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	0.173134
2	2	2	0.0333481
3	3	1	0.180963
4	4	2	0.113274
5	1	1	0.836035
6	2	2	0.0155187
7	3	1	0.340925
8	4	2	0.468375

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	0.173134
2	1	1	0.836035

⋮

Last Group (2 rows): id = 4

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.113274
2	4	2	0.468375

groupby(x, [])

GroupedDataFrame with 1 group based on key:

First Group (8 rows):

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.173134
2	2	2	0.0333481
3	3	1	0.180963
4	4	2	0.113274
5	1	1	0.836035
6	2	2	0.0155187
7	3	1	0.340925
8	4	2	0.468375

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	0.173134
2	1	1	0.836035

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.113274
2	4	2	0.468375

get the parent DataFrame

parent(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.173134
2	2	2	0.0333481
3	3	1	0.180963
4	4	2	0.113274
5	1	1	0.836035
6	2	2	0.0155187
7	3	1	0.340925
8	4	2	0.468375

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	0.173134
2	1	1	0.836035
3	2	2	0.0333481
4	2	2	0.0155187
5	3	1	0.180963
6	3	1	0.340925
7	4	2	0.113274
8	4	2	0.468375

the same as above

DataFrame(gx2)

8×3 DataFrame

Row	id	id2	v
	Int64	Int64	Float64
1	1	1	0.173134
2	1	1	0.836035
3	2	2	0.0333481
4	2	2	0.0155187
5	3	1	0.180963
6	3	1	0.340925
7	4	2	0.113274
8	4	2	0.468375

drop grouping columns when creating a data frame

DataFrame(gx2, keepkeys=false)

8×1 DataFrame

Row	v
	Float64
1	0.173134
2	0.836035
3	0.0333481
4	0.0155187
5	0.180963
6	0.340925
7	0.113274
8	0.468375

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	0.173134
2	1	1	0.836035

⋮

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

Row	id	id2	v
	Int64	Int64	Float64
1	4	2	0.113274
2	4	2	0.468375

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  0.173134
   2 │     1      1  0.836035, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.173134
   2 │     1      1  0.836035, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.173134
   2 │     1      1  0.836035, 2×3 SubDataFrame
 Row │ id     id2    v        
     │ Int64  Int64  Float64  
─────┼────────────────────────
   1 │     1      1  0.173134
   2 │     1      1  0.836035)

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	0.59953
2	d	0.963186
3	b	0.912677
4	c	0.46845
5	b	0.640382
6	b	0.340461
7	c	0.716181
8	b	0.748398
9	c	0.0640978
10	b	0.706182
11	b	0.161233
12	c	0.261802
13	c	0.15591
⋮	⋮	⋮
89	b	0.467717
90	a	0.128613
91	d	0.401218
92	a	0.0066471
93	a	0.609879
94	b	0.200713
95	c	0.666582
96	a	0.770652
97	a	0.297559
98	c	0.797879
99	b	0.350133
100	b	0.229405

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	0.479639
2	b	0.502398
3	c	0.548179
4	a	0.426175

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	0.59953	1	0.479639
2	d	0.963186	2	0.479639
3	b	0.912677	3	0.502398
4	c	0.46845	4	0.548179
5	b	0.640382	5	0.502398
6	b	0.340461	6	0.502398
7	c	0.716181	7	0.548179
8	b	0.748398	8	0.502398
9	c	0.0640978	9	0.548179
10	b	0.706182	10	0.502398
11	b	0.161233	11	0.502398
12	c	0.261802	12	0.548179
13	c	0.15591	13	0.548179
⋮	⋮	⋮	⋮	⋮
89	b	0.467717	89	0.502398
90	a	0.128613	90	0.426175
91	d	0.401218	91	0.479639
92	a	0.0066471	92	0.426175
93	a	0.609879	93	0.426175
94	b	0.200713	94	0.502398
95	c	0.666582	95	0.548179
96	a	0.770652	96	0.426175
97	a	0.297559	97	0.426175
98	c	0.797879	98	0.548179
99	b	0.350133	99	0.502398
100	b	0.229405	100	0.502398

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	0.479639
2	d	2	0.479639
3	d	15	0.479639
4	d	20	0.479639
5	d	30	0.479639
6	d	31	0.479639
7	d	32	0.479639
8	d	35	0.479639
9	d	43	0.479639
10	d	48	0.479639
11	d	49	0.479639
12	d	56	0.479639
13	d	58	0.479639
⋮	⋮	⋮	⋮
89	a	62	0.426175
90	a	65	0.426175
91	a	67	0.426175
92	a	71	0.426175
93	a	72	0.426175
94	a	75	0.426175
95	a	79	0.426175
96	a	90	0.426175
97	a	92	0.426175
98	a	93	0.426175
99	a	96	0.426175
100	a	97	0.426175

