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<h1 id="data-top" class="title">Data Processing</h1>
<p>Machine learning is all about building models from data. However, data scientists
frequently talk about models and algorithms first, which very likely generates
suboptimal results. The other approach is to play with the data first. Even simple
statistics and plots can help us get feelings of data and problems, which more likely
lead us to better modelling.</p>
<h2 id="features">Features</h2>
<p>A feature is an individual measurable property of a phenomenon being observed.
Features are also called explanatory variables, independent variables, predictors, regressors, etc.
Any attribute could be a feature, but choosing informative, discriminating and
independent features is a crucial step for effective algorithms in machine learning.
Features are usually numeric and a set of numeric features can be conveniently
described by a feature vector. Structural features such as strings, sequences and
graphs are also used in areas such as natural language processing, computational biology, etc.</p>
<p>Feature engineering is the process of using domain knowledge of the data to create features that make
machine learning algorithms work. Feature engineering is fundamental to the application of machine
learning, and is both difficult and expensive. It requires the experimentation of multiple
possibilities and the combination of automated
techniques with the intuition and knowledge of the domain expert.</p>
<h2 title="data-type">Data Type</h2>
<p>Generally speaking, there are two major types of attributes:</p>
<dl>
<dt>Qualitative variables:</dt>
<dd><p>The data values are non-numeric categories. Examples: Blood type, Gender.</p></dd>
<dt>Quantitative variables:</dt>
<dd><p>The data values are counts or numerical measurements. A quantitative
variable can be either discrete such as the number of students receiving
an 'A' in a class, or continuous such as GPA, salary and so on.</p></dd>
</dl>
<p>Another way of classifying data is by the measurement scales. In statistics,
there are four generally used measurement scales:</p>
<dl>
<dt>Nominal data:</dt>
<dd><p>Data values are non-numeric group labels. For example, Gender variable
can be defined as male = 0 and female =1.</p></dd>
<dt>Ordinal data:</dt>
<dd><p>Data values are categorical and may be ranked in some numerically
meaningful way. For example, strongly disagree to strong agree may be
defined as 1 to 5.</p></dd>
<dt>Continuous data:</dt>
<dd><ul>
<li><strong>Interval data:</strong>
Data values are ranged in a real interval, which can be as large as
from negative infinity to positive infinity. The difference between two
values are meaningful, however, the ratio of two interval data is not
meaningful. For example temperature, IQ. </li>
<li><strong>Ratio data:</strong>
Both difference and ratio of two values are meaningful. For example,
salary, weight.</li>
</ul></dd>
</dl>
<p>Many machine learning algorithms can only handle numeric attributes while a few
such as decision trees can process nominal attribute directly. Date attribute
is useful in plotting. With some feature engineering, values like day of week
can be used as nominal attribute. String attribute could be used in text mining
and natural language processing.</p>
<h2 id="DataFrame">DataFrame</h2>
<p>While some Smile algorithms take simple <code>double[]</code> as input, we often use
the encapsulation class <a href="api/java/smile/data/DataFrame.html">DataFrame</a>.
DataFrame is a two-dimensional data structure like a table with rows
and columns. Each column is a <a href="api/java/smile/data/vector/ValueVector.html">ValueVector</a>
that is a one-dimensional labeled abstraction to store a sequence of values having the same type.
