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	0.274977
2	2	2	0.0764892
3	3	1	0.620511
4	4	2	0.0601085
5	1	1	0.162512
6	2	2	0.318162
7	3	1	0.407531
8	4	2	0.851718

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	0.274977
2	1	1	0.162512

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.0601085
2	4	2	0.851718

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.274977
2	2	2	0.0764892
3	3	1	0.620511
4	4	2	0.0601085
5	1	1	0.162512
6	2	2	0.318162
7	3	1	0.407531
8	4	2	0.851718

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	0.274977
2	1	1	0.162512

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.0601085
2	4	2	0.851718

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.274977
2	2	2	0.0764892
3	3	1	0.620511
4	4	2	0.0601085
5	1	1	0.162512
6	2	2	0.318162
7	3	1	0.407531
8	4	2	0.851718

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	0.274977
2	1	1	0.162512
3	2	2	0.0764892
4	2	2	0.318162
5	3	1	0.620511
6	3	1	0.407531
7	4	2	0.0601085
8	4	2	0.851718

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.274977
2	1	1	0.162512
3	2	2	0.0764892
4	2	2	0.318162
5	3	1	0.620511
6	3	1	0.407531
7	4	2	0.0601085
8	4	2	0.851718

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.274977
2	0.162512
3	0.0764892
4	0.318162
5	0.620511
6	0.407531
7	0.0601085
8	0.851718

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	0.274977
2	1	1	0.162512

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.0601085
2	4	2	0.851718

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  0.274977
   2 │     1      1  0.162512, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.274977
   2 │     1      1  0.162512, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.274977
   2 │     1      1  0.162512, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.274977
   2 │     1      1  0.162512)

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	b	0.453848
2	c	0.909895
3	d	0.703891
4	b	0.027346
5	d	0.248954
6	d	0.223325
7	a	0.632575
8	c	0.0887604
9	a	0.56193
10	a	0.874694
11	c	0.192252
12	d	0.611955
13	a	0.63133
⋮	⋮	⋮
89	c	0.582991
90	d	0.456848
91	b	0.892767
92	c	0.622906
93	a	0.492163
94	d	0.459594
95	b	0.923333
96	d	0.956767
97	a	0.344608
98	d	0.374078
99	a	0.597576
100	b	0.880164

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	b	0.549499
2	c	0.551201
3	d	0.501363
4	a	0.480635

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	b	0.453848	1	0.549499
2	c	0.909895	2	0.551201
3	d	0.703891	3	0.501363
4	b	0.027346	4	0.549499
5	d	0.248954	5	0.501363
6	d	0.223325	6	0.501363
7	a	0.632575	7	0.480635
8	c	0.0887604	8	0.551201
9	a	0.56193	9	0.480635
10	a	0.874694	10	0.480635
11	c	0.192252	11	0.551201
12	d	0.611955	12	0.501363
13	a	0.63133	13	0.480635
⋮	⋮	⋮	⋮	⋮
89	c	0.582991	89	0.551201
90	d	0.456848	90	0.501363
91	b	0.892767	91	0.549499
92	c	0.622906	92	0.551201
93	a	0.492163	93	0.480635
94	d	0.459594	94	0.501363
95	b	0.923333	95	0.549499
96	d	0.956767	96	0.501363
97	a	0.344608	97	0.480635
98	d	0.374078	98	0.501363
99	a	0.597576	99	0.480635
100	b	0.880164	100	0.549499

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	b	1	0.549499
2	b	4	0.549499
3	b	14	0.549499
4	b	17	0.549499
5	b	20	0.549499
6	b	24	0.549499
7	b	27	0.549499
8	b	29	0.549499
9	b	30	0.549499
10	b	33	0.549499
11	b	34	0.549499
12	b	39	0.549499
13	b	47	0.549499
⋮	⋮	⋮	⋮
89	a	41	0.480635
90	a	50	0.480635
91	a	51	0.480635
92	a	54	0.480635
93	a	61	0.480635
94	a	63	0.480635
95	a	76	0.480635
96	a	77	0.480635
97	a	79	0.480635
98	a	93	0.480635
99	a	97	0.480635
100	a	99	0.480635

