Codes for stage A project.ipynb

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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data1 = pd.read_excel(r'C:\\Users\\Doris\\Downloads\\FoodBalanceSheets_E_Africa_NOFLAG.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91c71ad1",
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       "      <th>Item Code</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
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       "    <tr>\n",
       "      <th>Y2014</th>\n",
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       "      <td>5.0</td>\n",
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       "      <td>3418.0</td>\n",
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       "      <td>2501</td>\n",
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       "      <td>2907</td>\n",
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       "           Area Code  Item Code  Element Code     Y2015     Y2016     Y2017  \\\n",
       "Y2014                                                                         \n",
       "-1796.0          159       2657          5072     -73.0     -12.0       0.0   \n",
       "-1631.0          159       2924          5072       6.0     -77.0    -689.0   \n",
       "-849.0           266       5444         10340   -1118.0   -1119.0   -1200.0   \n",
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       "-685.0           143       2905          5072    3418.0   -3225.0     689.0   \n",
       "...              ...        ...           ...       ...       ...       ...   \n",
       " 90425.0          59       2501           511   92443.0   94447.0   96443.0   \n",
       " 98094.0         238       2501           511  100835.0  103603.0  106400.0   \n",
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       " 176405.0        159       2501           511  181137.0  185960.0  190873.0   \n",
       "\n",
       "              Y2018  \n",
       "Y2014                \n",
       "-1796.0         0.0  \n",
       "-1631.0         5.0  \n",
       "-849.0      -1196.0  \n",
       "-838.0       -567.0  \n",
       "-685.0        399.0  \n",
       "...             ...  \n",
       " 90425.0    98424.0  \n",
       " 98094.0   109224.0  \n",
       " 108756.0  117038.0  \n",
       " 109593.0  118172.0  \n",
       " 176405.0  195875.0  \n",
       "\n",
       "[4493 rows x 7 columns]"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data1.groupby(['Y2014']).sum() #to get the sum of the column Y2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd1c1f52",
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       "      <th>-1582.0</th>\n",
       "      <td>118</td>\n",
       "      <td>5445</td>\n",
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       "      <td>5462.0</td>\n",
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       "      <th>-860.0</th>\n",
       "      <td>215</td>\n",
       "      <td>2556</td>\n",
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       "      <td>480.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>-212.0</td>\n",
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       "      <td>2501</td>\n",
       "      <td>511</td>\n",
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       "      <td>100835.0</td>\n",
       "      <td>103603.0</td>\n",
       "      <td>109224.0</td>\n",
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       "      <th>112625.0</th>\n",
       "      <td>159</td>\n",
       "      <td>2907</td>\n",
       "      <td>5301</td>\n",
       "      <td>108756.0</td>\n",
       "      <td>110798.0</td>\n",
       "      <td>116483.0</td>\n",
       "      <td>117038.0</td>\n",
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       "      <th>117702.0</th>\n",
       "      <td>159</td>\n",
       "      <td>2907</td>\n",
       "      <td>5511</td>\n",
       "      <td>109593.0</td>\n",
       "      <td>111663.0</td>\n",
       "      <td>119330.0</td>\n",
       "      <td>118172.0</td>\n",
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       "      <td>159</td>\n",
       "      <td>2501</td>\n",
       "      <td>511</td>\n",
       "      <td>176405.0</td>\n",
       "      <td>181137.0</td>\n",
       "      <td>185960.0</td>\n",
       "      <td>195875.0</td>\n",
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      "text/plain": [
       "           Area Code  Item Code  Element Code     Y2014     Y2015     Y2016  \\\n",
       "Y2017                                                                         \n",
       "-1582.0          118       5445         10144    5462.0      94.0   -1970.0   \n",
       "-1200.0          144       2532          5072       0.0       0.0     915.0   \n",
       "-1159.0          144       2907          5072    -219.0      -3.0     915.0   \n",
       "-865.0           215       2913          5072    2308.0    2505.0    -195.0   \n",
       "-860.0           215       2556          5072     480.0     572.0    -212.0   \n",
       "...              ...        ...           ...       ...       ...       ...   \n",
       " 96443.0          59       2501           511   90425.0   92443.0   94447.0   \n",
       " 106400.0        238       2501           511   98094.0  100835.0  103603.0   \n",
       " 112625.0        159       2907          5301  108756.0  110798.0  116483.0   \n",
       " 117702.0        159       2907          5511  109593.0  111663.0  119330.0   \n",
       " 190873.0        159       2501           511  176405.0  181137.0  185960.0   \n",
       "\n",
       "              Y2018  \n",
       "Y2017                \n",
       "-1582.0     -4312.0  \n",
       "-1200.0     -1445.0  \n",
       "-1159.0     -1442.0  \n",
