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	0.668046
2	2	2	0.0217206
3	3	1	0.825651
4	4	2	0.835345
5	1	1	0.0516351
6	2	2	0.91682
7	3	1	0.669437
8	4	2	0.524971

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	0.668046
2	1	1	0.0516351

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.835345
2	4	2	0.524971

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.668046
2	2	2	0.0217206
3	3	1	0.825651
4	4	2	0.835345
5	1	1	0.0516351
6	2	2	0.91682
7	3	1	0.669437
8	4	2	0.524971

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	0.668046
2	1	1	0.0516351

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.835345
2	4	2	0.524971

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.668046
2	2	2	0.0217206
3	3	1	0.825651
4	4	2	0.835345
5	1	1	0.0516351
6	2	2	0.91682
7	3	1	0.669437
8	4	2	0.524971

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	0.668046
2	1	1	0.0516351
3	2	2	0.0217206
4	2	2	0.91682
5	3	1	0.825651
6	3	1	0.669437
7	4	2	0.835345
8	4	2	0.524971

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.668046
2	1	1	0.0516351
3	2	2	0.0217206
4	2	2	0.91682
5	3	1	0.825651
6	3	1	0.669437
7	4	2	0.835345
8	4	2	0.524971

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.668046
2	0.0516351
3	0.0217206
4	0.91682
5	0.825651
6	0.669437
7	0.835345
8	0.524971

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	0.668046
2	1	1	0.0516351

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.835345
2	4	2	0.524971

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  0.668046
   2 │     1      1  0.0516351, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.668046
   2 │     1      1  0.0516351, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.668046
   2 │     1      1  0.0516351, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.668046
   2 │     1      1  0.0516351)

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	0.170523
2	d	0.914176
3	d	0.204357
4	d	0.603191
5	b	0.887813
6	b	0.414486
7	c	0.0844315
8	d	0.0611222
9	a	0.960798
10	b	0.427352
11	d	0.527319
12	a	0.763995
13	a	0.0469857
⋮	⋮	⋮
89	d	0.15171
90	b	0.461184
91	b	0.583866
92	c	0.658358
93	c	0.24761
94	c	0.590359
95	b	0.432363
96	a	0.844712
97	b	0.825326
98	a	0.210774
99	a	0.0527467
100	c	0.985588

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	0.481783
2	d	0.534384
3	b	0.583599
4	c	0.45104

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	0.170523	1	0.481783
2	d	0.914176	2	0.534384
3	d	0.204357	3	0.534384
4	d	0.603191	4	0.534384
5	b	0.887813	5	0.583599
6	b	0.414486	6	0.583599
7	c	0.0844315	7	0.45104
8	d	0.0611222	8	0.534384
9	a	0.960798	9	0.481783
10	b	0.427352	10	0.583599
11	d	0.527319	11	0.534384
12	a	0.763995	12	0.481783
13	a	0.0469857	13	0.481783
⋮	⋮	⋮	⋮	⋮
89	d	0.15171	89	0.534384
90	b	0.461184	90	0.583599
91	b	0.583866	91	0.583599
92	c	0.658358	92	0.45104
93	c	0.24761	93	0.45104
94	c	0.590359	94	0.45104
95	b	0.432363	95	0.583599
96	a	0.844712	96	0.481783
97	b	0.825326	97	0.583599
98	a	0.210774	98	0.481783
99	a	0.0527467	99	0.481783
100	c	0.985588	100	0.45104

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	0.481783
2	a	9	0.481783
3	a	12	0.481783
4	a	13	0.481783
5	a	14	0.481783
6	a	16	0.481783
7	a	18	0.481783
8	a	23	0.481783
9	a	24	0.481783
10	a	28	0.481783
11	a	33	0.481783
12	a	34	0.481783
13	a	38	0.481783
⋮	⋮	⋮	⋮
89	c	44	0.45104
90	c	45	0.45104
91	c	60	0.45104
92	c	61	0.45104
93	c	66	0.45104
94	c	68	0.45104
95	c	77	0.45104
96	c	80	0.45104
97	c	92	0.45104
98	c	93	0.45104
99	c	94	0.45104
100	c	100	0.45104

