Question

我在Google BigQuery上有一个带有位置的数据表，称为TABLE_A。

这是TABLE_A的样子：

df_nan <- data.frame(
  Variable = colnames(df))
df_nan["%NA"] <- apply(df,2,function(x) round(sum(is.na(x))/NROW(x)*100,2)) #percentage over total
df_nan["#NA"] <- apply(df,2,function(x) sum(is.na(x))) #NA count
df_nan["%NA Y=1"] <- apply(df[df$Y == 1,],2,function(x) round(sum(is.na(x))/NROW(x)*100,2)) #percentage over total if Y=1
df_nan["%NA Y=0"] <- apply(df[df$Y == 0,],2,function(x) round(sum(is.na(x))/NROW(x)*100,2)) #percentage over total if Y=0
df_nan["#NA Y=1"] <- apply(df[df$Y == 1,],2,function(x) sum(is.na(x))) #NA count if Y=1
df_nan["#NA Y=0"] <- apply(df[df$Y == 0,],2,function(x) sum(is.na(x))) #NA count if Y=0

还有另一个包含不同项目的表，称为TABLE_B。 TABLE_B具有与TABLE_A相同的架构。这是来自TABLE_B的示例：

ID,Lat,Lon
1,32.95,65.567
2,33.95,65.566

，我想创建一个新表TABLE_C，其中每一行都包含TABLE_A和TABLE_B中的项，以使这些项最接近（即，联接表时经纬度对之间的距离是最小距离）。这将是带有以上示例数据的TABLE_C的示例：

ID,Lat,Lon
a,32.96,65.566
b,33.96,65.566

我的实际数据是一方面带经纬度对，另一方面带ID_A,ID_B 1,a 2,b的属性表（我正在寻找每个属性最近的气象站）。

Answer 1

以下是用于BigQuery标准SQL

#standardSQL
SELECT AS VALUE ARRAY_AGG(STRUCT<id_a INT64, id_b STRING>(a.id, b.id) ORDER BY ST_DISTANCE(a.point, b.point) LIMIT 1)[OFFSET(0)] 
FROM (SELECT id, ST_GEOGPOINT(lon, lat) point FROM `project.dataset.table_a`) a
CROSS JOIN (SELECT id, ST_GEOGPOINT(lon, lat) point FROM `project.dataset.table_b`) b 
GROUP BY a.id

您可以使用问题中的伪数据作为

进行测试，玩耍

#standardSQL
WITH `project.dataset.table_a` AS (
  SELECT 1 id, 32.95 lat, 65.567 lon UNION ALL
  SELECT 2, 33.95, 65.566
), `project.dataset.table_b` AS (
  SELECT 'a' id, 32.96 lat, 65.566 lon UNION ALL
  SELECT 'b', 33.96, 65.566
)
SELECT AS VALUE ARRAY_AGG(STRUCT<id_a INT64, id_b STRING>(a.id, b.id) ORDER BY ST_DISTANCE(a.point, b.point) LIMIT 1)[OFFSET(0)] 
FROM (SELECT id, ST_GEOGPOINT(lon, lat) point FROM `project.dataset.table_a`) a
CROSS JOIN (SELECT id, ST_GEOGPOINT(lon, lat) point FROM `project.dataset.table_b`) b 
GROUP BY a.id

有结果

Row id_a    id_b     
1   1       a    
2   2       b

SQL：在Google BigQuery上找到最接近的纬度/经度记录

1 个答案: