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	0.452854
2	2	2	0.604998
3	3	1	0.589787
4	4	2	0.224989
5	1	1	0.259274
6	2	2	0.663381
7	3	1	0.637711
8	4	2	0.0547333

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	0.452854
2	1	1	0.259274

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.224989
2	4	2	0.0547333

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.452854
2	2	2	0.604998
3	3	1	0.589787
4	4	2	0.224989
5	1	1	0.259274
6	2	2	0.663381
7	3	1	0.637711
8	4	2	0.0547333

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	0.452854
2	1	1	0.259274

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.224989
2	4	2	0.0547333

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.452854
2	2	2	0.604998
3	3	1	0.589787
4	4	2	0.224989
5	1	1	0.259274
6	2	2	0.663381
7	3	1	0.637711
8	4	2	0.0547333

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	0.452854
2	1	1	0.259274
3	2	2	0.604998
4	2	2	0.663381
5	3	1	0.589787
6	3	1	0.637711
7	4	2	0.224989
8	4	2	0.0547333

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.452854
2	1	1	0.259274
3	2	2	0.604998
4	2	2	0.663381
5	3	1	0.589787
6	3	1	0.637711
7	4	2	0.224989
8	4	2	0.0547333

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.452854
2	0.259274
3	0.604998
4	0.663381
5	0.589787
6	0.637711
7	0.224989
8	0.0547333

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	0.452854
2	1	1	0.259274

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.224989
2	4	2	0.0547333

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  0.452854
   2 │     1      1  0.259274, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.452854
   2 │     1      1  0.259274, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.452854
   2 │     1      1  0.259274, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.452854
   2 │     1      1  0.259274)

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	0.687815
2	a	0.907297
3	d	0.753421
4	c	0.746023
5	d	0.184972
6	d	0.519753
7	d	0.819507
8	c	0.171625
9	b	0.651596
10	c	0.578907
11	b	0.457389
12	a	0.242115
13	b	0.929259
⋮	⋮	⋮
89	c	0.0398338
90	a	0.504607
91	a	0.0691471
92	d	0.0866579
93	b	0.105838
94	d	0.761518
95	d	0.418352
96	c	0.198222
97	c	0.354353
98	c	0.605072
99	c	0.474177
100	d	0.277162

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	0.502598
2	a	0.555364
3	d	0.509115
4	c	0.52345

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	0.687815	1	0.502598
2	a	0.907297	2	0.555364
3	d	0.753421	3	0.509115
4	c	0.746023	4	0.52345
5	d	0.184972	5	0.509115
6	d	0.519753	6	0.509115
7	d	0.819507	7	0.509115
8	c	0.171625	8	0.52345
9	b	0.651596	9	0.502598
10	c	0.578907	10	0.52345
11	b	0.457389	11	0.502598
12	a	0.242115	12	0.555364
13	b	0.929259	13	0.502598
⋮	⋮	⋮	⋮	⋮
89	c	0.0398338	89	0.52345
90	a	0.504607	90	0.555364
91	a	0.0691471	91	0.555364
92	d	0.0866579	92	0.509115
93	b	0.105838	93	0.502598
94	d	0.761518	94	0.509115
95	d	0.418352	95	0.509115
96	c	0.198222	96	0.52345
97	c	0.354353	97	0.52345
98	c	0.605072	98	0.52345
99	c	0.474177	99	0.52345
100	d	0.277162	100	0.509115

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	0.502598
2	b	9	0.502598
3	b	11	0.502598
4	b	13	0.502598
5	b	14	0.502598
6	b	29	0.502598
7	b	31	0.502598
8	b	37	0.502598
9	b	38	0.502598
10	b	39	0.502598
11	b	47	0.502598
12	b	53	0.502598
13	b	58	0.502598
⋮	⋮	⋮	⋮
89	c	65	0.52345
90	c	70	0.52345
91	c	74	0.52345
92	c	76	0.52345
93	c	77	0.52345
94	c	85	0.52345
95	c	88	0.52345
96	c	89	0.52345
97	c	96	0.52345
98	c	97	0.52345
99	c	98	0.52345
100	c	99	0.52345

