Series
Related Docs:
object Series
| package framian
Concatenates that onto the end of this Series.
Transforms the cells in this series using f.
Transforms the cells, indexed by their key, in this series using f.
Return the cells of this series as a vector in index order.
Combines 2 series together using the functions provided to handle each case.
Combines 2 series together using the functions provided to handle each
case. If a value exists in both this and that, then both is used
to combine the value to a new one, otherwise either left or right
are used, unless both are missing, then the missing value is returned
(NA is both are NA and NM otherwise).
Returns a compacted version of this Series.
Returns a compacted version of this Series. The new series will be equal
to the old one, but the backing column will be dropped and replaced with a
version that only contains the values needed for this series. It will also
remove any indirection in the underlying column, such as that caused by
reindexing, shifting, mapping values, etc.
Returns the framian.reduce.Count reduction of this series.
Returns the framian.reduce.Count reduction of this series.
the framian.reduce.Count reduction of this series.
Returns the framian.reduce.Count reduction of this series by key.
Returns the framian.reduce.Count reduction of this series by key.
the framian.reduce.Count reduction of this series by key.
Returns an iterator over the key-value pairs of the series.
Returns an iterator over the key-value pairs of the series.
This iterator assumes that the series is dense, so it will skip over any non value cells if, in fact, the series is sparse.
an iterator over the key-value pairs of the series.
Returns the framian.reduce.Exists reduction of this series.
Returns the framian.reduce.Exists reduction of this series.
the framian.reduce.Exists reduction of this series.
Returns the framian.reduce.Exists reduction of this series by key.
Returns the framian.reduce.Exists reduction of this series by key.
the framian.reduce.Exists reduction of this series by key.
Select all key-cell pairs of this series where the cells satisfy a predicate.
Select all key-cell pairs of this series where the cells satisfy a predicate.
This method preserves the orderedness of the underlying index.
the predicate used to test cells.
a new series consisting of all key-call pairs of this
series where the cells satisfy the given predicate p.
This method is a specialized and optimized version of filterEntries, where
s.filterEntries { (_, c) => p(c) } == s.filterByCells(p)
Select all key-cell pairs of this series where the keys satisfy a predicate.
Select all key-cell pairs of this series where the keys satisfy a predicate.
This method preserves the orderedness of the underlying index.
the predicate used to test keys.
a new series consisting of all key-call pairs of this
series where the keys satisfy the given predicate p.
This method is a specialized and optimized version of filterEntries, where
s.filterEntries { (k, _) => p(k) } == s.filterByKeys(p)
Selects all key-value pairs of this series where the values satisfy a predicate.
Selects all key-value pairs of this series where the values satisfy a predicate.
This method preserves the orderedness of the underlying index. It also assumes this series is dense, so any non values will also be filtered out. The column that backs the new series will be dense.
the predicate used to test values.
a new series consisting of all key-value pairs of this
series where the values satisfy the given predicate p.
This method is a specialized and optimized version of filterEntries, where
s.filterEntries {
case (_, Value(v)) => p(v)
case _ => false
} == s.filterByValues(p)
Select all key-cell pairs of this series where the pairs satisfy a predicate.
Select all key-cell pairs of this series where the pairs satisfy a predicate.
This method preserves the orderedness of the underlying index.
the predicate used to test key-cell pairs.
a new series consisting of all key-cell pairs of this
series where the pairs satisfy the given predicate p.
Returns the first defined result of f when scanning the series in acending order.
Returns the first defined result of f when scanning the series in acending order.
The parameter f is predicate on the key-values pairs of the
series; however, it also returns a value when satisfied, hence
the return type of Option[B].
To ensure efficient access to the values of the series, the
predicate is supplied with the column and the index into the
column, rather than the cell. (Contrast (K, Cell[V]) to
(K, Column[V], Int)).
the return type of the predicate
a predicate on key–value pairs in the series that returns a value when satisfied
the first defined result of f when scanning the series in acending order.
findFirstValue is defined as,
findAsc((key, col, row) => if (col.isValueAt(row)) Some(key -> column.valueAt(row)) else None )
Returns the first defined result of f when scanning the series in descending order.
