Khmer_Markdown_SRU_HW1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "d2cf834e-4b3f-427c-860a-dfb6db01fc0e",
      "metadata": {
        "id": "d2cf834e-4b3f-427c-860a-dfb6db01fc0e"
      },
      "source": [
        "## Khmer bilingual Notebook Development"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "097742fb-1932-47ff-852c-6388d054ae44",
      "metadata": {
        "id": "097742fb-1932-47ff-852c-6388d054ae44"
      },
      "source": [
        "The purpose of this notebook is to develop a bilingual Khmer and English notebook for a first time user to start working on Juptyer notebooks for learnign Data Science\n",
        "\n",
        "Possible goals would be to develop a Lab 0  -\n",
        "\n",
        "Some good resources come from our [Curriculum Guide](https://ds-modules.github.io/curriculum-guide/intro.html)\n",
        "\n",
        " - [Lab 0](https://ds-modules.github.io/curriculum-guide/technology/pedagogy-and-technology/notebook-zero.html)\n",
        " - [Authoring Assignments](https://ds-modules.github.io/curriculum-guide/workflow/creating-notebooks.html)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdb1e1cd-3e95-460c-a9d9-df7f69835d90",
      "metadata": {
        "id": "fdb1e1cd-3e95-460c-a9d9-df7f69835d90"
      },
      "source": [
        "The point here is that Khmer can be passed in as an alternative language in the Markdown Cells - as long as a Jupyter extensions module is installed\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9a4ec5c-ef97-4643-96e8-cdbdea530de3",
      "metadata": {
        "id": "d9a4ec5c-ef97-4643-96e8-cdbdea530de3"
      },
      "outputs": [],
      "source": [
        "# if not installed\n",
        "# !pip install jupyter-contrib-nbextensions"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b01a81c-b340-4073-a18b-57f5dd9deb3e",
      "metadata": {
        "id": "9b01a81c-b340-4073-a18b-57f5dd9deb3e"
      },
      "source": [
        "<div lang=\"km\">\n",
        "ស្វាគមន៍មកការប្រតិបត្តិកម្មវិធី Jupyter notebooks!...\n",
        "    \n",
        "    អ្នកប្រាជ្ញកម្ពុជានឹងបង្កើតកម្មវិធីសិក្សាដ៏ស្រស់ស្អាត ដោយមានជំនួយពី UC Berkeley in collaboration with SRU\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97ef23c0-f374-474c-9464-5e79fe0f9aea",
      "metadata": {
        "id": "97ef23c0-f374-474c-9464-5e79fe0f9aea"
      },
      "source": [
        "<div lang=\"km\">\n",
        "ស្វាគមន៍មកការប្រតិបត្តិកម្មវិធី Jupyter notebooks!...\n",
        "    \n",
        "    អ្នកប្រាជ្ញកម្ពុជានឹងបង្កើតកម្មវិធីសិក្សាដ៏ស្រស់ស្អាត ដោយមានជំនួយពី UC Berkeley\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c2342e8-eca2-4144-aa71-fee53ed5e3f6",
      "metadata": {
        "id": "8c2342e8-eca2-4144-aa71-fee53ed5e3f6"
      },
      "source": [
        "## Let's get started - Lab 0 Intro Lab"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e245580-5210-4586-80ed-4b29d7c32196",
      "metadata": {
        "id": "0e245580-5210-4586-80ed-4b29d7c32196"
      },
      "source": [
        "From here the point would be to start to make cells that can explain to a new user how to use a Jupyter notebook in Khmer - and potentially to provide a reference for key terms that they need to learn in Python English\n",
        " - Table\n",
        " - Numbers\n",
        " - String\n",
        " - Package\n",
        " - Error\n",
        " - Import\n",
        " - Plot\n",
        "\n",
        "Etc....\n",
        " -"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b3beff3-c1f0-4633-847c-b8bba1cbf525",
      "metadata": {
        "id": "5b3beff3-c1f0-4633-847c-b8bba1cbf525"
      },
      "source": [
        "## Localizing Data for Cambodia\n",
        "\n",
        "### Maybe we could do Data 8 Lab 2 with Cambodia data?\n",
        "[Data 8 Lab 2](https://github.com/data-8/materials-sp22-colab-no-footprint/blob/main/lab/lab02/lab02.ipynb)\n",
        "\n",
        "\n",
        "Maybe some [Census Data](https://www.nis.gov.kh/nis/Census2019/Provisional%20Population%20Census%202019_English_FINAL.pdf)?\n",
        "\n",
        "Lots of Data at Open Data Portal [OpenDevelopmentCambodia](https://data.opendevelopmentcambodia.net/en/organization/cambodia-organization)\n",
        "\n",
        "\n",
        "[Census Data from 2008](https://data.opendevelopmentcambodia.net/en/dataset/census-2008/resource/4b99aba0-ce2d-4790-8d74-8fb30f83f5b7)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "2c1e7eae-ee1f-407f-86b9-1b9ef873ad93",
      "metadata": {
        "id": "2c1e7eae-ee1f-407f-86b9-1b9ef873ad93"
      },
      "outputs": [],
      "source": [
        "from datascience import *"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_dataset = \"https://raw.githubusercontent.com/ds-modules/DS4Cambodia/main/provisional_census2019.csv\""
      ],
      "metadata": {
        "id": "Einzu4xukNbH"
      },
      "id": "Einzu4xukNbH",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "08395072-c597-428c-8ee8-1b63c92a125d",
      "metadata": {
        "id": "08395072-c597-428c-8ee8-1b63c92a125d",
