diff --git a/README.md b/README.md index 06368f49f1..a2b374db36 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ You could find the following articles there: * [Rendering to HTML](https://kotlin.github.io/dataframe/tohtml.html#jupyter-notebooks) ### What's new -Check out this [notebook with new features](examples/notebooks/feature_overviews/0.14.0/new_features.ipynb) in development for the next release. +Check out this [notebook with new features](examples/notebooks/feature_overviews/0.15/new_features.ipynb) in v0.15. The DataFrame compiler plugin has reached public preview! Here's a [compiler plugin demo project](https://github.com/koperagen/df-plugin-demo) that works with [IntelliJ IDEA](https://www.jetbrains.com/idea/) 2024.2. diff --git a/examples/README.md b/examples/README.md index feb0158e22..108c7dec84 100644 --- a/examples/README.md +++ b/examples/README.md @@ -42,7 +42,7 @@ ___ * imdb sql database ([notebook](https://github.com/zaleslaw/KotlinDataFrame-SQL-Examples/blob/master/notebooks/imdb.ipynb)) – In this notebook, we use Kotlin DataFrame and Kandy library to analyze data from [IMDB](https://datasets.imdbws.com/) (SQL dump for the MariaDB database with the name "imdb" could be downloaded by this [link](https://drive.google.com/file/d/10HnOu0Yem2Tkz_34SfvDoHTVqF_8b4N7/view?usp=sharing)). --- -* 0.14.0 Dev Feature Overview [notebook](notebooks/feature_overviews/0.14.0/new_features.ipynb) - Overview of new features available in 0.14.0-dev +* 0.14 Feature Overview [notebook](notebooks/feature_overviews/0.14/new_features.ipynb) + Overview of new features available in 0.14 These example notebooks are tested by the [:core module](../core/src/test/kotlin/org/jetbrains/kotlinx/dataframe/jupyter). diff --git a/examples/notebooks/feature_overviews/0.14.0/data.csv b/examples/notebooks/feature_overviews/0.14/data.csv similarity index 100% rename from examples/notebooks/feature_overviews/0.14.0/data.csv rename to examples/notebooks/feature_overviews/0.14/data.csv diff --git a/examples/notebooks/feature_overviews/0.14.0/data1.csv b/examples/notebooks/feature_overviews/0.14/data1.csv similarity index 100% rename from examples/notebooks/feature_overviews/0.14.0/data1.csv rename to examples/notebooks/feature_overviews/0.14/data1.csv diff --git a/examples/notebooks/feature_overviews/0.14.0/mixed_column.xlsx b/examples/notebooks/feature_overviews/0.14/mixed_column.xlsx similarity index 100% rename from examples/notebooks/feature_overviews/0.14.0/mixed_column.xlsx rename to examples/notebooks/feature_overviews/0.14/mixed_column.xlsx diff --git a/examples/notebooks/feature_overviews/0.14.0/new_features.ipynb b/examples/notebooks/feature_overviews/0.14/new_features.ipynb similarity index 100% rename from examples/notebooks/feature_overviews/0.14.0/new_features.ipynb rename to examples/notebooks/feature_overviews/0.14/new_features.ipynb diff --git a/examples/notebooks/feature_overviews/0.15/irisDataset.tar b/examples/notebooks/feature_overviews/0.15/irisDataset.tar new file mode 100644 index 0000000000..857e3c477d Binary files /dev/null and b/examples/notebooks/feature_overviews/0.15/irisDataset.tar differ diff --git a/examples/notebooks/feature_overviews/0.15/new_features.ipynb b/examples/notebooks/feature_overviews/0.15/new_features.ipynb new file mode 100644 index 0000000000..a80ef3d3f3 --- /dev/null +++ b/examples/notebooks/feature_overviews/0.15/new_features.ipynb @@ -0,0 +1,13033 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 0.15: New Features\n", + "\n", + "- Experimental new CSV parser based on [Deephaven-CSV](https://github.com/deephaven/deephaven-csv)\n", + "- Experimental new `GeoDataFrame` class for working with geographical data (from GeoJson/Shapefile) and plotting it with [Kandy](https://github.com/Kotlin/kandy)\n", + "- Custom SQL Database support by passing the `dbType` parameter to read functions\n", + "- Full `BigInteger` support\n", + "- Improved parsing" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:14:52.870403Z", + "start_time": "2024-12-06T13:14:51.110838Z" + } + }, + "cell_type": "code", + "source": [ + "// loading dependencies for the SQL examples\n", + "// this needs to be called before importing dataframe itself\n", + "USE {\n", + " dependencies {\n", + " implementation(\"com.h2database:h2:2.3.232\")\n", + " implementation(\"com.mysql:mysql-connector-j:9.1.0\")\n", + " }\n", + "}" + ], + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-12-06T13:15:04.824210Z", + "start_time": "2024-12-06T13:14:52.895019Z" + } + }, + "cell_type": "code", + "source": [ + "%useLatestDescriptors\n", + "\n", + "// you can enable the new experimental modules (in notebooks) in the following way:\n", + "%use dataframe(v=0.15.0, enableExperimentalCsv=true, enableExperimentalGeo=true)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enabling experimental CSV module: dataframe-csv\n", + "Enabling experimental Geo module: dataframe-geo\n" + ] + } + ], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Experimental new CSV parser based on Deephaven-CSV\n", + "\n", + "DataFrame's CSV parsing has been based on [Apache Commons CSV](https://commons.apache.org/proper/commons-csv/) from the beginning. While this has been sufficient for most applications, it had some issues like running out of memory, performance, and our API lacking in clarity, documentation, and completeness.\n", + "([Related issue #827](https://github.com/Kotlin/dataframe/issues/827))\n", + "\n", + "For DataFrame 0.15, we introduce a new separate package [`org.jetbrains.kotlinx:dataframe-csv`](https://central.sonatype.com/artifact/org.jetbrains.kotlinx/dataframe-csv) which tries to solve all these issues at once. It's based on [Deephaven-CSV](https://github.com/deephaven/deephaven-csv) which makes it faster and more memory efficient. And since we built it from the ground up, we made sure the API was complete, predictable, and documented carefully.\n", + "\n", + "To try it yourself, explicitly add the dependency [`org.jetbrains.kotlinx:dataframe-csv`](https://central.sonatype.com/artifact/org.jetbrains.kotlinx/dataframe-csv) to your project. In notebooks you can add `enableExperimentalCsv=true` to the %use-magic, as seen above." + ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Given a large CSV file, such as below, the chances of running out of memory are now (still possible, but) lower:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-02T12:48:59.669795Z", + "start_time": "2024-12-02T12:47:40.785374Z" + }, + "tags": [] + }, + "cell_type": "code", + "source": [ + "// Old csv function:\n", + "DataFrame.readCSV(\n", + " \"../../../../dataframe-csv/src/test/resources/largeCsv.csv.gz\",\n", + ")" + ], + "outputs": [ + { + "ename": "java.lang.OutOfMemoryError", + "evalue": "Ran out of memory reading this CSV-like file. You can try our new experimental CSV reader by adding the dependency \"org.jetbrains.kotlinx:dataframe-csv:{VERSION}\" and using `DataFrame.readCsv()` instead of `DataFrame.readCSV()`.", + "output_type": "error", + "traceback": [ + "java.lang.OutOfMemoryError: Ran out of memory reading this CSV-like file. You can try our new experimental CSV reader by adding the dependency \"org.jetbrains.kotlinx:dataframe-csv:{VERSION}\" and using `DataFrame.readCsv()` instead of `DataFrame.readCSV()`.", + "\tat org.jetbrains.kotlinx.dataframe.io.CsvKt.readDelim(csv.kt:367)", + "\tat org.jetbrains.kotlinx.dataframe.io.CsvKt.readDelim(csv.kt:285)", + "\tat org.jetbrains.kotlinx.dataframe.io.CsvKt.readCSV$lambda$2(csv.kt:152)", + "\tat org.jetbrains.kotlinx.dataframe.io.CommonKt.catchHttpResponse(common.kt:27)", + "\tat org.jetbrains.kotlinx.dataframe.io.CsvKt.readCSV(csv.kt:151)", + "\tat org.jetbrains.kotlinx.dataframe.io.CsvKt.readCSV$default(csv.kt:138)", + "\tat Line_11_jupyter.