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	0.339102
2	2	2	0.76979
3	3	1	0.743467
4	4	2	0.179302
5	1	1	0.0578305
6	2	2	0.043014
7	3	1	0.126171
8	4	2	0.00631068

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	0.339102
2	1	1	0.0578305

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.179302
2	4	2	0.00631068

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.339102
2	2	2	0.76979
3	3	1	0.743467
4	4	2	0.179302
5	1	1	0.0578305
6	2	2	0.043014
7	3	1	0.126171
8	4	2	0.00631068

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	0.339102
2	1	1	0.0578305

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.179302
2	4	2	0.00631068

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.339102
2	2	2	0.76979
3	3	1	0.743467
4	4	2	0.179302
5	1	1	0.0578305
6	2	2	0.043014
7	3	1	0.126171
8	4	2	0.00631068

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	0.339102
2	1	1	0.0578305
3	2	2	0.76979
4	2	2	0.043014
5	3	1	0.743467
6	3	1	0.126171
7	4	2	0.179302
8	4	2	0.00631068

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.339102
2	1	1	0.0578305
3	2	2	0.76979
4	2	2	0.043014
5	3	1	0.743467
6	3	1	0.126171
7	4	2	0.179302
8	4	2	0.00631068

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.339102
2	0.0578305
3	0.76979
4	0.043014
5	0.743467
6	0.126171
7	0.179302
8	0.00631068

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	0.339102
2	1	1	0.0578305

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.179302
2	4	2	0.00631068

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  0.339102
   2 │     1      1  0.0578305, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.339102
   2 │     1      1  0.0578305, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.339102
   2 │     1      1  0.0578305, 2×3 SubDataFrame
 Row │ id     id2    v         
     │ Int64  Int64  Float64   
─────┼─────────────────────────
   1 │     1      1  0.339102
   2 │     1      1  0.0578305)

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	0.370334
2	b	0.993104
3	d	0.635
4	c	0.616631
5	b	0.35559
6	c	0.124394
7	b	0.511263
8	d	0.874141
9	b	0.933005
10	d	0.588118
11	a	0.12122
12	b	0.822107
13	c	0.58134
⋮	⋮	⋮
89	d	0.54355
90	d	0.113034
91	b	0.817364
92	c	0.0342757
93	b	0.657528
94	a	0.877121
95	b	0.203659
96	b	0.241867
97	c	0.945331
98	a	0.0922752
99	a	0.928773
100	b	0.258577

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	0.424412
2	b	0.492894
3	d	0.47484
4	c	0.517263

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	0.370334	1	0.424412
2	b	0.993104	2	0.492894
3	d	0.635	3	0.47484
4	c	0.616631	4	0.517263
5	b	0.35559	5	0.492894
6	c	0.124394	6	0.517263
7	b	0.511263	7	0.492894
8	d	0.874141	8	0.47484
9	b	0.933005	9	0.492894
10	d	0.588118	10	0.47484
11	a	0.12122	11	0.424412
12	b	0.822107	12	0.492894
13	c	0.58134	13	0.517263
⋮	⋮	⋮	⋮	⋮
89	d	0.54355	89	0.47484
90	d	0.113034	90	0.47484
91	b	0.817364	91	0.492894
92	c	0.0342757	92	0.517263
93	b	0.657528	93	0.492894
94	a	0.877121	94	0.424412
95	b	0.203659	95	0.492894
96	b	0.241867	96	0.492894
97	c	0.945331	97	0.517263
98	a	0.0922752	98	0.424412
99	a	0.928773	99	0.424412
100	b	0.258577	100	0.492894

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	0.424412
2	a	11	0.424412
3	a	17	0.424412
4	a	20	0.424412
5	a	22	0.424412
6	a	25	0.424412
7	a	30	0.424412
8	a	32	0.424412
9	a	41	0.424412
10	a	55	0.424412
11	a	61	0.424412
12	a	66	0.424412
13	a	67	0.424412
⋮	⋮	⋮	⋮
89	c	56	0.517263
90	c	57	0.517263
91	c	59	0.517263
92	c	62	0.517263
93	c	72	0.517263
94	c	74	0.517263
95	c	77	0.517263
96	c	79	0.517263
97	c	83	0.517263
98	c	84	0.517263
99	c	92	0.517263
100	c	97	0.517263

