如何在 BigQuery 中透视表

Question

我正在使用 Google Big Query，并且我正在尝试从公共样本数据集中获取旋转结果。

对现有表的简单查询是：

SELECT * 
FROM publicdata:samples.shakespeare
LIMIT 10;

此查询返回以下结果集。

现在我要做的是，从表格中获取结果，如果单词是勇敢的，选择“勇敢”作为column_1，如果单词是出席，选择“ATTENDED”作为column_2，然后聚合这 2 个字数。

这是我正在使用的查询。

SELECT
(CASE WHEN word = 'brave' THEN 'BRAVE' ELSE '' END) AS column_1,
(CASE WHEN word = 'attended' THEN 'ATTENDED' ELSE '' END) AS column_2,
SUM (word_count)
FROM publicdata:samples.shakespeare
WHERE (word = 'brave' OR word = 'attended')
GROUP BY column_1, column_2
LIMIT 10;

但是，这个查询返回数据

我一直在寻找的是

我知道这个数据集的这个支点没有意义。但我只是以此为例来说明问题。如果您能给我一些指示，那就太好了。

已编辑：我也提到了How to simulate a pivot table with BigQuery?，它似乎也有我在这里提到的同样问题。

Answer 1

2020 年更新：

请致电fhoffa.x.pivot()，详情请参阅这篇文章：

• https://medium.com/@hoffa/easy-pivot-in-bigquery-one-step-5a1f13c6c710

以 2019 年为例，例如：

CREATE OR REPLACE VIEW `fh-bigquery.temp.a` AS (
 SELECT * EXCEPT(SensorName), REGEXP_REPLACE(SensorName, r'.*/', '') SensorName
 FROM `data-sensing-lab.io_sensor_data.moscone_io13`
);

CALL fhoffa.x.pivot(
  'fh-bigquery.temp.a'
  , 'fh-bigquery.temp.delete_pivotted' # destination table
  , ['MoteName', 'TIMESTAMP_TRUNC(Timestamp, HOUR) AS hour'] # row_ids
  , 'SensorName' # pivot_col_name
  , 'Data' # pivot_col_value
  , 8 # max_columns
  , 'AVG' # aggregation
  , 'LIMIT 10' # optional_limit
);

2019 年更新：

由于这是一个流行的问题，让我更新到#standardSQL 和更一般的旋转案例。在这种情况下，我们有多个行，每个传感器查看不同类型的属性。要旋转它，我们会执行以下操作：

#standardSQL
SELECT MoteName
  , TIMESTAMP_TRUNC(Timestamp, hour) hour
  , AVG(IF(SensorName LIKE '%altitude', Data, null)) altitude
  , AVG(IF(SensorName LIKE '%light', Data, null)) light
  , AVG(IF(SensorName LIKE '%mic', Data, null)) mic
  , AVG(IF(SensorName LIKE '%temperature', Data, null)) temperature
FROM `data-sensing-lab.io_sensor_data.moscone_io13`
WHERE MoteName = 'XBee_40670F5F'
GROUP BY 1, 2

作为AVG() 的替代品，您可以尝试MAX()、ANY_VALUE() 等。

---

以前：

我不确定您要做什么，但是：

SELECT NTH(1, words) WITHIN RECORD column_1, NTH(2, words) WITHIN RECORD column_2, f0_
FROM (
  SELECT NEST(word) words, SUM(c)  
  FROM (
    SELECT word, SUM(word_count) c
    FROM publicdata:samples.shakespeare
    WHERE word in ('brave', 'attended')
    GROUP BY 1
  )
)

更新：相同的结果，更简单的查询：

SELECT NTH(1, word) column_1, NTH(2, word) column_2, SUM(c)
FROM (
    SELECT word, SUM(word_count) c
    FROM publicdata:samples.shakespeare
    WHERE word in ('brave', 'attended')
    GROUP BY 1
)

Answer 2

2021 年更新：

BigQuery 中引入了新的 PIVOT 运算符。

在使用 PIVOT 将销售额和季度轮换到 Q1、Q2、Q3、Q4 列之前：

product	sales	quarter
Kale	51	Q1
Kale	23	Q2
Kale	45	Q3
Kale	3	Q4
Apple	77	Q1
Apple	0	Q2
Apple	25	Q3
Apple	2	Q4

使用 PIVOT 将销售额和季度轮换到 Q1、Q2、Q3、Q4 列之后：

product	Q1	Q2	Q3	Q4
Apple	77	0	25	2
Kale	51	23	45	3

查询：

with Produce AS (
  SELECT 'Kale' as product, 51 as sales, 'Q1' as quarter UNION ALL
  SELECT 'Kale', 23, 'Q2' UNION ALL
  SELECT 'Kale', 45, 'Q3' UNION ALL
  SELECT 'Kale', 3, 'Q4' UNION ALL
  SELECT 'Apple', 77, 'Q1' UNION ALL
  SELECT 'Apple', 0, 'Q2' UNION ALL
  SELECT 'Apple', 25, 'Q3' UNION ALL
  SELECT 'Apple', 2, 'Q4')
SELECT * FROM
  (SELECT product, sales, quarter FROM Produce)
  PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3', 'Q4'))

