in 2017?\n",
1341 |     "\n",
1342 |     "#### Ans:\n",
1343 |     "    \n",
1344 |     "B - Guinea-Bissau"
1345 |    ]
1346 |   },
1347 |   {
1348 |    "cell_type": "code",
1349 |    "execution_count": 12,
1350 |    "id": "d085c27f",
1351 |    "metadata": {},
1352 |    "outputs": [
1353 |     {
1354 |      "name": "stdout",
1355 |      "output_type": "stream",
1356 |      "text": [
1357 |       "                           Area       Y2017\n",
1358 |       "42               Sudan (former)        0.00\n",
1359 |       "16                 Ethiopia PDR        0.00\n",
1360 |       "9                       Comoros       59.84\n",
1361 |       "38                   Seychelles      442.34\n",
1362 |       "36        Sao Tome and Principe    12662.63\n",
1363 |       "5                    Cabo Verde    14650.74\n",
1364 |       "21                Guinea-Bissau    19102.77\n",
1365 |       "23                      Lesotho    21267.96\n",
1366 |       "3                      Botswana    22101.30\n",
1367 |       "12                     Djibouti    22729.91\n",
1368 |       "18                       Gambia    23154.18\n",
1369 |       "17                        Gabon    27979.64\n",
1370 |       "24                      Liberia    29342.20\n",
1371 |       "32                      Namibia    29874.89\n",
1372 |       "7      Central African Republic    29937.00\n",
1373 |       "10                        Congo    41181.68\n",
1374 |       "43                         Togo    49841.88\n",
1375 |       "29                    Mauritius    51114.83\n",
1376 |       "14                     Eswatini    54343.33\n",
1377 |       "39                 Sierra Leone    55311.33\n",
1378 |       "8                          Chad    71594.68\n",
1379 |       "35                       Rwanda    73663.69\n",
1380 |       "48                     Zimbabwe    75919.34\n",
1381 |       "37                      Senegal    95681.15\n",
1382 |       "20                       Guinea    98138.87\n",
1383 |       "4                  Burkina Faso   101855.07\n",
1384 |       "47                       Zambia   103223.77\n",
1385 |       "44                      Tunisia   124167.20\n",
1386 |       "2                         Benin   124771.22\n",
1387 |       "33                        Niger   126707.58\n",
1388 |       "25                   Madagascar   131197.73\n",
1389 |       "27                         Mali   149928.33\n",
1390 |       "28                   Mauritania   156665.46\n",
1391 |       "31                   Mozambique   161407.98\n",
1392 |       "26                       Malawi   181098.71\n",
1393 |       "45                       Uganda   213950.38\n",
1394 |       "11                Côte d'Ivoire   224599.01\n",
1395 |       "1                        Angola   229159.57\n",
1396 |       "6                      Cameroon   232030.43\n",
1397 |       "41                        Sudan   239931.92\n",
1398 |       "22                        Kenya   264660.66\n",
1399 |       "46  United Republic of Tanzania   322616.85\n",
1400 |       "0                       Algeria   325644.27\n",
1401 |       "19                        Ghana   337599.06\n",
1402 |       "30                      Morocco   388495.36\n",
1403 |       "15                     Ethiopia   448683.76\n",
1404 |       "40                 South Africa   517590.54\n",
1405 |       "13                        Egypt   866379.92\n",
1406 |       "34                      Nigeria  1483268.23\n",
1407 |       "The Area with the 7th lowest sum in 2017 is: Guinea-Bissau\n"
1408 |      ]
1409 |     }
1410 |    ],
1411 |    "source": [
1412 |     "# Select columns 'Y2017' and 'Area'\n",
1413 |     "selected_columns = df_FBS[['Y2017', 'Area']]\n",
1414 |     "\n",
1415 |     "# Group by 'Area' and calculate the sum of 'Y2017' for each Area\n",
1416 |     "grouped_data = selected_columns.groupby('Area')['Y2017'].sum().reset_index()\n",
1417 |     "\n",
1418 |     "# Sort the data by the sum of 'Y2017' in ascending order\n",
1419 |     "sorted_data = grouped_data.sort_values(by='Y2017', ascending=True)\n",
1420 |     "print(sorted_data)\n",
1421 |     "\n",
1422 |     "# Get the Area with the 7th lowest sum in 2017\n",
1423 |     "seventh_lowest_area = sorted_data.iloc[6]['Area']\n",
1424 |     "\n",
1425 |     "# Print the result\n",
1426 |     "print(\"The Area with the 7th lowest sum in 2017 is:\", seventh_lowest_area)"
1427 |    ]
1428 |   },
1429 |   {
1430 |    "cell_type": "code",
1431 |    "execution_count": null,
1432 |    "id": "5db878e7",
1433 |    "metadata": {},
1434 |    "outputs": [],
1435 |    "source": []
1436 |   },
1437 |   {
1438 |    "cell_type": "markdown",
1439 |    "id": "4e38ed0b",
1440 |    "metadata": {},
1441 |    "source": [
1442 |     "#### Question 8\n",
1443 |     "\n",
1444 |     "Which year had the least correlation with ‘Element Code’?\n",
1445 |     "\n",
1446 |     "#### Ans:\n",
1447 |     "    \n",
1448 |     "D - 2016"
1449 |    ]
1450 |   },
1451 |   {
1452 |    "cell_type": "code",
1453 |    "execution_count": 13,
1454 |    "id": "038e18ef",
1455 |    "metadata": {},
1456 |    "outputs": [
1457 |     {
1458 |      "data": {
1459 |       "text/html": [
1460 |        "<div>\n",
1461 |        "<style scoped>\n",
1462 |        "    .dataframe tbody tr th:only-of-type {\n",
1463 |        "        vertical-align: middle;\n",
1464 |        "    }\n",
1465 |        "\n",
1466 |        "    .dataframe tbody tr th {\n",
1467 |        "        vertical-align: top;\n",
1468 |        "    }\n",
1469 |        "\n",
1470 |        "    .dataframe thead th {\n",
1471 |        "        text-align: right;\n",
1472 |        "    }\n",
1473 |        "</style>\n",
1474 |        "<table border=\"1\" class=\"dataframe\">\n",
1475 |        "  <thead>\n",
1476 |        "    <tr style=\"text-align: right;\">\n",
1477 |        "      <th></th>\n",
1478 |        "      <th>Element Code</th>\n",
1479 |        "      <th>Y2014</th>\n",
1480 |        "      <th>Y2015</th>\n",
1481 |        "      <th>Y2016</th>\n",
1482 |        "      <th>Y2017</th>\n",
1483 |        "      <th>Y2018</th>\n",
1484 |        "    </tr>\n",
1485 |        "  </thead>\n",
1486 |        "  <tbody>\n",
1487 |        "    <tr>\n",
1488 |        "      <th>Element Code</th>\n",
1489 |        "      <td>1.000000</td>\n",
1490 |        "      <td>0.024457</td>\n",
1491 |        "      <td>0.023889</td>\n",
1492 |        "      <td>0.023444</td>\n",
1493 |        "      <td>0.024254</td>\n",
1494 |        "      <td>0.024279</td>\n",
1495 |        "    </tr>\n",
1496 |        "    <tr>\n",
1497 |        "      <th>Y2014</th>\n",
1498 |        "      <td>0.024457</td>\n",
1499 |        "      <td>1.000000</td>\n",
1500 |        "      <td>0.994647</td>\n",
1501 |        "      <td>0.996081</td>\n",
1502 |        "      <td>0.995230</td>\n",
1503 |        "      <td>0.994872</td>\n",
1504 |        "    </tr>\n",
1505 |        "    <tr>\n",
1506 |        "      <th>Y2015</th>\n",
1507 |        "      <td>0.023889</td>\n",
1508 |        "      <td>0.994647</td>\n",
1509 |        "      <td>1.000000</td>\n",
1510 |        "      <td>0.995739</td>\n",
1511 |        "      <td>0.988048</td>\n",
1512 |        "      <td>0.988208</td>\n",
1513 |        "    </tr>\n",
1514 |        "    <tr>\n",
1515 |        "      <th>Y2016</th>\n",
1516 |        "      <td>0.023444</td>\n",
1517 |        "      <td>0.996081</td>\n",
1518 |        "      <td>0.995739</td>\n",
1519 |        "      <td>1.000000</td>\n",
1520 |        "      <td>0.992785</td>\n",
1521 |        "      <td>0.992757</td>\n",
1522 |        "    </tr>\n",
1523 |        "    <tr>\n",
1524 |        "      <th>Y2017</th>\n",
1525 |        "      <td>0.024254</td>\n",
1526 |        "      <td>0.995230</td>\n",
1527 |        "      <td>0.988048</td>\n",
1528 |        "      <td>0.992785</td>\n",
1529 |        "      <td>1.000000</td>\n",
1530 |        "      <td>0.998103</td>\n",
1531 |        "    </tr>\n",
1532 |        "    <tr>\n",
1533 |        "      <th>Y2018</th>\n",
1534 |        "      <td>0.024279</td>\n",
1535 |        "      <td>0.994872</td>\n",
1536 |        "      <td>0.988208</td>\n",
1537 |        "      <td>0.992757</td>\n",
1538 |        "      <td>0.998103</td>\n",
1539 |        "      <td>1.000000</td>\n",
1540 |        "    </tr>\n",
1541 |        "  </tbody>\n",
1542 |        "</table>\n",
1543 |        "</div>"
1544 |       ],
1545 |       "text/plain": [
1546 |        "              Element Code     Y2014     Y2015     Y2016     Y2017     Y2018\n",
1547 |        "Element Code      1.000000  0.024457  0.023889  0.023444  0.024254  0.024279\n",
1548 |        "Y2014             0.024457  1.000000  0.994647  0.996081  0.995230  0.994872\n",
1549 |        "Y2015             0.023889  0.994647  1.000000  0.995739  0.988048  0.988208\n",
1550 |        "Y2016             0.023444  0.996081  0.995739  1.000000  0.992785  0.992757\n",
1551 |        "Y2017             0.024254  0.995230  0.988048  0.992785  1.000000  0.998103\n",
1552 |        "Y2018             0.024279  0.994872  0.988208  0.992757  0.998103  1.000000"
1553 |       ]
1554 |      },
1555 |      "execution_count": 13,
1556 |      "metadata": {},
1557 |      "output_type": "execute_result"
1558 |     }
1559 |    ],
1560 |    "source": [
1561 |     "# Get a dataframe of the required columns : Element Code and the year columns\n",
1562 |     "numeric_columns = df_FBS[['Element Code', 'Y2014', 'Y2015', 'Y2016', 'Y2017', 'Y2018']]\n",
1563 |     "\n",
1564 |     "# Get the correlaton of the year column with Element code and assign the result to correlation_matrix\n",
1565 |     "correlation_matrix = numeric_columns.corr()\n",
1566 |     "correlation_matrix"
1567 |    ]
1568 |   },
1569 |   {
1570 |    "cell_type": "code",
1571 |    "execution_count": null,
1572 |    "id": "dda0b2d8",
1573 |    "metadata": {},
1574 |    "outputs": [],
1575 |    "source": []
1576 |   },
1577 |   {
1578 |    "cell_type": "markdown",
1579 |    "id": "d42e992b",
1580 |    "metadata": {},
1581 |    "source": [
1582 |     "#### Question 9\n",
1583 |     "\n",
1584 |     "Which of the following dataframe methods can be used to access elements across rows and columns?\n",
1585 |     "\n",
1586 |     "#### Ans:\n",
1587 |     "    \n",
1588 |     "E - c and d"
1589 |    ]
1590 |   },
1591 |   {
1592 |    "cell_type": "code",
1593 |    "execution_count": null,
1594 |    "id": "ce5bd980",
1595 |    "metadata": {},
1596 |    "outputs": [],
1597 |    "source": []
1598 |   },
1599 |   {
1600 |    "cell_type": "markdown",
1601 |    "id": "71adcf7e",
1602 |    "metadata": {},
1603 |    "source": [
1604 |     "#### Question 10\n",
1605 |     "\n",
1606 |     "Which of these python data structures is unorderly?\n",
1607 |     "\n",
1608 |     "#### Ans:\n",
1609 |     "    \n",
1610 |     "A - Set\n",
1611 |     "\n",
1612 |     "B - Dictionary"
1613 |    ]
1614 |   },
1615 |   {
1616 |    "cell_type": "code",
1617 |    "execution_count": null,
1618 |    "id": "a6e92161",
1619 |    "metadata": {},
1620 |    "outputs": [],
1621 |    "source": []
1622 |   },
1623 |   {
1624 |    "cell_type": "markdown",
1625 |    "id": "980416e2",
1626 |    "metadata": {},
1627 |    "source": [
1628 |     "#### Question 11\n",
1629 |     "\n",
1630 |     "If you have the following list\n",
1631 |     "\n",
1632 |     "lst = [[35, 'Portugal', 94], [33, 'Argentina', 93], [30 , 'Brazil', 92]]\n",
1633 |     "\n",
1634 |     "col = [‘Age’,’Nationality’,’Overall’]\n",
1635 |     "\n",
1636 |     "How do you create a pandas DataFrame using this list, to look like the table below?\n",
1637 |     "\n",
1638 |     "#### Ans:\n",
1639 |     "    \n",
1640 |     "B - pd.Dataframe(lst, columns = col, index = [1,2,3])"
1641 |    ]
1642 |   },
1643 |   {
1644 |    "cell_type": "code",
1645 |    "execution_count": null,
1646 |    "id": "a1b672d4",
1647 |    "metadata": {},
1648 |    "outputs": [],
1649 |    "source": []
1650 |   },
1651 |   {
1652 |    "cell_type": "markdown",
1653 |    "id": "20928f97",
1654 |    "metadata": {},
1655 |    "source": [
1656 |     "#### Question 12\n",
1657 |     "\n",
1658 |     "Perform a groupby operation on ‘Element’.  What is the total number of the sum of Processing in 2017?\n",
1659 |     "\n",
1660 |     "#### Ans:\n",
1661 |     "    \n",
1662 |     "E - 292836.00"
1663 |    ]
1664 |   },
1665 |   {
1666 |    "cell_type": "code",
1667 |    "execution_count": 14,
1668 |    "id": "3670d912",
1669 |    "metadata": {},
1670 |    "outputs": [
1671 |     {
1672 |      "data": {
1673 |       "text/html": [
1674 |        "<div>\n",
1675 |        "<style scoped>\n",
1676 |        "    .dataframe tbody tr th:only-of-type {\n",
1677 |        "        vertical-align: middle;\n",
1678 |        "    }\n",
1679 |        "\n",
1680 |        "    .dataframe tbody tr th {\n",
1681 |        "        vertical-align: top;\n",
1682 |        "    }\n",
1683 |        "\n",
1684 |        "    .dataframe thead th {\n",
1685 |        "        text-align: right;\n",
1686 |        "    }\n",
1687 |        "</style>\n",
1688 |        "<table border=\"1\" class=\"dataframe\">\n",
1689 |        "  <thead>\n",
1690 |        "    <tr style=\"text-align: right;\">\n",
1691 |        "      <th></th>\n",
1692 |        "      <th>Y2014</th>\n",
1693 |        "      <th>Y2015</th>\n",
1694 |        "      <th>Y2016</th>\n",
1695 |        "      <th>Y2017</th>\n",
1696 |        "      <th>Y2018</th>\n",
1697 |        "    </tr>\n",
1698 |        "    <tr>\n",
1699 |        "      <th>Element</th>\n",
1700 |        "      <th></th>\n",
1701 |        "      <th></th>\n",
1702 |        "      <th></th>\n",
1703 |        "      <th></th>\n",
1704 |        "      <th></th>\n",
1705 |        "    </tr>\n",
1706 |        "  </thead>\n",
1707 |        "  <tbody>\n",
1708 |        "    <tr>\n",
1709 |        "      <th>Domestic supply quantity</th>\n",
1710 |        "      <td>1996716.35</td>\n",
1711 |        "      <td>2021493.55</td>\n",
1712 |        "      <td>2044842.70</td>\n",
1713 |        "      <td>2088198.10</td>\n",
1714 |        "      <td>2161192.10</td>\n",
1715 |        "    </tr>\n",
1716 |        "    <tr>\n",
1717 |        "      <th>Export Quantity</th>\n",
1718 |        "      <td>150020.64</td>\n",
1719 |        "      <td>157614.47</td>\n",
1720 |        "      <td>151920.46</td>\n",
1721 |        "      <td>182338.80</td>\n",
1722 |        "      <td>181594.80</td>\n",
1723 |        "    </tr>\n",
1724 |        "    <tr>\n",
1725 |        "      <th>Fat supply quantity (g/capita/day)</th>\n",
1726 |        "      <td>10225.56</td>\n",
1727 |        "      <td>10235.74</td>\n",
1728 |        "      <td>10102.77</td>\n",
1729 |        "      <td>10253.84</td>\n",
1730 |        "      <td>10258.69</td>\n",
1731 |        "    </tr>\n",
1732 |        "    <tr>\n",
1733 |        "      <th>Feed</th>\n",
1734 |        "      <td>216927.89</td>\n",
1735 |        "      <td>225050.22</td>\n",
1736 |        "      <td>228958.65</td>\n",
1737 |        "      <td>223705.68</td>\n",
1738 |        "      <td>233489.68</td>\n",
1739 |        "    </tr>\n",
1740 |        "    <tr>\n",
1741 |        "      <th>Food</th>\n",
1742 |        "      <td>1212332.49</td>\n",
1743 |        "      <td>1232361.10</td>\n",
1744 |        "      <td>1247022.17</td>\n",
1745 |        "      <td>1258888.28</td>\n",
1746 |        "      <td>1303841.28</td>\n",
1747 |        "    </tr>\n",
1748 |        "    <tr>\n",
1749 |        "      <th>Food supply (kcal/capita/day)</th>\n",
1750 |        "      <td>454257.00</td>\n",
1751 |        "      <td>453383.00</td>\n",
1752 |        "      <td>451810.00</td>\n",
1753 |        "      <td>454681.00</td>\n",
