Thursday, 29 May 2025

194.IN-DEPTH ANALYSIS AND VISUALIZATION OF INDIAN PALACES DATASET | USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SORT | PROC SQL | MACROS FOR AUTOMATION | DATA CLEANING TECHNIQUES | STATISTICAL SUMMARY | ADVANCED REPORT GENERATION | INSIGHTS ON ARCHITECTURE AND TOURISM IMPACT

IN-DEPTH ANALYSIS AND VISUALIZATION OF INDIAN PALACES DATASET | USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SORT | PROC SQL | MACROS FOR AUTOMATION | DATA CLEANING TECHNIQUES | STATISTICAL SUMMARY | ADVANCED REPORT GENERATION | INSIGHTS ON ARCHITECTURE AND TOURISM IMPACT

/*Creating a unique dataset about different types of Indian palaces*/

Step 1: Create a Unique Dataset of Indian Palaces

data indian_palaces;

  length Palace_ID $5 Palace_Name $20 Location $15 State $15 Architect $30 Style $20 Condition $10;

  input Palace_ID :$5.

        Palace_Name :$20.

        Location :$15.

        State :$15.

        Architect :$30.

        Style :$20.

        Condition :$10.

        Year_Built

        Visitors_per_year

        Ticket_Price

        INR_Rs_in_Lakhs;

datalines;

P001 MysorePalace Mysore Karnataka HH_Mohan_Das Hindu Excellent 1912 2000000 300 500

P002 CityPalace Jaipur Rajasthan Maharaja_Sawai_Jai_Singh Rajput Good 1727 1500000 250 300

P003 UdaipurPalace Udaipur Rajasthan Maharana_Udai_Singh Rajput Excellent 1559 1000000 200 400

P004 HawaMahal Jaipur Rajasthan Lal_Chand_Ustad Rajput Good 1799 1200000 150 250

P005 AmberFort Jaipur Rajasthan Raja_Man_Singh Rajput Good 1592 1800000 200 350

P006 FalaknumaPalace Hyderabad Telangana Nawab_Vikar_ul_Mulk European Fair 1893 300000 1000 900

P007 ChowmahallaPalace Hyderabad Telangana Asaf_Jahi_Dynasty European Good 1750 400000 500 600

P008 CityPalace Bikaner Rajasthan Maharaja_Karan_Singh Rajput Fair 1594 500000 100 200

P009 UmaidBhawanPalace Jodhpur Rajasthan Henry_Lutyens Indo_Saracenic Excellent 1943 600000 600 700

P010 LaxmiVilasPalace Vadodara Gujarat Major_Charles_Mant Indo_Saracenic Fair 1890 250000 400 500

P011 JaiMahalPalace Jaipur Rajasthan Maharaja_Sawai_Man_Singh_II Rajput Good 1922 350000 300 400

P013 LalQila Delhi Delhi Shah_Jahan Mughal Excellent 1648 5000000 400 200

P014 RedFort Delhi Delhi Shah_Jahan Mughal Good 1639 4500000 350 180

P015 GolcondaFort Hyderabad Telangana Sultan_Quli_Qutub_Shah Fortification Fair 1518 700000 100 150

P016 JaisalmerFort Jaisalmer Rajasthan Rawal_Jaisal Rajput Good 1156 600000 150 100

;

run;

proc print;

run;

Output:

Obs Palace_ID Palace_Name Location State Architect Style Condition Year_Built Visitors_per_year Ticket_Price INR_Rs_in_Lakhs
1 P001 MysorePalace Mysore Karnataka HH_Mohan_Das Hindu Excellent 1912 2000000 300 500
2 P002 CityPalace Jaipur Rajasthan Maharaja_Sawai_Jai_Singh Rajput Good 1727 1500000 250 300
3 P003 UdaipurPalace Udaipur Rajasthan Maharana_Udai_Singh Rajput Excellent 1559 1000000 200 400
4 P004 HawaMahal Jaipur Rajasthan Lal_Chand_Ustad Rajput Good 1799 1200000 150 250
5 P005 AmberFort Jaipur Rajasthan Raja_Man_Singh Rajput Good 1592 1800000 200 350
6 P006 FalaknumaPalace Hyderabad Telangana Nawab_Vikar_ul_Mulk European Fair 1893 300000 1000 900
7 P007 ChowmahallaPalace Hyderabad Telangana Asaf_Jahi_Dynasty European Good 1750 400000 500 600
8 P008 CityPalace Bikaner Rajasthan Maharaja_Karan_Singh Rajput Fair 1594 500000 100 200
9 P009 UmaidBhawanPalace Jodhpur Rajasthan Henry_Lutyens Indo_Saracenic Excellent 1943 600000 600 700
10 P010 LaxmiVilasPalace Vadodara Gujarat Major_Charles_Mant Indo_Saracenic Fair 1890 250000 400 500
11 P011 JaiMahalPalace Jaipur Rajasthan Maharaja_Sawai_Man_Singh_II Rajput Good 1922 350000 300 400
12 P013 LalQila Delhi Delhi Shah_Jahan Mughal Excellent 1648 5000000 400 200
13 P014 RedFort Delhi Delhi Shah_Jahan Mughal Good 1639 4500000 350 180
14 P015 GolcondaFort Hyderabad Telangana Sultan_Quli_Qutub_Shah Fortification Fair 1518 700000 100 150
15 P016 JaisalmerFort Jaisalmer Rajasthan Rawal_Jaisal Rajput Good 1156 600000 150 100

Step 2: PROC PRINT — View the Dataset

proc print data=indian_palaces label noobs;

  title "List of Famous Indian Palaces with Attributes";

  var Palace_ID Palace_Name Location State Year_Built Architect Style Visitors_per_year Ticket_Price INR_Rs_in_Lakhs Condition;

run;

Output:

List of Famous Indian Palaces with Attributes

Palace_ID Palace_Name Location State Year_Built Architect Style Visitors_per_year Ticket_Price INR_Rs_in_Lakhs Condition
P001 MysorePalace Mysore Karnataka 1912 HH_Mohan_Das Hindu 2000000 300 500 Excellent
P002 CityPalace Jaipur Rajasthan 1727 Maharaja_Sawai_Jai_Singh Rajput 1500000 250 300 Good
P003 UdaipurPalace Udaipur Rajasthan 1559 Maharana_Udai_Singh Rajput 1000000 200 400 Excellent
P004 HawaMahal Jaipur Rajasthan 1799 Lal_Chand_Ustad Rajput 1200000 150 250 Good
P005 AmberFort Jaipur Rajasthan 1592 Raja_Man_Singh Rajput 1800000 200 350 Good
P006 FalaknumaPalace Hyderabad Telangana 1893 Nawab_Vikar_ul_Mulk European 300000 1000 900 Fair
P007 ChowmahallaPalace Hyderabad Telangana 1750 Asaf_Jahi_Dynasty European 400000 500 600 Good
P008 CityPalace Bikaner Rajasthan 1594 Maharaja_Karan_Singh Rajput 500000 100 200 Fair
P009 UmaidBhawanPalace Jodhpur Rajasthan 1943 Henry_Lutyens Indo_Saracenic 600000 600 700 Excellent
P010 LaxmiVilasPalace Vadodara Gujarat 1890 Major_Charles_Mant Indo_Saracenic 250000 400 500 Fair
P011 JaiMahalPalace Jaipur Rajasthan 1922 Maharaja_Sawai_Man_Singh_II Rajput 350000 300 400 Good
P013 LalQila Delhi Delhi 1648 Shah_Jahan Mughal 5000000 400 200 Excellent
P014 RedFort Delhi Delhi 1639 Shah_Jahan Mughal 4500000 350 180 Good
P015 GolcondaFort Hyderabad Telangana 1518 Sultan_Quli_Qutub_Shah Fortification 700000 100 150 Fair
P016 JaisalmerFort Jaisalmer Rajasthan 1156 Rawal_Jaisal Rajput 600000 150 100 Good

Step 3: PROC CONTENTS — Get Dataset Metadata

proc contents data=indian_palaces varnum;

  title "Metadata for Indian Palaces Dataset";

run;

Output:

Metadata for Indian Palaces Dataset

The CONTENTS Procedure

Data Set Name WORK.INDIAN_PALACES Observations 15
Member Type DATA Variables 11
Engine V9 Indexes 0
Created 14/09/2015 00:30:06 Observation Length 152
Last Modified 14/09/2015 00:30:06 Deleted Observations 0
Protection   Compressed NO
Data Set Type   Sorted NO
Label      
Data Representation WINDOWS_64    
Encoding wlatin1 Western (Windows)    