Columns in a DataFrame may have different data types. There are concrete subclasses of ValueVector
for each primitive data type and generic object types. Creating a ValueVector by passing a list of values.</p>
<ul class="nav nav-tabs">
<li class="active"><a href="#java_0" data-toggle="tab">Java</a></li>
<li><a href="#scala_0" data-toggle="tab">Scala</a></li>
<li><a href="#kotlin_0" data-toggle="tab">Kotlin</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane" id="scala_0">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-scala"><code>
smile> import java.time._
import smile.data._
import smile.data.vector._
smile> ValueVector.of("A", 1.0, 2.0, 3.0)
$val res0: smile.data.vector.DoubleVector = A[1, 2, 3]
smile> ValueVector.of("B", Instant.now())
val res1: smile.data.vector.ObjectVector[java.time.Instant] = B[2025-01-19T01:58:06.813727400Z]
smile> ValueVector.nominal("C", "test", "train", "test", "train")
val res2: smile.data.vector.ValueVector = C[test, train, test, train]
smile> ValueVector.of("D",
"this is a string vector",
"Nominal/ordinal vectors store data as integers internally")
val res3: smile.data.vector.StringVector = D[this is a string vector, Nominal/ordinal vectors store data as integers internally]
smile> ObjectVector.of("E", Index.range(0, 4).toArray(), Array(3, 3, 3, 3))
val res4: smile.data.vector.ObjectVector[Array[Int]] = E[[0, 1, 2, 3], [3, 3, 3, 3]]
</code></pre>
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<div class="tab-pane active" id="java_0">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> import java.time.*
import smile.data.*
import smile.data.vector.*
smile> ValueVector.of("A", 1.0, 2.0, 3.0)
$2 ==> A[1, 2, 3]
smile> ValueVector.of("B", Instant.now())
$3 ==> B[2025-01-18T23:08:54.375073600Z]
smile> ValueVector.nominal("C", "test", "train", "test", "train")
$4 ==> C[test, train, test, train]
smile> ValueVector.of("D",
"this is a string vector",
"Nominal/ordinal vectors store data as integers internally")
$5 ==> D[this is a string vector, Nominal/ordinal vectors store data as integers internally]
smile> ObjectVector.of("E", Index.range(0, 4).toArray(), new int[]{3, 3, 3, 3})
$6 ==> E[[0, 1, 2, 3], [3, 3, 3, 3]]
</code></pre>
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<div class="tab-pane" id="kotlin_0">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-kotlin"><code>
>>> import java.time.*
import smile.data.*
import smile.data.vector.*
import smile.util.*
>>> ValueVector.of("A", 1.0, 2.0, 3.0)
res4: smile.data.vector.DoubleVector = A[1, 2, 3]
>>> ValueVector.of("B", Instant.now())
res5: smile.data.vector.ObjectVector<java.time.Instant?> = B[2025-01-19T01:58:06.813727400Z]
>>> ValueVector.nominal("C", "test", "train", "test", "train")
res6: smile.data.vector.ValueVector = C[test, train, test, train]
>>> ValueVector.of("D",
"this is a string vector",
"Nominal/ordinal vectors store data as integers internally")
res7: smile.data.vector.StringVector = D[this is a string vector, Nominal/ordinal vectors store data as integers internally]
>>> ObjectVector.of("E", Index.range(0, 4).toArray(), intArrayOf(3, 3, 3, 3))
res8: smile.data.vector.ObjectVector<kotlin.IntArray?> = E[[0, 1, 2, 3], [3, 3, 3, 3]]
</code></pre>
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</div>
<p>Note that the <code>nominal</code> and <code>ordinal</code> methods
factorize string values and store them as integral values internally,
which are more efficient, compact, and friendly for machine learning
algorithm. In contrast, the <code>ValueVector.of(String...)</code>
method returns a <code>StringVector</code> that store string values
as is, which is useful for text processing.</p>
<h3 id="DataFrame_Creation">Creation</h3>
<p>For illustration, we create a DataFrame with a 2-dimensional array in below example.
If no optional column names are passed, the default column names will be
V1, V2, etc. It is also easy to create a DataFrame by passing a list of
columns. The columns of the second DataFrame have different data types.
