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	0.660288
2	2	2	0.00519032
3	3	1	0.0942121
4	4	2	0.458129
5	1	1	0.26202
6	2	2	0.849367
7	3	1	0.522893
8	4	2	0.24494

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	0.660288
2	1	1	0.26202

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.458129
2	4	2	0.24494

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.660288
2	2	2	0.00519032
3	3	1	0.0942121
4	4	2	0.458129
5	1	1	0.26202
6	2	2	0.849367
7	3	1	0.522893
8	4	2	0.24494

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	0.660288
2	1	1	0.26202

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.458129
2	4	2	0.24494

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.660288
2	2	2	0.00519032
3	3	1	0.0942121
4	4	2	0.458129
5	1	1	0.26202
6	2	2	0.849367
7	3	1	0.522893
8	4	2	0.24494

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	0.660288
2	1	1	0.26202
3	2	2	0.00519032
4	2	2	0.849367
5	3	1	0.0942121
6	3	1	0.522893
7	4	2	0.458129
8	4	2	0.24494

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.660288
2	1	1	0.26202
3	2	2	0.00519032
4	2	2	0.849367
5	3	1	0.0942121
6	3	1	0.522893
7	4	2	0.458129
8	4	2	0.24494

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.660288
2	0.26202
3	0.00519032
4	0.849367
5	0.0942121
6	0.522893
7	0.458129
8	0.24494

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	0.660288
2	1	1	0.26202

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.458129
2	4	2	0.24494

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  0.660288
   2 │     1      1  0.26202, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.660288
   2 │     1      1  0.26202, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.660288
   2 │     1      1  0.26202, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.660288
   2 │     1      1  0.26202)

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	d	0.248966
2	b	0.677136
3	d	0.0390287
4	d	0.338539
5	a	0.914206
6	c	0.119065
7	b	0.572222
8	d	0.403218
9	c	0.907131
10	d	0.419591
11	a	0.438969
12	b	0.0641178
13	b	0.983015
⋮	⋮	⋮
89	a	0.0495181
90	d	0.106207
91	a	0.00476801
92	c	0.985394
93	d	0.122091
94	d	0.867029
95	a	0.616144
96	d	0.99123
97	b	0.13803
98	b	0.390887
99	b	0.372689
100	b	0.927418

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	d	0.461606
2	b	0.498758
3	a	0.529388
4	c	0.546276

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	d	0.248966	1	0.461606
2	b	0.677136	2	0.498758
3	d	0.0390287	3	0.461606
4	d	0.338539	4	0.461606
5	a	0.914206	5	0.529388
6	c	0.119065	6	0.546276
7	b	0.572222	7	0.498758
8	d	0.403218	8	0.461606
9	c	0.907131	9	0.546276
10	d	0.419591	10	0.461606
11	a	0.438969	11	0.529388
12	b	0.0641178	12	0.498758
13	b	0.983015	13	0.498758
⋮	⋮	⋮	⋮	⋮
89	a	0.0495181	89	0.529388
90	d	0.106207	90	0.461606
91	a	0.00476801	91	0.529388
92	c	0.985394	92	0.546276
93	d	0.122091	93	0.461606
94	d	0.867029	94	0.461606
95	a	0.616144	95	0.529388
96	d	0.99123	96	0.461606
97	b	0.13803	97	0.498758
98	b	0.390887	98	0.498758
99	b	0.372689	99	0.498758
100	b	0.927418	100	0.498758

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	d	1	0.461606
2	d	3	0.461606
3	d	4	0.461606
4	d	8	0.461606
5	d	10	0.461606
6	d	16	0.461606
7	d	22	0.461606
8	d	24	0.461606
9	d	32	0.461606
10	d	34	0.461606
11	d	37	0.461606
12	d	38	0.461606
13	d	40	0.461606
⋮	⋮	⋮	⋮
89	c	47	0.546276
90	c	48	0.546276
91	c	53	0.546276
92	c	54	0.546276
93	c	65	0.546276
94	c	71	0.546276
95	c	73	0.546276
96	c	74	0.546276
97	c	78	0.546276
98	c	86	0.546276
99	c	87	0.546276
100	c	92	0.546276

