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	0.325215
2	2	2	0.369038
3	3	1	0.814633
4	4	2	0.377291
5	1	1	0.0800426
6	2	2	0.796786
7	3	1	0.21076
8	4	2	0.75457

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	0.325215
2	1	1	0.0800426

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.377291
2	4	2	0.75457

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.325215
2	2	2	0.369038
3	3	1	0.814633
4	4	2	0.377291
5	1	1	0.0800426
6	2	2	0.796786
7	3	1	0.21076
8	4	2	0.75457

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	0.325215
2	1	1	0.0800426

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.377291
2	4	2	0.75457

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.325215
2	2	2	0.369038
3	3	1	0.814633
4	4	2	0.377291
5	1	1	0.0800426
6	2	2	0.796786
7	3	1	0.21076
8	4	2	0.75457

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	0.325215
2	1	1	0.0800426
3	2	2	0.369038
4	2	2	0.796786
5	3	1	0.814633
6	3	1	0.21076
7	4	2	0.377291
8	4	2	0.75457

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.325215
2	1	1	0.0800426
3	2	2	0.369038
4	2	2	0.796786
5	3	1	0.814633
6	3	1	0.21076
7	4	2	0.377291
8	4	2	0.75457

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.325215
2	0.0800426
3	0.369038
4	0.796786
5	0.814633
6	0.21076
7	0.377291
8	0.75457

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	0.325215
2	1	1	0.0800426

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.377291
2	4	2	0.75457

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  0.325215
   2 │     1      1  0.0800426, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.325215
   2 │     1      1  0.0800426, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.325215
   2 │     1      1  0.0800426, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.325215
   2 │     1      1  0.0800426)

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	d	0.911682
2	a	0.336318
3	c	0.569249
4	a	0.860833
5	d	0.990385
6	d	0.671075
7	b	0.456261
8	a	0.663899
9	a	0.673223
10	c	0.822798
11	b	0.948415
12	d	0.462216
13	b	0.772046
⋮	⋮	⋮
89	b	0.736596
90	c	0.010389
91	d	0.958571
92	b	0.132037
93	c	0.132203
94	d	0.6734
95	c	0.581931
96	d	0.732172
97	a	0.647178
98	a	0.746204
99	a	0.0444478
100	c	0.206397

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	d	0.579567
2	a	0.50468
3	c	0.512914
4	b	0.584901

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	d	0.911682	1	0.579567
2	a	0.336318	2	0.50468
3	c	0.569249	3	0.512914
4	a	0.860833	4	0.50468
5	d	0.990385	5	0.579567
6	d	0.671075	6	0.579567
7	b	0.456261	7	0.584901
8	a	0.663899	8	0.50468
9	a	0.673223	9	0.50468
10	c	0.822798	10	0.512914
11	b	0.948415	11	0.584901
12	d	0.462216	12	0.579567
13	b	0.772046	13	0.584901
⋮	⋮	⋮	⋮	⋮
89	b	0.736596	89	0.584901
90	c	0.010389	90	0.512914
91	d	0.958571	91	0.579567
92	b	0.132037	92	0.584901
93	c	0.132203	93	0.512914
94	d	0.6734	94	0.579567
95	c	0.581931	95	0.512914
96	d	0.732172	96	0.579567
97	a	0.647178	97	0.50468
98	a	0.746204	98	0.50468
99	a	0.0444478	99	0.50468
100	c	0.206397	100	0.512914

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	d	1	0.579567
2	d	5	0.579567
3	d	6	0.579567
4	d	12	0.579567
5	d	16	0.579567
6	d	22	0.579567
7	d	28	0.579567
8	d	30	0.579567
9	d	35	0.579567
10	d	37	0.579567
11	d	38	0.579567
12	d	39	0.579567
13	d	40	0.579567
⋮	⋮	⋮	⋮
89	b	58	0.584901
90	b	59	0.584901
91	b	64	0.584901
92	b	65	0.584901
93	b	69	0.584901
94	b	71	0.584901
95	b	74	0.584901
96	b	77	0.584901
97	b	84	0.584901
98	b	88	0.584901
99	b	89	0.584901
100	b	92	0.584901

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	d	0.579567
2	a	0.50468
3	c	0.512914
4	b	0.584901

