9  Data Frame

9.1 Create DF

using DataFrames

9.1.1 Using Column Vector

df = DataFrame(
    ID = 1:4, 
    Subject = repeat(["M", "F"], outer=2),
    Is_OK = true
    )

4 rows × 3 columns

	ID	Subject	Is_OK
	Int64	String	Bool
1	1	M	1
2	2	F	1
3	3	M	1
4	4	F	1

typeof(df)

DataFrame

names(df)

3-element Vector{String}:
 "ID"
 "Subject"
 "Is_OK"

9.1.2 Create from Dict

dict = Dict(:first_name => ["Rohit", "Rahul", "Akshat"],
            :customer_age => [15, 20, 25]
            )
df2 = DataFrame(dict)

3 rows × 2 columns

	customer_age	first_name
	Int64	String
1	15	Rohit
2	20	Rahul
3	25	Akshat

Convert df2 back to Dict (Ref)

pairs(eachcol(df2)) |> Dict

Dict{Symbol, AbstractVector} with 2 entries:
  :customer_age => [15, 20, 25]
  :first_name   => ["Rohit", "Rahul", "Akshat"]

pairs(df2.first_name) |> Dict

Dict{Int64, String} with 3 entries:
  2 => "Rahul"
  3 => "Akshat"
  1 => "Rohit"

Pair.(df2.first_name, df2.customer_age) |> Dict

Dict{String, Int64} with 3 entries:
  "Akshat" => 25
  "Rahul"  => 20
  "Rohit"  => 15

9.2 Subsetting

9.2.1 By Column

df.ID

4-element Vector{Int64}:
 1
 2
 3
 4

Return a copy of the column by:

df[:, :ID]

4-element Vector{Int64}:
 1
 2
 3
 4

This will modify df in-place:

df[!, :ID][1] = 5

5

df

4 rows × 3 columns

	ID	Subject	Is_OK
	Int64	String	Bool
1	5	M	1
2	2	F	1
3	3	M	1
4	4	F	1