Overview

Dataset statistics

Number of variables112
Number of observations10000
Missing cells506020
Missing cells (%)45.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory9.2 MiB
Average record size in memory966.0 B

Variable types

Categorical44
DateTime14
Unsupported10
Boolean9
Numeric26
Text9

Dataset

Description산림복지전문업지원시스템에서 추출한 산림복지 전문업 결제정보입니다.
Author한국산림복지진흥원
URLhttps://www.data.go.kr/data/15091540/fileData.do

Alerts

유효여부(VLDTY_YN) has constant value ""Constant
환불사유(RFNDM_RSN) has constant value ""Constant
환불완료일시(RFNDM_CMPLE_DTM) has constant value ""Constant
최종수정일시(LAST_UPDT_DTM).2 has constant value ""Constant
유효여부(VLDTY_YN).2 has constant value ""Constant
유효여부(VLDTY_YN).3 has constant value ""Constant
시도명(CTPRV_NM) has constant value ""Constant
등록일시(VLDTY_YN) has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
상품(ID) is highly imbalanced (98.6%)Imbalance
시설대분류코드(FCLT_LRCLS_CD) is highly imbalanced (99.0%)Imbalance
시설용도구분코드(FCLT_PRPSE_TPCD) is highly imbalanced (98.6%)Imbalance
시즌구분코드(SSN_TPCD) is highly imbalanced (99.0%)Imbalance
일자구분코드(DT_TPCD) is highly imbalanced (99.0%)Imbalance
위약규정일수(PNLT_RGLTN_DCNT) is highly imbalanced (99.3%)Imbalance
공제율비(DDCTN_RATE) is highly imbalanced (99.4%)Imbalance
취소사유(CANCELMSG) is highly imbalanced (82.5%)Imbalance
부분취소구분(PARTIALCANCELCODE) is highly imbalanced (88.1%)Imbalance
결제순번(STTLM_SEQ) is highly imbalanced (97.2%)Imbalance
유효여부(VLDTY_YN).1 is highly imbalanced (75.6%)Imbalance
환불유형코드(RFNDM_TPE_CD) is highly imbalanced (96.1%)Imbalance
부서ID(DPRTM_ID) is highly imbalanced (92.5%)Imbalance
결제순번(STTLM_SEQ).2 is highly imbalanced (96.6%)Imbalance
위약금금액(PNLT_AMT).1 is highly imbalanced (96.6%)Imbalance
결제순번(STTLM_SEQ).3 is highly imbalanced (80.0%)Imbalance
시설대분류코드(FCLT_LRCLS_CD).1 is highly imbalanced (99.0%)Imbalance
시설용도구분코드(FCLT_PRPSE_TPCD).1 is highly imbalanced (98.6%)Imbalance
시즌구분코드(SSN_TPCD).1 is highly imbalanced (99.0%)Imbalance
일자구분코드(DT_TPCD).1 is highly imbalanced (99.0%)Imbalance
위약규정일수(PNLT_RGLTN_DCNT).1 is highly imbalanced (99.3%)Imbalance
공제율비(DDCTN_RATE).1 is highly imbalanced (99.4%)Imbalance
시설용도구분코드(FCLT_PRPSE_TPCD).2 is highly imbalanced (82.7%)Imbalance
시설예약구분코드(FCLT_RSRVT_TPCD) is highly imbalanced (84.9%)Imbalance
시설이미지맵좌표(FCLT_IMAGE_MAP_CRD) is highly imbalanced (97.3%)Imbalance
구역명(ZONE_NM) is highly imbalanced (91.9%)Imbalance
면적단위(AREA_UNIT) is highly imbalanced (81.8%)Imbalance
시설폐쇄일자(FCLT_CLSNG_DTM) is highly imbalanced (89.6%)Imbalance
난방시설유형코드(HEAT_FCLT_TPE_CD) is highly imbalanced (90.3%)Imbalance
시설상태코드(FCLT_STCD) is highly imbalanced (80.0%)Imbalance
AS-IS 자료(PRODUCT_CODE) is highly imbalanced (95.2%)Imbalance
가로크기(FCLT_SIZE_W) is highly imbalanced (96.2%)Imbalance
세로크기(FCLT_SIZE_H) is highly imbalanced (96.0%)Imbalance
산여부(MNTN_YN) is highly imbalanced (94.3%)Imbalance
지하여부(UNDRG_YN) is highly imbalanced (99.7%)Imbalance
공동건물여부(CPRTI_BLDNG_YN) is highly imbalanced (83.0%)Imbalance
등록일시(RGTER_ID) has 9987 (99.9%) missing valuesMissing
최종수정일시(LAST_UPDT_DTM) has 10000 (100.0%) missing valuesMissing
유효여부(VLDTY_YN) has 9987 (99.9%) missing valuesMissing
취소금액(CANCELAMT) has 8504 (85.0%) missing valuesMissing
부분취소구분(PARTIALCANCELCODE) has 8504 (85.0%) missing valuesMissing
취소결과코드(CANCELRESULTCODE) has 8504 (85.0%) missing valuesMissing
위약금금액(PNLT_AMT) has 8513 (85.1%) missing valuesMissing
결제만기일시(STTLM_MT_DTM) has 9325 (93.2%) missing valuesMissing
결제완료일시(STTLM_CMPLE_DTM) has 114 (1.1%) missing valuesMissing
최종수정일시(LAST_UPDT_DTM).1 has 9626 (96.3%) missing valuesMissing
환불테이블순번(RFNDM_TABLE_SEQ) has 9908 (99.1%) missing valuesMissing
예약순번(RSRVT_SEQ) has 9909 (99.1%) missing valuesMissing
환불금액(RFNDM_AMT) has 9908 (99.1%) missing valuesMissing
환불사유(RFNDM_RSN) has 9999 (> 99.9%) missing valuesMissing
환불요청일시(RFNDM_RQUST_DTM) has 9908 (99.1%) missing valuesMissing
환불완료일시(RFNDM_CMPLE_DTM) has 9999 (> 99.9%) missing valuesMissing
등록일시(RGSTN_DTM).1 has 9908 (99.1%) missing valuesMissing
최종수정일시(LAST_UPDT_DTM).2 has 9999 (> 99.9%) missing valuesMissing
유효여부(VLDTY_YN).2 has 9908 (99.1%) missing valuesMissing
결제상품금액(AMT) has 5810 (58.1%) missing valuesMissing
거래타입(TRANSTYPE) has 10000 (100.0%) missing valuesMissing
상점예비정보(MALLRESERVED) has 10000 (100.0%) missing valuesMissing
가상계좌입금만료일(VBANKEXPDATE) has 9888 (98.9%) missing valuesMissing
소켓이용유무(SOCKETYN) has 10000 (100.0%) missing valuesMissing
전문생성일시코드(EDIDATECODE) has 5860 (58.6%) missing valuesMissing
해쉬값(ENCRYPTDATA) has 10000 (100.0%) missing valuesMissing
승인일자코드(AUTHDATECODE) has 5822 (58.2%) missing valuesMissing
결과코드(RESULTCODE) has 5820 (58.2%) missing valuesMissing
등록일시(RGTER_ID).1 has 9987 (99.9%) missing valuesMissing
최종수정일시(LAST_UPDT_DTM).3 has 10000 (100.0%) missing valuesMissing
유효여부(VLDTY_YN).3 has 9987 (99.9%) missing valuesMissing
부서ID(DPRTM_ID).1 has 9499 (95.0%) missing valuesMissing
시설ID(FCLT_ID) has 9499 (95.0%) missing valuesMissing
시설명(FCLT_NM) has 9499 (95.0%) missing valuesMissing
시설설명(FCLT_DSCRT) has 9504 (95.0%) missing valuesMissing
시설대분류코드(FCLT_LRCLS_CD).2 has 9499 (95.0%) missing valuesMissing
시설중분류코드(FCLT_MDCLS_CD) has 9499 (95.0%) missing valuesMissing
시설소분류코드(FCLT_SMCLS_CD) has 9535 (95.3%) missing valuesMissing
배치물품내용(PSTNG_THING_CONT) has 9773 (97.7%) missing valuesMissing
면적합계(AREA_SUM) has 9688 (96.9%) missing valuesMissing
최소수용수(MNMM_ACCPT_CNT) has 9499 (95.0%) missing valuesMissing
최대수용수(MXMM_ACCPT_CNT) has 9499 (95.0%) missing valuesMissing
시설오픈일자(FCLT_OPEN_DTM) has 9620 (96.2%) missing valuesMissing
전기시설여부(ELCTY_FCLT_YN) has 9499 (95.0%) missing valuesMissing
난방시설여부(HEAT_FCLT_YN) has 9499 (95.0%) missing valuesMissing
등록일시(RGSTN_DTM).2 has 9499 (95.0%) missing valuesMissing
최종수정일시(LAST_UPDT_DTM).4 has 9656 (96.6%) missing valuesMissing
유효여부(VLDTY_YN).4 has 9499 (95.0%) missing valuesMissing
(PPL_CNT) has 9812 (98.1%) missing valuesMissing
리명(LI_NM) has 10000 (100.0%) missing valuesMissing
건물명(BLDNG_NM) has 8380 (83.8%) missing valuesMissing
상세건물명(DTL_BLDNG_NM) has 9905 (99.1%) missing valuesMissing
기타명(ETC_NM) has 9954 (99.5%) missing valuesMissing
변경구분코드(CHNG_TPCD) has 10000 (100.0%) missing valuesMissing
변경일(CHDT) has 10000 (100.0%) missing valuesMissing
최종수정일시(LAST_UPDT_DTM) is an unsupported type, check if it needs cleaning or further analysisUnsupported
거래타입(TRANSTYPE) is an unsupported type, check if it needs cleaning or further analysisUnsupported
상점예비정보(MALLRESERVED) is an unsupported type, check if it needs cleaning or further analysisUnsupported
소켓이용유무(SOCKETYN) is an unsupported type, check if it needs cleaning or further analysisUnsupported
해쉬값(ENCRYPTDATA) is an unsupported type, check if it needs cleaning or further analysisUnsupported
결과코드(RESULTCODE) is an unsupported type, check if it needs cleaning or further analysisUnsupported
최종수정일시(LAST_UPDT_DTM).3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
리명(LI_NM) is an unsupported type, check if it needs cleaning or further analysisUnsupported
변경구분코드(CHNG_TPCD) is an unsupported type, check if it needs cleaning or further analysisUnsupported
변경일(CHDT) is an unsupported type, check if it needs cleaning or further analysisUnsupported
결제금액(STTLM_AMT) has 1527 (15.3%) zerosZeros
위약금금액(PNLT_AMT) has 1409 (14.1%) zerosZeros
면적합계(AREA_SUM) has 207 (2.1%) zerosZeros
시작부번지명(BGN_SUB_LTNMB_NM) has 499 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-12 17:39:33.726518
Analysis finished2023-12-12 17:39:38.012583
Duration4.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상품(ID)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
G20160727
 
13

Length

Max length9
Median length4
Mean length4.0065
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
G20160727 13
 
0.1%

Length

2023-12-13T02:39:38.087756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:38.195885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
g20160727 13
 
0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
7
 
10
3
 
3

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
7 10
 
0.1%
3 3
 
< 0.1%

Length

2023-12-13T02:39:38.297050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:38.393164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
7 10
 
0.1%
3 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
1
 
13

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
1 13
 
0.1%

Length

2023-12-13T02:39:38.501992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:38.612818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
1 13
 
0.1%

시즌구분코드(SSN_TPCD)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
2
 
9
1
 
4

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
2 9
 
0.1%
1 4
 
< 0.1%

Length

2023-12-13T02:39:38.939919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:39.066670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
2 9
 
0.1%
1 4
 
< 0.1%

일자구분코드(DT_TPCD)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
1
 
8
2
 
5

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
1 8
 
0.1%
2 5
 
0.1%

Length

2023-12-13T02:39:39.185671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:39.294414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
1 8
 
0.1%
2 5
 
< 0.1%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
2
 
4
1
 
3
4
 
2
3
 
2

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
2 4
 
< 0.1%
1 3
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
0 2
 
< 0.1%

Length

2023-12-13T02:39:39.406567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:39.505094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
2 4
 
< 0.1%
1 3
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
0 2
 
< 0.1%

공제율비(DDCTN_RATE)
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
30
 
9
90
 
1
80
 
1
100
 
1

Length

Max length4
Median length4
Mean length3.9974
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
30 9
 
0.1%
90 1
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
0 1
 
< 0.1%

Length

2023-12-13T02:39:39.625603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:39.768733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
30 9
 
0.1%
90 1
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
0 1
 
< 0.1%
Distinct3
Distinct (%)23.1%
Missing9987
Missing (%)99.9%
Memory size156.2 KiB
Minimum2018-06-22 11:21:00
Maximum2018-06-22 13:07:00
2023-12-13T02:39:39.884815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:39.985545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

최종수정일시(LAST_UPDT_DTM)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

유효여부(VLDTY_YN)
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)7.7%
Missing9987
Missing (%)99.9%
Memory size97.7 KiB
True
 
13
(Missing)
9987 
ValueCountFrequency (%)
True 13
 
0.1%
(Missing) 9987
99.9%
2023-12-13T02:39:40.098631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

취소금액(CANCELAMT)
Real number (ℝ)

MISSING 

Distinct461
Distinct (%)30.8%
Missing8504
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean137442.46
Minimum0
Maximum1079500
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:40.238984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27000
Q164000
median100000
Q3168000
95-th percentile374000
Maximum1079500
Range1079500
Interquartile range (IQR)104000

Descriptive statistics

Standard deviation119452.73
Coefficient of variation (CV)0.86911085
Kurtosis9.1649026
Mean137442.46
Median Absolute Deviation (MAD)48000
Skewness2.471099
Sum2.0561392 × 108
Variance1.4268955 × 1010
MonotonicityNot monotonic
2023-12-13T02:39:40.405376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90000 117
 
1.2%
50000 47
 
0.5%
46000 31
 
0.3%
30000 29
 
0.3%
70000 29
 
0.3%
60000 29
 
0.3%
120000 27
 
0.3%
82000 27
 
0.3%
27000 26
 
0.3%
74000 23
 
0.2%
Other values (451) 1111
 
11.1%
(Missing) 8504
85.0%
ValueCountFrequency (%)
0 3
< 0.1%
1790 2
 
< 0.1%
4000 5
0.1%
4500 2
 
< 0.1%
6000 6
0.1%
6350 1
 
< 0.1%
7000 6
0.1%
9000 2
 
< 0.1%
9200 1
 
< 0.1%
9750 3
< 0.1%
ValueCountFrequency (%)
1079500 1
< 0.1%
1038200 1
< 0.1%
798000 1
< 0.1%
795000 1
< 0.1%
703000 1
< 0.1%
678500 1
< 0.1%
673000 1
< 0.1%
667000 1
< 0.1%
661000 1
< 0.1%
650000 1
< 0.1%

취소사유(CANCELMSG)
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8507 
취소 성공
1357 
취소성공
 
32
취소패스워드 불일치
 
32
취소금액이 미정산 금액보다 큽니다.
 