要动态构建列列表，请使用execute immediate：

execute immediate (             
  select '''
    select * 
    from (select product, sales, quarter from Produce)
    pivot(sum(sales) for quarter in ("''' ||  string_agg(distinct quarter, '", "' order by quarter)  || '''"))
  '''
  from Produce
);

Answer 3

同样受到 How to simulate a pivot table with BigQuery? 的启发，以下使用 subselect 的请求会产生您想要的结果：

SELECT
  MAX(column_1),
  MAX(column_2),
  SUM(wc),
FROM (
  SELECT
  (CASE WHEN word = 'brave' THEN 'BRAVE' ELSE '' END) AS column_1,
  (CASE WHEN word = 'attended' THEN 'ATTENDED' ELSE '' END) AS column_2,
  SUM (word_count) AS wc
  FROM publicdata:samples.shakespeare
  WHERE (word = 'brave' OR word = 'attended')
  GROUP BY column_1, column_2
  LIMIT 10
)

诀窍在于MAX(NULL, 'ATTENDED', NULL, ...) 等于'ATTENDED'。

Answer 4

使用 case/if 语句创建透视列是一种方法。但是，如果旋转列的数量开始增长，就会变得非常烦人。为了解决这个问题，我使用 python pandas 创建了一个 Python 模块，它自动生成 SQL 查询，然后可以在 BigQuery 中运行。这里对其做一个小介绍：

https://yashuseth.blog/2018/06/06/how-to-pivot-large-tables-in-bigquery

github宕机时的相关github代码：

import re
import pandas as pd

class BqPivot():
    """
    Class to generate a SQL query which creates pivoted tables in BigQuery.

    Example
    -------

    The following example uses the kaggle's titanic data. It can be found here -
    `https://www.kaggle.com/c/titanic/data`

    This data is only 60 KB and it has been used for a demonstration purpose.
    This module comes particularly handy with huge datasets for which we would need
    BigQuery(https://en.wikipedia.org/wiki/BigQuery).

    >>> from bq_pivot import BqPivot
    >>> import pandas as pd
    >>> data = pd.read_csv("titanic.csv").head()
    >>> gen = BqPivot(data=data, index_col=["Pclass", "Survived", "PassengenId"],
                      pivot_col="Name", values_col="Age",
                      add_col_nm_suffix=False)
    >>> print(gen.generate_query())

    select Pclass, Survived, PassengenId, 
    sum(case when Name = "Braund, Mr. Owen Harris" then Age else 0 end) as braund_mr_owen_harris,
    sum(case when Name = "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" then Age else 0 end) as cumings_mrs_john_bradley_florence_briggs_thayer,
    sum(case when Name = "Heikkinen, Miss. Laina" then Age else 0 end) as heikkinen_miss_laina,
    sum(case when Name = "Futrelle, Mrs. Jacques Heath (Lily May Peel)" then Age else 0 end) as futrelle_mrs_jacques_heath_lily_may_peel,
    sum(case when Name = "Allen, Mr. William Henry" then Age else 0 end) as allen_mr_william_henry
    from <--insert-table-name-here-->
    group by 1,2,3

    """
    def __init__(self, data, index_col, pivot_col, values_col, agg_fun="sum",
                 table_name=None, not_eq_default="0", add_col_nm_suffix=True, custom_agg_fun=None,
                 prefix=None, suffix=None):
        """
        Parameters
        ----------

        data: pandas.core.frame.DataFrame or string
            The input data can either be a pandas dataframe or a string path to the pandas
            data frame. The only requirement of this data is that it must have the column
            on which the pivot it to be done.

        index_col: list
            The names of the index columns in the query (the columns on which the group by needs to be performed)

        pivot_col: string
            The name of the column on which the pivot needs to be done.

        values_col: string
            The name of the column on which aggregation needs to be performed.

        agg_fun: string
            The name of the sql aggregation function.

        table_name: string
            The name of the table in the query.

        not_eq_default: numeric, optional
            The value to take when the case when statement is not satisfied. For example,
            if one is doing a sum aggregation on the value column then the not_eq_default should
            be equal to 0. Because the case statement part of the sql query would look like - 

            ... ...
            sum(case when <pivot_col> = <some_pivot_col_value> then values_col else 0)
            ... ...