Returns the first defined result of f when scanning the series in descending order.
The parameter f is predicate on the key-values pairs of the
series; however, it also returns a value when satisfied, hence
the return type of Option[B].
To ensure efficient access to the values of the series, the
predicate is supplied with the column and the index into the
column, rather than the cell. (Contrast (K, Cell[V]) to
(K, Column[V], Int)).
the return type of the predicate
a predicate on key–value pairs in the series that returns a value when satisfied
the first defined result of f when scanning the series in descending order.
findLastValue is defined as,
findDesc((key, col, row) => if (col.isValueAt(row)) Some(key -> col.valueAt(row)) else None )
Returns the first key-value in the series.
Returns the first key-value in the series.
The returned key-value pair is the first in the series where the value is both available and meaningful.
the first key-value in the series.
Returns the last key-value in the series.
Returns the last key-value in the series.
The returned key-value pair is the last in the series where the value is both available and meaningful.
the last key-value in the series.
Returns the framian.reduce.First reduction of this series.
Returns the framian.reduce.First reduction of this series.
the framian.reduce.First reduction of this series.
Returns the framian.reduce.First reduction of this series by key.
Returns the framian.reduce.First reduction of this series by key.
the framian.reduce.First reduction of this series by key.
Returns the framian.reduce.FirstN reduction of this series.
Returns the framian.reduce.FirstN reduction of this series.
the framian.reduce.FirstN reduction of this series.
Returns the framian.reduce.FirstN reduction of this series by key.
Returns the framian.reduce.FirstN reduction of this series by key.
the framian.reduce.FirstN reduction of this series by key.
Map the value of this series to a cell.
Returns the framian.reduce.ForAll reduction of this series.
Returns the framian.reduce.ForAll reduction of this series.
the framian.reduce.ForAll reduction of this series.
Returns the framian.reduce.ForAll reduction of this series by key.
Returns the framian.reduce.ForAll reduction of this series by key.
the framian.reduce.ForAll reduction of this series by key.
Applies a function f to all key-cell pairs of the series.
Applies a function f to all key-cell pairs of the series.
The series is traversed in index order.
the function that is applied for its side-effect
to every key-cell pair. The result of the
function f is discarded
Applies a function f to all cells of the series.
Applies a function f to all cells of the series.
The series is traversed in index order.
the function that is applied for its side-effect
to every cell. The result of the
function f is discarded
Applies a function f to all key-value pairs of the series.
Applies a function f to all key-value pairs of the series.
The series is traversed in index order.
This method assumes that the series is dense, so it will skip over any non value cells if, in fact, the series is sparse.
the function that is applied for its side-effect
to every key-value pair. The result of the
function f is discarded
Applies a function f to all keys of the series.
Applies a function f to all keys of the series.
The series is traversed in index order.
the function that is applied for its side-effect
to every key. The result of the
function f is discarded
Applies a function f to all values of the series.
Applies a function f to all values of the series.
The series is traversed in index order.
This method assumes that the series is dense, so it will skip over any non value cells if, in fact, the series is sparse.
the function that is applied for its side-effect
to every value. The result of the
function f is discarded
Returns all cells with with key of key.
Returns all values with with key of key.
Returns true if at least 1 value exists in this series.
Returns true if at least 1 value exists in this series. A series with
only NAs and/or NMs will return false.
Computes a histogram of the values in this series.
