Julia でやる方法は幾通りかあるが，Query を使う方法を示しておこう。ただし，Query が対応していないものについては，別法で。

using DataFrames, CSV
df = CSV.read("df.csv", DataFrame);
df_long = CSV.read("df_long.csv", DataFrame);

df

3×4 DataFrame

Row	date	USDJPY	EURJPY	SPX
	Date	Float64	Float64	Int64
1	2023-01-01	130.0	140.0	3800
2	2023-01-02	131.0	141.0	3900
3	2023-01-03	130.5	143.5	4000

df_long

9×3 DataFrame

Row	date	index	value
	Date	String7	Float64
1	2023-01-01	USDJPY	130.0
2	2023-01-01	EURJPY	140.0
3	2023-01-01	SPX	3800.0
4	2023-01-02	USDJPY	131.0
5	2023-01-02	EURJPY	141.0
6	2023-01-02	SPX	3900.0
7	2023-01-03	USDJPY	130.5
8	2023-01-03	EURJPY	143.5
9	2023-01-03	SPX	4000.0

using Query

# 列の選択
df |>
    @select(:date, :USDJPY) |>
    DataFrame

3×2 DataFrame

# 列の変更・追加
df |>
    @mutate(USDJPY = 2_.USDJPY) |>
    DataFrame

3×4 DataFrame

Row	date	USDJPY	EURJPY	SPX
	Date	Float64	Float64	Int64
1	2023-01-01	260.0	140.0	3800
2	2023-01-02	262.0	141.0	3900
3	2023-01-03	261.0	143.5	4000

df |>
    @mutate(EURUSD = _.EURJPY / _.USDJPY) |>
    DataFrame

3×5 DataFrame

Row	date	USDJPY	EURJPY	SPX	EURUSD
	Date	Float64	Float64	Int64	Float64
1	2023-01-01	130.0	140.0	3800	1.07692
2	2023-01-02	131.0	141.0	3900	1.07634
3	2023-01-03	130.5	143.5	4000	1.09962

# 複数追加する場合は @mutate を繰り返す
df |>
    @mutate(EURUSD = _.EURJPY / _.USDJPY) |>
    @mutate(foo = 100_.EURUSD) |>
    DataFrame

3×6 DataFrame

Row	date	USDJPY	EURJPY	SPX	EURUSD	foo
	Date	Float64	Float64	Int64	Float64	Float64
1	2023-01-01	130.0	140.0	3800	1.07692	107.692
2	2023-01-02	131.0	141.0	3900	1.07634	107.634
3	2023-01-03	130.5	143.5	4000	1.09962	109.962

# 行の選択
df |>
    @filter(_.date == Date("2023-01-02")) |>
    DataFrame

1×4 DataFrame

Row	date	USDJPY	EURJPY	SPX
	Date	Float64	Float64	Int64
1	2023-01-02	131.0	141.0	3900

# ソート
df |>
    @orderby(_.date) |>
    DataFrame

3×4 DataFrame

Row	date	USDJPY	EURJPY	SPX
	Date	Float64	Float64	Int64
1	2023-01-01	130.0	140.0	3800
2	2023-01-02	131.0	141.0	3900
3	2023-01-03	130.5	143.5	4000

df |>
    @orderby_descending(_.date) |>
    DataFrame

3×4 DataFrame

Row	date	USDJPY	EURJPY	SPX
	Date	Float64	Float64	Int64
1	2023-01-03	130.5	143.5	4000
2	2023-01-02	131.0	141.0	3900
3	2023-01-01	130.0	140.0	3800

# 重複行の削除 全列を対象にする
df_long |>
    @unique() |>
    DataFrame

9×3 DataFrame

Row	date	index	value
	Date	String7	Float64
1	2023-01-01	USDJPY	130.0
2	2023-01-01	EURJPY	140.0
3	2023-01-01	SPX	3800.0
4	2023-01-02	USDJPY	131.0
5	2023-01-02	EURJPY	141.0
6	2023-01-02	SPX	3900.0
7	2023-01-03	USDJPY	130.5
8	2023-01-03	EURJPY	143.5
9	2023-01-03	SPX	4000.0

# 重複行の削除 特定の列を対象にする
df_long |>
    @unique(_.date) |>
    DataFrame

3×3 DataFrame

Row	date	index	value
	Date	String7	Float64
1	2023-01-01	USDJPY	130.0
2	2023-01-02	USDJPY	131.0
3	2023-01-03	USDJPY	130.5

# 縦長データに変換
stack(df, [:USDJPY, :EURJPY, :SPX])

9×3 DataFrame

Row	date	variable	value
	Date	String	Float64
1	2023-01-01	USDJPY	130.0
2	2023-01-02	USDJPY	131.0
3	2023-01-03	USDJPY	130.5
4	2023-01-01	EURJPY	140.0
5	2023-01-02	EURJPY	141.0
6	2023-01-03	EURJPY	143.5
7	2023-01-01	SPX	3800.0
8	2023-01-02	SPX	3900.0
9	2023-01-03	SPX	4000.0

# 横長データに変換
unstack(df_long, :index, :value)

3×4 DataFrame

Row	date	USDJPY	EURJPY	SPX
	Date	Float64?	Float64?	Float64?
1	2023-01-01	130.0	140.0	3800.0
2	2023-01-02	131.0	141.0	3900.0
3	2023-01-03	130.5	143.5	4000.0

# グループ化
gd = groupby(df_long, :index);
using StatsBase
combine(gd, :value => mean => :ave, :value => maximum => :max, :value => minimum => :min)

3×4 DataFrame

Row	index	ave	max	min
	String7	Float64	Float64	Float64
1	USDJPY	130.5	131.0	130.0
2	EURJPY	141.5	143.5	140.0
3	SPX	3900.0	4000.0	3800.0