Quiz_1_Project_Food_Balance_Sheets_Africa.ipynb

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      "name": "Quiz 1 Project - Food_Balance_Sheets_Africa.ipynb",
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        "<a href=\"https://colab.research.google.com/github/JayOaks/HDSI-Study-Projects/blob/main/Quiz_1_Project_Food_Balance_Sheets_Africa.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Importing pandas library"
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      "metadata": {
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      "source": [
        "import pandas as pd"
      ]
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      "cell_type": "markdown",
      "source": [
        "Creating dataframe / reading file.\n",
        "Remember to upload file seperately into contents folder under the files tab or file will not be located."
      ],
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      "source": [
        "#Reading the file failed because utf-8 could not encode some values hence the encoding in 'latin-1'\n",
        "df = pd.read_csv('FoodBalanceSheetsAfrica.csv', encoding='latin-1')"
      ],
      "metadata": {
        "id": "Iyhbty7ddfA9"
      },
      "execution_count": 31,
      "outputs": []
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        "Visualizing dataframe"
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        "df.head()"
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              "   Area Code     Area  Item Code         Item  Element Code  \\\n",
              "0          4  Algeria       2501   Population           511   \n",
              "1          4  Algeria       2501   Population          5301   \n",
              "2          4  Algeria       2901  Grand Total           664   \n",
              "3          4  Algeria       2901  Grand Total           674   \n",
              "4          4  Algeria       2901  Grand Total           684   \n",
              "\n",
              "                                  Element             Unit     Y2014  \\\n",
              "0           Total Population - Both sexes     1000 persons  38924.00   \n",
              "1                Domestic supply quantity      1000 tonnes      0.00   \n",
              "2           Food supply (kcal/capita/day)  kcal/capita/day   3377.00   \n",
              "3  Protein supply quantity (g/capita/day)     g/capita/day     94.90   \n",
              "4      Fat supply quantity (g/capita/day)     g/capita/day     80.06   \n",
              "\n",
              "      Y2015     Y2016     Y2017     Y2018  \n",
              "0  39728.00  40551.00  41389.00  42228.00  \n",
              "1      0.00      0.00      0.00      0.00  \n",
              "2   3379.00   3372.00   3341.00   3322.00  \n",
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        "Finding the sum of each item and grouping them by item"
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      "source": [
        "#Grouping each item in the 'Item' column and finding their sums\n",
        "df.groupby(['Item']).sum()"
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              "                     Area Code  Item Code  Element Code      Y2014      Y2015  \\\n",
              "Item                                                                            \n",
              "Alcohol, Non-Food        38447     752497       1515090    2403.00    2180.00   \n",
              "Alcoholic Beverages      75016    1649136       2171381  102410.11   98783.72   \n",
              "Animal Products          18060     397035         90990   11935.65   11811.73   \n",
              "Animal fats              85279    1882494       2567303  209460.54  200675.72   \n",
              "Apples and products      69280    1352989       1920418    9499.23   10559.15   \n",
              "...                        ...        ...           ...        ...        ...   \n",
              "Vegetables, Other        77779    1518715       2275409  155038.96  158104.08   \n",
              "Vegetal Products         18060     391905         90990  107145.19  107064.17   \n",
              "Wheat and products       87073    1639683       2654934  232670.13  234710.51   \n",
              "Wine                     66206    1319535       1818328    4497.36    4251.81   \n",
              "Yams                     50798     970905       1525745  200396.96  203151.78   \n",
              "\n",
              "                         Y2016      Y2017      Y2018  \n",
              "Item                                                  \n",
              "Alcohol, Non-Food      2223.00    2348.00    2293.00  \n",
              "Alcoholic Beverages   96958.75   95581.06   97847.27  \n",
              "Animal Products       11661.69   11547.65   11578.61  \n",
              "Animal fats          183314.22  269617.53  269648.27  \n",
              "Apples and products    9853.26   10198.90    9640.51  \n",
              "...                        ...        ...        ...  \n",
              "Vegetables, Other    156218.90  157752.59  163987.21  \n",
              "Vegetal Products     106792.72  107655.20  107775.39  \n",
              "Wheat and products   239377.07  240047.62  242645.19  \n",
              "Wine                   3872.09    4178.02    4039.32  \n",
