queryWrapper .select("op_airline as airline", "cabin_type as bigCabin")

时间: 2023-05-21 17:02:23 浏览: 59
这是一个数据库查询语句,其中使用了 select 函数来选择 op_airline 和 cabin_type 两个字段,并将它们分别命名为 airline 和 bigCabin。具体的实现需要根据具体的数据库和编程语言来进行。
相关问题

SELECT PIS.SHOW_FLT_DETAIL AS SHOW_FLT_DETAIL -- new , PIS.SHOW_AWB_DETAIL AS SHOW_AWB_DETAIL -- new , PIS.DISPLAY_AIRLINE_CODE AS CARRIER_CODE , DECODE(PIS.REVERT_FLOW,'N',PIS.FLOW_TYPE,DECODE(PIS.FLOW_TYPE,'I','E','I')) AS FLOW_TYPE , PIS.SHIP_TO_LOCATION AS SHIP_TO_LOCATION , PIS.INVOICE_SEQUENCE AS INVOICE_SEQUENCE , PFT.FLIGHT_DATE AS FLIGHT_DATE , PFT.FLIGHT_CARRIER_CODE AS FLIGHT_CARRIER_CODE , PFT.FLIGHT_SERIAL_NUMBER AS FLIGHT_SERIAL_NUMBER , PFT.FLOW_TYPE AS AIRCRAFT_FLOW , FAST.AIRCRAFT_SERVICE_TYPE AS AIRCRAFT_SERVICE_TYPE , PPT.AWB_NUMBER AS AWB_NUMBER , PPT.WEIGHT AS WEIGHT , PPT.CARGO_HANDLING_OPERATOR AS CARGO_HANDLING_OPERATOR , PPT.SHIPMENT_PACKING_TYPE AS SHIPMENT_PACKING_TYPE , PPT.SHIPMENT_FLOW_TYPE AS SHIPMENT_FLOW_TYPE , PPT.SHIPMENT_BUILD_TYPE AS SHIPMENT_BUILD_TYPE , PPT.SHIPMENT_CARGO_TYPE AS SHIPMENT_CARGO_TYPE , PPT.REVENUE_TYPE AS REVENUE_TYPE , PFT.JV_FLIGHT_CARRIER_CODE AS JV_FLIGHT_CARRIER_CODE , PPT.PORT_TONNAGE_UID AS PORT_TONNAGE_UID , PPT.AWB_UID AS AWB_UID , PIS.INVOICE_SEPARATION_UID AS INVOICE_SEPARATION_UID , PFT.FLIGHT_TONNAGE_UID AS FLIGHT_TONNAGE_UID FROM PN_FLT_TONNAGES PFT , FZ_AIRLINES FA , PN_TONNAGE_FLT_PORTS PTFP , PN_PORT_TONNAGES PPT , FF_AIRCRAFT_SERVICE_TYPES FAST , SR_PN_INVOICE_SEPARATIONS PIS --new , SR_PN_INVOICE_SEP_DETAILS PISD--new , SR_PN_INV_SEP_PORT_TONNAGES PISPT --new WHERE PFT.FLIGHT_OPERATION_DATE >= trunc( CASE :rundate WHEN TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN ADD_MONTHS(SYSDATE,-1) ELSE ADD_MONTHS(:rundate,-1) END, 'MON') AND PFT.FLIGHT_OPERATION_DATE < trunc( CASE :rundate WHEN TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN TRUNC(SYSDATE) ELSE TRUNC(:rundate) END, 'MON') AND PFT.TYPE IN ('C', 'F') AND PFT.RECORD_TYPE = 'M' AND (PFT.TERMINAL_OPERATOR NOT IN ('X', 'A') OR (PFT.TERMINAL_OPERATOR <> 'X' AND FA.CARRIER_CODE IN (SELECT * FROM SPECIAL_HANDLING_AIRLINE) AND PPT.REVENUE_TYPE IN (SELECT * FROM SPECIAL_REVENUE_TYPE) AND PPT.SHIPMENT_FLOW_TYPE IN (SELECT * FROM SPECIAL_SHIPMENT_FLOW_TYPE) AND PFT.FLIGHT_OPERATION_DATE >= (select EFF_DATE from SPECIAL_HANDLING_EFF_DATE) )) AND PFT.DELETING_DATETIME IS NULL AND FA.AIRLINE_UID = PFT.AIRLINE_UID AND FA.DELETING_DATETIME IS NULL AND PTFP.FLIGHT_TONNAGE_UID = PFT.FLIGHT_TONNAGE_UID AND PTFP.RECORD_TYPE = 'M' AND PTFP.DELETING_DATETIME IS NULL AND PPT.TONNAGE_FLIGHT_PORT_UID (+)= PTFP.TONNAGE_FLIGHT_PORT_UID AND PPT.RECORD_TYPE (+)= 'M' AND PPT.DISCREPANCY_TYPE (+)= 'NONE' AND PPT.ADJUSTMENT_INC_FLAG (+)= 'Y' AND PPT.DELETING_DATETIME (+) IS NULL AND FAST.AIRCRAFT_SERVICE_TYPE_UID = PFT.AIRCRAFT_SERVICE_TYPE_UID AND FAST.DELETING_DATETIME IS NULL AND PIS.TEMPORAL_NAME = TO_CHAR((CASE :rundate --new WHEN TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN TRUNC(SYSDATE) ELSE TRUNC(:rundate) END ), 'YYYYMM') || '00' AND PIS.INVOICE_SEPARATION_UID = PISD.INVOICE_SEPARATION_UID --new AND PISD.INVOICE_SEP_DETAIL_UID = PISPT.INVOICE_SEP_DETAIL_UID --new AND PISPT.PORT_TONNAGE_UID = PPT.PORT_TONNAGE_UID --new AND PIS.PRINT_SUPPORTING_DOC = 'Y';上面是oracle的写法,请转成spark SQL的写法。

