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	0.973202
2	2	2	0.046608
3	3	1	0.252007
4	4	2	0.628115
5	1	1	0.110051
6	2	2	0.544258
7	3	1	0.891864
8	4	2	0.24464

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	0.973202
2	1	1	0.110051

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.628115
2	4	2	0.24464

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.973202
2	2	2	0.046608
3	3	1	0.252007
4	4	2	0.628115
5	1	1	0.110051
6	2	2	0.544258
7	3	1	0.891864
8	4	2	0.24464

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	0.973202
2	1	1	0.110051

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.628115
2	4	2	0.24464

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.973202
2	2	2	0.046608
3	3	1	0.252007
4	4	2	0.628115
5	1	1	0.110051
6	2	2	0.544258
7	3	1	0.891864
8	4	2	0.24464

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	0.973202
2	1	1	0.110051
3	2	2	0.046608
4	2	2	0.544258
5	3	1	0.252007
6	3	1	0.891864
7	4	2	0.628115
8	4	2	0.24464

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.973202
2	1	1	0.110051
3	2	2	0.046608
4	2	2	0.544258
5	3	1	0.252007
6	3	1	0.891864
7	4	2	0.628115
8	4	2	0.24464

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.973202
2	0.110051
3	0.046608
4	0.544258
5	0.252007
6	0.891864
7	0.628115
8	0.24464

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	0.973202
2	1	1	0.110051

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.628115
2	4	2	0.24464

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  0.973202
   2 │     1      1  0.110051, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.973202
   2 │     1      1  0.110051, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.973202
   2 │     1      1  0.110051, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.973202
   2 │     1      1  0.110051)

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	c	0.847674
2	a	0.894818
3	d	0.133443
4	d	0.744233
5	d	0.963738
6	b	0.74572
7	c	0.861151
8	c	0.699599
9	a	0.776682
10	a	0.383058
11	c	0.368769
12	d	0.712692
13	b	0.0873433
⋮	⋮	⋮
89	d	0.326667
90	a	0.92526
91	b	0.5512
92	d	0.538515
93	a	0.543957
94	c	0.404102
95	c	0.787083
96	d	0.931296
97	a	0.298847
98	b	0.445243
99	c	0.506366
100	d	0.264476

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	c	0.493781
2	a	0.507957
3	d	0.507968
4	b	0.443487

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	c	0.847674	1	0.493781
2	a	0.894818	2	0.507957
3	d	0.133443	3	0.507968
4	d	0.744233	4	0.507968
5	d	0.963738	5	0.507968
6	b	0.74572	6	0.443487
7	c	0.861151	7	0.493781
8	c	0.699599	8	0.493781
9	a	0.776682	9	0.507957
10	a	0.383058	10	0.507957
11	c	0.368769	11	0.493781
12	d	0.712692	12	0.507968
13	b	0.0873433	13	0.443487
⋮	⋮	⋮	⋮	⋮
89	d	0.326667	89	0.507968
90	a	0.92526	90	0.507957
91	b	0.5512	91	0.443487
92	d	0.538515	92	0.507968
93	a	0.543957	93	0.507957
94	c	0.404102	94	0.493781
95	c	0.787083	95	0.493781
96	d	0.931296	96	0.507968
97	a	0.298847	97	0.507957
98	b	0.445243	98	0.443487
99	c	0.506366	99	0.493781
100	d	0.264476	100	0.507968

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	c	1	0.493781
2	c	7	0.493781
3	c	8	0.493781
4	c	11	0.493781
5	c	14	0.493781
6	c	19	0.493781
7	c	24	0.493781
8	c	26	0.493781
9	c	27	0.493781
10	c	30	0.493781
11	c	37	0.493781
12	c	39	0.493781
13	c	47	0.493781
⋮	⋮	⋮	⋮
89	b	46	0.443487
90	b	65	0.443487
91	b	66	0.443487
92	b	69	0.443487
93	b	72	0.443487
94	b	75	0.443487
95	b	77	0.443487
96	b	78	0.443487
97	b	83	0.443487
98	b	85	0.443487
99	b	91	0.443487
100	b	98	0.443487

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	c	0.493781
2	a	0.507957
3	d	0.507968
4	b	0.443487