The method <code>schema()</code> will describe the column names,
data types, whether they can be null.</p>
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<div class="tab-pane active" id="java_0.1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> var df = DataFrame.of(MathEx.randn(6, 4))
df ==>
+---+---------+---------+---------+---------+
| | V1| V2| V3| V4|
+---+---------+---------+---------+---------+
| 0|-0.201469| 0.970363| 2.726932|-0.146014|
| 1| 1.872161| 0.495932| 0.553859|-0.028237|
| 2|-0.504866|-0.179409| 0.201377| 0.281267|
| 3| 0.894446| 0.791521| 0.053346| 0.213519|
| 4| 0.200011|-0.203736|-0.349196|-1.193759|
| 5| 1.52529|-1.407597| 1.16758| -1.78291|
+---+---------+---------+---------+---------+
smile> var df = new DataFrame(
ValueVector.of("A", 1.0),
ValueVector.of("B", LocalDate.parse("2013-01-02")),
ValueVector.of("C", "foo"),
ObjectVector.of("D", Index.range(0, 4).toArray()),
ObjectVector.of("E", new int[]{3, 3, 3, 3})
)
df ==>
+---+---+----------+---+------------+------------+
| | A| B| C| D| E|
+---+---+----------+---+------------+------------+
| 0| 1|2013-01-02|foo|[0, 1, 2, 3]|[3, 3, 3, 3]|
+---+---+----------+---+------------+------------+
smile> df.schema()
$4 ==> {
A: double NOT NULL,
B: Date,
C: String,
D: int[],
E: int[]
}
</code></pre>
</div>
</div>
</div>
<p>We can create a DataFrame with a collection of records or beans.</p>
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<li class="active"><a href="#java_0.2" data-toggle="tab">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_0.2">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code style="white-space: preserve nowrap;">
smile> enum Gender {Male, Female}
record Person(String name, Gender gender, String state, LocalDate birthday, int age, Double salary) { }
List<Person> persons = new ArrayList<>();
persons.add(new Person("Alex", Gender.Male, "NY", LocalDate.of(1980, 10, 1), 38, 10000.));
persons.add(new Person("Bob", Gender.Male, "AZ", LocalDate.of(1995, 3, 4), 23, null));
persons.add(new Person("Jane", Gender.Female, "CA", LocalDate.of(1970, 3, 1), 48, 230000.));
persons.add(new Person("Amy", Gender.Female, "NY", LocalDate.of(2005, 12, 10), 13, null));
var df = DataFrame.of(Person.class, persons);
df ==>
+---+----+------+-----+----------+---+------+
| |name|gender|state| birthday|age|salary|
+---+----+------+-----+----------+---+------+
| 0|Alex| Male| NY|1980-10-01| 38| 10000|
| 1| Bob| Male| AZ|1995-03-04| 23| null|
| 2|Jane|Female| CA|1970-03-01| 48|230000|
| 3| Amy|Female| NY|2005-12-10| 13| null|
+---+----+------+-----+----------+---+------+
</code></pre>
</div>
</div>
</div>
<p>In above example, the column 'state' is of string type. Apparently, it is a categorical variable.
We can factorize such string columns to categorical values:</p>
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<div class="tab-pane active" id="java_0.3">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> var df2 = df.factorize("state");
df2 ==>
+---+----+------+-----+----------+---+------+
| |name|gender|state| birthday|age|salary|
+---+----+------+-----+----------+---+------+
| 0|Alex| Male| NY|1980-10-01| 38| 10000|
| 1| Bob| Male| AZ|1995-03-04| 23| null|
| 2|Jane|Female| CA|1970-03-01| 48|230000|
| 3| Amy|Female| NY|2005-12-10| 13| null|
+---+----+------+-----+----------+---+------+
smile> df.get(0, 2)
$25 ==> "NY"
smile> df2.get(0, 2)
$27 ==> 2
</code></pre>
</div>
</div>
</div>
<p>On the surface, everything seems same. But the column 'state' is actually converted
to integral values under the hood.</p>
<p>Smile provides many parsers for popular data formats. In fact, the output of most Smile
data parsers is a DataFrame.</p>
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<li class="active"><a href="#java_1" data-toggle="tab">Java</a></li>
<li><a href="#scala_1" data-toggle="tab">Scala</a></li>
<li><a href="#kotlin_1" data-toggle="tab">Kotlin</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane" id="scala_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-scala"><code>
smile> val iris = read.arff("data/weka/iris.arff")
[main] INFO smile.io.Arff - Read ARFF relation iris
iris: DataFrame =
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.1| 3.5| 1.4| 0.2|Iris-setosa|
| 1| 4.9| 3| 1.4| 0.2|Iris-setosa|
| 2| 4.7| 3.2| 1.3| 0.2|Iris-setosa|
| 3| 4.6| 3.1| 1.5| 0.2|Iris-setosa|
| 4| 5| 3.6| 1.4| 0.2|Iris-setosa|
| 5| 5.4| 3.9| 1.7| 0.4|Iris-setosa|
| 6| 4.6| 3.4| 1.4| 0.3|Iris-setosa|
| 7| 5| 3.4| 1.5| 0.2|Iris-setosa|
| 8| 4.4| 2.9| 1.4| 0.2|Iris-setosa|
| 9| 4.9| 3.1| 1.5| 0.1|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
140 more rows...
