Difference between revisions of "ParseCSV"

Latest revision as of 16:04, 27 May 2021

Converts a text value read in from a file with CSV (Comma-Separated Values) format into a 2D array. By default, it converts numbers and dates into Analytica numbers and dates. It offers lots of optional parameters to control the column and row indexes, and handle the many variants of the CSV format -- for example, nonstandard characters that separate columns, rows, and for the quotes around text.

ParseCSV( csvText, columnIndex, rowIndex, separator, firstLineIsHeader, trimCells, columnsToKeep, rowIndexLabelColumn, parseFlags, dateTemplate, decimal, quote )

Converts text «csvText» in a CSV (Comma-Separated Values) format into a two-dimensional array. You usually use it with ReadTextFile() to read the file:

ParseCSV(ReadTextFile( filename ))

It assumes these defaults:

The usual conventions for CSV files (like Excel): Comma to separate fields, newline to separate rows, and double quotes (") to enclose elements that may contain commas or newlines.
It returns a 2D array with local indexes, .Column and .Row.
Local index .Column gets the column headers from the first row.
Local index .Row is numbered 1 to n-1, where n is the number of rows.

You can override any of these defaults with these optional parameters:

«columnIndex» and «rowIndex»

Specify «columnIndex» or «rowIndex» to use an existing index instead of the default local indexes .Column or .Row. If you specify a «columnIndex» it treats the first row as data and assumes that the columns are in the correct order. If the first row contains column headers that you want to ignore, perhaps because you want to rename some or all with «columnIndex», set «firstLineIsHeader» to true. If «columnIndex» (or «rowIndex») is shorter than the number of columns (rows) in the original, it ignores the extra columns or rows. If it is longer, it pads the result with NULL for the extra columns (rows.)

You can use ParseCSV to update indexes «columnIndex» and/or «rowIndex» with values from the column or row headers from the CSV. To do this, you define the global «columnIndex» and/or «rowIndex» as ComputedBy the variable or function containing the ParseCSV() call. For example, suppose you define, a variable TableFromCSV as: Variable TableFromCSV := ParseCSV(ReadTextFile('data.csv'), Cols, Rows, firstLineIsHeader: True, rowIndexLabelColumn: 1) Index Cols := ComputedBy(TableFromCSV) Index Rows := ComputedBy(TableFromCSV) It automatically updates Cols and rows with the header values from the csv table, when one of those indexes or TableFromCSV is first computed.

«firstLineIsHeader»

If the CSV has no column headers, set «firstLineIsHeader» to False, so that it treats the first row as data. If you don't specify a «columnIndex», it uses local index .Column containing numbers 1 to M (the number of columns).

«columnsToKeep» to select, reorder, or rename columns

You can select just a single column from «csvText» by specifying «columnsToKeep» as a column name from the first line of the csv file, or as the column number. You should then omit «columnIndex». It returns a vector (1D table) with no column index.

You can also select several columns and/or reorder columns by setting «columnIndex» to an existing index containing only the headers you want in the order you want. If it contains column names from the CSV (in any order), you can specify it also to «columnsToKeep», e.g.,

ParseCSV(Csv, Col, columnsToKeep: Col)

If you want to rename the columns, you can pass «columnsToKeep» a 1D array containing the column names or numbers from the CSV text indexed by «columnIndex» that contains the new names. (Some could be the same and some different.)

If you pass a list or array to «columnsToKeep» it's best to also specify «columnIndex». Without a «columnIndex» it will still extract the specified columns, but less efficiently because array abstraction repeats the call for each element in the array passed to «columnsToKeep», and it parses the «csvText» anew each time.

«rowIndexLabelsColumn» for row labels

By default, the local index .Row contains numbers 1 to the number of rows. But, you can also use a column to use for the labels for the local row index by setting «rowIndexLabelsColumn» to the number or label of that column. For example,

ParseCSV(CsvText, rowIndexLabelsColumn: 1)

treats the first column as row index labels rather than as array cells. It removes the selected column from the result array unless you include that column in «columnIndex» or «columnsToKeep».