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	0.502598
2	a	0.555364
3	d	0.509115
4	c	0.52345

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	0.502598	9.54936	19
2	a	0.555364	12.7734	23
3	d	0.509115	14.2552	28
4	c	0.52345	15.7035	30

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	d	28
2	c	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	0.106617	0.291168	0.1674	0.775344	0.943925	0.166796	0.490957	0.53846	0.463252	0.741993
2	0.662203	0.130301	0.992243	0.138432	0.281077	0.438627	0.757478	0.979952	0.508883	0.573489
3	0.136419	0.835384	0.251823	0.318747	0.956128	0.438975	0.439469	0.0138696	0.191399	0.216512
4	0.575429	0.74441	0.798861	0.60023	0.74901	0.677474	0.259728	0.437118	0.675392	0.50832
5	0.948811	0.166646	0.201158	0.705186	0.681665	0.89616	0.823825	0.695029	0.3091	0.985866
6	0.560098	0.837752	0.0390727	0.54336	0.187887	0.59732	0.49907	0.515912	0.566915	0.335263
7	0.145522	0.596191	0.0446629	0.765127	0.852365	0.996652	0.351631	0.865093	0.377556	0.121889
8	0.1456	0.245567	0.441424	0.585407	0.572443	0.90318	0.557173	0.561593	0.818471	0.384634
9	0.90192	0.718953	0.0471103	0.0332092	0.152148	0.696649	0.269912	0.379309	0.373111	0.121797
10	0.761204	0.332511	0.845687	0.792485	0.202757	0.131407	0.737986	0.0398569	0.311286	0.502011

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	0.494382	0.489888	0.382944	0.525753	0.55794	0.594324	0.518723	0.502619	0.459537	0.449177

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}:
4943824664189296
48988820236298974
38294400298210396
5257527517021974
5579404589540388
594324121350188
5187228558595499
5026192006128546
45953654364177865
4491773322836904

an iteration returns a Pair with column name and values

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

x1: 0.4943824664189296
x2: 0.48988820236298974
x3: 0.38294400298210396
x4: 0.5257527517021974
x5: 0.5579404589540388
x6: 0.594324121350188
x7: 0.5187228558595499
x8: 0.5026192006128546
x9: 0.45953654364177865
x10: 0.4491773322836904

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}:
36617154763225235
082108318350985
16330072246858027
7730008652075995
693568306175609
6685728829468034
2440863067533868
5929160989190951
2544915085157406
2892576622142773

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}:
10661731577051836
6622031498542199
1364187656650997
5754294556458643
9488110585459724
5600981459786365
14552211973238283
14560044116986715
9019200455048797
7612041663218551

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	0.106617	0.291168	0.1674	0.775344	0.943925	0.166796	0.490957	0.53846	0.463252	0.741993
2	0.662203	0.130301	0.992243	0.138432	0.281077	0.438627	0.757478	0.979952	0.508883	0.573489
3	0.136419	0.835384	0.251823	0.318747	0.956128	0.438975	0.439469	0.0138696	0.191399	0.216512
4	0.575429	0.74441	0.798861	0.60023	0.74901	0.677474	0.259728	0.437118	0.675392	0.50832
5	0.948811	0.166646	0.201158	0.705186	0.681665	0.89616	0.823825	0.695029	0.3091	0.985866
6	0.560098	0.837752	0.0390727	0.54336	0.187887	0.59732	0.49907	0.515912	0.566915	0.335263
7	0.145522	0.596191	0.0446629	0.765127	0.852365	0.996652	0.351631	0.865093	0.377556	0.121889
8	0.1456	0.245567	0.441424	0.585407	0.572443	0.90318	0.557173	0.561593	0.818471	0.384634
9	0.90192	0.718953	0.0471103	0.0332092	0.152148	0.696649	0.269912	0.379309	0.373111	0.121797
10	0.761204	0.332511	0.845687	0.792485	0.202757	0.131407	0.737986	0.0398569	0.311286	0.502011

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
10661731577051836
6622031498542199
1364187656650997
5754294556458643
9488110585459724
5600981459786365
14552211973238283
14560044116986715
9019200455048797
7612041663218551

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#