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	b	0.549499
2	c	0.551201
3	d	0.501363
4	a	0.480635

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	b	0.549499	13.188	24
2	c	0.551201	14.3312	26
3	d	0.501363	15.0409	30
4	a	0.480635	9.6127	20

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	c	26
2	d	30

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	0.142933	0.698027	0.800833	0.239132	0.178874	0.0204503	0.831044	0.394313	0.155643	0.821355
2	0.871697	0.486461	0.733391	0.98954	0.788407	0.939166	0.264716	0.940042	0.441365	0.546164
3	0.729192	0.629316	0.450316	0.0734478	0.173671	0.900968	0.7131	0.366368	0.431975	0.0348618
4	0.863221	0.247543	0.733563	0.00921637	0.92269	0.200339	0.347216	0.226552	0.469603	0.723935
5	0.212953	0.551312	0.887163	0.243485	0.411023	0.740894	0.937903	0.359857	0.870435	0.763588
6	0.450141	0.443776	0.0689358	0.620169	0.310059	0.352924	0.344005	0.954429	0.27418	0.506943
7	0.978055	0.213576	0.94304	0.381025	0.472879	0.974818	0.703878	0.953461	0.921067	0.676587
8	0.00193463	0.76328	0.831439	0.608385	0.103024	0.901616	0.68105	0.748914	0.267894	0.268814
9	0.458591	0.693498	0.174774	0.689581	0.0934355	0.671297	0.058933	0.0900385	0.115305	0.779629
10	0.172119	0.736147	0.571974	0.121839	0.754063	0.840438	0.580782	0.835718	0.494714	0.0780636

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	0.488084	0.546294	0.619543	0.397582	0.420812	0.654291	0.546263	0.586969	0.444218	0.519994

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}:
4880837305347603
5462936532258832
6195428722469729
3975818076667079
42081249608994337
6542910592139737
546262570576392
5869692073307355
44421820059897693
5199939474914792

an iteration returns a Pair with column name and values

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

x1: 0.4880837305347603
x2: 0.5462936532258832
x3: 0.6195428722469729
x4: 0.3975818076667079
x5: 0.42081249608994337
x6: 0.6542910592139737
x7: 0.546262570576392
x8: 0.5869692073307355
x9: 0.44421820059897693
x10: 0.5199939474914792

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}:
20476684814042187
7919161167443853
1587060545644823
4871599001661853
38626626380992646
0143419056246494
579421293717041
0025346325040194943
6612724362340907
2338102187801741

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}:
14293288724939057
8716972684308065
7291924954982303
8632214579604041
21295310607107132
4501409310895359
9780545986593611
0019346332871597038
4585911654757868
1721187616258557

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	0.142933	0.698027	0.800833	0.239132	0.178874	0.0204503	0.831044	0.394313	0.155643	0.821355
2	0.871697	0.486461	0.733391	0.98954	0.788407	0.939166	0.264716	0.940042	0.441365	0.546164
3	0.729192	0.629316	0.450316	0.0734478	0.173671	0.900968	0.7131	0.366368	0.431975	0.0348618
4	0.863221	0.247543	0.733563	0.00921637	0.92269	0.200339	0.347216	0.226552	0.469603	0.723935
5	0.212953	0.551312	0.887163	0.243485	0.411023	0.740894	0.937903	0.359857	0.870435	0.763588
6	0.450141	0.443776	0.0689358	0.620169	0.310059	0.352924	0.344005	0.954429	0.27418	0.506943
7	0.978055	0.213576	0.94304	0.381025	0.472879	0.974818	0.703878	0.953461	0.921067	0.676587
8	0.00193463	0.76328	0.831439	0.608385	0.103024	0.901616	0.68105	0.748914	0.267894	0.268814
9	0.458591	0.693498	0.174774	0.689581	0.0934355	0.671297	0.058933	0.0900385	0.115305	0.779629
10	0.172119	0.736147	0.571974	0.121839	0.754063	0.840438	0.580782	0.835718	0.494714	0.0780636

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
14293288724939057
8716972684308065
7291924954982303
8632214579604041
21295310607107132
4501409310895359
9780545986593611
0019346332871597038
4585911654757868
1721187616258557

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#