(Line_11.jupyter.kts:2) at Cell In[6], line 2", + "\tat java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)", + "\tat java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)", + "\tat java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)", + "\tat java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)", + "\tat kotlin.script.experimental.jvm.BasicJvmScriptEvaluator.evalWithConfigAndOtherScriptsResults(BasicJvmScriptEvaluator.kt:122)", + "\tat kotlin.script.experimental.jvm.BasicJvmScriptEvaluator.invoke$suspendImpl(BasicJvmScriptEvaluator.kt:48)", + "\tat kotlin.script.experimental.jvm.BasicJvmScriptEvaluator.invoke(BasicJvmScriptEvaluator.kt)", + "\tat kotlin.script.experimental.jvm.BasicJvmReplEvaluator.eval(BasicJvmReplEvaluator.kt:49)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.InternalEvaluatorImpl$eval$resultWithDiagnostics$1.invokeSuspend(InternalEvaluatorImpl.kt:137)", + "\tat kotlin.coroutines.jvm.internal.BaseContinuationImpl.resumeWith(ContinuationImpl.kt:33)", + "\tat kotlinx.coroutines.DispatchedTask.run(DispatchedTask.kt:104)", + "\tat kotlinx.coroutines.EventLoopImplBase.processNextEvent(EventLoop.common.kt:277)", + "\tat kotlinx.coroutines.BlockingCoroutine.joinBlocking(Builders.kt:95)", + "\tat kotlinx.coroutines.BuildersKt__BuildersKt.runBlocking(Builders.kt:69)", + "\tat kotlinx.coroutines.BuildersKt.runBlocking(Unknown Source)", + "\tat kotlinx.coroutines.BuildersKt__BuildersKt.runBlocking$default(Builders.kt:48)", + "\tat kotlinx.coroutines.BuildersKt.runBlocking$default(Unknown Source)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.InternalEvaluatorImpl.eval(InternalEvaluatorImpl.kt:137)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$execute$1$result$1.invoke(CellExecutorImpl.kt:80)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl$execute$1$result$1.invoke(CellExecutorImpl.kt:78)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl.withHost(ReplForJupyterImpl.kt:774)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.CellExecutorImpl.execute-L4Nmkdk(CellExecutorImpl.kt:78)", + "\tat org.jetbrains.kotlinx.jupyter.repl.execution.CellExecutor$DefaultImpls.execute-L4Nmkdk$default(CellExecutor.kt:13)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl.evaluateUserCode-wNURfNM(ReplForJupyterImpl.kt:596)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl.evalExImpl(ReplForJupyterImpl.kt:454)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl.access$evalExImpl(ReplForJupyterImpl.kt:141)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl$evalEx$1.invoke(ReplForJupyterImpl.kt:447)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl$evalEx$1.invoke(ReplForJupyterImpl.kt:446)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl.withEvalContext(ReplForJupyterImpl.kt:427)", + "\tat org.jetbrains.kotlinx.jupyter.repl.impl.ReplForJupyterImpl.evalEx(ReplForJupyterImpl.kt:446)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor$processExecuteRequest$1$response$1$1.invoke(IdeCompatibleMessageRequestProcessor.kt:171)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor$processExecuteRequest$1$response$1$1.invoke(IdeCompatibleMessageRequestProcessor.kt:170)", + "\tat org.jetbrains.kotlinx.jupyter.streams.BlockingSubstitutionEngine.withDataSubstitution(SubstitutionEngine.kt:70)", + "\tat org.jetbrains.kotlinx.jupyter.streams.StreamSubstitutionManager.withSubstitutedStreams(StreamSubstitutionManager.kt:118)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor.withForkedIn(IdeCompatibleMessageRequestProcessor.kt:347)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor.access$withForkedIn(IdeCompatibleMessageRequestProcessor.kt:67)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor$evalWithIO$1$1.invoke(IdeCompatibleMessageRequestProcessor.kt:361)", + "\tat org.jetbrains.kotlinx.jupyter.streams.BlockingSubstitutionEngine.withDataSubstitution(SubstitutionEngine.kt:70)", + "\tat org.jetbrains.kotlinx.jupyter.streams.StreamSubstitutionManager.withSubstitutedStreams(StreamSubstitutionManager.kt:118)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor.withForkedErr(IdeCompatibleMessageRequestProcessor.kt:336)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor.access$withForkedErr(IdeCompatibleMessageRequestProcessor.kt:67)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor$evalWithIO$1.invoke(IdeCompatibleMessageRequestProcessor.kt:360)", + "\tat org.jetbrains.kotlinx.jupyter.streams.BlockingSubstitutionEngine.withDataSubstitution(SubstitutionEngine.kt:70)", + "\tat org.jetbrains.kotlinx.jupyter.streams.StreamSubstitutionManager.withSubstitutedStreams(StreamSubstitutionManager.kt:118)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor.withForkedOut(IdeCompatibleMessageRequestProcessor.kt:328)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor.evalWithIO(IdeCompatibleMessageRequestProcessor.kt:359)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor$processExecuteRequest$1$response$1.invoke(IdeCompatibleMessageRequestProcessor.kt:170)", + "\tat org.jetbrains.kotlinx.jupyter.messaging.IdeCompatibleMessageRequestProcessor$processExecuteRequest$1$response$1.invoke(IdeCompatibleMessageRequestProcessor.kt:169)", + "\tat org.jetbrains.kotlinx.jupyter.execution.JupyterExecutorImpl$Task.execute(JupyterExecutorImpl.kt:41)", + "\tat org.jetbrains.kotlinx.jupyter.execution.JupyterExecutorImpl$executorThread$1.invoke(JupyterExecutorImpl.kt:81)", + "\tat org.jetbrains.kotlinx.jupyter.execution.JupyterExecutorImpl$executorThread$1.invoke(JupyterExecutorImpl.kt:79)", + "\tat kotlin.concurrent.ThreadsKt$thread$thread$1.run(Thread.kt:30)", + "", + "java.lang.OutOfMemoryError: Ran out of memory reading this CSV-like file. You can try our new experimental CSV reader by adding the dependency \"org.jetbrains.kotlinx:dataframe-csv:{VERSION}\" and using `DataFrame.readCsv()` instead of `DataFrame.readCSV()`.", + "at Cell In[6], line 2", + "" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-02T12:53:41.064469Z", + "start_time": "2024-12-02T12:53:08.127522Z" + } + }, + "cell_type": "code", + "source": [ + "// New csv function:\n", + "DataFrame.readCsv(\n", + " \"../../../../dataframe-csv/src/test/resources/largeCsv.csv.gz\",\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
YearAgeEthnicSexAreacount
201801101795
2018011025067
2018011032229
2018011041356
201801105180
201801106738
201801107630
2018011081188
2018011092157
201801112177
2018011132823
2018011141020
201801115516
201801116222
201801117219
201801118234
201801199..C
2018011999919563
20180117719557
201801201768
