Transformation to DataFrames

Transformation to DataFrames#

Split-apply-combine

using DataFrames

Grouping a data frame#

groupby

x = DataFrame(id=[1, 2, 3, 4, 1, 2, 3, 4], id2=[1, 2, 1, 2, 1, 2, 1, 2], v=rand(8))

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	2	2	0.37718
3	3	1	0.654804
4	4	2	0.393161
5	1	1	0.531108
6	2	2	0.202536
7	3	1	0.33627
8	4	2	0.374237

groupby(x, :id)

GroupedDataFrame with 4 groups based on key: id

First Group (2 rows): id = 1

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	1	1	0.531108

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.393161
2	4	2	0.374237

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	2	2	0.37718
3	3	1	0.654804
4	4	2	0.393161
5	1	1	0.531108
6	2	2	0.202536
7	3	1	0.33627
8	4	2	0.374237

gx2 = groupby(x, [:id, :id2])

GroupedDataFrame with 4 groups based on keys: id, id2

First Group (2 rows): id = 1, id2 = 1

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	1	1	0.531108

⋮

Last Group (2 rows): id = 4, id2 = 2

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.393161
2	4	2	0.374237

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	2	2	0.37718
3	3	1	0.654804
4	4	2	0.393161
5	1	1	0.531108
6	2	2	0.202536
7	3	1	0.33627
8	4	2	0.374237

back to the DataFrame, but in a different order of rows than the original

vcat(gx2...)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	1	1	0.531108
3	2	2	0.37718
4	2	2	0.202536
5	3	1	0.654804
6	3	1	0.33627
7	4	2	0.393161
8	4	2	0.374237

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	1	1	0.531108
3	2	2	0.37718
4	2	2	0.202536
5	3	1	0.654804
6	3	1	0.33627
7	4	2	0.393161
8	4	2	0.374237

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.213093
2	0.531108
3	0.37718
4	0.202536
5	0.654804
6	0.33627
7	0.393161
8	0.374237

vector of names of grouping variables

groupcols(gx2)

2-element Vector{Symbol}:
 :id
 :id2

and non-grouping variables

valuecols(gx2)

1-element Vector{Symbol}:
 :v

group indices in parent(gx2)

groupindices(gx2)

8-element Vector{Union{Missing, Int64}}:
 1
 2
 3
 4
 1
 2
 3
 4

kgx2 = keys(gx2)

4-element DataFrames.GroupKeys{DataFrames.GroupedDataFrame{DataFrames.DataFrame}}:
 GroupKey: (id = 1, id2 = 1)
 GroupKey: (id = 2, id2 = 2)
 GroupKey: (id = 3, id2 = 1)
 GroupKey: (id = 4, id2 = 2)

You can index into a GroupedDataFrame like to a vector or to a dictionary. The second form accepts GroupKey, NamedTuple or a Tuple.

gx2

GroupedDataFrame with 4 groups based on keys: id, id2

First Group (2 rows): id = 1, id2 = 1

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.213093
2	1	1	0.531108

⋮

Last Group (2 rows): id = 4, id2 = 2

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.393161
2	4	2	0.374237

k = keys(gx2)[1]

GroupKey: (id = 1, id2 = 1)

ntk = NamedTuple(k)

(id = 1, id2 = 1)

tk = Tuple(k)

(1, 1)

the operations below produce the same result and are proformant

gx2[1], gx2[k], gx2[ntk], gx2[tk]

(2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.213093
   2 │     1      1  0.531108, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.213093
   2 │     1      1  0.531108, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.213093
   2 │     1      1  0.531108, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.213093
   2 │     1      1  0.531108)

handling missing values

x = DataFrame(id=[missing, 5, 1, 3, missing], x=1:5)

5×2 DataFrame

Row	id	x
	Int64?	Int64
1	missing	1
2	5	2
3	1	3
4	3	4
5	missing	5

by default groups include missing values and their order is not guaranteed

groupby(x, :id)

GroupedDataFrame with 4 groups based on key: id

First Group (1 row): id = 1

Row	id	x
	Int64?	Int64
1	1	3

⋮

Last Group (2 rows): id = missing

Row	id	x
	Int64?	Int64
1	missing	1
2	missing	5

but we can change it; now they are sorted

groupby(x, :id, sort=true, skipmissing=true)

GroupedDataFrame with 3 groups based on key: id

First Group (1 row): id = 1

Row	id	x
	Int64?	Int64
1	1	3

⋮

Last Group (1 row): id = 5

Row	id	x
	Int64?	Int64
1	5	2

and now they are in the order they appear in the source data frame

groupby(x, :id, sort=false)