1754 |        "      <td>455261.00</td>\n",
1755 |        "    </tr>\n",
1756 |        "    <tr>\n",
1757 |        "      <th>Food supply quantity (kg/capita/yr)</th>\n",
1758 |        "      <td>49650.63</td>\n",
1759 |        "      <td>49345.13</td>\n",
1760 |        "      <td>48985.28</td>\n",
1761 |        "      <td>48690.04</td>\n",
1762 |        "      <td>49056.85</td>\n",
1763 |        "    </tr>\n",
1764 |        "    <tr>\n",
1765 |        "      <th>Import Quantity</th>\n",
1766 |        "      <td>274144.48</td>\n",
1767 |        "      <td>267018.46</td>\n",
1768 |        "      <td>286582.78</td>\n",
1769 |        "      <td>294559.09</td>\n",
1770 |        "      <td>287997.09</td>\n",
1771 |        "    </tr>\n",
1772 |        "    <tr>\n",
1773 |        "      <th>Losses</th>\n",
1774 |        "      <td>153223.00</td>\n",
1775 |        "      <td>155439.00</td>\n",
1776 |        "      <td>157787.00</td>\n",
1777 |        "      <td>160614.00</td>\n",
1778 |        "      <td>163902.00</td>\n",
1779 |        "    </tr>\n",
1780 |        "    <tr>\n",
1781 |        "      <th>Other uses (non-food)</th>\n",
1782 |        "      <td>78718.13</td>\n",
1783 |        "      <td>66254.41</td>\n",
1784 |        "      <td>69563.68</td>\n",
1785 |        "      <td>91645.97</td>\n",
1786 |        "      <td>91300.97</td>\n",
1787 |        "    </tr>\n",
1788 |        "    <tr>\n",
1789 |        "      <th>Processing</th>\n",
1790 |        "      <td>282923.00</td>\n",
1791 |        "      <td>287929.00</td>\n",
1792 |        "      <td>280631.00</td>\n",
1793 |        "      <td>292836.00</td>\n",
1794 |        "      <td>308429.00</td>\n",
1795 |        "    </tr>\n",
1796 |        "    <tr>\n",
1797 |        "      <th>Production</th>\n",
1798 |        "      <td>1931287.75</td>\n",
1799 |        "      <td>1947019.39</td>\n",
1800 |        "      <td>1943537.15</td>\n",
1801 |        "      <td>2030056.89</td>\n",
1802 |        "      <td>2075072.89</td>\n",
1803 |        "    </tr>\n",
1804 |        "    <tr>\n",
1805 |        "      <th>Protein supply quantity (g/capita/day)</th>\n",
1806 |        "      <td>11836.46</td>\n",
1807 |        "      <td>11833.95</td>\n",
1808 |        "      <td>11779.69</td>\n",
1809 |        "      <td>11842.45</td>\n",
1810 |        "      <td>11833.56</td>\n",
1811 |        "    </tr>\n",
1812 |        "    <tr>\n",
1813 |        "      <th>Residuals</th>\n",
1814 |        "      <td>30149.00</td>\n",
1815 |        "      <td>30045.00</td>\n",
1816 |        "      <td>37224.00</td>\n",
1817 |        "      <td>35500.00</td>\n",
1818 |        "      <td>34864.00</td>\n",
1819 |        "    </tr>\n",
1820 |        "    <tr>\n",
1821 |        "      <th>Seed</th>\n",
1822 |        "      <td>21922.92</td>\n",
1823 |        "      <td>23976.82</td>\n",
1824 |        "      <td>23389.20</td>\n",
1825 |        "      <td>24870.14</td>\n",
1826 |        "      <td>25263.14</td>\n",
1827 |        "    </tr>\n",
1828 |        "    <tr>\n",
1829 |        "      <th>Stock Variation</th>\n",
1830 |        "      <td>58749.83</td>\n",
1831 |        "      <td>34910.99</td>\n",
1832 |        "      <td>33140.12</td>\n",
1833 |        "      <td>54316.91</td>\n",
1834 |        "      <td>20577.91</td>\n",
1835 |        "    </tr>\n",
1836 |        "    <tr>\n",
1837 |        "      <th>Total Population - Both sexes</th>\n",
1838 |        "      <td>1031585.00</td>\n",
1839 |        "      <td>1058081.00</td>\n",
1840 |        "      <td>1085107.00</td>\n",
1841 |        "      <td>1112641.00</td>\n",
1842 |        "      <td>1140605.00</td>\n",
1843 |        "    </tr>\n",
1844 |        "    <tr>\n",
1845 |        "      <th>Tourist consumption</th>\n",
1846 |        "      <td>416.00</td>\n",
1847 |        "      <td>349.00</td>\n",
1848 |        "      <td>89.00</td>\n",
1849 |        "      <td>91.00</td>\n",
1850 |        "      <td>90.00</td>\n",
1851 |        "    </tr>\n",
1852 |        "  </tbody>\n",
1853 |        "</table>\n",
1854 |        "</div>"
1855 |       ],
1856 |       "text/plain": [
1857 |        "                                             Y2014       Y2015       Y2016  \\\n",
1858 |        "Element                                                                      \n",
1859 |        "Domestic supply quantity                1996716.35  2021493.55  2044842.70   \n",
1860 |        "Export Quantity                          150020.64   157614.47   151920.46   \n",
1861 |        "Fat supply quantity (g/capita/day)        10225.56    10235.74    10102.77   \n",
1862 |        "Feed                                     216927.89   225050.22   228958.65   \n",
1863 |        "Food                                    1212332.49  1232361.10  1247022.17   \n",
1864 |        "Food supply (kcal/capita/day)            454257.00   453383.00   451810.00   \n",
1865 |        "Food supply quantity (kg/capita/yr)       49650.63    49345.13    48985.28   \n",
1866 |        "Import Quantity                          274144.48   267018.46   286582.78   \n",
1867 |        "Losses                                   153223.00   155439.00   157787.00   \n",
1868 |        "Other uses (non-food)                     78718.13    66254.41    69563.68   \n",
1869 |        "Processing                               282923.00   287929.00   280631.00   \n",
1870 |        "Production                              1931287.75  1947019.39  1943537.15   \n",
1871 |        "Protein supply quantity (g/capita/day)    11836.46    11833.95    11779.69   \n",
1872 |        "Residuals                                 30149.00    30045.00    37224.00   \n",
1873 |        "Seed                                      21922.92    23976.82    23389.20   \n",
1874 |        "Stock Variation                           58749.83    34910.99    33140.12   \n",
1875 |        "Total Population - Both sexes           1031585.00  1058081.00  1085107.00   \n",
1876 |        "Tourist consumption                         416.00      349.00       89.00   \n",
1877 |        "\n",
1878 |        "                                             Y2017       Y2018  \n",
1879 |        "Element                                                         \n",
1880 |        "Domestic supply quantity                2088198.10  2161192.10  \n",
1881 |        "Export Quantity                          182338.80   181594.80  \n",
1882 |        "Fat supply quantity (g/capita/day)        10253.84    10258.69  \n",
1883 |        "Feed                                     223705.68   233489.68  \n",
1884 |        "Food                                    1258888.28  1303841.28  \n",
1885 |        "Food supply (kcal/capita/day)            454681.00   455261.00  \n",
1886 |        "Food supply quantity (kg/capita/yr)       48690.04    49056.85  \n",
1887 |        "Import Quantity                          294559.09   287997.09  \n",
1888 |        "Losses                                   160614.00   163902.00  \n",
1889 |        "Other uses (non-food)                     91645.97    91300.97  \n",
1890 |        "Processing                               292836.00   308429.00  \n",
1891 |        "Production                              2030056.89  2075072.89  \n",
1892 |        "Protein supply quantity (g/capita/day)    11842.45    11833.56  \n",
1893 |        "Residuals                                 35500.00    34864.00  \n",
1894 |        "Seed                                      24870.14    25263.14  \n",
1895 |        "Stock Variation                           54316.91    20577.91  \n",
1896 |        "Total Population - Both sexes           1112641.00  1140605.00  \n",
1897 |        "Tourist consumption                          91.00       90.00  "
1898 |       ]
1899 |      },
1900 |      "execution_count": 14,
1901 |      "metadata": {},
1902 |      "output_type": "execute_result"
1903 |     }
1904 |    ],
1905 |    "source": [
1906 |     "# Get the list of years\n",
1907 |     "year_list = ['Y2014', 'Y2015', 'Y2016', 'Y2017', 'Y2018']\n",
1908 |     "\n",
1909 |     "# Use group by operation on 'Element' and get the total sum for each 'Element' values\n",
1910 |     "df_FBS.groupby(['Element'])[year_list].agg(np.sum)"
1911 |    ]
1912 |   },
1913 |   {
1914 |    "cell_type": "code",
1915 |    "execution_count": null,
1916 |    "id": "0be87109",
1917 |    "metadata": {},
1918 |    "outputs": [],
1919 |    "source": []
1920 |   },
1921 |   {
1922 |    "cell_type": "markdown",
1923 |    "id": "81c79403",
1924 |    "metadata": {},
1925 |    "source": [
1926 |     "#### Question 13\n",
1927 |     "\n",
1928 |     "How would you check for the number of rows and columns in a pandas DataFrame named df?\n",
1929 |     "\n",
1930 |     "#### Ans:\n",
1931 |     "    \n",
1932 |     "C - df.shape"
1933 |    ]
1934 |   },
1935 |   {
1936 |    "cell_type": "code",
1937 |    "execution_count": null,
1938 |    "id": "ea2f98ee",
1939 |    "metadata": {},
1940 |    "outputs": [],
1941 |    "source": []
1942 |   },
1943 |   {
1944 |    "cell_type": "markdown",
1945 |    "id": "a4a4446f",
1946 |    "metadata": {},
1947 |    "source": [
1948 |     "#### Question 14\n",
1949 |     "\n",
1950 |     "What is the total number and percentage of missing data in 2014 to 3 decimal places?\n",
1951 |     "\n",
1952 |     "#### Ans:\n",
1953 |     "    \n",
1954 |     "E - 1589 and 2.607%"
1955 |    ]
1956 |   },
1957 |   {
1958 |    "cell_type": "code",
1959 |    "execution_count": 15,
1960 |    "id": "bb50e610",
1961 |    "metadata": {},
1962 |    "outputs": [
1963 |     {
1964 |      "data": {
1965 |       "text/plain": [
1966 |        "Area Code          0\n",
1967 |        "Area               0\n",
1968 |        "Item Code          0\n",
1969 |        "Item               0\n",
1970 |        "Element Code       0\n",
1971 |        "Element            0\n",
1972 |        "Unit               0\n",
1973 |        "Y2014           1589\n",
1974 |        "Y2015           1548\n",
1975 |        "Y2016           1535\n",
1976 |        "Y2017           1506\n",
1977 |        "Y2018           1436\n",
1978 |        "dtype: int64"
1979 |       ]
1980 |      },
1981 |      "execution_count": 15,
1982 |      "metadata": {},
1983 |      "output_type": "execute_result"
1984 |     }
1985 |    ],
1986 |    "source": [
1987 |     "# Get the total sum of missing values in each column in the dataframe\n",
1988 |     "df_FBS.isnull().sum()"
1989 |    ]
1990 |   },
1991 |   {
1992 |    "cell_type": "code",
1993 |    "execution_count": 16,
1994 |    "id": "0af275a7",
1995 |    "metadata": {},
1996 |    "outputs": [
1997 |     {
1998 |      "data": {
1999 |       "text/plain": [
2000 |        "2.6073544131401474"
2001 |       ]
2002 |      },
2003 |      "execution_count": 16,
2004 |      "metadata": {},
2005 |      "output_type": "execute_result"
2006 |     }
2007 |    ],
2008 |    "source": [
2009 |     "# percentage of missing values\n",
2010 |     "df_FBS['Y2014'].isnull().sum() * 100 / len(df_FBS)"
2011 |    ]
2012 |   },
2013 |   {
2014 |    "cell_type": "code",
2015 |    "execution_count": null,
2016 |    "id": "b63eac08",
2017 |    "metadata": {},
2018 |    "outputs": [],
2019 |    "source": []
2020 |   },
2021 |   {
2022 |    "cell_type": "markdown",
2023 |    "id": "0ded550f",
2024 |    "metadata": {},
2025 |    "source": [
2026 |     "#### Question 15\n",
2027 |     "\n",
2028 |     "Select columns ‘Y2017’ and ‘Area’, Perform a groupby operation on ‘Area’.  Which of these Areas had the highest sum in 2017?\n",
2029 |     "\n",
2030 |     "#### Ans:\n",
2031 |     "    \n",
2032 |     "A - Nigeria"
2033 |    ]
2034 |   },
2035 |   {
2036 |    "cell_type": "code",
2037 |    "execution_count": 17,
2038 |    "id": "cd3e2ab4",
2039 |    "metadata": {
2040 |     "scrolled": true
2041 |    },
2042 |    "outputs": [
2043 |     {
2044 |      "name": "stdout",
2045 |      "output_type": "stream",
2046 |      "text": [
2047 |       "                           Area       Y2017\n",
2048 |       "34                      Nigeria  1483268.23\n",
2049 |       "13                        Egypt   866379.92\n",
2050 |       "40                 South Africa   517590.54\n",
2051 |       "15                     Ethiopia   448683.76\n",
2052 |       "30                      Morocco   388495.36\n",
2053 |       "19                        Ghana   337599.06\n",
2054 |       "0                       Algeria   325644.27\n",
2055 |       "46  United Republic of Tanzania   322616.85\n",
2056 |       "22                        Kenya   264660.66\n",
2057 |       "41                        Sudan   239931.92\n",
2058 |       "6                      Cameroon   232030.43\n",
2059 |       "1                        Angola   229159.57\n",
2060 |       "11                Côte d'Ivoire   224599.01\n",
2061 |       "45                       Uganda   213950.38\n",
2062 |       "26                       Malawi   181098.71\n",
2063 |       "31                   Mozambique   161407.98\n",
2064 |       "28                   Mauritania   156665.46\n",
2065 |       "27                         Mali   149928.33\n",
2066 |       "25                   Madagascar   131197.73\n",
2067 |       "33                        Niger   126707.58\n",
2068 |       "2                         Benin   124771.22\n",
2069 |       "44                      Tunisia   124167.20\n",
2070 |       "47                       Zambia   103223.77\n",
2071 |       "4                  Burkina Faso   101855.07\n",
2072 |       "20                       Guinea    98138.87\n",
2073 |       "37                      Senegal    95681.15\n",
2074 |       "48                     Zimbabwe    75919.34\n",
2075 |       "35                       Rwanda    73663.69\n",
2076 |       "8                          Chad    71594.68\n",
2077 |       "39                 Sierra Leone    55311.33\n",
2078 |       "14                     Eswatini    54343.33\n",
2079 |       "29                    Mauritius    51114.83\n",
2080 |       "43                         Togo    49841.88\n",
2081 |       "10                        Congo    41181.68\n",
2082 |       "7      Central African Republic    29937.00\n",
2083 |       "32                      Namibia    29874.89\n",
2084 |       "24                      Liberia    29342.20\n",
2085 |       "17                        Gabon    27979.64\n",
2086 |       "18                       Gambia    23154.18\n",
2087 |       "12                     Djibouti    22729.91\n",
2088 |       "3                      Botswana    22101.30\n",
2089 |       "23                      Lesotho    21267.96\n",
2090 |       "21                Guinea-Bissau    19102.77\n",
2091 |       "5                    Cabo Verde    14650.74\n",
2092 |       "36        Sao Tome and Principe    12662.63\n",
2093 |       "38                   Seychelles      442.34\n",
2094 |       "9                       Comoros       59.84\n",
2095 |       "42               Sudan (former)        0.00\n",
2096 |       "16                 Ethiopia PDR        0.00\n",
2097 |       "The Area with the highest sum in 2017 is: Nigeria\n"
2098 |      ]
2099 |     }
2100 |    ],
2101 |    "source": [
2102 |     "# Select columns 'Y2017' and 'Area'\n",
2103 |     "selected_columns = df_FBS[['Y2017', 'Area']]\n",
2104 |     "\n",
2105 |     "# Group by 'Area' and calculate the sum of 'Y2017' for each Area\n",
2106 |     "grouped_data = selected_columns.groupby('Area')['Y2017'].sum().reset_index()\n",
2107 |     "\n",
2108 |     "# Sort the data by the sum of 'Y2017' in ascending order\n",
2109 |     "sorted_data = grouped_data.sort_values(by='Y2017', ascending=False)\n",
2110 |     "print(sorted_data)\n",
2111 |     "\n",
2112 |     "# Get the Area with the 7th lowest sum in 2017\n",
