tech.tablesaw.api.IntColumn Maven / Gradle / Ivy
package tech.tablesaw.api;
import static com.google.common.base.Preconditions.checkArgument;
import com.google.common.base.Preconditions;
import it.unimi.dsi.fastutil.ints.*;
import java.nio.ByteBuffer;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Set;
import java.util.stream.IntStream;
import tech.tablesaw.columns.AbstractColumnParser;
import tech.tablesaw.columns.Column;
import tech.tablesaw.columns.numbers.DoubleColumnType;
import tech.tablesaw.columns.numbers.IntColumnType;
import tech.tablesaw.columns.numbers.NumberColumnFormatter;
import tech.tablesaw.selection.BitmapBackedSelection;
import tech.tablesaw.selection.Selection;
/** A column that contains int values */
public class IntColumn extends NumberColumn
implements CategoricalColumn {
protected final IntArrayList data;
protected IntColumn(final String name, IntArrayList data) {
super(IntColumnType.instance(), name, IntColumnType.DEFAULT_PARSER);
setPrintFormatter(NumberColumnFormatter.ints());
this.data = data;
}
public static IntColumn create(final String name) {
return new IntColumn(name, new IntArrayList());
}
public static IntColumn create(final String name, final int... arr) {
return new IntColumn(name, new IntArrayList(arr));
}
public static IntColumn create(final String name, final Integer[] arr) {
IntColumn newColumn = IntColumn.create(name, 0);
for (Integer integer : arr) {
newColumn.append(integer);
}
return newColumn;
}
public static IntColumn create(String name, int initialSize) {
IntColumn column = new IntColumn(name, new IntArrayList(initialSize));
for (int i = 0; i < initialSize; i++) {
column.appendMissing();
}
return column;
}
public static IntColumn create(String name, IntStream stream) {
IntArrayList list = new IntArrayList();
stream.forEach(list::add);
return new IntColumn(name, list);
}
/** {@inheritDoc} */
@Override
public IntColumn createCol(String name, int initialSize) {
return create(name, initialSize);
}
/** {@inheritDoc} */
@Override
public IntColumn createCol(String name) {
return create(name);
}
/**
* Returns a new numeric column initialized with the given name and size. The values in the column
* are integers beginning at startsWith and continuing through size (exclusive), monotonically
* increasing by 1 TODO consider a generic fill function including steps or random samples from
* various distributions
*/
public static IntColumn indexColumn(
final String columnName, final int size, final int startsWith) {
final IntColumn indexColumn = IntColumn.create(columnName, size);
for (int i = 0; i < size; i++) {
indexColumn.set(i, i + startsWith);
}
return indexColumn;
}
/** {@inheritDoc} */
@Override
public int size() {
return data.size();
}
/** {@inheritDoc} */
@Override
public int valueHash(int rowNumber) {
return getInt(rowNumber);
}
/** {@inheritDoc} */
@Override
public boolean equals(int rowNumber1, int rowNumber2) {
return getInt(rowNumber1) == getInt(rowNumber2);
}
/** {@inheritDoc} */
@Override
public void clear() {
data.clear();
}
public static boolean valueIsMissing(int value) {
return IntColumnType.valueIsMissing(value);
}
/** {@inheritDoc} */
@Override
public Integer get(int index) {
int result = getInt(index);
return isMissingValue(result) ? null : result;
}
public int[] asIntArray() {
return data.toIntArray();
}
/** {@inheritDoc} */
@Override
public IntColumn subset(final int[] rows) {
final IntColumn c = this.emptyCopy();
for (final int row : rows) {
c.append(getInt(row));
}
return c;
}
/** {@inheritDoc} */
@Override
public IntColumn unique() {
final IntSet values = new IntOpenHashSet();
for (int i = 0; i < size(); i++) {
values.add(getInt(i));
}
final IntColumn column = IntColumn.create(name() + " Unique values");
for (int value : values) {
column.append(value);
}
return column;
}
/** {@inheritDoc} */
@Override
public IntColumn top(int n) {
final IntArrayList top = new IntArrayList();
final int[] values = data.toIntArray();
IntArrays.parallelQuickSort(values, IntComparators.OPPOSITE_COMPARATOR);
for (int i = 0; i < n && i < values.length; i++) {
top.add(values[i]);
}
return new IntColumn(name() + "[Top " + n + "]", top);
}
/** {@inheritDoc} */
@Override
public IntColumn bottom(final int n) {
final IntArrayList bottom = new IntArrayList();
final int[] values = data.toIntArray();
IntArrays.parallelQuickSort(values);
for (int i = 0; i < n && i < values.length; i++) {
