How to Pivot table in BigQuery

Question

I am using Google Big Query, and I am trying to get a pivoted result out from public sample data set.

A simple query to an existing table is:

SELECT * 
FROM publicdata:samples.shakespeare
LIMIT 10;

This query returns following result set.

Now what I am trying to do is, get the results from the table in such way that if the word is brave, select "BRAVE" as column_1 and if the word is attended, select "ATTENDED" as column_2, and aggregate the word count for these 2.

Here is the query that I am using.

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;

But, this query returns the data

What I was looking for is

I know this pivot for this data set does not make sense. But I am just taking this as an example to explain the problem. It will be great if you can put in some directions for me.

EDITED: I also referred to How to simulate a pivot table with BigQuery? and it seems it also has the same issue I mentioned here.

Answer 1

Update 2020:

Just call fhoffa.x.pivot(), as detailed in this post:

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

For the 2019 example, for example:

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
);

Update 2019:

Since this is a popular question, let me update to #standardSQL and a more general case of pivoting. In this case we have multiple rows, and each sensor looks at a different type of property. To pivot it, we would do something like:

#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

As an alternative to AVG() you can try MAX(), ANY_VALUE(), etc.

---

Previously:

I'm not sure what you are trying to do, but:

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
  )
)

UPDATE: Same results, simpler query:

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

Also inspired by How to simulate a pivot table with BigQuery? , the following request using subselect yields your exact desired result :

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
)

The trick is that MAX(NULL, 'ATTENDED', NULL, ...) equals 'ATTENDED'.

Answer 3

Using case/if statements to create pivoted columns is one way to go about it. But it gets very annoying if the number of pivoted columns start to grow. To deal with this, I have created a Python module using python pandas that automatically generates the SQL query which can then be run in BigQuery. Here is a small introduction to it:

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

Relevant github code in case github goes down:

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 4

Update 2021:

A new PIVOT operator has been introduced into BigQuery.

Before PIVOT is used to rotate sales and quarter into Q1, Q2, Q3, Q4 columns:

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

After PIVOT is used to rotate sales and quarter into Q1, Q2, Q3, Q4 columns:

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

Query:

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'))

To build list of columns dynamically use 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 5

Try this

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 6

Not everyone can use python nor pandas (think about dataAnalysts and BI dudes :) ) here is a dynamic pivot procedure in standard SQL@Bigquery. it does not aggregate yet. at first, you need to provide a table with already pe-KPI agregated values (if needed) . but it automatically creates a table and generates all the pivoted columns.

the start hypothesis is that you have in input a table myDataset.myTable like this :
LONG,LAT,KPI,US,EUR
A,1,temp, 78,45
A,1,pressure, 120,114
B,1,temp,12,8
B,1,pressure, 85,52

if you call below procedure like this :

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

you'll get a new table called myDataset.myTable_pivot like this :

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

here is the code :

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;

Strange things : nested while loops doesn't work in BQ. only the last while loop is executed. That's why in the procedure code you have a mix of WHILE and LOOP

Answer 7

There is also 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;