biteytech / game-of-life-x11

Using Panama to render the Game of Life with X11

dataframe

Another dataframe library for Java, inspired by Tablesaw, built on nio buffers.

To add a dependency on dataframe using Maven, use the following:

<dependency>
  <groupId>tech.bitey</groupId>
  <artifactId>dataframe</artifactId>
  <version>1.2.11</version>
</dependency>

Requires Java 17 or higher. The last version supporting Java 11 was 1.1.7.

Sample Usages

Release Notes

What's different about this dataframe library?

• It's geared towards making it easier to ship around tabular data for Java backend developers - rather than for data science. This is not Pandas for Java.
• Data is stored in ByteBuffers, so the dataframes can read/write to Channels with minimal overhead (save to files, send over network).
• Optimized for space. For example, booleans take one bit each, DateTimes take one long (with microsecond precision).
• Nulls are stored in a separate bitset (also backed by ByteBuffer), taking up two bits per Column length. No extra space is used if all values are non-null.

Features

• Supports the most common types: String, int, long, short, byte, boolean, double, float, Date, DateTime, and BigDecimal; as well as Time, UUID, Instant, and InputStream.
• Column and DataFrame are immutable. Columns can be created from collections, arrays, streams, or with builders.
• Read/write to File or Channel with minimal overhead. Supports memory-mapping from a file.
• Read/write CSV files
• Read from ResultSet, write with PreparedStatement
• Backing ByteBuffers can be on heap or off as a global property: -Dtech.bitey.allocateDirect=true or false, defaults to false
• Column implements List. DataFrame implements List<Row>. If the DataFrame has a key column it can be viewed as a NavigableMap<T, Row>
• Basic filtering, joining, grouping
• No additional dependencies
• Extensive testing

Sample Use Cases

• Great as a ResultSet cache. Have an expensive query that needs to run every time your app starts and it's slowing down your development? Cache it locally on disk in a DataFrame! Because DataFrame can be viewed as a ResultSet, you can plug it into existing code with minimal changes. Or cache it in a service and pull it over the network (reads/writes directly to Channel).
• Great for generating Excel reports via POI. Stage the data in a DataFrame first, then write POI code against the DataFrame. This separates concerns and is easier than writing POI directly against a ResultSet.