       "-865.0        -14.0  \n",
       "-860.0        -61.0  \n",
       "...             ...  \n",
       " 96443.0    98424.0  \n",
       " 106400.0  109224.0  \n",
       " 112625.0  117038.0  \n",
       " 117702.0  118172.0  \n",
       " 190873.0  195875.0  \n",
       "\n",
       "[4537 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to get the sum of  Y2017\n",
    "data1.groupby(['Y2017']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13337a00",
   "metadata": {},
   "outputs": [
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       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
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       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y2015</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>-3161.0</th>\n",
       "      <td>202</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2905</td>\n",
       "      <td>Cereals - Excluding Beer</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>439.0</td>\n",
       "      <td>-1499.0</td>\n",
       "      <td>2665.0</td>\n",
       "      <td>-296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2152.0</th>\n",
       "      <td>202</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2514</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>626.0</td>\n",
       "      <td>-1324.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>-597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1284.0</th>\n",
       "      <td>276</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>2905</td>\n",
       "      <td>Cereals - Excluding Beer</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>4569.0</td>\n",
       "      <td>3266.0</td>\n",
       "      <td>-607.0</td>\n",
       "      <td>1898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-907.0</th>\n",
       "      <td>251</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2905</td>\n",
       "      <td>Cereals - Excluding Beer</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>520.0</td>\n",
       "      <td>-506.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>-529.0</td>\n",
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       "    <tr>\n",
       "      <th>-887.0</th>\n",
       "      <td>276</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>2518</td>\n",
       "      <td>Sorghum and products</td>\n",
       "      <td>5072</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3134.0</td>\n",
       "      <td>2432.0</td>\n",
       "      <td>-819.0</td>\n",
       "      <td>163.0</td>\n",
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       "    <tr>\n",
       "      <th>92443.0</th>\n",
       "      <td>59</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>511</td>\n",
       "      <td>Total Population - Both sexes</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>90425.0</td>\n",
       "      <td>94447.0</td>\n",
       "      <td>96443.0</td>\n",
       "      <td>98424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835.0</th>\n",
       "      <td>238</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>511</td>\n",
       "      <td>Total Population - Both sexes</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>98094.0</td>\n",
       "      <td>103603.0</td>\n",
       "      <td>106400.0</td>\n",
       "      <td>109224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110798.0</th>\n",
       "      <td>159</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2907</td>\n",
       "      <td>Starchy Roots</td>\n",
       "      <td>5301</td>\n",
       "      <td>Domestic supply quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>108756.0</td>\n",
       "      <td>116483.0</td>\n",
       "      <td>112625.0</td>\n",
       "      <td>117038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111663.0</th>\n",
       "      <td>159</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2907</td>\n",
       "      <td>Starchy Roots</td>\n",
       "      <td>5511</td>\n",
       "      <td>Production</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>109593.0</td>\n",
       "      <td>119330.0</td>\n",
       "      <td>117702.0</td>\n",
       "      <td>118172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181137.0</th>\n",
       "      <td>159</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>511</td>\n",
       "      <td>Total Population - Both sexes</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>176405.0</td>\n",
       "      <td>185960.0</td>\n",
       "      <td>190873.0</td>\n",
       "      <td>195875.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4520 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Code          Area  Item Code                      Item  \\\n",
       "Y2015                                                                     \n",
       "-3161.0          202  South Africa       2905  Cereals - Excluding Beer   \n",
       "-2152.0          202  South Africa       2514        Maize and products   \n",
       "-1284.0          276         Sudan       2905  Cereals - Excluding Beer   \n",
       "-907.0           251        Zambia       2905  Cereals - Excluding Beer   \n",
       "-887.0           276         Sudan       2518      Sorghum and products   \n",
       "...              ...           ...        ...                       ...   \n",
       " 92443.0          59         Egypt       2501                Population   \n",
       " 100835.0        238      Ethiopia       2501                Population   \n",
       " 110798.0        159       Nigeria       2907             Starchy Roots   \n",
       " 111663.0        159       Nigeria       2907             Starchy Roots   \n",
       " 181137.0        159       Nigeria       2501                Population   \n",
       "\n",