        "outputId": "013391bd-4f22-4e16-ecec-cf0fe598c225",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pro_code | Provinces_kh | Provinces        | Households | Males  | Females | Total   | Household size | area_km2 | pop_km2 | reference\n",
              "1        | បន្ទាយមានជ័យ | Banteay Meanchey | 177526     | 426104 | 433441  | 859545  | 4.8            | 6679     | 129     | Provisional_Population_Census__00.06.2019.pdf\n",
              "2        | បាត់ដំបង     | Battambang       | 218584     | 458902 | 528498  | 987400  | 4.5            | 11702    | 84      | Provisional_Population_Census__00.06.2019.pdf\n",
              "3        | កំពង់ចាម     | Kampong Cham     | 215923     | 428481 | 467282  | 895763  | 4.1            | 4549     | 197     | Provisional_Population_Census__00.06.2019.pdf\n",
              "4        | កំពង់ឆ្នាំង  | Kampong Chhnang  | 122925     | 251895 | 274037  | 525932  | 4.3            | 5521     | 95      | Provisional_Population_Census__00.06.2019.pdf\n",
              "5        | កំពង់ស្ពឺ    | Kampong Speu     | 187835     | 424039 | 448180  | 872219  | 4.6            | 7017     | 124     | Provisional_Population_Census__00.06.2019.pdf\n",
              "6        | កំពង់ធំ      | Kampong Thom     | 154458     | 327013 | 350247  | 677260  | 4.4            | 13814    | 49      | Provisional_Population_Census__00.06.2019.pdf\n",
              "7        | កំពត         | Kampot           | 138374     | 280537 | 312308  | 592845  | 4.3            | 4873     | 122     | Provisional_Population_Census__00.06.2019.pdf\n",
              "8        | កណ្តាល       | Kandal           | 273111     | 580129 | 615418  | 1195547 | 4.4            | 3179     | 376     | Provisional_Population_Census__00.06.2019.pdf\n",
              "9        | កោះកុង       | Koh Kong         | 26716      | 62304  | 61314   | 123618  | 4.6            | 10090    | 12      | Provisional_Population_Census__00.06.2019.pdf\n",
              "10       | ក្រចេះ       | Kratie           | 86137      | 185429 | 187396  | 372825  | 4.3            | 11094    | 34      | Provisional_Population_Census__00.06.2019.pdf\n",
              "... (16 rows omitted)"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>pro_code</th> <th>Provinces_kh</th> <th>Provinces</th> <th>Households</th> <th>Males</th> <th>Females</th> <th>Total</th> <th>Household size</th> <th>area_km2</th> <th>pop_km2</th> <th>reference</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>1       </td> <td>បន្ទាយមានជ័យ</td> <td>Banteay Meanchey</td> <td>177526    </td> <td>426104</td> <td>433441 </td> <td>859545 </td> <td>4.8           </td> <td>6679    </td> <td>129    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>2       </td> <td>បាត់ដំបង    </td> <td>Battambang      </td> <td>218584    </td> <td>458902</td> <td>528498 </td> <td>987400 </td> <td>4.5           </td> <td>11702   </td> <td>84     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>3       </td> <td>កំពង់ចាម    </td> <td>Kampong Cham    </td> <td>215923    </td> <td>428481</td> <td>467282 </td> <td>895763 </td> <td>4.1           </td> <td>4549    </td> <td>197    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>4       </td> <td>កំពង់ឆ្នាំង </td> <td>Kampong Chhnang </td> <td>122925    </td> <td>251895</td> <td>274037 </td> <td>525932 </td> <td>4.3           </td> <td>5521    </td> <td>95     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>5       </td> <td>កំពង់ស្ពឺ   </td> <td>Kampong Speu    </td> <td>187835    </td> <td>424039</td> <td>448180 </td> <td>872219 </td> <td>4.6           </td> <td>7017    </td> <td>124    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>6       </td> <td>កំពង់ធំ     </td> <td>Kampong Thom    </td> <td>154458    </td> <td>327013</td> <td>350247 </td> <td>677260 </td> <td>4.4           </td> <td>13814   </td> <td>49     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>7       </td> <td>កំពត        </td> <td>Kampot          </td> <td>138374    </td> <td>280537</td> <td>312308 </td> <td>592845 </td> <td>4.3           </td> <td>4873    </td> <td>122    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>8       </td> <td>កណ្តាល      </td> <td>Kandal          </td> <td>273111    </td> <td>580129</td> <td>615418 </td> <td>1195547</td> <td>4.4           </td> <td>3179    </td> <td>376    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>9       </td> <td>កោះកុង      </td> <td>Koh Kong        </td> <td>26716     </td> <td>62304 </td> <td>61314  </td> <td>123618 </td> <td>4.6           </td> <td>10090   </td> <td>12     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>10      </td> <td>ក្រចេះ      </td> <td>Kratie          </td> <td>86137     </td> <td>185429</td> <td>187396 </td> <td>372825 </td> <td>4.3           </td> <td>11094   </td> <td>34     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (16 rows omitted)</p>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "kh_census = Table.read_table(first_dataset)\n",