26
Other values (12)
 
46

Length

Max length46
Median length4
Mean length4.2586
Min length4

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 8507
85.1%
취소 성공 1357
 
13.6%
취소성공 32
 
0.3%
취소패스워드 불일치 32
 
0.3%
취소금액이 미정산 금액보다 큽니다. 26
 
0.3%
타상점 거래 취소 불가. 14
 
0.1%
해당거래 취소실패(기취소성공) : 전화 문의(1661-0808) 9
 
0.1%
전체금액취소 불가. 6
 
0.1%
부분취소 불가능 복합결제 5
 
0.1%
취소 요청금액 0원 이하 취소불가 3
 
< 0.1%
Other values (7) 9
 
0.1%

Length

2023-12-13T02:39:40.539508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8507
73.3%
취소 1376
 
11.9%
성공 1359
 
11.7%
불일치 33
 
0.3%
취소성공 32
 
0.3%
취소패스워드 32
 
0.3%
취소금액이 28
 
0.2%
미정산 26
 
0.2%
금액보다 26
 
0.2%
큽니다 26
 
0.2%
Other values (35) 154
 
1.3%

부분취소구분(PARTIALCANCELCODE)
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing8504
Missing (%)85.0%
Memory size97.7 KiB
True
1472 
False
 
24
(Missing)
8504 
ValueCountFrequency (%)
True 1472
 
14.7%
False 24
 
0.2%
(Missing) 8504
85.0%
2023-12-13T02:39:40.661801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

취소결과코드(CANCELRESULTCODE)
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)1.0%
Missing8504
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean2002.8543
Minimum1535
Maximum2211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:40.828009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1535
5-th percentile2001
Q12001
median2001
Q32001
95-th percentile2024
Maximum2211
Range676
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.748948
Coefficient of variation (CV)0.0083625393
Kurtosis454.49321
Mean2002.8543
Median Absolute Deviation (MAD)0
Skewness-10.460868
Sum2996270
Variance280.52725
MonotonicityNot monotonic
2023-12-13T02:39:40.999017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2001 1392
 
13.9%
2024 32
 
0.3%
2026 26
 
0.3%
2035 14
 
0.1%
2015 8
 
0.1%
2031 6
 
0.1%
2051 5
 
0.1%
2010 3
 
< 0.1%
2211 3
 
< 0.1%
2032 2
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 8504
85.0%
ValueCountFrequency (%)
1535 1
 
< 0.1%
2001 1392
13.9%
2010 3
 
< 0.1%
2011 1
 
< 0.1%
2015 8
 
0.1%
2024 32
 
0.3%
2026 26
 
0.3%
2030 1
 
< 0.1%
2031 6
 
0.1%
2032 2
 
< 0.1%
ValueCountFrequency (%)
2211 3
 
< 0.1%
2051 5
 
0.1%
2047 1
 
< 0.1%
2035 14
0.1%
2033 1
 
< 0.1%
2032 2
 
< 0.1%
2031 6
 
0.1%
2030 1
 
< 0.1%
2026 26
0.3%
2024 32
0.3%

결제순번(STTLM_SEQ)
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9926 
2
 
47
3
 
21
4
 
3
5
 
2

Length

Max length4
Median length4
Mean length3.9779
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9926
99.3%
2 47
 
0.5%
3 21
 
0.2%
4 3
 
< 0.1%
5 2
 
< 0.1%
13 1
 
< 0.1%

Length

2023-12-13T02:39:41.192864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:41.335458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9926
99.3%
2 47
 
0.5%
3 21
 
0.2%
4 3
 
< 0.1%
5 2
 
< 0.1%
13 1
 
< 0.1%

결제순번(STTLM_SEQ).1
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.481
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:41.449921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92082778
Coefficient of variation (CV)0.62176082
Kurtosis68.369229
Mean1.481
Median Absolute Deviation (MAD)0
Skewness5.7928895
Sum14810
Variance0.84792379
MonotonicityNot monotonic
2023-12-13T02:39:41.595084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 6590
65.9%
2 2437
 
24.4%
3 805
 
8.1%
4 122
 
1.2%
11 20
 
0.2%
5 9
 
0.1%
6 7
 
0.1%
13 2
 
< 0.1%
16 2
 
< 0.1%
20 1
 
< 0.1%
Other values (5) 5
 
0.1%
ValueCountFrequency (%)
1 6590
65.9%
2 2437
 
24.4%
3 805
 
8.1%
4 122
 
1.2%
5 9
 
0.1%
6 7
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 20
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
11 20
0.2%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 7
 
0.1%

결제금액(STTLM_AMT)
Real number (ℝ)

ZEROS 

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41237.848
Minimum0
Maximum1502000
Zeros1527
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:41.757238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19000
median32000
Q360000
95-th percentile104000
Maximum1502000
Range1502000
Interquartile range (IQR)51000

Descriptive statistics

Standard deviation56819.14
Coefficient of variation (CV)1.3778396
Kurtosis110.47938
Mean41237.848
Median Absolute Deviation (MAD)25000
Skewness6.9806083
Sum4.1237848 × 108
Variance3.2284147 × 109
MonotonicityNot monotonic
2023-12-13T02:39:41.994957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 1783
17.8%
0 1527
15.3%
32000 650
 
6.5%
40000 624
 
6.2%
73000 485
 
4.9%
50000 462
 
4.6%
58000 358
 
3.6%
57000 275
 
2.8%
89000 271
 
2.7%
7000 250
 
2.5%
Other values (249) 3315
33.1%
ValueCountFrequency (%)
0 1527
15.3%
1000 10
 
0.1%
1300 1
 
< 0.1%
1500 1
 
< 0.1%
2000 28
 
0.3%
2300 2
 
< 0.1%
2500 2
 
< 0.1%
2600 6
 
0.1%
3000 10
 
0.1%
3200 1
 
< 0.1%
ValueCountFrequency (%)
1502000 1
 
< 0.1%
1354000 1
 
< 0.1%
964000 1
 
< 0.1%
952000 2
< 0.1%
767000 1
 
< 0.1%
691000 1
 
< 0.1%
663000 1
 
< 0.1%
642000 3
< 0.1%
550000 3
< 0.1%
519000 1
 
< 0.1%

위약금금액(PNLT_AMT)
Real number (ℝ)

MISSING  ZEROS 

Distinct37
Distinct (%)2.5%
Missing8513
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean502.89845
Minimum0
Maximum84000
Zeros1409
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:42.189214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile600
Maximum84000
Range84000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3572.3295
Coefficient of variation (CV)7.1034808
Kurtosis253.68304
Mean502.89845
Median Absolute Deviation (MAD)0
Skewness13.714873
Sum747810
Variance12761538
MonotonicityNot monotonic
2023-12-13T02:39:42.404892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 1409
 
14.1%
600 9
 
0.1%
1200 6
 
0.1%
10000 4
 
< 0.1%
17800 4
 
< 0.1%
4000 3
 
< 0.1%
11600 3
 
< 0.1%
7400 3
 
< 0.1%
6400 3
 
< 0.1%
5800 3
 
< 0.1%
Other values (27) 40
 
0.4%
(Missing) 8513
85.1%
ValueCountFrequency (%)
0 1409
14.1%
600 9
 
0.1%
750 1
 
< 0.1%
1100 2
 
< 0.1%
1200 6
 
0.1%
1400 1
 
< 0.1%
1800 1
 
< 0.1%
2300 2
 
< 0.1%
3200 2
 
< 0.1%
4000 3
 
< 0.1%
ValueCountFrequency (%)
84000 1
 
< 0.1%
51000 1
 
< 0.1%
43500 1
 
< 0.1%
37500 1
 
< 0.1%
23400 1
 
< 0.1%
20800 2
< 0.1%
20400 1
 
< 0.1%
18000 1
 
< 0.1%
17800 4
< 0.1%
14600 2
< 0.1%
Distinct83
Distinct (%)12.3%
Missing9325
Missing (%)93.2%
Memory size156.2 KiB
Minimum2013-06-08 23:59:00
Maximum2017-06-27 23:59:00
2023-12-13T02:39:42.635562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:42.861506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7764
Distinct (%)78.5%
Missing114
Missing (%)1.1%
Memory size156.2 KiB
Minimum2013-05-15 09:02:00
Maximum2017-06-23 17:33:00
2023-12-13T02:39:43.081034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:43.311773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3001
5050 
CASH
1682 
2001
1110 
4110
915 
4100
672 
Other values (5)
571 

Length

Max length5
Median length4
Mean length4.0024
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4000
2nd rowCASH
3rd row3001
4th row4110
5th row2001

Common Values

ValueCountFrequency (%)
3001 5050
50.5%
CASH 1682
 
16.8%
2001 1110
 
11.1%
4110 915
 
9.2%
4100 672
 
6.7%
4000 455
 
4.5%
2211 75
 
0.8%
RFNDM 24
 
0.2%
9999 9
 
0.1%
2024 8
 
0.1%

Length

2023-12-13T02:39:43.552137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:43.715676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3001 5050
50.5%
cash 1682
 
16.8%
2001 1110
 
11.1%
4110 915
 
9.2%
4100 672
 
6.7%
4000 455
 
4.5%
2211 75
 
0.8%
rfndm 24
 
0.2%
9999 9
 
0.1%
2024 8
 
0.1%
Distinct7142
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2013-05-15 09:00:00
Maximum2017-06-23 17:33:00
2023-12-13T02:39:43.865508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:44.024118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct301
Distinct (%)80.5%
Missing9626
Missing (%)96.3%
Memory size156.2 KiB
Minimum2013-05-15 09:00:00
Maximum2017-06-23 09:48:00
2023-12-13T02:39:44.193315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:44.381788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

유효여부(VLDTY_YN).1
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
9596 
False
 
404
ValueCountFrequency (%)
True 9596
96.0%
False 404
 
4.0%
2023-12-13T02:39:44.552357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

환불테이블순번(RFNDM_TABLE_SEQ)
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)100.0%
Missing9908
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean20573.283
Minimum20372
Maximum20777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:44.687214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20372
5-th percentile20397.75
Q120466
median20556.5
Q320707.25
95-th percentile20766.25
Maximum20777
Range405
Interquartile range (IQR)241.25

Descriptive statistics

Standard deviation126.088
Coefficient of variation (CV)0.0061287253
Kurtosis-1.3452165
Mean20573.283
Median Absolute Deviation (MAD)125
Skewness0.13991989
Sum1892742
Variance15898.183
MonotonicityNot monotonic
2023-12-13T02:39:45.253389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20557 1
 
< 0.1%
20418 1
 
< 0.1%
20598 1
 
< 0.1%
20459 1
 
< 0.1%
20463 1
 
< 0.1%
20728 1
 
< 0.1%
20575 1
 
< 0.1%
20769 1
 
< 0.1%
20478 1
 
< 0.1%
20533 1
 
< 0.1%
Other values (82) 82
 
0.8%
(Missing) 9908
99.1%
ValueCountFrequency (%)
20372 1
< 0.1%
20384 1
< 0.1%
20387 1
< 0.1%
20389 1
< 0.1%
20395 1
< 0.1%
20400 1
< 0.1%
20401 1
< 0.1%
20402 1
< 0.1%
20404 1
< 0.1%
20406 1
< 0.1%
ValueCountFrequency (%)
20777 1
< 0.1%
20776 1
< 0.1%
20773 1
< 0.1%
20772 1
< 0.1%
20769 1
< 0.1%
20764 1
< 0.1%
20758 1
< 0.1%
20750 1
< 0.1%
20743 1
< 0.1%
20742 1
< 0.1%

환불유형코드(RFNDM_TPE_CD)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9908 
1
 
89
3
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.9724
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9908
99.1%
1 89
 
0.9%
3 2
 
< 0.1%
4 1
 
< 0.1%

Length

2023-12-13T02:39:45.422907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:45.546887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9908
99.1%
1 89
 
0.9%
3 2
 
< 0.1%
4 1
 
< 0.1%

부서ID(DPRTM_ID)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9908 
101
 
92

Length

Max length4
Median length4
Mean length3.9908
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9908
99.1%
101 92
 
0.9%

Length

2023-12-13T02:39:45.698395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:45.825593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9908
99.1%
101 92
 
0.9%

예약순번(RSRVT_SEQ)
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)19.8%
Missing9909
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean8.2527473
Minimum3
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:45.970063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median6
Q311
95-th percentile18
Maximum23
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7902526
Coefficient of variation (CV)0.58044339
Kurtosis0.98273551
Mean8.2527473
Median Absolute Deviation (MAD)2
Skewness1.1749061
Sum751
Variance22.94652
MonotonicityNot monotonic
2023-12-13T02:39:46.131906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 20
 
0.2%
11 12
 
0.1%
3 10
 
0.1%
6 9
 
0.1%
4 8
 
0.1%
8 6
 
0.1%
12 6
 
0.1%
13 3
 
< 0.1%
7 3
 
< 0.1%
18 2
 
< 0.1%
Other values (8) 12
 
0.1%
(Missing) 9909
99.1%
ValueCountFrequency (%)
3 10
0.1%
4 8
 
0.1%
5 20
0.2%
6 9
0.1%
7 3
 
< 0.1%
8 6
 
0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
11 12
0.1%
12 6
 
0.1%
ValueCountFrequency (%)
23 1
 
< 0.1%
22 2
 
< 0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 3
 
< 0.1%
12 6
0.1%
11 12
0.1%

결제순번(STTLM_SEQ).2
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9910 
2
 
38
3
 
37
4
 
12
5
 
2

Length

Max length4
Median length4
Mean length3.973
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9910
99.1%
2 38
 
0.4%
3 37
 
0.4%
4 12
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%

Length

2023-12-13T02:39:46.295451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:46.439879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9910
99.1%
2 38
 
0.4%
3 37
 
0.4%
4 12
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%

환불금액(RFNDM_AMT)
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)26.1%
Missing9908
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean16954.783
Minimum1500
Maximum90000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:46.651088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1500
Q14000
median6000
Q321000
95-th percentile68890
Maximum90000
Range88500
Interquartile range (IQR)17000

Descriptive statistics

Standard deviation22216.516
Coefficient of variation (CV)1.3103392
Kurtosis2.8957707
Mean16954.783
Median Absolute Deviation (MAD)4000
Skewness1.9136676
Sum1559840
Variance4.935736 × 108
MonotonicityNot monotonic
2023-12-13T02:39:46.883065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6000 22
 