       "           Element Code                        Element          Unit  \\\n",
       "Y2015                                                                  \n",
       "-3161.0            5072                Stock Variation   1000 tonnes   \n",
       "-2152.0            5072                Stock Variation   1000 tonnes   \n",
       "-1284.0            5072                Stock Variation   1000 tonnes   \n",
       "-907.0             5072                Stock Variation   1000 tonnes   \n",
       "-887.0             5072                Stock Variation   1000 tonnes   \n",
       "...                 ...                            ...           ...   \n",
       " 92443.0            511  Total Population - Both sexes  1000 persons   \n",
       " 100835.0           511  Total Population - Both sexes  1000 persons   \n",
       " 110798.0          5301       Domestic supply quantity   1000 tonnes   \n",
       " 111663.0          5511                     Production   1000 tonnes   \n",
       " 181137.0           511  Total Population - Both sexes  1000 persons   \n",
       "\n",
       "              Y2014     Y2016     Y2017     Y2018  \n",
       "Y2015                                              \n",
       "-3161.0       439.0   -1499.0    2665.0    -296.0  \n",
       "-2152.0       626.0   -1324.0    2555.0    -597.0  \n",
       "-1284.0      4569.0    3266.0    -607.0    1898.0  \n",
       "-907.0        520.0    -506.0     532.0    -529.0  \n",
       "-887.0       3134.0    2432.0    -819.0     163.0  \n",
       "...             ...       ...       ...       ...  \n",
       " 92443.0    90425.0   94447.0   96443.0   98424.0  \n",
       " 100835.0   98094.0  103603.0  106400.0  109224.0  \n",
       " 110798.0  108756.0  116483.0  112625.0  117038.0  \n",
       " 111663.0  109593.0  119330.0  117702.0  118172.0  \n",
       " 181137.0  176405.0  185960.0  190873.0  195875.0  \n",
       "\n",
       "[4520 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Qs 12: mean and standard deviation to 3 decimal places\n",
    "data1.groupby(['Y2015']).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5eca8870",
   "metadata": {},
   "outputs": [],
   "source": [
    "# QS 13: What is the total number and percentage of missing data in 2016 to 2 decimal places?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "718fe741",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Y2014</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Element Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>Total Population - Both sexes</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>176405.00</td>\n",
       "      <td>181137.00</td>\n",
       "      <td>185960.00</td>\n",
       "      <td>190873.00</td>\n",
       "      <td>195875.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Food supply quantity (kg/capita/yr)</td>\n",
       "      <td>kg</td>\n",
       "      <td>428.27</td>\n",
       "      <td>418.21</td>\n",
       "      <td>419.48</td>\n",
       "      <td>433.38</td>\n",
       "      <td>443.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Food supply (kcal/capita/day)</td>\n",
       "      <td>kcal/capita/day</td>\n",
       "      <td>3435.00</td>\n",
       "      <td>3454.00</td>\n",
       "      <td>3451.00</td>\n",
       "      <td>3457.00</td>\n",
       "      <td>3450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Protein supply quantity (g/capita/day)</td>\n",
       "      <td>g/capita/day</td>\n",
       "      <td>100.34</td>\n",
       "      <td>100.33</td>\n",
       "      <td>100.94</td>\n",
       "      <td>100.10</td>\n",
       "      <td>101.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Fat supply quantity (g/capita/day)</td>\n",
       "      <td>g/capita/day</td>\n",
       "      <td>93.73</td>\n",
       "      <td>96.79</td>\n",
       "      <td>94.33</td>\n",
       "      <td>97.13</td>\n",
       "      <td>94.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5072</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Stock Variation</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>4569.00</td>\n",
       "      <td>3418.00</td>\n",
       "      <td>3266.00</td>\n",
       "      <td>5089.00</td>\n",
       "      <td>2226.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5123</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2949</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Losses</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>9807.00</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>10432.00</td>\n",
       "      <td>10342.00</td>\n",
       "      <td>10355.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5131</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Wine</td>\n",
       "      <td>Processing</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>18383.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>21739.00</td>\n",
       "      <td>22174.00</td>\n",
       "      <td>22609.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5142</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>49014.00</td>\n",
       "      <td>50705.00</td>\n",
       "      <td>52612.00</td>\n",
       "      <td>51220.00</td>\n",
       "      <td>54077.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5154</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Other uses (non-food)</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>12552.00</td>\n",
       "      <td>6001.00</td>\n",
       "      <td>6099.00</td>\n",
       "      <td>17219.00</td>\n",
       "      <td>17221.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5170</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2949</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Residuals</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>16697.00</td>\n",