Engine/Host Dependent Information
Data Set Page Size 65536
Number of Data Set Pages 1
First Data Page 1
Max Obs per Page 430
Obs in First Data Page 15
Number of Data Set Repairs 0
ExtendObsCounter YES
Filename C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD8864_DESKTOP-QFAA4KV_\indian_palaces.sas7bdat
Release Created 9.0401M2
Host Created X64_8HOME


Variables in Creation Order
# Variable Type Len
1 Palace_ID Char 5
2 Palace_Name Char 20
3 Location Char 15
4 State Char 15
5 Architect Char 30
6 Style Char 20
7 Condition Char 10
8 Year_Built Num 8
9 Visitors_per_year Num 8
10 Ticket_Price Num 8
11 INR_Rs_in_Lakhs Num 8


Step 4: PROC FREQ — Frequency Distribution of Palace Conditions and Styles

proc freq data=indian_palaces order=freq;

  tables Condition Style / nocum nopercent;

  title "Frequency Distribution of Palace Condition and Architectural Style";

run;

Output:

Frequency Distribution of Palace Condition and Architectural Style

The FREQ Procedure

Condition Frequency
Good 7
Excellent 4
Fair 4


Style Frequency
Rajput 7
European 2
Indo_Saracenic 2
Mughal 2
Fortification 1
Hindu 1

Step 5: PROC MEANS — Statistical Summary of Numeric Variables

proc means data=indian_palaces mean median min max std n;

  var Year_Built Visitors_per_year Ticket_Price INR_Rs_in_Lakhs;

  title "Summary Statistics for Numeric Attributes of Indian Palaces";

run;

Output:

Summary Statistics for Numeric Attributes of Indian Palaces

The MEANS Procedure

Variable Mean Median Minimum Maximum Std Dev N
Year_Built
Visitors_per_year
Ticket_Price
INR_Rs_in_Lakhs
1702.80
1380000.00
333.3333333
382.0000000
1727.00
700000.00
300.0000000
350.0000000
1156.00
250000.00
100.0000000
100.0000000
1943.00
5000000.00
1000.00
900.0000000
210.0687643
1476216.21
235.0278606
225.3631989
15
15
15
15


Step 6: PROC SORT — Sort Palaces by Visitors and Ticket Price

proc sort data=indian_palaces out=palaces_sorted;

  by descending Visitors_per_year Ticket_Price;

run;


proc print data=palaces_sorted(obs=10) label noobs;

  title "Top 10 Palaces by Visitors and Ticket Price";

  var Palace_Name Visitors_per_year Ticket_Price Condition;

run;

Output:

Top 10 Palaces by Visitors and Ticket Price

Palace_Name Visitors_per_year Ticket_Price Condition
LalQila 5000000 400 Excellent
RedFort 4500000 350 Good
MysorePalace 2000000 300 Excellent
AmberFort 1800000 200 Good
CityPalace 1500000 250 Good
HawaMahal 1200000 150 Good
UdaipurPalace 1000000 200 Excellent
GolcondaFort 700000 100 Fair
JaisalmerFort 600000 150 Good
UmaidBhawanPalace 600000 600 Excellent

Step 7: PROC SQL — Advanced Queries

Query 1: Palaces with Visitors More Than 1 Million and Good or Excellent Condition

proc sql;

  title "Palaces with >1 Million Visitors and Good/Excellent Condition";

  select Palace_Name, Location, State, Visitors_per_year, Ticket_Price, Condition

  from indian_palaces

  where Visitors_per_year > 1000000 and Condition in ('Good', 'Excellent')

  order by Visitors_per_year desc;

quit;

Output:

Palaces with >1 Million Visitors and Good/Excellent Condition

Palace_Name Location State Visitors_per_year Ticket_Price Condition
LalQila Delhi Delhi 5000000 400 Excellent
RedFort Delhi Delhi 4500000 350 Good
MysorePalace Mysore Karnataka 2000000 300 Excellent
AmberFort Jaipur Rajasthan 1800000 200 Good
CityPalace Jaipur Rajasthan 1500000 250 Good
HawaMahal Jaipur Rajasthan 1200000 150 Good


Query 2: Average Ticket Price by State

proc sql;

  title "Average Ticket Price by State";

  select State, round(mean(Ticket_Price), 1) as Avg_Ticket_Price format=8.2

  from indian_palaces

  group by State

  order by Avg_Ticket_Price desc;

quit;