2113 |     "highest_sum_area = sorted_data.iloc[0]['Area']\n",
2114 |     "\n",
2115 |     "# Print the result\n",
2116 |     "print(\"The Area with the highest sum in 2017 is:\", highest_sum_area)"
2117 |    ]
2118 |   },
2119 |   {
2120 |    "cell_type": "code",
2121 |    "execution_count": null,
2122 |    "id": "20548412",
2123 |    "metadata": {},
2124 |    "outputs": [],
2125 |    "source": []
2126 |   },
2127 |   {
2128 |    "cell_type": "markdown",
2129 |    "id": "233087eb",
2130 |    "metadata": {},
2131 |    "source": [
2132 |     "#### Question 16\n",
2133 |     "\n",
2134 |     "Consider the following list of tuples:\n",
2135 |     "\n",
2136 |     "y = [(2, 4), (7, 8), (1, 5, 9)]\n",
2137 |     "\n",
2138 |     "How would you assign element 8 from the list to a variable x?\n",
2139 |     "\n",
2140 |     "#### Ans:\n",
2141 |     "    \n",
2142 |     "B - x = y[1][1]"
2143 |    ]
2144 |   },
2145 |   {
2146 |    "cell_type": "code",
2147 |    "execution_count": null,
2148 |    "id": "b0e1ac71",
2149 |    "metadata": {},
2150 |    "outputs": [],
2151 |    "source": []
2152 |   },
2153 |   {
2154 |    "cell_type": "markdown",
2155 |    "id": "55249720",
2156 |    "metadata": {},
2157 |    "source": [
2158 |     "#### Question 17\n",
2159 |     "\n",
2160 |     "A pandas Dataframe with dimensions (100,3) has how many features and observations?\n",
2161 |     "\n",
2162 |     "#### Ans:\n",
2163 |     "    \n",
2164 |     "B - 3 features, 100 observations"
2165 |    ]
2166 |   },
2167 |   {
2168 |    "cell_type": "code",
2169 |    "execution_count": null,
2170 |    "id": "6e0e7026",
2171 |    "metadata": {},
2172 |    "outputs": [],
2173 |    "source": []
2174 |   },
2175 |   {
2176 |    "cell_type": "markdown",
2177 |    "id": "a2cb829b",
2178 |    "metadata": {},
2179 |    "source": [
2180 |     "#### Question 18\n",
2181 |     "\n",
2182 |     "Which of the following is a python inbuilt module?\n",
2183 |     "\n",
2184 |     "#### Ans:\n",
2185 |     "    \n",
2186 |     "C - Math"
2187 |    ]
2188 |   },
2189 |   {
2190 |    "cell_type": "code",
2191 |    "execution_count": null,
2192 |    "id": "197ce575",
2193 |    "metadata": {},
2194 |    "outputs": [],
2195 |    "source": []
2196 |   },
2197 |   {
2198 |    "cell_type": "markdown",
2199 |    "id": "30f39308",
2200 |    "metadata": {},
2201 |    "source": [
2202 |     "#### Question 19\n",
2203 |     "\n",
2204 |     "What is the total number of unique countries in the dataset?\n",
2205 |     "\n",
2206 |     "#### Ans:\n",
2207 |     "    \n",
2208 |     "A - 49"
2209 |    ]
2210 |   },
2211 |   {
2212 |    "cell_type": "code",
2213 |    "execution_count": 18,
2214 |    "id": "fe9cd365",
2215 |    "metadata": {},
2216 |    "outputs": [
2217 |     {
2218 |      "data": {
2219 |       "text/plain": [
2220 |        "49"
2221 |       ]
2222 |      },
2223 |      "execution_count": 18,
2224 |      "metadata": {},
2225 |      "output_type": "execute_result"
2226 |     }
2227 |    ],
2228 |    "source": [
2229 |     "# Get the total number of unique 'Area' in the dataframe\n",
2230 |     "df_FBS['Area'].nunique()"
2231 |    ]
2232 |   },
2233 |   {
2234 |    "cell_type": "code",
2235 |    "execution_count": null,
2236 |    "id": "9a000af9",
2237 |    "metadata": {},
2238 |    "outputs": [],
2239 |    "source": []
2240 |   },
2241 |   {
2242 |    "cell_type": "markdown",
2243 |    "id": "ca723cc5",
2244 |    "metadata": {},
2245 |    "source": [
2246 |     "#### Question 20\n",
2247 |     "\n",
2248 |     "What would be the output for?\n",
2249 |     "\n",
2250 |     "S = [['him', 'sell'], [90, 28, 43]]\n",
2251 |     "\n",
2252 |     "S[0][1][1]\n",
2253 |     "\n",
2254 |     "#### Ans:\n",
2255 |     "    \n",
2256 |     "D - 'e'"
2257 |    ]
2258 |   },
2259 |   {
2260 |    "cell_type": "code",
2261 |    "execution_count": null,
2262 |    "id": "00188d0d",
2263 |    "metadata": {},
2264 |    "outputs": [],
2265 |    "source": []
2266 |   },
2267 |   {
2268 |    "cell_type": "code",
2269 |    "execution_count": null,
2270 |    "id": "dce33880",
2271 |    "metadata": {},
2272 |    "outputs": [],
2273 |    "source": []
2274 |   }
2275 |  ],
2276 |  "metadata": {
2277 |   "kernelspec": {
2278 |    "display_name": "Python 3 (ipykernel)",
2279 |    "language": "python",
2280 |    "name": "python3"
2281 |   },
2282 |   "language_info": {
2283 |    "codemirror_mode": {
2284 |     "name": "ipython",
2285 |     "version": 3
2286 |    },
2287 |    "file_extension": ".py",
2288 |    "mimetype": "text/x-python",
2289 |    "name": "python",
2290 |    "nbconvert_exporter": "python",
2291 |    "pygments_lexer": "ipython3",
2292 |    "version": "3.11.5"
2293 |   }
2294 |  },
2295 |  "nbformat": 4,
2296 |  "nbformat_minor": 5
2297 | }
2298 | 


--------------------------------------------------------------------------------
/Stage_A_Quiz/Stage_A_Quiz.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "629963e4",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "## Food Balance Sheet Analysis"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "code",
  13 |    "execution_count": 1,
  14 |    "id": "2f535948",
  15 |    "metadata": {},
  16 |    "outputs": [],
  17 |    "source": [
  18 |     "# import all packages and set plots to be embedded inline\n",
  19 |     "import numpy as np\n",
  20 |     "import pandas as pd\n",
  21 |     "import matplotlib.pyplot as plt\n",
  22 |     "import seaborn as sb\n",
  23 |     "\n",
  24 |     "%matplotlib inline\n",
  25 |     "\n",
  26 |     "# suppress warnings from final output\n",
  27 |     "import warnings\n",
  28 |     "warnings.simplefilter(\"ignore\")"
  29 |    ]
  30 |   },
  31 |   {
  32 |    "cell_type": "markdown",
  33 |    "id": "4dd46ce2",
  34 |    "metadata": {},
  35 |    "source": [
  36 |     "## Gathering and Assessing the Data"
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "code",
  41 |    "execution_count": 2,
  42 |    "id": "ef43ca9c",
  43 |    "metadata": {},
  44 |    "outputs": [
  45 |     {
  46 |      "name": "stdout",
  47 |      "output_type": "stream",
  48 |      "text": [
  49 |       "(60943, 12)\n"
  50 |      ]
  51 |     },
  52 |     {
  53 |      "data": {
  54 |       "text/html": [
  55 |        "<div>\n",
  56 |        "<style scoped>\n",
  57 |        "    .dataframe tbody tr th:only-of-type {\n",
  58 |        "        vertical-align: middle;\n",
  59 |        "    }\n",
  60 |        "\n",
  61 |        "    .dataframe tbody tr th {\n",
  62 |        "        vertical-align: top;\n",
  63 |        "    }\n",
  64 |        "\n",
  65 |        "    .dataframe thead th {\n",
  66 |        "        text-align: right;\n",
  67 |        "    }\n",
  68 |        "</style>\n",
  69 |        "<table border=\"1\" class=\"dataframe\">\n",
  70 |        "  <thead>\n",
  71 |        "    <tr style=\"text-align: right;\">\n",
  72 |        "      <th></th>\n",
  73 |        "      <th>Area Code</th>\n",
  74 |        "      <th>Area</th>\n",
  75 |        "      <th>Item Code</th>\n",
  76 |        "      <th>Item</th>\n",
  77 |        "      <th>Element Code</th>\n",
  78 |        "      <th>Element</th>\n",
  79 |        "      <th>Unit</th>\n",
  80 |        "      <th>Y2014</th>\n",
  81 |        "      <th>Y2015</th>\n",
  82 |        "      <th>Y2016</th>\n",
  83 |        "      <th>Y2017</th>\n",
  84 |        "      <th>Y2018</th>\n",
  85 |        "    </tr>\n",
  86 |        "  </thead>\n",
  87 |        "  <tbody>\n",
  88 |        "    <tr>\n",
  89 |        "      <th>0</th>\n",
  90 |        "      <td>4</td>\n",
  91 |        "      <td>Algeria</td>\n",
  92 |        "      <td>2501</td>\n",
  93 |        "      <td>Population</td>\n",
  94 |        "      <td>511</td>\n",
  95 |        "      <td>Total Population - Both sexes</td>\n",
  96 |        "      <td>1000 persons</td>\n",
  97 |        "      <td>38924.00</td>\n",
  98 |        "      <td>39728.00</td>\n",
  99 |        "      <td>40551.00</td>\n",
 100 |        "      <td>41389.00</td>\n",
 101 |        "      <td>42228.00</td>\n",
 102 |        "    </tr>\n",
 103 |        "    <tr>\n",
 104 |        "      <th>1</th>\n",
 105 |        "      <td>4</td>\n",
 106 |        "      <td>Algeria</td>\n",
 107 |        "      <td>2501</td>\n",
 108 |        "      <td>Population</td>\n",
 109 |        "      <td>5301</td>\n",
 110 |        "      <td>Domestic supply quantity</td>\n",
 111 |        "      <td>1000 tonnes</td>\n",
 112 |        "      <td>0.00</td>\n",
 113 |        "      <td>0.00</td>\n",
 114 |        "      <td>0.00</td>\n",
 115 |        "      <td>0.00</td>\n",
 116 |        "      <td>0.00</td>\n",
 117 |        "    </tr>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>2</th>\n",
 120 |        "      <td>4</td>\n",
 121 |        "      <td>Algeria</td>\n",
 122 |        "      <td>2901</td>\n",
 123 |        "      <td>Grand Total</td>\n",
 124 |        "      <td>664</td>\n",
 125 |        "      <td>Food supply (kcal/capita/day)</td>\n",
 126 |        "      <td>kcal/capita/day</td>\n",
 127 |        "      <td>3377.00</td>\n",
 128 |        "      <td>3379.00</td>\n",
 129 |        "      <td>3372.00</td>\n",
 130 |        "      <td>3341.00</td>\n",
 131 |        "      <td>3322.00</td>\n",
 132 |        "    </tr>\n",
 133 |        "    <tr>\n",
 134 |        "      <th>3</th>\n",
 135 |        "      <td>4</td>\n",
 136 |        "      <td>Algeria</td>\n",
 137 |        "      <td>2901</td>\n",
 138 |        "      <td>Grand Total</td>\n",
 139 |        "      <td>674</td>\n",
 140 |        "      <td>Protein supply quantity (g/capita/day)</td>\n",
 141 |        "      <td>g/capita/day</td>\n",
 142 |        "      <td>94.90</td>\n",
 143 |        "      <td>94.35</td>\n",
 144 |        "      <td>94.72</td>\n",
 145 |        "      <td>92.82</td>\n",
 146 |        "      <td>91.83</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>4</th>\n",
 150 |        "      <td>4</td>\n",
 151 |        "      <td>Algeria</td>\n",
 152 |        "      <td>2901</td>\n",
 153 |        "      <td>Grand Total</td>\n",
 154 |        "      <td>684</td>\n",
 155 |        "      <td>Fat supply quantity (g/capita/day)</td>\n",
 156 |        "      <td>g/capita/day</td>\n",
 157 |        "      <td>80.06</td>\n",
 158 |        "      <td>79.36</td>\n",
 159 |        "      <td>77.40</td>\n",
 160 |        "      <td>80.19</td>\n",
 161 |        "      <td>77.28</td>\n",
 162 |        "    </tr>\n",
 163 |        "  </tbody>\n",
 164 |        "</table>\n",
 165 |        "</div>"
 166 |       ],
 167 |       "text/plain": [
 168 |        "   Area Code     Area  Item Code         Item  Element Code  \\\n",
 169 |        "0          4  Algeria       2501   Population           511   \n",
 170 |        "1          4  Algeria       2501   Population          5301   \n",
 171 |        "2          4  Algeria       2901  Grand Total           664   \n",
 172 |        "3          4  Algeria       2901  Grand Total           674   \n",
 173 |        "4          4  Algeria       2901  Grand Total           684   \n",
 174 |        "\n",
 175 |        "                                  Element             Unit     Y2014  \\\n",
 176 |        "0           Total Population - Both sexes     1000 persons  38924.00   \n",
 177 |        "1                Domestic supply quantity      1000 tonnes      0.00   \n",
 178 |        "2           Food supply (kcal/capita/day)  kcal/capita/day   3377.00   \n",
 179 |        "3  Protein supply quantity (g/capita/day)     g/capita/day     94.90   \n",
 180 |        "4      Fat supply quantity (g/capita/day)     g/capita/day     80.06   \n",
 181 |        "\n",
 182 |        "      Y2015     Y2016     Y2017     Y2018  \n",
 183 |        "0  39728.00  40551.00  41389.00  42228.00  \n",
 184 |        "1      0.00      0.00      0.00      0.00  \n",
 185 |        "2   3379.00   3372.00   3341.00   3322.00  \n",
 186 |        "3     94.35     94.72     92.82     91.83  \n",
 187 |        "4     79.36     77.40     80.19     77.28  "
 188 |       ]
 189 |      },
 190 |      "execution_count": 2,
 191 |      "metadata": {},
 192 |      "output_type": "execute_result"
 193 |     }
 194 |    ],
 195 |    "source": [
 196 |     "# import dataset to pandas dataframe\n",
 197 |     "df_FBS = pd.read_csv('FoodBalanceSheets_E_Africa_NOFLAG.csv', encoding='Latin-1')\n",
 198 |     "\n",
 199 |     "# Checking the total number of rows and columns in our dataframe\n",
 200 |     "print(df_FBS.shape)\n",
 201 |     "\n",
 202 |     "#Looking at the first five rows in our dataframe\n",
 203 |     "df_FBS.head()"
 204 |    ]
 205 |   },
 206 |   {
 207 |    "cell_type": "code",
 208 |    "execution_count": 3,
 209 |    "id": "c3a2e817",
 210 |    "metadata": {},
 211 |    "outputs": [
 212 |     {
 213 |      "data": {
 214 |       "text/html": [
 215 |        "<div>\n",
 216 |        "<style scoped>\n",
 217 |        "    .dataframe tbody tr th:only-of-type {\n",
 218 |        "        vertical-align: middle;\n",
 219 |        "    }\n",
 220 |        "\n",
 221 |        "    .dataframe tbody tr th {\n",
 222 |        "        vertical-align: top;\n",
 223 |        "    }\n",
 224 |        "\n",
 225 |        "    .dataframe thead th {\n",
 226 |        "        text-align: right;\n",
 227 |        "    }\n",
 228 |        "</style>\n",
 229 |        "<table border=\"1\" class=\"dataframe\">\n",
 230 |        "  <thead>\n",
 231 |        "    <tr style=\"text-align: right;\">\n",
 232 |        "      <th></th>\n",
 233 |        "      <th>Area Code</th>\n",
 234 |        "      <th>Area</th>\n",
 235 |        "      <th>Item Code</th>\n",
 236 |        "      <th>Item</th>\n",
 237 |        "      <th>Element Code</th>\n",
 238 |        "      <th>Element</th>\n",
 239 |        "      <th>Unit</th>\n",
 240 |        "      <th>Y2014</th>\n",
 241 |        "      <th>Y2015</th>\n",
 242 |        "      <th>Y2016</th>\n",
 243 |        "      <th>Y2017</th>\n",
 244 |        "      <th>Y2018</th>\n",
 245 |        "    </tr>\n",
 246 |        "  </thead>\n",
 247 |        "  <tbody>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>60938</th>\n",
 250 |        "      <td>181</td>\n",
 251 |        "      <td>Zimbabwe</td>\n",
 252 |        "      <td>2899</td>\n",
 253 |        "      <td>Miscellaneous</td>\n",
 254 |        "      <td>5142</td>\n",
 255 |        "      <td>Food</td>\n",
 256 |        "      <td>1000 tonnes</td>\n",
 257 |        "      <td>42.00</td>\n",
 258 |        "      <td>46.00</td>\n",
 259 |        "      <td>33.00</td>\n",
 260 |        "      <td>19.00</td>\n",
 261 |        "      <td>16.00</td>\n",
 262 |        "    </tr>\n",
 263 |        "    <tr>\n",
 264 |        "      <th>60939</th>\n",
 265 |        "      <td>181</td>\n",
 266 |        "      <td>Zimbabwe</td>\n",
 267 |        "      <td>2899</td>\n",
 268 |        "      <td>Miscellaneous</td>\n",
 269 |        "      <td>645</td>\n",
 270 |        "      <td>Food supply quantity (kg/capita/yr)</td>\n",
 271 |        "      <td>kg</td>\n",
 272 |        "      <td>3.06</td>\n",
 273 |        "      <td>3.33</td>\n",
 274 |        "      <td>2.35</td>\n",
 275 |        "      <td>1.33</td>\n",
 276 |        "      <td>1.08</td>\n",
 277 |        "    </tr>\n",
 278 |        "    <tr>\n",
 279 |        "      <th>60940</th>\n",
 280 |        "      <td>181</td>\n",
 281 |        "      <td>Zimbabwe</td>\n",
 282 |        "      <td>2899</td>\n",
 283 |        "      <td>Miscellaneous</td>\n",
 284 |        "      <td>664</td>\n",
 285 |        "      <td>Food supply (kcal/capita/day)</td>\n",
 286 |        "      <td>kcal/capita/day</td>\n",