        "kh_census"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "0f34f0c9-422d-4615-ab83-df2eea37c797",
      "metadata": {
        "id": "0f34f0c9-422d-4615-ab83-df2eea37c797",
        "outputId": "297a0a9c-f1ed-4b06-9d9b-9d25f073e76d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pro_code | Provinces_kh | Provinces        | Households | Males  | Females | Total   | Household size | area_km2 | pop_km2 | reference\n",
              "1        | បន្ទាយមានជ័យ | Banteay Meanchey | 177526     | 426104 | 433441  | 859545  | 4.8            | 6679     | 129     | Provisional_Population_Census__00.06.2019.pdf\n",
              "2        | បាត់ដំបង     | Battambang       | 218584     | 458902 | 528498  | 987400  | 4.5            | 11702    | 84      | Provisional_Population_Census__00.06.2019.pdf\n",
              "3        | កំពង់ចាម     | Kampong Cham     | 215923     | 428481 | 467282  | 895763  | 4.1            | 4549     | 197     | Provisional_Population_Census__00.06.2019.pdf\n",
              "4        | កំពង់ឆ្នាំង  | Kampong Chhnang  | 122925     | 251895 | 274037  | 525932  | 4.3            | 5521     | 95      | Provisional_Population_Census__00.06.2019.pdf\n",
              "5        | កំពង់ស្ពឺ    | Kampong Speu     | 187835     | 424039 | 448180  | 872219  | 4.6            | 7017     | 124     | Provisional_Population_Census__00.06.2019.pdf\n",
              "6        | កំពង់ធំ      | Kampong Thom     | 154458     | 327013 | 350247  | 677260  | 4.4            | 13814    | 49      | Provisional_Population_Census__00.06.2019.pdf\n",
              "7        | កំពត         | Kampot           | 138374     | 280537 | 312308  | 592845  | 4.3            | 4873     | 122     | Provisional_Population_Census__00.06.2019.pdf\n",
              "8        | កណ្តាល       | Kandal           | 273111     | 580129 | 615418  | 1195547 | 4.4            | 3179     | 376     | Provisional_Population_Census__00.06.2019.pdf\n",
              "9        | កោះកុង       | Koh Kong         | 26716      | 62304  | 61314   | 123618  | 4.6            | 10090    | 12      | Provisional_Population_Census__00.06.2019.pdf\n",
              "10       | ក្រចេះ       | Kratie           | 86137      | 185429 | 187396  | 372825  | 4.3            | 11094    | 34      | Provisional_Population_Census__00.06.2019.pdf\n",
              "... (16 rows omitted)"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>pro_code</th> <th>Provinces_kh</th> <th>Provinces</th> <th>Households</th> <th>Males</th> <th>Females</th> <th>Total</th> <th>Household size</th> <th>area_km2</th> <th>pop_km2</th> <th>reference</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>1       </td> <td>បន្ទាយមានជ័យ</td> <td>Banteay Meanchey</td> <td>177526    </td> <td>426104</td> <td>433441 </td> <td>859545 </td> <td>4.8           </td> <td>6679    </td> <td>129    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>2       </td> <td>បាត់ដំបង    </td> <td>Battambang      </td> <td>218584    </td> <td>458902</td> <td>528498 </td> <td>987400 </td> <td>4.5           </td> <td>11702   </td> <td>84     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>3       </td> <td>កំពង់ចាម    </td> <td>Kampong Cham    </td> <td>215923    </td> <td>428481</td> <td>467282 </td> <td>895763 </td> <td>4.1           </td> <td>4549    </td> <td>197    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>4       </td> <td>កំពង់ឆ្នាំង </td> <td>Kampong Chhnang </td> <td>122925    </td> <td>251895</td> <td>274037 </td> <td>525932 </td> <td>4.3           </td> <td>5521    </td> <td>95     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>5       </td> <td>កំពង់ស្ពឺ   </td> <td>Kampong Speu    </td> <td>187835    </td> <td>424039</td> <td>448180 </td> <td>872219 </td> <td>4.6           </td> <td>7017    </td> <td>124    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>6       </td> <td>កំពង់ធំ     </td> <td>Kampong Thom    </td> <td>154458    </td> <td>327013</td> <td>350247 </td> <td>677260 </td> <td>4.4           </td> <td>13814   </td> <td>49     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>7       </td> <td>កំពត        </td> <td>Kampot          </td> <td>138374    </td> <td>280537</td> <td>312308 </td> <td>592845 </td> <td>4.3           </td> <td>4873    </td> <td>122    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>8       </td> <td>កណ្តាល      </td> <td>Kandal          </td> <td>273111    </td> <td>580129</td> <td>615418 </td> <td>1195547</td> <td>4.4           </td> <td>3179    </td> <td>376    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>9       </td> <td>កោះកុង      </td> <td>Koh Kong        </td> <td>26716     </td> <td>62304 </td> <td>61314  </td> <td>123618 </td> <td>4.6           </td> <td>10090   </td> <td>12     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>10      </td> <td>ក្រចេះ      </td> <td>Kratie          </td> <td>86137     </td> <td>185429</td> <td>187396 </td> <td>372825 </td> <td>4.3           </td> <td>11094   </td> <td>34     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (16 rows omitted)</p>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "kh_census"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff59b809-e6cf-461f-a7db-3b73c9ad45b2",