GroupedDataFrame with 4 groups based on key: id

First Group (2 rows): id = missing

Row	id	x
	Int64?	Int64
1	missing	1
2	missing	5

⋮

Last Group (1 row): id = 3

Row	id	x
	Int64?	Int64
1	3	4

Performing transformations#

by group using combine, select, select!, transform, and transform!

using Statistics
using Chain

x = DataFrame(id=rand('a':'d', 100), v=rand(100))

100×2 DataFrame

75 rows omitted

Row	id	v
	Char	Float64
1	a	0.801883
2	d	0.952816
3	d	0.0163985
4	d	0.628305
5	d	0.200988
6	c	0.679717
7	b	0.406815
8	d	0.0890824
9	d	0.647735
10	b	0.743218
11	a	0.979649
12	d	0.307343
13	c	0.29788
⋮	⋮	⋮
89	a	0.127252
90	c	0.123745
91	c	0.460812
92	b	0.818189
93	b	0.138276
94	b	0.676854
95	a	0.587076
96	d	0.613215
97	d	0.609037
98	b	0.823303
99	a	0.0850082
100	a	0.0878467

apply a function to each group of a data frame combine keeps as many rows as are returned from the function

@chain x begin
    groupby(:id)
    combine(:v => mean)
end

4×2 DataFrame

Row	id	v_mean
	Char	Float64
1	a	0.497656
2	d	0.457387
3	c	0.425741
4	b	0.568237

x.id2 = axes(x, 1)

Base.OneTo(100)

Select and transform keep as many rows as are in the source data frame and in correct order. Additionally, transform keeps all columns from the source.

@chain x begin
    groupby(:id)
    transform(:v => mean)
end

100×4 DataFrame

75 rows omitted

Row	id	v	id2	v_mean
	Char	Float64	Int64	Float64
1	a	0.801883	1	0.497656
2	d	0.952816	2	0.457387
3	d	0.0163985	3	0.457387
4	d	0.628305	4	0.457387
5	d	0.200988	5	0.457387
6	c	0.679717	6	0.425741
7	b	0.406815	7	0.568237
8	d	0.0890824	8	0.457387
9	d	0.647735	9	0.457387
10	b	0.743218	10	0.568237
11	a	0.979649	11	0.497656
12	d	0.307343	12	0.457387
13	c	0.29788	13	0.425741
⋮	⋮	⋮	⋮	⋮
89	a	0.127252	89	0.497656
90	c	0.123745	90	0.425741
91	c	0.460812	91	0.425741
92	b	0.818189	92	0.568237
93	b	0.138276	93	0.568237
94	b	0.676854	94	0.568237
95	a	0.587076	95	0.497656
96	d	0.613215	96	0.457387
97	d	0.609037	97	0.457387
98	b	0.823303	98	0.568237
99	a	0.0850082	99	0.497656
100	a	0.0878467	100	0.497656

note that combine reorders rows by group of GroupedDataFrame

@chain x begin
    groupby(:id)
    combine(:id2, :v => mean)
end

100×3 DataFrame

75 rows omitted

Row	id	id2	v_mean
	Char	Int64	Float64
1	a	1	0.497656
2	a	11	0.497656
3	a	15	0.497656
4	a	19	0.497656
5	a	25	0.497656
6	a	34	0.497656
7	a	40	0.497656
8	a	49	0.497656
9	a	53	0.497656
10	a	55	0.497656
11	a	59	0.497656
12	a	76	0.497656
13	a	77	0.497656
⋮	⋮	⋮	⋮
89	b	44	0.568237
90	b	46	0.568237
91	b	58	0.568237
92	b	61	0.568237
93	b	62	0.568237
94	b	66	0.568237
95	b	70	0.568237
96	b	73	0.568237
97	b	92	0.568237
98	b	93	0.568237
99	b	94	0.568237
100	b	98	0.568237

we give a custom name for the result column

@chain x begin
    groupby(:id)
    combine(:v => mean => :res)
end

4×2 DataFrame

Row	id	res
	Char	Float64
1	a	0.497656
2	d	0.457387
3	c	0.425741
4	b	0.568237

you can have multiple operations

@chain x begin
    groupby(:id)
    combine(:v => mean => :res1, :v => sum => :res2, nrow => :n)
end

4×4 DataFrame

Row	id	res1	res2	n
	Char	Float64	Float64	Int64
1	a	0.497656	10.4508	21
2	d	0.457387	14.6364	32
3	c	0.425741	11.0693	26
4	b	0.568237	11.933	21

Additional notes:

select! and transform! perform operations in-place
The general syntax for transformation is source_columns => function => target_column
if you pass multiple columns to a function they are treated as positional arguments
ByRow and AsTable work exactly like discussed for operations on data frames in 05_columns.ipynb
you can automatically groupby again the result of combine, select etc. by passing ungroup=false keyword argument to them
similarly keepkeys keyword argument allows you to drop grouping columns from the resulting data frame

It is also allowed to pass a function to all these functions (also - as a special case, as a first argument). In this case the return value can be a table. In particular it allows for an easy dropping of groups if you return an empty table from the function.

