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Treweek","email":"samtreweek@gmail.com"},"license":"MIT","homepage":"https://github.com/serialbandicoot/html-table-to-dataframe#readme","keywords":["html","table","dataframe","typescript","jsdom","cli-table3","pretty-print"],"repository":{"type":"git","url":"git+https://github.com/serialbandicoot/html-table-to-dataframe.git"},"description":"Convert HTML tables to data-frames","maintainers":[{"name":"serialbandicoot","email":"samtreweek@gmail.com"}],"readme":"# html-table-to-dataframe\n\nConvert an HTML table to a DataFrame.\n\n![CI Status](https://github.com/serialbandicoot/html-table-to-dataframe/actions/workflows/ci.yml/badge.svg)\n\n## Overview\n\nThis project provides utilities to convert HTML tables into structured data formats and pretty-print them as tables. It uses `jsdom` for parsing HTML and `cli-table3` for displaying data in a formatted table.\n\n**Usage:**\n\n| Person | Like                  | Age |\n|--------|-----------------------|-----|\n| Chris  | HTML tables           |  22 |\n| Dennis | Web accessibility     |  45 |\n| Sarah  | JavaScript frameworks |  29 |\n| Karen  | Web performance       |  36 |\n\n```typescript\nimport { toDataFrame } from 'html-table-to-dataframe';\n\nconst dataFrame = toDataFrame(htmlString, ['Person', 'Likes', 'Age']);\n\nconsole.log(dataFrame)\n// data => [\n//  { Person: \"Chris\", Likes: \"HTML tables\", Age: \"22\" },\n//];\n\nawait toPrettyPrint(data);\n\n```\n### In Print Form\n\n```typescript\n┌─────────┬──────────────────────┬─────┐\n│ Person  │ Likes                │ Age │\n├─────────┼──────────────────────┼─────┤\n│ Chris   │ HTML tables          │ 22  │\n│ Dennis  │ Web accessibility    │ 45  │\n│ Sarah   │ JavaScript frameworks│ 29  │\n│ Karen   │ Web performance      │ 36  │\n└─────────┴──────────────────────┴─────┘\n```\n\n## Accessible Expectations\n\n- [toHaveTableRowCountGreaterThan](#tohavetablerowcountgreaterthan)\n- [toHaveColumnToBeValue](#tohavecolumntobevalue)\n- [toHaveColumnValuesToMatchRegex](#tohavecolumnvaluestomatchregex)\n- [toHaveColumnsValuesToMatchRegex](#tohavecolumnsvaluestomatchregex)\n- [toHaveColumnValuesToBeInRange](#tohavecolumnvaluestobeeinrange)\n- [toHaveColumnValuesToBeNumbers](#tohavecolumnvaluestobenumbers)\n- [toHaveColumnToMatchWhenFilteredBy](#tohavecolumntomatchwhenfilteredby)\n- [toHaveColumnToMatchGroupWhenFilteredBy](#tohavecolumntomatchgroupwhenfilteredby)\n- [toHaveColumnToNotMatch](#tohavecolumntonotmatch)\n- [toHaveTableRowCount](#tohavetablerowcount)\n- [toHaveColumnGroupToBeValue](#tohavecolumngrouptobevalue)\n- [toHaveColumnGroupToBeValues](#tohavecolumngrouptobeevalues)\n- [toHaveTableToNotMatch](#tohavetabletonotmatch)\n- [toHaveTableToMatch](#tohavetabletomatch)\n\n### toDataFrame\n\nThe toDataFrame function converts an HTML string into a data frame structure, which is an array of objects where each object represents a row in the table. This function is essential for transforming raw HTML tables into a format that can be easily manipulated and tested.\n\n### toHaveColumnToBeValue\n\nChecks if a single row in the table has the specified value in a given column.\n\n#### toHaveColumnToBeValue(tableData, 'col_2', '3');\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' }\n];\n\ntoHaveColumnToBeValue(tableData, 'col_2', '3'); // Passes, as the value in 'col_2' is '3'\n```\n\n### toHaveTableRowCountGreaterThan\n\nChecks if the table contains more rows than the specified count.