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	0.481783
2	d	0.534384
3	b	0.583599
4	c	0.45104

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	0.481783	13.9717	29
2	d	0.534384	13.3596	25
3	b	0.583599	16.3408	28
4	c	0.45104	8.11873	18

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	a	29
2	d	25
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	0.868459	0.00654418	0.433637	0.930547	0.23168	0.42777	0.7547	0.520275	0.425056	0.861338
2	0.144681	0.0194137	0.111178	0.659497	0.524944	0.0739465	0.598811	0.128128	0.261384	0.512681
3	0.973916	0.481915	0.800024	0.0407536	0.629961	0.913566	0.110335	0.72225	0.768512	0.692188
4	0.341584	0.312496	0.0284095	0.67526	0.115266	0.92251	0.741232	0.502286	0.82653	0.354182
5	0.387646	0.670977	0.325491	0.173094	0.2506	0.990914	0.31726	0.28657	0.574434	0.132789
6	0.717522	0.906963	0.447598	0.265681	0.164826	0.136781	0.66608	0.477339	0.665717	0.995873
7	0.669004	0.763295	0.686524	0.13838	0.100231	0.993652	0.578947	0.423681	0.160622	0.959578
8	0.573946	0.265492	0.312578	0.417322	0.82173	0.438493	0.0523987	0.331511	0.519	0.159041
9	0.546238	0.100002	0.426146	0.17062	0.0232923	0.321438	0.150506	0.674669	0.544913	0.614595
10	0.74745	0.192785	0.261677	0.081463	0.86766	0.0788269	0.463088	0.340394	0.728178	0.672423

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	0.597045	0.371988	0.383326	0.355262	0.373019	0.52979	0.443336	0.44071	0.547435	0.595469

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}:
5970446200515614
371988280043345
3833261421741702
35526177851212193
3730189771544299
5297896374308705
4433358543846618
4407101912829486
5474346036000817
5954687362554464

an iteration returns a Pair with column name and values

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

x1: 0.5970446200515614
x2: 0.371988280043345
x3: 0.3833261421741702
x4: 0.35526177851212193
x5: 0.3730189771544299
x6: 0.5297896374308705
x7: 0.4433358543846618
x8: 0.4407101912829486
x9: 0.5474346036000817
x10: 0.5954687362554464

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}:
70712754424997
452529393425048
0209313586052504
0930815434814145
5777342899285142
7911259171572509
8764693136551583
161819129709327
4622427942466985
8771071571962517

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}:
8684590156304837
144681492763982
9739162879177052
341583618406165
38764646475147546
7175215916255719
6690043125147219
5739460726996791
5462377762078239
7474495679980068

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	0.868459	0.00654418	0.433637	0.930547	0.23168	0.42777	0.7547	0.520275	0.425056	0.861338
2	0.144681	0.0194137	0.111178	0.659497	0.524944	0.0739465	0.598811	0.128128	0.261384	0.512681
3	0.973916	0.481915	0.800024	0.0407536	0.629961	0.913566	0.110335	0.72225	0.768512	0.692188
4	0.341584	0.312496	0.0284095	0.67526	0.115266	0.92251	0.741232	0.502286	0.82653	0.354182
5	0.387646	0.670977	0.325491	0.173094	0.2506	0.990914	0.31726	0.28657	0.574434	0.132789
6	0.717522	0.906963	0.447598	0.265681	0.164826	0.136781	0.66608	0.477339	0.665717	0.995873
7	0.669004	0.763295	0.686524	0.13838	0.100231	0.993652	0.578947	0.423681	0.160622	0.959578
8	0.573946	0.265492	0.312578	0.417322	0.82173	0.438493	0.0523987	0.331511	0.519	0.159041
9	0.546238	0.100002	0.426146	0.17062	0.0232923	0.321438	0.150506	0.674669	0.544913	0.614595
10	0.74745	0.192785	0.261677	0.081463	0.86766	0.0788269	0.463088	0.340394	0.728178	0.672423

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
8684590156304837
144681492763982
9739162879177052
341583618406165
38764646475147546
7175215916255719
6690043125147219
5739460726996791
5462377762078239
7474495679980068

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#