 287 |        "      <td>3.00</td>\n",
 288 |        "      <td>4.00</td>\n",
 289 |        "      <td>3.00</td>\n",
 290 |        "      <td>1.00</td>\n",
 291 |        "      <td>1.00</td>\n",
 292 |        "    </tr>\n",
 293 |        "    <tr>\n",
 294 |        "      <th>60941</th>\n",
 295 |        "      <td>181</td>\n",
 296 |        "      <td>Zimbabwe</td>\n",
 297 |        "      <td>2899</td>\n",
 298 |        "      <td>Miscellaneous</td>\n",
 299 |        "      <td>674</td>\n",
 300 |        "      <td>Protein supply quantity (g/capita/day)</td>\n",
 301 |        "      <td>g/capita/day</td>\n",
 302 |        "      <td>0.10</td>\n",
 303 |        "      <td>0.11</td>\n",
 304 |        "      <td>0.08</td>\n",
 305 |        "      <td>0.04</td>\n",
 306 |        "      <td>0.04</td>\n",
 307 |        "    </tr>\n",
 308 |        "    <tr>\n",
 309 |        "      <th>60942</th>\n",
 310 |        "      <td>181</td>\n",
 311 |        "      <td>Zimbabwe</td>\n",
 312 |        "      <td>2899</td>\n",
 313 |        "      <td>Miscellaneous</td>\n",
 314 |        "      <td>684</td>\n",
 315 |        "      <td>Fat supply quantity (g/capita/day)</td>\n",
 316 |        "      <td>g/capita/day</td>\n",
 317 |        "      <td>0.04</td>\n",
 318 |        "      <td>0.05</td>\n",
 319 |        "      <td>0.03</td>\n",
 320 |        "      <td>0.02</td>\n",
 321 |        "      <td>0.01</td>\n",
 322 |        "    </tr>\n",
 323 |        "  </tbody>\n",
 324 |        "</table>\n",
 325 |        "</div>"
 326 |       ],
 327 |       "text/plain": [
 328 |        "       Area Code      Area  Item Code           Item  Element Code  \\\n",
 329 |        "60938        181  Zimbabwe       2899  Miscellaneous          5142   \n",
 330 |        "60939        181  Zimbabwe       2899  Miscellaneous           645   \n",
 331 |        "60940        181  Zimbabwe       2899  Miscellaneous           664   \n",
 332 |        "60941        181  Zimbabwe       2899  Miscellaneous           674   \n",
 333 |        "60942        181  Zimbabwe       2899  Miscellaneous           684   \n",
 334 |        "\n",
 335 |        "                                      Element             Unit  Y2014  Y2015  \\\n",
 336 |        "60938                                    Food      1000 tonnes  42.00  46.00   \n",
 337 |        "60939     Food supply quantity (kg/capita/yr)               kg   3.06   3.33   \n",
 338 |        "60940           Food supply (kcal/capita/day)  kcal/capita/day   3.00   4.00   \n",
 339 |        "60941  Protein supply quantity (g/capita/day)     g/capita/day   0.10   0.11   \n",
 340 |        "60942      Fat supply quantity (g/capita/day)     g/capita/day   0.04   0.05   \n",
 341 |        "\n",
 342 |        "       Y2016  Y2017  Y2018  \n",
 343 |        "60938  33.00  19.00  16.00  \n",
 344 |        "60939   2.35   1.33   1.08  \n",
 345 |        "60940   3.00   1.00   1.00  \n",
 346 |        "60941   0.08   0.04   0.04  \n",
 347 |        "60942   0.03   0.02   0.01  "
 348 |       ]
 349 |      },
 350 |      "execution_count": 3,
 351 |      "metadata": {},
 352 |      "output_type": "execute_result"
 353 |     }
 354 |    ],
 355 |    "source": [
 356 |     "# Looking at the last five rows in our dataframe\n",
 357 |     "df_FBS.tail()"
 358 |    ]
 359 |   },
 360 |   {
 361 |    "cell_type": "code",
 362 |    "execution_count": 4,
 363 |    "id": "79025bfc",
 364 |    "metadata": {},
 365 |    "outputs": [
 366 |     {
 367 |      "name": "stdout",
 368 |      "output_type": "stream",
 369 |      "text": [
 370 |       "<class 'pandas.core.frame.DataFrame'>\n",
 371 |       "RangeIndex: 60943 entries, 0 to 60942\n",
 372 |       "Data columns (total 12 columns):\n",
 373 |       " #   Column        Non-Null Count  Dtype  \n",
 374 |       "---  ------        --------------  -----  \n",
 375 |       " 0   Area Code     60943 non-null  int64  \n",
 376 |       " 1   Area          60943 non-null  object \n",
 377 |       " 2   Item Code     60943 non-null  int64  \n",
 378 |       " 3   Item          60943 non-null  object \n",
 379 |       " 4   Element Code  60943 non-null  int64  \n",
 380 |       " 5   Element       60943 non-null  object \n",
 381 |       " 6   Unit          60943 non-null  object \n",
 382 |       " 7   Y2014         59354 non-null  float64\n",
 383 |       " 8   Y2015         59395 non-null  float64\n",
 384 |       " 9   Y2016         59408 non-null  float64\n",
 385 |       " 10  Y2017         59437 non-null  float64\n",
 386 |       " 11  Y2018         59507 non-null  float64\n",
 387 |       "dtypes: float64(5), int64(3), object(4)\n",
 388 |       "memory usage: 5.6+ MB\n"
 389 |      ]
 390 |     }
 391 |    ],
 392 |    "source": [
 393 |     "# Looking at the information of our data\n",
 394 |     "df_FBS.info()"
 395 |    ]
 396 |   },
 397 |   {
 398 |    "cell_type": "code",
 399 |    "execution_count": 5,
 400 |    "id": "2c26aebf",
 401 |    "metadata": {},
 402 |    "outputs": [
 403 |     {
 404 |      "data": {
 405 |       "text/html": [
 406 |        "<div>\n",
 407 |        "<style scoped>\n",
 408 |        "    .dataframe tbody tr th:only-of-type {\n",
 409 |        "        vertical-align: middle;\n",
 410 |        "    }\n",
 411 |        "\n",
 412 |        "    .dataframe tbody tr th {\n",
 413 |        "        vertical-align: top;\n",
 414 |        "    }\n",
 415 |        "\n",
 416 |        "    .dataframe thead th {\n",
 417 |        "        text-align: right;\n",
 418 |        "    }\n",
 419 |        "</style>\n",
 420 |        "<table border=\"1\" class=\"dataframe\">\n",
 421 |        "  <thead>\n",
 422 |        "    <tr style=\"text-align: right;\">\n",
 423 |        "      <th></th>\n",
 424 |        "      <th>Area Code</th>\n",
 425 |        "      <th>Item Code</th>\n",
 426 |        "      <th>Element Code</th>\n",
 427 |        "      <th>Y2014</th>\n",
 428 |        "      <th>Y2015</th>\n",
 429 |        "      <th>Y2016</th>\n",
 430 |        "      <th>Y2017</th>\n",
 431 |        "      <th>Y2018</th>\n",
 432 |        "    </tr>\n",
 433 |        "  </thead>\n",
 434 |        "  <tbody>\n",
 435 |        "    <tr>\n",
 436 |        "      <th>count</th>\n",
 437 |        "      <td>60943.000000</td>\n",
 438 |        "      <td>60943.000000</td>\n",
 439 |        "      <td>60943.000000</td>\n",
 440 |        "      <td>59354.000000</td>\n",
 441 |        "      <td>59395.000000</td>\n",
 442 |        "      <td>59408.000000</td>\n",
 443 |        "      <td>59437.000000</td>\n",
 444 |        "      <td>59507.000000</td>\n",
 445 |        "    </tr>\n",
 446 |        "    <tr>\n",
 447 |        "      <th>mean</th>\n",
 448 |        "      <td>134.265576</td>\n",
 449 |        "      <td>2687.176706</td>\n",
 450 |        "      <td>3814.856456</td>\n",
 451 |        "      <td>134.196282</td>\n",
 452 |        "      <td>135.235966</td>\n",
 453 |        "      <td>136.555222</td>\n",
 454 |        "      <td>140.917765</td>\n",
 455 |        "      <td>143.758381</td>\n",
 456 |        "    </tr>\n",
 457 |        "    <tr>\n",
 458 |        "      <th>std</th>\n",
 459 |        "      <td>72.605709</td>\n",
 460 |        "      <td>146.055739</td>\n",
 461 |        "      <td>2212.007033</td>\n",
 462 |        "      <td>1567.663696</td>\n",
 463 |        "      <td>1603.403984</td>\n",
 464 |        "      <td>1640.007194</td>\n",
 465 |        "      <td>1671.862359</td>\n",
 466 |        "      <td>1710.782658</td>\n",
 467 |        "    </tr>\n",
 468 |        "    <tr>\n",
 469 |        "      <th>min</th>\n",
 470 |        "      <td>4.000000</td>\n",
 471 |        "      <td>2501.000000</td>\n",
 472 |        "      <td>511.000000</td>\n",
 473 |        "      <td>-1796.000000</td>\n",
 474 |        "      <td>-3161.000000</td>\n",
 475 |        "      <td>-3225.000000</td>\n",
 476 |        "      <td>-1582.000000</td>\n",
 477 |        "      <td>-3396.000000</td>\n",
 478 |        "    </tr>\n",
 479 |        "    <tr>\n",
 480 |        "      <th>25%</th>\n",
 481 |        "      <td>74.000000</td>\n",
 482 |        "      <td>2562.000000</td>\n",
 483 |        "      <td>684.000000</td>\n",
 484 |        "      <td>0.000000</td>\n",
 485 |        "      <td>0.000000</td>\n",
 486 |        "      <td>0.000000</td>\n",
 487 |        "      <td>0.000000</td>\n",
 488 |        "      <td>0.000000</td>\n",
 489 |        "    </tr>\n",
 490 |        "    <tr>\n",
 491 |        "      <th>50%</th>\n",
 492 |        "      <td>136.000000</td>\n",
 493 |        "      <td>2630.000000</td>\n",
 494 |        "      <td>5142.000000</td>\n",
 495 |        "      <td>0.090000</td>\n",
 496 |        "      <td>0.080000</td>\n",
 497 |        "      <td>0.080000</td>\n",
 498 |        "      <td>0.100000</td>\n",
 499 |        "      <td>0.070000</td>\n",
 500 |        "    </tr>\n",
 501 |        "    <tr>\n",
 502 |        "      <th>75%</th>\n",
 503 |        "      <td>195.000000</td>\n",
 504 |        "      <td>2775.000000</td>\n",
 505 |        "      <td>5511.000000</td>\n",
 506 |        "      <td>8.340000</td>\n",
 507 |        "      <td>8.460000</td>\n",
 508 |        "      <td>8.430000</td>\n",
 509 |        "      <td>9.000000</td>\n",
 510 |        "      <td>9.000000</td>\n",
 511 |        "    </tr>\n",
 512 |        "    <tr>\n",
 513 |        "      <th>max</th>\n",
 514 |        "      <td>276.000000</td>\n",
 515 |        "      <td>2961.000000</td>\n",
 516 |        "      <td>5911.000000</td>\n",
 517 |        "      <td>176405.000000</td>\n",
 518 |        "      <td>181137.000000</td>\n",
 519 |        "      <td>185960.000000</td>\n",
 520 |        "      <td>190873.000000</td>\n",
 521 |        "      <td>195875.000000</td>\n",
 522 |        "    </tr>\n",
 523 |        "  </tbody>\n",
 524 |        "</table>\n",
 525 |        "</div>"
 526 |       ],
 527 |       "text/plain": [
 528 |        "          Area Code     Item Code  Element Code          Y2014          Y2015  \\\n",
 529 |        "count  60943.000000  60943.000000  60943.000000   59354.000000   59395.000000   \n",
 530 |        "mean     134.265576   2687.176706   3814.856456     134.196282     135.235966   \n",
 531 |        "std       72.605709    146.055739   2212.007033    1567.663696    1603.403984   \n",
 532 |        "min        4.000000   2501.000000    511.000000   -1796.000000   -3161.000000   \n",
 533 |        "25%       74.000000   2562.000000    684.000000       0.000000       0.000000   \n",
 534 |        "50%      136.000000   2630.000000   5142.000000       0.090000       0.080000   \n",
 535 |        "75%      195.000000   2775.000000   5511.000000       8.340000       8.460000   \n",
 536 |        "max      276.000000   2961.000000   5911.000000  176405.000000  181137.000000   \n",
 537 |        "\n",
 538 |        "               Y2016          Y2017          Y2018  \n",
 539 |        "count   59408.000000   59437.000000   59507.000000  \n",
 540 |        "mean      136.555222     140.917765     143.758381  \n",
 541 |        "std      1640.007194    1671.862359    1710.782658  \n",
 542 |        "min     -3225.000000   -1582.000000   -3396.000000  \n",
 543 |        "25%         0.000000       0.000000       0.000000  \n",
 544 |        "50%         0.080000       0.100000       0.070000  \n",
 545 |        "75%         8.430000       9.000000       9.000000  \n",
 546 |        "max    185960.000000  190873.000000  195875.000000  "
 547 |       ]
 548 |      },
 549 |      "execution_count": 5,
 550 |      "metadata": {},
 551 |      "output_type": "execute_result"
 552 |     }
 553 |    ],
 554 |    "source": [
 555 |     "# Looking at the statistical description of our data\n",
 556 |     "df_FBS.describe()"
 557 |    ]
 558 |   },
 559 |   {
 560 |    "cell_type": "code",
 561 |    "execution_count": 6,
 562 |    "id": "02f2f527",
 563 |    "metadata": {},
 564 |    "outputs": [
 565 |     {
 566 |      "data": {
 567 |       "text/plain": [
 568 |        "Area Code          0\n",
 569 |        "Area               0\n",
 570 |        "Item Code          0\n",
 571 |        "Item               0\n",
 572 |        "Element Code       0\n",
 573 |        "Element            0\n",
 574 |        "Unit               0\n",
 575 |        "Y2014           1589\n",
 576 |        "Y2015           1548\n",
 577 |        "Y2016           1535\n",
 578 |        "Y2017           1506\n",
 579 |        "Y2018           1436\n",
 580 |        "dtype: int64"
 581 |       ]
 582 |      },
 583 |      "execution_count": 6,
 584 |      "metadata": {},
 585 |      "output_type": "execute_result"
 586 |     }
 587 |    ],
 588 |    "source": [
 589 |     "# Checking for missing values in our dataframe\n",
 590 |     "df_FBS.isnull().sum()"
 591 |    ]
 592 |   },
 593 |   {
 594 |    "cell_type": "code",
 595 |    "execution_count": 7,
 596 |    "id": "a02277ed",
 597 |    "metadata": {},
 598 |    "outputs": [
 599 |     {
 600 |      "data": {
 601 |       "text/plain": [
 602 |        "False"
 603 |       ]
 604 |      },
 605 |      "execution_count": 7,
 606 |      "metadata": {},
 607 |      "output_type": "execute_result"
 608 |     }
 609 |    ],
 610 |    "source": [
 611 |     "# Checking if we have any duplicated rows\n",
 612 |     "df_FBS.duplicated().any()"
 613 |    ]
 614 |   },
 615 |   {
 616 |    "cell_type": "code",
 617 |    "execution_count": null,
 618 |    "id": "adca0331",
 619 |    "metadata": {},
 620 |    "outputs": [],
 621 |    "source": []
 622 |   },
 623 |   {
 624 |    "cell_type": "markdown",
 625 |    "id": "eea55dee",
 626 |    "metadata": {},
 627 |    "source": [
 628 |     "## Answering the Quiz Questions"
 629 |    ]
 630 |   },
 631 |   {
 632 |    "cell_type": "markdown",
 633 |    "id": "75509885",
 634 |    "metadata": {},
 635 |    "source": [
 636 |     "#### Question 1\n",
 637 |     "\n",
 638 |     "What is the total Protein supply quantity in Madagascar in 2015?\n",
 639 |     "\n",
 640 |     "#### Ans:\n",
 641 |     "    \n",
 642 |     "B - 173.05"
 643 |    ]
 644 |   },
 645 |   {
 646 |    "cell_type": "code",
 647 |    "execution_count": 8,
 648 |    "id": "2b67fe96",
 649 |    "metadata": {},
 650 |    "outputs": [
 651 |     {
 652 |      "name": "stdout",
 653 |      "output_type": "stream",
 654 |      "text": [
 655 |       "Total Protein supply quantity in Madagascar in 2015: 173.04999999999998 g/capita/day\n"
 656 |      ]
 657 |     }
 658 |    ],
 659 |    "source": [
 660 |     "# Filter the dataframe to select rows for Madagascar and the year 2015\n",
 661 |     "madagascar_2015 = df_FBS[(df_FBS['Area'] == 'Madagascar') & (df_FBS['Element'] == 'Protein supply quantity (g/capita/day)') & (df_FBS['Y2015'] != 0)]\n",
 662 |     "\n",
 663 |     "# Calculate the total protein supply quantity in Madagascar for 2015\n",
 664 |     "total_protein_supply_2015 = madagascar_2015['Y2015'].sum()\n",
 665 |     "\n",
 666 |     "# Print the result\n",
 667 |     "print(\"Total Protein supply quantity in Madagascar in 2015:\", total_protein_supply_2015, \"g/capita/day\")\n"
 668 |    ]
 669 |   },
 670 |   {
 671 |    "cell_type": "code",
 672 |    "execution_count": null,
 673 |    "id": "37b882a2",
 674 |    "metadata": {},
 675 |    "outputs": [],
 676 |    "source": []
 677 |   },
 678 |   {
 679 |    "cell_type": "markdown",
 680 |    "id": "ed6981a3",
 681 |    "metadata": {},
 682 |    "source": [
 683 |     "#### Question 2\n",
 684 |     "\n",
 685 |     "What is the mean and standard deviation across the whole dataset for the year 2017 to 2 decimal places?\n",
 686 |     "\n",
 687 |     "#### Ans:\n",
 688 |     "    \n",
