JarCastingCSV-DATA
The Superfly-CSV project was born from a need to work with small data sets within an automated testing project. Excel sheets were cumbersome to import due to less than ideal libraries available, and more importantly, are nearly impossible to work with effectively in a source control system such as Git. CSV, on the other hand, provides the ability to organize data in a meaningful way, while not suffering the same challenges with source control and collaboration as Excel.
Usage
Loading CSV Files
Loading a CSV file is as simple as calling the CsvLoader object, passing the path and name of the file as the argument.
CsvFile csv = CsvLoader.load("testCsv.csv");
The path to the CSV file is relative to the classpath on the consuming project. For Maven, this simply means the file would be stored in the resources folder. For non-Maven projects, the directory storing CSV files would need to be added to the classpath.
For example, if the CSV file resides in a directory beneath the standard resources folder in a Maven project, the constructor would be expressed as follows:
CsvFile csv = CsvLoader.load("test-data/data.csv");
CSV File Structure
This tool employs two constraints on CSV formatting. First, the first row is always assumed to be a header row.
Failure to add headers will result in data being offset by one row, and values being interpreted as header information. Second, a separator is required for each row. Rows with no comma separator are ignored and will not be parsed onto the CsvFile object. For instances where only a single column is represented on a CSV file, each line must end with a separator.
Leading and Trailing Spaces
Due to the way some tools may present CSV data to the user, there may be some extraneous leading and/or trailing spaces within segments of the CSV lines. This tool provides some flexibility in handling this scenario.
The header row is always stripped of leading and trailing spaces. The result of this is easier to use inputs when calling out specific column headers throughout the use of the CsvFile object.
Row data can be stripped (or not) at the time the CSV file is loaded, by including the preserveSpaces argument to the CsvLoader.
CsvFile csv = CsvLoader.load("data.csv", true);
In the example above, the loader will refrain from stripping the CSV segments of any leading or trailing spaces.
This method is overloaded, so the preserveSpaces option is only required if the CSV data is to be presented in it's original state. By not using the option, leading and trailing spaces will be removed by default.
Managing CSV Data
There are currently two ways to reduce the data set to a more focused set of data, filter(), and exclude().
This example CSV file will be used in the descriptions below.
a,b,c
foo,bar,baz
foo,baz,bar
bar,foo,baz
bar,baz,foo
baz,foo,bar
baz,bar,foo
Filtering Data
Filtering data will return only rows that match the given criteria in a column. To accomplish this, the target column value, and the filter value must be given in the method call.
CsvFile csv = CsvLoader.load("test.csv");
csv.filter("b", "foo");
... will reduce the example CSV above to:
a,b,c
bar,foo,baz
baz,foo,bar
CSV Data can be filtered repeatedly as long as rows continue to exist. Adding to the example above...
csv.filter("c", "bar");
... will reduce the data set to:
a,b,c
baz,foo,bar
Finally, a convenience method exists to automatically set the filter target to the first column.
.filter(String column, String filterBy)will filter the data set based on the values in the given column..filter(String filterBy)will filter data based on the values in the first column.
Excluding Data
Excluding data will remove all the rows that match the given criteria in a column. To accomplish this, the target column value, and the exclusion value must be given in the method call.
CsvFile csv = CsvLoader.load("test.csv");
csv.exclude("c", "baz");
... will reduce the example CSV above to:
a,b,c
foo,baz,bar
bar,baz,foo
baz,foo,bar
baz,bar,foo
CSV Data can be excluded repeatedly as long as rows continue to exist. Adding to the example above...
csv.exclude("b", "foo");
... will reduce the data set to:
a,b,c
foo,baz,bar
bar,baz,foo
baz,bar,foo
Finally, a convenience method exists to automatically set the exclusion target to the first column.
.exclude(String column, String excludeBy)will exclude data based on the values in the given column..exclude(String excludeBy)will exclude data based on the values in the first column.
Retrieving Data
Navigating the CSV
By default, focus is given to the first row of the CSV data when a CSV file is loaded. Filtering and excluding rows will also reset focus to the first row. Focus is reassigned by one of several methods.
The nextRow() method will advance focus to the next row below the current row, if one exists. If no next row exists, an exception is thrown.
The previousRow() method will change focus to the row above the current row, if one exists. If no previous row exists, an exception is thrown.
The setCurrentRow(Int row) method will change focus directly to the given row. A constraint check is performed to ensure the newly assigned row is within bounds of the rows on the current data set. An exception is thrown if the new value is out of bounds.
Single CSV Data Value
Single values can be retrieved from the row with focus be referencing the column header. With the following CSV...
a,b,c
foo,bar,baz
... when csv.valueOf("a") is called, the value foo is returned.
List of Values by Column
A list of values can be retrieved from all rows based on a referenced column header value. With the following CSV...
a,b,c
foo,bar,baz
bar,baz,foo
baz,foo,bar
... when csv.columnValues("a") is called, the values foo, bar, and baz will be returned as a List<String>.
List of Values by Row
A list of values can be retrieved from the current row. With the following CSV...
a,b,c
baz,bar,foo
... when csv.currentRowValues() is called, the values baz, bar, and foo will be returned as a String[].
Managing Instances
Multiple instances of a CsvFile can be created by using the .clone() method. This method allows the current state of a CSV file to be replicated to a separate instance of the object. Cloning a CsvFile instance will enable more sophisticated data filtering and exclusion, as well as creating a "save point" of sorts with a virtualized CSV file.
Iterating over CSV rows
The recommended approach to iterating over rows on a CSV is to create an enhanced for loop on getRows (), then performing any desired functions or calls to the CsvRow object.
for (CsvRow row : csv.getRows()) {
// add tasks to be completed for each currently stored row.
}
The CsvFile is not an iterator, and does not behave as such. Thus, while loops that conclude with calls to nextRow() will result in the final row being skipped, or in the case of single-row CSV, the loop exiting without running any of the iterative commands. The getRows() method does return an ArrayList, which in turn makes an Iterator available, if one is required.