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"Year\",\"Age\",\"Ethnic\",\"Sex\",\"Area\",\"count\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":31999999,\"ncol\":6},\"kotlin_dataframe\":[{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"01\",\"count\":\"795\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"02\",\"count\":\"5067\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"03\",\"count\":\"2229\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"04\",\"count\":\"1356\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"05\",\"count\":\"180\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"06\",\"count\":\"738\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"07\",\"count\":\"630\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"08\",\"count\":\"1188\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"09\",\"count\":\"2157\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"12\",\"count\":\"177\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"13\",\"count\":\"2823\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"14\",\"count\":\"1020\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"15\",\"count\":\"516\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"16\",\"count\":\"222\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"17\",\"count\":\"219\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"18\",\"count\":\"234\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"99\",\"count\":\"..C\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"9999\",\"count\":\"19563\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":1,\"Area\":\"77\",\"count\":\"19557\"},{\"Year\":2018,\"Age\":0,\"Ethnic\":1,\"Sex\":2,\"Area\":\"01\",\"count\":\"768\"}]}" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "40 million rows! Not bad, right?\n", + "\n", + "The fact we can now read this file is due to Deephaven CSV's ability to parse columns directly to the target type, like `Int`, or `Double`, instead of reading and storing everything as a `String` first and then parsing it. This saves both memory and running time. Deephaven made a [blogpost](https://deephaven.io/blog/2022/02/23/csv-reader/) if you're curious about the specifics.\n", + "\n", + "DataFrame still reads everything into (boxed) memory, so there are limits to the size of the file you can read, but now the CSV reader is not a limiting factor anymore.\n", + "(Check the \"Max heap size\" setting if you're running this notebook in IntelliJ, and you're still running out of memory for large files).\n", + "\n", + "Switching to the new API, in most cases, is as easy as swapping `readCSV` with `readCsv` (and `readTSV` with `readTsv`, etc.). However, there are a few differences in the API, so be sure to check the KDocs of the new functions.\n", + "\n", + "Here's a small demonstration of the new API:" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:21:46.445799Z", + "start_time": "2024-12-06T13:21:45.263111Z" + } + }, + "cell_type": "code", + "source": [ + "import java.util.Locale\n", + "\n", + "DataFrame.readCsv(\n", + " \"../../../../dataframe-csv/src/test/resources/irisDataset.csv\",\n", + " delimiter = ',',\n", + "\n", + " // overwriting the given header\n", + " header = listOf(\"sepalLength\", \"sepalWidth\", \"petalLength\", \"petalWidth\", \"species\"),\n", + "\n", + " // skipping the first line in the file with old header\n", + " skipLines = 1,\n", + "\n", + " // reading only 50 lines\n", + " readLines = 50,\n", + "\n", + " // manually specifying the types of the columns, will be inferred otherwise\n", + " colTypes = mapOf(\n", + " \"species\" to ColType.String, // setting the type of the species column to String\n", + " ColType.DEFAULT to ColType.Double, // setting type of all other columns to Double\n", + " ),\n", + "\n", + " // manually specifying some parser options\n", + " // Will be read from the global parser options `DataFrame.parser` otherwise\n", + " parserOptions = ParserOptions(\n", + " // setting the locale to US, uses `DataFrame.parser.locale` or `Locale.getDefault()` otherwise\n", + " locale = Locale.US,\n", + " // overriding null strings\n", + " nullStrings = DEFAULT_DELIM_NULL_STRINGS + \"nothing\",\n", + " // using the new faster double parser, true by default for readCsv\n", + " useFastDoubleParser = true,\n", + " ),\n", + "\n", + " // new! specifying the quote character\n", + " quote = '\\\"',\n", + "\n", + " // specifying whether to ignore empty lines in between rows in the file, and plenty more options...\n", + " ignoreEmptyLines = false,\n", + " allowMissingColumns = true,\n", + " ignoreSurroundingSpaces = true,\n", + " trimInsideQuoted = false,\n", + " parseParallel = true,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"sepalLength\",\"sepalWidth\",\"petalLength\",\"petalWidth\",\"species\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":50,\"ncol\":5},\"kotlin_dataframe\":[{\"sepalLength\":5.1,\"sepalWidth\":3.5,\"petalLength\":1.4,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.9,\"sepalWidth\":3.0,\"petalLength\":1.4,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.7,\"sepalWidth\":3.2,\"petalLength\":1.3,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.6,\"sepalWidth\":3.1,\"petalLength\":1.5,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":5.0,\"sepalWidth\":3.6,\"petalLength\":1.4,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":5.4,\"sepalWidth\":3.9,\"petalLength\":1.7,\"petalWidth\":0.4,\"species\":\"Setosa\"},{\"sepalLength\":4.6,\"sepalWidth\":3.4,\"petalLength\":1.4,\"petalWidth\":0.3,\"species\":\"Setosa\"},{\"sepalLength\":5.0,\"sepalWidth\":3.4,\"petalLength\":1.5,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.4,\"sepalWidth\":2.9,\"petalLength\":1.4,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.9,\"sepalWidth\":3.1,\"petalLength\":1.5,\"petalWidth\":0.1,\"species\":\"Setosa\"},{\"sepalLength\":5.4,\"sepalWidth\":3.7,\"petalLength\":1.5,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.8,\"sepalWidth\":3.4,\"petalLength\":1.6,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":4.8,\"sepalWidth\":3.0,\"petalLength\":1.4,\"petalWidth\":0.1,\"species\":\"Setosa\"},{\"sepalLength\":4.3,\"sepalWidth\":3.0,\"petalLength\":1.1,\"petalWidth\":0.1,\"species\":\"Setosa\"},{\"sepalLength\":5.8,\"sepalWidth\":4.0,\"petalLength\":1.2,\"petalWidth\":0.2,\"species\":\"Setosa\"},{\"sepalLength\":5.7,\"sepalWidth\":4.4,\"petalLength\":1.5,\"petalWidth\":0.4,\"species\":\"Setosa\"},{\"sepalLength\":5.4,\"sepalWidth\":3.9,\"petalLength\":1.3,\"petalWidth\":0.4,\"species\":\"Setosa\"},{\"sepalLength\":5.1,\"sepalWidth\":3.5,\"petalLength\":1.4,\"petalWidth\":0.3,\"species\":\"Setosa\"},{\"sepalLength\":5.7,\"sepalWidth\":3.8,\"petalLength\":1.7,\"petalWidth\":0.3,\"species\":\"Setosa\"},{\"sepalLength\":5.1,\"sepalWidth\":3.8,\"petalLength\":1.5,\"petalWidth\":0.3,\"species\":\"Setosa\"}]}" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Since deephaven supports it, we can now also read multi-space separated files, like logs ([Relevant issue #746](https://github.com/Kotlin/dataframe/issues/746)):" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:21:51.445969Z", + "start_time": "2024-12-06T13:21:51.285692Z" + } + }, + "cell_type": "code", + "source": [ + "DataFrame.readDelimStr(\n", + " \"\"\"\n", + " NAME STATUS AGE LABELS\n", + " argo-events Active 2y77d app.kubernetes.io/instance=argo-events,kubernetes.io/metadata.name=argo-events\n", + " argo-workflows Active 2y77d app.kubernetes.io/instance=argo-workflows,kubernetes.io/metadata.name=argo-workflows\n", + " argocd Active 5y18d kubernetes.io/metadata.name=argocd\n", + " beta Active 4y235d kubernetes.io/metadata.name=beta\n", + " \"\"\".trimIndent(),\n", + " hasFixedWidthColumns = true,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NAMESTATUSAGELABELS
argo-eventsActive2y77dapp.kubernetes.io/instance=argo-event...
argo-workflowsActive2y77dapp.kubernetes.io/instance=argo-workf...