 689 |     "E - 140.92 and 1671.86"
 690 |    ]
 691 |   },
 692 |   {
 693 |    "cell_type": "code",
 694 |    "execution_count": 9,
 695 |    "id": "49c627f0",
 696 |    "metadata": {},
 697 |    "outputs": [
 698 |     {
 699 |      "data": {
 700 |       "text/html": [
 701 |        "<div>\n",
 702 |        "<style scoped>\n",
 703 |        "    .dataframe tbody tr th:only-of-type {\n",
 704 |        "        vertical-align: middle;\n",
 705 |        "    }\n",
 706 |        "\n",
 707 |        "    .dataframe tbody tr th {\n",
 708 |        "        vertical-align: top;\n",
 709 |        "    }\n",
 710 |        "\n",
 711 |        "    .dataframe thead th {\n",
 712 |        "        text-align: right;\n",
 713 |        "    }\n",
 714 |        "</style>\n",
 715 |        "<table border=\"1\" class=\"dataframe\">\n",
 716 |        "  <thead>\n",
 717 |        "    <tr style=\"text-align: right;\">\n",
 718 |        "      <th></th>\n",
 719 |        "      <th>Area Code</th>\n",
 720 |        "      <th>Item Code</th>\n",
 721 |        "      <th>Element Code</th>\n",
 722 |        "      <th>Y2014</th>\n",
 723 |        "      <th>Y2015</th>\n",
 724 |        "      <th>Y2016</th>\n",
 725 |        "      <th>Y2017</th>\n",
 726 |        "      <th>Y2018</th>\n",
 727 |        "    </tr>\n",
 728 |        "  </thead>\n",
 729 |        "  <tbody>\n",
 730 |        "    <tr>\n",
 731 |        "      <th>count</th>\n",
 732 |        "      <td>60943.000000</td>\n",
 733 |        "      <td>60943.000000</td>\n",
 734 |        "      <td>60943.000000</td>\n",
 735 |        "      <td>59354.000000</td>\n",
 736 |        "      <td>59395.000000</td>\n",
 737 |        "      <td>59408.000000</td>\n",
 738 |        "      <td>59437.000000</td>\n",
 739 |        "      <td>59507.000000</td>\n",
 740 |        "    </tr>\n",
 741 |        "    <tr>\n",
 742 |        "      <th>mean</th>\n",
 743 |        "      <td>134.265576</td>\n",
 744 |        "      <td>2687.176706</td>\n",
 745 |        "      <td>3814.856456</td>\n",
 746 |        "      <td>134.196282</td>\n",
 747 |        "      <td>135.235966</td>\n",
 748 |        "      <td>136.555222</td>\n",
 749 |        "      <td>140.917765</td>\n",
 750 |        "      <td>143.758381</td>\n",
 751 |        "    </tr>\n",
 752 |        "    <tr>\n",
 753 |        "      <th>std</th>\n",
 754 |        "      <td>72.605709</td>\n",
 755 |        "      <td>146.055739</td>\n",
 756 |        "      <td>2212.007033</td>\n",
 757 |        "      <td>1567.663696</td>\n",
 758 |        "      <td>1603.403984</td>\n",
 759 |        "      <td>1640.007194</td>\n",
 760 |        "      <td>1671.862359</td>\n",
 761 |        "      <td>1710.782658</td>\n",
 762 |        "    </tr>\n",
 763 |        "    <tr>\n",
 764 |        "      <th>min</th>\n",
 765 |        "      <td>4.000000</td>\n",
 766 |        "      <td>2501.000000</td>\n",
 767 |        "      <td>511.000000</td>\n",
 768 |        "      <td>-1796.000000</td>\n",
 769 |        "      <td>-3161.000000</td>\n",
 770 |        "      <td>-3225.000000</td>\n",
 771 |        "      <td>-1582.000000</td>\n",
 772 |        "      <td>-3396.000000</td>\n",
 773 |        "    </tr>\n",
 774 |        "    <tr>\n",
 775 |        "      <th>25%</th>\n",
 776 |        "      <td>74.000000</td>\n",
 777 |        "      <td>2562.000000</td>\n",
 778 |        "      <td>684.000000</td>\n",
 779 |        "      <td>0.000000</td>\n",
 780 |        "      <td>0.000000</td>\n",
 781 |        "      <td>0.000000</td>\n",
 782 |        "      <td>0.000000</td>\n",
 783 |        "      <td>0.000000</td>\n",
 784 |        "    </tr>\n",
 785 |        "    <tr>\n",
 786 |        "      <th>50%</th>\n",
 787 |        "      <td>136.000000</td>\n",
 788 |        "      <td>2630.000000</td>\n",
 789 |        "      <td>5142.000000</td>\n",
 790 |        "      <td>0.090000</td>\n",
 791 |        "      <td>0.080000</td>\n",
 792 |        "      <td>0.080000</td>\n",
 793 |        "      <td>0.100000</td>\n",
 794 |        "      <td>0.070000</td>\n",
 795 |        "    </tr>\n",
 796 |        "    <tr>\n",
 797 |        "      <th>75%</th>\n",
 798 |        "      <td>195.000000</td>\n",
 799 |        "      <td>2775.000000</td>\n",
 800 |        "      <td>5511.000000</td>\n",
 801 |        "      <td>8.340000</td>\n",
 802 |        "      <td>8.460000</td>\n",
 803 |        "      <td>8.430000</td>\n",
 804 |        "      <td>9.000000</td>\n",
 805 |        "      <td>9.000000</td>\n",
 806 |        "    </tr>\n",
 807 |        "    <tr>\n",
 808 |        "      <th>max</th>\n",
 809 |        "      <td>276.000000</td>\n",
 810 |        "      <td>2961.000000</td>\n",
 811 |        "      <td>5911.000000</td>\n",
 812 |        "      <td>176405.000000</td>\n",
 813 |        "      <td>181137.000000</td>\n",
 814 |        "      <td>185960.000000</td>\n",
 815 |        "      <td>190873.000000</td>\n",
 816 |        "      <td>195875.000000</td>\n",
 817 |        "    </tr>\n",
 818 |        "  </tbody>\n",
 819 |        "</table>\n",
 820 |        "</div>"
 821 |       ],
 822 |       "text/plain": [
 823 |        "          Area Code     Item Code  Element Code          Y2014          Y2015  \\\n",
 824 |        "count  60943.000000  60943.000000  60943.000000   59354.000000   59395.000000   \n",
 825 |        "mean     134.265576   2687.176706   3814.856456     134.196282     135.235966   \n",
 826 |        "std       72.605709    146.055739   2212.007033    1567.663696    1603.403984   \n",
 827 |        "min        4.000000   2501.000000    511.000000   -1796.000000   -3161.000000   \n",
 828 |        "25%       74.000000   2562.000000    684.000000       0.000000       0.000000   \n",
 829 |        "50%      136.000000   2630.000000   5142.000000       0.090000       0.080000   \n",
 830 |        "75%      195.000000   2775.000000   5511.000000       8.340000       8.460000   \n",
 831 |        "max      276.000000   2961.000000   5911.000000  176405.000000  181137.000000   \n",
 832 |        "\n",
 833 |        "               Y2016          Y2017          Y2018  \n",
 834 |        "count   59408.000000   59437.000000   59507.000000  \n",
 835 |        "mean      136.555222     140.917765     143.758381  \n",
 836 |        "std      1640.007194    1671.862359    1710.782658  \n",
 837 |        "min     -3225.000000   -1582.000000   -3396.000000  \n",
 838 |        "25%         0.000000       0.000000       0.000000  \n",
 839 |        "50%         0.080000       0.100000       0.070000  \n",
 840 |        "75%         8.430000       9.000000       9.000000  \n",
 841 |        "max    185960.000000  190873.000000  195875.000000  "
 842 |       ]
 843 |      },
 844 |      "execution_count": 9,
 845 |      "metadata": {},
 846 |      "output_type": "execute_result"
 847 |     }
 848 |    ],
 849 |    "source": [
 850 |     "# Get the descriptive statistcs of the columns\n",
 851 |     "df_FBS.describe()"
 852 |    ]
 853 |   },
 854 |   {
 855 |    "cell_type": "code",
 856 |    "execution_count": null,
 857 |    "id": "e043e507",
 858 |    "metadata": {},
 859 |    "outputs": [],
 860 |    "source": []
 861 |   },
 862 |   {
 863 |    "cell_type": "markdown",
 864 |    "id": "faa62caa",
 865 |    "metadata": {},
 866 |    "source": [
 867 |     "#### Question 3\n",
 868 |     "\n",
 869 |     "Perform a groupby operation on ‘Element’.  What year has the highest sum of Stock Variation?\n",
 870 |     "\n",
 871 |     "#### Ans:\n",
 872 |     "    \n",
 873 |     "A - 2014"
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "code",
 878 |    "execution_count": 10,
 879 |    "id": "f3b782b1",
 880 |    "metadata": {},
 881 |    "outputs": [
 882 |     {
 883 |      "data": {
 884 |       "text/html": [
 885 |        "<div>\n",
 886 |        "<style scoped>\n",
 887 |        "    .dataframe tbody tr th:only-of-type {\n",
 888 |        "        vertical-align: middle;\n",
 889 |        "    }\n",
 890 |        "\n",
 891 |        "    .dataframe tbody tr th {\n",
 892 |        "        vertical-align: top;\n",
 893 |        "    }\n",
 894 |        "\n",
 895 |        "    .dataframe thead th {\n",
 896 |        "        text-align: right;\n",
 897 |        "    }\n",
 898 |        "</style>\n",
 899 |        "<table border=\"1\" class=\"dataframe\">\n",
 900 |        "  <thead>\n",
 901 |        "    <tr style=\"text-align: right;\">\n",
 902 |        "      <th></th>\n",
 903 |        "      <th>Y2014</th>\n",
 904 |        "      <th>Y2015</th>\n",
 905 |        "      <th>Y2016</th>\n",
 906 |        "      <th>Y2017</th>\n",
 907 |        "      <th>Y2018</th>\n",
 908 |        "    </tr>\n",
 909 |        "    <tr>\n",
 910 |        "      <th>Element</th>\n",
 911 |        "      <th></th>\n",
 912 |        "      <th></th>\n",
 913 |        "      <th></th>\n",
 914 |        "      <th></th>\n",
 915 |        "      <th></th>\n",
 916 |        "    </tr>\n",
 917 |        "  </thead>\n",
 918 |        "  <tbody>\n",
 919 |        "    <tr>\n",
 920 |        "      <th>Domestic supply quantity</th>\n",
 921 |        "      <td>1996716.35</td>\n",
 922 |        "      <td>2021493.55</td>\n",
 923 |        "      <td>2044842.70</td>\n",
 924 |        "      <td>2088198.10</td>\n",
 925 |        "      <td>2161192.10</td>\n",
 926 |        "    </tr>\n",
 927 |        "    <tr>\n",
 928 |        "      <th>Export Quantity</th>\n",
 929 |        "      <td>150020.64</td>\n",
 930 |        "      <td>157614.47</td>\n",
 931 |        "      <td>151920.46</td>\n",
 932 |        "      <td>182338.80</td>\n",
 933 |        "      <td>181594.80</td>\n",
 934 |        "    </tr>\n",
 935 |        "    <tr>\n",
 936 |        "      <th>Fat supply quantity (g/capita/day)</th>\n",
 937 |        "      <td>10225.56</td>\n",
 938 |        "      <td>10235.74</td>\n",
 939 |        "      <td>10102.77</td>\n",
 940 |        "      <td>10253.84</td>\n",
 941 |        "      <td>10258.69</td>\n",
 942 |        "    </tr>\n",
 943 |        "    <tr>\n",
 944 |        "      <th>Feed</th>\n",
 945 |        "      <td>216927.89</td>\n",
 946 |        "      <td>225050.22</td>\n",
 947 |        "      <td>228958.65</td>\n",
 948 |        "      <td>223705.68</td>\n",
 949 |        "      <td>233489.68</td>\n",
 950 |        "    </tr>\n",
 951 |        "    <tr>\n",
 952 |        "      <th>Food</th>\n",
 953 |        "      <td>1212332.49</td>\n",
 954 |        "      <td>1232361.10</td>\n",
 955 |        "      <td>1247022.17</td>\n",
 956 |        "      <td>1258888.28</td>\n",
 957 |        "      <td>1303841.28</td>\n",
 958 |        "    </tr>\n",
 959 |        "    <tr>\n",
 960 |        "      <th>Food supply (kcal/capita/day)</th>\n",
 961 |        "      <td>454257.00</td>\n",
 962 |        "      <td>453383.00</td>\n",
 963 |        "      <td>451810.00</td>\n",
 964 |        "      <td>454681.00</td>\n",
 965 |        "      <td>455261.00</td>\n",
 966 |        "    </tr>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>Food supply quantity (kg/capita/yr)</th>\n",
 969 |        "      <td>49650.63</td>\n",
 970 |        "      <td>49345.13</td>\n",
 971 |        "      <td>48985.28</td>\n",
 972 |        "      <td>48690.04</td>\n",
 973 |        "      <td>49056.85</td>\n",
 974 |        "    </tr>\n",
 975 |        "    <tr>\n",
 976 |        "      <th>Import Quantity</th>\n",
 977 |        "      <td>274144.48</td>\n",
 978 |        "      <td>267018.46</td>\n",
 979 |        "      <td>286582.78</td>\n",
 980 |        "      <td>294559.09</td>\n",
 981 |        "      <td>287997.09</td>\n",
 982 |        "    </tr>\n",
 983 |        "    <tr>\n",
 984 |        "      <th>Losses</th>\n",
 985 |        "      <td>153223.00</td>\n",
 986 |        "      <td>155439.00</td>\n",
 987 |        "      <td>157787.00</td>\n",
 988 |        "      <td>160614.00</td>\n",
 989 |        "      <td>163902.00</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>Other uses (non-food)</th>\n",
 993 |        "      <td>78718.13</td>\n",
 994 |        "      <td>66254.41</td>\n",
 995 |        "      <td>69563.68</td>\n",
 996 |        "      <td>91645.97</td>\n",
 997 |        "      <td>91300.97</td>\n",
 998 |        "    </tr>\n",
 999 |        "    <tr>\n",
1000 |        "      <th>Processing</th>\n",
1001 |        "      <td>282923.00</td>\n",
1002 |        "      <td>287929.00</td>\n",
1003 |        "      <td>280631.00</td>\n",
1004 |        "      <td>292836.00</td>\n",
1005 |        "      <td>308429.00</td>\n",
1006 |        "    </tr>\n",
1007 |        "    <tr>\n",
1008 |        "      <th>Production</th>\n",
1009 |        "      <td>1931287.75</td>\n",
1010 |        "      <td>1947019.39</td>\n",
1011 |        "      <td>1943537.15</td>\n",
1012 |        "      <td>2030056.89</td>\n",
1013 |        "      <td>2075072.89</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>Protein supply quantity (g/capita/day)</th>\n",
1017 |        "      <td>11836.46</td>\n",
1018 |        "      <td>11833.95</td>\n",
1019 |        "      <td>11779.69</td>\n",
1020 |        "      <td>11842.45</td>\n",
1021 |        "      <td>11833.56</td>\n",
1022 |        "    </tr>\n",
1023 |        "    <tr>\n",
1024 |        "      <th>Residuals</th>\n",
1025 |        "      <td>30149.00</td>\n",
1026 |        "      <td>30045.00</td>\n",
1027 |        "      <td>37224.00</td>\n",
1028 |        "      <td>35500.00</td>\n",
1029 |        "      <td>34864.00</td>\n",
1030 |        "    </tr>\n",
1031 |        "    <tr>\n",
1032 |        "      <th>Seed</th>\n",
1033 |        "      <td>21922.92</td>\n",
1034 |        "      <td>23976.82</td>\n",
1035 |        "      <td>23389.20</td>\n",
1036 |        "      <td>24870.14</td>\n",
1037 |        "      <td>25263.14</td>\n",
1038 |        "    </tr>\n",
1039 |        "    <tr>\n",
1040 |        "      <th>Stock Variation</th>\n",
1041 |        "      <td>58749.83</td>\n",
1042 |        "      <td>34910.99</td>\n",
1043 |        "      <td>33140.12</td>\n",
1044 |        "      <td>54316.91</td>\n",
1045 |        "      <td>20577.91</td>\n",
1046 |        "    </tr>\n",
1047 |        "    <tr>\n",
1048 |        "      <th>Total Population - Both sexes</th>\n",
1049 |        "      <td>1031585.00</td>\n",
1050 |        "      <td>1058081.00</td>\n",
1051 |        "      <td>1085107.00</td>\n",
1052 |        "      <td>1112641.00</td>\n",
1053 |        "      <td>1140605.00</td>\n",
1054 |        "    </tr>\n",
1055 |        "    <tr>\n",
1056 |        "      <th>Tourist consumption</th>\n",
1057 |        "      <td>416.00</td>\n",
1058 |        "      <td>349.00</td>\n",
1059 |        "      <td>89.00</td>\n",
1060 |        "      <td>91.00</td>\n",
1061 |        "      <td>90.00</td>\n",
1062 |        "    </tr>\n",
1063 |        "  </tbody>\n",
1064 |        "</table>\n",
1065 |        "</div>"
1066 |       ],
1067 |       "text/plain": [
1068 |        "                                             Y2014       Y2015       Y2016  \\\n",
1069 |        "Element                                                                      \n",
1070 |        "Domestic supply quantity                1996716.35  2021493.55  2044842.70   \n",
1071 |        "Export Quantity                          150020.64   157614.47   151920.46   \n",
1072 |        "Fat supply quantity (g/capita/day)        10225.56    10235.74    10102.77   \n",
1073 |        "Feed                                     216927.89   225050.22   228958.65   \n",
1074 |        "Food                                    1212332.49  1232361.10  1247022.17   \n",
1075 |        "Food supply (kcal/capita/day)            454257.00   453383.00   451810.00   \n",
