There are following ways to read CSV file in Java. The default separator of a CSV file is a comma ,. Step 3: Now, save the file. Now, click on the Save button. Step 2: Write some data into file separated by comma ,. For example:. Vivek, Singh, 23, , Chandigarh. Java Scanner class provide various methods by which we can read CSV file.
The Scanner class provides a constructor that produces values scanned from the specified file. It breaks data into the token form. It uses a delimiter pattern which by default matches white space. The resulting tokens then converted into values of different types using the next methods. The method parses a delimiting regular expression. The method returns an array of string computed by splitting this string around matches of the given regular expression. That's all folks for reading and parsing a CSV file in Java.
For simple CSV file formats where column values do not contain the delimiter itself, core Java is a good choice. If you are looking to create and download a CSV file in a Spring Boot application, check out this excellent tutorial I wrote a while ago. Follow me on Twitter and LinkedIn. You can also subscribe to RSS Feed. Access to subordinate beans is accomplished the same way it is in the rest of opencsv: accessor methods where available, and Reflection otherwise. There may be times when you receive differently formatted input files that nonetheless have the same data, and you will want to map them to the same bean.
Here are two example inputs:. As you can see, the inputs have mostly the same information, but the formats are incompatible for the purposes of using exactly one bean. Profiles allow you to resolve these superficial differences and use the same data bean for both inputs. All annotations save CsvRecurse include a "profiles" parameter for this purpose.
CsvRecurse does not include the parameter because all annotations in recursively included beans are likewise subject to profile selection. The first annotation does not specify any profiles, so it is used when no annotation for a specific profile is found, or when no profile is specified on parsing.
It says that the default setting is to find a column named "last name" to bind to the field. The second annotation stipulates it is to be used with the profiles "customer 2" and "customer 5" whose data we have not seen in this example. It does not name a column, so the typical fallback for header naming is used: the name of the field.
The field "name" is annotated with three CsvBindByName annotations. The first does just what one would expect: it binds the field "name" to the column "name" from the input. This annotation does not specify a profile, so it is the default profile.
The second annotation is only for the profile "customer 1", and it binds the field to the input column named "first name". The third annotation is similar in function. The field "initial" is annotated with only one CsvBindByName, which binds the input column "middle initial" to the field. It uses the default profile. This field is also annotated with a CsvIgnore which says the field will be ignored for the profile "customer 2". It is worth noting, though, that both are connected to one named profile each.
If a different profile is specified for parsing, e. The field is also annotated with two CsvNumber annotations: one each for the profiles specified in the CsvBindByName annotations, as it would happen. The two annotations simply provide different format strings for the input numbers. The field "heightInCentimeters" has only one CsvBindByName annotation to bind the field to the input column "height" independent of profile since it is the default profile, and no other binding annotations exist for the field.
After that come two CsvNumber annotations that demonstrate the same principle as the binding annotations: the first is for the default profile, the second is only for the profile "customer 2".
If annotations are anathema to you, you can bypass them with carefully structured data and beans. With a header name mapping strategy, things are even easier. As long as you do not annotate anything in the bean, the header name mapping strategy will assume that all columns may be matched to a member variable of the bean with precisely the same name save capitalization.
Every field is considered optional. If no annotations of any kind are present, the header name mapping strategy is automatically chosen for you. If you explicitly specify the mapping strategy FuzzyMappingStrategy, all annotated member variables are respected, if any are present, and if any input fields are left unmapped, they will be mapped to the best non-annotated member variable.
Everything will work like you want it to with a minimum of annotating. After that, the fuzzy mapping strategy will compute that the header name "first header" is closest to the member variable name "firstHeader", "second header" is closest to "secondHeader", and "mispeling" is closest to "misspelling".
The mappings will be initialized appropriately. The dangers of this mapping strategy should be obvious. Even though the algorithm for computing the closest match is stable, the results might not be obvious to you. If you have headers named "header 1" with two spaces and "header 11", and only one member variable named "header1" perhaps you wish to ignore "header 11" in the input , it is non-deterministic which of the two input columns will be mapped to the member variable "header1".
You might accidentally get stuck with the wrong mapping. A similar problem can arise if the structure of your input data is not stable. If someone else is in control of the input and may add or delete columns at any time, fuzzy mappings that have worked fine for a long time may stop working because the new input file has a better match between header name and member variable.
Finally, if you have headers that should remain unmatched and member variables without annotations that should also remain unmatched, you will have a problem. This mapping strategy will map any unused field to the best unused member variable, no matter how poor the match. If you need to get around this, the best way is to annotate the member variable to be skipped and map it to a fictitious but optional header. Nonetheless, if you know your data, and a mismapping will not cause catastrophic failure of a critical system, this mapping strategy can save you some burdensome annotating for obvious mappings.
Since this matching strategy only makes sense for reading, it is not supported for writing, but it should behave exactly as HeaderColumnNameMappingStrategy. With some input it can be helpful to skip the first few lines. This will skip the first few lines of the raw input, not the CSV data, in case some input provides heaven knows what before the first line of CSV data, such as a legal disclaimer or copyright information.
Verifying is slightly different. With verifying, a complete finished bean is checked for desirability and consistency.
Beans can be silently filtered if they are simply undesirable data sets, or if the data are inconsistent and this is considered an error for the surrounding logic, CsvConstraintViolationException may be thrown. Incidentally, though it is a well-kept secret, the bean passed to a BeanVerifier is not a copy, so any changes made to the bean will be kept.