            Similarly if the aggregation function is min then the not_eq_default should be
            positive infinity.

        add_col_nm_suffix: boolean, optional
            If True, then the original values column name will be added as suffix in the new 
            pivoted columns.

        custom_agg_fun: string, optional
            Can be used if one wants to give customized aggregation function. The values col name 
            should be replaced with {}. For example, if we want an aggregation function like - 
            sum(coalesce(values_col, 0)) then the custom_agg_fun argument would be - 
            sum(coalesce({}, 0)). 
            If provided this would override the agg_fun argument.

        prefix: string, optional
            A fixed string to add as a prefix in the pivoted column names separated by an
            underscore.

        suffix: string, optional
            A fixed string to add as a suffix in the pivoted column names separated by an
            underscore.        
        """
        self.query = ""

        self.index_col = list(index_col)
        self.values_col = values_col
        self.pivot_col = pivot_col

        self.not_eq_default = not_eq_default
        self.table_name = self._get_table_name(table_name)

        self.piv_col_vals = self._get_piv_col_vals(data)
        self.piv_col_names = self._create_piv_col_names(add_col_nm_suffix, prefix, suffix)

        self.function = custom_agg_fun if custom_agg_fun else agg_fun + "({})"

    def _get_table_name(self, table_name):
        """
        Returns the table name or a placeholder if the table name is not provided.
        """
        return table_name if table_name else "<--insert-table-name-here-->"

    def _get_piv_col_vals(self, data):
        """
        Gets all the unique values of the pivot column.
        """
        if isinstance(data, pd.DataFrame):
            self.data = data
        elif isinstance(data, str):
            self.data = pd.read_csv(data)
        else:
            raise ValueError("Provided data must be a pandas dataframe or a csv file path.")

        if self.pivot_col not in self.data.columns:
            raise ValueError("The provided data must have the column on which pivot is to be done. "\
                             "Also make sure that the column name in the data is same as the name "\
                             "provided to the pivot_col parameter.")

        return self.data[self.pivot_col].astype(str).unique().tolist()

    def _clean_col_name(self, col_name):
        """
        The pivot column values can have arbitrary strings but in order to 
        convert them to column names some cleaning is required. This method 
        takes a string as input and returns a clean column name.
        """

        # replace spaces with underscores
        # remove non alpha numeric characters other than underscores
        # replace multiple consecutive underscores with one underscore
        # make all characters lower case
        # remove trailing underscores
        return re.sub("_+", "_", re.sub('[^0-9a-zA-Z_]+', '', re.sub(" ", "_", col_name))).lower().rstrip("_")

    def _create_piv_col_names(self, add_col_nm_suffix, prefix, suffix):
        """
        The method created a list of pivot column names of the new pivoted table.
        """
        prefix = prefix + "_" if prefix else ""
        suffix = "_" + suffix if suffix else ""

        if add_col_nm_suffix:
            piv_col_names = ["{0}{1}_{2}{3}".format(prefix, self._clean_col_name(piv_col_val), self.values_col.lower(), suffix)
                             for piv_col_val in self.piv_col_vals]
        else:
            piv_col_names = ["{0}{1}{2}".format(prefix, self._clean_col_name(piv_col_val), suffix)
                             for piv_col_val in self.piv_col_vals]

        return piv_col_names

    def _add_select_statement(self):
        """
        Adds the select statement part of the query.
        """
        query = "select " + "".join([index_col + ", " for index_col in self.index_col]) + "\n"
        return query

    def _add_case_statement(self):
        """
        Adds the case statement part of the query.
        """
        case_query = self.function.format("case when {0} = \"{1}\" then {2} else {3} end") + " as {4},\n"

        query = "".join([case_query.format(self.pivot_col, piv_col_val, self.values_col,
                                           self.not_eq_default, piv_col_name)
                         for piv_col_val, piv_col_name in zip(self.piv_col_vals, self.piv_col_names)])

        query = query[:-2] + "\n"
        return query

    def _add_from_statement(self):
        """
        Adds the from statement part of the query.
        """
        query =  "from {0}\n".format(self.table_name)
        return query

    def _add_group_by_statement(self):
        """
        Adds the group by part of the query.
        """
        query = "group by " + "".join(["{0},".format(x) for x in range(1, len(self.index_col) + 1)])
        return query[:-1]

    def generate_query(self):
        """
        Returns the query to create the pivoted table.
        """
        self.query = self._add_select_statement() +\
                     self._add_case_statement() +\
                     self._add_from_statement() +\
                     self._add_group_by_statement()

        return self.query

    def write_query(self, output_file):
        """
        Writes the query to a text file.
        """
        text_file = open(output_file, "w")
        text_file.write(self.generate_query())
        text_file.close()