</code></pre>
</div>
</div>
<div class="tab-pane active" id="java_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> import smile.data.*
import smile.datasets.*
import smile.io.*
var iris = Read.arff("data/weka/iris.arff")
[main] INFO smile.io.Arff - Read ARFF relation iris
$3 ==>
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.1| 3.5| 1.4| 0.2|Iris-setosa|
| 1| 4.9| 3| 1.4| 0.2|Iris-setosa|
| 2| 4.7| 3.2| 1.3| 0.2|Iris-setosa|
| 3| 4.6| 3.1| 1.5| 0.2|Iris-setosa|
| 4| 5| 3.6| 1.4| 0.2|Iris-setosa|
| 5| 5.4| 3.9| 1.7| 0.4|Iris-setosa|
| 6| 4.6| 3.4| 1.4| 0.3|Iris-setosa|
| 7| 5| 3.4| 1.5| 0.2|Iris-setosa|
| 8| 4.4| 2.9| 1.4| 0.2|Iris-setosa|
| 9| 4.9| 3.1| 1.5| 0.1|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
140 more rows...
</code></pre>
</div>
</div>
<div class="tab-pane" id="kotlin_1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-kotlin"><code>
>>> import smile.*
import smile.data.*
import smile.io.*
val iris = Read.arff("data/weka/iris.arff")
[main] INFO smile.io.Arff - Read ARFF relation iris
>>> iris
res3: smile.data.DataFrame! =
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.1| 3.5| 1.4| 0.2|Iris-setosa|
| 1| 4.9| 3| 1.4| 0.2|Iris-setosa|
| 2| 4.7| 3.2| 1.3| 0.2|Iris-setosa|
| 3| 4.6| 3.1| 1.5| 0.2|Iris-setosa|
| 4| 5| 3.6| 1.4| 0.2|Iris-setosa|
| 5| 5.4| 3.9| 1.7| 0.4|Iris-setosa|
| 6| 4.6| 3.4| 1.4| 0.3|Iris-setosa|
| 7| 5| 3.4| 1.5| 0.2|Iris-setosa|
| 8| 4.4| 2.9| 1.4| 0.2|Iris-setosa|
| 9| 4.9| 3.1| 1.5| 0.1|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
140 more rows...
</code></pre>
</div>
</div>
</div>
<p>The <code>smile.datasets</code> package also provide many public datasets.</p>
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<li class="active"><a href="#java_1.1" data-toggle="tab">Java</a></li>
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<div class="tab-pane active" id="java_1.1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> var iris = new Iris().data() // use built-in dataset object
[main] INFO smile.io.Arff - Read ARFF relation iris
iris ==>
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.1| 3.5| 1.4| 0.2|Iris-setosa|
| 1| 4.9| 3| 1.4| 0.2|Iris-setosa|
| 2| 4.7| 3.2| 1.3| 0.2|Iris-setosa|
| 3| 4.6| 3.1| 1.5| 0.2|Iris-setosa|
| 4| 5| 3.6| 1.4| 0.2|Iris-setosa|
| 5| 5.4| 3.9| 1.7| 0.4|Iris-setosa|
| 6| 4.6| 3.4| 1.4| 0.3|Iris-setosa|
| 7| 5| 3.4| 1.5| 0.2|Iris-setosa|
| 8| 4.4| 2.9| 1.4| 0.2|Iris-setosa|
| 9| 4.9| 3.1| 1.5| 0.1|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
140 more rows...