Non-comma separator: «separator»

You might expect that CSV ("comma-separated value") format would always use commas to separate values! But, sometimes "CSV" files use another separator character, such as code>'|', 'Chr(9)' (tab), or ';'. It is convenient to choose a separator that never appears within a value, as ',' often does. If the CSV does use comma separators, you must put the «quote» character (default double quote) around the value to avoid confusing the parser.

Spaces around values: «trimCells»

By default it trims away any leading or trailing spaces around each value. Set «trimCells» to False if you want to retain those spaces. It never trims spaces inside quotes.

«parseFlags» to interpret numbers and dates

By default ParseCSV converts each number, whether enclosed in quotes or not, into a number or date, following Excel conventions. It treats any cell that starts with "=" (e.g. an Excel formula) as text. You can change these defaults with «parseFlags». This parameter is a bit-field of flags, which means that you can add flags together to combine their effects:

0 = (default) Return quoted ("52") and unquoted (52) numbers as numbers, and cells that look like dates ('6-May-2016' or '9/4/2020 15:04') as dates.
1 = Don't parse any cells -- e.g. return ="52", "52" and 52 as text.
2 = Don't parse quoted cells -- e.g. treat "52" as text, but 52 as a number.
4 = Disable the '=' prefix (="0042" normally suppresses parsing).
8 = Recognize backslash-escaped quotes to allow quotes inside a quote (in addition to the doubling of quotes, which is the usual CSV standard). For example "He said \"Hello\"." would return the text "He said "Hello"."
16 = Disable generalized date parsing. When you do this, it still recognizes format 6-May-2016 as a date but not other formats. This flag dramatically speeds up parsing times for data with no other date formats.

«dateTemplate» Date Formats

Use the «dateTemplate» parameter to specify the ordering for international dates. Use the letters "d", "M" and "y" to specify the ordering of these components. For example, with «dateTemplate» of "d/M/y", parses "11/10/9" as 11-Oct-2009, but with «dateTemplate» of "y/M/d" it parses it as 9-Oct-2011, and with "M/d/y" it would be 10-Nov-2009.

This parameter uses the same conventions used in the Custom date formats template in the Number Format Dialog, but only the relative ordering of the day, month and year patterns matter. Hence, all these work equivalently: "dMy", "dd/MM/yyyy", "d-M-yy", "d M yyyy". Also, upper/lower case matters! "M" must be uppercase (because "m" means minutes").

«decimal» point and thousands separators

By default, it assumes that numbers look like "2,525,948.77" with a decimal point '.' (dot) to separate the whole and decimal part, and commas ',' to group digits into threes (thousands, millions, billions, etc.). That is the convention in English-speaking countries. To use the opposite convention -- e.g. "2.525.948,77" -- common in the rest of the World, set the «decimal» parameter to ',' (comma) instead of the default '.'.

When using comma for «decimal», it's usual to specify «separator» as semi-colon ';' or something else other than comma. If «decimal» and «separator» are both commas, you need to use quotes around any numbers containing decimals.

«quote» character

The default quote character to enclose numbers or text that may contain commas, new lines, or other separators is double quote ("). You can specify a different character or text to the «quote» parameter. The most common other quote character is the single quote ('). To type a single quote in Analytica, you can type doubleQuote SingleQuote doubleQuote ("'"). Analytica lets you use either matching single quotes or matching double quotes to enclose a text string.

The CSV format

There is no single well-defined CSV standard. Undocumented conventions used by Excel are often treated as the "definitive" specification. The article at Comma Separated Values (CSV) Standard File Format is a good overview, but not comprehensive.

Analytica's ParseCSV() and MakeCSV() follows these Excel conventions (which you can override with parseFlags):

Each "record" (or row) in a CSV file is delineated by a newline, either CR, LF or CRLF.
Each value within a row is separated from the next field by a separator character, usually comma (hence the name "Comma-separated values"), although other characters or character sequences are also sometimes used, including commonly TAB (Chr(9)), bar ('|'), semi-colon (';') and even a simple space.