argocdActive5y18dkubernetes.io/metadata.name=argocd
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"NAME\",\"STATUS\",\"AGE\",\"LABELS\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":3,\"ncol\":4},\"kotlin_dataframe\":[{\"NAME\":\"argo-events\",\"STATUS\":\"Active\",\"AGE\":\"2y77d\",\"LABELS\":\"app.kubernetes.io/instance=argo-events,kubernetes.io/metadata.name=argo-events\"},{\"NAME\":\"argo-workflows\",\"STATUS\":\"Active\",\"AGE\":\"2y77d\",\"LABELS\":\"app.kubernetes.io/instance=argo-workflows,kubernetes.io/metadata.name=argo-workflows\"},{\"NAME\":\"argocd\",\"STATUS\":\"Active\",\"AGE\":\"5y18d\",\"LABELS\":\"kubernetes.io/metadata.name=argocd\"}]}" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We provide single overload (with `InputStream`) which exposes the underlying implementation for when ours is not sufficient for your needs ([Relevant issue #787](https://github.com/Kotlin/dataframe/issues/787))." + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:21:59.773321Z", + "start_time": "2024-12-06T13:21:59.216960Z" + } + }, + "cell_type": "code", + "source": [ + "import io.deephaven.csv.containers.ByteSlice\n", + "import io.deephaven.csv.tokenization.Tokenizer\n", + "import java.io.InputStream\n", + "\n", + "DataFrame.readCsv(\n", + " inputStream = File(\"../../../../dataframe-csv/src/test/resources/irisDataset.csv\").inputStream(),\n", + " adjustCsvSpecs = {\n", + " this\n", + " .headerLegalizer {\n", + " it.map { it.lowercase().replace('.', '_') }.toTypedArray()\n", + " }\n", + " .customDoubleParser(object : Tokenizer.CustomDoubleParser {\n", + " override fun parse(bs: ByteSlice?): Double = TODO(\"Not yet implemented\")\n", + " override fun parse(cs: CharSequence?): Double = TODO(\"Not yet implemented\")\n", + " })\n", + " // etc..\n", + " },\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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4.6000003.1000001.5000000.200000Setosa
5.0000003.6000001.4000000.200000Setosa
5.4000003.9000001.7000000.400000Setosa
4.6000003.4000001.4000000.300000Setosa
5.0000003.4000001.5000000.200000Setosa
4.4000002.9000001.4000000.200000Setosa
4.9000003.1000001.5000000.100000Setosa
5.4000003.7000001.5000000.200000Setosa
4.8000003.4000001.6000000.200000Setosa
4.8000003.0000001.4000000.100000Setosa
4.3000003.0000001.1000000.100000Setosa
5.8000004.0000001.2000000.200000Setosa
5.7000004.4000001.5000000.400000Setosa
5.4000003.9000001.3000000.400000Setosa
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5.7000003.8000001.7000000.300000Setosa
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\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"sepal_length\",\"sepal_width\",\"petal_length\",\"petal_width\",\"variety\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":150,\"ncol\":5},\"kotlin_dataframe\":[{\"sepal_length\":5.1,\"sepal_width\":3.5,\"petal_length\":1.4,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.9,\"sepal_width\":3.0,\"petal_length\":1.4,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.7,\"sepal_width\":3.2,\"petal_length\":1.3,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.6,\"sepal_width\":3.1,\"petal_length\":1.5,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":5.0,\"sepal_width\":3.6,\"petal_length\":1.4,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":5.4,\"sepal_width\":3.9,\"petal_length\":1.7,\"petal_width\":0.4,\"variety\":\"Setosa\"},{\"sepal_length\":4.6,\"sepal_width\":3.4,\"petal_length\":1.4,\"petal_width\":0.3,\"variety\":\"Setosa\"},{\"sepal_length\":5.0,\"sepal_width\":3.4,\"petal_length\":1.5,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.4,\"sepal_width\":2.9,\"petal_length\":1.4,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.9,\"sepal_width\":3.1,\"petal_length\":1.5,\"petal_width\":0.1,\"variety\":\"Setosa\"},{\"sepal_length\":5.4,\"sepal_width\":3.7,\"petal_length\":1.5,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.8,\"sepal_width\":3.4,\"petal_length\":1.6,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":4.8,\"sepal_width\":3.0,\"petal_length\":1.4,\"petal_width\":0.1,\"variety\":\"Setosa\"},{\"sepal_length\":4.3,\"sepal_width\":3.0,\"petal_length\":1.1,\"petal_width\":0.1,\"variety\":\"Setosa\"},{\"sepal_length\":5.8,\"sepal_width\":4.0,\"petal_length\":1.2,\"petal_width\":0.2,\"variety\":\"Setosa\"},{\"sepal_length\":5.7,\"sepal_width\":4.4,\"petal_length\":1.5,\"petal_width\":0.4,\"variety\":\"Setosa\"},{\"sepal_length\":5.4,\"sepal_width\":3.9,\"petal_length\":1.3,\"petal_width\":0.4,\"variety\":\"Setosa\"},{\"sepal_length\":5.1,\"sepal_width\":3.5,\"petal_length\":1.4,\"petal_width\":0.3,\"variety\":\"Setosa\"},{\"sepal_length\":5.7,\"sepal_width\":3.8,\"petal_length\":1.7,\"petal_width\":0.3,\"variety\":\"Setosa\"},{\"sepal_length\":5.1,\"sepal_width\":3.8,\"petal_length\":1.5,\"petal_width\":0.3,\"variety\":\"Setosa\"}]}" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Finally, we now support reading from ZIP files directly, along with GZIP (already demonstrated above) and custom compression formats ([Relevant issue #469](https://github.com/Kotlin/dataframe/issues/469)):" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:06.763690Z", + "start_time": "2024-12-06T13:22:06.524446Z" + } + }, + "cell_type": "code", + "source": [ + "DataFrame.readCsv(\n", + " \"../../../../dataframe-csv/src/test/resources/testCSV.zip\",\n", + " // this can be manually specified, but is inferred automatically from the file extension\n", + " // compression = Compression.Zip,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
untitleduser_idnameduplicateusernameduplicate1duplicate11doublenumbertimeempty
04Georgenullabcanull1203.000000599.2130002021-01-07T15:12:32null
15Paulnullpaulnullnullnull214.2110002021-01-14T14:36:19null
28Johnnynullqwertybnull20.000000412.2140002021-02-23T19:47null
310Jacknullbuknullnull2414.0000001.0100002021-03-08T23:38:52null
412SamuelnullqwertynullnullInfinity0.0000002021-04-01T02:30:22null
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"untitled\",\"user_id\",\"name\",\"duplicate\",\"username\",\"duplicate1\",\"duplicate11\",\"double\",\"number\",\"time\",\"empty\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Char?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDateTime\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"}],\"nrow\":5,\"ncol\":11},\"kotlin_dataframe\":[{\"untitled\":0,\"user_id\":4,\"name\":\"George\",\"duplicate\":null,\"username\":\"abc\",\"duplicate1\":\"a\",\"duplicate11\":null,\"double\":1203.0,\"number\":599.213,\"time\":\"2021-01-07T15:12:32\",\"empty\":null},{\"untitled\":1,\"user_id\":5,\"name\":\"Paul\",\"duplicate\":null,\"username\":\"paul\",\"duplicate1\":null,\"duplicate11\":null,\"double\":null,\"number\":214.211,\"time\":\"2021-01-14T14:36:19\",\"empty\":null},{\"untitled\":2,\"user_id\":8,\"name\":\"Johnny\",\"duplicate\":null,\"username\":\"qwerty\",\"duplicate1\":\"b\",\"duplicate11\":null,\"double\":20.0,\"number\":412.214,\"time\":\"2021-02-23T19:47\",\"empty\":null},{\"untitled\":3,\"user_id\":10,\"name\":\"Jack\",\"duplicate\":null,\"username\":\"buk\",\"duplicate1\":null,\"duplicate11\":null,\"double\":2414.0,\"number\":1.01,\"time\":\"2021-03-08T23:38:52\",\"empty\":null},{\"untitled\":4,\"user_id\":12,\"name\":\"Samuel\",\"duplicate\":null,\"username\":\"qwerty\",\"duplicate1\":null,\"duplicate11\":null,\"double\":Infinity,\"number\":0.0,\"time\":\"2021-04-01T02:30:22\",\"empty\":null}]}" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:10.963588Z", + "start_time": "2024-12-06T13:22:09.273678Z" + } + }, + "cell_type": "code", + "source": "USE { dependencies(\"org.tukaani:xz:1.10\", \"org.apache.commons:commons-compress:1.27.1\") }", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:11.841585Z", + "start_time": "2024-12-06T13:22:11.256663Z" + } + }, + "cell_type": "code", + "source": [ + "import org.apache.commons.compress.archivers.tar.TarFile\n", + "import org.apache.commons.io.IOUtils\n", + "import org.apache.commons.compress.utils.SeekableInMemoryByteChannel\n", + "\n", + "// custom compression format by specifying how to convert a compressed InputStream to a normal one\n", + "val tarCompression = Compression.Custom({ tarInputStream ->\n", + " val tar = TarFile(SeekableInMemoryByteChannel(IOUtils.toByteArray(tarInputStream)))\n", + " tar.getInputStream(tar.entries.first())\n", + "})\n", + "\n", + "DataFrame.readCsv(\"irisDataset.tar\", compression = tarCompression)" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sepal.lengthsepal.widthpetal.lengthpetal.widthvariety
