探索分组数据
Julia 中的数据操作
Katerina Zahradova
Instructor
为分组筛选
# 不同国家的公司
choc_comp_france = filter(chocolates -> chocolates.company_location == "France", chocolates)
choc_comp_brazil = filter(chocolates -> chocolates.company_location == "Brazil", chocolates)
choc_comp_peru = filter(chocolates -> chocolates.company_location == "Peru", chocolates)
choc_comp_belgium = filter(chocolates -> chocolates.company_location == "Belgium", chocolates)
...
groupby() 来帮忙！
GroupedDataFrame
groupby() 实战
# 按 company_location 分组
groupby(chocolates, :company_location)
GroupedDataFrame with 60 groups based on key: company_location
First Group (156 rows): company_location = "France"
Row company bean_origin ...
String String ...
_______________________________
1 A. Morin Agua Grande ...
...
Last Group(4 rows): company_location = "Ireland"
Row company bean_origin ...
String String ...
_______________________________
1 Wilkie's Organic Amazonas ...
多列上的 groupby()
groupby(chocolates, [:company_location, :cocoa])
GroupedDataFrame with 373 groups based on keys: company_location, cocoa
First Group (5 rows): company_location = "France", cocoa = 63.0
Row company bean_origin ...
String String ...
_______________________________
1 A. Morin Agua Grande ...
...
Last Group (1 row): company_location = "Ireland", cocoa = 89.0
Row company bean_origin ...
String String ...
_______________________________
1 Wilkie's Organic Amazonas ...
顺序很重要
记录数
# 按国家分组
chocolates_by_country = groupby(chocolates, :company_location)
# 每组记录数
combine(chocolates_by_country, nrow => :count)
60x2 DataFrame
Row company_location nrow
String31 Int64
______________________________
1 France 156
2 U.S.A. 764
3 Fiji 4
...
按记录数排序
# 按国家分组
chocolates_by_country = groupby(chocolates, :company_location)
# 按记录数排序
sort(combine(chocolates_by_country, nrow => :count), :count, rev = true)
60x2 DataFrame
Row company_location nrow
String31 Int64
______________________________
1 U.S.A 764
2 France 156
3 Canada 125
...
向量上的 unique()
# 唯一元素
unique(chocolates.company_location)
60-element Vector{String31}:
"France"
"U.S.A."
"Fiji"
"Ecuador"
"Mexico"
"Switzerland"
"Netherlands"
...
DataFrame 上的 unique()
# 仅保留唯一行的新数据框
unique(chocolates)
1795×10 DataFrame
Row company bean_origin ...
String String ...
_________________________________
1 A. Morin Agua Grande ...
...
指定列的 unique()
# 唯一的公司记录
unique(chocolates, :company)
416×10 DataFrame
Row company bean_origin ...
String String ...
_________________________________
1 A. Morin Agua Grande ...
...
# 唯一的公司与可可含量组合
unique(chocolates, [:company, :cocoa])
968×10 DataFrame
Row company bean_origin ...
String String ...
_________________________________
1 A. Morin Agua Grande ...
...
Vamos praticar!
Julia 中的数据操作