       "      <td>16136.00</td>\n",
       "      <td>18077.00</td>\n",
       "      <td>17476.00</td>\n",
       "      <td>17323.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5171</th>\n",
       "      <td>193</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>2949</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Tourist consumption</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>62.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5301</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Domestic supply quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>108756.00</td>\n",
       "      <td>110798.00</td>\n",
       "      <td>116483.00</td>\n",
       "      <td>112625.00</td>\n",
       "      <td>117038.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5511</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Production</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>109593.00</td>\n",
       "      <td>111663.00</td>\n",
       "      <td>119330.00</td>\n",
       "      <td>117702.00</td>\n",
       "      <td>118172.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5521</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>29797.00</td>\n",
       "      <td>30472.00</td>\n",
       "      <td>31607.00</td>\n",
       "      <td>29580.00</td>\n",
       "      <td>31567.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5527</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Seed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>1258.00</td>\n",
       "      <td>1352.00</td>\n",
       "      <td>1402.00</td>\n",
       "      <td>1453.00</td>\n",
       "      <td>1474.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5611</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Import Quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>19582.00</td>\n",
       "      <td>18824.00</td>\n",
       "      <td>19858.00</td>\n",
       "      <td>18627.00</td>\n",
       "      <td>18036.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5911</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>Export Quantity</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>39686.00</td>\n",
       "      <td>52578.00</td>\n",
       "      <td>35420.00</td>\n",
       "      <td>34641.00</td>\n",
       "      <td>34641.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Area Code                   Area  Item Code        Item  \\\n",
       "Element Code                                                            \n",
       "511                 276               Zimbabwe       2501  Population   \n",
       "645                 276               Zimbabwe       2961        Yams   \n",
       "664                 276               Zimbabwe       2961        Yams   \n",
       "674                 276               Zimbabwe       2961        Yams   \n",
       "684                 276               Zimbabwe       2961        Yams   \n",
       "5072                276               Zimbabwe       2961        Yams   \n",
       "5123                276               Zimbabwe       2949        Yams   \n",
       "5131                276               Zimbabwe       2948        Wine   \n",
       "5142                276               Zimbabwe       2961        Yams   \n",
       "5154                276               Zimbabwe       2961        Yams   \n",
       "5170                276               Zimbabwe       2949        Yams   \n",
       "5171                193  Sao Tome and Principe       2949        Yams   \n",
       "5301                276               Zimbabwe       2961        Yams   \n",
       "5511                276               Zimbabwe       2961        Yams   \n",
       "5521                276               Zimbabwe       2960        Yams   \n",
       "5527                276               Zimbabwe       2960        Yams   \n",
       "5611                276               Zimbabwe       2961        Yams   \n",
       "5911                276               Zimbabwe       2961        Yams   \n",
       "\n",
       "                                             Element             Unit  \\\n",
       "Element Code                                                            \n",
       "511                    Total Population - Both sexes     1000 persons   \n",
       "645              Food supply quantity (kg/capita/yr)               kg   \n",
       "664                    Food supply (kcal/capita/day)  kcal/capita/day   \n",
       "674           Protein supply quantity (g/capita/day)     g/capita/day   \n",
       "684               Fat supply quantity (g/capita/day)     g/capita/day   \n",
       "5072                                 Stock Variation      1000 tonnes   \n",
       "5123                                          Losses      1000 tonnes   \n",
       "5131                                      Processing      1000 tonnes   \n",
       "5142                                            Food      1000 tonnes   \n",
       "5154                           Other uses (non-food)      1000 tonnes   \n",
       "5170                                       Residuals      1000 tonnes   \n",
       "5171                             Tourist consumption      1000 tonnes   \n",
       "5301                        Domestic supply quantity      1000 tonnes   \n",
       "5511                                      Production      1000 tonnes   \n",
       "5521                                            Feed      1000 tonnes   \n",
       "5527                                            Seed      1000 tonnes   \n",
       "5611                                 Import Quantity      1000 tonnes   \n",
       "5911                                 Export Quantity      1000 tonnes   \n",
       "\n",