      "metadata": {
        "id": "ff59b809-e6cf-461f-a7db-3b73c9ad45b2"
      },
      "source": [
        "## Exploring a Dataset\n",
        "\n",
        "For your reference, here's a table of all the functions and methods we can use to explore the data.\n",
        "We'll learn more methods to add to this table in the coming week!\n",
        "\n",
        "|Name|Example|Purpose|\n",
        "|-|-|-|\n",
        "|`sort`|`tbl.sort(\"N\")`|Create a copy of a table sorted by the values in a column|\n",
        "|`where`|`tbl.where(\"N\", are.above(2))`|Create a copy of a table with only the rows that match some *predicate*|\n",
        "|`num_rows`|`tbl.num_rows`|Compute the number of rows in a table|\n",
        "|`num_columns`|`tbl.num_columns`|Compute the number of columns in a table|\n",
        "|`select`|`tbl.select(\"N\")`|Create a copy of a table with only some of the columns|\n",
        "|`drop`|`tbl.drop(\"N\")`|Create a copy of a table without some of the columns|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "4c4b370f-2ab4-482f-a80b-fe4497456e75",
      "metadata": {
        "id": "4c4b370f-2ab4-482f-a80b-fe4497456e75",
        "outputId": "95c76a3a-e3d2-40ff-ee7b-80eeb6b1924a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "kh_census.num_rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "2849fdfe-e847-4396-ba5e-fb8555315b42",
      "metadata": {
        "id": "2849fdfe-e847-4396-ba5e-fb8555315b42",
        "outputId": "02f798fe-0ec6-4ced-be27-43ef573543df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "kh_census.num_columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "ebebcbb0-b817-4a25-89a1-b3cadfbfd768",
      "metadata": {
        "id": "ebebcbb0-b817-4a25-89a1-b3cadfbfd768",
        "outputId": "dde8e008-17c9-4d2a-cb94-92b7e8707840",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pro_code | Provinces_kh | Provinces        | Households | Males   | Females | Total    | Household size | area_km2 | pop_km2 | reference\n",
              "nan      | nan          | Total            | 3341770    | 7418577 | 7869912 | 15288489 | 4.6            | 178035   | 86      | Provisional_Population_Census__00.06.2019.pdf\n",
              "12       | ភ្នំពេញ      | Phnom Penh       | 399203     | 1039192 | 1090179 | 2129371  | 5.3            | 679      | 3136    | Provisional_Population_Census__00.06.2019.pdf\n",
              "8        | កណ្តាល       | Kandal           | 273111     | 580129  | 615418  | 1195547  | 4.4            | 3179     | 376     | Provisional_Population_Census__00.06.2019.pdf\n",
              "14       | ព្រៃវែង      | Prey Veng        | 227008     | 501346  | 556082  | 1057428  | 4.7            | 4883     | 217     | Provisional_Population_Census__00.06.2019.pdf\n",
              "17       | សៀមរាប       | Siem Reap        | 218659     | 491568  | 514944  | 1006512  | 4.6            | 10299    | 98      | Provisional_Population_Census__00.06.2019.pdf\n",
              "2        | បាត់ដំបង     | Battambang       | 218584     | 458902  | 528498  | 987400   | 4.5            | 11702    | 84      | Provisional_Population_Census__00.06.2019.pdf\n",
              "21       | តាកែវ        | Takeo            | 199362     | 432649  | 466836  | 899485   | 4.5            | 3563     | 252     | Provisional_Population_Census__00.06.2019.pdf\n",
              "3        | កំពង់ចាម     | Kampong Cham     | 215923     | 428481  | 467282  | 895763   | 4.1            | 4549     | 197     | Provisional_Population_Census__00.06.2019.pdf\n",
              "5        | កំពង់ស្ពឺ    | Kampong Speu     | 187835     | 424039  | 448180  | 872219   | 4.6            | 7017     | 124     | Provisional_Population_Census__00.06.2019.pdf\n",
              "1        | បន្ទាយមានជ័យ | Banteay Meanchey | 177526     | 426104  | 433441  | 859545   | 4.8            | 6679     | 129     | Provisional_Population_Census__00.06.2019.pdf\n",
              "... (16 rows omitted)"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>pro_code</th> <th>Provinces_kh</th> <th>Provinces</th> <th>Households</th> <th>Males</th> <th>Females</th> <th>Total</th> <th>Household size</th> <th>area_km2</th> <th>pop_km2</th> <th>reference</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>nan     </td> <td>nan         </td> <td>Total           </td> <td>3341770   </td> <td>7418577</td> <td>7869912</td> <td>15288489</td> <td>4.6           </td> <td>178035  </td> <td>86     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>12      </td> <td>ភ្នំពេញ     </td> <td>Phnom Penh      </td> <td>399203    </td> <td>1039192</td> <td>1090179</td> <td>2129371 </td> <td>5.3           </td> <td>679     </td> <td>3136   </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>8       </td> <td>កណ្តាល      </td> <td>Kandal          </td> <td>273111    </td> <td>580129 </td> <td>615418 </td> <td>1195547 </td> <td>4.4           </td> <td>3179    </td> <td>376    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>14      </td> <td>ព្រៃវែង     </td> <td>Prey Veng       </td> <td>227008    </td> <td>501346 </td> <td>556082 </td> <td>1057428 </td> <td>4.7           </td> <td>4883    </td> <td>217    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>17      </td> <td>សៀមរាប      </td> <td>Siem Reap       </td> <td>218659    </td> <td>491568 </td> <td>514944 </td> <td>1006512 </td> <td>4.6           </td> <td>10299   </td> <td>98     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>2       </td> <td>បាត់ដំបង    </td> <td>Battambang      </td> <td>218584    </td> <td>458902 </td> <td>528498 </td> <td>987400  </td> <td>4.5           </td> <td>11702   </td> <td>84     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>21      </td> <td>តាកែវ       </td> <td>Takeo           </td> <td>199362    </td> <td>432649 </td> <td>466836 </td> <td>899485  </td> <td>4.5           </td> <td>3563    </td> <td>252    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>3       </td> <td>កំពង់ចាម    </td> <td>Kampong Cham    </td> <td>215923    </td> <td>428481 </td> <td>467282 </td> <td>895763  </td> <td>4.1           </td> <td>4549    </td> <td>197    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>5       </td> <td>កំពង់ស្ពឺ   </td> <td>Kampong Speu    </td> <td>187835    </td> <td>424039 </td> <td>448180 </td> <td>872219  </td> <td>4.6           </td> <td>7017    </td> <td>124    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>1       </td> <td>បន្ទាយមានជ័យ</td> <td>Banteay Meanchey</td> <td>177526    </td> <td>426104 </td> <td>433441 </td> <td>859545  </td> <td>4.8           </td> <td>6679    </td> <td>129    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (16 rows omitted)</p>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Sort Provinces  by Population Size\n",
        "kh_census.sort(\"Total\", descending=True) # Annoying Spaces in Column name need to clean up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "a692a1db-faed-43ed-9b7b-b3989b785aa0",
      "metadata": {
        "id": "a692a1db-faed-43ed-9b7b-b3989b785aa0",
        "outputId": "6ba7632e-776d-472d-d210-6d4061d65e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pro_code | Provinces_kh | Provinces      | Households | Males   | Females | Total   | Household size | area_km2 | pop_km2 | reference\n",
              "23       | កែប          | Kep            | 9347       | 20615   | 21183   | 41798   | 4.5            | 336      | 124     | Provisional_Population_Census__00.06.2019.pdf\n",
              "12       | ភ្នំពេញ      | Phnom Penh     | 399203     | 1039192 | 1090179 | 2129371 | 5.3            | 679      | 3136    | Provisional_Population_Census__00.06.2019.pdf\n",
              "24       | ប៉ៃលិន       | Pailin         | 16833      | 36151   | 35449   | 71600   | 4.3            | 803      | 89      | Provisional_Population_Census__00.06.2019.pdf\n",
              "18       | ព្រះសីហនុ    | Preah Sihanouk | 51983      | 153255  | 149632  | 302887  | 5.8            | 1938     | 156     | Provisional_Population_Census__00.06.2019.pdf\n",
              "20       | ស្វាយរៀង     | Svay Rieng     | 131937     | 249446  | 275108  | 524554  | 4              | 2966     | 177     | Provisional_Population_Census__00.06.2019.pdf\n",
              "8        | កណ្តាល       | Kandal         | 273111     | 580129  | 615418  | 1195547 | 4.4            | 3179     | 376     | Provisional_Population_Census__00.06.2019.pdf\n",
              "21       | តាកែវ        | Takeo          | 199362     | 432649  | 466836  | 899485  | 4.5            | 3563     | 252     | Provisional_Population_Census__00.06.2019.pdf\n",
              "3        | កំពង់ចាម     | Kampong Cham   | 215923     | 428481  | 467282  | 895763  | 4.1            | 4549     | 197     | Provisional_Population_Census__00.06.2019.pdf\n",
              "7        | កំពត         | Kampot         | 138374     | 280537  | 312308  | 592845  | 4.3            | 4873     | 122     | Provisional_Population_Census__00.06.2019.pdf\n",
              "14       | ព្រៃវែង      | Prey Veng      | 227008     | 501346  | 556082  | 1057428 | 4.7            | 4883     | 217     | Provisional_Population_Census__00.06.2019.pdf\n",