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5.0000003.6000001.4000000.200000Setosa
5.4000003.9000001.7000000.400000Setosa
4.6000003.4000001.4000000.300000Setosa
5.0000003.4000001.5000000.200000Setosa
4.4000002.9000001.4000000.200000Setosa
4.9000003.1000001.5000000.100000Setosa
5.4000003.7000001.5000000.200000Setosa
4.8000003.4000001.6000000.200000Setosa
4.8000003.0000001.4000000.100000Setosa
4.3000003.0000001.1000000.100000Setosa
5.8000004.0000001.2000000.200000Setosa
5.7000004.4000001.5000000.400000Setosa
5.4000003.9000001.3000000.400000Setosa
5.1000003.5000001.4000000.300000Setosa
5.7000003.8000001.7000000.300000Setosa
5.1000003.8000001.5000000.300000Setosa
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"sepal.length\",\"sepal.width\",\"petal.length\",\"petal.width\",\"variety\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":150,\"ncol\":5},\"kotlin_dataframe\":[{\"sepal.length\":5.1,\"sepal.width\":3.5,\"petal.length\":1.4,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.9,\"sepal.width\":3.0,\"petal.length\":1.4,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.7,\"sepal.width\":3.2,\"petal.length\":1.3,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.6,\"sepal.width\":3.1,\"petal.length\":1.5,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":5.0,\"sepal.width\":3.6,\"petal.length\":1.4,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":5.4,\"sepal.width\":3.9,\"petal.length\":1.7,\"petal.width\":0.4,\"variety\":\"Setosa\"},{\"sepal.length\":4.6,\"sepal.width\":3.4,\"petal.length\":1.4,\"petal.width\":0.3,\"variety\":\"Setosa\"},{\"sepal.length\":5.0,\"sepal.width\":3.4,\"petal.length\":1.5,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.4,\"sepal.width\":2.9,\"petal.length\":1.4,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.9,\"sepal.width\":3.1,\"petal.length\":1.5,\"petal.width\":0.1,\"variety\":\"Setosa\"},{\"sepal.length\":5.4,\"sepal.width\":3.7,\"petal.length\":1.5,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.8,\"sepal.width\":3.4,\"petal.length\":1.6,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":4.8,\"sepal.width\":3.0,\"petal.length\":1.4,\"petal.width\":0.1,\"variety\":\"Setosa\"},{\"sepal.length\":4.3,\"sepal.width\":3.0,\"petal.length\":1.1,\"petal.width\":0.1,\"variety\":\"Setosa\"},{\"sepal.length\":5.8,\"sepal.width\":4.0,\"petal.length\":1.2,\"petal.width\":0.2,\"variety\":\"Setosa\"},{\"sepal.length\":5.7,\"sepal.width\":4.4,\"petal.length\":1.5,\"petal.width\":0.4,\"variety\":\"Setosa\"},{\"sepal.length\":5.4,\"sepal.width\":3.9,\"petal.length\":1.3,\"petal.width\":0.4,\"variety\":\"Setosa\"},{\"sepal.length\":5.1,\"sepal.width\":3.5,\"petal.length\":1.4,\"petal.width\":0.3,\"variety\":\"Setosa\"},{\"sepal.length\":5.7,\"sepal.width\":3.8,\"petal.length\":1.7,\"petal.width\":0.3,\"variety\":\"Setosa\"},{\"sepal.length\":5.1,\"sepal.width\":3.8,\"petal.length\":1.5,\"petal.width\":0.3,\"variety\":\"Setosa\"}]}" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Writing is also supported; it still uses Apache Commons CSV under the hood.\n", + "The API is similar to the reading API:" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:16.631206Z", + "start_time": "2024-12-06T13:22:15.902813Z" + } + }, + "cell_type": "code", + "source": [ + "val irisDf = DataFrame.readCsv(\"../../../../dataframe-csv/src/test/resources/irisDataset.csv\")\n", + "\n", + "irisDf.writeCsv(\"irisDataset.csv\")" + ], + "outputs": [], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "some options can be specified:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:18.602183Z", + "start_time": "2024-12-06T13:22:18.504388Z" + } + }, + "cell_type": "code", + "source": [ + "irisDf.writeDelim(\n", + " path = \"irisDataset.csv\",\n", + " delimiter = ';',\n", + " includeHeader = false,\n", + " quoteMode = QuoteMode.ALL,\n", + " escapeChar = '\\\\',\n", + " commentChar = '#',\n", + " headerComments = listOf(\"This is a comment\", \"This is another comment\"),\n", + " recordSeparator = \"\\n\",\n", + ")" + ], + "outputs": [], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "and similarly we have a single overload which exposes the underlying implementation:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:22.013636Z", + "start_time": "2024-12-06T13:22:21.751085Z" + } + }, + "cell_type": "code", + "source": [ + "irisDf.writeCsv(\n", + " writer = File(\"irisDataset.csv\").writer(),\n", + " adjustCsvFormat = {\n", + " this\n", + " .setSkipHeaderRecord(true)\n", + " .setHeader(\"sepalLength\", \"sepalWidth\", \"petalLength\", \"petalWidth\", \"species\")\n", + " .setTrailingData(true)\n", + " .setNullString(\"null\")\n", + " // etc..\n", + " },\n", + ")" + ], + "outputs": [], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Experimental new GeoDataFrame with Kandy\n", + "\n", + "[Kandy](https://github.com/Kotlin/kandy) v0.8 introduces geo-plotting which allows you to visualize geospatial/geographical data using the awesome Kandy DSL. To make working with this geographical data (from GeoJson/Shapefile) easier, we happily accepted the [GeoDataFrame PR](https://github.com/Kotlin/dataframe/pull/909) from the Kandy team ([Relevant issue #875](https://github.com/Kotlin/dataframe/issues/875))\n", + "\n", + "To try it yourself, explicitly add the dependency [`org.jetbrains.kotlinx:dataframe-geo`](https://central.sonatype.com/artifact/org.jetbrains.kotlinx/dataframe-geo) to your project (with the repository `maven(\"https://repo.osgeo.org/repository/release\")`) or add `enableExperimentalGeo=true` to the %use-magic, as seen at the start of the notebook.\n", + "\n", + "Then use `GeoDataFrame.readGeoJson()` or `GeoDataFrame.readShapeFile()` to get started!" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:29.984700Z", + "start_time": "2024-12-06T13:22:25.732250Z" + } + }, + "cell_type": "code", + "source": [ + "USE {\n", + " repositories(\"https://repo.osgeo.org/repository/release\")\n", + " dependencies(\"org.jetbrains.kotlinx:kandy-geo:0.8.0-dev-57\")\n", + "}" + ], + "outputs": [], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Here's a small demonstration of the new API, reading and plotting a GeoJson file:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:35.873834Z", + "start_time": "2024-12-06T13:22:33.595757Z" + } + }, + "cell_type": "code", + "source": [ + "val usaGeo = GeoDataFrame.readGeoJson(\"https://echarts.apache.org/examples/data/asset/geo/USA.json\")\n", + "usaGeo.df" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-12-06T13:22:34.883693Z Execution of code 'val usaGeo = GeoData...' ERROR Log4j2 could not find a logging implementation. Please add log4j-core to the classpath. Using SimpleLogger to log to the console...\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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"metadata": {}, + "cell_type": "markdown", + "source": "Let's modify the `GeoDataFrame` a bit by adding some population data and plotting that too:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:47.012228Z", + "start_time": "2024-12-06T13:22:46.478159Z" + } + }, + "cell_type": "code", + "source": [ + "val usPopByState = DataFrame.readCsv(\"us_pop_by_state.csv\")\n", + "usPopByState" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
rankstatestate_code2020_censuspercent_of_total
1.000000CaliforniaCA395382230.119100
2.000000TexasTX291455050.087400
3.000000FloridaFL215381870.064700
4.000000New YorkNY202012490.058600
5.000000PennsylvaniaPA130027000.038600
6.000000IllinoisIL128019890.038200
7.000000OhioOH117994480.035200
8.000000GeorgiaGA107119080.032000
9.000000North CarolinaNC104393880.031600
10.000000MichiganMI100773310.030100
11.000000New JerseyNJ92889940.026800
12.000000VirginiaVA86313930.025700
13.000000WashingtonWA77052810.022900
14.000000ArizonaAZ71515020.021900
15.000000MassachusettsMA70299170.020900
16.000000TennesseeTN69108400.020600
17.000000IndianaIN67855280.020300
