Dataframe 计算值与选定列中的条件匹配的列数

我有一个pyspark数据框，看起来如下所示：

+---------------+-------------------+-------------------------------------+
|fields_0_method| fields_1_method  |fields_2_method
+-------+-----------------------------------------------------------------+
|NIGHT           |MORNING          |       NIGHT                          |
+-------+--------------------------------------------------------------------+  +-------+-----------------------------------------------------------------+
|NIGHT           |NIGHT         |       NIGHT                          |
+-------+--------------------------------------------------------------------

如何创建一个新列来计算“fields\u 0\u method”、“fields\u 1\u methods”和“fields\u 2\u methods”中具有“NIGHT”值的列数

理想的数据帧如下所示：

+---------------+-------------------+-------------------------------------+
|fields_0_method| fields_1_method  |fields_2_method    | field_method_Night_Count
+-------+-----------------------------------------------------------------+
|NIGHT           |MORNING          |       NIGHT       |    2
+-------+--------------------------------------------------------------------+   +-------+-----------------------------------------------------------------+
|NIGHT           |NIGHT         |       NIGHT          |    3
+-------+--------------------------------------------------------------------

更新： 我遵循了拉马努斯的方法，但出现了以下错误：

ParseException: u"\nextraneous input '>' expecting {'(', 'SELECT', 'FROM', 'ADD', 'AS', 'ALL', 'DISTINCT', 'WHERE', 'GROUP', 'BY', 'GROUPING', 'SETS', 'CUBE', 'ROLLUP', 'ORDER', 'HAVING', 'LIMIT', 'AT', 'OR', 'AND', 'IN', NOT, 'NO', 'EXISTS', 'BETWEEN', 'LIKE', RLIKE, 'IS', 'NULL', 'TRUE', 'FALSE', 'NULLS', 'ASC', 'DESC', 'FOR', 'INTERVAL', 'CASE', 'WHEN', 'THEN', 'ELSE', 'END', 'JOIN', 'CROSS', 'OUTER', 'INNER', 'LEFT', 'SEMI', 'RIGHT', 'FULL', 'NATURAL', 'ON', 'LATERAL', 'WINDOW', 'OVER', 'PARTITION', 'RANGE', 'ROWS', 'UNBOUNDED', 'PRECEDING', 'FOLLOWING', 'CURRENT', 'FIRST', 'AFTER', 'LAST', 'ROW', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'DIRECTORY', 'VIEW', 'REPLACE', 'INSERT', 'DELETE', 'INTO', 'DESCRIBE', 'EXPLAIN', 'FORMAT', 'LOGICAL', 'CODEGEN', 'COST', 'CAST', 'SHOW', 'TABLES', 'COLUMNS', 'COLUMN', 'USE', 'PARTITIONS', 'FUNCTIONS', 'DROP', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'TO', 'TABLESAMPLE', 'STRATIFY', 'ALTER', 'RENAME', 'ARRAY', 'MAP', 'STRUCT', 'COMMENT', 'SET', 'RESET', 'DATA', 'START', 'TRANSACTION', 'COMMIT', 'ROLLBACK', 'MACRO', 'IGNORE', 'BOTH', 'LEADING', 'TRAILING', 'IF', 'POSITION', '+', '-', '*', 'DIV', '~', 'PERCENT', 'BUCKET', 'OUT', 'OF', 'SORT', 'CLUSTER', 'DISTRIBUTE', 'OVERWRITE', 'TRANSFORM', 'REDUCE', 'SERDE', 'SERDEPROPERTIES', 'RECORDREADER', 'RECORDWRITER', 'DELIMITED', 'FIELDS', 'TERMINATED', 'COLLECTION', 'ITEMS', 'KEYS', 'ESCAPED', 'LINES', 'SEPARATED', 'FUNCTION', 'EXTENDED', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'LAZY', 'FORMATTED', 'GLOBAL', TEMPORARY, 'OPTIONS', 'UNSET', 'TBLPROPERTIES', 'DBPROPERTIES', 'BUCKETS', 'SKEWED', 'STORED', 'DIRECTORIES', 'LOCATION', 'EXCHANGE', 'ARCHIVE', 'UNARCHIVE', 'FILEFORMAT', 'TOUCH', 'COMPACT', 'CONCATENATE', 'CHANGE', 'CASCADE', 'RESTRICT', 'CLUSTERED', 'SORTED', 'PURGE', 'INPUTFORMAT', 'OUTPUTFORMAT', DATABASE, DATABASES, 'DFS', 'TRUNCATE', 'ANALYZE', 'COMPUTE', 'LIST', 'STATISTICS', 'PARTITIONED', 'EXTERNAL', 'DEFINED', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'REPAIR', 'RECOVER', 'EXPORT', 'IMPORT', 'LOAD', 'ROLE', 'ROLES', 'COMPACTIONS', 'PRINCIPALS', 'TRANSACTIONS', 'INDEX', 'INDEXES', 'LOCKS', 'OPTION', 'ANTI', 'LOCAL', 'INPATH', STRING, BIGINT_LITERAL, SMALLINT_LITERAL, TINYINT_LITERAL, INTEGER_VALUE, DECIMAL_VALUE, DOUBLE_LITERAL, BIGDECIMAL_LITERAL, IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 153)\n\n== SQL ==\naggregate(array(fields_shippingDetails_0_shippingMethod_x,fields_shippingDetails_1_shippingMethod,fields_shippingDetails_2_shippingMethod), 0, (acc, x) -> if(x == 'DROPOFFSHIPPINGGROUP', acc + 1, acc))\n---------------------------------------------------------------------------------------------------------------------------------------------------------^^^\n"

---

您可以创建一个数组并使用聚合函数来计算所需的条件

df1 = df.withColumn('count', expr("aggregate(array(fields_0_method, fields_1_method, fields_2_method), 0, (acc, x) -> if(x == 'NIGHT', acc + 1, acc))"))
df1.show()

+---------------+---------------+---------------+-----+
|fields_0_method|fields_1_method|fields_2_method|count|
+---------------+---------------+---------------+-----+
|          NIGHT|        MORNING|          NIGHT|    2|
|          NIGHT|          NIGHT|          NIGHT|    3|
+---------------+---------------+---------------+-----+

---

实现这一点的一种方法是创建一个接收整行的UDF，该UDF将计算“夜间”的计数并返回它。因此，您可以将此UDF与df.withColumn（'new_col'，UDF（row））一起使用。@ChristianGomes如何实现这一点？我使用了这种方法，但它给了我一个错误（请参阅更新）