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	0.923554
2	2	2	0.525914
3	3	1	0.611834
4	4	2	0.105175
5	1	1	0.926859
6	2	2	0.843111
7	3	1	0.173183
8	4	2	0.439764

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	0.923554
2	1	1	0.926859

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.105175
2	4	2	0.439764

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.923554
2	2	2	0.525914
3	3	1	0.611834
4	4	2	0.105175
5	1	1	0.926859
6	2	2	0.843111
7	3	1	0.173183
8	4	2	0.439764

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	0.923554
2	1	1	0.926859

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.105175
2	4	2	0.439764

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.923554
2	2	2	0.525914
3	3	1	0.611834
4	4	2	0.105175
5	1	1	0.926859
6	2	2	0.843111
7	3	1	0.173183
8	4	2	0.439764

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	0.923554
2	1	1	0.926859
3	2	2	0.525914
4	2	2	0.843111
5	3	1	0.611834
6	3	1	0.173183
7	4	2	0.105175
8	4	2	0.439764

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.923554
2	1	1	0.926859
3	2	2	0.525914
4	2	2	0.843111
5	3	1	0.611834
6	3	1	0.173183
7	4	2	0.105175
8	4	2	0.439764

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.923554
2	0.926859
3	0.525914
4	0.843111
5	0.611834
6	0.173183
7	0.105175
8	0.439764

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	0.923554
2	1	1	0.926859

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.105175
2	4	2	0.439764

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  0.923554
   2 │     1      1  0.926859, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.923554
   2 │     1      1  0.926859, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.923554
   2 │     1      1  0.926859, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.923554
   2 │     1      1  0.926859)

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	0.835577
2	c	0.281793
3	a	0.578997
4	b	0.0623499
5	a	0.627195
6	d	0.871266
7	a	0.0218157
8	c	0.404754
9	d	0.787053
10	c	0.498892
11	b	0.229484
12	d	0.315011
13	a	0.921946
⋮	⋮	⋮
89	d	0.067035
90	a	0.933599
91	c	0.849821
92	c	0.906701
93	b	0.736553
94	b	0.779055
95	b	0.985637
96	a	0.34354
97	d	0.935088
98	c	0.742881
99	a	0.932195
100	a	0.730541

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	0.585795
2	c	0.460639
3	b	0.528949
4	d	0.585117

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	0.835577	1	0.585795
2	c	0.281793	2	0.460639
3	a	0.578997	3	0.585795
4	b	0.0623499	4	0.528949
5	a	0.627195	5	0.585795
6	d	0.871266	6	0.585117
7	a	0.0218157	7	0.585795
8	c	0.404754	8	0.460639
9	d	0.787053	9	0.585117
10	c	0.498892	10	0.460639
11	b	0.229484	11	0.528949
12	d	0.315011	12	0.585117
13	a	0.921946	13	0.585795
⋮	⋮	⋮	⋮	⋮
89	d	0.067035	89	0.585117
90	a	0.933599	90	0.585795
91	c	0.849821	91	0.460639
92	c	0.906701	92	0.460639
93	b	0.736553	93	0.528949
94	b	0.779055	94	0.528949
95	b	0.985637	95	0.528949
96	a	0.34354	96	0.585795
97	d	0.935088	97	0.585117
98	c	0.742881	98	0.460639
99	a	0.932195	99	0.585795
100	a	0.730541	100	0.585795

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	0.585795
2	a	3	0.585795
3	a	5	0.585795
4	a	7	0.585795
5	a	13	0.585795
6	a	17	0.585795
7	a	19	0.585795
8	a	25	0.585795
9	a	26	0.585795
10	a	27	0.585795
11	a	28	0.585795
12	a	30	0.585795
13	a	37	0.585795
⋮	⋮	⋮	⋮
89	d	64	0.585117
90	d	65	0.585117
91	d	68	0.585117
92	d	71	0.585117
93	d	77	0.585117
94	d	78	0.585117
95	d	80	0.585117
96	d	85	0.585117
97	d	87	0.585117
98	d	88	0.585117
99	d	89	0.585117
100	d	97	0.585117

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	0.585795
2	c	0.460639
3	b	0.528949
4	d	0.585117