       "                  Y2014      Y2015      Y2016      Y2017      Y2018  \n",
       "Element Code                                                         \n",
       "511           176405.00  181137.00  185960.00  190873.00  195875.00  \n",
       "645              428.27     418.21     419.48     433.38     443.56  \n",
       "664             3435.00    3454.00    3451.00    3457.00    3450.00  \n",
       "674              100.34     100.33     100.94     100.10     101.08  \n",
       "684               93.73      96.79      94.33      97.13      94.94  \n",
       "5072            4569.00    3418.00    3266.00    5089.00    2226.00  \n",
       "5123            9807.00    9999.00   10432.00   10342.00   10355.00  \n",
       "5131           18383.00   21609.00   21739.00   22174.00   22609.00  \n",
       "5142           49014.00   50705.00   52612.00   51220.00   54077.00  \n",
       "5154           12552.00    6001.00    6099.00   17219.00   17221.00  \n",
       "5170           16697.00   16136.00   18077.00   17476.00   17323.00  \n",
       "5171              62.00      48.00       6.00       6.00       6.00  \n",
       "5301          108756.00  110798.00  116483.00  112625.00  117038.00  \n",
       "5511          109593.00  111663.00  119330.00  117702.00  118172.00  \n",
       "5521           29797.00   30472.00   31607.00   29580.00   31567.00  \n",
       "5527            1258.00    1352.00    1402.00    1453.00    1474.00  \n",
       "5611           19582.00   18824.00   19858.00   18627.00   18036.00  \n",
       "5911           39686.00   52578.00   35420.00   34641.00   34641.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Qs 14: Which year had the highest correlation with ‘Element Code’? Answer is Y2017\n",
    "data1.groupby(\"Element Code\").max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67981be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Y2014</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Element</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Domestic supply quantity</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5301</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>108756.00</td>\n",
       "      <td>110798.00</td>\n",
       "      <td>116483.00</td>\n",
       "      <td>112625.00</td>\n",
       "      <td>117038.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Export Quantity</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5911</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>39686.00</td>\n",
       "      <td>52578.00</td>\n",
       "      <td>35420.00</td>\n",
       "      <td>34641.00</td>\n",
       "      <td>34641.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fat supply quantity (g/capita/day)</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>684</td>\n",
       "      <td>g/capita/day</td>\n",
       "      <td>93.73</td>\n",
       "      <td>96.79</td>\n",
       "      <td>94.33</td>\n",
       "      <td>97.13</td>\n",
       "      <td>94.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feed</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5521</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>29797.00</td>\n",
       "      <td>30472.00</td>\n",
       "      <td>31607.00</td>\n",
       "      <td>29580.00</td>\n",
       "      <td>31567.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5142</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>49014.00</td>\n",
       "      <td>50705.00</td>\n",
       "      <td>52612.00</td>\n",
       "      <td>51220.00</td>\n",
       "      <td>54077.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food supply (kcal/capita/day)</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>664</td>\n",
       "      <td>kcal/capita/day</td>\n",
       "      <td>3435.00</td>\n",
       "      <td>3454.00</td>\n",
       "      <td>3451.00</td>\n",
       "      <td>3457.00</td>\n",
       "      <td>3450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food supply quantity (kg/capita/yr)</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>645</td>\n",
       "      <td>kg</td>\n",
       "      <td>428.27</td>\n",
       "      <td>418.21</td>\n",
       "      <td>419.48</td>\n",
       "      <td>433.38</td>\n",
       "      <td>443.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Import Quantity</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5611</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>19582.00</td>\n",
       "      <td>18824.00</td>\n",
       "      <td>19858.00</td>\n",
       "      <td>18627.00</td>\n",
       "      <td>18036.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Losses</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2949</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5123</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>9807.00</td>\n",
       "      <td>9999.00</td>\n",
       "      <td>10432.00</td>\n",
       "      <td>10342.00</td>\n",
       "      <td>10355.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other uses (non-food)</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5154</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>12552.00</td>\n",
       "      <td>6001.00</td>\n",
       "      <td>6099.00</td>\n",
       "      <td>17219.00</td>\n",
       "      <td>17221.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Processing</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Wine</td>\n",
       "      <td>5131</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>18383.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>21739.00</td>\n",
       "      <td>22174.00</td>\n",
       "      <td>22609.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Production</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5511</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>109593.00</td>\n",