1076 |        "Food supply quantity (kg/capita/yr)       49650.63    49345.13    48985.28   \n",
1077 |        "Import Quantity                          274144.48   267018.46   286582.78   \n",
1078 |        "Losses                                   153223.00   155439.00   157787.00   \n",
1079 |        "Other uses (non-food)                     78718.13    66254.41    69563.68   \n",
1080 |        "Processing                               282923.00   287929.00   280631.00   \n",
1081 |        "Production                              1931287.75  1947019.39  1943537.15   \n",
1082 |        "Protein supply quantity (g/capita/day)    11836.46    11833.95    11779.69   \n",
1083 |        "Residuals                                 30149.00    30045.00    37224.00   \n",
1084 |        "Seed                                      21922.92    23976.82    23389.20   \n",
1085 |        "Stock Variation                           58749.83    34910.99    33140.12   \n",
1086 |        "Total Population - Both sexes           1031585.00  1058081.00  1085107.00   \n",
1087 |        "Tourist consumption                         416.00      349.00       89.00   \n",
1088 |        "\n",
1089 |        "                                             Y2017       Y2018  \n",
1090 |        "Element                                                         \n",
1091 |        "Domestic supply quantity                2088198.10  2161192.10  \n",
1092 |        "Export Quantity                          182338.80   181594.80  \n",
1093 |        "Fat supply quantity (g/capita/day)        10253.84    10258.69  \n",
1094 |        "Feed                                     223705.68   233489.68  \n",
1095 |        "Food                                    1258888.28  1303841.28  \n",
1096 |        "Food supply (kcal/capita/day)            454681.00   455261.00  \n",
1097 |        "Food supply quantity (kg/capita/yr)       48690.04    49056.85  \n",
1098 |        "Import Quantity                          294559.09   287997.09  \n",
1099 |        "Losses                                   160614.00   163902.00  \n",
1100 |        "Other uses (non-food)                     91645.97    91300.97  \n",
1101 |        "Processing                               292836.00   308429.00  \n",
1102 |        "Production                              2030056.89  2075072.89  \n",
1103 |        "Protein supply quantity (g/capita/day)    11842.45    11833.56  \n",
1104 |        "Residuals                                 35500.00    34864.00  \n",
1105 |        "Seed                                      24870.14    25263.14  \n",
1106 |        "Stock Variation                           54316.91    20577.91  \n",
1107 |        "Total Population - Both sexes           1112641.00  1140605.00  \n",
1108 |        "Tourist consumption                          91.00       90.00  "
1109 |       ]
1110 |      },
1111 |      "execution_count": 10,
1112 |      "metadata": {},
1113 |      "output_type": "execute_result"
1114 |     }
1115 |    ],
1116 |    "source": [
1117 |     "# Get the list of years\n",
1118 |     "year_list = ['Y2014', 'Y2015', 'Y2016', 'Y2017', 'Y2018']\n",
1119 |     "\n",
1120 |     "#groupby by Element to view the highst sum of Stock Varaiation\n",
1121 |     "df_FBS.groupby(['Element'])[year_list].agg(np.sum)"
1122 |    ]
1123 |   },
1124 |   {
1125 |    "cell_type": "code",
1126 |    "execution_count": null,
1127 |    "id": "92b90a86",
1128 |    "metadata": {},
1129 |    "outputs": [],
1130 |    "source": []
1131 |   },
1132 |   {
1133 |    "cell_type": "markdown",
1134 |    "id": "3a478719",
1135 |    "metadata": {},
1136 |    "source": [
1137 |     "#### Question 4\n",
1138 |     "\n",
1139 |     "Answer the following questions based on the African food production dataset provided by the FAO website already provided\n",
1140 |     "\n",
1141 |     "What is the total sum of Wine produced in 2015 and 2018 respectively?\n",
1142 |     "\n",
1143 |     "Hint:Perform a groupby sum aggregation on ‘Item’\n",
1144 |     "\n",
1145 |     "#### Ans:\n",
1146 |     "    \n",
1147 |     "B - 4251.81 and 4039.32"
1148 |    ]
1149 |   },
1150 |   {
1151 |    "cell_type": "code",
1152 |    "execution_count": 11,
1153 |    "id": "cb373e12",
1154 |    "metadata": {},
1155 |    "outputs": [
1156 |     {
1157 |      "data": {
1158 |       "text/html": [
1159 |        "<div>\n",
1160 |        "<style scoped>\n",
1161 |        "    .dataframe tbody tr th:only-of-type {\n",
1162 |        "        vertical-align: middle;\n",
1163 |        "    }\n",
1164 |        "\n",
1165 |        "    .dataframe tbody tr th {\n",
1166 |        "        vertical-align: top;\n",
1167 |        "    }\n",
1168 |        "\n",
1169 |        "    .dataframe thead th {\n",
1170 |        "        text-align: right;\n",
1171 |        "    }\n",
1172 |        "</style>\n",
1173 |        "<table border=\"1\" class=\"dataframe\">\n",
1174 |        "  <thead>\n",
1175 |        "    <tr style=\"text-align: right;\">\n",
1176 |        "      <th></th>\n",
1177 |        "      <th>Y2015</th>\n",
1178 |        "      <th>Y2018</th>\n",
1179 |        "    </tr>\n",
1180 |        "    <tr>\n",
1181 |        "      <th>Item</th>\n",
1182 |        "      <th></th>\n",
1183 |        "      <th></th>\n",
1184 |        "    </tr>\n",
1185 |        "  </thead>\n",
1186 |        "  <tbody>\n",
1187 |        "    <tr>\n",
1188 |        "      <th>Alcohol, Non-Food</th>\n",
1189 |        "      <td>2180.00</td>\n",
1190 |        "      <td>2293.00</td>\n",
1191 |        "    </tr>\n",
1192 |        "    <tr>\n",
1193 |        "      <th>Alcoholic Beverages</th>\n",
1194 |        "      <td>98783.72</td>\n",
1195 |        "      <td>97847.27</td>\n",
1196 |        "    </tr>\n",
1197 |        "    <tr>\n",
1198 |        "      <th>Animal Products</th>\n",
1199 |        "      <td>11811.73</td>\n",
1200 |        "      <td>11578.61</td>\n",
1201 |        "    </tr>\n",
1202 |        "    <tr>\n",
1203 |        "      <th>Animal fats</th>\n",
1204 |        "      <td>200675.72</td>\n",
1205 |        "      <td>269648.27</td>\n",
1206 |        "    </tr>\n",
1207 |        "    <tr>\n",
1208 |        "      <th>Apples and products</th>\n",
1209 |        "      <td>10559.15</td>\n",
1210 |        "      <td>9640.51</td>\n",
1211 |        "    </tr>\n",
1212 |        "    <tr>\n",
1213 |        "      <th>...</th>\n",
1214 |        "      <td>...</td>\n",
1215 |        "      <td>...</td>\n",
1216 |        "    </tr>\n",
1217 |        "    <tr>\n",
1218 |        "      <th>Vegetables, Other</th>\n",
1219 |        "      <td>158104.08</td>\n",
1220 |        "      <td>163987.21</td>\n",
1221 |        "    </tr>\n",
1222 |        "    <tr>\n",
1223 |        "      <th>Vegetal Products</th>\n",
1224 |        "      <td>107064.17</td>\n",
1225 |        "      <td>107775.39</td>\n",
1226 |        "    </tr>\n",
1227 |        "    <tr>\n",
1228 |        "      <th>Wheat and products</th>\n",
1229 |        "      <td>234710.51</td>\n",
1230 |        "      <td>242645.19</td>\n",
1231 |        "    </tr>\n",
1232 |        "    <tr>\n",
1233 |        "      <th>Wine</th>\n",
1234 |        "      <td>4251.81</td>\n",
1235 |        "      <td>4039.32</td>\n",
1236 |        "    </tr>\n",
1237 |        "    <tr>\n",
1238 |        "      <th>Yams</th>\n",
1239 |        "      <td>203151.78</td>\n",
1240 |        "      <td>221272.09</td>\n",
1241 |        "    </tr>\n",
1242 |        "  </tbody>\n",
1243 |        "</table>\n",
1244 |        "<p>119 rows × 2 columns</p>\n",
1245 |        "</div>"
1246 |       ],
1247 |       "text/plain": [
1248 |        "                         Y2015      Y2018\n",
1249 |        "Item                                     \n",
1250 |        "Alcohol, Non-Food      2180.00    2293.00\n",
1251 |        "Alcoholic Beverages   98783.72   97847.27\n",
1252 |        "Animal Products       11811.73   11578.61\n",
1253 |        "Animal fats          200675.72  269648.27\n",
1254 |        "Apples and products   10559.15    9640.51\n",
1255 |        "...                        ...        ...\n",
1256 |        "Vegetables, Other    158104.08  163987.21\n",
1257 |        "Vegetal Products     107064.17  107775.39\n",
1258 |        "Wheat and products   234710.51  242645.19\n",
1259 |        "Wine                   4251.81    4039.32\n",
1260 |        "Yams                 203151.78  221272.09\n",
1261 |        "\n",
1262 |        "[119 rows x 2 columns]"
1263 |       ]
1264 |      },
1265 |      "execution_count": 11,
1266 |      "metadata": {},
1267 |      "output_type": "execute_result"
1268 |     }
1269 |    ],
1270 |    "source": [
1271 |     "# Grouo by sum aggregation of Item for the year columns 2015 and 2018\n",
1272 |     "wine_2015_and_2018 = df_FBS.groupby('Item')[['Y2015', 'Y2018']].sum()\n",
1273 |     "wine_2015_and_2018"
1274 |    ]
1275 |   },
1276 |   {
1277 |    "cell_type": "code",
1278 |    "execution_count": null,
1279 |    "id": "1afa88a2",
1280 |    "metadata": {},
1281 |    "outputs": [],
1282 |    "source": []
1283 |   },
1284 |   {
1285 |    "cell_type": "markdown",
1286 |    "id": "fe6e9cc0",
1287 |    "metadata": {},
1288 |    "source": [
1289 |     "#### Question 5\n",
1290 |     "\n",
1291 |     "Given the following numpy array \n",
1292 |     "\n",
1293 |     "array  = ([[94, 89, 63],\n",
1294 |     "\n",
1295 |     "        [93, 92, 48],\n",
1296 |     "\n",
1297 |     "        [92, 94, 56]])\n",
1298 |     "\n",
1299 |     "How would you select  the elements in bold and italics from the array?\n",
1300 |     "\n",
1301 |     "#### Ans:\n",
1302 |     "    \n",
1303 |     "C - array[ : 2, 1 : ] "
1304 |    ]
1305 |   },
1306 |   {
1307 |    "cell_type": "code",
1308 |    "execution_count": null,
1309 |    "id": "27dc9ffc",
1310 |    "metadata": {},
1311 |    "outputs": [],
1312 |    "source": []
1313 |   },
1314 |   {
1315 |    "cell_type": "markdown",
1316 |    "id": "493a77cd",
1317 |    "metadata": {},
1318 |    "source": [
1319 |     "#### Question 6\n",
1320 |     "\n",
1321 |     "Given the following python code, what would the output of the code give?\n",
1322 |     "\n",
1323 |     "my_tuppy = (1,2,5,8)\n",
1324 |     "\n",
1325 |     "my_tuppy[2] = 6\n",
1326 |     "\n",
1327 |     "#### Ans:\n",
1328 |     "    \n",
1329 |     "B - Type Error"
1330 |    ]
1331 |   },
1332 |   {
1333 |    "cell_type": "code",
1334 |    "execution_count": null,
1335 |    "id": "fb11ec1f",
1336 |    "metadata": {},
1337 |    "outputs": [],
1338 |    "source": []
1339 |   },
1340 |   {
1341 |    "cell_type": "markdown",
1342 |    "id": "2db2a42a",
1343 |    "metadata": {},
1344 |    "source": [
1345 |     "#### Question 7\n",
1346 |     "\n",
1347 |     "Select columns ‘Y2017’ and ‘Area’, Perform a groupby operation on ‘Area’.  Which of these Areas had the 7th lowest\n",
1348 |     "sum in 2017?\n",
1349 |     "\n",
1350 |     "#### Ans:\n",
1351 |     "    \n",
1352 |     "B - Guinea-Bissau"
1353 |    ]
1354 |   },
1355 |   {
1356 |    "cell_type": "code",
1357 |    "execution_count": 12,
1358 |    "id": "d085c27f",
1359 |    "metadata": {},
1360 |    "outputs": [
1361 |     {
1362 |      "name": "stdout",
1363 |      "output_type": "stream",
1364 |      "text": [
1365 |       "                           Area       Y2017\n",
1366 |       "42               Sudan (former)        0.00\n",
1367 |       "16                 Ethiopia PDR        0.00\n",
1368 |       "9                       Comoros       59.84\n",
1369 |       "38                   Seychelles      442.34\n",
1370 |       "36        Sao Tome and Principe    12662.63\n",
1371 |       "5                    Cabo Verde    14650.74\n",
1372 |       "21                Guinea-Bissau    19102.77\n",
1373 |       "23                      Lesotho    21267.96\n",
1374 |       "3                      Botswana    22101.30\n",
1375 |       "12                     Djibouti    22729.91\n",
1376 |       "18                       Gambia    23154.18\n",
1377 |       "17                        Gabon    27979.64\n",
1378 |       "24                      Liberia    29342.20\n",
1379 |       "32                      Namibia    29874.89\n",
1380 |       "7      Central African Republic    29937.00\n",
1381 |       "10                        Congo    41181.68\n",
1382 |       "43                         Togo    49841.88\n",
1383 |       "29                    Mauritius    51114.83\n",
1384 |       "14                     Eswatini    54343.33\n",
1385 |       "39                 Sierra Leone    55311.33\n",
1386 |       "8                          Chad    71594.68\n",
1387 |       "35                       Rwanda    73663.69\n",
1388 |       "48                     Zimbabwe    75919.34\n",
1389 |       "37                      Senegal    95681.15\n",
1390 |       "20                       Guinea    98138.87\n",
1391 |       "4                  Burkina Faso   101855.07\n",
1392 |       "47                       Zambia   103223.77\n",
1393 |       "44                      Tunisia   124167.20\n",
1394 |       "2                         Benin   124771.22\n",
1395 |       "33                        Niger   126707.58\n",
1396 |       "25                   Madagascar   131197.73\n",
1397 |       "27                         Mali   149928.33\n",
1398 |       "28                   Mauritania   156665.46\n",
1399 |       "31                   Mozambique   161407.98\n",
1400 |       "26                       Malawi   181098.71\n",
1401 |       "45                       Uganda   213950.38\n",
1402 |       "11                Côte d'Ivoire   224599.01\n",
1403 |       "1                        Angola   229159.57\n",
1404 |       "6                      Cameroon   232030.43\n",
1405 |       "41                        Sudan   239931.92\n",
1406 |       "22                        Kenya   264660.66\n",
1407 |       "46  United Republic of Tanzania   322616.85\n",
1408 |       "0                       Algeria   325644.27\n",
1409 |       "19                        Ghana   337599.06\n",
1410 |       "30                      Morocco   388495.36\n",
1411 |       "15                     Ethiopia   448683.76\n",
1412 |       "40                 South Africa   517590.54\n",
1413 |       "13                        Egypt   866379.92\n",
1414 |       "34                      Nigeria  1483268.23\n",
1415 |       "The Area with the 7th lowest sum in 2017 is: Guinea-Bissau\n"
1416 |      ]
1417 |     }
1418 |    ],
1419 |    "source": [
1420 |     "# Select columns 'Y2017' and 'Area'\n",
1421 |     "selected_columns = df_FBS[['Y2017', 'Area']]\n",
1422 |     "\n",
1423 |     "# Group by 'Area' and calculate the sum of 'Y2017' for each Area\n",
1424 |     "grouped_data = selected_columns.groupby('Area')['Y2017'].sum().reset_index()\n",
1425 |     "\n",
1426 |     "# Sort the data by the sum of 'Y2017' in ascending order\n",
1427 |     "sorted_data = grouped_data.sort_values(by='Y2017', ascending=True)\n",
1428 |     "print(sorted_data)\n",
1429 |     "\n",
1430 |     "# Get the Area with the 7th lowest sum in 2017\n",
1431 |     "seventh_lowest_area = sorted_data.iloc[6]['Area']\n",
1432 |     "\n",
1433 |     "# Print the result\n",
1434 |     "print(\"The Area with the 7th lowest sum in 2017 is:\", seventh_lowest_area)"
1435 |    ]
1436 |   },
1437 |   {
1438 |    "cell_type": "code",
1439 |    "execution_count": null,
1440 |    "id": "5db878e7",
1441 |    "metadata": {},
1442 |    "outputs": [],
1443 |    "source": []
1444 |   },
1445 |   {
1446 |    "cell_type": "markdown",
1447 |    "id": "4e38ed0b",
1448 |    "metadata": {},
1449 |    "source": [
1450 |     "#### Question 8\n",
1451 |     "\n",
1452 |     "Which year had the least correlation with ‘Element Code’?\n",