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	c	0.493781	14.8134	30
2	a	0.507957	12.6989	25
3	d	0.507968	12.6992	25
4	b	0.443487	8.86974	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

3×2 DataFrame

Row	id	n
	Char	Int64
1	c	30
2	a	25
3	d	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	0.263127	0.877198	0.584535	0.899592	0.754093	0.288598	0.23089	0.276577	0.105592	0.976852
2	0.593438	0.113225	0.920428	0.823655	0.18551	0.264829	0.811713	0.150274	0.282963	0.288698
3	0.520668	0.277364	0.504678	0.389945	0.300603	0.568672	0.65554	0.818769	0.177598	0.666094
4	0.248962	0.667065	0.191287	0.394665	0.925936	0.0543978	0.287819	0.765772	0.680041	0.241801
5	0.461436	0.533172	0.364565	0.370468	0.366276	0.207401	0.0467888	0.469072	0.603109	0.983973
6	0.921979	0.0827353	0.917015	0.408304	0.15775	0.811074	0.509842	0.0597666	0.382358	0.307684
7	0.27564	0.944287	0.790453	0.942799	0.0492421	0.958079	0.26433	0.0949598	0.232186	0.87194
8	0.0962007	0.54146	0.370044	0.54388	0.851827	0.00907529	0.592196	0.0633686	0.163662	0.71178
9	0.83328	0.645829	0.625591	0.0473215	0.837015	0.327945	0.963545	0.344426	0.00757466	0.210299
10	0.232741	0.519151	0.180921	0.991015	0.896622	0.0761436	0.930661	0.904419	0.97458	0.930629

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	0.444747	0.520148	0.544952	0.581165	0.532487	0.356622	0.529333	0.39474	0.360966	0.618975

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}:
44474714682317557
5201484905953546
5449515714761632
5811645813409286
5324872656441735
3566215707533047
5293326134324855
39474042721143554
3609663947412353
6189750547489952

an iteration returns a Pair with column name and values

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

x1: 0.44474714682317557
x2: 0.5201484905953546
x3: 0.5449515714761632
x4: 0.5811645813409286
x5: 0.5324872656441735
x6: 0.3566215707533047
x7: 0.5293326134324855
x8: 0.39474042721143554
x9: 0.3609663947412353
x10: 0.6189750547489952

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}:
29996299302778767
24124800610297
877198550350215
37321980323752285
865454240024211
143722651309448
29190334065555323
17766891870799123
2902488983315856
44831027267186824

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}:
26312679794452243
5934383993898801
5206675282484069
2489616895928357
4614360974663768
9219794376761827
2756404170403999
09620066883911571
8332796762631839
23274075577085218

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	0.263127	0.877198	0.584535	0.899592	0.754093	0.288598	0.23089	0.276577	0.105592	0.976852
2	0.593438	0.113225	0.920428	0.823655	0.18551	0.264829	0.811713	0.150274	0.282963	0.288698
3	0.520668	0.277364	0.504678	0.389945	0.300603	0.568672	0.65554	0.818769	0.177598	0.666094
4	0.248962	0.667065	0.191287	0.394665	0.925936	0.0543978	0.287819	0.765772	0.680041	0.241801
5	0.461436	0.533172	0.364565	0.370468	0.366276	0.207401	0.0467888	0.469072	0.603109	0.983973
6	0.921979	0.0827353	0.917015	0.408304	0.15775	0.811074	0.509842	0.0597666	0.382358	0.307684
7	0.27564	0.944287	0.790453	0.942799	0.0492421	0.958079	0.26433	0.0949598	0.232186	0.87194
8	0.0962007	0.54146	0.370044	0.54388	0.851827	0.00907529	0.592196	0.0633686	0.163662	0.71178
9	0.83328	0.645829	0.625591	0.0473215	0.837015	0.327945	0.963545	0.344426	0.00757466	0.210299
10	0.232741	0.519151	0.180921	0.991015	0.896622	0.0761436	0.930661	0.904419	0.97458	0.930629

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
26312679794452243
5934383993898801
5206675282484069
2489616895928357
4614360974663768
9219794376761827
2756404170403999
09620066883911571
8332796762631839
23274075577085218

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#