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	0.585795	16.4023	28
2	c	0.460639	8.29149	18
3	b	0.528949	11.6369	22
4	d	0.585117	18.7237	32

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	a	28
2	d	32

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	0.888222	0.0809605	0.403429	0.511608	0.965816	0.478898	0.371856	0.957915	0.129378	0.876398
2	0.969358	0.0217866	0.901886	0.410378	0.812917	0.255134	0.91058	0.0088501	0.888141	0.743186
3	0.0350118	0.277128	0.672571	0.911096	0.972657	0.783727	0.298356	0.187414	0.555125	0.681353
4	0.31299	0.141234	0.0983423	0.590939	0.0682573	0.384461	0.998603	0.704168	0.200136	0.379971
5	0.256835	0.458744	0.84923	0.470579	0.525386	0.128736	0.696099	0.192121	0.734684	0.397709
6	0.675231	0.226636	0.11204	0.046839	0.743986	0.415168	0.326053	0.612582	0.708217	0.456039
7	0.409624	0.719965	0.693778	0.713999	0.255868	0.941009	0.460104	0.137039	0.0450871	0.426524
8	0.361776	0.0872905	0.755017	0.97836	0.658533	0.376703	0.321328	0.0635676	0.317717	0.130425
9	0.502291	0.09431	0.807559	0.56433	0.588048	0.0412632	0.731746	0.158557	0.378116	0.406653
10	0.0238244	0.222155	0.844815	0.488589	0.74649	0.411805	0.262241	0.352572	0.0119243	0.1269

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	0.443516	0.233021	0.613867	0.568672	0.633796	0.42169	0.537696	0.337479	0.396853	0.462516

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}:
44351626991809645
23302107000340802
6138666895806199
5686716035619371
633795739464339
4216904417231671
5376964271412635
33747865010160033
3968525913053566
4625157129085385

an iteration returns a Pair with column name and values

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

x1: 0.44351626991809645
x2: 0.23302107000340802
x3: 0.6138666895806199
x4: 0.5686716035619371
x5: 0.633795739464339
x6: 0.4216904417231671
x7: 0.5376964271412635
x8: 0.33747865010160033
x9: 0.3968525913053566
x10: 0.4625157129085385

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}:
971054379628248
49338049460191
1263380337364685
2161041599136193
5598644975669603
9793662606153277
5689497869814226
144501187588225
325959217502414
10724220030834118

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}:
8882216562522717
9693580655050634
03501184143705083
31299002717386293
2568346884017747
6752310702251116
40962414133358827
36177556410656886
5022912541860961
02382439055957608

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	0.888222	0.0809605	0.403429	0.511608	0.965816	0.478898	0.371856	0.957915	0.129378	0.876398
2	0.969358	0.0217866	0.901886	0.410378	0.812917	0.255134	0.91058	0.0088501	0.888141	0.743186
3	0.0350118	0.277128	0.672571	0.911096	0.972657	0.783727	0.298356	0.187414	0.555125	0.681353
4	0.31299	0.141234	0.0983423	0.590939	0.0682573	0.384461	0.998603	0.704168	0.200136	0.379971
5	0.256835	0.458744	0.84923	0.470579	0.525386	0.128736	0.696099	0.192121	0.734684	0.397709
6	0.675231	0.226636	0.11204	0.046839	0.743986	0.415168	0.326053	0.612582	0.708217	0.456039
7	0.409624	0.719965	0.693778	0.713999	0.255868	0.941009	0.460104	0.137039	0.0450871	0.426524
8	0.361776	0.0872905	0.755017	0.97836	0.658533	0.376703	0.321328	0.0635676	0.317717	0.130425
9	0.502291	0.09431	0.807559	0.56433	0.588048	0.0412632	0.731746	0.158557	0.378116	0.406653
10	0.0238244	0.222155	0.844815	0.488589	0.74649	0.411805	0.262241	0.352572	0.0119243	0.1269

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
8882216562522717
9693580655050634
03501184143705083
31299002717386293
2568346884017747
6752310702251116
40962414133358827
36177556410656886
5022912541860961
02382439055957608

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#