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dataframe - 如何在 IndexedTable 中添加/编辑值？

问题描述

如何在 IndexedTable 中添加或编辑值？从文档中我了解到 IndexedTable 对象本身是不可变的，但不是基础数据，所以我“理解”为什么这样的东西不起作用，但我不知道如何使用新数据获取新的 IndexedTable：

myTable = ndsparse((
             region      = ["US","US","US","US","EU","EU","EU","EU"],
             product     = ["apple","apple","banana","banana","apple","apple","banana","banana"],
             year        = [2011,2010,2011,2010,2011,2010,2011,2010]
           ),(
             production  = [3.3,3.2,2.3,2.1,2.7,2.8,1.5,1.3],
             consumption = [4.3,7.4,2.5,9.8,3.2,4.3,6.5,3.0]
          ))
myTable["EU","banana",2011] = (2.5, 7.5) # ERROR: type Tuple has no field region
myTable["EU","banana",2012] = (2.5, 7.5) # ERROR: type Tuple has no field region
myTable["EU","banana",2011] = (production = 2.5, consumption = 7.5) # ERROR: type Tuple has no field region

标签： dataframejulia

解决方案

看起来您想要的功能在 JuliaDB 的Base.merge.

合并（a::NDSparse，a::NDSparse；聚合）

将 a 的行与 b 的行合并。为了保持唯一键，来自 b 的值优先。提供的函数 agg 将聚合来自 a 和 b 的具有相同键的值。

Example:

a = table((x = 1:5, y = rand(5)); pkey = :x)
b = table((x = 6:10, y = rand(5)); pkey = :x)
merge(a, b)

a = ndsparse([1,3,5], [1,2,3])
b = ndsparse([2,3,4], [4,5,6])
merge(a, b)
merge(a, b; agg = (x,y) -> x)

基于您的问题的工作示例：

# tested on Julia 1.0.4
julia> using JuliaDB
julia> myTable = ndsparse((
                    region      = ["US","US","US","US","EU","EU","EU","EU"],
                    product     = ["apple","apple","banana","banana","apple","apple","banana","banana"],
                    year        = [2011,2010,2011,2010,2011,2010,2011,2010]
                  ),(
                    production  = [3.3,3.2,2.3,2.1,2.7,2.8,1.5,1.3],
                    consumption = [4.3,7.4,2.5,9.8,3.2,4.3,6.5,3.0]
                 ))
3-d NDSparse with 8 values (2 field named tuples):
region  product   year │ production  consumption
───────────────────────┼────────────────────────
"EU"    "apple"   2010 │ 2.8         4.3
"EU"    "apple"   2011 │ 2.7         3.2
"EU"    "banana"  2010 │ 1.3         3.0
"EU"    "banana"  2011 │ 1.5         6.5 # Note the old value
"US"    "apple"   2010 │ 3.2         7.4
"US"    "apple"   2011 │ 3.3         4.3
"US"    "banana"  2010 │ 2.1         9.8
"US"    "banana"  2011 │ 2.3         2.5

julia> updated_myTable = ndsparse((
                    region      = ["EU"],
                    product     = ["banana"],
                    year        = [2011]
                  ),(
                    production  = [2.5], # new values here
                    consumption = [7.5]
                 ))
3-d NDSparse with 1 values (2 field named tuples):
region  product   year │ production  consumption
───────────────────────┼────────────────────────
"EU"    "banana"  2011 │ 2.5         7.5

julia> newTable = merge(updated_myTable, myTable, agg = (x,y) -> x)
3-d NDSparse with 8 values (2 field named tuples):
region  product   year │ production  consumption
───────────────────────┼────────────────────────
"EU"    "apple"   2010 │ 2.8         4.3
"EU"    "apple"   2011 │ 2.7         3.2
"EU"    "banana"  2010 │ 1.3         3.0
"EU"    "banana"  2011 │ 2.5         7.5 # Note the updated values here!
"US"    "apple"   2010 │ 3.2         7.4
"US"    "apple"   2011 │ 3.3         4.3
"US"    "banana"  2010 │ 2.1         9.8
"US"    "banana"  2011 │ 2.3         2.5

请注意，agg在发生冲突的情况下，该函数如何首选第一个参数中的键。

另一种方法是在发现正确的索引后直接编辑数据元素。

julia> i = findfirst(isequal((region = "EU", product = "banana", year = 2011)), myTable.index)
4

julia> myTable.data[i]
(production = 1.5, consumption = 6.5)

julia> myTable.data[i] = (production = 2.5, consumption = 7.5)
(production = 2.5, consumption = 7.5)

julia> myTable
3-d NDSparse with 8 values (2 field named tuples):
region  product   year │ production  consumption
───────────────────────┼────────────────────────
"EU"    "apple"   2010 │ 2.8         4.3
"EU"    "apple"   2011 │ 2.7         3.2
"EU"    "banana"  2010 │ 1.3         3.0
"EU"    "banana"  2011 │ 2.5         7.5 # Note the updated values here!
"US"    "apple"   2010 │ 3.2         7.4
"US"    "apple"   2011 │ 3.3         4.3
"US"    "banana"  2010 │ 2.1         9.8
"US"    "banana"  2011 │ 2.3         2.5

希望有帮助。

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