0.2%
10000 15
 
0.1%
1500 13
 
0.1%
3000 8
 
0.1%
4000 5
 
0.1%
90000 3
 
< 0.1%
46000 3
 
< 0.1%
30000 3
 
< 0.1%
21000 2
 
< 0.1%
32000 2
 
< 0.1%
Other values (14) 16
 
0.2%
(Missing) 9908
99.1%
ValueCountFrequency (%)
1500 13
0.1%
3000 8
 
0.1%
4000 5
 
0.1%
4500 1
 
< 0.1%
5000 1
 
< 0.1%
6000 22
0.2%
7000 1
 
< 0.1%
7200 1
 
< 0.1%
10000 15
0.1%
20840 1
 
< 0.1%
ValueCountFrequency (%)
90000 3
< 0.1%
74000 1
 
< 0.1%
71200 1
 
< 0.1%
67000 1
 
< 0.1%
58000 2
< 0.1%
50500 1
 
< 0.1%
50000 2
< 0.1%
46000 3
< 0.1%
40000 1
 
< 0.1%
32000 2
< 0.1%

위약금금액(PNLT_AMT).1
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9908 
0
 
89
1800
 
1
6400
 
1
17800
 
1

Length

Max length5
Median length4
Mean length3.9734
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9908
99.1%
0 89
 
0.9%
1800 1
 
< 0.1%
6400 1
 
< 0.1%
17800 1
 
< 0.1%

Length

2023-12-13T02:39:47.121248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:47.284980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9908
99.1%
0 89
 
0.9%
1800 1
 
< 0.1%
6400 1
 
< 0.1%
17800 1
 
< 0.1%

환불사유(RFNDM_RSN)
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-13T02:39:47.430770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row할인환불
ValueCountFrequency (%)
할인환불 1
100.0%
2023-12-13T02:39:47.796229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct29
Distinct (%)31.5%
Missing9908
Missing (%)99.1%
Memory size156.2 KiB
Minimum2013-07-24 23:59:00
Maximum2016-09-13 23:59:00
2023-12-13T02:39:47.967205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:48.120492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

환불완료일시(RFNDM_CMPLE_DTM)
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
Minimum2014-02-19 16:50:00
Maximum2014-02-19 16:50:00
2023-12-13T02:39:48.259867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:48.362313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
Distinct65
Distinct (%)70.7%
Missing9908
Missing (%)99.1%
Memory size156.2 KiB
Minimum2013-07-24 10:38:00
Maximum2016-08-27 15:03:00
2023-12-13T02:39:48.497805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:48.652343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최종수정일시(LAST_UPDT_DTM).2
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
Minimum2014-02-19 16:50:00
Maximum2014-02-19 16:50:00
2023-12-13T02:39:48.749894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:48.842349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효여부(VLDTY_YN).2
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.1%
Missing9908
Missing (%)99.1%
Memory size97.7 KiB
True
 
92
(Missing)
9908 
ValueCountFrequency (%)
True 92
 
0.9%
(Missing) 9908
99.1%
2023-12-13T02:39:48.923880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5810 
CARD
4006 
VBANK
 
112
BANK
 
72

Length

Max length5
Median length4
Mean length4.0112
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5810
58.1%
CARD 4006
40.1%
VBANK 112
 
1.1%
BANK 72
 
0.7%

Length

2023-12-13T02:39:49.004436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:49.087725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5810
58.1%
card 4006
40.1%
vbank 112
 
1.1%
bank 72
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5860 
1
4140 

Length

Max length4
Median length4
Mean length2.758
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5860
58.6%
1 4140
41.4%

Length

2023-12-13T02:39:49.202607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:49.290553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5860
58.6%
1 4140
41.4%

결제상품금액(AMT)
Real number (ℝ)

MISSING 

Distinct476
Distinct (%)11.4%
Missing5810
Missing (%)58.1%
Infinite0
Infinite (%)0.0%
Mean154573.98
Minimum1000
Maximum1334000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:49.384917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile30000
Q172000
median120000
Q3187000
95-th percentile413000
Maximum1334000
Range1333000
Interquartile range (IQR)115000

Descriptive statistics

Standard deviation128517.4
Coefficient of variation (CV)0.83142972
Kurtosis8.2113723
Mean154573.98
Median Absolute Deviation (MAD)55250
Skewness2.2795687
Sum6.4766498 × 108
Variance1.6516722 × 1010
MonotonicityNot monotonic
2023-12-13T02:39:49.520641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90000 310
 
3.1%
50000 142
 
1.4%
30000 101
 
1.0%
46000 82
 
0.8%
120000 80
 
0.8%
82000 75
 
0.8%
144000 75
 
0.8%
60000 73
 
0.7%
64000 69
 
0.7%
70000 62
 
0.6%
Other values (466) 3121
31.2%
(Missing) 5810
58.1%
ValueCountFrequency (%)
1000 7
0.1%
1790 2
 
< 0.1%
2000 1
 
< 0.1%
2400 1
 
< 0.1%
2500 1
 
< 0.1%
4000 12
0.1%
6000 16
0.2%
7000 5
 
0.1%
7500 8
0.1%
8600 1
 
< 0.1%
ValueCountFrequency (%)
1334000 1
< 0.1%
1326000 1
< 0.1%
1102000 1
< 0.1%
990000 1
< 0.1%
964000 1
< 0.1%
800000 1
< 0.1%
795000 2
< 0.1%
789000 1
< 0.1%
784000 1
< 0.1%
780000 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6093 
302840
3907 

Length

Max length6
Median length4
Mean length4.7814
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6093
60.9%
302840 3907
39.1%

Length

2023-12-13T02:39:49.646254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:49.734412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6093
60.9%
302840 3907
39.1%

거래타입(TRANSTYPE)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

상점예비정보(MALLRESERVED)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct69
Distinct (%)61.6%
Missing9888
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean19617322
Minimum41974
Maximum20181204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:49.831796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41974
5-th percentile20130676
Q120140315
median20170627
Q320170716
95-th percentile20180628
Maximum20181204
Range20139230
Interquartile range (IQR)30401

Descriptive statistics

Standard deviation3262188
Coefficient of variation (CV)0.1662912
Kurtosis33.906765
Mean19617322
Median Absolute Deviation (MAD)10002.5
Skewness-5.9414097
Sum2.19714 × 109
Variance1.0641871 × 1013
MonotonicityNot monotonic
2023-12-13T02:39:49.957435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170728 4
 
< 0.1%
20170714 4
 
< 0.1%
20170702 4
 
< 0.1%
20170627 4
 
< 0.1%
20140809 3
 
< 0.1%
20141104 3
 
< 0.1%
20130727 3
 
< 0.1%
20170722 3
 
< 0.1%
20170705 3
 
< 0.1%
20131125 3
 
< 0.1%
Other values (59) 78
 
0.8%
(Missing) 9888
98.9%
ValueCountFrequency (%)
41974 1
 
< 0.1%
42096 2
< 0.1%
20130623 1
 
< 0.1%
20130628 1
 
< 0.1%
20130629 1
 
< 0.1%
20130715 2
< 0.1%
20130727 3
< 0.1%
20130913 1
 
< 0.1%
20131103 1
 
< 0.1%
20131107 1
 
< 0.1%
ValueCountFrequency (%)
20181204 1
 
< 0.1%
20181018 1
 
< 0.1%
20180929 1
 
< 0.1%
20180630 1
 
< 0.1%
20180629 1
 
< 0.1%
20180628 2
< 0.1%
20170728 4
< 0.1%
20170727 2
< 0.1%
20170726 1
 
< 0.1%
20170724 1
 
< 0.1%

소켓이용유무(SOCKETYN)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

전문생성일시코드(EDIDATECODE)
Real number (ℝ)

MISSING 

Distinct85
Distinct (%)2.1%
Missing5860
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean2.0193118 × 1013
Minimum2.01306 × 1013
Maximum2.02109 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:50.083862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.01306 × 1013
5-th percentile2.01609 × 1013
Q12.01807 × 1013
median2.02005 × 1013
Q32.02104 × 1013
95-th percentile2.02108 × 1013
Maximum2.02109 × 1013
Range8.03 × 1010
Interquartile range (IQR)2.97 × 1010

Descriptive statistics

Standard deviation1.7520778 × 1010
Coefficient of variation (CV)0.00086766086
Kurtosis0.69985259
Mean2.0193118 × 1013
Median Absolute Deviation (MAD)1.01 × 1010
Skewness-1.0270608
Sum8.3599508 × 1016
Variance3.0697767 × 1020
MonotonicityNot monotonic
2023-12-13T02:39:50.247677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210700000000 345
 
3.5%
20200700000000 264
 
2.6%
20210800000000 232
 
2.3%
20210600000000 207
 
2.1%
20210400000000 143
 
1.4%
20210500000000 132
 
1.3%
20201000000000 124
 
1.2%
20200600000000 116
 
1.2%
20190700000000 115
 
1.1%
20170700000000 112
 
1.1%
Other values (75) 2350
23.5%
(Missing) 5860
58.6%
ValueCountFrequency (%)
20130600000000 3
 
< 0.1%
20130700000000 11
0.1%
20130800000000 1
 
< 0.1%
20130900000000 1
 
< 0.1%
20131000000000 1
 
< 0.1%
20131100000000 2
 
< 0.1%
20140200000000 1
 
< 0.1%
20140300000000 6
0.1%
20140500000000 10
0.1%
20140800000000 10
0.1%
ValueCountFrequency (%)
20210900000000 58
 
0.6%
20210800000000 232
2.3%
20210700000000 345
3.5%
20210600000000 207
2.1%
20210500000000 132
 
1.3%
20210400000000 143
1.4%
20210300000000 96
 
1.0%
20210200000000 48
 
0.5%
20210100000000 43
 
0.4%
20201200000000 52
 
0.5%

해쉬값(ENCRYPTDATA)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5860 
KRW
4140 

Length

Max length4
Median length4
Mean length3.586
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5860
58.6%
KRW 4140
41.4%

Length

2023-12-13T02:39:50.393567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:50.470947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5860
58.6%
krw 4140
41.4%

승인일자코드(AUTHDATECODE)
Real number (ℝ)

MISSING 

Distinct1342
Distinct (%)32.1%
Missing5822
Missing (%)58.2%
Infinite0
Infinite (%)0.0%
Mean1.9252022 × 1011
Minimum1.30619 × 1011
Maximum2.10908 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:50.571249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.30619 × 1011
5-th percentile1.608905 × 1011
Q11.80713 × 1011
median2.00518 × 1011
Q32.10419 × 1011
95-th percentile2.1081 × 1011
Maximum2.10908 × 1011
Range8.0289 × 1010
Interquartile range (IQR)2.9706 × 1010

Descriptive statistics

Standard deviation1.853463 × 1010
Coefficient of variation (CV)0.09627368
Kurtosis1.1316986
Mean1.9252022 × 1011
Median Absolute Deviation (MAD)1.01015 × 1010
Skewness-1.1590658
Sum8.0434946 × 1014
Variance3.4353249 × 1020
MonotonicityNot monotonic
2023-12-13T02:39:50.704118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210716000000 20
 
0.2%
210718000000 18
 
0.2%
210719000000 17
 
0.2%
180928000000 17
 
0.2%
131128000000 16
 
0.2%
210707000000 16
 
0.2%
200721000000 16
 
0.2%
210823000000 16
 
0.2%
210809000000 15
 
0.1%
201017000000 15
 
0.1%
Other values (1332) 4012
40.1%
(Missing) 5822
58.2%
ValueCountFrequency (%)
130619000000 2
< 0.1%
130625000000 1
 
< 0.1%
130701000000 1
 
< 0.1%
130704000000 1
 
< 0.1%
130712000000 2
< 0.1%
130716000000 1
 
< 0.1%
130717000000 1
 
< 0.1%
130724000000 3
< 0.1%
130725000000 2
< 0.1%
130826000000 1
 
< 0.1%
ValueCountFrequency (%)
210908000000 5
0.1%
210907000000 6
0.1%
210906000000 12
0.1%
210905000000 7
0.1%
210904000000 9
0.1%
210903000000 4
 
< 0.1%
210902000000 9
0.1%
210901000000 6
0.1%
210831000000 9
0.1%
210830000000 2
 
< 0.1%

결과코드(RESULTCODE)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5820
Missing (%)58.2%
Memory size156.2 KiB

결제순번(STTLM_SEQ).3
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9053 
1
933 
3
 
10
2
 
3
4
 
1

Length

Max length4
Median length4
Mean length3.7159
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9053
90.5%
1 933
 
9.3%
3 10
 
0.1%
2 3
 
< 0.1%
4 1
 
< 0.1%

Length

2023-12-13T02:39:50.826261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:50.915184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9053
90.5%
1 933
 
9.3%
3 10
 
0.1%
2 3
 
< 0.1%
4 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
7
 
10
3
 
3

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
7 10
 
0.1%
3 3
 
< 0.1%

Length

2023-12-13T02:39:51.008411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:51.091385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
7 10
 
0.1%
3 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
1
 
13

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
1 13
 
0.1%

Length

2023-12-13T02:39:51.185373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:51.263120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
1 13
 
0.1%

시즌구분코드(SSN_TPCD).1
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
2
 
9
1
 
4

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
2 9
 
0.1%
1 4
 
< 0.1%

Length

2023-12-13T02:39:51.354687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:51.446272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
2 9
 
0.1%
1 4
 
< 0.1%

일자구분코드(DT_TPCD).1
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
1
 
8
2
 
5

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
1 8
 
0.1%
2 5
 
0.1%

Length

2023-12-13T02:39:51.539355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:51.628237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
1 8
 
0.1%
2 5
 
< 0.1%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
2
 
4
1
 
3
4
 
2
3
 
2

Length

Max length4
Median length4
Mean length3.9961
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
2 4
 
< 0.1%
1 3
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
0 2
 
< 0.1%

Length

2023-12-13T02:39:51.715073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:51.809440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
2 4
 
< 0.1%
1 3
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
0 2
 
< 0.1%

공제율비(DDCTN_RATE).1
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9987 
30
 
9
90
 
1
80
 
1
100
 
1

Length

Max length4
Median length4
Mean length3.9974
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9987
99.9%
30 9
 
0.1%
90 1
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
0 1
 
< 0.1%

Length

2023-12-13T02:39:52.209458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:52.326963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9987
99.9%
30 9
 
0.1%
90 1
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
0 1
 
< 0.1%
Distinct3
Distinct (%)23.1%
Missing9987
Missing (%)99.9%
Memory size156.2 KiB
Minimum2018-06-22 11:21:00
Maximum2018-06-22 13:07:00
2023-12-13T02:39:52.429483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:52.601377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

최종수정일시(LAST_UPDT_DTM).3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

유효여부(VLDTY_YN).3
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)7.7%
Missing9987
Missing (%)99.9%
Memory size97.7 KiB
True
 
13
(Missing)
9987 
ValueCountFrequency (%)
True 13
 
0.1%
(Missing) 9987
99.9%
2023-12-13T02:39:52.788936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

부서ID(DPRTM_ID).1
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)2.0%
Missing9499
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean105.2475
Minimum101
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:52.899961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median104
Q3107
95-th percentile113
Maximum113
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9898144
Coefficient of variation (CV)0.037908874
Kurtosis-0.90419519
Mean105.2475
Median Absolute Deviation (MAD)3
Skewness0.66325951
Sum52729
Variance15.918619
MonotonicityNot monotonic
2023-12-13T02:39:53.046671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
101 103
 
1.0%
106 86
 
0.9%
102 79
 
0.8%
103 58
 
0.6%
113 32
 
0.3%
110 32
 
0.3%
107 32
 
0.3%
112 31
 
0.3%
104 26
 
0.3%
111 22
 
0.2%
(Missing) 9499
95.0%
ValueCountFrequency (%)
101 103
1.0%
102 79
0.8%
103 58
0.6%
104 26
 
0.3%
106 86
0.9%
107 32
 
0.3%
110 32
 
0.3%
111 22
 
0.2%
112 31
 
0.3%
113 32
 
0.3%
ValueCountFrequency (%)
113 32
 
0.3%
112 31
 
0.3%
111 22
 
0.2%
110 32
 
0.3%
107 32
 
0.3%
106 86
0.9%
104 26
 
0.3%
103 58
0.6%
102 79
0.8%
101 103
1.0%

시설ID(FCLT_ID)
Text

MISSING 

Distinct501
Distinct (%)100.0%
Missing9499
Missing (%)95.0%
Memory size156.2 KiB
2023-12-13T02:39:53.348097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length13.171657
Min length10

Characters and Unicode

Total characters6599
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique501 ?
Unique (%)100.0%

Sample

1st rowF01130301000101
2nd rowF01100300150114
3rd rowF011302007
4th rowF010702005
5th rowF010308000055
ValueCountFrequency (%)
f011107001002 1
 
0.2%
f01040300100406 1
 
0.2%
f011201101001 1
 
0.2%
f010201001002 1
 
0.2%
f010307003006 1
 
0.2%
f01010300100714 1
 
0.2%
f011202001 1
 
0.2%
f011002006 1
 
0.2%
f010204001231 1
 
0.2%
f011101201001 1
 
0.2%
Other values (491) 491
98.0%
2023-12-13T02:39:53.830335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3069
46.5%
1 1392
21.1%
F 501
 
7.6%
2 402
 
6.1%
3 304
 
4.6%
4 221
 
3.3%
5 195
 
3.0%
6 191
 
2.9%
7 165
 
2.5%
8 99
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6098
92.4%
Uppercase Letter 501
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3069
50.3%
1 1392
22.8%
2 402
 
6.6%
3 304
 
5.0%
4 221
 
3.6%
5 195
 
3.2%
6 191
 
3.1%
7 165
 
2.7%
8 99
 
1.6%
9 60
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
F 501
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6098
92.4%
Latin 501
 
7.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3069
50.3%
1 1392
22.8%
2 402
 
6.6%
3 304
 
5.0%
4 221
 
3.6%
5 195
 
3.2%
6 191
 
3.1%
7 165
 
2.7%
8 99
 
1.6%
9 60
 
1.0%
Latin
ValueCountFrequency (%)
F 501
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3069
46.5%
1 1392
21.1%
F 501
 
7.6%
2 402
 
6.1%
3 304
 
4.6%
4 221
 
3.3%
5 195
 
3.0%
6 191
 
2.9%
7 165
 
2.5%
8 99
 
1.5%

시설명(FCLT_NM)
Text

MISSING 

Distinct339
Distinct (%)67.7%
Missing9499
Missing (%)95.0%
Memory size156.2 KiB
2023-12-13T02:39:54.252280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length10.550898
Min length2

Characters and Unicode

Total characters5286
Distinct characters385
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique263 ?
Unique (%)52.5%

Sample

1st row수풀동(A) 가덕산 201호
2nd row활엽수동 204호 서어나무
3rd row면제차량
4th row국가유공자등록차량
5th row땅속 지도 만들기
ValueCountFrequency (%)
만들기 25
 
2.3%
숙박 21
 
1.9%
기준인원외 18
 
1.6%
문필마을 15
 
1.4%
어린이 13
 
1.2%
13
 
1.2%
뒷말 11
 
1.0%
활엽수동 10
 
0.9%
수련센터 10
 
0.9%
추가 10
 
0.9%
Other values (490) 962
86.8%
2023-12-13T02:39:54.900279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
611
 
11.6%
( 224
 
4.2%
) 224
 
4.2%
119
 
2.3%
116
 
2.2%
1 78
 
1.5%
77
 
1.5%
72
 
1.4%
68
 
1.3%
0 67
 
1.3%
Other values (375) 3630
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3627
68.6%
Space Separator 611
 
11.6%
Decimal Number 309
 
5.8%
Open Punctuation 224
 
4.2%
Close Punctuation 224
 
4.2%
Lowercase Letter 136
 
2.6%
Uppercase Letter 126
 
2.4%
Other Punctuation 25
 
0.5%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
119
 
3.3%
116
 
3.2%
77
 
2.1%
72
 
2.0%
68
 
1.9%
65
 
1.8%
64
 
1.8%
64
 
1.8%
63
 
1.7%
61
 
1.7%
Other values (327) 2858
78.8%
Uppercase Letter
ValueCountFrequency (%)
A 19
15.1%
M 19
15.1%
C 15
11.9%
S 12
9.5%
B 11
8.7%
H 8
6.3%
P 7
 
5.6%
O 6
 
4.8%
F 6
 
4.8%
D 5
 
4.0%
Other values (8) 18
14.3%
Lowercase Letter
ValueCountFrequency (%)
a 22
16.2%
l 16
11.8%
i 16
11.8%
e 15
11.0%
y 13
9.6%
s 12
8.8%
p 8
 
5.9%
r 8
 
5.9%
n 7
 
5.1%
o 6
 
4.4%
Other values (3) 13
9.6%
Decimal Number
ValueCountFrequency (%)
1 78
25.2%
0 67
21.7%
2 63
20.4%
3 46
14.9%
4 17
 
5.5%
6 15
 
4.9%
5 10
 
3.2%
8 5
 
1.6%
7 4
 
1.3%
9 4
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 20
80.0%
. 4
 
16.0%
% 1
 
4.0%
Space Separator
ValueCountFrequency (%)
611
100.0%
Open Punctuation
ValueCountFrequency (%)
( 224
100.0%
Close Punctuation
ValueCountFrequency (%)
) 224
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3601
68.1%
Common 1397
 
26.4%
Latin 262
 
5.0%
Han 26
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
119
 
3.3%
116
 
3.2%
77
 
2.1%
72
 
2.0%
68
 
1.9%
65
 
1.8%
64
 
1.8%
64
 
1.8%
63
 
1.7%
61
 
1.7%
Other values (320) 2832
78.6%
Latin
ValueCountFrequency (%)
a 22
 
8.4%
A 19
 
7.3%
M 19
 
7.3%
l 16
 
6.1%
i 16
 
6.1%
e 15
 
5.7%
C 15
 
5.7%
y 13
 
5.0%
s 12
 
4.6%
S 12
 
4.6%
Other values (21) 103
39.3%
Common
ValueCountFrequency (%)
611
43.7%
( 224
 
16.0%
) 224
 
16.0%
1 78
 
5.6%
0 67
 
4.8%
2 63
 
4.5%
3 46
 
3.3%
, 20
 
1.4%
4 17
 
1.2%
6 15
 
1.1%
Other values (7) 32
 
2.3%
Han
ValueCountFrequency (%)
13
50.0%
7
26.9%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3601
68.1%
ASCII 1659
31.4%
CJK Compat Ideographs 20
 
0.4%
CJK 6
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
611
36.8%
( 224
 
13.5%
) 224
 
13.5%
1 78
 
4.7%
0 67
 
4.0%
2 63
 
3.8%
3 46
 
2.8%
a 22
 
1.3%
, 20
 
1.2%
A 19
 
1.1%
Other values (38) 285
17.2%
Hangul
ValueCountFrequency (%)
119
 
3.3%
116
 
3.2%
77
 
2.1%
72
 
2.0%
68
 
1.9%
65
 
1.8%
64
 
1.8%
64
 
1.8%
63
 
1.7%
61
 
1.7%
Other values (320) 2832
78.6%
CJK Compat Ideographs
ValueCountFrequency (%)
13
65.0%
7
35.0%
CJK
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Distinct329
Distinct (%)66.3%
Missing9504
Missing (%)95.0%
Memory size156.2 KiB
2023-12-13T02:39:55.202874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length113
Median length80
Mean length19.961694
Min length1

Characters and Unicode

Total characters9901
Distinct characters475
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)49.4%

Sample

1st row가덕산 201호(2인실)
2nd row(2/3명)
3rd row주차시설/면제차량
4th row주차시설/국가유공자등록차량
5th row숲에 살고 있지만 눈에 잘 보이지 않는 토양생물과 하층의 곤충을 찾아보고 역할을 이해함으로써 숲 구성원으로서의 소중함을 느끼도록 한다.
ValueCountFrequency (%)
프로그램 58
 
2.7%
44
 
2.1%
29
 
1.4%
숙박 20
 
0.9%
19
 
0.9%
18
 
0.8%
tv가 18
 
0.8%
있고 18
 
0.8%
설명 18
 
0.8%
부연 18
 
0.8%
Other values (854) 1879
87.8%
2023-12-13T02:39:55.753661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1658
 
16.7%
) 294
 
3.0%
( 294
 
3.0%
181
 
1.8%
172
 
1.7%
139
 
1.4%
118
 
1.2%
115
 
1.2%
112
 
1.1%
104
 
1.1%
Other values (465) 6714
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6972
70.4%
Space Separator 1658
 
16.7%
Decimal Number 332
 
3.4%
Close Punctuation 297
 
3.0%
Open Punctuation 297
 
3.0%
Other Punctuation 161
 
1.6%
Uppercase Letter 123
 
1.2%
Lowercase Letter 45
 
0.5%
Control 13
 
0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
181
 
2.6%
172
 
2.5%
139
 
2.0%
118
 
1.7%
115
 
1.6%
112
 
1.6%
104
 
1.5%
102
 
1.5%
101
 
1.4%
100
 
1.4%
Other values (408) 5728
82.2%
Uppercase Letter
ValueCountFrequency (%)
V 18
14.6%
M 18
14.6%
T 18
14.6%
A 16
13.0%
S 12
9.8%
B 8
6.5%
C 8
6.5%
J 4
 
3.3%
H 4
 
3.3%
O 3
 
2.4%
Other values (9) 14
11.4%
Lowercase Letter
ValueCountFrequency (%)
a 7
15.6%
e 7
15.6%
i 6
13.3%
l 4
8.9%
n 4
8.9%
s 4
8.9%
g 3
6.7%
r 3
6.7%
p 2
 
4.4%
o 2
 
4.4%
Other values (3) 3
6.7%
Decimal Number
ValueCountFrequency (%)
2 98
29.5%
1 63
19.0%
3 55
16.6%
0 53
16.0%
4 22
 
6.6%
5 16
 
4.8%
6 15
 
4.5%
8 4
 
1.2%
9 3
 
0.9%
7 3
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 69
42.9%
, 52
32.3%
. 23
 
14.3%
' 6
 
3.7%
* 5
 
3.1%
· 4
 
2.5%
% 1
 
0.6%
! 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 294
99.0%
3
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 294
99.0%
3
 
1.0%
Space Separator
ValueCountFrequency (%)
1658
100.0%
Control
ValueCountFrequency (%)
13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6969
70.4%
Common 2761
 
27.9%
Latin 168
 
1.7%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
181
 
2.6%
172
 
2.5%
139
 
2.0%
118
 
1.7%
115
 
1.7%
112
 
1.6%
104
 
1.5%
102
 
1.5%
101
 
1.4%
100
 
1.4%
Other values (405) 5725
82.1%
Latin
ValueCountFrequency (%)
V 18
 
10.7%
M 18
 
10.7%
T 18
 
10.7%
A 16
 
9.5%
S 12
 
7.1%
B 8
 
4.8%
C 8
 
4.8%
a 7
 
4.2%
e 7
 
4.2%
i 6
 
3.6%
Other values (22) 50
29.8%
Common
ValueCountFrequency (%)
1658
60.1%
) 294
 
10.6%
( 294
 
10.6%
2 98
 
3.5%
/ 69
 
2.5%
1 63
 
2.3%
3 55
 
2.0%
0 53
 
1.9%
, 52
 
1.9%
. 23
 
0.8%
Other values (15) 102
 
3.7%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6969
70.4%
ASCII 2919
29.5%
None 10
 
0.1%
CJK 2
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1658
56.8%
) 294
 
10.1%
( 294
 
10.1%
2 98
 
3.4%
/ 69
 
2.4%
1 63
 
2.2%
3 55
 
1.9%
0 53
 
1.8%
, 52
 
1.8%
. 23
 
0.8%
Other values (44) 260
 
8.9%
Hangul
ValueCountFrequency (%)
181
 
2.6%
172
 
2.5%
139
 
2.0%
118
 
1.7%
115
 
1.7%
112
 
1.6%
104
 
1.5%
102
 
1.5%
101
 
1.4%
100
 
1.4%
Other values (405) 5725
82.1%
None
ValueCountFrequency (%)
· 4
40.0%
3
30.0%
3
30.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

시설대분류코드(FCLT_LRCLS_CD).2
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)1.6%
Missing9499
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean4.1177645
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:55.920763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0228949
Coefficient of variation (CV)0.49126047
Kurtosis-0.62807157
Mean4.1177645
Median Absolute Deviation (MAD)1
Skewness0.28559041
Sum2063
Variance4.0921038
MonotonicityNot monotonic
2023-12-13T02:39:56.112044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 109
 
1.1%
3 95
 
0.9%
5 95
 
0.9%
1 66
 
0.7%
7 49
 
0.5%
8 40
 
0.4%
2 36
 
0.4%
6 11
 
0.1%
(Missing) 9499
95.0%
ValueCountFrequency (%)
1 66
0.7%
2 36
 
0.4%
3 95
0.9%
4 109
1.1%
5 95
0.9%
6 11
 
0.1%
7 49
0.5%
8 40
 
0.4%
ValueCountFrequency (%)
8 40
 
0.4%
7 49
0.5%
6 11
 
0.1%
5 95
0.9%
4 109
1.1%
3 95
0.9%
2 36
 
0.4%
1 66
0.7%

시설중분류코드(FCLT_MDCLS_CD)
Real number (ℝ)

MISSING 

Distinct126
Distinct (%)25.1%
Missing9499
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean4149.7106
Minimum1001
Maximum9001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:56.286789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1008
Q13001
median4001
Q35080
95-th percentile8001
Maximum9001
Range8000
Interquartile range (IQR)2079

Descriptive statistics

Standard deviation2020.5396
Coefficient of variation (CV)0.48691098
Kurtosis-0.64461952
Mean4149.7106
Median Absolute Deviation (MAD)1049
Skewness0.31191031
Sum2079005
Variance4082580.5
MonotonicityNot monotonic
2023-12-13T02:39:56.499275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4001 109
 
1.1%
3001 67
 
0.7%
8001 40
 
0.4%
7002 21
 
0.2%
7003 17
 
0.2%
3002 11
 
0.1%
7001 11
 
0.1%
1201 10
 
0.1%
1005 6
 
0.1%
1401 6
 
0.1%
Other values (116) 203
 
2.0%
(Missing) 9499
95.0%
ValueCountFrequency (%)
1001 3
< 0.1%
1002 6
0.1%
1003 1
 
< 0.1%
1004 3
< 0.1%
1005 6
0.1%
1006 4
< 0.1%
1007 2
 
< 0.1%
1008 4
< 0.1%
1009 4
< 0.1%
1010 2
 
< 0.1%
ValueCountFrequency (%)
9001 1
 
< 0.1%
8001 40
0.4%
7003 17
0.2%
7002 21
0.2%
7001 11
 
0.1%
6022 1
 
< 0.1%
6020 1
 
< 0.1%
6013 1
 
< 0.1%
6012 1
 
< 0.1%
6010 1
 
< 0.1%

시설소분류코드(FCLT_SMCLS_CD)
Real number (ℝ)

MISSING 

Distinct329
Distinct (%)70.8%
Missing9535
Missing (%)95.3%
Infinite0
Infinite (%)0.0%
Mean4315663.8
Minimum1001001
Maximum9001001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:56.691794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001001
5-th percentile1007001
Q13001501
median4001249
Q35095001
95-th percentile8000065.8
Maximum9001001
Range8000000
Interquartile range (IQR)2093500

Descriptive statistics

Standard deviation2003631.7
Coefficient of variation (CV)0.46426964
Kurtosis-0.58124045
Mean4315663.8
Median Absolute Deviation (MAD)1030752
Skewness0.18965815
Sum2.0067837 × 109
Variance4.0145399 × 1012
MonotonicityNot monotonic
2023-12-13T02:39:56.890191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3001004 12
 
0.1%
3001501 10
 
0.1%
7002002 6
 
0.1%
3001007 6
 
0.1%
3001102 6
 
0.1%
1201001 5
 
0.1%
3002020 5
 
0.1%
1201002 5
 
0.1%
1011001 5
 
0.1%
3010001 5
 
0.1%
Other values (319) 400
 
4.0%
(Missing) 9535
95.3%
ValueCountFrequency (%)
1001001 2
< 0.1%
1001002 1
 
< 0.1%
1002001 1
 
< 0.1%
1002011 1
 
< 0.1%
1002012 4
< 0.1%
1003002 1
 
< 0.1%
1004001 2
< 0.1%
1004003 1
 
< 0.1%
1005001 2
< 0.1%
1005002 1
 
< 0.1%
ValueCountFrequency (%)
9001001 1
< 0.1%
8000117 1
< 0.1%
8000111 1
< 0.1%
8000109 1
< 0.1%
8000108 1
< 0.1%
8000105 1
< 0.1%
8000104 1
< 0.1%
8000102 1
< 0.1%
8000099 1
< 0.1%
8000098 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9499 
1
 
352
10
 
85
11
 
64

Length

Max length4
Median length4
Mean length3.8646
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9499
95.0%
1 352
 
3.5%
10 85
 
0.9%
11 64
 
0.6%

Length

2023-12-13T02:39:57.058801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:57.201603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9499
95.0%
1 352
 
3.5%
10 85
 
0.9%
11 64
 
0.6%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9499 
1
 
359
11
 
82
10
 
36
2
 
24

Length

Max length4
Median length4
Mean length3.8615
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9499
95.0%
1 359
 
3.6%
11 82
 
0.8%
10 36
 
0.4%
2 24
 
0.2%

Length

2023-12-13T02:39:57.358865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:57.499346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9499
95.0%
1 359
 
3.6%
11 82
 
0.8%
10 36
 
0.4%
2 24
 
0.2%
Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9860 
 
118
소쩍새(5/7명)
 
1
숲오름(2/4명)
 
1
도덕봉(2/4명)
 
1
Other values (19)
 
19

Length

Max length11
Median length4
Mean length3.9761
Min length1

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9860
98.6%
118
 
1.2%
소쩍새(5/7명) 1
 
< 0.1%
숲오름(2/4명) 1
 
< 0.1%
도덕봉(2/4명) 1
 
< 0.1%
진주구름(2/4명) 1
 
< 0.1%
비늘구름(2/4명) 1
 
< 0.1%
뭉게구름(2/4명) 1
 
< 0.1%
작은집(2/3명) 1
 
< 0.1%
계족산(2/4명) 1
 
< 0.1%
Other values (14) 14
 
0.1%

Length

2023-12-13T02:39:57.632347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9860
99.8%
삼악산(2/4명 1
 
< 0.1%
꽃마루(2/4명 1
 
< 0.1%
빛마루(2/4명 1
 
< 0.1%
물총새(5/7명 1
 
< 0.1%
4동(2/3명 1
 
< 0.1%
숲마루(2/4명 1
 
< 0.1%
올빼미(10/12명 1
 
< 0.1%
갈참나무(5/7명 1
 
< 0.1%
금수봉(2/4명 1
 
< 0.1%
Other values (13) 13
 
0.1%
Distinct61
Distinct (%)26.9%
Missing9773
Missing (%)97.7%
Memory size156.2 KiB
2023-12-13T02:39:57.846741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length1
Mean length9.6255507
Min length1

Characters and Unicode

Total characters2185
Distinct characters165
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)20.7%

Sample

1st row수풀동 가덕산(2/4명)
2nd row(서어나무) (2/3명)
3rd row(오리나무) (2/3명)
4th row대강당
5th row
ValueCountFrequency (%)
58
 
12.8%
프로그램 29
 
6.4%
체험 29
 
6.4%
29
 
6.4%
힐링 28
 
6.2%
숙박 28
 
6.2%
식사 28
 
6.2%
2인실 20
 
4.4%
숙박시설+무료프로그램(선택 17
 
3.7%
뒷말 11
 
2.4%
Other values (89) 177
39.0%
2023-12-13T02:39:58.321341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
345
 
15.8%
118
 
5.4%
) 114
 
5.2%
( 114
 
5.2%
+ 81
 
3.7%
2 59
 
2.7%
52
 
2.4%
49
 
2.2%
49
 
2.2%
48
 
2.2%
Other values (155) 1156
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1210
55.4%
Space Separator 345
 
15.8%
Decimal Number 165
 
7.6%
Control 118
 
5.4%
Close Punctuation 114
 
5.2%
Open Punctuation 114
 
5.2%
Math Symbol 86
 
3.9%
Other Punctuation 33
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
52
 
4.3%
49
 
4.0%
49
 
4.0%
48
 
4.0%
47
 
3.9%
47
 
3.9%
47
 
3.9%
47
 
3.9%
41
 
3.4%
36
 
3.0%
Other values (139) 747
61.7%
Decimal Number
ValueCountFrequency (%)
2 59
35.8%
3 25
15.2%
4 22
 
13.3%
5 19
 
11.5%
1 12
 
7.3%
7 10
 
6.1%
0 8
 
4.8%
6 7
 
4.2%
8 3
 
1.8%
Math Symbol
ValueCountFrequency (%)
+ 81
94.2%
~ 5
 
5.8%
Space Separator
ValueCountFrequency (%)
345
100.0%
Control
ValueCountFrequency (%)
118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1210
55.4%
Common 975
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
52
 
4.3%
49
 
4.0%
49
 
4.0%
48
 
4.0%
47
 
3.9%
47
 
3.9%
47
 
3.9%
47
 
3.9%
41
 
3.4%
36
 
3.0%
Other values (139) 747
61.7%
Common
ValueCountFrequency (%)
345
35.4%
118
 
12.1%
) 114
 
11.7%
( 114
 
11.7%
+ 81
 
8.3%
2 59
 
6.1%
/ 33
 
3.4%
3 25
 
2.6%
4 22
 
2.3%
5 19
 
1.9%
Other values (6) 45
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1210
55.4%
ASCII 975
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
345
35.4%
118
 
12.1%
) 114
 
11.7%
( 114
 
11.7%
+ 81
 
8.3%
2 59
 
6.1%
/ 33
 
3.4%
3 25
 
2.6%
4 22
 
2.3%
5 19
 
1.9%
Other values (6) 45
 
4.6%
Hangul
ValueCountFrequency (%)
52
 
4.3%
49
 
4.0%
49
 
4.0%
48
 
4.0%
47
 
3.9%
47
 
3.9%
47
 
3.9%
47
 
3.9%
41
 
3.4%
36
 
3.0%
Other values (139) 747
61.7%

구역명(ZONE_NM)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9845 
 
118
0
 
37

Length

Max length4
Median length4
Mean length3.9535
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9845
98.5%
118
 
1.2%
0 37
 
0.4%

Length

2023-12-13T02:39:58.512903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:58.659487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9845
99.6%
0 37
 
0.4%

면적단위(AREA_UNIT)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9579 
 
303
 
118

Length

Max length4
Median length4
Mean length3.8737
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9579
95.8%
303
 
3.0%
118
 
1.2%

Length

2023-12-13T02:39:58.822166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:39:58.953223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9579
96.9%
303
 
3.1%

면적합계(AREA_SUM)
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)11.5%
Missing9688
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean18.604391
Minimum0
Maximum585
Zeros207
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:59.089828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331.86
95-th percentile53
Maximum585
Range585
Interquartile range (IQR)31.86

Descriptive statistics

Standard deviation46.723459
Coefficient of variation (CV)2.5114211
Kurtosis78.245966
Mean18.604391
Median Absolute Deviation (MAD)0
Skewness7.53509
Sum5804.57
Variance2183.0817
MonotonicityNot monotonic
2023-12-13T02:39:59.239994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 207
 
2.1%
53.0 30
 
0.3%
31.86 9
 
0.1%
32.4 7
 
0.1%
46.57 5
 
0.1%
30.24 5
 
0.1%
49.5 4
 
< 0.1%
18.0 4
 
< 0.1%
45.36 3
 
< 0.1%
37.98 3
 
< 0.1%
Other values (26) 35
 
0.4%
(Missing) 9688
96.9%
ValueCountFrequency (%)
0.0 207
2.1%
18.0 4
 
< 0.1%
20.8 3
 
< 0.1%
25.47 1
 
< 0.1%
25.65 2
 
< 0.1%
25.92 2
 
< 0.1%
27.0 1
 
< 0.1%
27.36 1
 
< 0.1%
27.4 1
 
< 0.1%
30.0 1
 
< 0.1%
ValueCountFrequency (%)
585.0 1
< 0.1%
342.0 1
< 0.1%
244.0 1
< 0.1%
213.0 1
< 0.1%
109.0 2
< 0.1%
100.0 1
< 0.1%
82.0 1
< 0.1%
75.6 1
< 0.1%
73.0 1
< 0.1%
58.56 1
< 0.1%

최소수용수(MNMM_ACCPT_CNT)
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)1.8%
Missing9499
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean5.2335329
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:59.367301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile20
Maximum30
Range29
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.7837878
Coefficient of variation (CV)1.4872913
Kurtosis0.46503755
Mean5.2335329
Median Absolute Deviation (MAD)0
Skewness1.4933984
Sum2622
Variance60.587353
MonotonicityNot monotonic
2023-12-13T02:39:59.506923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 349
 
3.5%
20 96
 
1.0%
4 36
 
0.4%
2 10
 
0.1%
30 4
 
< 0.1%
5 2
 
< 0.1%
10 2
 
< 0.1%
24 1
 
< 0.1%
15 1
 
< 0.1%
(Missing) 9499
95.0%
ValueCountFrequency (%)
1 349
3.5%
2 10
 
0.1%
4 36
 
0.4%
5 2
 
< 0.1%
10 2
 
< 0.1%
15 1
 
< 0.1%
20 96
 
1.0%
24 1
 
< 0.1%
30 4
 
< 0.1%
ValueCountFrequency (%)
30 4
 
< 0.1%
24 1
 
< 0.1%
20 96
 
1.0%
15 1
 
< 0.1%
10 2
 
< 0.1%
5 2
 
< 0.1%
4 36
 
0.4%
2 10
 
0.1%
1 349
3.5%

최대수용수(MXMM_ACCPT_CNT)
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)5.2%
Missing9499
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean11699.022
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:39:59.661314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median80
Q39999
95-th percentile99999
Maximum99999
Range99998
Interquartile range (IQR)9992

Descriptive statistics

Standard deviation27456.11
Coefficient of variation (CV)2.3468723
Kurtosis6.3128813
Mean11699.022
Median Absolute Deviation (MAD)79
Skewness2.8254126
Sum5861210
Variance7.5383798 × 108
MonotonicityNot monotonic
2023-12-13T02:39:59.824523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
9999 155
 
1.6%
80 57
 
0.6%
1 51
 
0.5%
99999 43
 
0.4%
4 40
 
0.4%
20 40
 
0.4%
100 28
 
0.3%
7 26
 
0.3%
3 14
 
0.1%
30 6
 
0.1%
Other values (16) 41
 
0.4%
(Missing) 9499
95.0%
ValueCountFrequency (%)
1 51
0.5%
2 1
 
< 0.1%
3 14
 
0.1%
4 40
0.4%
7 26
0.3%
8 4
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 5
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
99999 43
 
0.4%
9999 155
1.6%
300 2
 
< 0.1%
200 5
 
0.1%
150 1
 
< 0.1%
100 28
 
0.3%
85 4
 
< 0.1%
80 57
 
0.6%
60 2
 
< 0.1%
40 1
 
< 0.1%
Distinct17
Distinct (%)4.5%
Missing9620
Missing (%)96.2%
Memory size156.2 KiB
Minimum2000-01-01 00:00:00
Maximum2021-07-08 00:00:00
2023-12-13T02:39:59.935878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.057345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

시설폐쇄일자(FCLT_CLSNG_DTM)
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9620 
2999-01-01 12:00
 
192
2099-01-01
 
60
2999-01-01
 
42
2999-01-01 23:59
 
38
Other values (3)
 
48

Length

Max length16
Median length4
Mean length4.3888
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9620
96.2%
2999-01-01 12:00 192
 