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	0.424412
2	b	0.492894
3	d	0.47484
4	c	0.517263

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	0.424412	8.91264	21
2	b	0.492894	14.7868	30
3	d	0.47484	11.3962	24
4	c	0.517263	12.9316	25

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	b	30
2	c	25

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	0.464199	0.197573	0.471682	0.598597	0.203353	0.39853	0.707139	0.633737	0.906073	0.948284
2	0.412674	0.251407	0.114538	0.146708	0.46244	0.0758774	0.553053	0.0886115	0.914786	0.998204
3	0.173466	0.724795	0.129264	0.0100064	0.79954	0.0753851	0.949451	0.26599	0.866778	0.264724
4	0.453579	0.071489	0.0425121	0.64335	0.264881	0.0722715	0.616745	0.736837	0.804739	0.948438
5	0.11875	0.106227	0.9622	0.504896	0.57059	0.644028	0.372035	0.872128	0.73128	0.510869
6	0.325553	0.13065	0.893474	0.651575	0.67349	0.585531	0.191917	0.502565	0.359287	0.922355
7	0.905637	0.499335	0.303188	0.268708	0.638603	0.118515	0.310534	0.291868	0.0122852	0.637567
8	0.102074	0.622027	0.0834084	0.500928	0.342573	0.492162	0.674333	0.813899	0.0632016	0.396585
9	0.248494	0.0175227	0.0689599	0.145855	0.429778	0.255679	0.162785	0.961914	0.438382	0.110069
10	0.75359	0.340263	0.826692	0.743853	0.30519	0.400359	0.208686	0.400674	0.996291	0.7375

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	0.395802	0.296129	0.389592	0.421448	0.469044	0.311834	0.474668	0.556822	0.60931	0.647459

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}:
3958017176190256
2961289269833874
38959191218230754
4214476688437806
4690437545883105
31183387085376063
4746679779896309
55682222654873
6093101882415054
6474594562251919

an iteration returns a Pair with column name and values

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

x1: 0.3958017176190256
x2: 0.2961289269833874
x3: 0.38959191218230754
x4: 0.4214476688437806
x5: 0.4690437545883105
x6: 0.31183387085376063
x7: 0.4746679779896309
x8: 0.55682222654873
x9: 0.6093101882415054
x10: 0.6474594562251919

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}:
3495068850231715
6414563044865957
23933124317182303
3447452579481505
1178820382382946
4917889463094918
8136875370352246
16409978501458122
181299738280472
214727543588411

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}:
4641990970776172
4126742463842531
1734660417075179
45357920179274114
11874965606061971
3255530246477467
9056369528299589
10207449347088948
24849417821930797
7535902839996041

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	0.464199	0.197573	0.471682	0.598597	0.203353	0.39853	0.707139	0.633737	0.906073	0.948284
2	0.412674	0.251407	0.114538	0.146708	0.46244	0.0758774	0.553053	0.0886115	0.914786	0.998204
3	0.173466	0.724795	0.129264	0.0100064	0.79954	0.0753851	0.949451	0.26599	0.866778	0.264724
4	0.453579	0.071489	0.0425121	0.64335	0.264881	0.0722715	0.616745	0.736837	0.804739	0.948438
5	0.11875	0.106227	0.9622	0.504896	0.57059	0.644028	0.372035	0.872128	0.73128	0.510869
6	0.325553	0.13065	0.893474	0.651575	0.67349	0.585531	0.191917	0.502565	0.359287	0.922355
7	0.905637	0.499335	0.303188	0.268708	0.638603	0.118515	0.310534	0.291868	0.0122852	0.637567
8	0.102074	0.622027	0.0834084	0.500928	0.342573	0.492162	0.674333	0.813899	0.0632016	0.396585
9	0.248494	0.0175227	0.0689599	0.145855	0.429778	0.255679	0.162785	0.961914	0.438382	0.110069
10	0.75359	0.340263	0.826692	0.743853	0.30519	0.400359	0.208686	0.400674	0.996291	0.7375

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
4641990970776172
4126742463842531
1734660417075179
45357920179274114
11874965606061971
3255530246477467
9056369528299589
10207449347088948
24849417821930797
7535902839996041

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#