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	d	0.579567	15.6483	27
2	a	0.50468	9.08424	18
3	c	0.512914	13.8487	27
4	b	0.584901	16.3772	28

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

3×2 DataFrame

Row	id	n
	Char	Int64
1	d	27
2	c	27
3	b	28

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	0.0497713	0.174038	0.335658	0.98617	0.818046	0.315802	0.176087	0.88693	0.331478	0.565418
2	0.469067	0.532094	0.522563	0.698294	0.684567	0.713736	0.236339	0.436262	0.438289	0.501549
3	0.960727	0.792708	0.219594	0.991814	0.853047	0.661315	0.227833	0.796672	0.830572	0.0412893
4	0.954378	0.464174	0.469449	0.207181	0.98027	0.811747	0.908094	0.187583	0.127347	0.11277
5	0.490458	0.356026	0.220018	0.672967	0.0462723	0.578973	0.838352	0.145225	0.718935	0.254138
6	0.226693	0.975931	0.82475	0.132941	0.486135	0.189352	0.305374	0.478535	0.457187	0.0475593
7	0.520552	0.0662263	0.154305	0.440129	0.508249	0.531972	0.604084	0.38234	0.665801	0.563294
8	0.881169	0.782526	0.13947	0.571747	0.240582	0.556918	0.981525	0.813337	0.07942	0.616259
9	0.615944	0.740941	0.155328	0.450876	0.731792	0.414014	0.995569	0.825418	0.808066	0.795116
10	0.171597	0.927998	0.945204	0.761721	0.000796149	0.700339	0.824226	0.786892	0.33651	0.839618

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	0.534036	0.581266	0.398634	0.591384	0.534976	0.547417	0.609748	0.573919	0.479361	0.433701

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}:
5340356166118051
581266221422634
39863395825014675
5913839384886548
5349757146385773
5474167357793596
6097484043642387
5739193910908156
47936052129134554
4337009355738717

an iteration returns a Pair with column name and values

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

x1: 0.5340356166118051
x2: 0.581266221422634
x3: 0.39863395825014675
x4: 0.5913839384886548
x5: 0.5349757146385773
x6: 0.5474167357793596
x7: 0.6097484043642387
x8: 0.5739193910908156
x9: 0.47936052129134554
x10: 0.4337009355738717

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}:
28598028361408456
8815489568706143
211955077276455
056076699409006
3775932036047553
23228344373783585
860203109455978
1260573845407145
8312995463574625
18491066455919594

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}:
04977131658967582
4690673288817153
9607270015546927
9543781600816001
49045845644444097
22669264058140504
5205522834249062
8811686488976916
6159435670365279
17159676262539625

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	0.0497713	0.174038	0.335658	0.98617	0.818046	0.315802	0.176087	0.88693	0.331478	0.565418
2	0.469067	0.532094	0.522563	0.698294	0.684567	0.713736	0.236339	0.436262	0.438289	0.501549
3	0.960727	0.792708	0.219594	0.991814	0.853047	0.661315	0.227833	0.796672	0.830572	0.0412893
4	0.954378	0.464174	0.469449	0.207181	0.98027	0.811747	0.908094	0.187583	0.127347	0.11277
5	0.490458	0.356026	0.220018	0.672967	0.0462723	0.578973	0.838352	0.145225	0.718935	0.254138
6	0.226693	0.975931	0.82475	0.132941	0.486135	0.189352	0.305374	0.478535	0.457187	0.0475593
7	0.520552	0.0662263	0.154305	0.440129	0.508249	0.531972	0.604084	0.38234	0.665801	0.563294
8	0.881169	0.782526	0.13947	0.571747	0.240582	0.556918	0.981525	0.813337	0.07942	0.616259
9	0.615944	0.740941	0.155328	0.450876	0.731792	0.414014	0.995569	0.825418	0.808066	0.795116
10	0.171597	0.927998	0.945204	0.761721	0.000796149	0.700339	0.824226	0.786892	0.33651	0.839618

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
04977131658967582
4690673288817153
9607270015546927
9543781600816001
49045845644444097
22669264058140504
5205522834249062
8811686488976916
6159435670365279
17159676262539625

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#