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	0.479639
2	b	0.502398
3	c	0.548179
4	a	0.426175

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	0.479639	11.0317	23
2	b	0.502398	16.5791	33
3	c	0.548179	13.1563	24
4	a	0.426175	8.5235	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

1×2 DataFrame

Row	id	n
	Char	Int64
1	b	33

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	0.406316	0.983192	0.384771	0.92523	0.939731	0.955698	0.366889	0.623042	0.19006	0.846325
2	0.461318	0.630919	0.137374	0.831208	0.250198	0.264345	0.518362	0.262207	0.122696	0.307633
3	0.311336	0.261178	0.991151	0.972152	0.509253	0.66056	0.907303	0.51472	0.412227	0.256053
4	0.419724	0.903528	0.297772	0.515809	0.0105733	0.000237585	0.946771	0.463093	0.976676	0.978568
5	0.643806	0.152396	0.636482	0.650665	0.40173	0.515477	0.301423	0.316934	0.0873756	0.139309
6	0.605538	0.253532	0.253467	0.35848	0.214893	0.268126	0.299708	0.83004	0.69916	0.711818
7	0.503239	0.500504	0.914121	0.697106	0.801714	0.876675	0.190561	0.45116	0.374046	0.0171316
8	0.699878	0.246049	0.912662	0.219399	0.862877	0.539972	0.554607	0.821112	0.253741	0.277274
9	0.60524	0.828592	0.623395	0.740417	0.296086	0.0719441	0.181372	0.328845	0.697748	0.983598
10	0.404152	0.118663	0.918418	0.659753	0.242187	0.850887	0.737558	0.0118075	0.136959	0.544532

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	0.506055	0.487855	0.606961	0.657022	0.452924	0.500392	0.500455	0.462296	0.395069	0.506224

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}:
5060547920796004
48785535814481973
6069613476270621
6570220327155232
45292425664895275
5003921008238743
5004553926468702
4622961689025299
39506884836796574
5062243237787427

an iteration returns a Pair with column name and values

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

x1: 0.5060547920796004
x2: 0.48785535814481973
x3: 0.6069613476270621
x4: 0.6570220327155232
x5: 0.45292425664895275
x6: 0.5003921008238743
x7: 0.5004553926468702
x8: 0.4622961689025299
x9: 0.39506884836796574
x10: 0.5062243237787427

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}:
4132624389343559
7311832529054909
1920438782095928
46453955353950066
224551393630487
388414434655677
0054654848736022
8444695221189074
730443081489957
4058671583882494

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}:
4063163827820543
4613176613354725
31133551371983115
4197243987932313
6438063265650948
6055384860738079
5032393113313084
6998783593017472
6052395617941637
40415191909929205

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	0.406316	0.983192	0.384771	0.92523	0.939731	0.955698	0.366889	0.623042	0.19006	0.846325
2	0.461318	0.630919	0.137374	0.831208	0.250198	0.264345	0.518362	0.262207	0.122696	0.307633
3	0.311336	0.261178	0.991151	0.972152	0.509253	0.66056	0.907303	0.51472	0.412227	0.256053
4	0.419724	0.903528	0.297772	0.515809	0.0105733	0.000237585	0.946771	0.463093	0.976676	0.978568
5	0.643806	0.152396	0.636482	0.650665	0.40173	0.515477	0.301423	0.316934	0.0873756	0.139309
6	0.605538	0.253532	0.253467	0.35848	0.214893	0.268126	0.299708	0.83004	0.69916	0.711818
7	0.503239	0.500504	0.914121	0.697106	0.801714	0.876675	0.190561	0.45116	0.374046	0.0171316
8	0.699878	0.246049	0.912662	0.219399	0.862877	0.539972	0.554607	0.821112	0.253741	0.277274
9	0.60524	0.828592	0.623395	0.740417	0.296086	0.0719441	0.181372	0.328845	0.697748	0.983598
10	0.404152	0.118663	0.918418	0.659753	0.242187	0.850887	0.737558	0.0118075	0.136959	0.544532

you can access columns of a parent data frame directly

ec.x1

10-element Vector{Float64}:
4063163827820543
4613176613354725
31133551371983115
4197243987932313
6438063265650948
6055384860738079
5032393113313084
6998783593017472
6052395617941637
40415191909929205

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#