1.9%
2099-01-01 60
 
0.6%
2999-01-01 42
 
0.4%
2999-01-01 23:59 38
 
0.4%
2999-12-31 23:59 37
 
0.4%
2030-12-31 10
 
0.1%
2030-12-31 12:00 1
 
< 0.1%

Length

2023-12-13T02:40:00.187401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:00.300535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9620
93.7%
2999-01-01 272
 
2.6%
12:00 193
 
1.9%
23:59 75
 
0.7%
2099-01-01 60
 
0.6%
2999-12-31 37
 
0.4%
2030-12-31 11
 
0.1%
Distinct2
Distinct (%)0.4%
Missing9499
Missing (%)95.0%
Memory size97.7 KiB
False
 
343
True
 
158
(Missing)
9499 
ValueCountFrequency (%)
False 343
 
3.4%
True 158
 
1.6%
(Missing) 9499
95.0%
2023-12-13T02:40:00.413147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)0.4%
Missing9499
Missing (%)95.0%
Memory size97.7 KiB
True
 
279
False
 
222
(Missing)
9499 
ValueCountFrequency (%)
True 279
 
2.8%
False 222
 
2.2%
(Missing) 9499
95.0%
2023-12-13T02:40:00.493631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9811 
 
118
0
 
71

Length

Max length4
Median length4
Mean length3.9433
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9811
98.1%
118
 
1.2%
0 71
 
0.7%

Length

2023-12-13T02:40:00.593685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:00.705052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9811
99.3%
0 71
 
0.7%

시설상태코드(FCLT_STCD)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9499 
1
 
425
2
 
76

Length

Max length4
Median length4
Mean length3.8497
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9499
95.0%
1 425
 
4.2%
2 76
 
0.8%

Length

2023-12-13T02:40:00.802156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:00.906237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9499
95.0%
1 425
 
4.2%
2 76
 
0.8%
Distinct125
Distinct (%)25.0%
Missing9499
Missing (%)95.0%
Memory size156.2 KiB
Minimum2009-11-30 18:15:00
Maximum2021-08-25 09:51:00
2023-12-13T02:40:01.027596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.169008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct126
Distinct (%)36.6%
Missing9656
Missing (%)96.6%
Memory size156.2 KiB
Minimum2016-07-29 09:45:00
Maximum2021-08-25 21:15:00
2023-12-13T02:40:01.299285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.452455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

유효여부(VLDTY_YN).4
Boolean

MISSING 

Distinct2
Distinct (%)0.4%
Missing9499
Missing (%)95.0%
Memory size97.7 KiB
True
 
380
False
 
121
(Missing)
9499 
ValueCountFrequency (%)
True 380
 
3.8%
False 121
 
1.2%
(Missing) 9499
95.0%
2023-12-13T02:40:01.586192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

AS-IS 자료(PRODUCT_CODE)
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9842 
1
 
57
G20160727
 
49
2
 
29
4
 
11
Other values (4)
 
12

Length

Max length9
Median length4
Mean length3.9918
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9842
98.4%
1 57
 
0.6%
G20160727 49
 
0.5%
2 29
 
0.3%
4 11
 
0.1%
3 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%

Length

2023-12-13T02:40:01.692822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:02.097104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9842
98.4%
1 57
 
0.6%
g20160727 49
 
0.5%
2 29
 
0.3%
4 11
 
0.1%
3 8
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%

가로크기(FCLT_SIZE_W)
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9851 
 
40
90000
 
25
140000
 
19
100000
 
16
Other values (13)
 
49

Length

Max length6
Median length4
Mean length4.006
Min length1

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9851
98.5%
40
 
0.4%
90000 25
 
0.2%
140000 19
 
0.2%
100000 16
 
0.2%
130000 14
 
0.1%
150000 9
 
0.1%
93000 8
 
0.1%
193000 7
 
0.1%
153000 3
 
< 0.1%
Other values (8) 8
 
0.1%

Length

2023-12-13T02:40:02.219212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9851
98.9%
90000 25
 
0.3%
140000 19
 
0.2%
100000 16
 
0.2%
130000 14
 
0.1%
150000 9
 
0.1%
93000 8
 
0.1%
193000 7
 
0.1%
153000 3
 
< 0.1%
0 1
 
< 0.1%
Other values (7) 7
 
0.1%

세로크기(FCLT_SIZE_H)
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9851 
 
40
180000
 
28
153000
 
13
170000
 
10
Other values (12)
 
58

Length

Max length6
Median length4
Mean length4.0093
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9851
98.5%
40
 
0.4%
180000 28
 
0.3%
153000 13
 
0.1%
170000 10
 
0.1%
160000 10
 
0.1%
110000 9
 
0.1%
210000 7
 
0.1%
143000 6
 
0.1%
163000 6
 
0.1%
Other values (7) 20
 
0.2%

Length

2023-12-13T02:40:02.339631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9851
98.9%
180000 28
 
0.3%
153000 13
 
0.1%
170000 10
 
0.1%
160000 10
 
0.1%
110000 9
 
0.1%
210000 7
 
0.1%
143000 6
 
0.1%
163000 6
 
0.1%
113000 5
 
0.1%
Other values (6) 15
 
0.2%

(PPL_CNT)
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)4.3%
Missing9812
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean2.1117021
Minimum0
Maximum10
Zeros44
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:02.461559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5.65
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1353333
Coefficient of variation (CV)1.0111906
Kurtosis1.3076743
Mean2.1117021
Median Absolute Deviation (MAD)1
Skewness1.2984881
Sum397
Variance4.5596484
MonotonicityNot monotonic
2023-12-13T02:40:02.586450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 49
 
0.5%
2 47
 
0.5%
0 44
 
0.4%
5 22
 
0.2%
4 16
 
0.2%
8 8
 
0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 9812
98.1%
ValueCountFrequency (%)
0 44
0.4%
1 49
0.5%
2 47
0.5%
4 16
 
0.2%
5 22
0.2%
6 1
 
< 0.1%
8 8
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 8
 
0.1%
6 1
 
< 0.1%
5 22
0.2%
4 16
 
0.2%
2 47
0.5%
1 49
0.5%
0 44
0.4%

법정동코드(LGDNG_CD)
Real number (ℝ)

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0177384 × 109
Minimum3.0110101 × 109
Maximum3.0230126 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:02.732390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0110101 × 109
5-th percentile3.0110107 × 109
Q13.0110114 × 109
median3.0230101 × 109
Q33.0230109 × 109
95-th percentile3.0230126 × 109
Maximum3.0230126 × 109
Range12002500
Interquartile range (IQR)11999500

Descriptive statistics

Standard deviation5955785.9
Coefficient of variation (CV)0.0019735925
Kurtosis-1.9407365
Mean3.0177384 × 109
Median Absolute Deviation (MAD)1800
Skewness-0.24423685
Sum3.0177384 × 1013
Variance3.5471385 × 1013
MonotonicityNot monotonic
2023-12-13T02:40:02.895103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3023010100 1030
 
10.3%
3023010900 917
 
9.2%
3011011400 640
 
6.4%
3011011800 572
 
5.7%
3023012600 519
 
5.2%
3023010200 468
 
4.7%
3011011000 459
 
4.6%
3023010700 364
 
3.6%
3023011000 363
 
3.6%
3011011600 357
 
3.6%
Other values (57) 4311
43.1%
ValueCountFrequency (%)
3011010100 121
 
1.2%
3011010200 51
 
0.5%
3011010300 10
 
0.1%
3011010400 26
 
0.3%
3011010500 9
 
0.1%
3011010600 232
2.3%
3011010700 294
2.9%
3011010800 20
 
0.2%
3011010900 104
 
1.0%
3011011000 459
4.6%
ValueCountFrequency (%)
3023012600 519
5.2%
3023012500 40
 
0.4%
3023012400 12
 
0.1%
3023012100 12
 
0.1%
3023012000 16
 
0.2%
3023011900 13
 
0.1%
3023011800 100
 
1.0%
3023011700 177
 
1.8%
3023011600 184
 
1.8%
3023011500 272
2.7%

시도명(CTPRV_NM)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대전광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시
2nd row대전광역시
3rd row대전광역시
4th row대전광역시
5th row대전광역시

Common Values

ValueCountFrequency (%)
대전광역시 10000
100.0%

Length

2023-12-13T02:40:03.025549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:03.120220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 10000
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대덕구
5606 
동구
4394 

Length

Max length3
Median length3
Mean length2.5606
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동구
2nd row동구
3rd row동구
4th row대덕구
5th row동구

Common Values

ValueCountFrequency (%)
대덕구 5606
56.1%
동구 4394
43.9%

Length

2023-12-13T02:40:03.223557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:03.328501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대덕구 5606
56.1%
동구 4394
43.9%
Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T02:40:03.521703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9417
Min length2

Characters and Unicode

Total characters29417
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row삼성동
2nd row소제동
3rd row대별동
4th row문평동
5th row삼성동
ValueCountFrequency (%)
오정동 1030
 
10.3%
중리동 917
 
9.2%
가양동 640
 
6.4%
삼성동 572
 
5.7%
신탄진동 519
 
5.2%
대화동 468
 
4.7%
대동 459
 
4.6%
송촌동 364
 
3.6%
비래동 363
 
3.6%
성남동 357
 
3.6%
Other values (56) 4311
43.1%
2023-12-13T02:40:03.895734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10000
34.0%
1161
 
3.9%
1136
 
3.9%
1087
 
3.7%
1049
 
3.6%
996
 
3.4%
975
 
3.3%
917
 
3.1%
758
 
2.6%
649
 
2.2%
Other values (61) 10689
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29417
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10000
34.0%
1161
 
3.9%
1136
 
3.9%
1087
 
3.7%
1049
 
3.6%
996
 
3.4%
975
 
3.3%
917
 
3.1%
758
 
2.6%
649
 
2.2%
Other values (61) 10689
36.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29417
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10000
34.0%
1161
 
3.9%
1136
 
3.9%
1087
 
3.7%
1049
 
3.6%
996
 
3.4%
975
 
3.3%
917
 
3.1%
758
 
2.6%
649
 
2.2%
Other values (61) 10689
36.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29417
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
10000
34.0%
1161
 
3.9%
1136
 
3.9%
1087
 
3.7%
1049
 
3.6%
996
 
3.4%
975
 
3.3%
917
 
3.1%
758
 
2.6%
649
 
2.2%
Other values (61) 10689
36.3%

리명(LI_NM)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

산여부(MNTN_YN)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9934 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9934
99.3%
1 66
 
0.7%

Length

2023-12-13T02:40:04.018975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:04.104179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9934
99.3%
1 66
 
0.7%
Distinct682
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.9724
Minimum1
Maximum1696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:04.203417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1109
median219
Q3354
95-th percentile542
Maximum1696
Range1695
Interquartile range (IQR)245

Descriptive statistics

Standard deviation197.52522
Coefficient of variation (CV)0.79656133
Kurtosis13.764687
Mean247.9724
Median Absolute Deviation (MAD)120
Skewness2.4257314
Sum2479724
Variance39016.214
MonotonicityNot monotonic
2023-12-13T02:40:04.338068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 191
 
1.9%
305 142
 
1.4%
299 93
 
0.9%
16 81
 
0.8%
1 79
 
0.8%
200 71
 
0.7%
61 66
 
0.7%
161 65
 
0.7%
295 60
 
0.6%
194 53
 
0.5%
Other values (672) 9099
91.0%
ValueCountFrequency (%)
1 79
0.8%
2 21
 
0.2%
3 22
 
0.2%
4 14
 
0.1%
5 43
0.4%
6 22
 
0.2%
7 22
 
0.2%
8 19
 
0.2%
9 28
 
0.3%
10 44
0.4%
ValueCountFrequency (%)
1696 4
< 0.1%
1695 3
 
< 0.1%
1694 3
 
< 0.1%
1693 2
 
< 0.1%
1691 1
 
< 0.1%
1690 3
 
< 0.1%
1689 1
 
< 0.1%
1688 6
0.1%
1686 1
 
< 0.1%
1684 9
0.1%

시작부번지명(BGN_SUB_LTNMB_NM)
Real number (ℝ)

ZEROS 

Distinct450
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.6085
Minimum0
Maximum1217
Zeros499
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:04.514024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q323
95-th percentile145.1
Maximum1217
Range1217
Interquartile range (IQR)19

Descriptive statistics

Standard deviation88.35485
Coefficient of variation (CV)2.709565
Kurtosis51.61458
Mean32.6085
Median Absolute Deviation (MAD)8
Skewness6.3308731
Sum326085
Variance7806.5795
MonotonicityNot monotonic
2023-12-13T02:40:04.669815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 826
 
8.3%
2 562
 
5.6%
0 499
 
5.0%
3 492
 
4.9%
4 442
 
4.4%
5 433
 
4.3%
6 408
 
4.1%
7 370
 
3.7%
9 342
 
3.4%
8 335
 
3.4%
Other values (440) 5291
52.9%
ValueCountFrequency (%)
0 499
5.0%
1 826
8.3%
2 562
5.6%
3 492
4.9%
4 442
4.4%
5 433
4.3%
6 408
4.1%
7 370
3.7%
8 335
3.4%
9 342
3.4%
ValueCountFrequency (%)
1217 1
< 0.1%
1199 1
< 0.1%
1197 1
< 0.1%
1190 1
< 0.1%
1141 1
< 0.1%
1133 1
< 0.1%
1131 1
< 0.1%
1117 1
< 0.1%
1112 1
< 0.1%
1093 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
302000000000
5606 
301000000000
4394 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row301000000000
2nd row301000000000
3rd row301000000000
4th row302000000000
5th row301000000000

Common Values

ValueCountFrequency (%)
302000000000 5606
56.1%
301000000000 4394
43.9%

Length

2023-12-13T02:40:04.834582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:04.940098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
302000000000 5606
56.1%
301000000000 4394
43.9%

지하여부(UNDRG_YN)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9998
> 99.9%
1 2
 
< 0.1%

Length

2023-12-13T02:40:05.057930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:05.175973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9998
> 99.9%
1 2
 
< 0.1%

건물명(BLDNG_NM)
Text

MISSING 

Distinct1381
Distinct (%)85.2%
Missing8380
Missing (%)83.8%
Memory size156.2 KiB
2023-12-13T02:40:05.542480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length5.3987654
Min length2

Characters and Unicode

Total characters8746
Distinct characters547
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1230 ?
Unique (%)75.9%

Sample

1st row(주)로얄방충망
2nd row송촌빌라
3rd row가양연립
4th row세영빌라
5th row읍내동현대아파트
ValueCountFrequency (%)
한국타이어(주)대전공장 18
 