1453 |     "\n",
1454 |     "#### Ans:\n",
1455 |     "    \n",
1456 |     "D - 2016"
1457 |    ]
1458 |   },
1459 |   {
1460 |    "cell_type": "code",
1461 |    "execution_count": 13,
1462 |    "id": "038e18ef",
1463 |    "metadata": {},
1464 |    "outputs": [
1465 |     {
1466 |      "data": {
1467 |       "text/html": [
1468 |        "<div>\n",
1469 |        "<style scoped>\n",
1470 |        "    .dataframe tbody tr th:only-of-type {\n",
1471 |        "        vertical-align: middle;\n",
1472 |        "    }\n",
1473 |        "\n",
1474 |        "    .dataframe tbody tr th {\n",
1475 |        "        vertical-align: top;\n",
1476 |        "    }\n",
1477 |        "\n",
1478 |        "    .dataframe thead th {\n",
1479 |        "        text-align: right;\n",
1480 |        "    }\n",
1481 |        "</style>\n",
1482 |        "<table border=\"1\" class=\"dataframe\">\n",
1483 |        "  <thead>\n",
1484 |        "    <tr style=\"text-align: right;\">\n",
1485 |        "      <th></th>\n",
1486 |        "      <th>Element Code</th>\n",
1487 |        "      <th>Y2014</th>\n",
1488 |        "      <th>Y2015</th>\n",
1489 |        "      <th>Y2016</th>\n",
1490 |        "      <th>Y2017</th>\n",
1491 |        "      <th>Y2018</th>\n",
1492 |        "    </tr>\n",
1493 |        "  </thead>\n",
1494 |        "  <tbody>\n",
1495 |        "    <tr>\n",
1496 |        "      <th>Element Code</th>\n",
1497 |        "      <td>1.000000</td>\n",
1498 |        "      <td>0.024457</td>\n",
1499 |        "      <td>0.023889</td>\n",
1500 |        "      <td>0.023444</td>\n",
1501 |        "      <td>0.024254</td>\n",
1502 |        "      <td>0.024279</td>\n",
1503 |        "    </tr>\n",
1504 |        "    <tr>\n",
1505 |        "      <th>Y2014</th>\n",
1506 |        "      <td>0.024457</td>\n",
1507 |        "      <td>1.000000</td>\n",
1508 |        "      <td>0.994647</td>\n",
1509 |        "      <td>0.996081</td>\n",
1510 |        "      <td>0.995230</td>\n",
1511 |        "      <td>0.994872</td>\n",
1512 |        "    </tr>\n",
1513 |        "    <tr>\n",
1514 |        "      <th>Y2015</th>\n",
1515 |        "      <td>0.023889</td>\n",
1516 |        "      <td>0.994647</td>\n",
1517 |        "      <td>1.000000</td>\n",
1518 |        "      <td>0.995739</td>\n",
1519 |        "      <td>0.988048</td>\n",
1520 |        "      <td>0.988208</td>\n",
1521 |        "    </tr>\n",
1522 |        "    <tr>\n",
1523 |        "      <th>Y2016</th>\n",
1524 |        "      <td>0.023444</td>\n",
1525 |        "      <td>0.996081</td>\n",
1526 |        "      <td>0.995739</td>\n",
1527 |        "      <td>1.000000</td>\n",
1528 |        "      <td>0.992785</td>\n",
1529 |        "      <td>0.992757</td>\n",
1530 |        "    </tr>\n",
1531 |        "    <tr>\n",
1532 |        "      <th>Y2017</th>\n",
1533 |        "      <td>0.024254</td>\n",
1534 |        "      <td>0.995230</td>\n",
1535 |        "      <td>0.988048</td>\n",
1536 |        "      <td>0.992785</td>\n",
1537 |        "      <td>1.000000</td>\n",
1538 |        "      <td>0.998103</td>\n",
1539 |        "    </tr>\n",
1540 |        "    <tr>\n",
1541 |        "      <th>Y2018</th>\n",
1542 |        "      <td>0.024279</td>\n",
1543 |        "      <td>0.994872</td>\n",
1544 |        "      <td>0.988208</td>\n",
1545 |        "      <td>0.992757</td>\n",
1546 |        "      <td>0.998103</td>\n",
1547 |        "      <td>1.000000</td>\n",
1548 |        "    </tr>\n",
1549 |        "  </tbody>\n",
1550 |        "</table>\n",
1551 |        "</div>"
1552 |       ],
1553 |       "text/plain": [
1554 |        "              Element Code     Y2014     Y2015     Y2016     Y2017     Y2018\n",
1555 |        "Element Code      1.000000  0.024457  0.023889  0.023444  0.024254  0.024279\n",
1556 |        "Y2014             0.024457  1.000000  0.994647  0.996081  0.995230  0.994872\n",
1557 |        "Y2015             0.023889  0.994647  1.000000  0.995739  0.988048  0.988208\n",
1558 |        "Y2016             0.023444  0.996081  0.995739  1.000000  0.992785  0.992757\n",
1559 |        "Y2017             0.024254  0.995230  0.988048  0.992785  1.000000  0.998103\n",
1560 |        "Y2018             0.024279  0.994872  0.988208  0.992757  0.998103  1.000000"
1561 |       ]
1562 |      },
1563 |      "execution_count": 13,
1564 |      "metadata": {},
1565 |      "output_type": "execute_result"
1566 |     }
1567 |    ],
1568 |    "source": [
1569 |     "# Get a dataframe of the required columns : Element Code and the year columns\n",
1570 |     "numeric_columns = df_FBS[['Element Code', 'Y2014', 'Y2015', 'Y2016', 'Y2017', 'Y2018']]\n",
1571 |     "\n",
1572 |     "# Get the correlaton of the year column with Element code and assign the result to correlation_matrix\n",
1573 |     "correlation_matrix = numeric_columns.corr()\n",
1574 |     "correlation_matrix"
1575 |    ]
1576 |   },
1577 |   {
1578 |    "cell_type": "code",
1579 |    "execution_count": null,
1580 |    "id": "dda0b2d8",
1581 |    "metadata": {},
1582 |    "outputs": [],
1583 |    "source": []
1584 |   },
1585 |   {
1586 |    "cell_type": "markdown",
1587 |    "id": "d42e992b",
1588 |    "metadata": {},
1589 |    "source": [
1590 |     "#### Question 9\n",
1591 |     "\n",
1592 |     "Which of the following dataframe methods can be used to access elements across rows and columns?\n",
1593 |     "\n",
1594 |     "#### Ans:\n",
1595 |     "    \n",
1596 |     "E - c and d"
1597 |    ]
1598 |   },
1599 |   {
1600 |    "cell_type": "code",
1601 |    "execution_count": null,
1602 |    "id": "ce5bd980",
1603 |    "metadata": {},
1604 |    "outputs": [],
1605 |    "source": []
1606 |   },
1607 |   {
1608 |    "cell_type": "markdown",
1609 |    "id": "71adcf7e",
1610 |    "metadata": {},
1611 |    "source": [
1612 |     "#### Question 10\n",
1613 |     "\n",
1614 |     "Which of these python data structures is unorderly?\n",
1615 |     "\n",
1616 |     "#### Ans:\n",
1617 |     "    \n",
1618 |     "A - Set\n",
1619 |     "\n",
1620 |     "B - Dictionary"
1621 |    ]
1622 |   },
1623 |   {
1624 |    "cell_type": "code",
1625 |    "execution_count": null,
1626 |    "id": "a6e92161",
1627 |    "metadata": {},
1628 |    "outputs": [],
1629 |    "source": []
1630 |   },
1631 |   {
1632 |    "cell_type": "markdown",
1633 |    "id": "980416e2",
1634 |    "metadata": {},
1635 |    "source": [
1636 |     "#### Question 11\n",
1637 |     "\n",
1638 |     "If you have the following list\n",
1639 |     "\n",
1640 |     "lst = [[35, 'Portugal', 94], [33, 'Argentina', 93], [30 , 'Brazil', 92]]\n",
1641 |     "\n",
1642 |     "col = [‘Age’,’Nationality’,’Overall’]\n",
1643 |     "\n",
1644 |     "How do you create a pandas DataFrame using this list, to look like the table below?\n",
1645 |     "\n",
1646 |     "#### Ans:\n",
1647 |     "    \n",
1648 |     "B - pd.Dataframe(lst, columns = col, index = [1,2,3])"
1649 |    ]
1650 |   },
1651 |   {
1652 |    "cell_type": "code",
1653 |    "execution_count": null,
1654 |    "id": "a1b672d4",
1655 |    "metadata": {},
1656 |    "outputs": [],
1657 |    "source": []
1658 |   },
1659 |   {
1660 |    "cell_type": "markdown",
1661 |    "id": "20928f97",
1662 |    "metadata": {},
1663 |    "source": [
1664 |     "#### Question 12\n",
1665 |     "\n",
1666 |     "Perform a groupby operation on ‘Element’.  What is the total number of the sum of Processing in 2017?\n",
1667 |     "\n",
1668 |     "#### Ans:\n",
1669 |     "    \n",
1670 |     "E - 292836.00"
1671 |    ]
1672 |   },
1673 |   {
1674 |    "cell_type": "code",
1675 |    "execution_count": 14,
1676 |    "id": "3670d912",
1677 |    "metadata": {},
1678 |    "outputs": [
1679 |     {
1680 |      "data": {
1681 |       "text/html": [
1682 |        "<div>\n",
1683 |        "<style scoped>\n",
1684 |        "    .dataframe tbody tr th:only-of-type {\n",
1685 |        "        vertical-align: middle;\n",
1686 |        "    }\n",
1687 |        "\n",
1688 |        "    .dataframe tbody tr th {\n",
1689 |        "        vertical-align: top;\n",
1690 |        "    }\n",
1691 |        "\n",
1692 |        "    .dataframe thead th {\n",
1693 |        "        text-align: right;\n",
1694 |        "    }\n",
1695 |        "</style>\n",
1696 |        "<table border=\"1\" class=\"dataframe\">\n",
1697 |        "  <thead>\n",
1698 |        "    <tr style=\"text-align: right;\">\n",
1699 |        "      <th></th>\n",
1700 |        "      <th>Y2014</th>\n",
1701 |        "      <th>Y2015</th>\n",
1702 |        "      <th>Y2016</th>\n",
1703 |        "      <th>Y2017</th>\n",
1704 |        "      <th>Y2018</th>\n",
1705 |        "    </tr>\n",
1706 |        "    <tr>\n",
1707 |        "      <th>Element</th>\n",
1708 |        "      <th></th>\n",
1709 |        "      <th></th>\n",
1710 |        "      <th></th>\n",
1711 |        "      <th></th>\n",
1712 |        "      <th></th>\n",
1713 |        "    </tr>\n",
1714 |        "  </thead>\n",
1715 |        "  <tbody>\n",
1716 |        "    <tr>\n",
1717 |        "      <th>Domestic supply quantity</th>\n",
1718 |        "      <td>1996716.35</td>\n",
1719 |        "      <td>2021493.55</td>\n",
1720 |        "      <td>2044842.70</td>\n",
1721 |        "      <td>2088198.10</td>\n",
1722 |        "      <td>2161192.10</td>\n",
1723 |        "    </tr>\n",
1724 |        "    <tr>\n",
1725 |        "      <th>Export Quantity</th>\n",
1726 |        "      <td>150020.64</td>\n",
1727 |        "      <td>157614.47</td>\n",
1728 |        "      <td>151920.46</td>\n",
1729 |        "      <td>182338.80</td>\n",
1730 |        "      <td>181594.80</td>\n",
1731 |        "    </tr>\n",
1732 |        "    <tr>\n",
1733 |        "      <th>Fat supply quantity (g/capita/day)</th>\n",
1734 |        "      <td>10225.56</td>\n",
1735 |        "      <td>10235.74</td>\n",
1736 |        "      <td>10102.77</td>\n",
1737 |        "      <td>10253.84</td>\n",
1738 |        "      <td>10258.69</td>\n",
1739 |        "    </tr>\n",
1740 |        "    <tr>\n",
1741 |        "      <th>Feed</th>\n",
1742 |        "      <td>216927.89</td>\n",
1743 |        "      <td>225050.22</td>\n",
1744 |        "      <td>228958.65</td>\n",
1745 |        "      <td>223705.68</td>\n",
1746 |        "      <td>233489.68</td>\n",
1747 |        "    </tr>\n",
1748 |        "    <tr>\n",
1749 |        "      <th>Food</th>\n",
1750 |        "      <td>1212332.49</td>\n",
1751 |        "      <td>1232361.10</td>\n",
1752 |        "      <td>1247022.17</td>\n",
1753 |        "      <td>1258888.28</td>\n",
1754 |        "      <td>1303841.28</td>\n",
1755 |        "    </tr>\n",
1756 |        "    <tr>\n",
1757 |        "      <th>Food supply (kcal/capita/day)</th>\n",
1758 |        "      <td>454257.00</td>\n",
1759 |        "      <td>453383.00</td>\n",
1760 |        "      <td>451810.00</td>\n",
1761 |        "      <td>454681.00</td>\n",
1762 |        "      <td>455261.00</td>\n",
1763 |        "    </tr>\n",
1764 |        "    <tr>\n",
1765 |        "      <th>Food supply quantity (kg/capita/yr)</th>\n",
1766 |        "      <td>49650.63</td>\n",
1767 |        "      <td>49345.13</td>\n",
1768 |        "      <td>48985.28</td>\n",
1769 |        "      <td>48690.04</td>\n",
1770 |        "      <td>49056.85</td>\n",
1771 |        "    </tr>\n",
1772 |        "    <tr>\n",
1773 |        "      <th>Import Quantity</th>\n",
1774 |        "      <td>274144.48</td>\n",
1775 |        "      <td>267018.46</td>\n",
1776 |        "      <td>286582.78</td>\n",
1777 |        "      <td>294559.09</td>\n",
1778 |        "      <td>287997.09</td>\n",
1779 |        "    </tr>\n",
1780 |        "    <tr>\n",
1781 |        "      <th>Losses</th>\n",
1782 |        "      <td>153223.00</td>\n",
1783 |        "      <td>155439.00</td>\n",
1784 |        "      <td>157787.00</td>\n",
1785 |        "      <td>160614.00</td>\n",
1786 |        "      <td>163902.00</td>\n",
1787 |        "    </tr>\n",
1788 |        "    <tr>\n",
1789 |        "      <th>Other uses (non-food)</th>\n",
1790 |        "      <td>78718.13</td>\n",
1791 |        "      <td>66254.41</td>\n",
1792 |        "      <td>69563.68</td>\n",
1793 |        "      <td>91645.97</td>\n",
1794 |        "      <td>91300.97</td>\n",
1795 |        "    </tr>\n",
1796 |        "    <tr>\n",
1797 |        "      <th>Processing</th>\n",
1798 |        "      <td>282923.00</td>\n",
1799 |        "      <td>287929.00</td>\n",
1800 |        "      <td>280631.00</td>\n",
1801 |        "      <td>292836.00</td>\n",
1802 |        "      <td>308429.00</td>\n",
1803 |        "    </tr>\n",
1804 |        "    <tr>\n",
1805 |        "      <th>Production</th>\n",
1806 |        "      <td>1931287.75</td>\n",
1807 |        "      <td>1947019.39</td>\n",
1808 |        "      <td>1943537.15</td>\n",
1809 |        "      <td>2030056.89</td>\n",
1810 |        "      <td>2075072.89</td>\n",
1811 |        "    </tr>\n",
1812 |        "    <tr>\n",
1813 |        "      <th>Protein supply quantity (g/capita/day)</th>\n",
1814 |        "      <td>11836.46</td>\n",
1815 |        "      <td>11833.95</td>\n",
1816 |        "      <td>11779.69</td>\n",
1817 |        "      <td>11842.45</td>\n",
1818 |        "      <td>11833.56</td>\n",
1819 |        "    </tr>\n",
1820 |        "    <tr>\n",
1821 |        "      <th>Residuals</th>\n",
1822 |        "      <td>30149.00</td>\n",
1823 |        "      <td>30045.00</td>\n",
1824 |        "      <td>37224.00</td>\n",
1825 |        "      <td>35500.00</td>\n",
1826 |        "      <td>34864.00</td>\n",
1827 |        "    </tr>\n",
1828 |        "    <tr>\n",
1829 |        "      <th>Seed</th>\n",
1830 |        "      <td>21922.92</td>\n",
1831 |        "      <td>23976.82</td>\n",
1832 |        "      <td>23389.20</td>\n",
1833 |        "      <td>24870.14</td>\n",
1834 |        "      <td>25263.14</td>\n",
1835 |        "    </tr>\n",
1836 |        "    <tr>\n",
1837 |        "      <th>Stock Variation</th>\n",
1838 |        "      <td>58749.83</td>\n",
1839 |        "      <td>34910.99</td>\n",
1840 |        "      <td>33140.12</td>\n",
1841 |        "      <td>54316.91</td>\n",
1842 |        "      <td>20577.91</td>\n",
1843 |        "    </tr>\n",
1844 |        "    <tr>\n",
1845 |        "      <th>Total Population - Both sexes</th>\n",
1846 |        "      <td>1031585.00</td>\n",
1847 |        "      <td>1058081.00</td>\n",
1848 |        "      <td>1085107.00</td>\n",
1849 |        "      <td>1112641.00</td>\n",
1850 |        "      <td>1140605.00</td>\n",
1851 |        "    </tr>\n",
1852 |        "    <tr>\n",
1853 |        "      <th>Tourist consumption</th>\n",
1854 |        "      <td>416.00</td>\n",
1855 |        "      <td>349.00</td>\n",
1856 |        "      <td>89.00</td>\n",
1857 |        "      <td>91.00</td>\n",
1858 |        "      <td>90.00</td>\n",
1859 |        "    </tr>\n",
1860 |        "  </tbody>\n",
1861 |        "</table>\n",
1862 |        "</div>"
1863 |       ],
1864 |       "text/plain": [
1865 |        "                                             Y2014       Y2015       Y2016  \\\n",
1866 |        "Element                                                                      \n",
1867 |        "Domestic supply quantity                1996716.35  2021493.55  2044842.70   \n",
1868 |        "Export Quantity                          150020.64   157614.47   151920.46   \n",
1869 |        "Fat supply quantity (g/capita/day)        10225.56    10235.74    10102.77   \n",
1870 |        "Feed                                     216927.89   225050.22   228958.65   \n",
1871 |        "Food                                    1212332.49  1232361.10  1247022.17   \n",