A row of data might look like this:

California, CA, "39,144,818", 163696, Sacramento, "Edmund Gerald ""Jerry"" Brown, Jr.", ="95814", 9/9/1850

Text and numeric values may or may not be surrounded by quotes. Any value containing the separator character or a newline must be surrounded by quotes.
Quotes are always the double-quote character (").
A value may also be a date (e.g., 9/9/1850).
If a value parses as a valid date, ParseCSV() treats it as a date, otherwise it tries to parse it as a number. If that fails, it treats it as text. The «parseFlags» parameter can be used to alter this behavior somewhat. T
To force a value to be read as text, even when it would be a valid number, use the equal-quote form, ="95814".
It trims spaces to the left or right of a value, unless you set «textTrim» to False.
A new line can appear within a quoted value, so simply splitting CSV data on newlines does not reliably separate the data into rows.
The first line of a CSV file often consists of columns names, with data starting on the second row. But, some CSV files have no column headers and the data starts on the first line.

Examples

The simplest usage is:

ParseCSV( csvText )

which follows the most common format and Excel standards.

To read in a CSV file:

ParseCSV( ReadTextFile( filename ) )

History

Analytica 5.3 introduced the ability to update the columns and rows at the same time you parse the CSV, using the ComputedBy function.

We introduced this function in Analytica 5.0. For earlier releases, the Flat-file library contains ReadCsvFile and ParseCsvText functions. ParseCSV is a substantial improvement on these.