Computes a histogram of the values in this series. This will create
contiguous, disjoint buckets starting from min, each with width
stepSize, except for (possibly) the last. So, we could represent the
buckets like this,
[min, min + stepSize), [min + 2 * stepSize), ..., [min + k * stepSize, max]You'll note that the last bucket *includes* max. For each bucket, we
calculate the number of values from this series that fall within it. The
Series returned is keyed by a tuple of the start and end of each
bucket (keep in mind only the last bucket is inclusive on the right).
the maximum value to include in the histogram (inclusive)
the width of each bucket, starting from min
a series keyed by the buckets, with values being the count of items
Compute a histogram of the values in this series, using a bucket width of
stepSize.
Compute a histogram of the values in this series, using a bucket width of
stepSize. This just calls histogram(min, max, stepSize), where min
and max are the minimum and maximum values in this series.
the width of each bucket, starting from minimum value in this series
a series keyed by the buckets, with values being the count of items
Returns true if the series is logically empty.
Returns an iterator over the key-cell pairs of the series.
Returns an iterator over the key-cell pairs of the series.
an iterator over the key-cell pairs of the series.
If the series is known to be dense, or the non values can ignored, then one should use denseIterator instead.
Returns the keys of this series as a vector in index order.
Returns the framian.reduce.Last reduction of this series.
Returns the framian.reduce.Last reduction of this series.
the framian.reduce.Last reduction of this series.
Returns the framian.reduce.Last reduction of this series by key.
Returns the framian.reduce.Last reduction of this series by key.
the framian.reduce.Last reduction of this series by key.
Returns the framian.reduce.LastN reduction of this series.
Returns the framian.reduce.LastN reduction of this series.
the framian.reduce.LastN reduction of this series.
Returns the framian.reduce.LastN reduction of this series by key.
Returns the framian.reduce.LastN reduction of this series by key.
the framian.reduce.LastN reduction of this series by key.
Map the keys of this series.
Map the keys of this series. This will maintain the same iteration order as the old series.
Map the values of this series only.
Map the values of this series only. Note that the function f will be
called every time a value is accessed. To prevent this, you must compact
the Series.
Map the values of this series, using both the *key* and *value* of each cell.
Returns the framian.reduce.Max reduction of this series.
Returns the framian.reduce.Max reduction of this series.
the framian.reduce.Max reduction of this series.
Returns the framian.reduce.Max reduction of this series by key.
Returns the framian.reduce.Max reduction of this series by key.
the framian.reduce.Max reduction of this series by key.
Returns the framian.reduce.Mean reduction of this series.
Returns the framian.reduce.Mean reduction of this series.
the framian.reduce.Mean reduction of this series.
Returns the framian.reduce.Mean reduction of this series by key.
Returns the framian.reduce.Mean reduction of this series by key.
the framian.reduce.Mean reduction of this series by key.
Returns the framian.reduce.Median reduction of this series.
Returns the framian.reduce.Median reduction of this series.
the framian.reduce.Median reduction of this series.
Returns the framian.reduce.Median reduction of this series by key.
Returns the framian.reduce.Median reduction of this series by key.
the framian.reduce.Median reduction of this series by key.
Merges 2 series together using a semigroup to append values.
Returns the framian.reduce.Min reduction of this series.
Returns the framian.reduce.Min reduction of this series.
the framian.reduce.Min reduction of this series.
Returns the framian.reduce.Min reduction of this series by key.
Returns the framian.reduce.Min reduction of this series by key.
the framian.reduce.Min reduction of this series by key.
Computes a normalized histogram of the values in this series.
Computes a normalized histogram of the values in this series. This will
create contiguous, disjoint buckets starting from min, each with width
stepSize, except for (possibly) the last. So, we could represent the
buckets like this,
[min, min + stepSize), [min + 2 * stepSize), ..., [min + k * stepSize, max]You'll note that the last bucket *includes* max. For each bucket, we
calculate the number of values from this series that fall within it. The
Series returned is keyed by a tuple of the start and end of each
bucket (keep in mind only the last bucket is inclusive on the right). The
values are the proportion of values that fell into that bucket, as a
percentage of the total size of this series - including NAs and NMs.
the maximum value to include in the histogram (inclusive)
the width of each bucket, starting from min
a series keyed by the buckets, with values being the count of items
Compute a histogram of the values in this series, using a bucket width of
stepSize.