以下是转换为 Spark SQL 查询的代码: ``` SELECT PIS.SHOW_FLT_DETAIL AS SHOW_FLT_DETAIL, PIS.SHOW_AWB_DETAIL AS SHOW_AWB_DETAIL, PIS.DISPLAY_AIRLINE_CODE AS CARRIER_CODE, CASE WHEN PIS.REVERT_FLOW = 'N' THEN PIS.FLOW_TYPE ELSE CASE WHEN PIS.FLOW_TYPE = 'I' THEN 'E' ELSE 'I' END END AS FLOW_TYPE, PIS.SHIP_TO_LOCATION AS SHIP_TO_LOCATION, PIS.INVOICE_SEQUENCE AS INVOICE_SEQUENCE, PFT.FLIGHT_DATE AS FLIGHT_DATE, PFT.FLIGHT_CARRIER_CODE AS FLIGHT_CARRIER_CODE, PFT.FLIGHT_SERIAL_NUMBER AS FLIGHT_SERIAL_NUMBER, PFT.FLOW_TYPE AS AIRCRAFT_FLOW, FAST.AIRCRAFT_SERVICE_TYPE AS AIRCRAFT_SERVICE_TYPE, PPT.AWB_NUMBER AS AWB_NUMBER, PPT.WEIGHT AS WEIGHT, PPT.CARGO_HANDLING_OPERATOR AS CARGO_HANDLING_OPERATOR, PPT.SHIPMENT_PACKING_TYPE AS SHIPMENT_PACKING_TYPE, PPT.SHIPMENT_FLOW_TYPE AS SHIPMENT_FLOW_TYPE, PPT.SHIPMENT_BUILD_TYPE AS SHIPMENT_BUILD_TYPE, PPT.SHIPMENT_CARGO_TYPE AS SHIPMENT_CARGO_TYPE, PPT.REVENUE_TYPE AS REVENUE_TYPE, PFT.JV_FLIGHT_CARRIER_CODE AS JV_FLIGHT_CARRIER_CODE, PPT.PORT_TONNAGE_UID AS PORT_TONNAGE_UID, PPT.AWB_UID AS AWB_UID, PIS.INVOICE_SEPARATION_UID AS INVOICE_SEPARATION_UID, PFT.FLIGHT_TONNAGE_UID AS FLIGHT_TONNAGE_UID FROM PN_FLT_TONNAGES PFT JOIN FZ_AIRLINES FA ON FA.AIRLINE_UID = PFT.AIRLINE_UID AND FA.DELETING_DATETIME IS NULL JOIN PN_TONNAGE_FLT_PORTS PTFP ON PTFP.FLIGHT_TONNAGE_UID = PFT.FLIGHT_TONNAGE_UID AND PTFP.RECORD_TYPE = 'M' AND PTFP.DELETING_DATETIME IS NULL LEFT JOIN PN_PORT_TONNAGES PPT ON PPT.TONNAGE_FLIGHT_PORT_UID = PTFP.TONNAGE_FLIGHT_PORT_UID AND PPT.RECORD_TYPE = 'M' AND PPT.DISCREPANCY_TYPE = 'NONE' AND PPT.ADJUSTMENT_INC_FLAG = 'Y' AND PPT.DELETING_DATETIME IS NULL JOIN FF_AIRCRAFT_SERVICE_TYPES FAST ON FAST.AIRCRAFT_SERVICE_TYPE_UID = PFT.AIRCRAFT_SERVICE_TYPE_UID AND FAST.DELETING_DATETIME IS NULL JOIN SR_PN_INVOICE_SEPARATIONS PIS ON PIS.TEMPORAL_NAME = CONCAT(YEAR(:rundate), RIGHT(CONCAT('0', MONTH(:rundate)), 2), '00') AND PIS.INVOICE_SEPARATION_UID = PISD.INVOICE_SEPARATION_UID AND PIS.PRINT_SUPPORTING_DOC = 'Y' JOIN SR_PN_INVOICE_SEP_DETAILS PISD ON PISD.INVOICE_SEP_DETAIL_UID = PISPT.INVOICE_SEP_DETAIL_UID JOIN SR_PN_INV_SEP_PORT_TONNAGES PISPT ON PISPT.PORT_TONNAGE_UID = PPT.PORT_TONNAGE_UID WHERE PFT.FLIGHT_OPERATION_DATE >= trunc(CASE WHEN :rundate = TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN ADD_MONTHS(SYSDATE, -1) ELSE ADD_MONTHS(:rundate, -1) END, 'MON') AND PFT.FLIGHT_OPERATION_DATE < trunc(CASE WHEN :rundate = TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN TRUNC(SYSDATE) ELSE TRUNC(:rundate) END, 'MON') AND PFT.TYPE IN ('C', 'F') AND PFT.RECORD_TYPE = 'M' AND ( PFT.TERMINAL_OPERATOR NOT IN ('X', 'A') OR ( PFT.TERMINAL_OPERATOR <> 'X' AND FA.CARRIER_CODE IN (SELECT * FROM SPECIAL_HANDLING_AIRLINE) AND PPT.REVENUE_TYPE IN (SELECT * FROM SPECIAL_REVENUE_TYPE) AND PPT.SHIPMENT_FLOW_TYPE IN (SELECT * FROM SPECIAL_SHIPMENT_FLOW_TYPE) AND PFT.FLIGHT_OPERATION_DATE >= (SELECT EFF_DATE FROM SPECIAL_HANDLING_EFF_DATE) ) ) AND PFT.DELETING_DATETIME IS NULL; ``` 请注意,需要将 `TRUNC` 和 `CONCAT` 函数替换为 Spark SQL 中的相应函数,并使用 `JOIN` 语句替换 `LEFT JOIN` 语句。还需要更改零散的子查询,以便在 Spark SQL 中使用。