bottom.add(values[i]);
}
return new IntColumn(name() + "[Bottoms " + n + "]", bottom);
}
/** {@inheritDoc} */
@Override
public IntColumn lag(int n) {
final int srcPos = n >= 0 ? 0 : -n;
final int[] dest = new int[size()];
final int destPos = Math.max(n, 0);
final int length = n >= 0 ? size() - n : size() + n;
for (int i = 0; i < size(); i++) {
dest[i] = IntColumnType.missingValueIndicator();
}
int[] array = data.toIntArray();
System.arraycopy(array, srcPos, dest, destPos, length);
return new IntColumn(name() + " lag(" + n + ")", new IntArrayList(dest));
}
/** {@inheritDoc} */
@Override
public IntColumn removeMissing() {
IntColumn result = copy();
result.clear();
IntListIterator iterator = data.iterator();
while (iterator.hasNext()) {
final int v = iterator.nextInt();
if (!isMissingValue(v)) {
result.append(v);
}
}
return result;
}
public IntColumn append(int i) {
data.add(i);
return this;
}
/** {@inheritDoc} */
@Override
public IntColumn append(Integer val) {
if (val == null) {
appendMissing();
} else {
append(val.intValue());
}
return this;
}
/** {@inheritDoc} */
@Override
public IntColumn copy() {
IntColumn copy = new IntColumn(name(), data.clone());
copy.setPrintFormatter(getPrintFormatter());
copy.locale = locale;
return copy;
}
/** {@inheritDoc} */
@Override
public Iterator iterator() {
return data.iterator();
}
public IntIterator intIterator() {
return data.iterator();
}
/** {@inheritDoc} */
@Override
public Integer[] asObjectArray() {
final Integer[] output = new Integer[size()];
for (int i = 0; i < size(); i++) {
if (!isMissing(i)) {
output[i] = getInt(i);
} else {
output[i] = null;
}
}
return output;
}
/** {@inheritDoc} */
@Override
public int compare(Integer o1, Integer o2) {
return Integer.compare(o1, o2);
}
/** {@inheritDoc} */
@Override
public IntColumn set(int i, Integer val) {
return val == null ? setMissing(i) : set(i, (int) val);
}
public IntColumn set(int i, int val) {
data.set(i, val);
return this;
}
/** {@inheritDoc} */
@Override
public IntColumn append(final Column column) {
checkArgument(
column.type() == this.type(),
"Column '%s' has type %s, but column '%s' has type %s.",
name(),
type(),
column.name(),
column.type());
final IntColumn numberColumn = (IntColumn) column;
final int size = numberColumn.size();
for (int i = 0; i < size; i++) {
append(numberColumn.getInt(i));
}
return this;
}
/** {@inheritDoc} */
@Override
public IntColumn append(Column column, int row) {
Preconditions.checkArgument(
column.type() == this.type(),
"Column '%s' has type %s, but column '%s' has type %s.",
name(),
type(),
column.name(),
column.type());
return append(((IntColumn) column).getInt(row));
}
/** {@inheritDoc} */
@Override
public IntColumn set(int row, Column column, int sourceRow) {
Preconditions.checkArgument(
column.type() == this.type(),
"Column '%s' has type %s, but column '%s' has type %s.",
name(),
type(),
column.name(),
column.type());
return set(row, ((IntColumn) column).getInt(sourceRow));
}
/** {@inheritDoc} */
@Override
public Column set(int row, String stringValue, AbstractColumnParser parser) {
return set(row, parser.parseInt(stringValue));
}
/** {@inheritDoc} */
@Override
public IntColumn appendMissing() {
return append(IntColumnType.missingValueIndicator());
}
/** {@inheritDoc} */
@Override
public byte[] asBytes(int rowNumber) {
return ByteBuffer.allocate(IntColumnType.instance().byteSize())
.putInt(getInt(rowNumber))
.array();
}
/** {@inheritDoc} */
@Override
public String getString(final int row) {
final int value = getInt(row);
return String.valueOf(getPrintFormatter().format(value));
}
/** {@inheritDoc} */
@Override
public int countUnique() {
IntSet uniqueElements = new IntOpenHashSet();
for (int i = 0; i < size(); i++) {
uniqueElements.add(getInt(i));
}
return uniqueElements.size();
}
/**
* Returns the value at the given index. The actual value is returned if the ColumnType is
* INTEGER. Otherwise the value is rounded as described below.
*
* Returns the closest {@code int} to the argument, with ties rounding to positive infinity.
*
*
Special cases:
*
*
* - If the argument is NaN, the result is 0.
*
- If the argument is positive infinity or any value greater than or equal to the value of
* {@code Integer.MAX_VALUE}, an error will be thrown
*
*
* @param row the index of the value to be rounded to an integer.
* @return the value of the argument rounded to the nearest {@code int} value.
