我的代码如下：
print df.groupBy('offer_id', 'record_id').avg().collect()
哪个有效，但我有一个字符串：
print df.groupBy(stringNamesDF).avg().collect()
哪个失败了：
org.apache.spark.sql.AnalysisException: cannot resolve ''record_id', 'assigned_offer_id', 'accepted_offer_flag', 'current_offer_flag', 'offer_good_until_date', 'rescinded_date', 'first_pymt_date', 'contract_date', 'acct_nbr', 'acct_nbr_assigned_dttm', 'acct_expiration_dttm', 'offer_desc', 'offer_sales_script', 'presentable_flag', 'insrt_dttm', 'insrt_usr_id', 'chng_dttm', 'chng_usr_id', 'actv_flag', 'correlation_id', 'offer_status_type_cd', 'presentation_instrument_nbr'' given input columns pymt, rescindable_days, rescinded_date, market_cell_id, offer_sales_script, assigned_offer_id, offer_desc, rate_index_type_cd, nbr_of_pymts, campaign_id, down_pymt, offer_status_type_cd, offer_type_cd, acct_expiration_dttm, record_id, origination_fee_rate, insrt_usr_id, promo_id, term_mm, min_amount, offer_good_until_date, decision_id, insrt_dttm, late_fee_min_amount, late_fee_percent, offer_id, origination_fee_amount, presentation_instrument_nbr, offer_order, chng_usr_id, correlation_id, acct_nbr_assigned_dttm, chng_dttm, presentable_flag, accepted_offer_flag, amount, min_rate, max_rate, acct_nbr, actv_flag, sub_product_id, cs_result_id, current_offer_flag, finance_charge, annual_fee_waived_mm, cs_result_usage_type_cd, max_amount, total_pymts, contract_date, index_rate, first_pymt_date, annual_fee_amount, rate, amount_financed, pymt_method_type_cd;
stringNamesDF打印如下：
'record_id', 'assigned_offer_id', 'accepted_offer_flag', 'current_offer_flag', 'offer_good_until_date', 'rescinded_date', 'first_pymt_date', 'contract_date', 'acct_nbr', 'acct_nbr_assigned_dttm', 'acct_expiration_dttm', 'offer_desc', 'offer_sales_script', 'presentable_flag', 'insrt_dttm', 'insrt_usr_id', 'chng_dttm', 'chng_usr_id', 'actv_flag', 'correlation_id', 'offer_status_type_cd', 'presentation_instrument_nbr'
我也尝试过使用stringNamesDF：
record_id, assigned_offer_id, accepted_offer_flag, current_offer_flag, offer_good_until_date, rescinded_date, first_pymt_date, contract_date, acct_nbr, acct_nbr_assigned_dttm, acct_expiration_dttm, offer_desc, offer_sales_script, presentable_flag, insrt_dttm, insrt_usr_id, chng_dttm, chng_usr_id, actv_flag, correlation_id, offer_status_type_cd, presentation_instrument_nbr
但得到这个：
org.apache.spark.sql.AnalysisException: cannot resolve 'record_id, assigned_offer_id, accepted_offer_flag, current_offer_flag, offer_good_until_date, rescinded_date, first_pymt_date, contract_date, acct_nbr, acct_nbr_assigned_dttm, acct_expiration_dttm, offer_desc, offer_sales_script, presentable_flag, insrt_dttm, insrt_usr_id, chng_dttm, chng_usr_id, actv_flag, correlation_id, offer_status_type_cd, presentation_instrument_nbr' given input columns pymt, rescindable_days, rescinded_date, market_cell_id, offer_sales_script, assigned_offer_id, offer_desc, rate_index_type_cd, nbr_of_pymts, campaign_id, down_pymt, offer_status_type_cd, offer_type_cd, acct_expiration_dttm, record_id, origination_fee_rate, insrt_usr_id, promo_id, term_mm, min_amount, offer_good_until_date, decision_id, insrt_dttm, late_fee_min_amount, late_fee_percent, offer_id, origination_fee_amount, presentation_instrument_nbr, offer_order, chng_usr_id, correlation_id, acct_nbr_assigned_dttm, chng_dttm, presentable_flag, accepted_offer_flag, amount, min_rate, max_rate, acct_nbr, actv_flag, sub_product_id, cs_result_id, current_offer_flag, finance_charge, annual_fee_waived_mm, cs_result_usage_type_cd, max_amount, total_pymts, contract_date, index_rate, first_pymt_date, annual_fee_amount, rate, amount_financed, pymt_method_type_cd;
编辑：我尝试过：stringNames []看起来没有成功：
['record_id', 'assigned_offer_id', 'accepted_offer_flag', 'current_offer_flag', 'offer_good_until_date', 'rescinded_date', 'first_pymt_date', 'contract_date', 'acct_nbr', 'acct_nbr_assigned_dttm', 'acct_expiration_dttm', 'offer_desc', 'offer_sales_script', 'presentable_flag', 'insrt_dttm', 'insrt_usr_id', 'chng_dttm', 'chng_usr_id', 'actv_flag', 'correlation_id', 'offer_status_type_cd', 'presentation_instrument_nbr']
并获取AttributeError：'list'对象没有属性'_get_object_id'
答案 0 :(得分：0)
尝试：
print df.groupBy(stringNamesDF.split(", ")).avg().collect()
答案 1 :(得分：-1)
当stringNamesDF是一个列表时：
stringNamesDF=['record_id', 'assigned_offer_id', 'accepted_offer_flag', 'current_offer_flag', 'offer_good_until_date', 'rescinded_date', 'first_pymt_date', 'contract_date', 'acct_nbr', 'acct_nbr_assigned_dttm', 'acct_expiration_dttm', 'offer_desc', 'offer_sales_script', 'presentable_flag', 'insrt_dttm', 'insrt_usr_id', 'chng_dttm', 'chng_usr_id', 'actv_flag', 'correlation_id', 'offer_status_type_cd', 'presentation_instrument_nbr']
使用：
df.groupBy(*stringNamesDF).avg().collect()
来自https://spark.apache.org/docs/latest/api/python/pyspark.sql.html
GROUPBY（* COLS）
使用指定的列对DataFrame进行分组，因此我们可以对它们进行聚合。
参数：cols - 要分组的列的列表。每个元素应该是列名（字符串）或表达式（列）。
示例：
l = [（'Alice'，'2015-02-02'，1），（'Alice'，'2015-02-02'，2），（'Alice'，'2015-02-03' ，1），（'Bob'，'2015-02-03'，1），（'Bob'，'2015-02-03'，3）]
ddf = sqlContext.createDataFrame（l，['name'，'date'，'clicks']）
ddf.groupBy（* ['name'，'date'））。avg（）。collect（）