WITH SPECIAL_HANDLING_AIRLINE as (select regexp_substr(str, '[^,]+', 1, LEVEL) from (select AP.PARAMETER_VALUE str from FM_APPLICATION_PARAMETERS AP WHERE PARAMETER_CODE='TONNAGE_SPECIAL_HANDLING_AIRLINE') CONNECT BY LEVEL <= regexp_count(str, ',') + 1), SPECIAL_REVENUE_TYPE as (select regexp_substr(str, '[^,]+', 1, LEVEL) from (select AP.PARAMETER_VALUE str from FM_APPLICATION_PARAMETERS AP WHERE PARAMETER_CODE='TONNAGE_SPECIAL_REVENUE_TYPE') CONNECT BY LEVEL <= regexp_count(str, ',') + 1), SPECIAL_SHIPMENT_FLOW_TYPE as (select regexp_substr(str, '[^,]+', 1, LEVEL) from (select AP.PARAMETER_VALUE str from FM_APPLICATION_PARAMETERS AP WHERE PARAMETER_CODE='TONNAGE_SPECIAL_SHIPMENT_FLOW_TYPE') CONNECT BY LEVEL <= regexp_count(str, ',') + 1), SPECIAL_HANDLING_EFF_DATE as ( SELECT TO_DATE(PARAMETER_VALUE, 'YYYYMMDD') EFF_DATE from FM_APPLICATION_PARAMETERS where PARAMETER_CODE='TONNAGE_SPECIAL_HANDLING_EFFECTIVE_DATE' ) select * from SPECIAL_HANDLING_AIRLINE;以上是oracle的写法,请转换成spark SQL的写法。