              "Yams                 220626.20  229174.59  221272.09  \n",
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              "      <th>Alcohol, Non-Food</th>\n",
              "      <td>38447</td>\n",
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              "      <th>Animal fats</th>\n",
              "      <td>85279</td>\n",
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              "      <td>209460.54</td>\n",
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              "      <td>183314.22</td>\n",
              "      <td>269617.53</td>\n",
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              "      <td>158104.08</td>\n",
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              "      <th>Wheat and products</th>\n",
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              "      <td>239377.07</td>\n",
              "      <td>240047.62</td>\n",
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              "      <td>4497.36</td>\n",
              "      <td>4251.81</td>\n",
              "      <td>3872.09</td>\n",
              "      <td>4178.02</td>\n",
              "      <td>4039.32</td>\n",
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              "      <td>200396.96</td>\n",
              "      <td>203151.78</td>\n",
              "      <td>220626.20</td>\n",
              "      <td>229174.59</td>\n",
              "      <td>221272.09</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the Mean across the entire dataset"
      ],
      "metadata": {
        "id": "5HQZdfxrkiDE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating the mean across the entire dataset\n",
        "df.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJcPiJYwhdDO",
        "outputId": "103a0eed-9616-4b1f-8094-c05e6aab6976"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area Code        134.265576\n",
              "Item Code       2687.176706\n",
              "Element Code    3814.856456\n",
              "Y2014            134.196282\n",
              "Y2015            135.235966\n",
              "Y2016            136.555222\n",
              "Y2017            140.917765\n",
              "Y2018            143.758381\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the Standard Deviation across the entire dataset"
      ],
      "metadata": {
        "id": "lxZC5pdeku3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating the standard deviation across the entire dataset\n",
        "df.std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VkZ33I9h4jf",
        "outputId": "4299981b-8c18-4b95-b9bc-74d6198b6718"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area Code         72.605709\n",
              "Item Code        146.055739\n",
              "Element Code    2212.007033\n",
              "Y2014           1567.663696\n",
              "Y2015           1603.403984\n",
              "Y2016           1640.007194\n",
              "Y2017           1671.862359\n",
              "Y2018           1710.782658\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating the total numer and percentage of missing data in \n",
        " - the entire dataset\n",
        " - 2016"
      ],
      "metadata": {
        "id": "YQCmHz4miayv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z32y-IeQiRc3",
        "outputId": "5d7000ba-ff70-43f7-8175-4b9c128f6db4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7614"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum().sum() * 100 / len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IA2ZTtbjVS2",
        "outputId": "8adcf2a3-fea5-44b7-c90f-12b872393f7e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.493641599527427"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Y2016'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdIdXUm8iwvR",
        "outputId": "e73da79a-fdec-4626-fee4-fd3095655790"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1535"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Y2016'].isna().sum() * 100 / len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNPbLv2YjGa7",
        "outputId": "6ec45fcf-e1d9-4077-d3ce-3635f7343695"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.518747025909456"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation"
      ],
      "metadata": {
        "id": "j8KU8Jm4mIti"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Finding the year with the highest correlation to 'Element Code'\n",
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SolwuelPlP7d",
        "outputId": "a39924e4-2ddc-4363-b02f-aea33c69376d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Area Code  Item Code  Element Code     Y2014     Y2015  \\\n",
              "Area Code      1.000000  -0.005159     -0.000209  0.006164  0.005472   \n",
              "Item Code     -0.005159   1.000000     -0.024683  0.021722  0.020857   \n",
              "Element Code  -0.000209  -0.024683      1.000000  0.024457  0.023889   \n",
              "Y2014          0.006164   0.021722      0.024457  1.000000  0.994647   \n",
              "Y2015          0.005472   0.020857      0.023889  0.994647  1.000000   \n",
              "Y2016          0.005247   0.020109      0.023444  0.996081  0.995739   \n",