\n\n#### toHaveTableRowCountGreaterThan(tableData, 3);\n\n```typescript\nconst tableData = [\n  { one: '1', two: '3' },\n  { one: '2', two: '100' }\n];\n\ntoHaveTableRowCountGreaterThan(tableData, 3); // Fails, as row count is 2\n```\n\n### toHaveColumnValuesToMatchRegex & toHaveColumnsValuesToMatchRegex\n\nChecks if the values in the specified column match the given regular expression pattern.\n\n#### toHaveColumnValuesToMatchRegex(tableData, \"two\", \"\\\\d\\\\d\");\n\n```typescript\nconst tableData = [\n  { one: '1', two: '3' },\n  { one: '2', two: '100' }\n];\n\ntoHaveColumnValuesToMatchRegex(tableData, \"two\", \"\\\\d\\\\d\"); // Fails, as {\"two\":\"100\"} has 3 digits\ntoHaveColumnsValuesToMatchRegex(tableData, [\"one\", \"two\"], \"\\\\d\\\\d\"); // Fails, as {\"two\":\"100\"} has 3 digits\n```\n\n### toHaveColumnValuesToBeInRange\n\nChecks if the values in the specified column fall within the given range.\n\n#### toHaveColumnValuesToBeInRange(tableData, \"two\", 0, 4);\n\n```typescript\nconst tableData = [\n  { one: '1', two: '3' },\n  { one: '2', two: '100' }\n];\n\ntoHaveColumnValuesToBeInRange(tableData, \"two\", 0, 4); // Fails, as {\"two\":\"100\"} is greater than 4\n```\n\n### toHaveColumnValuesToBeNumbers\n\nChecks if the values in the specified column are numbers.\n\n#### toHaveColumnValuesToBeNumbers(tableData, \"two\");\n\n```typescript\nconst tableData = [\n  { one: '1', two: '3' },\n  { one: '2', two: '1e' }\n];\n\ntoHaveColumnValuesToBeNumbers(tableData, \"two\"); // Fails, as {\"two\":\"1e\"} is not a number\n```\n\n### toHaveColumnToMatchWhenFilteredBy\n\nChecks if a target column/value pair exists when filtered by a specified column/value.\n\n#### toHaveColumnToMatchWhenFilteredBy(tableData, \"col_1\", \"2\", \"col_2\", \"xyz\");\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' },\n  { col_1: '2', col_2: '1e' }\n];\n\ntoHaveColumnToMatchWhenFilteredBy(tableData, \"col_1\", \"2\", \"col_2\", \"xyz\"); \n// Fails, as {\"col_1\":\"2\"} is found, but {\"col_2\":\"xyz\"} is not\n```\n\n### toHaveColumnToMatchGroupWhenFilteredBy\n\nUses an array of `GroupType` to check if a target column/value pair exists when filtered by each specified column/value.\n\n#### toHaveColumnToMatchGroupWhenFilteredBy(tableData, \"col_1\", \"1\", group);\n\n```typescript\ntype GroupType = {\n  filterColumn: string;\n  filterValue: string;\n};\n\nconst tableData = [\n  { col_1: '1', col_2: 'a', col_3: 'b' }\n];\n\nconst group: GroupType[] = [\n  { filterColumn: \"col_2\", filterValue: \"a\" },\n  { filterColumn: \"col_3\", filterValue: \"a\" }\n];\n\ntoHaveColumnToMatchGroupWhenFilteredBy(tableData, \"col_1\", \"1\", group);\n// Fails, as {\"col_2\": \"a\"} is OK; however, {\"col_3\": \"a\"} should be \"b\"\n```\n\n### toHaveColumnToNotMatch\n\nConfirms that a specified column no longer contains a certain value. This is useful for checking if a row has been deleted or archived.\n\n#### toHaveColumnToNotMatch(tableData, \"col_1\", \"2\");\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' },\n  { col_1: '2', col_2: '1e' }\n];\n\ntoHaveColumnToNotMatch(tableData, \"col_1\", \"2\");\n// Fails, as {\"col_1\":\"2\"} is found and should not be.\n```\n\n### toHaveTableRowCount\n\nMatches the row count of the table data against the expected value.