@@ Line 1: / Line 1: @@
 [[category:Text Functions]]
-''new to [[Analytica 5.0]]''
 Converts a text value read in from a file with CSV (Comma-Separated Values) format into a 2D array.
-By default, it converts values that appear to be numbers and dates into Analytica numbers and dates. It offers lots of optional parameters to control the column and row indexes, and handle the many variants of the CSV format -- for example, with nonstandard characters that separate columns, rows, and for the quotes around text.
+By default, it converts numbers and dates into Analytica numbers and dates. It offers lots of optional parameters to control the column and row indexes, and handle the many variants of the CSV format -- for example, nonstandard characters that separate columns, rows, and for the quotes around text.
 == ParseCSV( csvText'', columnIndex, rowIndex, separator, firstLineIsHeader, trimCells, columnsToKeep, rowIndexLabelColumn, parseFlags, dateTemplate, decimal, quote '' ) ==
-Normally, you use this with [[ReadTextFile]]() to read in the file:
+Converts text «csvText» in a CSV (Comma-Separated Values) format into a two-dimensional array. You usually use it with [[ReadTextFile]]() to read the file:
+ <code>ParseCSV(ReadTextFile( filename ))</code>
+It assumes these defaults:
+* The usual conventions for CSV files (like Excel):  Comma to separate fields, newline to separate rows, and double quotes (<code>"</code>) to enclose elements that may contain commas or newlines.
+* It returns a 2D array with local indexes, <code>.Column</code> and <code>.Row</code>.
+* Local index <code>.Column</code> gets the column headers from the first row.
+* Local index <code>.Row</code> is numbered 1 to n-1, where n is the number of rows.
+You can override any of these defaults with these optional parameters:
+=== «columnIndex» and «rowIndex» ===
+Specify «columnIndex» or «rowIndex» to use an existing index instead of the default local indexes <code>.Column</code> or <code>.Row</code>. If you specify a «columnIndex» it treats the first row as data and assumes that the columns are in the correct order. If the first row contains column headers that you want to ignore, perhaps because you want to rename some or all with «columnIndex»,  set «firstLineIsHeader» to <code>true</code>.  If «columnIndex»  (or «rowIndex») is shorter than the number of columns (rows) in the original, it ignores the extra columns or rows. If it is longer, it pads the result with NULL for the extra columns (rows.)
+You can use ParseCSV to update indexes «columnIndex» and/or «rowIndex» with values from the column or row headers from the CSV.  To do this, you define the global «columnIndex» and/or «rowIndex» as [[ComputedBy]] the variable or function containing the ParseCSV() call. For example, suppose you define, a variable TableFromCSV as:
+<code>
+Variable TableFromCSV := ParseCSV(ReadTextFile('data.csv'),  Cols,  Rows,  firstLineIsHeader: True,  rowIndexLabelColumn: 1)
+Index Cols := ComputedBy(TableFromCSV)
+Index Rows := ComputedBy(TableFromCSV)
+</code>
+It automatically updates <code>Cols</code> and <code>rows</code> with the header values from the csv table, when one of those indexes or <code>TableFromCSV</code> is first computed.
-<code>ParseCSV(ReadTextFile( filename ))</code>
+=== «firstLineIsHeader» ===
-Given text in CSV-format (Comma Separated Values), it returns a two-dimensional array containing the data. By default, it assumes the same CSV format conventions as Excel -- that is, commas to separate columns, newline to separate rows, and double quotes to enclose elements that may contain commas or newlines.  By default, it returns the array with  local indexes, <code>.Column</code> and <code>.Row</code>.
+If the CSV has no column headers, set «firstLineIsHeader» to <code>False</code>, so that it treats the first row as data.  If you don't specify a «columnIndex», it uses local index <code>.Column</code> containing numbers 1 to M (the number of columns).
-=== Indexes for result ===
+=== «columnsToKeep» to select, reorder, or rename columns ===
-If you have an existing column or row index you want to use instead of the default local indexes  <code>.Column</code> and <code>.Row</code>, you can specify one or both as optional parameters «columnIndex» and «rowIndex». If «columnIndex»  (or «rowIndex») is shorter than the number of columns (or rows) in the original, it ignores the extra columns or rows. If the given index is is longer than expected, it pads the result with NULL for the extra columns (or rows.)
+You can select just a single column from «csvText» by specifying «columnsToKeep» as a column name from the first line of the csv file, or as the column number. You should then omit «columnIndex».  It returns a vector (1D table) with no column index.
-=== Non-comma separator ===
+You can also select several columns and/or reorder columns by setting «columnIndex» to an existing index containing only the headers you want in the order you want. If it contains column names from the CSV (in any order), you can specify it also to «columnsToKeep», e.g.,