18.000000MarylandMD61772240.018500
19.000000MissouriMO61549130.018200
20.000000WisconsinWI58937180.017500
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"rank\",\"state\",\"state_code\",\"2020_census\",\"percent_of_total\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double?\"}],\"nrow\":52,\"ncol\":5},\"kotlin_dataframe\":[{\"rank\":1.0,\"state\":\"California\",\"state_code\":\"CA\",\"2020_census\":39538223,\"percent_of_total\":0.1191},{\"rank\":2.0,\"state\":\"Texas\",\"state_code\":\"TX\",\"2020_census\":29145505,\"percent_of_total\":0.0874},{\"rank\":3.0,\"state\":\"Florida\",\"state_code\":\"FL\",\"2020_census\":21538187,\"percent_of_total\":0.0647},{\"rank\":4.0,\"state\":\"New York\",\"state_code\":\"NY\",\"2020_census\":20201249,\"percent_of_total\":0.0586},{\"rank\":5.0,\"state\":\"Pennsylvania\",\"state_code\":\"PA\",\"2020_census\":13002700,\"percent_of_total\":0.0386},{\"rank\":6.0,\"state\":\"Illinois\",\"state_code\":\"IL\",\"2020_census\":12801989,\"percent_of_total\":0.0382},{\"rank\":7.0,\"state\":\"Ohio\",\"state_code\":\"OH\",\"2020_census\":11799448,\"percent_of_total\":0.0352},{\"rank\":8.0,\"state\":\"Georgia\",\"state_code\":\"GA\",\"2020_census\":10711908,\"percent_of_total\":0.032},{\"rank\":9.0,\"state\":\"North Carolina\",\"state_code\":\"NC\",\"2020_census\":10439388,\"percent_of_total\":0.0316},{\"rank\":10.0,\"state\":\"Michigan\",\"state_code\":\"MI\",\"2020_census\":10077331,\"percent_of_total\":0.0301},{\"rank\":11.0,\"state\":\"New Jersey\",\"state_code\":\"NJ\",\"2020_census\":9288994,\"percent_of_total\":0.0268},{\"rank\":12.0,\"state\":\"Virginia\",\"state_code\":\"VA\",\"2020_census\":8631393,\"percent_of_total\":0.0257},{\"rank\":13.0,\"state\":\"Washington\",\"state_code\":\"WA\",\"2020_census\":7705281,\"percent_of_total\":0.0229},{\"rank\":14.0,\"state\":\"Arizona\",\"state_code\":\"AZ\",\"2020_census\":7151502,\"percent_of_total\":0.0219},{\"rank\":15.0,\"state\":\"Massachusetts\",\"state_code\":\"MA\",\"2020_census\":7029917,\"percent_of_total\":0.0209},{\"rank\":16.0,\"state\":\"Tennessee\",\"state_code\":\"TN\",\"2020_census\":6910840,\"percent_of_total\":0.0206},{\"rank\":17.0,\"state\":\"Indiana\",\"state_code\":\"IN\",\"2020_census\":6785528,\"percent_of_total\":0.0203},{\"rank\":18.0,\"state\":\"Maryland\",\"state_code\":\"MD\",\"2020_census\":6177224,\"percent_of_total\":0.0185},{\"rank\":19.0,\"state\":\"Missouri\",\"state_code\":\"MO\",\"2020_census\":6154913,\"percent_of_total\":0.0182},{\"rank\":20.0,\"state\":\"Wisconsin\",\"state_code\":\"WI\",\"2020_census\":5893718,\"percent_of_total\":0.0175}]}" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 15 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:49.082160Z", + "start_time": "2024-12-06T13:22:48.013841Z" + } + }, + "cell_type": "code", + "source": [ + "val usaGeoPopulation = usaGeo.modify {\n", + " this.join(\n", + " usPopByState.select { state and `2020_census`.named(\"population\") },\n", + " ) { name match right.state }\n", + "}\n", + "usaGeoPopulation.df" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namegeometrypopulation
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AlaskaMULTIPOLYGON (((-131.602021 55.117982...733391
ArizonaPOLYGON ((-109.042503 37.000263, -109...7151502
ArkansasPOLYGON ((-94.473842 36.501861, -90.1...3011524
CaliforniaPOLYGON ((-123.233256 42.006186, -122...39538223
ColoradoPOLYGON ((-107.919731 41.003906, -105...5773714
ConnecticutPOLYGON ((-73.053528 42.039048, -71.7...3605944
DelawarePOLYGON ((-75.414089 39.804456, -75.5...989948
FloridaPOLYGON ((-85.497137 30.997536, -85.0...21538187
GeorgiaPOLYGON ((-83.109191 35.00118, -83.32...10711908
HawaiiMULTIPOLYGON (((-155.634835 18.948267...1455271
IdahoPOLYGON ((-116.04751 49.000239, -116....1839106
IllinoisPOLYGON ((-90.639984 42.510065, -88.7...12801989
IndianaPOLYGON ((-85.990061 41.759724, -84.8...6785528
IowaPOLYGON ((-91.368417 43.501391, -91.2...3271616
KansasPOLYGON ((-101.90605 40.001626, -95.3...2937880
KentuckyPOLYGON ((-83.903347 38.769315, -83.6...4505836
LouisianaPOLYGON ((-93.608485 33.018527, -91.1...4657757
MainePOLYGON ((-70.703921 43.057759, -70.8...1362359
MarylandMULTIPOLYGON (((-75.994645 37.95325, ...6177224
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tooltips(name, population)\n", + " fillColor(population) {\n", + " scale = continuousColorViridis()\n", + " }\n", + " borderLine {\n", + " width = 0.1\n", + " color = Color.BLACK\n", + " }\n", + " alpha = 0.5\n", + " layout.style(Style.Void)\n", + " }\n", + "}" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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+ "{\"type\":\"Polygon\",\"coordinates\":[[[-109.080842,45.002073],[-105.91517,45.002073],[-104.058488,44.996596],[-104.053011,43.002989],[-104.053011,41.003906],[-105.728954,40.998429],[-107.919731,41.003906],[-109.04798,40.998429],[-111.047063,40.998429],[-111.047063,42.000709],[-111.047063,44.476286],[-111.05254,45.002073],[-109.080842,45.002073]]]}" + ] + }, + "tooltips": { + "variables": [ + "name", + "population" + ], + "disable_splitting": true + } + } + ], + "theme": { + "name": "classic", + "axis": { + "blank": true + }, + "line": { + "blank": true + }, + "axis_ontop": false, + "axis_ontop_y": false, + "axis_ontop_x": false + } + }, + "apply_color_scheme": true, + "swing_enabled": true + } + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 17 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Custom SQL Database support\n", + "\n", + "Our JDBC-based SQL integration for DataFrame has become extensible!\n", + "\n", + "This means that if you have an SQL database that we currently don't support, you can\n", + "create your own `DbType` instance and read from your database to a dataframe.\n", + "(Remember that we already support quite a few databases: MariaDB, PostgreSQL, MySQL, SQLite, MS SQL, and H2 (with dialects))" + ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "To get started, we need a custom `DbType`.\n", + "\n", + "For the sake of example, we'll create a custom DbType based on the `H2` Database. Ordinarily, you'd extend `DbType(\"jdbc name of your database\")`." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:55.881508Z", + "start_time": "2024-12-06T13:22:55.696856Z" + } + }, + "cell_type": "code", + "source": [ + "import org.jetbrains.kotlinx.dataframe.io.db.*\n", + "import org.jetbrains.kotlinx.dataframe.schema.ColumnSchema\n", + "import java.sql.ResultSet\n", + "import kotlin.reflect.KType\n", + "\n", + "object CustomDbType : H2(MySql) {\n", + "\n", + " /**\n", + " * Represents the JDBC driver class name for a given database type.\n", + " * Something like \"org.h2.Driver\".\n", + " */\n", + " override val driverClassName: String\n", + " get() = super.driverClassName\n", + "\n", + " /**\n", + " * Here you define which KType you expect the column to be based on [tableColumnMetadata].\n", + " * This is mostly for special cases, as DataFrame can already infer most types\n", + " * from the databse automatically.\n", + " *\n", + " * Return `null` to let DataFrame figure out the type.\n", + " */\n", + " override fun convertSqlTypeToKType(tableColumnMetadata: TableColumnMetadata): KType? {\n", + " return super.convertSqlTypeToKType(tableColumnMetadata)\n", + " }\n", + "\n", + " /**\n", + " * Similar to [convertSqlTypeToKType] but here you'll need to define a [ColumnSchema] for the column\n", + " * based on [tableColumnMetadata].\n", + " *\n", + " * Return `null` to let DataFrame figure out the schema.\n", + " */\n", + " override fun convertSqlTypeToColumnSchemaValue(tableColumnMetadata: TableColumnMetadata): ColumnSchema? {\n", + " return super.convertSqlTypeToColumnSchemaValue(tableColumnMetadata)\n", + " }\n", + "\n", + " /**\n", + " * Here you define where to get the table metadata for information about the database table,\n", + " * including its name, schema name, and catalogue name.\n", + " */\n", + " override fun buildTableMetadata(tables: ResultSet): TableMetadata {\n", + " return super.buildTableMetadata(tables)\n", + " }\n", + "\n", + " /**\n", + " * Return whether the table with metadata [tableMetadata] should be considered\n", + " * a system table or not.\n", + " *\n", + " * System tables are skipped when reading.