If you pass a function you can use a do block syntax. In case of passing a function it gets a SubDataFrame as its argument.

Here is an example:

combine(groupby(x, :id)) do sdf
    n = nrow(sdf)
    n < 25 ? DataFrame() : DataFrame(n=n) ## drop groups with low number of rows
end

2×2 DataFrame

Row	id	n
	Char	Int64
1	d	32
2	c	26

You can also produce multiple columns in a single operation:

df = DataFrame(id=[1, 1, 2, 2], val=[1, 2, 3, 4])

4×2 DataFrame

Row	id	val
	Int64	Int64
1	1	1
2	1	2
3	2	3
4	2	4

@chain df begin
    groupby(:id)
    combine(:val => (x -> [x]) => AsTable)
end

2×3 DataFrame

Row	id	x1	x2
	Int64	Int64	Int64
1	1	1	2
2	2	3	4

@chain df begin
    groupby(:id)
    combine(:val => (x -> [x]) => [:c1, :c2])
end

2×3 DataFrame

Row	id	c1	c2
	Int64	Int64	Int64
1	1	1	2
2	2	3	4

It is easy to unnest the column into multiple columns,

df = DataFrame(a=[(p=1, q=2), (p=3, q=4)])
select(df, :a => AsTable)

2×2 DataFrame

Row	p	q
	Int64	Int64
1	1	2
2	3	4

automatic column names generated

df = DataFrame(a=[[1, 2], [3, 4]])
select(df, :a => AsTable)

2×2 DataFrame

Row	x1	x2
	Int64	Int64
1	1	2
2	3	4

custom column names generated

select(df, :a => [:C1, :C2])

2×2 DataFrame

Row	C1	C2
	Int64	Int64
1	1	2
2	3	4

Finally, observe that one can conveniently apply multiple transformations using broadcasting:

df = DataFrame(id=repeat(1:10, 10), x1=1:100, x2=101:200)

100×3 DataFrame

75 rows omitted

Row	id	x1	x2
	Int64	Int64	Int64
1	1	1	101
2	2	2	102
3	3	3	103
4	4	4	104
5	5	5	105
6	6	6	106
7	7	7	107
8	8	8	108
9	9	9	109
10	10	10	110
11	1	11	111
12	2	12	112
13	3	13	113
⋮	⋮	⋮	⋮
89	9	89	189
90	10	90	190
91	1	91	191
92	2	92	192
93	3	93	193
94	4	94	194
95	5	95	195
96	6	96	196
97	7	97	197
98	8	98	198
99	9	99	199
100	10	100	200

@chain df begin
    groupby(:id)
    combine([:x1, :x2] .=> minimum)
end

10×3 DataFrame

Row	id	x1_minimum	x2_minimum
	Int64	Int64	Int64
1	1	1	101
2	2	2	102
3	3	3	103
4	4	4	104
5	5	5	105
6	6	6	106
7	7	7	107
8	8	8	108
9	9	9	109
10	10	10	110

@chain df begin
    groupby(:id)
    combine([:x1, :x2] .=> [minimum maximum])
end

10×5 DataFrame

Row	id	x1_minimum	x2_minimum	x1_maximum	x2_maximum
	Int64	Int64	Int64	Int64	Int64
1	1	1	101	91	191
2	2	2	102	92	192
3	3	3	103	93	193
4	4	4	104	94	194
5	5	5	105	95	195
6	6	6	106	96	196
7	7	7	107	97	197
8	8	8	108	98	198
9	9	9	109	99	199
10	10	10	110	100	200

Aggregation of a data frame using mapcols#

x = DataFrame(rand(10, 10), :auto)

10×10 DataFrame

Row	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	0.543187	0.654971	0.564967	0.0193184	0.0655621	0.333422	0.146635	0.696758	0.133701	0.441637
2	0.0216368	0.221818	0.812418	0.919989	0.499002	0.273137	0.269978	0.294127	0.125862	0.83274
3	0.0858233	0.729442	0.966261	0.682663	0.476421	0.903126	0.379712	0.0558343	0.89487	0.280855
4	0.44383	0.698613	0.879449	0.415938	0.484192	0.419159	0.275473	0.0465855	0.42337	0.87747
5	0.070336	0.935493	0.30444	0.801397	0.80048	0.467063	0.778272	0.480827	0.258181	0.310151
6	0.675448	0.663605	0.194207	0.924949	0.439675	0.0335385	0.742594	0.211452	0.528305	0.995182
7	0.496498	0.945806	0.168843	0.177435	0.0214421	0.411133	0.908437	0.423562	0.444285	0.0575748
8	0.39182	0.746754	0.583123	0.526052	0.406152	0.34729	0.663282	0.535099	0.0683799	0.502595
9	0.689919	0.480597	0.780622	0.153747	0.160571	0.941022	0.472905	0.476583	0.0153624	0.671748
10	0.317521	0.31849	0.950929	0.153774	0.733582	0.294143	0.828636	0.825528	0.630997	0.797777

mapcols(mean, x)

