TSFrames.TSFrame — Typestruct TSFrame
+ 0.108871 0.108871TSFrames.TSFrame — Typestruct TSFrame
+ 0.108871 0.108871TSFrames.TSFrame — Typestruct TSFrame
coredata :: DataFrame
end::TSFrame - A type to hold ordered data with an index.
A TSFrame object is essentially a DataFrame with a specific column marked as an index. The input DataFrame is sorted during construction and is stored under the property coredata. The index is stored in the Index column of coredata.
Permitted data inputs to the constructors are DataFrame, Vector, and 2-dimensional Array. If an index is already not present in the constructor then a sequential integer index is created automatically.
TSFrame(coredata::DataFrame): Here, the constructor looks for a column named Index in coredata as the index column, if this is not found then the first column of coredata is made the index by default. If coredata only has a single column then a new sequential index is generated.
Since TSFrame.coredata is a DataFrame it can be operated upon independently using methods provided by the DataFrames package (ex. transform, combine, etc.).
Constructors
TSFrame(coredata::DataFrame, index::Union{String, Symbol, Int}; issorted = false, copycols = true)
TSFrame(coredata::DataFrame, index::AbstractVector{T}; issorted = false, copycols = true) where {T<:Union{Int, TimeType}}
@@ -304,18 +304,18 @@
julia> ts = TSFrame(Date, [(Int64, "col1"), (String, "col2"), (Float64, "col3")]) # using strings instead of symbols
0×3 TSFrame with Date Index
-Base.:== — MethodEquality
Two TSFrame are considered equal if their `coredata` property is equal.
+Base.:== — MethodEquality
Two TSFrame are considered equal if their `coredata` property is equal.
Base.:(==)(tsf1::TSFrame, tsf2::TSFrame)::Bool
-Base.isequal(tsf1::TSFrame, tsf2::TSFrame)::BoolBase.first — MethodFirst Row
first(ts::TSFrame)Returns the first row of ts as a TSFrame object.
Examples
julia> first(TSFrame(1:10))
+Base.isequal(tsf1::TSFrame, tsf2::TSFrame)::BoolBase.first — MethodFirst Row
first(ts::TSFrame)Returns the first row of ts as a TSFrame object.
Examples
julia> first(TSFrame(1:10))
(10 x 1) TSFrame with Dates.Date Index
Index x1
Date Float64
───────────────────────
2022-02-01 0.768448
-Base.iterate — FunctionIterators
Base.iterate(tsf::TSFrame)
+Base.iterate — FunctionIterators
Base.iterate(tsf::TSFrame)
-Returns a row-based iterator for `tsf`.Base.join — MethodJoins/Column-binding
join(ts1::TSFrame, ts2::TSFrame, ts...; jointype::Symbol=:JoinAll)TSFrame objects can be combined together column-wise using Index as the column key. There are four kinds of column-binding operations possible as of now. Each join operation works by performing a Set operation on the Index column and then merging the datasets based on the output from the Set operation. Each operation changes column names in the final object automatically if the operation encounters duplicate column names amongst the TSFrame objects.
The following join types are supported:
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinInner) and join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinBoth)
a.k.a. inner join, takes the intersection of the indexes of ts1 and ts2, and then merges the columns of both the objects. The resulting object will only contain rows which are present in both the objects' indexes. The function will rename columns in the final object if they had same names in the TSFrame objects.
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinOuter) and join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinAll):
a.k.a. outer join, takes the union of the indexes of ts1 and ts2 before merging the other columns of input objects. The output will contain rows which are present in all the input objects while inserting missing values where a row was not present in any of the objects. This is the default behaviour if no jointype is provided.
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinLeft):
Left join takes the index values which are present in the left object ts1 and finds matching index values in the right object ts2. The resulting object includes all the rows from the left object, the column values from the left object, and the values associated with matching index rows on the right. The operation inserts missing values where in the unmatched rows of the right object.
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinRight)
Right join, similar to left join but works in the opposite direction. The final object contains all the rows from the right object while inserting missing values in rows missing from the left object.