       "      <td>111663.00</td>\n",
       "      <td>119330.00</td>\n",
       "      <td>117702.00</td>\n",
       "      <td>118172.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein supply quantity (g/capita/day)</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>674</td>\n",
       "      <td>g/capita/day</td>\n",
       "      <td>100.34</td>\n",
       "      <td>100.33</td>\n",
       "      <td>100.94</td>\n",
       "      <td>100.10</td>\n",
       "      <td>101.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residuals</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2949</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5170</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>16697.00</td>\n",
       "      <td>16136.00</td>\n",
       "      <td>18077.00</td>\n",
       "      <td>17476.00</td>\n",
       "      <td>17323.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seed</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5527</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>1258.00</td>\n",
       "      <td>1352.00</td>\n",
       "      <td>1402.00</td>\n",
       "      <td>1453.00</td>\n",
       "      <td>1474.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock Variation</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5072</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>4569.00</td>\n",
       "      <td>3418.00</td>\n",
       "      <td>3266.00</td>\n",
       "      <td>5089.00</td>\n",
       "      <td>2226.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Population - Both sexes</th>\n",
       "      <td>276</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2501</td>\n",
       "      <td>Population</td>\n",
       "      <td>511</td>\n",
       "      <td>1000 persons</td>\n",
       "      <td>176405.00</td>\n",
       "      <td>181137.00</td>\n",
       "      <td>185960.00</td>\n",
       "      <td>190873.00</td>\n",
       "      <td>195875.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tourist consumption</th>\n",
       "      <td>193</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>2949</td>\n",
       "      <td>Yams</td>\n",
       "      <td>5171</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>62.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Area Code                   Area  \\\n",
       "Element                                                                    \n",
       "Domestic supply quantity                      276               Zimbabwe   \n",
       "Export Quantity                               276               Zimbabwe   \n",
       "Fat supply quantity (g/capita/day)            276               Zimbabwe   \n",
       "Feed                                          276               Zimbabwe   \n",
       "Food                                          276               Zimbabwe   \n",
       "Food supply (kcal/capita/day)                 276               Zimbabwe   \n",
       "Food supply quantity (kg/capita/yr)           276               Zimbabwe   \n",
       "Import Quantity                               276               Zimbabwe   \n",
       "Losses                                        276               Zimbabwe   \n",
       "Other uses (non-food)                         276               Zimbabwe   \n",
       "Processing                                    276               Zimbabwe   \n",
       "Production                                    276               Zimbabwe   \n",
       "Protein supply quantity (g/capita/day)        276               Zimbabwe   \n",
       "Residuals                                     276               Zimbabwe   \n",
       "Seed                                          276               Zimbabwe   \n",
       "Stock Variation                               276               Zimbabwe   \n",
       "Total Population - Both sexes                 276               Zimbabwe   \n",
       "Tourist consumption                           193  Sao Tome and Principe   \n",
       "\n",
       "                                        Item Code        Item  Element Code  \\\n",
       "Element                                                                       \n",
       "Domestic supply quantity                     2961        Yams          5301   \n",
       "Export Quantity                              2961        Yams          5911   \n",
       "Fat supply quantity (g/capita/day)           2961        Yams           684   \n",
       "Feed                                         2960        Yams          5521   \n",
       "Food                                         2961        Yams          5142   \n",
       "Food supply (kcal/capita/day)                2961        Yams           664   \n",
       "Food supply quantity (kg/capita/yr)          2961        Yams           645   \n",
       "Import Quantity                              2961        Yams          5611   \n",
       "Losses                                       2949        Yams          5123   \n",
       "Other uses (non-food)                        2961        Yams          5154   \n",
       "Processing                                   2948        Wine          5131   \n",
       "Production                                   2961        Yams          5511   \n",
       "Protein supply quantity (g/capita/day)       2961        Yams           674   \n",
       "Residuals                                    2949        Yams          5170   \n",
       "Seed                                         2960        Yams          5527   \n",
       "Stock Variation                              2961        Yams          5072   \n",
       "Total Population - Both sexes                2501  Population           511   \n",