\n", + " */\n", + " override fun isSystemTable(tableMetadata: TableMetadata): Boolean {\n", + " return super.isSystemTable(tableMetadata)\n", + " }\n", + "\n", + " /**\n", + " * Can be overridden to change DataFrame limits queries in your specific DataBase type.\n", + " *\n", + " * By default it executes: `\"$sqlQuery LIMIT $limit\"`\n", + " */\n", + " override fun sqlQueryLimit(sqlQuery: String, limit: Int): String {\n", + " return super.sqlQueryLimit(sqlQuery, limit)\n", + " }\n", + "}" + ], + "outputs": [], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now that we have a custom `DbType` we can connect to our database (add some demo data) and retrieve it in a dataframe!" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:22:59.254923Z", + "start_time": "2024-12-06T13:22:58.635951Z" + } + }, + "cell_type": "code", + "source": [ + "import org.intellij.lang.annotations.Language\n", + "import java.sql.DriverManager\n", + "\n", + "val URL = \"jdbc:h2:mem:test5;DB_CLOSE_DELAY=-1;MODE=MySQL;DATABASE_TO_UPPER=false\"\n", + "val connection = DriverManager.getConnection(URL)\n", + "\n", + "// insert some demo data\n", + "val statements = listOf(\n", + " \"\"\"\n", + " CREATE TABLE Customer (\n", + " id INT PRIMARY KEY,\n", + " name VARCHAR(50),\n", + " age INT\n", + " )\n", + " \"\"\".trimIndent(),\n", + " \"\"\"\n", + " CREATE TABLE Sale (\n", + " id INT PRIMARY KEY,\n", + " customerId INT,\n", + " amount DECIMAL(10, 2) NOT NULL\n", + " )\n", + " \"\"\".trimIndent(),\n", + " \"INSERT INTO Customer (id, name, age) VALUES (1, 'John', 40)\",\n", + " \"INSERT INTO Customer (id, name, age) VALUES (2, 'Alice', 25)\",\n", + " \"INSERT INTO Customer (id, name, age) VALUES (3, 'Bob', 47)\",\n", + " \"INSERT INTO Customer (id, name, age) VALUES (4, NULL, NULL)\",\n", + " \"INSERT INTO Sale (id, customerId, amount) VALUES (1, 1, 100.50)\",\n", + " \"INSERT INTO Sale (id, customerId, amount) VALUES (2, 2, 50.00)\",\n", + " \"INSERT INTO Sale (id, customerId, amount) VALUES (3, 1, 75.25)\",\n", + " \"INSERT INTO Sale (id, customerId, amount) VALUES (4, 3, 35.15)\",\n", + ")\n", + "statements.forEach { connection.createStatement().execute(it) }" + ], + "outputs": [], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:00.639307Z", + "start_time": "2024-12-06T13:23:00.376403Z" + } + }, + "cell_type": "code", + "source": [ + "// and read it :)\n", + "DataFrame.readSqlQuery(connection, \"SELECT * FROM Customer\")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idnameage
1John40
2Alice25
3Bob47
4nullnull
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"id\",\"name\",\"age\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}],\"nrow\":4,\"ncol\":3},\"kotlin_dataframe\":[{\"id\":1,\"name\":\"John\",\"age\":40},{\"id\":2,\"name\":\"Alice\",\"age\":25},{\"id\":3,\"name\":\"Bob\",\"age\":47},{\"id\":4,\"name\":null,\"age\":null}]}" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "On the documentation website, you can find another [example](https://kotlin.github.io/dataframe/readSqlFromCustomDatabase.html) to support custom databases.\n", + "This time, it uses HSQLDB." + ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### `BigInteger` support\n", + "\n", + "Java has support for arbitrarily large decimal- and integer values: `BigDecimal` and `BigInteger`.\n", + "This is very helpful when working with huge numbers and `Double` and `Long` are not big enough.\n", + "Maybe Kotlin will even gain its own representation [in the future](https://youtrack.jetbrains.com/issue/KT-20912/BigDecimal-BigInteger-types-in-Kotlin-stdlib)!\n", + "\n", + "DataFrame has supported `BigDecimal` for a while, but it lacked `BigInteger` support. DataFrame 0.15 fixes that.\n", + "\n", + "Let's make a column with numbers so large that they can only be represented as `String`:" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:06.153948Z", + "start_time": "2024-12-06T13:23:05.802813Z" + } + }, + "cell_type": "code", + "source": [ + "import java.math.BigInteger\n", + "import kotlin.random.Random\n", + "import kotlin.random.nextLong\n", + "import kotlin.random.nextUInt\n", + "\n", + "val largestLong = Long.MAX_VALUE.toString()\n", + "val giantNumberCol: DataColumn by List(10) {\n", + " largestLong + abs(Random.nextLong()).toString()\n", + "}.toColumn()\n", + "\n", + "giantNumberCol" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
giantNumberCol
92233720368547758076895131956927986339
92233720368547758078046379596815421908
92233720368547758074448343410727247115
92233720368547758076039595862559697533
92233720368547758075144108214797648735
92233720368547758078096095849008105978
92233720368547758074672787943104667710
92233720368547758074824236907230894613
92233720368547758072945460066985633410
92233720368547758078189171310245143715
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"giantNumberCol\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":10,\"ncol\":1},\"kotlin_dataframe\":[{\"giantNumberCol\":\"92233720368547758076895131956927986339\"},{\"giantNumberCol\":\"92233720368547758078046379596815421908\"},{\"giantNumberCol\":\"92233720368547758074448343410727247115\"},{\"giantNumberCol\":\"92233720368547758076039595862559697533\"},{\"giantNumberCol\":\"92233720368547758075144108214797648735\"},{\"giantNumberCol\":\"92233720368547758078096095849008105978\"},{\"giantNumberCol\":\"92233720368547758074672787943104667710\"},{\"giantNumberCol\":\"92233720368547758074824236907230894613\"},{\"giantNumberCol\":\"92233720368547758072945460066985633410\"},{\"giantNumberCol\":\"92233720368547758078189171310245143715\"}]}" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 21 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "We now have overloads to convert/parse this column to `BigInteger`, just like for the other conversions.\n", + "This also allows us to perform mathematical operations with it!" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:09.524002Z", + "start_time": "2024-12-06T13:23:09.358644Z" + } + }, + "cell_type": "code", + "source": [ + "val bigIntCol = giantNumberCol.convertToBigInteger()\n", + "\n", + "DISPLAY(bigIntCol.type)\n", + "\n", + "bigIntCol * -1.toBigInteger()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "java.math.BigInteger" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
giantNumberCol
-92233720368547758076895131956927986339
-92233720368547758078046379596815421908
-92233720368547758074448343410727247115
-92233720368547758076039595862559697533
-92233720368547758075144108214797648735
-92233720368547758078096095849008105978
-92233720368547758074672787943104667710
-92233720368547758074824236907230894613
-92233720368547758072945460066985633410
-92233720368547758078189171310245143715
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"giantNumberCol\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"java.math.BigInteger\"}],\"nrow\":10,\"ncol\":1},\"kotlin_dataframe\":[{\"giantNumberCol\":\"-92233720368547758076895131956927986339\"},{\"giantNumberCol\":\"-92233720368547758078046379596815421908\"},{\"giantNumberCol\":\"-92233720368547758074448343410727247115\"},{\"giantNumberCol\":\"-92233720368547758076039595862559697533\"},{\"giantNumberCol\":\"-92233720368547758075144108214797648735\"},{\"giantNumberCol\":\"-92233720368547758078096095849008105978\"},{\"giantNumberCol\":\"-92233720368547758074672787943104667710\"},{\"giantNumberCol\":\"-92233720368547758074824236907230894613\"},{\"giantNumberCol\":\"-92233720368547758072945460066985633410\"},{\"giantNumberCol\":\"-92233720368547758078189171310245143715\"}]}" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 22 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We also support conversions from/to `BigInteger`, both on the column itself, and when the column is inside a dataframe:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:13.586831Z", + "start_time": "2024-12-06T13:23:13.267707Z" + } + }, + "cell_type": "code", + "source": [ + "val df = bigIntCol.toDataFrame()\n", + " .convert { bigIntCol }.toBigDecimal()\n", + "\n", + "DISPLAY(df.schema())\n", + "df" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "giantNumberCol: java.math.BigDecimal" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
giantNumberCol
9223372036854775807689513195692798633...