1×10 DataFrame

Row	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	0.373602	0.639559	0.620526	0.477526	0.408708	0.442303	0.546592	0.404636	0.352331	0.576773

Mapping rows and columns using eachcol and eachrow#

map a function over each column and return a vector

map(mean, eachcol(x))

10-element Vector{Float64}:
37360182782733153
6395588106842118
6205259472123151
4775262465128011
4087079638457814
4423033580345047
5465923243787321
40463569092326396
3523313871012412
5767728830531882

an iteration returns a Pair with column name and values

foreach(c -> println(c[1], ": ", mean(c[2])), pairs(eachcol(x)))

x1: 0.37360182782733153
x2: 0.6395588106842118
x3: 0.6205259472123151
x4: 0.4775262465128011
x5: 0.4087079638457814
x6: 0.4423033580345047
x7: 0.5465923243787321
x8: 0.40463569092326396
x9: 0.3523313871012412
x10: 0.5767728830531882

now the returned value is DataFrameRow which works as a NamedTuple but is a view to a parent DataFrame

map(r -> r.x1 / r.x2, eachrow(x))

10-element Vector{Float64}:
8293307869115067
0975428614803122
11765595862937013
6353016147079975
07518609472932634
0178462703245712
5249474255772854
5246970294853852
4355460069910808
9969559193720787

it prints like a data frame, only the caption is different so that you know the type of the object

er = eachrow(x)
er.x1 ## you can access columns of a parent data frame directly

10-element Vector{Float64}:
5431872051020343
02163675847737223
08582325596595097
4438299117560812
07033603762854568
6754479893590396
496498351529858
39181953559070604
6899186955395462
3175205373241805

it prints like a data frame, only the caption is different so that you know the type of the object

ec = eachcol(x)

10×10 DataFrameColumns

Row	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64
1	0.543187	0.654971	0.564967	0.0193184	0.0655621	0.333422	0.146635	0.696758	0.133701	0.441637
2	0.0216368	0.221818	0.812418	0.919989	0.499002	0.273137	0.269978	0.294127	0.125862	0.83274
3	0.0858233	0.729442	0.966261	0.682663	0.476421	0.903126	0.379712	0.0558343	0.89487	0.280855
4	0.44383	0.698613	0.879449	0.415938	0.484192	0.419159	0.275473	0.0465855	0.42337	0.87747
5	0.070336	0.935493	0.30444	0.801397	0.80048	0.467063	0.778272	0.480827	0.258181	0.310151
6	0.675448	0.663605	0.194207	0.924949	0.439675	0.0335385	0.742594	0.211452	0.528305	0.995182
7	0.496498	0.945806	0.168843	0.177435	0.0214421	0.411133	0.908437	0.423562	0.444285	0.0575748
8	0.39182	0.746754	0.583123	0.526052	0.406152	0.34729	0.663282	0.535099	0.0683799	0.502595
9	0.689919	0.480597	0.780622	0.153747	0.160571	0.941022	0.472905	0.476583	0.0153624	0.671748
10	0.317521	0.31849	0.950929	0.153774	0.733582	0.294143	0.828636	0.825528	0.630997	0.797777

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
5431872051020343
02163675847737223
08582325596595097
4438299117560812
07033603762854568
6754479893590396
496498351529858
39181953559070604
6899186955395462
3175205373241805

Transposing#

you can transpose a data frame using permutedims:

df = DataFrame(reshape(1:12, 3, 4), :auto)

3×4 DataFrame

Row	x1	x2	x3	x4
	Int64	Int64	Int64	Int64
1	1	4	7	10
2	2	5	8	11
3	3	6	9	12

df.names = ["a", "b", "c"]

3-element Vector{String}:
 "a"
 "b"
 "c"

permutedims(df, :names)

4×4 DataFrame

Row	names	a	b	c
	String	Int64	Int64	Int64
1	x1	1	2	3
2	x2	4	5	6
3	x3	7	8	9
4	x4	10	11	12

This notebook was generated using Literate.jl.

Transformation to DataFrames

Contents

Transformation to DataFrames#

Grouping a data frame#

Performing transformations#

Aggregation of a data frame using mapcols#

Mapping rows and columns using eachcol and eachrow#

Transposing#