1.1%
송촌빌라 7
 
0.4%
동양강철 7
 
0.4%
주)남북자동차종합시장 7
 
0.4%
한남대학교 6
 
0.4%
중리취수장 5
 
0.3%
농수산물도매시장 5
 
0.3%
아트빌라 5
 
0.3%
대전병원 5
 
0.3%
거성빌라 4
 
0.2%
Other values (1373) 1553
95.7%
2023-12-13T02:40:06.149308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282
 
3.2%
272
 
3.1%
262
 
3.0%
254
 
2.9%
175
 
2.0%
) 172
 
2.0%
( 170
 
1.9%
146
 
1.7%
134
 
1.5%
127
 
1.5%
Other values (537) 6752
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8274
94.6%
Close Punctuation 173
 
2.0%
Open Punctuation 171
 
2.0%
Uppercase Letter 79
 
0.9%
Decimal Number 30
 
0.3%
Other Punctuation 8
 
0.1%
Lowercase Letter 7
 
0.1%
Dash Punctuation 2
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
282
 
3.4%
272
 
3.3%
262
 
3.2%
254
 
3.1%
175
 
2.1%
146
 
1.8%
134
 
1.6%
127
 
1.5%
125
 
1.5%
125
 
1.5%
Other values (492) 6372
77.0%
Uppercase Letter
ValueCountFrequency (%)
G 11
13.9%
K 10
12.7%
L 9
11.4%
P 8
10.1%
C 5
 
6.3%
T 5
 
6.3%
F 4
 
5.1%
A 4
 
5.1%
S 4
 
5.1%
R 3
 
3.8%
Other values (11) 16
20.3%
Decimal Number
ValueCountFrequency (%)
2 12
40.0%
1 5
16.7%
0 4
 
13.3%
4 3
 
10.0%
3 2
 
6.7%
5 2
 
6.7%
7 1
 
3.3%
6 1
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
r 1
14.3%
t 1
14.3%
a 1
14.3%
i 1
14.3%
e 1
14.3%
p 1
14.3%
c 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 4
50.0%
& 2
25.0%
. 2
25.0%
Close Punctuation
ValueCountFrequency (%)
) 172
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 170
99.4%
[ 1
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8274
94.6%
Common 386
 
4.4%
Latin 86
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
282
 
3.4%
272
 
3.3%
262
 
3.2%
254
 
3.1%
175
 
2.1%
146
 
1.8%
134
 
1.6%
127
 
1.5%
125
 
1.5%
125
 
1.5%
Other values (492) 6372
77.0%
Latin
ValueCountFrequency (%)
G 11
12.8%
K 10
11.6%
L 9
 
10.5%
P 8
 
9.3%
C 5
 
5.8%
T 5
 
5.8%
F 4
 
4.7%
A 4
 
4.7%
S 4
 
4.7%
R 3
 
3.5%
Other values (18) 23
26.7%
Common
ValueCountFrequency (%)
) 172
44.6%
( 170
44.0%
2 12
 
3.1%
1 5
 
1.3%
, 4
 
1.0%
0 4
 
1.0%
4 3
 
0.8%
- 2
 
0.5%
3 2
 
0.5%
5 2
 
0.5%
Other values (7) 10
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8274
94.6%
ASCII 472
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
282
 
3.4%
272
 
3.3%
262
 
3.2%
254
 
3.1%
175
 
2.1%
146
 
1.8%
134
 
1.6%
127
 
1.5%
125
 
1.5%
125
 
1.5%
Other values (492) 6372
77.0%
ASCII
ValueCountFrequency (%)
) 172
36.4%
( 170
36.0%
2 12
 
2.5%
G 11
 
2.3%
K 10
 
2.1%
L 9
 
1.9%
P 8
 
1.7%
1 5
 
1.1%
C 5
 
1.1%
T 5
 
1.1%
Other values (35) 65
 
13.8%
Distinct62
Distinct (%)65.3%
Missing9905
Missing (%)99.1%
Memory size156.2 KiB
2023-12-13T02:40:06.454907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.8526316
Min length2

Characters and Unicode

Total characters366
Distinct characters135
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)51.6%

Sample

1st row한진PVC종합상사
2nd row1동
3rd row10가구
4th row전쎄빠
5th row숯불돼지마을
ValueCountFrequency (%)
2가구 10
 
10.5%
3가구 6
 
6.3%
2동 5
 
5.3%
6가구 4
 
4.2%
12가구 3
 
3.2%
4가구 3
 
3.2%
10가구 3
 
3.2%
다동 2
 
2.1%
18가구 2
 
2.1%
a동 2
 
2.1%
Other values (52) 55
57.9%
2023-12-13T02:40:07.055894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
 
11.2%
40
 
10.9%
22
 
6.0%
2 18
 
4.9%
1 16
 
4.4%
3 9
 
2.5%
6 7
 
1.9%
5
 
1.4%
5
 
1.4%
5
 
1.4%
Other values (125) 198
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 290
79.2%
Decimal Number 64
 
17.5%
Uppercase Letter 8
 
2.2%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
41
 
14.1%
40
 
13.8%
22
 
7.6%
5
 
1.7%
5
 
1.7%
5
 
1.7%
4
 
1.4%
4
 
1.4%
3
 
1.0%
3
 
1.0%
Other values (109) 158
54.5%
Decimal Number
ValueCountFrequency (%)
2 18
28.1%
1 16
25.0%
3 9
14.1%
6 7
 
10.9%
8 3
 
4.7%
5 3
 
4.7%
0 3
 
4.7%
4 3
 
4.7%
7 2
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 3
37.5%
B 2
25.0%
P 1
 
12.5%
C 1
 
12.5%
V 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 290
79.2%
Common 68
 
18.6%
Latin 8
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
41
 
14.1%
40
 
13.8%
22
 
7.6%
5
 
1.7%
5
 
1.7%
5
 
1.7%
4
 
1.4%
4
 
1.4%
3
 
1.0%
3
 
1.0%
Other values (109) 158
54.5%
Common
ValueCountFrequency (%)
2 18
26.5%
1 16
23.5%
3 9
13.2%
6 7
 
10.3%
8 3
 
4.4%
5 3
 
4.4%
0 3
 
4.4%
4 3
 
4.4%
( 2
 
2.9%
7 2
 
2.9%
Latin
ValueCountFrequency (%)
A 3
37.5%
B 2
25.0%
P 1
 
12.5%
C 1
 
12.5%
V 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 290
79.2%
ASCII 76
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
41
 
14.1%
40
 
13.8%
22
 
7.6%
5
 
1.7%
5
 
1.7%
5
 
1.7%
4
 
1.4%
4
 
1.4%
3
 
1.0%
3
 
1.0%
Other values (109) 158
54.5%
ASCII
ValueCountFrequency (%)
2 18
23.7%
1 16
21.1%
3 9
11.8%
6 7
 
9.2%
A 3
 
3.9%
8 3
 
3.9%
5 3
 
3.9%
0 3
 
3.9%
4 3
 
3.9%
( 2
 
2.6%
Other values (6) 9
11.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3020000000000000000000000
5606 
3010000000000000000000000
4394 

Length

Max length26
Median length26
Mean length26
Min length26

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3010000000000000000000000
2nd row3010000000000000000000000
3rd row3010000000000000000000000
4th row3020000000000000000000000
5th row3010000000000000000000000

Common Values

ValueCountFrequency (%)
3020000000000000000000000 5606
56.1%
3010000000000000000000000 4394
43.9%

Length

2023-12-13T02:40:07.278083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:07.440140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000000000000000000 5606
56.1%
3010000000000000000000000 4394
43.9%

읍면동ID(EMNDN_ID)
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4009
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:07.613893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2227546
Coefficient of variation (CV)0.872835
Kurtosis26.433318
Mean1.4009
Median Absolute Deviation (MAD)0
Skewness4.7540639
Sum14009
Variance1.4951287
MonotonicityNot monotonic
2023-12-13T02:40:07.796959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8117
81.2%
2 1178
 
11.8%
3 282
 
2.8%
4 127
 
1.3%
8 80
 
0.8%
5 63
 
0.6%
6 51
 
0.5%
7 40
 
0.4%
10 39
 
0.4%
12 14
 
0.1%
Other values (2) 9
 
0.1%
ValueCountFrequency (%)
1 8117
81.2%
2 1178
 
11.8%
3 282
 
2.8%
4 127
 
1.3%
5 63
 
0.6%
6 51
 
0.5%
7 40
 
0.4%
8 80
 
0.8%
9 8
 
0.1%
10 39
 
0.4%
ValueCountFrequency (%)
12 14
 
0.1%
11 1
 
< 0.1%
10 39
 
0.4%
9 8
 
0.1%
8 80
 
0.8%
7 40
 
0.4%
6 51
 
0.5%
5 63
 
0.6%
4 127
1.3%
3 282
2.8%

행정코드(ADMNS_CD)
Real number (ℝ)

Distinct28
Distinct (%)0.3%
Missing17
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.0177949 × 109
Minimum3.0110515 × 109
Maximum3.023061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:08.010404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0110515 × 109
5-th percentile3.0110515 × 109
Q13.011063 × 109
median3.023051 × 109
Q33.0230546 × 109
95-th percentile3.023057 × 109
Maximum3.023061 × 109
Range12009500
Interquartile range (IQR)11991600

Descriptive statistics

Standard deviation5950949.8
Coefficient of variation (CV)0.0019719531
Kurtosis-1.9390129
Mean3.0177949 × 109
Median Absolute Deviation (MAD)6000
Skewness-0.24773491
Sum3.0126646 × 1013
Variance3.5413804 × 1013
MonotonicityNot monotonic
2023-12-13T02:40:08.201018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3023051000 1031
 
10.3%
3023054600 915
 
9.2%
3011051500 664
 
6.6%
3023057000 634
 
6.3%
3023052500 624
 
6.2%
3023055000 611
 
6.1%
3011069500 572
 
5.7%
3023052000 466
 
4.7%
3011058500 461
 
4.6%
3011062000 382
 
3.8%
Other values (18) 3623
36.2%
ValueCountFrequency (%)
3011051500 664
6.6%
3011053000 45
 
0.4%
3011054500 283
2.8%
3011055100 157
 
1.6%
3011055200 153
 
1.5%
3011056000 103
 
1.0%
3011058500 461
4.6%
3011059000 118
 
1.2%
3011062000 382
3.8%
3011063000 246
 
2.5%
ValueCountFrequency (%)
3023061000 62
 
0.6%
3023060000 26
 
0.3%
3023058000 219
 
2.2%
3023057000 634
6.3%
3023056000 290
 
2.9%
3023055000 611
6.1%
3023054600 915
9.2%
3023054300 364
 
3.6%
3023053300 363
 
3.6%
3023052500 624
6.2%
Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
오정동
1031 
중리동
915 
중앙동
664 
덕암동
634 
회덕동
624 
Other values (24)
6132 

Length

Max length4
Median length3
Mean length3.106
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row삼성동
2nd row중앙동
3rd row산내동
4th row목상동
5th row삼성동

Common Values

ValueCountFrequency (%)
오정동 1031
 
10.3%
중리동 915
 
9.2%
중앙동 664
 
6.6%
덕암동 634
 
6.3%
회덕동 624
 
6.2%
신탄진동 611
 
6.1%
삼성동 572
 
5.7%
대화동 466
 
4.7%
대동 461
 
4.6%
가양1동 382
 
3.8%
Other values (19) 3640
36.4%

Length

2023-12-13T02:40:08.374824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
오정동 1031
 
10.3%
중리동 915
 
9.2%
중앙동 664
 
6.6%
덕암동 634
 
6.3%
회덕동 624
 
6.2%
신탄진동 611
 
6.1%
삼성동 572
 
5.7%
대화동 466
 
4.7%
대동 461
 
4.6%
가양1동 382
 
3.8%
Other values (19) 3640
36.4%

우편번호ID(ZIPCD_ID)
Real number (ℝ)

Distinct160
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean304005.25
Minimum300010
Maximum306830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:40:08.550731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300010
5-th percentile300080
Q1300811
median306130
Q3306817
95-th percentile306827
Maximum306830
Range6820
Interquartile range (IQR)6006

Descriptive statistics

Standard deviation3042.0412
Coefficient of variation (CV)0.010006541
Kurtosis-1.9031955
Mean304005.25
Median Absolute Deviation (MAD)696
Skewness-0.24694564
Sum3.0397485 × 109
Variance9254014.4
MonotonicityNot monotonic
2023-12-13T02:40:08.782212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306801 293
 
2.9%
300080 293
 
2.9%
306825 277
 
2.8%
306817 274
 
2.7%
300805 262
 
2.6%
306819 250
 
2.5%
306827 242
 
2.4%
306826 235
 
2.4%
300110 231
 
2.3%
300808 230
 
2.3%
Other values (150) 7412
74.1%
ValueCountFrequency (%)
300010 121
1.2%
300020 1
 
< 0.1%
300030 10
 
0.1%
300040 26
 
0.3%
300050 9
 
0.1%
300060 146
1.5%
300080 293
2.9%
300110 231
2.3%
300120 17
 
0.2%
300140 20
 
0.2%
ValueCountFrequency (%)
306830 158
1.6%
306829 48
 
0.5%
306828 120
1.2%
306827 242
2.4%
306826 235
2.4%
306825 277
2.8%
306824 161
1.6%
306823 89
 
0.9%
306822 167
1.7%
306821 113
1.1%

기타명(ETC_NM)
Text

MISSING 

Distinct31
Distinct (%)67.4%
Missing9954
Missing (%)99.5%
Memory size156.2 KiB
2023-12-13T02:40:09.160264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.2608696
Min length4

Characters and Unicode

Total characters288
Distinct characters86
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)47.8%

Sample

1st row현대아파트
2nd row주공아파트
3rd row세원풍남아파트
4th rowKT&G
5th row한남대학교
ValueCountFrequency (%)
한남대학교 6
 
13.0%
대전기관차사무소 3
 
6.5%
원창등마루아파트 3
 
6.5%
kt&g 2
 
4.3%
비래한신휴플러스 2
 
4.3%
우송공업대학 2
 
4.3%
새피앙아파트 2
 
4.3%
현대아파트 2
 
4.3%
주공아파트3단지 2
 
4.3%
우성빌딩 1
 
2.2%
Other values (21) 21
45.7%
2023-12-13T02:40:09.657275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
7.6%
22
 
7.6%
21
 
7.3%
19
 
6.6%
10
 
3.5%
9
 
3.1%
7
 
2.4%
7
 
2.4%
7
 
2.4%
7
 
2.4%
Other values (76) 157
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
95.1%
Uppercase Letter 6
 