Output:

Average Ticket Price by State

State Avg_Ticket_Price
Telangana 533.00
Gujarat 400.00
Delhi 375.00
Karnataka 300.00
Rajasthan 244.00


Query 3: Count of Palaces by Architectural Style

proc sql;

  title "Number of Palaces by Architectural Style";

  select Style, count(*) as Count

  from indian_palaces

  group by Style

  order by Count desc;

quit;

Output:

Number of Palaces by Architectural Style

Style Count
Rajput 7
Mughal 2
European 2
Indo_Saracenic 2
Hindu 1
Fortification 1


Step 8: PROC FORMAT — Create User-defined Formats

proc format;

  value $condfmt

    'Excellent' = 'Top Condition'

    'Good' = 'Well Maintained'

    'Fair' = 'Needs Attention';

run;

Log:

NOTE: Format $CONDFMT has been output.

Step 9: Use PROC REPORT with Formats for a Summary Table

proc report data=indian_palaces nowd;

  column State Style Palace_Name Visitors_per_year Ticket_Price Condition;

  define State / group;

  define Style / group;

  define Palace_Name / display;

  define Visitors_per_year / analysis sum format=comma12.;

  define Ticket_Price / analysis mean format=dollar8.2;

  define Condition / group format=$condfmt.;

  title "Palace Summary Report by State and Style with Condition Formatting";

run;

Output:

Palace Summary Report by State and Style with Condition Formatting

State Style Palace_Name Visitors_per_year Ticket_Price Condition
Delhi Mughal LalQila 5,000,000 $400.00 Top Condition
    RedFort 4,500,000 $350.00 Well Maintained
Gujarat Indo_Saracenic LaxmiVilasPalace 250,000 $400.00 Needs Attention
Karnataka Hindu MysorePalace 2,000,000 $300.00 Top Condition
Rajasthan Indo_Saracenic UmaidBhawanPalace 600,000 $600.00 Top Condition
  Rajput CityPalace 500,000 $100.00 Needs Attention
    UdaipurPalace 1,000,000 $200.00 Top Condition
    CityPalace 1,500,000 $250.00 Well Maintained
    HawaMahal 1,200,000 $150.00  
    AmberFort 1,800,000 $200.00  
    JaiMahalPalace 350,000 $300.00  
    JaisalmerFort 600,000 $150.00  
Telangana European FalaknumaPalace 300,000 $1000.00 Needs Attention
    ChowmahallaPalace 400,000 $500.00 Well Maintained
  Fortification GolcondaFort 700,000 $100.00 Needs Attention

Step 10: PROC TRANSPOSE — Reshape Data for Visitors and Ticket Price by Palace

proc transpose data=indian_palaces out=transposed_palaces(drop=_NAME_);

  by Palace_ID Palace_Name;

  var Visitors_per_year Ticket_Price;

run;


proc print data=transposed_palaces;

  title "Transposed Visitors and Ticket Price by Palace";

run;

Output:

Transposed Visitors and Ticket Price by Palace

Obs Palace_ID Palace_Name COL1
1 P001 MysorePalace 2000000
2 P001 MysorePalace 300
3 P002 CityPalace 1500000
4 P002 CityPalace 250
5 P003 UdaipurPalace 1000000
6 P003 UdaipurPalace 200
7 P004 HawaMahal 1200000
8 P004 HawaMahal 150
9 P005 AmberFort 1800000
10 P005 AmberFort 200
11 P006 FalaknumaPalace 300000
12 P006 FalaknumaPalace 1000
13 P007 ChowmahallaPalace 400000
14 P007 ChowmahallaPalace 500
15 P008 CityPalace 500000
16 P008 CityPalace 100
17 P009 UmaidBhawanPalace 600000
18 P009 UmaidBhawanPalace 600
19 P010 LaxmiVilasPalace 250000
20 P010 LaxmiVilasPalace 400
21 P011 JaiMahalPalace 350000
22 P011 JaiMahalPalace 300
23 P013 LalQila 5000000
24 P013 LalQila 400
25 P014 RedFort 4500000
26 P014 RedFort 350
27 P015 GolcondaFort 700000
28 P015 GolcondaFort 100
29 P016 JaisalmerFort 600000
30 P016 JaisalmerFort 150