The default behaviour is to assume jointype=:JoinAll if no jointype is provided to the join method.
Joining multiple TSFrames is also supported. The syntax is
join(ts1::TSFrame, ts2::TSFrame, ts...; jointype::Symbol)
where jointype must be one of :JoinInner, :JoinBoth, :JoinOuter, :JoinAll, :JoinLeft or :JoinRight. Note that join on multiple TSFrames is left associative.
cbind is an alias for join method.
Examples
julia> using Random;
+Returns a row-based iterator for `tsf`.Base.join — MethodJoins/Column-binding
join(ts1::TSFrame, ts2::TSFrame, ts...; jointype::Symbol=:JoinAll)TSFrame objects can be combined together column-wise using Index as the column key. There are four kinds of column-binding operations possible as of now. Each join operation works by performing a Set operation on the Index column and then merging the datasets based on the output from the Set operation. Each operation changes column names in the final object automatically if the operation encounters duplicate column names amongst the TSFrame objects.
The following join types are supported:
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinInner) and join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinBoth)
a.k.a. inner join, takes the intersection of the indexes of ts1 and ts2, and then merges the columns of both the objects. The resulting object will only contain rows which are present in both the objects' indexes. The function will rename columns in the final object if they had same names in the TSFrame objects.
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinOuter) and join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinAll):
a.k.a. outer join, takes the union of the indexes of ts1 and ts2 before merging the other columns of input objects. The output will contain rows which are present in all the input objects while inserting missing values where a row was not present in any of the objects. This is the default behaviour if no jointype is provided.
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinLeft):
Left join takes the index values which are present in the left object ts1 and finds matching index values in the right object ts2. The resulting object includes all the rows from the left object, the column values from the left object, and the values associated with matching index rows on the right. The operation inserts missing values where in the unmatched rows of the right object.
join(ts1::TSFrame, ts2::TSFrame; jointype=:JoinRight)
Right join, similar to left join but works in the opposite direction. The final object contains all the rows from the right object while inserting missing values in rows missing from the left object.
The default behaviour is to assume jointype=:JoinAll if no jointype is provided to the join method.
Joining multiple TSFrames is also supported. The syntax is
join(ts1::TSFrame, ts2::TSFrame, ts...; jointype::Symbol)
where jointype must be one of :JoinInner, :JoinBoth, :JoinOuter, :JoinAll, :JoinLeft or :JoinRight. Note that join on multiple TSFrames is left associative.
cbind is an alias for join method.
Examples
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -527,8 +527,8 @@
2017-01-08 0.0521332 0.0521332 0.0521332
2017-01-09 0.26864 0.26864 0.26864
2017-01-10 0.108871 0.108871 0.108871
-Base.size — MethodSize methods
size(ts::TSFrame)Return the number of rows and columns of ts as a tuple.
Examples
julia> TSFrames.size(TSFrame([collect(1:100) collect(1:100) collect(1:100)]))
-(100, 3)Base.vcat — MethodRow-merging (vcat/rbind)
vcat(ts1::TSFrame, ts2::TSFrame; colmerge::Symbol=:union)Concatenate rows of two TSFrame objects, append ts2 to ts1.
The colmerge keyword argument specifies the column merge strategy. The value of colmerge is directly passed to cols argument of DataFrames.vcat.
Currently, DataFrames.vcat supports four types of column-merge strategies:
:setequal: only merge if both objects have same column names, use the order of columns in ts1.:orderequal: only merge if both objects have same column names and columns are in the same order.:intersect: only merge the columns which are common to both objects, ignore the rest.:union: merge even if columns differ, the resulting object has all the columns filled with missing, if necessary.Examples
julia> using Random;
+Base.size — MethodSize methods
size(ts::TSFrame)Return the number of rows and columns of ts as a tuple.
Examples
julia> TSFrames.size(TSFrame([collect(1:100) collect(1:100) collect(1:100)]))
+(100, 3)Base.vcat — MethodRow-merging (vcat/rbind)
vcat(ts1::TSFrame, ts2::TSFrame; colmerge::Symbol=:union)Concatenate rows of two TSFrame objects, append ts2 to ts1.