              "Y2017          0.005006   0.021494      0.024254  0.995230  0.988048   \n",
              "Y2018          0.005665   0.021314      0.024279  0.994872  0.988208   \n",
              "\n",
              "                 Y2016     Y2017     Y2018  \n",
              "Area Code     0.005247  0.005006  0.005665  \n",
              "Item Code     0.020109  0.021494  0.021314  \n",
              "Element Code  0.023444  0.024254  0.024279  \n",
              "Y2014         0.996081  0.995230  0.994872  \n",
              "Y2015         0.995739  0.988048  0.988208  \n",
              "Y2016         1.000000  0.992785  0.992757  \n",
              "Y2017         0.992785  1.000000  0.998103  \n",
              "Y2018         0.992757  0.998103  1.000000  "
            ],
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              "      <th>Area Code</th>\n",
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              "      <th>Area Code</th>\n",
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              "      <th>Item Code</th>\n",
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              "      <th>Element Code</th>\n",
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              "    <tr>\n",
              "      <th>Y2014</th>\n",
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              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>Y2015</th>\n",
              "      <td>0.005472</td>\n",
              "      <td>0.020857</td>\n",
              "      <td>0.023889</td>\n",
              "      <td>0.994647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995739</td>\n",
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              "    <tr>\n",
              "      <th>Y2016</th>\n",
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              "      <td>0.020109</td>\n",
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              "      <td>0.996081</td>\n",
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              "      <td>0.992785</td>\n",
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              "    <tr>\n",
              "      <th>Y2017</th>\n",
              "      <td>0.005006</td>\n",
              "      <td>0.021494</td>\n",
              "      <td>0.024254</td>\n",
              "      <td>0.995230</td>\n",
              "      <td>0.988048</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <th>Y2018</th>\n",
              "      <td>0.005665</td>\n",
              "      <td>0.021314</td>\n",
              "      <td>0.024279</td>\n",
              "      <td>0.994872</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Highest Sum of Import Quantity, Sum of Production in 2014, Element with highest sum in 2018, Element with 3rd lowest sum in 2018"
      ],
      "metadata": {
        "id": "JB7RT-FzmaEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Finding the year with highest sum of 'Import Quantity', Sum of Production in 2014, Element with highest sum in 2018, Element with 3rd lowest sum in 2018\n",
        "df.groupby(['Element']).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "                                        Area Code  Item Code  Element Code  \\\n",
              "Element                                                                      \n",
              "Domestic supply quantity                   708993   14197445      28068795   \n",
              "Export Quantity                            599910   11840553      26026133   \n",
              "Fat supply quantity (g/capita/day)         675050   13535000       3435732   \n",
              "Feed                                       176272    3538507       7282199   \n",
              "Food                                       663295   13285035      25406622   \n",
              "Food supply (kcal/capita/day)              674057   13511060       3329296   \n",
              "Food supply quantity (kg/capita/yr)        658446   13185401       3163725   \n",
              "Import Quantity                            688174   13795966      28834929   \n",
              "Losses                                     274353    5424803      10292107   \n",
              "Other uses (non-food)                      235554    4729749       8926728   \n",
              "Processing                                 271940    5350416      10313310   \n",
              "Production                                 526751   10450053      21388191   \n",
              "Protein supply quantity (g/capita/day)     675050   13535000       3385502   \n",
              "Residuals                                  623271   12421089      24066350   \n",
              "Seed                                       103537    2035933       4211574   \n",
              "Stock Variation                            571566   11329527      21464704   \n",
              "Total Population - Both sexes                6020     112545         22995   \n",
              "Tourist consumption                         50308    1486528       2869905   \n",
              "\n",
              "                                             Y2014       Y2015       Y2016  \\\n",
              "Element                                                                      \n",
              "Domestic supply quantity                1996716.35  2021493.55  2044842.70   \n",