              "... (16 rows omitted)"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>pro_code</th> <th>Provinces_kh</th> <th>Provinces</th> <th>Households</th> <th>Males</th> <th>Females</th> <th>Total</th> <th>Household size</th> <th>area_km2</th> <th>pop_km2</th> <th>reference</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>23      </td> <td>កែប         </td> <td>Kep           </td> <td>9347      </td> <td>20615  </td> <td>21183  </td> <td>41798  </td> <td>4.5           </td> <td>336     </td> <td>124    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>12      </td> <td>ភ្នំពេញ     </td> <td>Phnom Penh    </td> <td>399203    </td> <td>1039192</td> <td>1090179</td> <td>2129371</td> <td>5.3           </td> <td>679     </td> <td>3136   </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>24      </td> <td>ប៉ៃលិន      </td> <td>Pailin        </td> <td>16833     </td> <td>36151  </td> <td>35449  </td> <td>71600  </td> <td>4.3           </td> <td>803     </td> <td>89     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>18      </td> <td>ព្រះសីហនុ   </td> <td>Preah Sihanouk</td> <td>51983     </td> <td>153255 </td> <td>149632 </td> <td>302887 </td> <td>5.8           </td> <td>1938    </td> <td>156    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>20      </td> <td>ស្វាយរៀង    </td> <td>Svay Rieng    </td> <td>131937    </td> <td>249446 </td> <td>275108 </td> <td>524554 </td> <td>4             </td> <td>2966    </td> <td>177    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>8       </td> <td>កណ្តាល      </td> <td>Kandal        </td> <td>273111    </td> <td>580129 </td> <td>615418 </td> <td>1195547</td> <td>4.4           </td> <td>3179    </td> <td>376    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>21      </td> <td>តាកែវ       </td> <td>Takeo         </td> <td>199362    </td> <td>432649 </td> <td>466836 </td> <td>899485 </td> <td>4.5           </td> <td>3563    </td> <td>252    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>3       </td> <td>កំពង់ចាម    </td> <td>Kampong Cham  </td> <td>215923    </td> <td>428481 </td> <td>467282 </td> <td>895763 </td> <td>4.1           </td> <td>4549    </td> <td>197    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>7       </td> <td>កំពត        </td> <td>Kampot        </td> <td>138374    </td> <td>280537 </td> <td>312308 </td> <td>592845 </td> <td>4.3           </td> <td>4873    </td> <td>122    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>14      </td> <td>ព្រៃវែង     </td> <td>Prey Veng     </td> <td>227008    </td> <td>501346 </td> <td>556082 </td> <td>1057428</td> <td>4.7           </td> <td>4883    </td> <td>217    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (16 rows omitted)</p>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Sort Provinces  by Total Area\n",
        "\n",
        "kh_census.sort(\"area_km2\", descending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e0b1b2e-9c6e-415d-97af-12dce37f843c",
      "metadata": {
        "id": "9e0b1b2e-9c6e-415d-97af-12dce37f843c"
      },
      "source": [
        "## Predicates\n",
        "\n",
        "|Predicate|Example|Result|\n",
        "|-|-|-|\n",
        "|`are.equal_to`|`are.equal_to(50)`|Find rows with values equal to 50|\n",
        "|`are.not_equal_to`|`are.not_equal_to(50)`|Find rows with values not equal to 50|\n",
        "|`are.above`|`are.above(50)`|Find rows with values above (and not equal to) 50|\n",
        "|`are.above_or_equal_to`|`are.above_or_equal_to(50)`|Find rows with values above 50 or equal to 50|\n",
        "|`are.below`|`are.below(50)`|Find rows with values below 50|\n",
        "|`are.between`|`are.between(2, 10)`|Find rows with values above or equal to 2 and below 10|\n",
        "|`are.between_or_equal_to`|`are.between_or_equal_to(2, 10)`|Find rows with values above or equal to 2 and below or equal to 10|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "ca125ecb-75c0-49f1-a35b-c1b92034bff2",
      "metadata": {
        "id": "ca125ecb-75c0-49f1-a35b-c1b92034bff2",
        "outputId": "34b83664-48de-4c52-d836-9aa207226e57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pro_code | Provinces_kh | Provinces  | Households | Males   | Females | Total    | Household size | area_km2 | pop_km2 | reference\n",
              "2        | បាត់ដំបង     | Battambang | 218584     | 458902  | 528498  | 987400   | 4.5            | 11702    | 84      | Provisional_Population_Census__00.06.2019.pdf\n",
              "8        | កណ្តាល       | Kandal     | 273111     | 580129  | 615418  | 1195547  | 4.4            | 3179     | 376     | Provisional_Population_Census__00.06.2019.pdf\n",
              "12       | ភ្នំពេញ      | Phnom Penh | 399203     | 1039192 | 1090179 | 2129371  | 5.3            | 679      | 3136    | Provisional_Population_Census__00.06.2019.pdf\n",
              "14       | ព្រៃវែង      | Prey Veng  | 227008     | 501346  | 556082  | 1057428  | 4.7            | 4883     | 217     | Provisional_Population_Census__00.06.2019.pdf\n",
              "17       | សៀមរាប       | Siem Reap  | 218659     | 491568  | 514944  | 1006512  | 4.6            | 10299    | 98      | Provisional_Population_Census__00.06.2019.pdf\n",