</code></pre>
</div>
</div>
</div>
<h3 id="DataFrame_Index">Indexing</h3>
<p>We can set a row index to the data frames that must be of length the number
of rows with no duplicates nor missing values. The row index serves as a
unique identifier for each row in a DataFrame. It allows you to access and
rows efficiently with the <code>loc()</code> method. Compared with the normal
ordinal index, the object-based row index often contains certain semantic
meaning.</p>
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<li class="active"><a href="#java_2.1" data-toggle="tab">Java</a></li>
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<div class="tab-pane active" id="java_2.1">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> var df = DataFrame.of(MathEx.randn(6, 4))
var dates = Dates.range(LocalDate.of(2025,2,1), 6)
df = df.setIndex(dates)
+----------+---------+---------+---------+---------+
| | V1| V2| V3| V4|
+----------+---------+---------+---------+---------+
|2025-02-01|-0.201469| 0.970363| 2.726932|-0.146014|
|2025-02-02| 1.872161| 0.495932| 0.553859|-0.028237|
|2025-02-03|-0.504866|-0.179409| 0.201377| 0.281267|
|2025-02-04| 0.894446| 0.791521| 0.053346| 0.213519|
|2025-02-05| 0.200011|-0.203736|-0.349196|-1.193759|
|2025-02-06| 1.52529|-1.407597| 1.16758| -1.78291|
+----------+---------+---------+---------+---------+
smile> df.loc(dates[1])
$8 ==> {
V1: -0.133296,
V2: -2.461161,
V3: 0.25011,
V4: 1.132062
}
smile> df.loc(dates[1], dates[2])
$9 ==>
+----------+---------+---------+---------+--------+
| | V1| V2| V3| V4|
+----------+---------+---------+---------+--------+
|2025-02-02|-0.133296|-2.461161| 0.25011|1.132062|
|2025-02-03| 0.25248|-0.063054|-1.128157| 0.37634|
+----------+---------+---------+---------+--------+
</code></pre>
</div>
</div>
</div>
<p>The row index may be an existing column, which will be removed in the
resulting data frame.</p>
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<div class="tab-pane active" id="java_2.2">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> var df = new DataFrame(
ValueVector.of("A", 1.0),
ValueVector.of("B", LocalDate.parse("2013-01-02")),
ValueVector.of("C", "foo"),
ObjectVector.of("D", Index.range(0, 4).toArray()),
ObjectVector.of("E", new int[]{3, 3, 3, 3})
)
df = df.setIndex("B")
df ==>
+----------+---+---+------------+------------+
| | A| C| D| E|
+----------+---+---+------------+------------+
|2013-01-02| 1|foo|[0, 1, 2, 3]|[3, 3, 3, 3]|
+----------+---+---+------------+------------+
</code></pre>
</div>
</div>
</div>
<h3 id="DataFrame_Viewing">Viewing Data</h3>
<p>By default, <code>DataFrame.toString()</code> returns a pretty print of
top 10 rows. You may also use <code>DataFrame.head()</code> and
<code>DataFrame.tail()</code> with a specified number to view the top
and bottom rows of the frame respectively.</p>
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<li class="active"><a href="#java_0.4" data-toggle="tab">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_0.4">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> println(df.head(3))
+----------+---------+---------+---------+---------+
| | V1| V2| V3| V4|
+----------+---------+---------+---------+---------+
|2025-02-01|-0.189794| 0.609897|-0.289189|-0.636956|
|2025-02-02|-0.133296|-2.461161| 0.25011| 1.132062|
|2025-02-03| 0.25248|-0.063054|-1.128157| 0.37634|
+----------+---------+---------+---------+---------+
3 more rows...
smile> println(df.tail(3))
+----------+---------+---------+---------+--------+
| | V1| V2| V3| V4|
+----------+---------+---------+---------+--------+
|2025-02-04|-1.391481| 1.398828| 0.294973|1.353308|
|2025-02-05| 2.812277| 0.82762|-0.294806|1.836631|
|2025-02-06| 1.091213|-0.190432| 1.963064|0.725228|
+----------+---------+---------+---------+--------+
</code></pre>
</div>
</div>
</div>
<p>The method <code>describe()</code> shows the data structure and statistic summary:</p>
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<div class="tab-pane active" id="java_0.5">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> iris.describe()
+---+-----------+-----+--------------------+-----+--------+--------+---+---+---+---+---+