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	d	0.461606
2	b	0.498758
3	a	0.529388
4	c	0.546276

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	d	0.461606	13.3866	29
2	b	0.498758	12.4689	25
3	a	0.529388	10.5878	20
4	c	0.546276	14.2032	26

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	d	29
2	b	25
3	c	26

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	0.857372	0.0604398	0.325589	0.338694	0.766581	0.794892	0.32041	0.0697253	0.944804	0.845565
2	0.66909	0.588814	0.0183379	0.80083	0.496673	0.0624174	0.48674	0.498084	0.173867	0.604959
3	0.6192	0.269699	0.0714028	0.0842104	0.0646443	0.875336	0.141855	0.236986	0.313379	0.0745637
4	0.851508	0.0858474	0.840433	0.19462	0.878192	0.00313476	0.203818	0.71006	0.963675	0.384315
5	0.865508	0.186952	0.260236	0.730858	0.92906	0.864217	0.997037	0.947447	0.739623	0.413455
6	0.00744464	0.803786	0.194378	0.794546	0.0273659	0.310378	0.622336	0.649783	0.532184	0.873814
7	0.12596	0.814005	0.0930538	0.185945	0.170085	0.214734	0.133885	0.597884	0.186861	0.893142
8	0.780113	0.766513	0.397515	0.432241	0.431676	0.519152	0.652428	0.0432491	0.237644	0.796689
9	0.338559	0.800977	0.790461	0.94481	0.163392	0.160652	0.482806	0.798111	0.583562	0.523887
10	0.188492	0.729708	0.109974	0.566246	0.260343	0.357419	0.282843	0.168503	0.905404	0.748549

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	0.530325	0.510674	0.310138	0.5073	0.418801	0.416233	0.432416	0.471983	0.5581	0.615894

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}:
5303245428009665
5106740318449894
31013809099463696
5073001344079734
4188011876439576
4162331753066006
43241578776594264
47198321172017604
5581001809827705
6158937739356688

an iteration returns a Pair with column name and values

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

x1: 0.5303245428009665
x2: 0.5106740318449894
x3: 0.31013809099463696
x4: 0.5073001344079734
x5: 0.4188011876439576
x6: 0.4162331753066006
x7: 0.43241578776594264
x8: 0.47198321172017604
x9: 0.5581001809827705
x10: 0.6158937739356688

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}:
185545224261652
1363353510787026
295892834255423
918850731427545
629583189916868
009261975035659207
1547412524365301
0177421879049602
4226822659171988
2583112239652292

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}:
857371938863679
6690899418911187
6191999149726781
8515080259405692
8655075819602075
007444641674264618
12596020343974845
7801126668947531
33855880913447767
18849170323816855

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	0.857372	0.0604398	0.325589	0.338694	0.766581	0.794892	0.32041	0.0697253	0.944804	0.845565
2	0.66909	0.588814	0.0183379	0.80083	0.496673	0.0624174	0.48674	0.498084	0.173867	0.604959
3	0.6192	0.269699	0.0714028	0.0842104	0.0646443	0.875336	0.141855	0.236986	0.313379	0.0745637
4	0.851508	0.0858474	0.840433	0.19462	0.878192	0.00313476	0.203818	0.71006	0.963675	0.384315
5	0.865508	0.186952	0.260236	0.730858	0.92906	0.864217	0.997037	0.947447	0.739623	0.413455
6	0.00744464	0.803786	0.194378	0.794546	0.0273659	0.310378	0.622336	0.649783	0.532184	0.873814
7	0.12596	0.814005	0.0930538	0.185945	0.170085	0.214734	0.133885	0.597884	0.186861	0.893142
8	0.780113	0.766513	0.397515	0.432241	0.431676	0.519152	0.652428	0.0432491	0.237644	0.796689
9	0.338559	0.800977	0.790461	0.94481	0.163392	0.160652	0.482806	0.798111	0.583562	0.523887
10	0.188492	0.729708	0.109974	0.566246	0.260343	0.357419	0.282843	0.168503	0.905404	0.748549

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
857371938863679
6690899418911187
6191999149726781
8515080259405692
8655075819602075
007444641674264618
12596020343974845
7801126668947531
33855880913447767
18849170323816855

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#