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	0.756392
2	2	2	0.405107
3	3	1	0.855122
4	4	2	0.989225
5	1	1	0.117164
6	2	2	0.3996
7	3	1	0.32268
8	4	2	0.317763

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	0.756392
2	1	1	0.117164

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.989225
2	4	2	0.317763

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.756392
2	2	2	0.405107
3	3	1	0.855122
4	4	2	0.989225
5	1	1	0.117164
6	2	2	0.3996
7	3	1	0.32268
8	4	2	0.317763

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	0.756392
2	1	1	0.117164

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.989225
2	4	2	0.317763

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.756392
2	2	2	0.405107
3	3	1	0.855122
4	4	2	0.989225
5	1	1	0.117164
6	2	2	0.3996
7	3	1	0.32268
8	4	2	0.317763

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	0.756392
2	1	1	0.117164
3	2	2	0.405107
4	2	2	0.3996
5	3	1	0.855122
6	3	1	0.32268
7	4	2	0.989225
8	4	2	0.317763

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.756392
2	1	1	0.117164
3	2	2	0.405107
4	2	2	0.3996
5	3	1	0.855122
6	3	1	0.32268
7	4	2	0.989225
8	4	2	0.317763

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.756392
2	0.117164
3	0.405107
4	0.3996
5	0.855122
6	0.32268
7	0.989225
8	0.317763

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	0.756392
2	1	1	0.117164

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.989225
2	4	2	0.317763

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  0.756392
   2 │     1      1  0.117164, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.756392
   2 │     1      1  0.117164, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.756392
   2 │     1      1  0.117164, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.756392
   2 │     1      1  0.117164)

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	0.164321
2	a	0.928849
3	c	0.177093
4	c	0.465703
5	d	0.270039
6	c	0.0326164
7	a	0.914675
8	c	0.420337
9	d	0.746542
10	c	0.609532
11	c	0.389072
12	b	0.80542
13	a	0.588681
⋮	⋮	⋮
89	d	0.99002
90	c	0.855962
91	c	0.425813
92	a	0.412515
93	b	0.158071
94	c	0.183782
95	c	0.347485
96	c	0.812393
97	d	0.574843
98	a	0.1684
99	b	0.118532
100	d	0.165367

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	0.443746
2	a	0.568155
3	d	0.515793
4	b	0.558574

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	0.164321	1	0.443746
2	a	0.928849	2	0.568155
3	c	0.177093	3	0.443746
4	c	0.465703	4	0.443746
5	d	0.270039	5	0.515793
6	c	0.0326164	6	0.443746
7	a	0.914675	7	0.568155
8	c	0.420337	8	0.443746
9	d	0.746542	9	0.515793
10	c	0.609532	10	0.443746
11	c	0.389072	11	0.443746
12	b	0.80542	12	0.558574
13	a	0.588681	13	0.568155
⋮	⋮	⋮	⋮	⋮
89	d	0.99002	89	0.515793
90	c	0.855962	90	0.443746
91	c	0.425813	91	0.443746
92	a	0.412515	92	0.568155
93	b	0.158071	93	0.558574
94	c	0.183782	94	0.443746
95	c	0.347485	95	0.443746
96	c	0.812393	96	0.443746
97	d	0.574843	97	0.515793
98	a	0.1684	98	0.568155
99	b	0.118532	99	0.558574
100	d	0.165367	100	0.515793

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	0.443746
2	c	3	0.443746
3	c	4	0.443746
4	c	6	0.443746
5	c	8	0.443746
6	c	10	0.443746
7	c	11	0.443746
8	c	19	0.443746
9	c	26	0.443746
10	c	30	0.443746
11	c	31	0.443746
12	c	34	0.443746
13	c	37	0.443746
⋮	⋮	⋮	⋮
89	b	14	0.558574
90	b	21	0.558574
91	b	27	0.558574
92	b	33	0.558574
93	b	36	0.558574
94	b	40	0.558574
95	b	66	0.558574
96	b	68	0.558574
97	b	72	0.558574
98	b	76	0.558574
99	b	93	0.558574
100	b	99	0.558574