The colmerge keyword argument specifies the column merge strategy. The value of colmerge is directly passed to cols argument of DataFrames.vcat.
Currently, DataFrames.vcat supports four types of column-merge strategies:
:setequal: only merge if both objects have same column names, use the order of columns in ts1.:orderequal: only merge if both objects have same column names and columns are in the same order.:intersect: only merge the columns which are common to both objects, ignore the rest.:union: merge even if columns differ, the resulting object has all the columns filled with missing, if necessary.Examples
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -655,7 +655,7 @@
2017-01-28 0.278259
2017-01-29 0.0231765
2017-01-30 0.516704
-RecipesBase.apply_recipe — FunctionPlotting
plot(ts::TSFrame, cols::Vector{Int} = collect(1:TSFrames.ncol(ts)))
+RecipesBase.apply_recipe — FunctionPlotting
plot(ts::TSFrame, cols::Vector{Int} = collect(1:TSFrames.ncol(ts)))
plot(ts::TSFrame, cols::Vector{T}) where {T<:Union{String, Symbol}}
plot(ts::TSFrame, cols::T) where {T<:Union{Int, String, Symbol}}Plots a TSFrame object with the index on the x-axis and selected cols on the y-axis. By default, plot all the columns. Columns can be selected using Int indexes, String(s), or Symbol(s).
Example
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -695,7 +695,7 @@
julia> # plot(ts[1:6], [:val1, :val3]);
# plot columns 1 and 2 on a specified window size
-julia> # plot(ts, [1, 2], size=(600, 400));TSFrames._check_consistency — MethodInternal function to check consistency of the Index of a TSFrame object.
TSFrames.apply — MethodApply/Period conversion
apply(ts::TSFrame,
+julia> # plot(ts, [1, 2], size=(600, 400));TSFrames._check_consistency — MethodInternal function to check consistency of the Index of a TSFrame object.
TSFrames.apply — MethodApply/Period conversion
apply(ts::TSFrame,
period::T,
fun::V,
index_at::Function=first;
@@ -797,7 +797,7 @@
2017-02-19 0.319216
2017-02-26 0.272058
2017-03-05 0.23651
-TSFrames.describe — MethodSummary statistics
describe(ts::TSFrame; cols=:)
+TSFrames.describe — MethodSummary statistics
describe(ts::TSFrame; cols=:)
describe(ts::TSFrame, stats::Union{Symbol, Pair}...; cols=:)Compute summary statistics of ts. The output is a DataFrame containing standard statistics along with number of missing values and data types of columns. The cols keyword controls which subset of columns from ts to be selected. The stats keyword is used to control which summary statistics are to be printed. For more information about these keywords, check out the corresponding documentation from DataFrames.jl.
Examples
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x...);
julia> ts = TSFrame(random(([1, 2, 3, 4, missing], 10)))
@@ -833,7 +833,7 @@
│ Symbol Int64 Int64
─────┼────────────────────────
1 │ x1 2 22
-TSFrames.endpoints — FunctionComputing end points
endpoints(timestamps::AbstractVector{T}, on::V) where {T<:Union{Date, DateTime, Time},
+TSFrames.endpoints — FunctionComputing end points
endpoints(timestamps::AbstractVector{T}, on::V) where {T<:Union{Date, DateTime, Time},
V<:Union{
Year,
Quarter,
@@ -1079,7 +1079,7 @@
2022-10-08T11:56:46
2022-10-08T12:32:08
2022-10-08T13:59:08
- 2022-10-08T14:37:17TSFrames.head — FunctionHead
head(ts::TSFrame, n::Int = 10)Returns the first n rows of ts.
Examples
julia> head(TSFrame(1:100))
+ 2022-10-08T14:37:17TSFrames.head — FunctionHead
head(ts::TSFrame, n::Int = 10)Returns the first n rows of ts.
Examples
julia> head(TSFrame(1:100))
(10 x 1) TSFrame with Int64 Index
Index x1
@@ -1094,7 +1094,7 @@
7 7
8 8
9 9
- 10 10TSFrames.index — MethodIndex column
index(ts::TSFrame)Return the index vector from the coredata DataFrame.