Step 11: PROC UNIVARIATE — Distribution Analysis of Visitors and Ticket Price

proc univariate data=indian_palaces;

  var Visitors_per_year Ticket_Price;

  histogram / normal;

  inset mean median std / position=ne;

  title "Univariate Analysis of Visitors and Ticket Price";

run;

Log:

NOTE: PROCEDURE UNIVARIATE used (Total process time):

      real time           4.89 seconds

      cpu time            0.90 seconds


Step 12: PROC SGPLOT — Visualizing Ticket Price vs Visitors

proc sgplot data=indian_palaces;

  scatter x=Visitors_per_year y=Ticket_Price / group=Condition markerattrs=(symbol=circlefilled size=10);

  reg x=Visitors_per_year y=Ticket_Price / nomarkers;

  xaxis label="Annual Visitors";

  yaxis label="Ticket Price (INR)";

  title "Scatter Plot of Ticket Price vs Annual Visitors by Palace Condition";

run;

Log:

NOTE: PROCEDURE SGPLOT used (Total process time):

      real time           0.79 seconds

      cpu time            0.06 seconds


NOTE: Listing image output written to SGPlot1.png.

NOTE: There were 15 observations read from the data set WORK.INDIAN_PALACES.

Step 13: Create a Macro to Filter Palaces Based on Minimum Visitors and Condition

%macro filter_palaces(min_visitors=, condition=);

  proc sql;

    title "Filtered Palaces with Visitors >= &min_visitors and Condition = &condition";

    select Palace_ID, Palace_Name, Location, State, Visitors_per_year, Ticket_Price, Condition

    from indian_palaces

    where Visitors_per_year >= &min_visitors and Condition = "&condition"

    order by Visitors_per_year desc;

  quit;

%mend;


%filter_palaces(min_visitors=500000, condition=Good)

Output:

Filtered Palaces with Visitors >= 500000 and Condition = Good

Palace_ID Palace_Name Location State Visitors_per_year Ticket_Price Condition
P014 RedFort Delhi Delhi 4500000 350 Good
P005 AmberFort Jaipur Rajasthan 1800000 200 Good
P002 CityPalace Jaipur Rajasthan 1500000 250 Good
P004 HawaMahal Jaipur Rajasthan 1200000 150 Good
P016 JaisalmerFort Jaisalmer Rajasthan 600000 150 Good

Step 14: PROC COMPARE — Compare Two Palace Datasets (Example: Original vs Modified)

data modified_palaces;

  set indian_palaces;

  if Palace_ID = "P002" then Ticket_Price = 300;

  if Palace_ID = "P009" then Condition = "Good";

run;


proc compare base=indian_palaces compare=modified_palaces;

  id Palace_ID;

  var Ticket_Price Condition;

  title "Comparison of Original vs Modified Palace Dataset";

run;

Output:



Step 15: PROC APPEND — Append a New Palace Record

data new_palace;

  length Palace_ID $5 Palace_Name $20 Location $15 State $15 Architect $30 Style $20 Condition $10;

  input Palace_ID :$5.

        Palace_Name :$20.

        Location :$15.

        State :$15.

        Architect :$30.

        Style :$20.

        Condition :$10.

        Year_Built

        Visitors_per_year

        Ticket_Price

        INR_Rs_in_Lakhs;

   datalines;

P017 RajwadaPalace Indore MadhyaPradesh Malhar_Rao_Holkar Maratha Fair 1747 300000 150 120

;

run;

proc append base=indian_palaces data=new_palace force;

run;

proc print data=indian_palaces;

  title "Indian Palaces Dataset After Adding a New Palace";

run;

Output:

Indian Palaces Dataset After Adding a New Palace

ObsPalace_IDPalace_NameLocationStateArchitectStyleConditionYear_BuiltVisitors_per_yearTicket_PriceINR_Rs_in_Lakhs
1P001MysorePalaceMysoreKarnatakaHH_Mohan_DasHinduExcellent19122000000300500
2P002CityPalaceJaipurRajasthanMaharaja_Sawai_Jai_SinghRajputGood17271500000250300
3P003UdaipurPalaceUdaipurRajasthanMaharana_Udai_SinghRajputExcellent15591000000200400
4P004HawaMahalJaipurRajasthanLal_Chand_UstadRajputGood17991200000150250
5P005AmberFortJaipurRajasthanRaja_Man_SinghRajputGood15921800000200350
6P006FalaknumaPalaceHyderabadTelanganaNawab_Vikar_ul_MulkEuropeanFair18933000001000900
7P007ChowmahallaPalaceHyderabadTelanganaAsaf_Jahi_DynastyEuropeanGood1750400000500600
8P008CityPalaceBikanerRajasthanMaharaja_Karan_SinghRajputFair1594500000100200
9P009UmaidBhawanPalaceJodhpurRajasthanHenry_LutyensIndo_SaracenicExcellent1943600000600700
10P010LaxmiVilasPalaceVadodaraGujaratMajor_Charles_MantIndo_SaracenicFair1890250000400500
11P011JaiMahalPalaceJaipurRajasthanMaharaja_Sawai_Man_Singh_IIRajputGood1922350000300400
12P013LalQilaDelhiDelhiShah_JahanMughalExcellent16485000000400200
13P014RedFortDelhiDelhiShah_JahanMughalGood16394500000350180
14P015GolcondaFortHyderabadTelanganaSultan_Quli_Qutub_ShahFortificationFair1518700000100150
15P016JaisalmerFortJaisalmerRajasthanRawal_JaisalRajputGood1156600000150100
16P017RajwadaPalaceIndoreMadhyaPradeshMalhar_Rao_HolkarMarathaFair1747300000150120

Step 16: PROC SQL Macro — Summarize Visitors and Ticket Price by State and Condition

%macro palace_summary(state=, condition=);

  proc sql;

    title "Summary for &state Palaces with Condition &condition";

    select count(*) as Num_Palaces,

           sum(Visitors_per_year) as Total_Visitors format=comma12.,

           mean(Ticket_Price) as Avg_Ticket_Price format=dollar8.2

    from indian_palaces

    where State = "&state" and Condition = "&condition";

  quit;

%mend;


%palace_summary(state=Rajasthan, condition=Good)

Output:

Summary for Rajasthan Palaces with Condition Good

Num_Palaces Total_Visitors Avg_Ticket_Price
5 5,450,000 $210.00

Step 17: Final Dataset Summary — PROC SUMMARY

proc summary data=indian_palaces print;

  class State Condition;

  var Visitors_per_year Ticket_Price;

  output out=summary_stats 

    mean= mean_Visitors mean_TicketPrice

    sum= sum_Visitors sum_TicketPrice;

  title "Summary Statistics by State and Condition";

run;


proc print data=summary_stats noobs;

  title "Detailed Summary by State and Condition";

run;

Output:

Detailed Summary by State and Condition

State Condition _TYPE_ _FREQ_ mean_Visitors mean_TicketPrice sum_Visitors sum_TicketPrice
    0 16 1312500.00 321.875 21000000 5150
  Excellent 1 4 2150000.00 375.000 8600000 1500
  Fair 1 5 410000.00 350.000 2050000 1750
  Good 1 7 1478571.43 271.429 10350000 1900
Delhi   2 2 4750000.00 375.000 9500000 750
Gujarat   2 1 250000.00 400.000 250000 400
Karnataka   2 1 2000000.00 300.000 2000000 300
MadhyaPradesh   2 1 300000.00 150.000 300000 150
Rajasthan   2 8 943750.00 243.750 7550000 1950
Telangana   2 3 466666.67 533.333 1400000 1600
Delhi Excellent 3 1 5000000.00 400.000 5000000 400
Delhi Good 3 1 4500000.00 350.000 4500000 350
Gujarat Fair 3 1 250000.00 400.000 250000 400
Karnataka Excellent 3 1 2000000.00 300.000 2000000 300
MadhyaPradesh Fair 3 1 300000.00 150.000 300000 150
Rajasthan Excellent 3 2 800000.00 400.000 1600000 800
Rajasthan Fair 3 1 500000.00 100.000 500000 100
Rajasthan Good 3 5 1090000.00 210.000 5450000 1050
Telangana Fair 3 2 500000.00 550.000 1000000 1100
Telangana Good 3 1 400000.00 500.000 400000 500


Step 18: PROC EXPORT — Export Dataset to CSV for Reporting

proc export data=indian_palaces

  outfile="/mnt/data/indian_palaces.csv"

  dbms=csv

  replace;

  putnames=yes;

run;

Log:

NOTE: "/mnt/data/indian_palaces.csv" file was successfully created.

NOTE: PROCEDURE EXPORT used (Total process time):

      real time           1.59 seconds

      cpu time            0.12 seconds


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