              "nan      | nan          | Total      | 3341770    | 7418577 | 7869912 | 15288489 | 4.6            | 178035   | 86      | Provisional_Population_Census__00.06.2019.pdf"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>pro_code</th> <th>Provinces_kh</th> <th>Provinces</th> <th>Households</th> <th>Males</th> <th>Females</th> <th>Total</th> <th>Household size</th> <th>area_km2</th> <th>pop_km2</th> <th>reference</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>2       </td> <td>បាត់ដំបង    </td> <td>Battambang</td> <td>218584    </td> <td>458902 </td> <td>528498 </td> <td>987400  </td> <td>4.5           </td> <td>11702   </td> <td>84     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>8       </td> <td>កណ្តាល      </td> <td>Kandal    </td> <td>273111    </td> <td>580129 </td> <td>615418 </td> <td>1195547 </td> <td>4.4           </td> <td>3179    </td> <td>376    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>12      </td> <td>ភ្នំពេញ     </td> <td>Phnom Penh</td> <td>399203    </td> <td>1039192</td> <td>1090179</td> <td>2129371 </td> <td>5.3           </td> <td>679     </td> <td>3136   </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>14      </td> <td>ព្រៃវែង     </td> <td>Prey Veng </td> <td>227008    </td> <td>501346 </td> <td>556082 </td> <td>1057428 </td> <td>4.7           </td> <td>4883    </td> <td>217    </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>17      </td> <td>សៀមរាប      </td> <td>Siem Reap </td> <td>218659    </td> <td>491568 </td> <td>514944 </td> <td>1006512 </td> <td>4.6           </td> <td>10299   </td> <td>98     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>nan     </td> <td>nan         </td> <td>Total     </td> <td>3341770   </td> <td>7418577</td> <td>7869912</td> <td>15288489</td> <td>4.6           </td> <td>178035  </td> <td>86     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "kh_census.where(\"Females\", are.above(500000))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yhAxtLD8mOaS"
      },
      "id": "yhAxtLD8mOaS",
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Question 1.1** what is the code for Area greater than 10000km2"
      ],
      "metadata": {
        "id": "kkJnDGutnaMW"
      },
      "id": "kkJnDGutnaMW"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mcsaLcbpno2_"
      },
      "id": "mcsaLcbpno2_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "wrbheFfOn7nq"
      },
      "id": "wrbheFfOn7nq",
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kh_census.hist(\"area_km2\")"
      ],
      "metadata": {
        "id": "2eYfFuI1n7u8",
        "outputId": "3fa75ca5-6cb2-45af-978f-c2b9b6e18b41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        }
      },
      "id": "2eYfFuI1n7u8",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kh_provinces=kh_census.where(\"area_km2\", are.below(178034))\n",
        "kh_provinces.sort(\"area_km2\", descending=True)"
      ],
      "metadata": {
        "id": "9MZ97cdLn9hE",
        "outputId": "5afc7c3e-5ea5-4c85-f001-bc744502e08e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "id": "9MZ97cdLn9hE",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pro_code | Provinces_kh | Provinces     | Households | Males  | Females | Total   | Household size | area_km2 | pop_km2 | reference\n",
              "11       | មណ្ឌលគីរី    | Mondul Kiri   | 19609      | 45533  | 43116   | 88649   | 4.5            | 14288    | 6       | Provisional_Population_Census__00.06.2019.pdf\n",
              "6        | កំពង់ធំ      | Kampong Thom  | 154458     | 327013 | 350247  | 677260  | 4.4            | 13814    | 49      | Provisional_Population_Census__00.06.2019.pdf\n",
              "13       | ព្រះវិហារ    | Preah Vihear  | 56331      | 126624 | 124728  | 251352  | 4.5            | 13788    | 18      | Provisional_Population_Census__00.06.2019.pdf\n",
              "15       | ពោធិ៍សាត់    | Pursat        | 102253     | 200392 | 211367  | 411759  | 4              | 12692    | 32      | Provisional_Population_Census__00.06.2019.pdf\n",
              "2        | បាត់ដំបង     | Battambang    | 218584     | 458902 | 528498  | 987400  | 4.5            | 11702    | 84      | Provisional_Population_Census__00.06.2019.pdf\n",
              "10       | ក្រចេះ       | Kratie        | 86137      | 185429 | 187396  | 372825  | 4.3            | 11094    | 34      | Provisional_Population_Census__00.06.2019.pdf\n",
              "19       | ស្ទឹងត្រែង   | Stung Treng   | 34627      | 83093  | 76472   | 159565  | 4.6            | 11092    | 14      | Provisional_Population_Census__00.06.2019.pdf\n",
              "16       | រតនគីរី      | Ratanak Kiri  | 47417      | 102325 | 101702  | 204027  | 4.3            | 10782    | 19      | Provisional_Population_Census__00.06.2019.pdf\n",
              "17       | សៀមរាប       | Siem Reap     | 218659     | 491568 | 514944  | 1006512 | 4.6            | 10299    | 98      | Provisional_Population_Census__00.06.2019.pdf\n",
              "9        | កោះកុង       | Koh Kong      | 26716      | 62304  | 61314   | 123618  | 4.6            | 10090    | 12      | Provisional_Population_Census__00.06.2019.pdf\n",