\n\n#### toHaveTableRowCount(tableData, 3);\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' },\n  { col_1: '2', col_2: '1e' }\n];\n\ntoHaveTableRowCount(tableData, 3);\n// Fails, as row count should be 2.\n```\n\n### toHaveColumnToBeValue\n\nExpects only 1 table row and checks if the column value matches the provided value.\n\n#### toHaveColumnToBeValue(tableData, \"col_2\", \"3\");\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' }\n];\n\ntoHaveColumnToBeValue(tableData, \"col_2\", \"3\");\n// Passes, as the column value is \"3\".\n```\n\n### toHaveColumnGroupToBeValue\n\nExpects only 1 table row and checks if the column values in a group match the provided values. Exceptions are made when the filter value is null or undefined.\n\n#### toHaveColumnGroupToBeValue(tableData, [{ filterColumn: \"col_2\", filterValue: \"3\" }]);\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' }\n];\n\nconst filterGroup = [\n  { filterColumn: \"col_2\", filterValue: \"3\" }\n];\n\ntoHaveColumnGroupToBeValue(tableData, filterGroup);\n// Passes, as the column value for \"col_2\" is \"3\".\n```\n\n### toHaveColumnGroupToBeValues\n\nPerforms multiple grouped checks using `toHaveColumnGroupToBeValue` for each row. The `tableData` must be the same length as the `filterGroups`, and each entry in `filterGroups` is applied to the corresponding row in `tableData`.\n\n#### toHaveColumnGroupToBeValues(tableData, filterGroups);\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' },\n  { col_1: '2', col_2: '4' }\n];\n\nconst filterGroups = [\n  [{ filterColumn: \"col_2\", filterValue: \"3\" }],\n  [{ filterColumn: \"col_2\", filterValue: \"4\" }]\n];\ntoHaveColumnGroupToBeValues(tableData, filterGroups);\n// Passes, as the column values match the expected values for each row.\n```\n\n### toHaveTableToNotMatch\n\nConverts two tables into strings and compares them for equality. This assertion checks that the two tables do not match exactly.\n\n#### toHaveTableToNotMatch(tableData1, tableData2);\n\n```typescript\nconst tableData1 = [\n  { col_1: '1', col_2: '3' }\n];\n\nconst tableData2 = [\n  { col_1: '1', col_2: '3' }\n];\n\ntoHaveTableToNotMatch(tableData1, tableData2);\n// Fails, as the two tables are identical\n```\n\n### toHaveTableToMatch\n\nConverts two tables into strings and compares them for equality. This assertion checks that the two tables match exactly.\n\n#### toHaveTableToMatch(tableData1, tableData2);\n\n```typescript\nconst tableData1 = [\n  { col_1: '1', col_2: '3' }\n];\n\nconst tableData2 = [\n  { col_1: '1', col_2: '4' }\n];\ntoHaveTableToMatch(tableData1, tableData2);\n// Fails, as the two tables are different\n```\n\n### toHaveTableRowCountEqualTo\n\nCheck the length of the table is equal to\n\n#### toHaveTableRowCountEqualTo(tableData, expectedLength);\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' },\n  { col_1: '2', col_2: '4' }\n];\n\ntoHaveTableRowCountEqualTo(tableData, 3);\n// Fails, as the tables are length of 2\n```\n\n### toHaveTableRowCountLessThan\n\nCheck the length of the table is equal to\n\n#### toHaveTableRowCountLessThan(tableData, expectedLength);\n\n```typescript\nconst tableData = [\n  { col_1: '1', col_2: '3' },\n  { col_1: '2', col_2: '4' }\n];\n\ntoHaveTableRowCountLessThan(tableData, 1);\n// Fails, as the tables are length of 2\n```\n","readmeFilename":"README.md"}