Examples
julia> using Random;
+ 10 10TSFrames.index — MethodIndex column
index(ts::TSFrame)Return the index vector from the coredata DataFrame.
Examples
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -1132,7 +1132,7 @@
2022-11-01
julia> eltype(index(ts))
-DateTSFrames.isregular — Methodisregular(timestamps::V, unit::T) where {V<:AbstractVector{TimeType}, T<:Dates.Period}
+DateTSFrames.isregular — Methodisregular(timestamps::V, unit::T) where {V<:AbstractVector{TimeType}, T<:Dates.Period}
isregular(timestamps::T) where {T<:AbstractVector{TimeType}}
isregular(timestamps::AbstractVector{V}, unit::Symbol = :firstdiff) where {V<:TimeType}
isregular(ts::TSFrame, unit::Symbol = :firstdiff)
@@ -1190,7 +1190,7 @@
false
julia> isregular(ts, :month)
-falseTSFrames.lag — FunctionLagging
lag(ts::TSFrame, lag_value::Int = 1)Lag the ts object by the specified lag_value. The rows corresponding to lagged values will be rendered as missing. Negative values of lag are also accepted (see TSFrames.lead).
Examples
julia> using Random, Statistics;
+falseTSFrames.lag — FunctionLagging
lag(ts::TSFrame, lag_value::Int = 1)Lag the ts object by the specified lag_value. The rows corresponding to lagged values will be rendered as missing. Negative values of lag are also accepted (see TSFrames.lead).
Examples
julia> using Random, Statistics;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -1248,7 +1248,7 @@
2017-01-08 0.662555
2017-01-09 0.586022
2017-01-10 0.0521332
-TSFrames.lead — FunctionLeading
lead(ts::TSFrame, lead_value::Int = 1)Similar to lag, this method leads the ts object by lead_value. The lead rows are inserted with missing. Negative values of lead are also accepted (see TSFrames.lag).
Examples
julia> using Random, Statistics;
+TSFrames.lead — FunctionLeading
lead(ts::TSFrame, lead_value::Int = 1)Similar to lag, this method leads the ts object by lead_value. The lead rows are inserted with missing. Negative values of lead are also accepted (see TSFrames.lag).
Examples
julia> using Random, Statistics;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -1314,7 +1314,7 @@
2017-01-08 0.108871
2017-01-09 0.163666
2017-01-10 0.473017
-TSFrames.ncol — MethodSize methods
ncol(ts::TSFrame)Return the number of columns of ts. nc is an alias for ncol.
Examples
julia> using Random;
+TSFrames.ncol — MethodSize methods
ncol(ts::TSFrame)Return the number of columns of ts. nc is an alias for ncol.
Examples
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x);
@@ -1322,10 +1322,10 @@
3
julia> nc(TSFrame([random(100) random(100) random(100)]))
-3TSFrames.nrow — MethodSize methods
nrow(ts::TSFrame)
+3TSFrames.nrow — MethodSize methods
nrow(ts::TSFrame)
nr(ts::TSFrame)Return the number of rows of ts. nr is an alias for nrow.
Examples
julia> ts = TSFrame(collect(1:10))
julia> TSFrames.nrow(ts)
-10TSFrames.rename! — MethodColumn Rename
rename!(ts::TSFrame, colnames::AbstractVector{String}; makeunique::Bool=false)
+10TSFrames.rename! — MethodColumn Rename
rename!(ts::TSFrame, colnames::AbstractVector{String}; makeunique::Bool=false)
rename!(ts::TSFrame, colnames::AbstractVector{Symbol}; makeunique::Bool=false)
rename!(ts::TSFrame, (from => to)::Pair...)
rename!(ts::TSFrame, d::AbstractDict)
@@ -1441,7 +1441,7 @@
2012-01-08 8 18
2012-01-09 9 19
2012-01-10 10 20
-TSFrames.rollapply — MethodRolling Functions
rollapply(ts::TSFrame, fun::Function, windowsize::Int; bycolumn=true)Apply function fun to rolling windows of ts. The output is a TSFrame object with (nrow(ts) - windowsize + 1) rows indexed with the last index value of each window.