              "Export Quantity                          150020.64   157614.47   151920.46   \n",
              "Fat supply quantity (g/capita/day)        10225.56    10235.74    10102.77   \n",
              "Feed                                     216927.89   225050.22   228958.65   \n",
              "Food                                    1212332.49  1232361.10  1247022.17   \n",
              "Food supply (kcal/capita/day)            454257.00   453383.00   451810.00   \n",
              "Food supply quantity (kg/capita/yr)       49650.63    49345.13    48985.28   \n",
              "Import Quantity                          274144.48   267018.46   286582.78   \n",
              "Losses                                   153223.00   155439.00   157787.00   \n",
              "Other uses (non-food)                     78718.13    66254.41    69563.68   \n",
              "Processing                               282923.00   287929.00   280631.00   \n",
              "Production                              1931287.75  1947019.39  1943537.15   \n",
              "Protein supply quantity (g/capita/day)    11836.46    11833.95    11779.69   \n",
              "Residuals                                 30149.00    30045.00    37224.00   \n",
              "Seed                                      21922.92    23976.82    23389.20   \n",
              "Stock Variation                           58749.83    34910.99    33140.12   \n",
              "Total Population - Both sexes           1031585.00  1058081.00  1085107.00   \n",
              "Tourist consumption                         416.00      349.00       89.00   \n",
              "\n",
              "                                             Y2017       Y2018  \n",
              "Element                                                         \n",
              "Domestic supply quantity                2088198.10  2161192.10  \n",
              "Export Quantity                          182338.80   181594.80  \n",
              "Fat supply quantity (g/capita/day)        10253.84    10258.69  \n",
              "Feed                                     223705.68   233489.68  \n",
              "Food                                    1258888.28  1303841.28  \n",
              "Food supply (kcal/capita/day)            454681.00   455261.00  \n",
              "Food supply quantity (kg/capita/yr)       48690.04    49056.85  \n",
              "Import Quantity                          294559.09   287997.09  \n",
              "Losses                                   160614.00   163902.00  \n",
              "Other uses (non-food)                     91645.97    91300.97  \n",
              "Processing                               292836.00   308429.00  \n",
              "Production                              2030056.89  2075072.89  \n",
              "Protein supply quantity (g/capita/day)    11842.45    11833.56  \n",
              "Residuals                                 35500.00    34864.00  \n",
              "Seed                                      24870.14    25263.14  \n",
              "Stock Variation                           54316.91    20577.91  \n",
              "Total Population - Both sexes           1112641.00  1140605.00  \n",
              "Tourist consumption                          91.00       90.00  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area Code</th>\n",
              "      <th>Item Code</th>\n",
              "      <th>Element Code</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Domestic supply quantity</th>\n",
              "      <td>708993</td>\n",
              "      <td>14197445</td>\n",
              "      <td>28068795</td>\n",
              "      <td>1996716.35</td>\n",
              "      <td>2021493.55</td>\n",
              "      <td>2044842.70</td>\n",
              "      <td>2088198.10</td>\n",
              "      <td>2161192.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Export Quantity</th>\n",
              "      <td>599910</td>\n",
              "      <td>11840553</td>\n",
              "      <td>26026133</td>\n",
              "      <td>150020.64</td>\n",
              "      <td>157614.47</td>\n",
              "      <td>151920.46</td>\n",
              "      <td>182338.80</td>\n",
              "      <td>181594.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fat supply quantity (g/capita/day)</th>\n",
              "      <td>675050</td>\n",
              "      <td>13535000</td>\n",
              "      <td>3435732</td>\n",
              "      <td>10225.56</td>\n",
              "      <td>10235.74</td>\n",
              "      <td>10102.77</td>\n",
              "      <td>10253.84</td>\n",
              "      <td>10258.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feed</th>\n",
              "      <td>176272</td>\n",
              "      <td>3538507</td>\n",
              "      <td>7282199</td>\n",
              "      <td>216927.89</td>\n",
              "      <td>225050.22</td>\n",
              "      <td>228958.65</td>\n",
              "      <td>223705.68</td>\n",
              "      <td>233489.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food</th>\n",
              "      <td>663295</td>\n",
              "      <td>13285035</td>\n",