Compute a histogram of the values in this series, using a bucket width of
stepSize. This just calls histogram(min, max, stepSize), where min
and max are the minimum and maximum values in this series. The values
are the proportion of values that fell into that bucket, as a percentage
of the total size of this series - including NAs and NMs.
the width of each bucket, starting from minimum value in this series
a series keyed by the buckets, with values being the count of items
Merges 2 series together, taking the first non-NA or NM value.
Returns the MultiplicativeMonoid reduction of this series.
Returns the MultiplicativeMonoid reduction of this series.
the MultiplicativeMonoid reduction of this series.
Returns the MultiplicativeMonoid reduction of this series by key.
Returns the MultiplicativeMonoid reduction of this series by key.
the MultiplicativeMonoid reduction of this series by key.
Returns the MultiplicativeSemigroup reduction of this series.
Returns the MultiplicativeSemigroup reduction of this series.
the MultiplicativeSemigroup reduction of this series.
Returns the MultiplicativeSemigroup reduction of this series by key.
Returns the MultiplicativeSemigroup reduction of this series by key.
the MultiplicativeSemigroup reduction of this series by key.
Reduce all the values in this Series using the given reducer.
For each unique key in this series, this reduces all the values for that key and returns a series with only the unique keys and reduced values.
For each unique key in this series, this reduces all the values for that key and returns a series with only the unique keys and reduced values. The new series will be in key order.
Rolls values and NMs forward, over NAs.
Rolls values and NMs forward, over NAs. This is similar to
rollForwardUpTo, but has no bounds checks. In fact, this is exactly
equivalent to
series.rollForwardUpTo(1)(TrivialMetricSpace[K], Order[Int]).
Roll-forward values and NMs over NAs.
Roll-forward values and NMs over NAs. It will rolls values in sequence
order (not sorted order). It will only roll over NAs whose key is within
delta of the last valid value or NM. This bounds check is inclusive.
An example of this behaviour is as follows:
Series(1 -> "a", 2 -> NA, 3 -> NA, 4 -> NM, 5 -> NA, 6 -> NA).rollForwardUpTo(1D) === Series(1 -> "a", 2 -> "a", 3 -> NA, 4 -> NM, 5 -> NM, 6 -> NA)
Sort this series by index keys and return it.
Sort this series by index keys and return it. The sort should be stable, so the relative order within a key will remain the same.
Returns the AdditiveMonoid reduction of this series.
Returns the AdditiveMonoid reduction of this series.
the AdditiveMonoid reduction of this series.
Returns the AdditiveMonoid reduction of this series by key.
Returns the AdditiveMonoid reduction of this series by key.
the AdditiveMonoid reduction of this series by key.
Returns the AdditiveSemigroup reduction of this series.
Returns the AdditiveSemigroup reduction of this series.
the AdditiveSemigroup reduction of this series.
Returns the AdditiveSemigroup reduction of this series by key.
Returns the AdditiveSemigroup reduction of this series by key.
the AdditiveSemigroup reduction of this series by key.
Returns this series as a collection of key/value pairs.
Convert this Series to a single column Frame.
Returns the framian.reduce.Unique reduction of this series.
Returns the framian.reduce.Unique reduction of this series.
the framian.reduce.Unique reduction of this series.
Returns the framian.reduce.Unique reduction of this series by key.
Returns the framian.reduce.Unique reduction of this series by key.
the framian.reduce.Unique reduction of this series by key.
Returns the values of this series as a vector in index order.
Perform an inner join with that and group the values in tuples.
Perform an inner join with that and group the values in tuples.
Equivalent to calling lhs.zipMap(rhs)((_, _)).
Performs an inner join on this Series with that.
Performs an inner join on this Series with that. Each pair of values
for a matching key is passed to f.