| | column| type| measure|count| mean| std|min|25%|50%|75%|max|
+---+-----------+-----+--------------------+-----+--------+--------+---+---+---+---+---+
| 0|sepallength|float| null| 150|5.843333|0.828066|4.3|5.1|5.8|6.4|7.9|
| 1| sepalwidth|float| null| 150| 3.054|0.433594| 2|2.8| 3|3.3|4.4|
| 2|petallength|float| null| 150|3.758667| 1.76442| 1|1.6|4.4|5.1|6.9|
| 3| petalwidth|float| null| 150|1.198667|0.763161|0.1|0.3|1.3|1.8|2.5|
| 4| class| byte|nominal[Iris-seto...| 150| NaN| NaN| 0| 0| 1| 2| 2|
+---+-----------+-----+--------------------+-----+--------+--------+---+---+---+---+---+
</code></pre>
</div>
</div>
</div>
<h3 id="DataFrame_Selection">Selection</h3>
<p>We can get a row with the array syntax or slice a subset of rows.</p>
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<li class="active"><a href="#java_2" data-toggle="tab">Java</a></li>
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<div class="code" style="text-align: left;">
<pre class="prettyprint lang-scala"><code>
smile> iris(0)
res5: Tuple = {
sepallength: 5.1,
sepalwidth: 3.5,
petallength: 1.4,
petalwidth: 0.2,
class: Iris-setosa
}
smile> iris(Index.range(10, 20))
res6: DataFrame =
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.4| 3.7| 1.5| 0.2|Iris-setosa|
| 1| 4.8| 3.4| 1.6| 0.2|Iris-setosa|
| 2| 4.8| 3| 1.4| 0.1|Iris-setosa|
| 3| 4.3| 3| 1.1| 0.1|Iris-setosa|
| 4| 5.8| 4| 1.2| 0.2|Iris-setosa|
| 5| 5.7| 4.4| 1.5| 0.4|Iris-setosa|
| 6| 5.4| 3.9| 1.3| 0.4|Iris-setosa|
| 7| 5.1| 3.5| 1.4| 0.3|Iris-setosa|
| 8| 5.7| 3.8| 1.7| 0.3|Iris-setosa|
| 9| 5.1| 3.8| 1.5| 0.3|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
</code></pre>
</div>
</div>
<div class="tab-pane active" id="java_2">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> iris.get(0)
$7 ==> {
sepallength: 5.1,
sepalwidth: 3.5,
petallength: 1.4,
petalwidth: 0.2,
class: Iris-setosa
}
smile> iris.get(Index.range(10, 20))
$8 ==>
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.4| 3.7| 1.5| 0.2|Iris-setosa|
| 1| 4.8| 3.4| 1.6| 0.2|Iris-setosa|
| 2| 4.8| 3| 1.4| 0.1|Iris-setosa|
| 3| 4.3| 3| 1.1| 0.1|Iris-setosa|
| 4| 5.8| 4| 1.2| 0.2|Iris-setosa|
| 5| 5.7| 4.4| 1.5| 0.4|Iris-setosa|
| 6| 5.4| 3.9| 1.3| 0.4|Iris-setosa|
| 7| 5.1| 3.5| 1.4| 0.3|Iris-setosa|
| 8| 5.7| 3.8| 1.7| 0.3|Iris-setosa|
| 9| 5.1| 3.8| 1.5| 0.3|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
</code></pre>
</div>
</div>
<div class="tab-pane" id="kotlin_2">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-kotlin"><code>
>>> iris[0]
res6: smile.data.Tuple! = {
sepallength: 5.1,
sepalwidth: 3.5,
petallength: 1.4,
petalwidth: 0.2,
class: Iris-setosa
}
>>> iris.get(Index.range(10, 20))
res7: smile.data.DataFrame! =
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.4| 3.7| 1.5| 0.2|Iris-setosa|
| 1| 4.8| 3.4| 1.6| 0.2|Iris-setosa|
| 2| 4.8| 3| 1.4| 0.1|Iris-setosa|
| 3| 4.3| 3| 1.1| 0.1|Iris-setosa|
| 4| 5.8| 4| 1.2| 0.2|Iris-setosa|
| 5| 5.7| 4.4| 1.5| 0.4|Iris-setosa|
| 6| 5.4| 3.9| 1.3| 0.4|Iris-setosa|
| 7| 5.1| 3.5| 1.4| 0.3|Iris-setosa|
| 8| 5.7| 3.8| 1.7| 0.3|Iris-setosa|
| 9| 5.1| 3.8| 1.5| 0.3|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
</code></pre>
</div>
</div>
</div>
<p>Similarly, we can refer a column by its name or select a few columns to create a new data frame.</p>
<ul class="nav nav-tabs">
<li class="active"><a href="#java_3" data-toggle="tab">Java</a></li>
<li><a href="#scala_3" data-toggle="tab">Scala</a></li>
<li><a href="#kotlin_3" data-toggle="tab">Kotlin</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane" id="scala_3">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-scala"><code>
smile> iris("sepallength")
res6: vector.ValueVector = sepallength[5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5, 4.4, 4.9, ..., 140 more]
smile> iris("sepallength", "sepalwidth")
res8: DataFrame =
+---+-----------+----------+
| |sepallength|sepalwidth|
+---+-----------+----------+
| 0| 5.1| 3.5|
| 1| 4.9| 3|
| 2| 4.7| 3.2|
| 3| 4.6| 3.1|
| 4| 5| 3.6|
| 5| 5.4| 3.9|
| 6| 4.6| 3.4|
| 7| 5| 3.4|
| 8| 4.4| 2.9|
| 9| 4.9| 3.1|
+---+-----------+----------+
140 more rows...