9223372036854775807804637959681542190...
9223372036854775807444834341072724711...
9223372036854775807603959586255969753...
9223372036854775807514410821479764873...
9223372036854775807809609584900810597...
9223372036854775807467278794310466771...
9223372036854775807482423690723089461...
9223372036854775807294546006698563341...
9223372036854775807818917131024514371...
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"giantNumberCol\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"java.math.BigDecimal\"}],\"nrow\":10,\"ncol\":1},\"kotlin_dataframe\":[{\"giantNumberCol\":\"92233720368547758076895131956927986339\"},{\"giantNumberCol\":\"92233720368547758078046379596815421908\"},{\"giantNumberCol\":\"92233720368547758074448343410727247115\"},{\"giantNumberCol\":\"92233720368547758076039595862559697533\"},{\"giantNumberCol\":\"92233720368547758075144108214797648735\"},{\"giantNumberCol\":\"92233720368547758078096095849008105978\"},{\"giantNumberCol\":\"92233720368547758074672787943104667710\"},{\"giantNumberCol\":\"92233720368547758074824236907230894613\"},{\"giantNumberCol\":\"92233720368547758072945460066985633410\"},{\"giantNumberCol\":\"92233720368547758078189171310245143715\"}]}" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 23 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Finally, statistics also support `BigInteger`, as well as all other number types.\n", + "\n", + "(`.describe()` now also works a bit better, both supporting `BigInteger` as well as columns with mixed number types [Relevant issue #558](https://github.com/Kotlin/dataframe/issues/558).\n", + "We'll continue to improve the statistics functions in the next releases)" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:16.423436Z", + "start_time": "2024-12-06T13:23:15.964868Z" + } + }, + "cell_type": "code", + "source": [ + "import java.math.BigDecimal\n", + "\n", + "val bigDecimalCol: DataColumn by bigIntCol.convertTo()\n", + "val mixedNumberCol: DataColumn by bigIntCol.map {\n", + " if (it % 2.toBigInteger() == 0.toBigInteger()) Random.nextDouble() else Random.nextInt()\n", + "}\n", + "\n", + "dataFrameOf(\n", + " bigIntCol named \"bigIntCol\",\n", + " bigDecimalCol,\n", + " mixedNumberCol,\n", + ").describe()" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypecountuniquenullstopfreqmeanstdminmedianmax
bigIntColjava.math.BigInteger101009223372036854775807689513195692798633919223372036854776000000000000000000000...0.000000922337203685477580729454600669856334109223372036854775807559185203867867313492233720368547758078189171310245143715
bigDecimalColjava.math.BigDecimal101009223372036854775807689513195692798633...19223372036854776000000000000000000000...1817876800039198720.0000009223372036854775807294546006698563341...9223372036854775807559185203867867313...9223372036854775807818917131024514371...
mixedNumberColNumber10100-5584080441-285953766.1559011186872780.278754-2071877832.0000000.0896721949813924.000000
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"name\",\"type\",\"count\",\"unique\",\"nulls\",\"top\",\"freq\",\"mean\",\"std\",\"min\",\"median\",\"max\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Number\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Number\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Number\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Number\"}],\"nrow\":3,\"ncol\":12},\"kotlin_dataframe\":[{\"name\":\"bigIntCol\",\"type\":\"java.math.BigInteger\",\"count\":10,\"unique\":10,\"nulls\":0,\"top\":\"92233720368547758076895131956927986339\",\"freq\":1,\"mean\":9.223372036854776E37,\"std\":0.0,\"min\":\"92233720368547758072945460066985633410\",\"median\":\"92233720368547758075591852038678673134\",\"max\":\"92233720368547758078189171310245143715\"},{\"name\":\"bigDecimalCol\",\"type\":\"java.math.BigDecimal\",\"count\":10,\"unique\":10,\"nulls\":0,\"top\":\"92233720368547758076895131956927986339\",\"freq\":1,\"mean\":9.223372036854776E37,\"std\":1.81787680003919872E18,\"min\":\"92233720368547758072945460066985633410\",\"median\":\"92233720368547758075591852038678673134\",\"max\":\"92233720368547758078189171310245143715\"},{\"name\":\"mixedNumberCol\",\"type\":\"Number\",\"count\":10,\"unique\":10,\"nulls\":0,\"top\":\"-558408044\",\"freq\":1,\"mean\":-2.8595376615590066E8,\"std\":1.1868727802787535E9,\"min\":\"-2.071877832E9\",\"median\":\"0.08967151389679417\",\"max\":\"1.949813924E9\"}]}" + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 24 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Improved Parsing\n", + "\n", + "[Parsing](https://kotlin.github.io/dataframe/parse.html), in DataFrame, is a special case of [`convert`](https://kotlin.github.io/dataframe/convert.html).\n", + "It can convert `String` columns to any other supported type by guessing.\n", + "This can be done manually, by calling `.parse()` on a dataframe, but it also happens automatically when reading from textual data, like CSV.\n", + "\n", + "In DataFrame 0.15:\n", + "- The speed of parsing and guessing types has improved\n", + "- We gained support for parsing strings to `Char`\n", + "- We have a new experimental double parser\n", + "\n", + "The new double parser is based on [FastDoubleParser](https://github.com/wrandelshofer/FastDoubleParser) and\n", + "can be enabled by setting `useFastDoubleParser = true` in the parser options." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:21.340462Z", + "start_time": "2024-12-06T13:23:21.180139Z" + } + }, + "cell_type": "code", + "source": [ + "// enabling the fast double parser globally can be done like\n", + "DataFrame.parser.apply {\n", + " useFastDoubleParser = true\n", + " // you can also set other global parsing options here\n", + "}\n", + "\n", + "// or you can choose to enable it per call\n", + "// Each function that parses strings should have the `parserOptions` argument:\n", + "DataFrame.readDelimStr(\n", + " text = \"\"\"\n", + " numbers\n", + " 0,12\n", + " 100.456,23\n", + " 1,00\n", + " \"\"\".trimIndent(),\n", + " delimiter = ';',\n", + " parserOptions = ParserOptions(\n", + " locale = Locale.GERMAN,\n", + " useFastDoubleParser = true,\n", + " )\n", + ")" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
numbers
0.120000
100456.230000
1.000000
\n", + " \n", + " \n", + " " + ], + "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"numbers\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}],\"nrow\":3,\"ncol\":1},\"kotlin_dataframe\":[{\"numbers\":0.12},{\"numbers\":100456.23},{\"numbers\":1.0}]}" + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 25 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Our implementation of `FastDoubleParser` is configured to be very forgiving, depending on the locale.\n", + "\n", + "For instance, in the French, numbers are often formatted like \"100 512,123\", which contains a non-breaking space character (\" \").\n", + "If you come across files which use normal spaces (\" \"), many double parsers would fail.\n", + "\n", + "The same holds for the Estonian minus (\"−\") which is expected if your locale is set to Estonian ([Relevant issue #607](https://github.com/Kotlin/dataframe/issues/607)).\n", + "\n", + "We now try to catch these cases and save you some headaches :).\n", + "Do not hesitate to provide feedback if you have a case that fails, and you think it should work!" + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T13:23:24.868732Z", + "start_time": "2024-12-06T13:23:24.657924Z" + } + }, + "cell_type": "code", + "source": [ + "val estonianNumbers by listOf(\n", + " \"12,45\",\n", + " \"−13,35\", // note the different minus sign '−' vs '-'\n", + " \"−204 235,23\", // note the different minus sign '−' vs '-'\n", + " \"100 123,35\", // space instead of NBSP\n", + " \"1,234e3\",\n", + " \"-345,122\", // 'ordinary' minus sign\n", + ").toColumn()\n", + "\n", + "estonianNumbers.parse(ParserOptions(locale = Locale.forLanguageTag(\"et-EE\"), useFastDoubleParser = true))" + ], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
estonianNumbers
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-13.350000
-204235.230000
100123.350000
1234.000000
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