       "Tourist consumption                          2949        Yams          5171   \n",
       "\n",
       "                                                   Unit      Y2014      Y2015  \\\n",
       "Element                                                                         \n",
       "Domestic supply quantity                    1000 tonnes  108756.00  110798.00   \n",
       "Export Quantity                             1000 tonnes   39686.00   52578.00   \n",
       "Fat supply quantity (g/capita/day)         g/capita/day      93.73      96.79   \n",
       "Feed                                        1000 tonnes   29797.00   30472.00   \n",
       "Food                                        1000 tonnes   49014.00   50705.00   \n",
       "Food supply (kcal/capita/day)           kcal/capita/day    3435.00    3454.00   \n",
       "Food supply quantity (kg/capita/yr)                  kg     428.27     418.21   \n",
       "Import Quantity                             1000 tonnes   19582.00   18824.00   \n",
       "Losses                                      1000 tonnes    9807.00    9999.00   \n",
       "Other uses (non-food)                       1000 tonnes   12552.00    6001.00   \n",
       "Processing                                  1000 tonnes   18383.00   21609.00   \n",
       "Production                                  1000 tonnes  109593.00  111663.00   \n",
       "Protein supply quantity (g/capita/day)     g/capita/day     100.34     100.33   \n",
       "Residuals                                   1000 tonnes   16697.00   16136.00   \n",
       "Seed                                        1000 tonnes    1258.00    1352.00   \n",
       "Stock Variation                             1000 tonnes    4569.00    3418.00   \n",
       "Total Population - Both sexes              1000 persons  176405.00  181137.00   \n",
       "Tourist consumption                         1000 tonnes      62.00      48.00   \n",
       "\n",
       "                                            Y2016      Y2017      Y2018  \n",
       "Element                                                                  \n",
       "Domestic supply quantity                116483.00  112625.00  117038.00  \n",
       "Export Quantity                          35420.00   34641.00   34641.00  \n",
       "Fat supply quantity (g/capita/day)          94.33      97.13      94.94  \n",
       "Feed                                     31607.00   29580.00   31567.00  \n",
       "Food                                     52612.00   51220.00   54077.00  \n",
       "Food supply (kcal/capita/day)             3451.00    3457.00    3450.00  \n",
       "Food supply quantity (kg/capita/yr)        419.48     433.38     443.56  \n",
       "Import Quantity                          19858.00   18627.00   18036.00  \n",
       "Losses                                   10432.00   10342.00   10355.00  \n",
       "Other uses (non-food)                     6099.00   17219.00   17221.00  \n",
       "Processing                               21739.00   22174.00   22609.00  \n",
       "Production                              119330.00  117702.00  118172.00  \n",
       "Protein supply quantity (g/capita/day)     100.94     100.10     101.08  \n",
       "Residuals                                18077.00   17476.00   17323.00  \n",
       "Seed                                      1402.00    1453.00    1474.00  \n",
       "Stock Variation                           3266.00    5089.00    2226.00  \n",
       "Total Population - Both sexes           185960.00  190873.00  195875.00  \n",
       "Tourist consumption                          6.00       6.00       6.00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Qs 15:What year has the highest sum of Import Quantity Answer is Y2016\n",
    "data1.groupby(\"Element\").max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6007fc20",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Production'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15848/3469476763.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Qs 16: What is the total number of the sum of Production in 2014?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Production\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Y2014\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\ANACONDA\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[0;32m   7629\u001b[0m         \u001b[1;31m# error: Argument \"squeeze\" to \"DataFrameGroupBy\" has incompatible type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7630\u001b[0m         \u001b[1;31m# \"Union[bool, NoDefault]\"; expected \"bool\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7631\u001b[1;33m         return DataFrameGroupBy(\n\u001b[0m\u001b[0;32m   7632\u001b[0m             \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7633\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\ANACONDA\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[0;32m    887\u001b[0m             \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[0;32m    890\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\ANACONDA\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[0;32m    860\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 862\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    863\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    864\u001b[0m             \u001b[1;31m# Add key to exclusions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Production'"
     ]
    }
   ],
   "source": [
    "#Qs 16: What is the total number of the sum of Production in 2014?\n",
    "data1.groupby([\"Production\"])([\"Y2014\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "804982cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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