</code></pre>
</div>
</div>
<div class="tab-pane active" id="java_3">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> iris.column("sepallength")
$8 ==> sepallength[5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5, 4.4, 4.9, ..., 140 more]
smile> iris.select("sepallength", "sepalwidth")
$9 ==>
+---+-----------+----------+
| |sepallength|sepalwidth|
+---+-----------+----------+
| 0| 5.1| 3.5|
| 1| 4.9| 3|
| 2| 4.7| 3.2|
| 3| 4.6| 3.1|
| 4| 5| 3.6|
| 5| 5.4| 3.9|
| 6| 4.6| 3.4|
| 7| 5| 3.4|
| 8| 4.4| 2.9|
| 9| 4.9| 3.1|
+---+-----------+----------+
140 more rows...
</code></pre>
</div>
</div>
<div class="tab-pane" id="kotlin_3">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-kotlin"><code>
>>> iris.column("sepallength")
res7: smile.data.vector.ValueVecotr! = sepallength[5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5, 4.4, 4.9, ..., 140 more]
>>> iris.select("sepallength", "sepalwidth")
res8: smile.data.DataFrame! =
+---+-----------+----------+
| |sepallength|sepalwidth|
+---+-----------+----------+
| 0| 5.1| 3.5|
| 1| 4.9| 3|
| 2| 4.7| 3.2|
| 3| 4.6| 3.1|
| 4| 5| 3.6|
| 5| 5.4| 3.9|
| 6| 4.6| 3.4|
| 7| 5| 3.4|
| 8| 4.4| 2.9|
| 9| 4.9| 3.1|
+---+-----------+----------+
140 more rows...
</code></pre>
</div>
</div>
</div>
<p>We can also select rows with boolean indexing. The below example uses
<code>isin()</code> method for filtering:</p>
<ul class="nav nav-tabs">
<li class="active"><a href="#java_2.3" data-toggle="tab">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_2.3">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> iris.get(iris.column("class").isin("Iris-setosa", "Iris-virginica"))
$10 ==>
+---+-----------+----------+-----------+----------+-----------+
| |sepallength|sepalwidth|petallength|petalwidth| class|
+---+-----------+----------+-----------+----------+-----------+
| 0| 5.1| 3.5| 1.4| 0.2|Iris-setosa|
| 1| 4.9| 3| 1.4| 0.2|Iris-setosa|
| 2| 4.7| 3.2| 1.3| 0.2|Iris-setosa|
| 3| 4.6| 3.1| 1.5| 0.2|Iris-setosa|
| 4| 5| 3.6| 1.4| 0.2|Iris-setosa|
| 5| 5.4| 3.9| 1.7| 0.4|Iris-setosa|
| 6| 4.6| 3.4| 1.4| 0.3|Iris-setosa|
| 7| 5| 3.4| 1.5| 0.2|Iris-setosa|
| 8| 4.4| 2.9| 1.4| 0.2|Iris-setosa|
| 9| 4.9| 3.1| 1.5| 0.1|Iris-setosa|
+---+-----------+----------+-----------+----------+-----------+
90 more rows...
</code></pre>
</div>
</div>
</div>
<h3 id="DataFrame_Setting">Setting</h3>
<p>Setting values by position:</p>
<ul class="nav nav-tabs">
<li class="active"><a href="#java_2.4" data-toggle="tab">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_2.4">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> iris.set(0, 0, 1.5)
</code></pre>
</div>
</div>
</div>
<p>Adding columns:</p>
<ul class="nav nav-tabs">
<li class="active"><a href="#java_2.5" data-toggle="tab">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_2.5">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> var df = DataFrame.of(MathEx.randn(150, 3))
iris.add(df.column("V1"), df.column("V3"))
</code></pre>
</div>
</div>
</div>
<p>Setting a column:</p>
<ul class="nav nav-tabs">
<li class="active"><a href="#java_2.6" data-toggle="tab">Java</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="java_2.6">
<div class="code" style="text-align: left;">
<pre class="prettyprint lang-java"><code>
smile> iris.set("V1", df.column("V3"))
</code></pre>
</div>