-By default, it assumes that cells (columns) are separated by commas (,), as you would expect in a "comma-separated" format!   You can specify any character (or a text with multiple characters) to the optional parameters  «separator». Common separators include <code>'|'</code>, <code>[[Chr]](9)</code> (tab), <code>';'</code>
+<code>[[ParseCSV]](Csv, Col, columnsToKeep: Col)</code>
-=== Spaces around values ===
+If you want to rename the columns, you can pass «columnsToKeep» a 1D array containing the column names or numbers from the CSV text indexed by «columnIndex» that contains the new names. (Some could be the same and some different.)
-By default it trims away any leading or trailing spaces around each value. Set «trimCells» to False if you want to retain those spaces. It never trims spaces inside quotes.
+If you pass a list or array to «columnsToKeep» it's best to also specify «columnIndex». Without a «columnIndex» it will still extract the specified columns, but less efficiently because array abstraction repeats the call for each element in the array passed to «columnsToKeep», and it parses the «csvText» anew each time.
-=== Header column in data ===
+===  «rowIndexLabelsColumn» for row labels ===
-By default, it assumes that the first row contains columns names, which is common in CSV file, and it puts those names into the local <code>.Column</code> index, unless you specify «columnIndex».   In that case, the first row does not appear in the data.  It also assumes the first row contains column names if you specify «columnsToKeep» or «rowIndexLabelsColumn».  You can override this default behavior by setting «firstLineIsHeader» to <code>false</code>, in which case it treats the first row as data.  Conversely, you can override the default of treating the first line as data when you specify a «columnIndex» by setting «firstLineIsHeader» to <code>true</code>.
+By default, the local index <code>.Row</code> contains numbers 1 to the number of rows. But, you can also use a column to use for the labels for the local row index by setting «rowIndexLabelsColumn» to the number or label of that column.  For example,
-=== Subset of or re-ordered columns ===
+<code>ParseCSV(CsvText, rowIndexLabelsColumn: 1)</code>
-The «columnsToKeep» parameter lets you select a subset of the columns, or re-order them.
+treats the first column as row index labels rather than as array cells. It removes the selected column from the result array unless you  include that column in «columnIndex» or «columnsToKeep».
-To extract just a single column, you can specify either the name (from the first line) or the number of the column that you want to keep to «columnsToKeep».  You can omit «columnIndex».
+=== Non-comma separator: «separator» ===
-To extract several columns or to reorder columns, you should specify a «columnIndex». If the column index contains the column names from the CSV (in any order), you can specify it also to «columnsToKeep»  e.g., <code>[[ParseCSV]](csv, col, columnsToKeep:col)</code>. Or you can pass «columnsToKeep» a 1D array of names or numbers indexed by «columnIndex». .
+You might expect that CSV ("comma-separated value") format would ''always'' use commas to separate values! But, sometimes "CSV" files use another separator character, such as code>'|'</code>, <code>'[[Chr]](9)'</code> (tab), or <code>';'</code>.  It is convenient to choose a separator that never appears within a value, as <code>','</code> often does.  If the CSV ''does'' use comma separators, you must put the «quote»  character (default double quote) around the value to avoid confusing the parser.
-If you pass a list or array to «columnsToKeep», but don't specify «columnIndex», it will still extract the specified columns, but less efficiently because array abstraction repeats the call for each element in the array passed to «columnsToKeep», and it parses the «csvText» anew each time.
+=== Spaces around values: «trimCells» ===
-===  Using a column for row labels ===
+By default it trims away any leading or trailing spaces around each value. Set «trimCells» to False if you want to retain those spaces. It never trims spaces inside quotes.
-You can use «rowIndexLabelsColumn» to specify a column number to use for the labels for the local row index. It then removes that column from the array (unless you specify «columnIndex» with «matchColumnLabels» and explicitly include that column). For example, <code>ParseCSV(csvText, rowIndexLabelsColumn: 1)</code> treats the first column as row index labels rather than as array cells.
+=== «parseFlags» to interpret numbers and dates ===
-=== Parse flags ===
+By default ParseCSV converts each number, whether enclosed in quotes or not,  into a number or date, following Excel conventions. It treats any cell that starts with "=" (e.g. an Excel formula) as text. You can change these defaults with «parseFlags».  This parameter is a bit-field of flags, which means that you can add flags together to combine their effects:
+* 0 = (default) Return quoted ("52") and unquoted (52) numbers as numbers, and cells that look like dates ('<code>6-May-2016</code>' or '9/4/2020 15:04') as dates.
+* 1 = Don't parse any cells -- e.g.  return <code>="52"</code>, <code>"52"</code> and <code>52</code> as text.
+* 2 = Don't parse quoted cells -- e.g.  treat <code>"52"</code> as text, but <code>52</code> as a number.