Answer 5

不是每个人都可以使用 python 和 pandas（想想 dataAnalysts 和 BI dudes :)） 这是标准 SQL@Bigquery 中的动态数据透视过程。 它还没有聚合。 首先，您需要提供一个包含 pe-KPI 汇总值的表格（如果需要）。 但它会自动创建一个表并生成所有透视列。

开始的假设是您输入了一个像这样的表 myDataset.myTable ：
LONG,LAT,KPI,US,EUR
A,1,温度, 78,45
A,1,压力, 120,114
B,1,temp,12,8
B,1,压力, 85,52

如果您像这样调用以下程序：

CALL warehouse.pivot ('myDataset','myTable',['LONG','LAT'],  'KPI');

您将获得一个名为 myDataset.myTable_pivot 的新表，如下所示：

LONG,LAT,temp_US,temp_EUR,pressure_US, pressure_EUR
A,1,78,45,120,114
B,1,12,8,85,52

这里是代码：

create or replace procedure warehouse.pivot (dataset STRING, table_to_pivot STRING, ls_pks ARRAY<STRING>, pivot_column STRING)
BEGIN
  DECLARE sql_pivot STRING;
  DECLARE sql_pk_string STRING;
  DECLARE sql_val_string STRING;
  DECLARE sql_pivot_cols STRING DEFAULT "";
  DECLARE pivot_cols_stmt STRING;
  DECLARE pivot_ls_values ARRAY<STRING>;
  DECLARE ls_pivot_value_columns ARRAY<STRING>;
  DECLARE nb_pivot_col_values INT64;
  DECLARE nb_pivot_val_values INT64;
  DECLARE loop_index INT64 DEFAULT 0;
  DECLARE loop2_index INT64 DEFAULT 0;

  SET sql_pk_string= ( array_to_string(ls_pks,',') ) ;
  /* get the values of pivot column to prepare the new columns in out put*/
  SET pivot_cols_stmt = concat(
          'SELECT array_agg(DISTINCT cast(', pivot_column ,' as string) ORDER BY ', pivot_column,' ) as pivot_ls_values, ',
          'count(distinct ',pivot_column,') as nb_pivot_col_values ',
          ' FROM ', dataset,'.', table_to_pivot
         );
  EXECUTE IMMEDIATE pivot_cols_stmt into pivot_ls_values, nb_pivot_col_values;

  /*get the name of value columns to preapre the new columns in output*/
  set sql_val_string =concat(
    "select array_agg(COLUMN_NAME) as ls_pivot_value_columns, count(distinct COLUMN_NAME) as nb_pivot_val_values ",
    "FROM ",dataset,".INFORMATION_SCHEMA.COLUMNS where TABLE_NAME='",table_to_pivot,"' ",
    "and COLUMN_NAME not in ('",array_to_string(ls_pks,"','"),"', '",pivot_column,"')"
  );
  EXECUTE IMMEDIATE sql_val_string
  into ls_pivot_value_columns, nb_pivot_val_values  ;

  /*create statement to populate the new columns*/
  while loop_index < nb_pivot_col_values DO
      set loop2_index =0;
      loop
              SET sql_pivot_cols= concat (
                                    sql_pivot_cols,
                                    "max( ",
                                    "if( ", pivot_column , "= '",pivot_ls_values[OFFSET (loop_index)],"' , ", ls_pivot_value_columns[OFFSET (loop2_index)], ", null) ",
                                    ") as ", pivot_ls_values[OFFSET (loop_index)], "_", ls_pivot_value_columns[OFFSET (loop2_index)],", "
                                  );

        SET loop2_index = loop2_index +1;
        if loop2_index >= nb_pivot_val_values then
          break;
        end if;
      END LOOP;

    SET loop_index =loop_index+ 1;
  END WHILE;

  SET sql_pivot =concat (
      "create or replace TABLE ", dataset,".",table_to_pivot,"_pivot as SELECT ",
      sql_pk_string, ",", sql_pivot_cols, " FROM ",dataset,".", table_to_pivot ,
      " GROUP BY ", sql_pk_string
   );

  EXECUTE IMMEDIATE sql_pivot;
END;

奇怪的事情：嵌套的 while 循环在 BQ 中不起作用。只执行最后一个 while 循环。这就是为什么在程序代码中混合了 WHILE 和 LOOP

Answer 6

试试这个

SELECT sum(CASE WHEN word = 'brave' THEN word_count ELSE 0 END) AS brave , sum(CASE WHEN word = 'attended' THEN word_count ELSE 0 END) AS attended, SUM (word_count) as total_word_count FROM publicdata:samples.shakespeare WHERE (word = 'brave' OR word = 'attended')

Answer 7

还有COUNTIF

https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#countif

SELECT COUNTIF(x<0) AS num_negative, COUNTIF(x>0) AS num_positive
FROM UNNEST([5, -2, 3, 6, -10, NULL, -7, 4, 0]) AS x;