1872 |        "Food supply (kcal/capita/day)            454257.00   453383.00   451810.00   \n",
1873 |        "Food supply quantity (kg/capita/yr)       49650.63    49345.13    48985.28   \n",
1874 |        "Import Quantity                          274144.48   267018.46   286582.78   \n",
1875 |        "Losses                                   153223.00   155439.00   157787.00   \n",
1876 |        "Other uses (non-food)                     78718.13    66254.41    69563.68   \n",
1877 |        "Processing                               282923.00   287929.00   280631.00   \n",
1878 |        "Production                              1931287.75  1947019.39  1943537.15   \n",
1879 |        "Protein supply quantity (g/capita/day)    11836.46    11833.95    11779.69   \n",
1880 |        "Residuals                                 30149.00    30045.00    37224.00   \n",
1881 |        "Seed                                      21922.92    23976.82    23389.20   \n",
1882 |        "Stock Variation                           58749.83    34910.99    33140.12   \n",
1883 |        "Total Population - Both sexes           1031585.00  1058081.00  1085107.00   \n",
1884 |        "Tourist consumption                         416.00      349.00       89.00   \n",
1885 |        "\n",
1886 |        "                                             Y2017       Y2018  \n",
1887 |        "Element                                                         \n",
1888 |        "Domestic supply quantity                2088198.10  2161192.10  \n",
1889 |        "Export Quantity                          182338.80   181594.80  \n",
1890 |        "Fat supply quantity (g/capita/day)        10253.84    10258.69  \n",
1891 |        "Feed                                     223705.68   233489.68  \n",
1892 |        "Food                                    1258888.28  1303841.28  \n",
1893 |        "Food supply (kcal/capita/day)            454681.00   455261.00  \n",
1894 |        "Food supply quantity (kg/capita/yr)       48690.04    49056.85  \n",
1895 |        "Import Quantity                          294559.09   287997.09  \n",
1896 |        "Losses                                   160614.00   163902.00  \n",
1897 |        "Other uses (non-food)                     91645.97    91300.97  \n",
1898 |        "Processing                               292836.00   308429.00  \n",
1899 |        "Production                              2030056.89  2075072.89  \n",
1900 |        "Protein supply quantity (g/capita/day)    11842.45    11833.56  \n",
1901 |        "Residuals                                 35500.00    34864.00  \n",
1902 |        "Seed                                      24870.14    25263.14  \n",
1903 |        "Stock Variation                           54316.91    20577.91  \n",
1904 |        "Total Population - Both sexes           1112641.00  1140605.00  \n",
1905 |        "Tourist consumption                          91.00       90.00  "
1906 |       ]
1907 |      },
1908 |      "execution_count": 14,
1909 |      "metadata": {},
1910 |      "output_type": "execute_result"
1911 |     }
1912 |    ],
1913 |    "source": [
1914 |     "# Get the list of years\n",
1915 |     "year_list = ['Y2014', 'Y2015', 'Y2016', 'Y2017', 'Y2018']\n",
1916 |     "\n",
1917 |     "# Use group by operation on 'Element' and get the total sum for each 'Element' values\n",
1918 |     "df_FBS.groupby(['Element'])[year_list].agg(np.sum)"
1919 |    ]
1920 |   },
1921 |   {
1922 |    "cell_type": "code",
1923 |    "execution_count": null,
1924 |    "id": "0be87109",
1925 |    "metadata": {},
1926 |    "outputs": [],
1927 |    "source": []
1928 |   },
1929 |   {
1930 |    "cell_type": "markdown",
1931 |    "id": "81c79403",
1932 |    "metadata": {},
1933 |    "source": [
1934 |     "#### Question 13\n",
1935 |     "\n",
1936 |     "How would you check for the number of rows and columns in a pandas DataFrame named df?\n",
1937 |     "\n",
1938 |     "#### Ans:\n",
1939 |     "    \n",
1940 |     "C - df.shape"
1941 |    ]
1942 |   },
1943 |   {
1944 |    "cell_type": "code",
1945 |    "execution_count": null,
1946 |    "id": "ea2f98ee",
1947 |    "metadata": {},
1948 |    "outputs": [],
1949 |    "source": []
1950 |   },
1951 |   {
1952 |    "cell_type": "markdown",
1953 |    "id": "a4a4446f",
1954 |    "metadata": {},
1955 |    "source": [
1956 |     "#### Question 14\n",
1957 |     "\n",
1958 |     "What is the total number and percentage of missing data in 2014 to 3 decimal places?\n",
1959 |     "\n",
1960 |     "#### Ans:\n",
1961 |     "    \n",
1962 |     "E - 1589 and 2.607%"
1963 |    ]
1964 |   },
1965 |   {
1966 |    "cell_type": "code",
1967 |    "execution_count": 15,
1968 |    "id": "bb50e610",
1969 |    "metadata": {},
1970 |    "outputs": [
1971 |     {
1972 |      "data": {
1973 |       "text/plain": [
1974 |        "Area Code          0\n",
1975 |        "Area               0\n",
1976 |        "Item Code          0\n",
1977 |        "Item               0\n",
1978 |        "Element Code       0\n",
1979 |        "Element            0\n",
1980 |        "Unit               0\n",
1981 |        "Y2014           1589\n",
1982 |        "Y2015           1548\n",
1983 |        "Y2016           1535\n",
1984 |        "Y2017           1506\n",
1985 |        "Y2018           1436\n",
1986 |        "dtype: int64"
1987 |       ]
1988 |      },
1989 |      "execution_count": 15,
1990 |      "metadata": {},
1991 |      "output_type": "execute_result"
1992 |     }
1993 |    ],
1994 |    "source": [
1995 |     "# Get the total sum of missing values in each column in the dataframe\n",
1996 |     "df_FBS.isnull().sum()"
1997 |    ]
1998 |   },
1999 |   {
2000 |    "cell_type": "code",
2001 |    "execution_count": 16,
2002 |    "id": "0af275a7",
2003 |    "metadata": {},
2004 |    "outputs": [
2005 |     {
2006 |      "data": {
2007 |       "text/plain": [
2008 |        "2.6073544131401474"
2009 |       ]
2010 |      },
2011 |      "execution_count": 16,
2012 |      "metadata": {},
2013 |      "output_type": "execute_result"
2014 |     }
2015 |    ],
2016 |    "source": [
2017 |     "# percentage of missing values\n",
2018 |     "df_FBS['Y2014'].isnull().sum() * 100 / len(df_FBS)"
2019 |    ]
2020 |   },
2021 |   {
2022 |    "cell_type": "code",
2023 |    "execution_count": null,
2024 |    "id": "b63eac08",
2025 |    "metadata": {},
2026 |    "outputs": [],
2027 |    "source": []
2028 |   },
2029 |   {
2030 |    "cell_type": "markdown",
2031 |    "id": "0ded550f",
2032 |    "metadata": {},
2033 |    "source": [
2034 |     "#### Question 15\n",
2035 |     "\n",
2036 |     "Select columns ‘Y2017’ and ‘Area’, Perform a groupby operation on ‘Area’.  Which of these Areas had the highest sum in 2017?\n",
2037 |     "\n",
2038 |     "#### Ans:\n",
2039 |     "    \n",
2040 |     "A - Nigeria"
2041 |    ]
2042 |   },
2043 |   {
2044 |    "cell_type": "code",
2045 |    "execution_count": 17,
2046 |    "id": "cd3e2ab4",
2047 |    "metadata": {
2048 |     "scrolled": true
2049 |    },
2050 |    "outputs": [
2051 |     {
2052 |      "name": "stdout",
2053 |      "output_type": "stream",
2054 |      "text": [
2055 |       "                           Area       Y2017\n",
2056 |       "34                      Nigeria  1483268.23\n",
2057 |       "13                        Egypt   866379.92\n",
2058 |       "40                 South Africa   517590.54\n",
2059 |       "15                     Ethiopia   448683.76\n",
2060 |       "30                      Morocco   388495.36\n",
2061 |       "19                        Ghana   337599.06\n",
2062 |       "0                       Algeria   325644.27\n",
2063 |       "46  United Republic of Tanzania   322616.85\n",
2064 |       "22                        Kenya   264660.66\n",
2065 |       "41                        Sudan   239931.92\n",
2066 |       "6                      Cameroon   232030.43\n",
2067 |       "1                        Angola   229159.57\n",
2068 |       "11                Côte d'Ivoire   224599.01\n",
2069 |       "45                       Uganda   213950.38\n",
2070 |       "26                       Malawi   181098.71\n",
2071 |       "31                   Mozambique   161407.98\n",
2072 |       "28                   Mauritania   156665.46\n",
2073 |       "27                         Mali   149928.33\n",
2074 |       "25                   Madagascar   131197.73\n",
2075 |       "33                        Niger   126707.58\n",
2076 |       "2                         Benin   124771.22\n",
2077 |       "44                      Tunisia   124167.20\n",
2078 |       "47                       Zambia   103223.77\n",
2079 |       "4                  Burkina Faso   101855.07\n",
2080 |       "20                       Guinea    98138.87\n",
2081 |       "37                      Senegal    95681.15\n",
2082 |       "48                     Zimbabwe    75919.34\n",
2083 |       "35                       Rwanda    73663.69\n",
2084 |       "8                          Chad    71594.68\n",
2085 |       "39                 Sierra Leone    55311.33\n",
2086 |       "14                     Eswatini    54343.33\n",
2087 |       "29                    Mauritius    51114.83\n",
2088 |       "43                         Togo    49841.88\n",
2089 |       "10                        Congo    41181.68\n",
2090 |       "7      Central African Republic    29937.00\n",
2091 |       "32                      Namibia    29874.89\n",
2092 |       "24                      Liberia    29342.20\n",
2093 |       "17                        Gabon    27979.64\n",
2094 |       "18                       Gambia    23154.18\n",
2095 |       "12                     Djibouti    22729.91\n",
2096 |       "3                      Botswana    22101.30\n",
2097 |       "23                      Lesotho    21267.96\n",
2098 |       "21                Guinea-Bissau    19102.77\n",
2099 |       "5                    Cabo Verde    14650.74\n",
2100 |       "36        Sao Tome and Principe    12662.63\n",
2101 |       "38                   Seychelles      442.34\n",
2102 |       "9                       Comoros       59.84\n",
2103 |       "42               Sudan (former)        0.00\n",
2104 |       "16                 Ethiopia PDR        0.00\n",
2105 |       "The Area with the highest sum in 2017 is: Nigeria\n"
2106 |      ]
2107 |     }
2108 |    ],
2109 |    "source": [
2110 |     "# Select columns 'Y2017' and 'Area'\n",
2111 |     "selected_columns = df_FBS[['Y2017', 'Area']]\n",
2112 |     "\n",
2113 |     "# Group by 'Area' and calculate the sum of 'Y2017' for each Area\n",
2114 |     "grouped_data = selected_columns.groupby('Area')['Y2017'].sum().reset_index()\n",
2115 |     "\n",
2116 |     "# Sort the data by the sum of 'Y2017' in ascending order\n",
2117 |     "sorted_data = grouped_data.sort_values(by='Y2017', ascending=False)\n",
2118 |     "print(sorted_data)\n",
2119 |     "\n",
2120 |     "# Get the Area with the 7th lowest sum in 2017\n",
2121 |     "highest_sum_area = sorted_data.iloc[0]['Area']\n",
2122 |     "\n",
2123 |     "# Print the result\n",
2124 |     "print(\"The Area with the highest sum in 2017 is:\", highest_sum_area)"
2125 |    ]
2126 |   },
2127 |   {
2128 |    "cell_type": "code",
2129 |    "execution_count": null,
2130 |    "id": "20548412",
2131 |    "metadata": {},
2132 |    "outputs": [],
2133 |    "source": []
2134 |   },
2135 |   {
2136 |    "cell_type": "markdown",
2137 |    "id": "233087eb",
2138 |    "metadata": {},
2139 |    "source": [
2140 |     "#### Question 16\n",
2141 |     "\n",
2142 |     "Consider the following list of tuples:\n",
2143 |     "\n",
2144 |     "y = [(2, 4), (7, 8), (1, 5, 9)]\n",
2145 |     "\n",
2146 |     "How would you assign element 8 from the list to a variable x?\n",
2147 |     "\n",
2148 |     "#### Ans:\n",
2149 |     "    \n",
2150 |     "B - x = y[1][1]"
2151 |    ]
2152 |   },
2153 |   {
2154 |    "cell_type": "code",
2155 |    "execution_count": null,
2156 |    "id": "b0e1ac71",
2157 |    "metadata": {},
2158 |    "outputs": [],
2159 |    "source": []
2160 |   },
2161 |   {
2162 |    "cell_type": "markdown",
2163 |    "id": "55249720",
2164 |    "metadata": {},
2165 |    "source": [
2166 |     "#### Question 17\n",
2167 |     "\n",
2168 |     "A pandas Dataframe with dimensions (100,3) has how many features and observations?\n",
2169 |     "\n",
2170 |     "#### Ans:\n",
2171 |     "    \n",
2172 |     "B - 3 features, 100 observations"
2173 |    ]
2174 |   },
2175 |   {
2176 |    "cell_type": "code",
2177 |    "execution_count": null,
2178 |    "id": "6e0e7026",
2179 |    "metadata": {},
2180 |    "outputs": [],
2181 |    "source": []
2182 |   },
2183 |   {
2184 |    "cell_type": "markdown",
2185 |    "id": "a2cb829b",
2186 |    "metadata": {},
2187 |    "source": [
2188 |     "#### Question 18\n",
2189 |     "\n",
2190 |     "Which of the following is a python inbuilt module?\n",
2191 |     "\n",
2192 |     "#### Ans:\n",
2193 |     "    \n",
2194 |     "C - Math"
2195 |    ]
2196 |   },
2197 |   {
2198 |    "cell_type": "code",
2199 |    "execution_count": null,
2200 |    "id": "197ce575",
2201 |    "metadata": {},
2202 |    "outputs": [],
2203 |    "source": []
2204 |   },
2205 |   {
2206 |    "cell_type": "markdown",
2207 |    "id": "30f39308",
2208 |    "metadata": {},
2209 |    "source": [
2210 |     "#### Question 19\n",
2211 |     "\n",
2212 |     "What is the total number of unique countries in the dataset?\n",
2213 |     "\n",
2214 |     "#### Ans:\n",
2215 |     "    \n",
2216 |     "A - 49"
2217 |    ]
2218 |   },
2219 |   {
2220 |    "cell_type": "code",
2221 |    "execution_count": 18,
2222 |    "id": "fe9cd365",
2223 |    "metadata": {},
2224 |    "outputs": [
2225 |     {
2226 |      "data": {
2227 |       "text/plain": [
2228 |        "49"
2229 |       ]
2230 |      },
2231 |      "execution_count": 18,
2232 |      "metadata": {},
2233 |      "output_type": "execute_result"
2234 |     }
2235 |    ],
2236 |    "source": [
2237 |     "# Get the total number of unique 'Area' in the dataframe\n",
2238 |     "df_FBS['Area'].nunique()"
2239 |    ]
2240 |   },
2241 |   {
2242 |    "cell_type": "code",
2243 |    "execution_count": null,
2244 |    "id": "9a000af9",
2245 |    "metadata": {},
2246 |    "outputs": [],
2247 |    "source": []
2248 |   },
2249 |   {
2250 |    "cell_type": "markdown",
2251 |    "id": "ca723cc5",
2252 |    "metadata": {},
2253 |    "source": [
2254 |     "#### Question 20\n",
2255 |     "\n",
2256 |     "What would be the output for?\n",
2257 |     "\n",
2258 |     "S = [['him', 'sell'], [90, 28, 43]]\n",
2259 |     "\n",
2260 |     "S[0][1][1]\n",
2261 |     "\n",
2262 |     "#### Ans:\n",
2263 |     "    \n",
2264 |     "D - 'e'"
2265 |    ]
2266 |   },
2267 |   {
2268 |    "cell_type": "code",
2269 |    "execution_count": null,
2270 |    "id": "00188d0d",
2271 |    "metadata": {},
2272 |    "outputs": [],
2273 |    "source": []
2274 |   },
2275 |   {
2276 |    "cell_type": "code",
2277 |    "execution_count": null,
2278 |    "id": "dce33880",
2279 |    "metadata": {},
2280 |    "outputs": [],
2281 |    "source": []
2282 |   }
2283 |  ],
2284 |  "metadata": {
2285 |   "kernelspec": {
2286 |    "display_name": "Python 3 (ipykernel)",
2287 |    "language": "python",
2288 |    "name": "python3"
2289 |   },
2290 |   "language_info": {
2291 |    "codemirror_mode": {
2292 |     "name": "ipython",
2293 |     "version": 3
2294 |    },
2295 |    "file_extension": ".py",
2296 |    "mimetype": "text/x-python",
2297 |    "name": "python",
2298 |    "nbconvert_exporter": "python",
2299 |    "pygments_lexer": "ipython3",
2300 |    "version": "3.11.5"
2301 |   }
2302 |  },
2303 |  "nbformat": 4,
2304 |  "nbformat_minor": 5
2305 | }
2306 | 


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