+* 4 = Disable the '=' prefix (<code>="0042"</code> normally suppresses parsing).
+* 8 = Recognize backslash-escaped quotes to allow quotes inside a quote (in addition to the doubling of quotes, which is the usual CSV standard). For example "He said \"Hello\"." would return the text "He said "Hello"."
+* 16 = Disable generalized date parsing.  When you do this, it still recognizes format <code>6-May-2016</code> as a date but not other formats. This flag dramatically speeds up parsing times for data with no other date formats.
-«parseFlags» is a bit-field of flags to control whether it should try to convert cells into a number or date. The default (0) corresponds to the standard Excel conventions -- i.e. it converts an unquoted number or a number between quotes into a number. You can add flags together to combine their effects:
+=== «dateTemplate» Date Formats ===
-* 0 = Parse both quoted ("52") and unquoted (52) cells.
-* 1 = Don't parse any cells. E.g., return <code>="52"</code>, <code>"52"</code> and <code>52</code> as text.
-* 2 = Don't parse quoted cells. E.g., <code>"52"</code> is text, but <code>52</code> is numeric.
-* 4 = Disable the '=' prefix (<code>="0042"</code> normally suppressed parsing).
-* 8 = Recognize backslash-escaped quotes inside quotes (in addition to the doubling of quotes, which is the usual CSV standard).
-* 16 = Disable generalized date parsing. When on, only the format <code>6-May-2016</code> is recognized as a date but not other formats. This flag dramatically speeds up parsing times for data with no other date formats.
-=== Date Formats ===
+Use the «dateTemplate» parameter to specify the ordering for international dates. Use the letters "d", "M" and "y" to specify the ordering of these components. For example, with «dateTemplate» of "d/M/y", parses "11/10/9" as 11-Oct-2009, but with «dateTemplate» of "y/M/d" it parses it as 9-Oct-2011, and with "M/d/y" it would be 10-Nov-2009.
-Use the «dateTemplate» parameter to specify the international ordering that should be used for parsing dates. Use the letters "d", "M" and "y" to specify the ordering of these components. For example, with a «dateTemplate» of "d/M/y", the text "11/10/9" parses as 11-Oct-2009, whereas with a «dateTemplate» of "y/M/d" it would be 9-Oct-2011, and with "M/d/y" it would be 10-Nov-2009.
-This parameter uses the same conventions used in the Custom date formats template in the [[Number Format Dialog]], but only the relative ordering of the day, month and year patterns matter. Hence, all these work equivalently: "dMy", "dd/MM/yyyy", "d-M-yy", "d M yyyy". Also, upper/lower case matters!  "M" must be capital (because "m" means minutes").
+This parameter uses the same conventions used in the Custom date formats template in the [[Number Format Dialog]], but only the relative ordering of the day, month and year patterns matter. Hence, all these work equivalently: "dMy", "dd/MM/yyyy", "d-M-yy", "d M yyyy". Also, upper/lower case matters!  "M" must be uppercase (because "m" means minutes").
-=== Decimal point and thousands separators ===
+=== «decimal» point and thousands separators ===
-By default, it assumes that <code>'.'</code> (dot or period) separates the whole number from the fractional part of a decimal number -- i.e. the convention used in English-speaking countries.  You can tell it to use  <code>','</code> (comma) by setting the «decimal» parameter. That parameter accepts only <code>'.'</code> (dot) or <code>','</code> (comma).
+By default, it assumes that numbers look like <code>"2,525,948.77"</code> with a decimal point <code>'.'</code> (dot) to separate the whole and decimal part, and commas <code>','</code> to group digits into threes (thousands, millions, billions, etc.). That is the convention in English-speaking countries. To use the opposite convention -- e.g.  <code>"2.525.948,77"</code> -- common in the rest of the World, set the «decimal» parameter to  <code>','</code> (comma) instead of the default <code>'.'</code>.
-When comma is used for «decimal», the «separator» is usually  something other than comma -- commonly semi-colon <code>';'</code>. If the «decimal» and «separator» are both commas, there must be quotes around any numbers containing decimals.
-When «decimal» is dot, you can use commas to group digits, such as <code>"525,948.77"</code>. Conversely, when «decimal» is comma, you can use dot to group digits, such as <code>"525.948,77"</code> (for five hundred twenty-five thousand, nine hundred eight and 77 hundredths).
+When using comma for «decimal», it's usual to specify «separator» as semi-colon <code>';'</code> or something else other than comma. If «decimal» and «separator» are both commas, you need to use quotes around any numbers containing decimals.
-=== Quote character ===
+=== «quote» character ===
-The quote character is used in CSV to enclose numbers or text that may contain separators. By default, the quote character is double quote (<code>"</code>). You can specify a different quote character using the «quote» parameter.  The most common other quote character used is the single quote ('). To type a single quote in Analytica, you can type doubleQuote SingleQuote doubleQuote (<code>"'"</code>). Analytica lets you use either matching single quotes or matching double quotes to enclose text strings.