              "... (15 rows omitted)"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>pro_code</th> <th>Provinces_kh</th> <th>Provinces</th> <th>Households</th> <th>Males</th> <th>Females</th> <th>Total</th> <th>Household size</th> <th>area_km2</th> <th>pop_km2</th> <th>reference</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>11      </td> <td>មណ្ឌលគីរី   </td> <td>Mondul Kiri  </td> <td>19609     </td> <td>45533 </td> <td>43116  </td> <td>88649  </td> <td>4.5           </td> <td>14288   </td> <td>6      </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>6       </td> <td>កំពង់ធំ     </td> <td>Kampong Thom </td> <td>154458    </td> <td>327013</td> <td>350247 </td> <td>677260 </td> <td>4.4           </td> <td>13814   </td> <td>49     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>13      </td> <td>ព្រះវិហារ   </td> <td>Preah Vihear </td> <td>56331     </td> <td>126624</td> <td>124728 </td> <td>251352 </td> <td>4.5           </td> <td>13788   </td> <td>18     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>15      </td> <td>ពោធិ៍សាត់   </td> <td>Pursat       </td> <td>102253    </td> <td>200392</td> <td>211367 </td> <td>411759 </td> <td>4             </td> <td>12692   </td> <td>32     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>2       </td> <td>បាត់ដំបង    </td> <td>Battambang   </td> <td>218584    </td> <td>458902</td> <td>528498 </td> <td>987400 </td> <td>4.5           </td> <td>11702   </td> <td>84     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>10      </td> <td>ក្រចេះ      </td> <td>Kratie       </td> <td>86137     </td> <td>185429</td> <td>187396 </td> <td>372825 </td> <td>4.3           </td> <td>11094   </td> <td>34     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>19      </td> <td>ស្ទឹងត្រែង  </td> <td>Stung Treng  </td> <td>34627     </td> <td>83093 </td> <td>76472  </td> <td>159565 </td> <td>4.6           </td> <td>11092   </td> <td>14     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>16      </td> <td>រតនគីរី     </td> <td>Ratanak Kiri </td> <td>47417     </td> <td>102325</td> <td>101702 </td> <td>204027 </td> <td>4.3           </td> <td>10782   </td> <td>19     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>17      </td> <td>សៀមរាប      </td> <td>Siem Reap    </td> <td>218659    </td> <td>491568</td> <td>514944 </td> <td>1006512</td> <td>4.6           </td> <td>10299   </td> <td>98     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>9       </td> <td>កោះកុង      </td> <td>Koh Kong     </td> <td>26716     </td> <td>62304 </td> <td>61314  </td> <td>123618 </td> <td>4.6           </td> <td>10090   </td> <td>12     </td> <td>Provisional_Population_Census__00.06.2019.pdf</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (15 rows omitted)</p>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kh_provinces.bar(\"Females\")"
      ],
      "metadata": {
        "id": "cveecrespAq9",
        "outputId": "5a9ea2a8-c9a1-41ac-ef79-41e8c59a32e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "id": "cveecrespAq9",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "The column 'Provinces_kh' contains non-numerical values. A plot cannot be drawn for this column.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-54cd0b8a27c0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkh_provinces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Females\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datascience/tables.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, column_for_categories, select, overlay, width, height, **vargs)\u001b[0m\n\u001b[1;32m   3629\u001b[0m                 \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstretch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ultra-condensed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_visualize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn_for_categories\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverlay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannotate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3633\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mibar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_for_categories\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverlay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datascience/tables.py\u001b[0m in \u001b[0;36m_visualize\u001b[0;34m(self, x_label, y_labels, ticks, overlay, draw, annotate, width, height)\u001b[0m\n\u001b[1;32m   4663\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4664\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4665\u001b[0;31m                 raise ValueError(\"The column '{0}' contains non-numerical \"\n\u001b[0m\u001b[1;32m   4666\u001b[0m                     \u001b[0;34m\"values. A plot cannot be drawn for this column.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4667\u001b[0m                     .format(label))\n",
            "\u001b[0;31mValueError\u001b[0m: The column 'Provinces_kh' contains non-numerical values. A plot cannot be drawn for this column."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kh_provinces.column(\"Females\").\n"
      ],
      "metadata": {
        "id": "cXNTX7-ZpYC3"
      },
      "id": "cXNTX7-ZpYC3",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.15"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}