This is a way to get a postprocessor for beans into opencsv. Ignoring applies to fields in beans, and can be achieved via annotation or method call. If a bean you are manipulating for reading or writing includes fields that you want opencsv to ignore even if they already bear binding annotations from opencsv , you can add CsvIgnore to them and opencsv will skip them in all reading and writing operations. If you have no source control over the beans you use, you can use the withIgnoreField method of the appropriate builder or the ignoreFields method of the mapping strategy to achieve the same effect.
Less often used, but just as comfortable as reading CSV files is writing them. And believe me, a lot of work went into making writing CSV files as comfortable as possible for you, our users. For example, to write a tab-separated file:. There is a constructor argument for this purpose. Thankfully, no more. Notice, please, we did not tell opencsv what kind of bean we are writing or what mapping strategy is to be used.
Annotations are not even strictly necessary: if there are no annotations, opencsv assumes you want to write the whole bean using the header name mapping strategy and uses the field names as the column headers. Just as we can use the "capture" option to the binding annotations, if you use annotations on writing, you can use the "format" option to dictate how the field should be formatted if simply writing the bean field value is not enough.
Please see the Javadoc for the annotations for details. Just as in reading into beans, there is a performance trade-off while writing that is left in your hands: ordered vs. If the order of the data written to the output and the order of any exceptions captured during processing do not matter to you, use StatefulBeanToCsv. If you do nothing, the order of the columns on writing will be ascending according to position for column index-based mappings, and ascending according to name for header name-based mappings.
You can change this order, if you must. The same method exists for ColumnPositionMappingStrategy. If you wish to use your own ordering, you must instantiate your own mapping strategy through the appropriate builder and pass it in to StatefulBeanToCsvBuilder.
We expect there will be plenty of people who find using a Comparator uncomfortable, because they have an exact order that they need that has nothing to do with any kind of rule-based ordering. For these people we have included com. It is instantiated with an array of strings for header name mapping or integers for column position mapping that define the order desired.
Please note, though, that LiteralComparator is deprecated as of opencsv 5. Commons Collections is a dependency of opencsv, so it is already in your classpath. You are strongly encouraged to examine the Comparators Commons Collections makes available to you.
They are quite flexible and very useful. It has always been, and always will be, our position that opencsv should be configurable enough to process almost all csv files but be extensible so that users can write their own parsers and mappers for the situations where it cannot. So we have added hooks for validators and processors. Validators allow for the injection of code to provide additional checks of data over and above what opencsv provides.
By allowing integration, developers can inject code for their specific requirements without adding performance overhead and an unnecessary burden to the users who do not need them. We are glad to help you with opencsv and the integration of your validators with opencsv but the bugs in the validators you write are NOT bugs with opencsv. That and we have unit tests with all types of validators so we know the validator integration works as designed.
Feel free to look at our unit tests if you are having issues with the validators or processors. Here is a crude diagram of csv data showing where the different types of validators and processors are called. The LineValidator interface is for the creation of validators upon a single line from the Reader before it is processed.
A LineValidator should only be used when your csv records take one and only one line no carriage returns or new line characters in any of the fields and any of the existing validations do not work for you - like the multiLineLimit that is set in the CSVReaderBuilder. The RowValidator interface is for the creation of validators for an array of Strings that are supplied by the CSVReader after they have been processed.
RowValidators should only be used if you have a very good understanding and control of the data being being processed, like the positions of the columns in the csv file. If you do not know the order, then RowValidator needs to be generic enough such that it can be applied to every element in the row.
The StringValidator allows for the validation of a String prior to the conversion and assignment to a field in a bean.
Of all the validators this is the most precise as the user knows the precise string that is going to be assigned to a given field and thus the only reason to make a validator generic is for reusability across multiple types of fields. Processors allow for the modification of data, typically for the removal of undesired data or changing the defaults empty string to null for example.
Great care must be taken to ensure that the Processors written are fully tested as a malformed processor can make the data unusable. RowProcessors take the array of String that is the entire row and will process it. It is up to the user to decide if only specific elements or the entire row is processed. The processColumnItem is currently not used directly in opencsv but was put in the interface directly in hopes that the implementors will use it when creating unit tests to verify their processors work correctly.
The StringProcessor allows for the processing of a String prior to the conversion and assignment to a field in a bean.
Because the user knows the precise string that is going to be processed for a given field and thus the only reason to make a StringProcessor generic is for reusability across multiple types of fields. But for those blessed few, here is how all of the pieces fit together for reading:. A Comma-Separated Values CSV file is just a normal plain-text file, store data in column by column, and split it by a separator e. Java 7 is currently the minimum supported version for OpenCSV. Get hold of all the important Java Foundation and Collections concepts with the Fundamentals of Java and Java Collections Course at a student-friendly price and become industry ready.
For maven project, you can include the OpenCSV maven dependency in pom. Read data line by line : Lets see how to read CSV file line by line. For reading data line by line, first we have to construct and initialize CSVReader object by passing the filereader object of CSV file.
We discussed almost all ways to write and read data from a CSV file. I hope you enjoy reading this article. You may be interested in reading other CSV related articles:. Follow me on Twitter and LinkedIn. You can also subscribe to RSS Feed. I started this blog as a place to share everything I have learned in the last decade. I write about modern JavaScript, Node. The newsletter is sent every week and includes early access to clear, concise, and easy-to-follow tutorials, and other stuff I think you'd enjoy!
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