+The default quote character to enclose numbers or text that may contain commas, new lines, or other separators is double quote (<code>"</code>). You can specify a different character or text to the «quote» parameter.  The most common other quote character is the single quote ('). To type a single quote in Analytica, you can type doubleQuote SingleQuote doubleQuote (<code>"'"</code>). Analytica lets you use either matching single quotes or matching double quotes to enclose a text string.
 == The CSV format ==
-There is no single well-defined CSV standard. Undocumented conventions used by Excel are often seen as the most definitive "specification". The article at [http://edoceo.com/utilitas/csv-file-format Comma Separated Values (CSV) Standard File Format] is relatively easy to read and covers the basics well, without being the most comprehensive reference.
+There is no single well-defined CSV standard. Undocumented conventions used by Excel are often treated as the "definitive" specification.  The article at [http://edoceo.com/utilitas/csv-file-format Comma Separated Values (CSV) Standard File Format] is a good overview, but not comprehensive.
-Each "record" (or row) is a CSV file is delineated by a newline, either CR, LF or CRLF.  Each value within a row is separated from the next field by a separator character, usually comma (hence the name "Comma-separated values"), although other characters or character sequences are also sometimes used, including commonly TAB (<code>[[Chr]](9)</code>), bar (<code>'|'</code>), semi-colon (<code>';'</code>) and even space.
+Analytica's ParseCSV() and MakeCSV() follows these Excel conventions  (which you can override with parseFlags):
+* Each "record" (or row) in a CSV file is delineated by a newline, either CR, LF or CRLF.
+* Each value within a row is separated from the next field by a separator character, usually comma (hence the name "Comma-separated values"), although other characters or character sequences are also sometimes used, including commonly TAB (<code>[[Chr]](9)</code>), bar (<code>'|'</code>), semi-colon (<code>';'</code>) and even a simple space.
 A row of data might look like this:
 :<code>California, CA, "39,144,818", 163696, Sacramento, "Edmund Gerald ""Jerry"" Brown, Jr.", ="95814", 9/9/1850</code>
-Both textual and numeric values may or may not be surrounded by quotes. Any value containing the separator character or a newline must be surrounded by quotes. Quotes are always the double-quote character (<code>"</code>). A value may also be a date (e.g., <code>9/9/1850</code>).  Spaces to the left or right of a value are normally trimmed (unless you set «textTrim» to false).
+* Text and numeric values may or may not be surrounded by quotes. Any value containing the separator character or a newline must be surrounded by quotes.
+* Quotes are always the double-quote character (<code>"</code>).
-When a value is read, if it parses as a valid date, it will be parsed as a date, otherwise if it parses as a valid number, it will parse as a number, and otherwise it will be read as text. The «parseFlags» parameter can be used to alter this behavior somewhat. To force a value to be read as text, even when it would be a valid number, use the equal-quote form, <code>="95814"</code>.
+* A value may also be a date (e.g., <code>9/9/1850</code>).
+* If a value parses as a valid date, ParseCSV() treats it as a date, otherwise it tries to parse it as a number. If that fails, it treats it as text. The «parseFlags» parameter can be used to alter this behavior somewhat. T
-Note that new lines can appear within quoted values, so simply splitting CSV data on newlines does not reliably separate the data into rows.
+* To force a value to be read as text, even when it would be a valid number, use the equal-quote form, <code>="95814"</code>.
+* It trims spaces to the left or right of a value, unless you set «textTrim» to False.
-The first line of a CSV file often consists of columns names, with data starting on the second row. But this is not always the case, some CSV files have no column headers with data starting on the first line.
+* A new line can appear within a quoted value, so simply splitting CSV data on newlines does not reliably separate the data into rows.
+* The first line of a CSV file often consists of columns names, with data starting on the second row. But, some CSV files have no column headers and the data starts on the first line.
 == Examples ==
@@ Line 99: / Line 119: @@
 == History ==
-This built-in function was introduced in [[Analytica 5.0]]. For earlier releases, the [[Standard_libraries#Flat_File_Library|Flat-file library]] contains [[ReadCsvFile]] and [[ParseCsvText]] functions. The [[ParseCSV]] is a substantial improvement.
+[[Analytica 5.3]] introduced the ability to update the columns and rows at the same time you parse the CSV, using the [[ComputedBy]] function.
+We introduced this function in [[Analytica 5.0]]. For earlier releases, the [[Standard_libraries#Flat_File_Library|Flat-file library]] contains [[ReadCsvFile]] and [[ParseCsvText]] functions. [[ParseCSV]] is a substantial improvement on these.
 == See Also ==
+* [[Parsing and formatting data]]
 * [[ReadTextFile]]
 * [[MakeCSV]]