以下是将Oracle SQL转换成Spark SQL的代码: ``` WITH SPECIAL_HANDLING_AIRLINE AS ( SELECT split(PARAMETER_VALUE, ',')[pos] FROM ( SELECT PARAMETER_VALUE, posexplode(split(PARAMETER_VALUE, ',')) FROM FM_APPLICATION_PARAMETERS WHERE PARAMETER_CODE = 'TONNAGE_SPECIAL_HANDLING_AIRLINE' ) ), SPECIAL_REVENUE_TYPE AS ( SELECT split(PARAMETER_VALUE, ',')[pos] FROM ( SELECT PARAMETER_VALUE, posexplode(split(PARAMETER_VALUE, ',')) FROM FM_APPLICATION_PARAMETERS WHERE PARAMETER_CODE = 'TONNAGE_SPECIAL_REVENUE_TYPE' ) ), SPECIAL_SHIPMENT_FLOW_TYPE AS ( SELECT split(PARAMETER_VALUE, ',')[pos] FROM ( SELECT PARAMETER_VALUE, posexplode(split(PARAMETER_VALUE, ',')) FROM FM_APPLICATION_PARAMETERS WHERE PARAMETER_CODE = 'TONNAGE_SPECIAL_SHIPMENT_FLOW_TYPE' ) ), SPECIAL_HANDLING_EFF_DATE AS ( SELECT to_date(PARAMETER_VALUE, 'yyyyMMdd') AS EFF_DATE FROM FM_APPLICATION_PARAMETERS WHERE PARAMETER_CODE = 'TONNAGE_SPECIAL_HANDLING_EFFECTIVE_DATE' ) SELECT * FROM SPECIAL_HANDLING_AIRLINE; ``` 注意:Spark SQL不支持Oracle SQL的所有函数和语法,因此需要根据具体情况进行转换。在这个例子中,我们使用了split函数和posexplode函数来实现Oracle SQL的regexp_substr函数和CONNECT BY LEVEL子句。同时,我们将to_date函数的格式参数从'YYYYMMDD'改为'yyyyMMdd'。

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import nltk.corpus import pandas as pd import re import matplotlib.pyplot as plt import seaborn as sns from stanfordcorenlp import StanfordCoreNLP # 导入数据 df = pd.read_csv('D:/file document/desktop/语料库大作业/Tweets.csv', usecols=['airline_sentiment', 'text']) def sentiment(x): if x == 'positive': return 1 elif x == 'negative': return -1 else: return 0 from nltk.corpus import stopwords from nltk.stem import SnowballStemmer from nltk.tokenize import RegexpTokenizer # 去除停用词 stopwords = nltk.corpus.stopwords.words('english') # 词还原 stemmer = SnowballStemmer('english') # 分词 tokenizer = RegexpTokenizer(r'\w+') # As this dataset is fetched from twitter so it has lots of people tag in tweets # we will remove them tags = r"@\w*" def preprocess_text(sentence, stem=False): # 去除text中一些影响文本分析的标签 sentence = [re.sub(tags, "", sentence)] text = [] for word in sentence: if word not in stopwords: if stem: text.append(stemmer.stem(word).lower()) else: text.append(word.lower()) return tokenizer.tokenize(" ".join(text)) # 将用preprocess_text() 函数处理后的text列保存回原始 DataFrame 的 text 列中 df['text'] = df['text'].map(preprocess_text) output_file = 'D:/file document/desktop/语料库大作业/output2.csv' # 输出文件路径 nlp = StanfordCoreNLP(r"D:/AppData/stanfordnlp", lang="en") # 定义函数,用于对指定文本进行依存句法分析 def dependency_parse(sentence): result = nlp.dependency_parse(sentence) return result # 对某一列进行依存句法分析,并将结果保存到新的一列中 df['dependency_parse'] = df['text'].apply(lambda x: dependency_parse(" ".join(x))) # 将结果保存到输出文件中 df.to_csv(output_file, index=False) nlp.close()优化这段代码

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