df.groupby('vote_cut')['revenue_adj'].max().plot.bar()

As an AI language model, I cannot execute this code as I do not have access to the DataFrame 'df'. However, I can explain what this code does.

This code is grouping the DataFrame 'df' by the values in the column 'vote_cut'. It then selects the maximum value of the column 'revenue_adj' for each group and plots a bar chart with the resulting values. The x-axis of the bar chart will show the unique values in the 'vote_cut' column and the y-axis will show the maximum value of 'revenue_adj' for each group.

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。

在以下代码中添加代码，使得保存的折线图更简洁更美观: import pandas as pd import matplotlib.pyplot as plt df=pd.read_csv('movies_revenue_starring_1950_2010.txt',sep=',') df.sort_values('Title',ascending=False,inplace=True) df_new=df[['Title','Revenue']].reset_index(drop=True) df_new=df_new.iloc[::65,:] #（进行位置索引）对title进行抽样 df_renew=df[['Title','Starring Actors Popularity']].reset_index(drop=True) df_renew=df_renew.iloc[::65,:] #可视化电影的revenue的值 plt.plot(df_new['Title'],df_new['Revenue'],color='red') plt.xticks(rotation=45) #x轴旋转45° plt.title('movies Revenue 1950-2010') plt.xlabel('title') plt.ylabel('Revenue') plt.savefig('movies_revenue_1950_2010.png',dpi=400) plt.show() #可视化电影的Starring Actors Popularity值 plt.plot(df_renew['Title'],df_renew['Starring Actors Popularity'],color='green') plt.xticks(rotation=45) plt.title('Starring Actors Popularity 1950-2010') plt.xlabel('title') plt.ylabel('Starring Actors Popularity') plt.savefig('movies_starpopularity_1950_2010.png',dpi=400) plt.show()

你可以尝试以下代码，使得保存的折线图更简洁更美观：

import pandas as pd 
import matplotlib.pyplot as plt 

df=pd.read_csv('movies_revenue_starring_1950_2010.txt',sep=',') 
df.sort_values('Title',ascending=False,inplace=True) 

df_new=df[['Title','Revenue']].reset_index(drop=True) 
df_new=df_new.iloc[::65,:]    #（进行位置索引）对title进行抽样 

df_renew=df[['Title','Starring Actors Popularity']].reset_index(drop=True) 
df_renew=df_renew.iloc[::65,:]   

#可视化电影的revenue的值 
plt.plot(df_new['Title'],df_new['Revenue'],color='red') 
plt.xticks(rotation=45) #x轴旋转45° 
plt.title('movies Revenue 1950-2010')  
plt.xlabel('title') 
plt.ylabel('Revenue') 
plt.tight_layout() # 自动调整子图间的间距和间隔 
plt.savefig('movies_revenue_1950_2010.png',dpi=400) 
plt.show() 

#可视化电影的Starring Actors Popularity值 
plt.plot(df_renew['Title'],df_renew['Starring Actors Popularity'],color='green') 
plt.xticks(rotation=45) 
plt.title('Starring Actors Popularity 1950-2010') 
plt.xlabel('title') 
plt.ylabel('Starring Actors Popularity') 
plt.tight_layout() # 自动调整子图间的间距和间隔 
plt.savefig('movies_starpopularity_1950_2010.png',dpi=400) 
plt.show()

使用 `plt.tight_layout()`函数可以自动调整子图间的间距和间隔，使得保存的折线图更加紧凑和美观。