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	0.443746
2	a	0.568155
3	d	0.515793
4	b	0.558574

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	0.443746	16.4186	37
2	a	0.568155	16.4765	29
3	d	0.515793	10.8316	21
4	b	0.558574	7.26146	13

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	37
2	a	29

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	0.428413	0.288273	0.380249	0.0962524	0.87573	0.892209	0.341893	0.967807	0.0425122	0.398241
2	0.641005	0.828817	0.292158	0.648656	0.561629	0.176464	0.680028	0.472814	0.0623693	0.355808
3	0.246123	0.535485	0.501676	0.767878	0.158613	0.218819	0.517465	0.55468	0.848898	0.508709
4	0.481773	0.883589	0.484177	0.976891	0.953131	0.131042	0.34662	0.42991	0.989193	0.0319736
5	0.72787	0.734414	0.277308	0.65552	0.297051	0.184805	0.81433	0.357484	0.240209	0.132601
6	0.246995	0.498433	0.158371	0.516696	0.735091	0.629004	0.812174	0.514785	0.0429962	0.0967131
7	0.795772	0.889987	0.913867	0.402521	0.625666	0.279997	0.337937	0.79828	0.0840332	0.227062
8	0.533317	0.437208	0.361738	0.610043	0.885454	0.842465	0.22449	0.570867	0.674115	0.695531
9	0.761087	0.04645	0.717471	0.849107	0.999313	0.201569	0.344265	0.883335	0.994071	0.364646
10	0.69455	0.0322291	0.0858867	0.161196	0.475494	0.608615	0.894533	0.895358	0.579155	0.0914591

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	0.55569	0.517489	0.41729	0.568476	0.656717	0.416499	0.531373	0.644532	0.455755	0.290274

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}:
5556904112031291
5174885931860757
4172901102513499
5684760119865997
6567171643899633
41649895612771
5313734330242246
6445319618589554
4557552139097991
29027441029936235

an iteration returns a Pair with column name and values

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

x1: 0.5556904112031291
x2: 0.5174885931860757
x3: 0.4172901102513499
x4: 0.5684760119865997
x5: 0.6567171643899633
x6: 0.41649895612771
x7: 0.5313734330242246
x8: 0.6445319618589554
x9: 0.4557552139097991
x10: 0.29027441029936235

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}:
4861381928287993
7733965185752516
45962608488578244
5452456518417225
9910894748966226
49554198049022985
8941381695084383
2198258072343713
385072165899587
550374040782003

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}:
42841287488654944
6410045042984526
24612297675874162
48177314942789873
7278698016799588
24699461528955446
7957717820207632
533317366688105
7610873308098037
6945497101714644

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	0.428413	0.288273	0.380249	0.0962524	0.87573	0.892209	0.341893	0.967807	0.0425122	0.398241
2	0.641005	0.828817	0.292158	0.648656	0.561629	0.176464	0.680028	0.472814	0.0623693	0.355808
3	0.246123	0.535485	0.501676	0.767878	0.158613	0.218819	0.517465	0.55468	0.848898	0.508709
4	0.481773	0.883589	0.484177	0.976891	0.953131	0.131042	0.34662	0.42991	0.989193	0.0319736
5	0.72787	0.734414	0.277308	0.65552	0.297051	0.184805	0.81433	0.357484	0.240209	0.132601
6	0.246995	0.498433	0.158371	0.516696	0.735091	0.629004	0.812174	0.514785	0.0429962	0.0967131
7	0.795772	0.889987	0.913867	0.402521	0.625666	0.279997	0.337937	0.79828	0.0840332	0.227062
8	0.533317	0.437208	0.361738	0.610043	0.885454	0.842465	0.22449	0.570867	0.674115	0.695531
9	0.761087	0.04645	0.717471	0.849107	0.999313	0.201569	0.344265	0.883335	0.994071	0.364646
10	0.69455	0.0322291	0.0858867	0.161196	0.475494	0.608615	0.894533	0.895358	0.579155	0.0914591

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
42841287488654944
6410045042984526
24612297675874162
48177314942789873
7278698016799588
24699461528955446
7957717820207632
533317366688105
7610873308098037
6945497101714644

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#