Prodigy__DataScience_Task1.ipynb

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      "metadata": {
        "id": "wjep08MciM1j"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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              "    Year 0-14 years 15-64 years 65 years +\n",
              "0   2022     25.31%       67.8%       6.9%\n",
              "1   2021     25.69%      67.51%       6.8%\n",
              "2   2020     26.11%      67.22%      6.67%\n",
              "3   2019     26.59%      66.93%      6.47%\n",
              "4   2018     27.09%      66.66%      6.25%\n",
              "5   2017      27.6%      66.36%      6.04%\n",
              "6   2016     28.12%      66.05%      5.84%\n",
              "7   2015     28.63%      65.73%      5.64%\n",
              "8   2014     29.14%       65.4%      5.46%\n",
              "9   2013     29.62%      65.06%      5.32%\n",
              "10  2012     30.08%       64.7%      5.22%"
            ],
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              "      <th>Year</th>\n",
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              "      <th>15-64 years</th>\n",
              "      <th>65 years +</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>25.31%</td>\n",
              "      <td>67.8%</td>\n",
              "      <td>6.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021</td>\n",
              "      <td>25.69%</td>\n",
              "      <td>67.51%</td>\n",
              "      <td>6.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020</td>\n",
              "      <td>26.11%</td>\n",
              "      <td>67.22%</td>\n",
              "      <td>6.67%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019</td>\n",
              "      <td>26.59%</td>\n",
              "      <td>66.93%</td>\n",
              "      <td>6.47%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018</td>\n",
              "      <td>27.09%</td>\n",
              "      <td>66.66%</td>\n",
              "      <td>6.25%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2017</td>\n",
              "      <td>27.6%</td>\n",
              "      <td>66.36%</td>\n",
              "      <td>6.04%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016</td>\n",
              "      <td>28.12%</td>\n",
              "      <td>66.05%</td>\n",
              "      <td>5.84%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2015</td>\n",
              "      <td>28.63%</td>\n",
              "      <td>65.73%</td>\n",
              "      <td>5.64%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2014</td>\n",
              "      <td>29.14%</td>\n",
              "      <td>65.4%</td>\n",
              "      <td>5.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2013</td>\n",
              "      <td>29.62%</td>\n",
              "      <td>65.06%</td>\n",
              "      <td>5.32%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2012</td>\n",
              "      <td>30.08%</td>\n",
              "      <td>64.7%</td>\n",
              "      <td>5.22%</td>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2012,\n        \"max\": 2022,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          2017,\n          2022,\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"0-14 years\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"27.6%\",\n          \"25.31%\",\n          \"29.62%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"15-64 years\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"66.36%\",\n          \"67.8%\",\n          \"65.06%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"65 years +\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"6.04%\",\n          \"6.9%\",\n          \"5.32%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Read data from CSV file using pandas\n",
        "filename = \"population_distribution.csv\"\n",
        "data = pd.read_csv(filename)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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            "text/plain": [
              "Index(['0-14 years', '15-64 years', '65 years +'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Extract relevant columns\n",
        "years = data['Year']\n",
        "age_groups = data.columns[1:]\n",
        "age_groups"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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          "height": 394
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        "id": "b03OgYX7xqQx",
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          "data": {
            "text/plain": [
              "    Year  0-14 years  15-64 years  65 years +\n",
              "0   2022       25.31        67.80        6.90\n",
              "1   2021       25.69        67.51        6.80\n",
              "2   2020       26.11        67.22        6.67\n",
              "3   2019       26.59        66.93        6.47\n",
              "4   2018       27.09        66.66        6.25\n",
              "5   2017       27.60        66.36        6.04\n",
              "6   2016       28.12        66.05        5.84\n",
              "7   2015       28.63        65.73        5.64\n",
              "8   2014       29.14        65.40        5.46\n",
              "9   2013       29.62        65.06        5.32\n",
              "10  2012       30.08        64.70        5.22"
            ],
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              "    .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>Year</th>\n",
              "      <th>0-14 years</th>\n",
              "      <th>15-64 years</th>\n",
              "      <th>65 years +</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>25.31</td>\n",
              "      <td>67.80</td>\n",
              "      <td>6.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021</td>\n",
              "      <td>25.69</td>\n",
              "      <td>67.51</td>\n",
              "      <td>6.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020</td>\n",
              "      <td>26.11</td>\n",
              "      <td>67.22</td>\n",
              "      <td>6.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019</td>\n",
              "      <td>26.59</td>\n",
              "      <td>66.93</td>\n",
              "      <td>6.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018</td>\n",
              "      <td>27.09</td>\n",
              "      <td>66.66</td>\n",
              "      <td>6.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2017</td>\n",
              "      <td>27.60</td>\n",
              "      <td>66.36</td>\n",
              "      <td>6.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016</td>\n",
              "      <td>28.12</td>\n",
              "      <td>66.05</td>\n",
              "      <td>5.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2015</td>\n",
              "      <td>28.63</td>\n",
              "      <td>65.73</td>\n",
              "      <td>5.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2014</td>\n",
              "      <td>29.14</td>\n",
              "      <td>65.40</td>\n",
              "      <td>5.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2013</td>\n",
              "      <td>29.62</td>\n",
              "      <td>65.06</td>\n",
              "      <td>5.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2012</td>\n",
              "      <td>30.08</td>\n",
              "      <td>64.70</td>\n",
              "      <td>5.22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "        document.querySelector('#df-a7dd4e27-f21a-42f6-9d17-686004fb135a button.colab-df-convert');\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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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-48efcce7-77a1-4433-8833-843c0f57a665 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_f574ac69-652e-4cb6-b9f0-689342e7b37a\">\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f574ac69-652e-4cb6-b9f0-689342e7b37a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        google.colab.notebook.generateWithVariable('data');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2012,\n        \"max\": 2022,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          2017,\n          2022,\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"0-14 years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.622130473398263,\n        \"min\": 25.31,\n        \"max\": 30.08,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          27.6,\n          25.31,\n          29.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"15-64 years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0190334101044425,\n        \"min\": 64.7,\n        \"max\": 67.8,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          66.36,\n          67.8,\n          65.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"65 years +\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6056131378423628,\n        \"min\": 5.22,\n        \"max\": 6.9,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6.04,\n          6.9,\n          5.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Remove '%' from data and convert to float\n",
        "for age_group in age_groups:\n",
        "    data[age_group] = data[age_group].str.rstrip('%').astype(float)\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "zjXHHXAHxDey",
        "outputId": "ef5e1b32-547e-401b-d2f2-fb8dc53be7b3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a bar chart for each age group\n",
        "plt.figure(figsize=(12, 8))\n",
        "for i, age_group in enumerate(age_groups):\n",
        "    plt.bar([x + i * 0.2 for x in years], data[age_group], width=0.2, label=age_group)\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Percentage')\n",
        "plt.title('Percentage of Population by Age Group')\n",
        "plt.legend(title='Age Group', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "plt.grid(True)\n",
        "plt.xticks(years)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "mnKTM94cqO9I",
        "outputId": "f3345989-bb2f-4951-edf5-8385601549a7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a bar chart for each age group\n",
        "plt.figure(figsize=(12, 8))\n",
        "for age_group in age_groups:\n",
        "    plt.plot(years, data[age_group], label=age_group)\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Percentage')\n",
        "plt.title('Percentage of Population by Age Group')\n",
        "plt.legend(title='Age Group', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO0KIpnELJo8P9CH49QK5gP",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}