* @throws ClassCastException if the absolute value of the value to be rounded is too large to be
* cast to an int
*/
public int getInt(int row) {
return data.getInt(row);
}
/** {@inheritDoc} */
@Override
public double getDouble(int row) {
int value = data.getInt(row);
if (isMissingValue(value)) {
return DoubleColumnType.missingValueIndicator();
}
return value;
}
public boolean isMissingValue(int value) {
return IntColumnType.valueIsMissing(value);
}
/** {@inheritDoc} */
@Override
public boolean isMissing(int rowNumber) {
return isMissingValue(getInt(rowNumber));
}
/** {@inheritDoc} */
@Override
public void sortAscending() {
data.sort(IntComparators.NATURAL_COMPARATOR);
}
/** {@inheritDoc} */
@Override
public void sortDescending() {
data.sort(IntComparators.OPPOSITE_COMPARATOR);
}
/** {@inheritDoc} */
@Override
public IntColumn appendObj(Object obj) {
if (obj == null) {
return appendMissing();
}
if (obj instanceof Integer) {
return append((int) obj);
}
throw new IllegalArgumentException("Could not append " + obj.getClass());
}
/** {@inheritDoc} */
@Override
public IntColumn appendCell(final String value) {
try {
return append(parser().parseInt(value));
} catch (final NumberFormatException e) {
throw new NumberFormatException(
"Error adding value to column " + name() + ": " + e.getMessage());
}
}
/** {@inheritDoc} */
@Override
public IntColumn appendCell(final String value, AbstractColumnParser parser) {
try {
return append(parser.parseInt(value));
} catch (final NumberFormatException e) {
throw new NumberFormatException(
"Error adding value to column " + name() + ": " + e.getMessage());
}
}
/** {@inheritDoc} */
@Override
public String getUnformattedString(final int row) {
final int value = getInt(row);
if (IntColumnType.valueIsMissing(value)) {
return "";
}
return String.valueOf(value);
}
/**
* Returns a new LongColumn containing a value for each value in this column
*
* A widening primitive conversion from int to long does not lose any information at all; the
* numeric value is preserved exactly.
*
*
A missing value in the receiver is converted to a missing value in the result
*/
@Override
public LongColumn asLongColumn() {
LongColumn result = LongColumn.create(name());
for (int d : data) {
if (IntColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append(d);
}
}
return result;
}
/**
* Returns a new FloatColumn containing a value for each value in this column, truncating if
* necessary.
*
*
A widening primitive conversion from an int to a float does not lose information about the
* overall magnitude of a numeric value. It may, however, result in loss of precision - that is,
* the result may lose some of the least significant bits of the value. In this case, the
* resulting floating-point value will be a correctly rounded version of the integer value, using
* IEEE 754 round-to-nearest mode.
*
*
Despite the fact that a loss of precision may occur, a widening primitive conversion never
* results in a run-time exception.
*
*
A missing value in the receiver is converted to a missing value in the result
*/
@Override
public FloatColumn asFloatColumn() {
FloatColumn result = FloatColumn.create(name());
for (int d : data) {
if (IntColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append(d);
}
}
return result;
}
public Selection isIn(final int... numbers) {
final Selection results = new BitmapBackedSelection();
final IntRBTreeSet intSet = new IntRBTreeSet(numbers);
for (int i = 0; i < size(); i++) {
if (intSet.contains(getInt(i))) {
results.add(i);
}
}
return results;
}
public Selection isNotIn(final int... numbers) {
final Selection results = new BitmapBackedSelection();
results.addRange(0, size());
results.andNot(isIn(numbers));
return results;
}
public Selection isNotIn(final IntColumn ints) {
final Selection results = new BitmapBackedSelection();
results.addRange(0, size());
results.andNot(isIn(ints.data.toIntArray()));
return results;
}
/**
* Returns a new DoubleColumn containing a value for each value in this column, truncating if
* necessary.
*
*
A widening primitive conversion from an int to a double does not lose information about the
* overall magnitude of a numeric value. It may, however, result in loss of precision - that is,
* the result may lose some of the least significant bits of the value. In this case, the
* resulting floating-point value will be a correctly rounded version of the integer value, using
* IEEE 754 round-to-nearest mode.
*
*
Despite the fact that a loss of precision may occur, a widening primitive conversion never
* results in a run-time exception.
*
*
A missing value in the receiver is converted to a missing value in the result
*/
@Override
public DoubleColumn asDoubleColumn() {
DoubleColumn result = DoubleColumn.create(name());
for (int d : data) {
if (IntColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append(d);
}
}
return result;
}
/**
* Returns a new ShortColumn containing a value for each value in this column
*
*
A narrowing conversion of a signed integer to an integral type T simply discards all but the
* n lowest order bits, where n is the number of bits used to represent type T. In addition to a
* possible loss of information about the magnitude of the numeric value, this may cause the sign
* of the resulting value to differ from the sign of the input value.
*
*
In other words, if the element being converted is larger (or smaller) than Short.MAX_VALUE
* (or Short.MIN_VALUE) you will not get a conventionally good conversion.
*
*
Despite the fact that overflow, underflow, or other loss of information may occur, a
* narrowing primitive conversion never results in a run-time exception.
*
*
A missing value in the receiver is converted to a missing value in the result
*/
@Override
public ShortColumn asShortColumn() {
ShortColumn result = ShortColumn.create(name());
for (int d : data) {
if (IntColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append((short) d);
}
}
return result;
}
/** {@inheritDoc} */
@Override
public IntColumn setMissing(int r) {
set(r, IntColumnType.missingValueIndicator());
return this;
}
/** {@inheritDoc} */
@Override
public Set asSet() {
return new HashSet<>(unique().asList());
}
}