The bycolumn argument should be set to true (default) if fun is to be applied to each column separately, and to false if fun takes a whole TSFrame as an input.
Examples
julia> rollapply(TSFrame([1:10 11:20]), mean, 5)
+TSFrames.rollapply — MethodRolling Functions
rollapply(ts::TSFrame, fun::Function, windowsize::Int; bycolumn=true)Apply function fun to rolling windows of ts. The output is a TSFrame object with (nrow(ts) - windowsize + 1) rows indexed with the last index value of each window.
The bycolumn argument should be set to true (default) if fun is to be applied to each column separately, and to false if fun takes a whole TSFrame as an input.
Examples
julia> rollapply(TSFrame([1:10 11:20]), mean, 5)
6×2 TSFrame with Int64 Index
Index rolling_x1_mean rolling_x2_mean
Int64 Float64 Float64
@@ -1488,7 +1488,7 @@
2001-01-07 ([1.0], 4.86475e-16)
2001-01-08 ([1.0], 7.43103e-16)
2001-01-09 ([1.0], 7.45753e-15)
- 2001-01-10 ([1.0], 9.28561e-15)TSFrames.subset — MethodSubsetting based on Index
subset(ts::TSFrame, from::T, to::T) where {T<:Union{Int, TimeType}}Create a subset of ts based on the Index starting from (inclusive) till to (inclusive).
Examples
julia> using Random;
+ 2001-01-10 ([1.0], 9.28561e-15)TSFrames.subset — MethodSubsetting based on Index
subset(ts::TSFrame, from::T, to::T) where {T<:Union{Int, TimeType}}Create a subset of ts based on the Index starting from (inclusive) till to (inclusive).
Examples
julia> using Random;
julia> random(x) = rand(MersenneTwister(123), x);
julia> dates = Date("2022-02-01"):Week(1):Date("2022-02-01")+Month(9);
julia> ts = TSFrame(random(length(dates)), dates)
@@ -1612,7 +1612,7 @@
2022-10-25 0.621379
2022-11-01 0.348173
-TSFrames.tail — FunctionTail
tail(ts::TSFrame, n::Int = 10)Returns the last n rows of ts.
julia> tail(TSFrame(1:100))
+TSFrames.tail — FunctionTail
tail(ts::TSFrame, n::Int = 10)Returns the last n rows of ts.
julia> tail(TSFrame(1:100))
(10 x 1) TSFrame with Int64 Index
Index x1
@@ -1627,7 +1627,7 @@
97 97
98 98
99 99
- 100 100TSFrames.to_period — MethodFrequency conversion
Set of convenience methods for frequency conversion of TimeType index types. Internally, they call endpoints() to do the actual conversion. n is the number of periods of the period type. For example, to_monthly(tsf, 2) will resample the time series to "every 2 months".
to_period(tsf::TSFrame, period::T)::TSFrame where {T<:Period}
+ 100 100TSFrames.to_period — MethodFrequency conversion
Set of convenience methods for frequency conversion of TimeType index types. Internally, they call endpoints() to do the actual conversion. n is the number of periods of the period type. For example, to_monthly(tsf, 2) will resample the time series to "every 2 months".
to_period(tsf::TSFrame, period::T)::TSFrame where {T<:Period}
to_yearly(tsf::TSFrame, n=1)::TSFrame
to_quarterly(tsf::TSFrame, n=1)::TSFrame
to_monthly(tsf::TSFrame, n=1)::TSFrame
@@ -1638,4 +1638,4 @@
to_seconds(tsf::TSFrame, n=1)::TSFrame
to_milliseconds(tsf::TSFrame, n=1)::TSFrame
to_microseconds(tsf::TSFrame, n=1)::TSFrame
-to_nanoseconds(tsf::TSFrame, n=1)::TSFrameSettings
This document was generated with Documenter.jl version 0.27.25 on Wednesday 20 March 2024. Using Julia version 1.10.2.