              "      <td>25406622</td>\n",
              "      <td>1212332.49</td>\n",
              "      <td>1232361.10</td>\n",
              "      <td>1247022.17</td>\n",
              "      <td>1258888.28</td>\n",
              "      <td>1303841.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food supply (kcal/capita/day)</th>\n",
              "      <td>674057</td>\n",
              "      <td>13511060</td>\n",
              "      <td>3329296</td>\n",
              "      <td>454257.00</td>\n",
              "      <td>453383.00</td>\n",
              "      <td>451810.00</td>\n",
              "      <td>454681.00</td>\n",
              "      <td>455261.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food supply quantity (kg/capita/yr)</th>\n",
              "      <td>658446</td>\n",
              "      <td>13185401</td>\n",
              "      <td>3163725</td>\n",
              "      <td>49650.63</td>\n",
              "      <td>49345.13</td>\n",
              "      <td>48985.28</td>\n",
              "      <td>48690.04</td>\n",
              "      <td>49056.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Import Quantity</th>\n",
              "      <td>688174</td>\n",
              "      <td>13795966</td>\n",
              "      <td>28834929</td>\n",
              "      <td>274144.48</td>\n",
              "      <td>267018.46</td>\n",
              "      <td>286582.78</td>\n",
              "      <td>294559.09</td>\n",
              "      <td>287997.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Losses</th>\n",
              "      <td>274353</td>\n",
              "      <td>5424803</td>\n",
              "      <td>10292107</td>\n",
              "      <td>153223.00</td>\n",
              "      <td>155439.00</td>\n",
              "      <td>157787.00</td>\n",
              "      <td>160614.00</td>\n",
              "      <td>163902.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other uses (non-food)</th>\n",
              "      <td>235554</td>\n",
              "      <td>4729749</td>\n",
              "      <td>8926728</td>\n",
              "      <td>78718.13</td>\n",
              "      <td>66254.41</td>\n",
              "      <td>69563.68</td>\n",
              "      <td>91645.97</td>\n",
              "      <td>91300.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Processing</th>\n",
              "      <td>271940</td>\n",
              "      <td>5350416</td>\n",
              "      <td>10313310</td>\n",
              "      <td>282923.00</td>\n",
              "      <td>287929.00</td>\n",
              "      <td>280631.00</td>\n",
              "      <td>292836.00</td>\n",
              "      <td>308429.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Production</th>\n",
              "      <td>526751</td>\n",
              "      <td>10450053</td>\n",
              "      <td>21388191</td>\n",
              "      <td>1931287.75</td>\n",
              "      <td>1947019.39</td>\n",
              "      <td>1943537.15</td>\n",
              "      <td>2030056.89</td>\n",
              "      <td>2075072.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Protein supply quantity (g/capita/day)</th>\n",
              "      <td>675050</td>\n",
              "      <td>13535000</td>\n",
              "      <td>3385502</td>\n",
              "      <td>11836.46</td>\n",
              "      <td>11833.95</td>\n",
              "      <td>11779.69</td>\n",
              "      <td>11842.45</td>\n",
              "      <td>11833.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residuals</th>\n",
              "      <td>623271</td>\n",
              "      <td>12421089</td>\n",
              "      <td>24066350</td>\n",
              "      <td>30149.00</td>\n",
              "      <td>30045.00</td>\n",
              "      <td>37224.00</td>\n",
              "      <td>35500.00</td>\n",
              "      <td>34864.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Seed</th>\n",
              "      <td>103537</td>\n",
              "      <td>2035933</td>\n",
              "      <td>4211574</td>\n",
              "      <td>21922.92</td>\n",
              "      <td>23976.82</td>\n",
              "      <td>23389.20</td>\n",
              "      <td>24870.14</td>\n",
              "      <td>25263.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stock Variation</th>\n",
              "      <td>571566</td>\n",
              "      <td>11329527</td>\n",
              "      <td>21464704</td>\n",
              "      <td>58749.83</td>\n",
              "      <td>34910.99</td>\n",
              "      <td>33140.12</td>\n",
              "      <td>54316.91</td>\n",
              "      <td>20577.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Population - Both sexes</th>\n",
              "      <td>6020</td>\n",
              "      <td>112545</td>\n",
              "      <td>22995</td>\n",
              "      <td>1031585.00</td>\n",
              "      <td>1058081.00</td>\n",
              "      <td>1085107.00</td>\n",
              "      <td>1112641.00</td>\n",
              "      <td>1140605.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tourist consumption</th>\n",
              "      <td>50308</td>\n",
              "      <td>1486528</td>\n",
              "      <td>2869905</td>\n",
              "      <td>416.00</td>\n",
              "      <td>349.00</td>\n",
              "      <td>89.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>90.00</td>\n",
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      "source": [
        "Finding the total Import Quantity in Algeria in 2018"
      ],
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        "id": "pdp5EV0Lq6i_"
      }
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    {
      "cell_type": "code",
      "source": [
        "#Calculating the total Import Quantity in Algeria in 2018\n",
        "df.groupby(['Area', 'Element']).sum()"
      ],
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        "outputId": "d09668b3-2875-41b0-ad1b-f97c854ae7c6"
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          "data": {
            "text/plain": [
              "                                                 Area Code  Item Code  \\\n",
              "Area     Element                                                        \n",
              "Algeria  Domestic supply quantity                      464     311204   \n",
              "         Export Quantity                               392     264011   \n",
              "         Fat supply quantity (g/capita/day)            432     291489   \n",
              "         Feed                                           88      58967   \n",
              "         Food                                          420     282744   \n",
              "...                                                    ...        ...   \n",
              "Zimbabwe Protein supply quantity (g/capita/day)      20453     304589   \n",
              "         Residuals                                   18824     277476   \n",
              "         Seed                                         3439      50560   \n",
              "         Stock Variation                             17195     254157   \n",
              "         Total Population - Both sexes                 181       2501   \n",
              "\n",
              "                                                 Element Code     Y2014  \\\n",
              "Area     Element                                                          \n",
              "Algeria  Domestic supply quantity                      614916  83755.94   \n",
              "         Export Quantity                               579278   1328.64   \n",
              "         Fat supply quantity (g/capita/day)             73872    320.19   \n",
              "         Feed                                          121462   9656.04   \n",
              "         Food                                          539910  60582.69   \n",
              "...                                                       ...       ...   \n",
              "Zimbabwe Protein supply quantity (g/capita/day)         76162    184.23   \n",
              "         Residuals                                     537680   -252.00   \n",
              "         Seed                                          105013    144.00   \n",
              "         Stock Variation                               481840   -644.00   \n",
              "         Total Population - Both sexes                    511  13587.00   \n",
              "\n",
              "                                                    Y2015     Y2016     Y2017  \\\n",
              "Area     Element                                                                \n",
              "Algeria  Domestic supply quantity                85972.86  87157.61  86810.42   \n",
              "         Export Quantity                          1104.62   1340.22   1494.54   \n",
              "         Fat supply quantity (g/capita/day)        317.40    309.54    320.74   \n",
              "         Feed                                    10308.00   9903.00   9571.00   \n",
              "         Food                                    61693.49  63081.43  62785.33   \n",
              "...                                                   ...       ...       ...   \n",
              "Zimbabwe Protein supply quantity (g/capita/day)    175.31    172.72    175.31   \n",
              "         Residuals                                -780.00   -882.00  -1083.00   \n",
              "         Seed                                      128.00    127.00    162.00   \n",
              "         Stock Variation                          -343.00    221.00    458.00   \n",
              "         Total Population - Both sexes           13815.00  14030.00  14237.00   \n",
              "\n",
              "                                                    Y2018  \n",
              "Area     Element                                           \n",
              "Algeria  Domestic supply quantity                90023.42  \n",
              "         Export Quantity                           811.54  \n",
              "         Fat supply quantity (g/capita/day)        309.09  \n",
              "         Feed                                    10668.00  \n",
              "         Food                                    63736.33  \n",
              "...                                                   ...  \n",
              "Zimbabwe Protein supply quantity (g/capita/day)    176.57  \n",
              "         Residuals                                -546.00  \n",
              "         Seed                                      140.00  \n",
              "         Stock Variation                          -447.00  \n",
              "         Total Population - Both sexes           14439.00  \n",
              "\n",
              "[803 rows x 8 columns]"
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              "      <th></th>\n",
              "      <th>Area Code</th>\n",
              "      <th>Item Code</th>\n",
              "      <th>Element Code</th>\n",
              "      <th>Y2014</th>\n",
              "      <th>Y2015</th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">Algeria</th>\n",
              "      <th>Domestic supply quantity</th>\n",
              "      <td>464</td>\n",
              "      <td>311204</td>\n",
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              "      <td>83755.94</td>\n",
              "      <td>85972.86</td>\n",
              "      <td>87157.61</td>\n",
              "      <td>86810.42</td>\n",
              "      <td>90023.42</td>\n",
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              "      <td>1104.62</td>\n",
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              "      <th>Fat supply quantity (g/capita/day)</th>\n",
              "      <td>432</td>\n",
              "      <td>291489</td>\n",
              "      <td>73872</td>\n",
              "      <td>320.19</td>\n",
              "      <td>317.40</td>\n",
              "      <td>309.54</td>\n",
              "      <td>320.74</td>\n",
              "      <td>309.09</td>\n",
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              "      <th>Feed</th>\n",
              "      <td>88</td>\n",
              "      <td>58967</td>\n",
              "      <td>121462</td>\n",
              "      <td>9656.04</td>\n",
              "      <td>10308.00</td>\n",
              "      <td>9903.00</td>\n",
              "      <td>9571.00</td>\n",
              "      <td>10668.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food</th>\n",
              "      <td>420</td>\n",
              "      <td>282744</td>\n",
              "      <td>539910</td>\n",
              "      <td>60582.69</td>\n",
              "      <td>61693.49</td>\n",
              "      <td>63081.43</td>\n",
              "      <td>62785.33</td>\n",
              "      <td>63736.33</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th rowspan=\"5\" valign=\"top\">Zimbabwe</th>\n",
              "      <th>Protein supply quantity (g/capita/day)</th>\n",
              "      <td>20453</td>\n",
              "      <td>304589</td>\n",
              "      <td>76162</td>\n",
              "      <td>184.23</td>\n",
              "      <td>175.31</td>\n",
              "      <td>172.72</td>\n",
              "      <td>175.31</td>\n",
              "      <td>176.57</td>\n",
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              "    <tr>\n",
              "      <th>Residuals</th>\n",
              "      <td>18824</td>\n",
              "      <td>277476</td>\n",
              "      <td>537680</td>\n",
              "      <td>-252.00</td>\n",
              "      <td>-780.00</td>\n",
              "      <td>-882.00</td>\n",
              "      <td>-1083.00</td>\n",
              "      <td>-546.00</td>\n",
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              "    <tr>\n",
              "      <th>Seed</th>\n",
              "      <td>3439</td>\n",
              "      <td>50560</td>\n",
              "      <td>105013</td>\n",
              "      <td>144.00</td>\n",
              "      <td>128.00</td>\n",
              "      <td>127.00</td>\n",
              "      <td>162.00</td>\n",
              "      <td>140.00</td>\n",
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              "    <tr>\n",
              "      <th>Stock Variation</th>\n",
              "      <td>17195</td>\n",
              "      <td>254157</td>\n",
              "      <td>481840</td>\n",
              "      <td>-644.00</td>\n",
              "      <td>-343.00</td>\n",
              "      <td>221.00</td>\n",
              "      <td>458.00</td>\n",
              "      <td>-447.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Population - Both sexes</th>\n",
              "      <td>181</td>\n",
              "      <td>2501</td>\n",
              "      <td>511</td>\n",
              "      <td>13587.00</td>\n",
              "      <td>13815.00</td>\n",
              "      <td>14030.00</td>\n",
              "      <td>14237.00</td>\n",
              "      <td>14439.00</td>\n",
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          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the total number of unique values/countries in the dataset"
      ],
      "metadata": {
        "id": "ybGiYd3SrirQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Counting the total number of unique countries in the dataset\n",
        "df['Area'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xz5jx6Jr8Dx",
        "outputId": "c4bd6d6f-e6a9-47b5-a1f9-f606546fd282"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    }
  ]
}