2.1%
Decimal Number 4
 
1.4%
Other Punctuation 2
 
0.7%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
8.0%
22
 
8.0%
21
 
7.7%
19
 
6.9%
10
 
3.6%
9
 
3.3%
7
 
2.6%
7
 
2.6%
7
 
2.6%
7
 
2.6%
Other values (67) 143
52.2%
Decimal Number
ValueCountFrequency (%)
3 2
50.0%
4 1
25.0%
6 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
G 2
33.3%
T 2
33.3%
K 2
33.3%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
95.1%
Common 8
 
2.8%
Latin 6
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
8.0%
22
 
8.0%
21
 
7.7%
19
 
6.9%
10
 
3.6%
9
 
3.3%
7
 
2.6%
7
 
2.6%
7
 
2.6%
7
 
2.6%
Other values (67) 143
52.2%
Common
ValueCountFrequency (%)
3 2
25.0%
& 2
25.0%
4 1
12.5%
6 1
12.5%
( 1
12.5%
) 1
12.5%
Latin
ValueCountFrequency (%)
G 2
33.3%
T 2
33.3%
K 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
95.1%
ASCII 14
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
22
 
8.0%
22
 
8.0%
21
 
7.7%
19
 
6.9%
10
 
3.6%
9
 
3.3%
7
 
2.6%
7
 
2.6%
7
 
2.6%
7
 
2.6%
Other values (67) 143
52.2%
ASCII
ValueCountFrequency (%)
3 2
14.3%
G 2
14.3%
T 2
14.3%
& 2
14.3%
K 2
14.3%
4 1
7.1%
6 1
7.1%
( 1
7.1%
) 1
7.1%

변경구분코드(CHNG_TPCD)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

변경일(CHDT)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

공동건물여부(CPRTI_BLDNG_YN)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9541 
1
 
458
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0003
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9541
95.4%
1 458
 
4.6%
<NA> 1
 
< 0.1%

Length

2023-12-13T02:40:09.843224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:10.047703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9541
95.4%
1 458
 
4.6%
na 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
Minimum2012-10-26 15:33:00
Maximum2012-10-26 15:43:00
2023-12-13T02:40:10.223834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:10.444601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

등록일시(VLDTY_YN)
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size97.7 KiB
True
9999 
(Missing)
 
1
ValueCountFrequency (%)
True 9999
> 99.9%
(Missing) 1
 
< 0.1%
2023-12-13T02:40:10.582631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

상품(ID)시설대분류코드(FCLT_LRCLS_CD)시설용도구분코드(FCLT_PRPSE_TPCD)시즌구분코드(SSN_TPCD)일자구분코드(DT_TPCD)위약규정일수(PNLT_RGLTN_DCNT)공제율비(DDCTN_RATE)등록일시(RGTER_ID)최종수정일시(LAST_UPDT_DTM)유효여부(VLDTY_YN)취소금액(CANCELAMT)취소사유(CANCELMSG)부분취소구분(PARTIALCANCELCODE)취소결과코드(CANCELRESULTCODE)결제순번(STTLM_SEQ)결제순번(STTLM_SEQ).1결제금액(STTLM_AMT)위약금금액(PNLT_AMT)결제만기일시(STTLM_MT_DTM)결제완료일시(STTLM_CMPLE_DTM)결제상태코드(STTLM_STCD)등록일시(RGSTN_DTM)최종수정일시(LAST_UPDT_DTM).1유효여부(VLDTY_YN).1환불테이블순번(RFNDM_TABLE_SEQ)환불유형코드(RFNDM_TPE_CD)부서ID(DPRTM_ID)예약순번(RSRVT_SEQ)결제순번(STTLM_SEQ).2환불금액(RFNDM_AMT)위약금금액(PNLT_AMT).1환불사유(RFNDM_RSN)환불요청일시(RFNDM_RQUST_DTM)환불완료일시(RFNDM_CMPLE_DTM)등록일시(RGSTN_DTM).1최종수정일시(LAST_UPDT_DTM).2유효여부(VLDTY_YN).2결제수단(PAYMETHOD)결제상품개수(GOODSCNT)결제상품금액(AMT)우편번호(BUYERPOSTNO)거래타입(TRANSTYPE)상점예비정보(MALLRESERVED)가상계좌입금만료일(VBANKEXPDATE)소켓이용유무(SOCKETYN)전문생성일시코드(EDIDATECODE)해쉬값(ENCRYPTDATA)통화구분(CURRENCY)승인일자코드(AUTHDATECODE)결과코드(RESULTCODE)결제순번(STTLM_SEQ).3시설대분류코드(FCLT_LRCLS_CD).1시설용도구분코드(FCLT_PRPSE_TPCD).1시즌구분코드(SSN_TPCD).1일자구분코드(DT_TPCD).1위약규정일수(PNLT_RGLTN_DCNT).1공제율비(DDCTN_RATE).1등록일시(RGTER_ID).1최종수정일시(LAST_UPDT_DTM).3유효여부(VLDTY_YN).3부서ID(DPRTM_ID).1시설ID(FCLT_ID)시설명(FCLT_NM)시설설명(FCLT_DSCRT)시설대분류코드(FCLT_LRCLS_CD).2시설중분류코드(FCLT_MDCLS_CD)시설소분류코드(FCLT_SMCLS_CD)시설용도구분코드(FCLT_PRPSE_TPCD).2시설예약구분코드(FCLT_RSRVT_TPCD)시설이미지맵좌표(FCLT_IMAGE_MAP_CRD)배치물품내용(PSTNG_THING_CONT)구역명(ZONE_NM)면적단위(AREA_UNIT)면적합계(AREA_SUM)최소수용수(MNMM_ACCPT_CNT)최대수용수(MXMM_ACCPT_CNT)시설오픈일자(FCLT_OPEN_DTM)시설폐쇄일자(FCLT_CLSNG_DTM)전기시설여부(ELCTY_FCLT_YN)난방시설여부(HEAT_FCLT_YN)난방시설유형코드(HEAT_FCLT_TPE_CD)시설상태코드(FCLT_STCD)등록일시(RGSTN_DTM).2최종수정일시(LAST_UPDT_DTM).4유효여부(VLDTY_YN).4AS-IS 자료(PRODUCT_CODE)가로크기(FCLT_SIZE_W)세로크기(FCLT_SIZE_H)(PPL_CNT)법정동코드(LGDNG_CD)시도명(CTPRV_NM)시군구명(SGNG_NM)읍면동명(EMNDN_NM)리명(LI_NM)산여부(MNTN_YN)시작주번지명(BGN_MIN_LTNMB_NM)시작부번지명(BGN_SUB_LTNMB_NM)도로코드(ROAD_CD)지하여부(UNDRG_YN)건물명(BLDNG_NM)상세건물명(DTL_BLDNG_NM)건물관리명(BLDNG_MNGME_NM)읍면동ID(EMNDN_ID)행정코드(ADMNS_CD)행정코드명(ADMNS_CD_NM)우편번호ID(ZIPCD_ID)기타명(ETC_NM)변경구분코드(CHNG_TPCD)변경일(CHDT)공동건물여부(CPRTI_BLDNG_YN)유효여부(RGSTN_DTM)등록일시(VLDTY_YN)
21703<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>140000<NA><NA>2013-06-17 20:4740002013-06-17 20:47<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011011800대전광역시동구삼성동<NA>0282323010000000000<NA><NA>301000000000000000000000013011069500삼성동300812<NA><NA><NA>02012-10-26 15:43Y
31259<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>20<NA><NA>2014-03-01 15:27CASH2014-03-01 15:27<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011011300대전광역시동구소제동<NA>02991393010000000000<NA><NA>301000000000000000000000013011051500중앙동300080<NA><NA><NA>02012-10-26 15:43Y
22652<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>125500<NA><NA>2013-07-25 19:1130012013-07-25 19:10<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011013700대전광역시동구대별동<NA>026903010000000000<NA><NA>301000000000000000000000013011074000산내동300260<NA><NA><NA>02012-10-26 15:43Y
16427<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>240000<NA><NA>2013-06-10 13:5441102013-06-10 13:54<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023011300대전광역시대덕구문평동<NA>06413020000000000(주)로얄방충망<NA>302000000000000000000000033023058000목상동306220<NA><NA><NA>02012-10-26 15:33Y
22068<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>290000<NA>2013-06-18 13:2320012013-06-18 13:23<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011011800대전광역시동구삼성동<NA>032083010000000000<NA><NA>301000000000000000000000013011069500삼성동300815<NA><NA><NA>02012-10-26 15:43Y
4729<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>37000취소 성공Y2001<NA>157000<NA><NA>2013-05-15 9:1241102013-05-15 9:10<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD1194000302840<NA><NA><NA><NA>20190400000000<NA>KRW1904140000003001<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023012600대전광역시대덕구신탄진동<NA>0123423020000000000<NA><NA>302000000000000000000000013023055000신탄진동306816<NA><NA><NA>02012-10-26 15:43Y
17075<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>158000<NA>2013-06-15 23:592013-06-12 13:1041002013-06-12 13:10<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023010200대전광역시대덕구대화동<NA>016353020000000000<NA><NA>302000000000000000000000023023052000대화동306800<NA><NA><NA>02012-10-26 15:43Y
6509<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>157000<NA><NA>2013-05-19 20:0630012013-05-19 20:03<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD1210000302840<NA><NA><NA><NA>20191100000000<NA>KRW1911280000003001<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023011100대전광역시대덕구석봉동<NA>0306233020000000000<NA><NA>302000000000000000000000013023056000석봉동306810<NA><NA><NA>02012-10-26 15:43Y
15564<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2700000<NA>2013-06-09 21:0320012013-06-09 21:03<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023010200대전광역시대덕구대화동<NA>0239203020000000000<NA><NA>302000000000000000000000023023052000대화동306801<NA><NA><NA>02012-10-26 15:43Y
28231<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2801008900<NA>2014-02-27 18:2720012014-02-27 18:27<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011011300대전광역시동구소제동<NA>03053393010000000000<NA><NA>301000000000000000000000013011051500중앙동300080<NA><NA><NA>02012-10-26 15:43Y
상품(ID)시설대분류코드(FCLT_LRCLS_CD)시설용도구분코드(FCLT_PRPSE_TPCD)시즌구분코드(SSN_TPCD)일자구분코드(DT_TPCD)위약규정일수(PNLT_RGLTN_DCNT)공제율비(DDCTN_RATE)등록일시(RGTER_ID)최종수정일시(LAST_UPDT_DTM)유효여부(VLDTY_YN)취소금액(CANCELAMT)취소사유(CANCELMSG)부분취소구분(PARTIALCANCELCODE)취소결과코드(CANCELRESULTCODE)결제순번(STTLM_SEQ)결제순번(STTLM_SEQ).1결제금액(STTLM_AMT)위약금금액(PNLT_AMT)결제만기일시(STTLM_MT_DTM)결제완료일시(STTLM_CMPLE_DTM)결제상태코드(STTLM_STCD)등록일시(RGSTN_DTM)최종수정일시(LAST_UPDT_DTM).1유효여부(VLDTY_YN).1환불테이블순번(RFNDM_TABLE_SEQ)환불유형코드(RFNDM_TPE_CD)부서ID(DPRTM_ID)예약순번(RSRVT_SEQ)결제순번(STTLM_SEQ).2환불금액(RFNDM_AMT)위약금금액(PNLT_AMT).1환불사유(RFNDM_RSN)환불요청일시(RFNDM_RQUST_DTM)환불완료일시(RFNDM_CMPLE_DTM)등록일시(RGSTN_DTM).1최종수정일시(LAST_UPDT_DTM).2유효여부(VLDTY_YN).2결제수단(PAYMETHOD)결제상품개수(GOODSCNT)결제상품금액(AMT)우편번호(BUYERPOSTNO)거래타입(TRANSTYPE)상점예비정보(MALLRESERVED)가상계좌입금만료일(VBANKEXPDATE)소켓이용유무(SOCKETYN)전문생성일시코드(EDIDATECODE)해쉬값(ENCRYPTDATA)통화구분(CURRENCY)승인일자코드(AUTHDATECODE)결과코드(RESULTCODE)결제순번(STTLM_SEQ).3시설대분류코드(FCLT_LRCLS_CD).1시설용도구분코드(FCLT_PRPSE_TPCD).1시즌구분코드(SSN_TPCD).1일자구분코드(DT_TPCD).1위약규정일수(PNLT_RGLTN_DCNT).1공제율비(DDCTN_RATE).1등록일시(RGTER_ID).1최종수정일시(LAST_UPDT_DTM).3유효여부(VLDTY_YN).3부서ID(DPRTM_ID).1시설ID(FCLT_ID)시설명(FCLT_NM)시설설명(FCLT_DSCRT)시설대분류코드(FCLT_LRCLS_CD).2시설중분류코드(FCLT_MDCLS_CD)시설소분류코드(FCLT_SMCLS_CD)시설용도구분코드(FCLT_PRPSE_TPCD).2시설예약구분코드(FCLT_RSRVT_TPCD)시설이미지맵좌표(FCLT_IMAGE_MAP_CRD)배치물품내용(PSTNG_THING_CONT)구역명(ZONE_NM)면적단위(AREA_UNIT)면적합계(AREA_SUM)최소수용수(MNMM_ACCPT_CNT)최대수용수(MXMM_ACCPT_CNT)시설오픈일자(FCLT_OPEN_DTM)시설폐쇄일자(FCLT_CLSNG_DTM)전기시설여부(ELCTY_FCLT_YN)난방시설여부(HEAT_FCLT_YN)난방시설유형코드(HEAT_FCLT_TPE_CD)시설상태코드(FCLT_STCD)등록일시(RGSTN_DTM).2최종수정일시(LAST_UPDT_DTM).4유효여부(VLDTY_YN).4AS-IS 자료(PRODUCT_CODE)가로크기(FCLT_SIZE_W)세로크기(FCLT_SIZE_H)(PPL_CNT)법정동코드(LGDNG_CD)시도명(CTPRV_NM)시군구명(SGNG_NM)읍면동명(EMNDN_NM)리명(LI_NM)산여부(MNTN_YN)시작주번지명(BGN_MIN_LTNMB_NM)시작부번지명(BGN_SUB_LTNMB_NM)도로코드(ROAD_CD)지하여부(UNDRG_YN)건물명(BLDNG_NM)상세건물명(DTL_BLDNG_NM)건물관리명(BLDNG_MNGME_NM)읍면동ID(EMNDN_ID)행정코드(ADMNS_CD)행정코드명(ADMNS_CD_NM)우편번호ID(ZIPCD_ID)기타명(ETC_NM)변경구분코드(CHNG_TPCD)변경일(CHDT)공동건물여부(CPRTI_BLDNG_YN)유효여부(RGSTN_DTM)등록일시(VLDTY_YN)
4802<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>116000취소 성공Y2001<NA>1117000<NA><NA>2013-05-20 21:4430012013-05-20 21:39<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD1158000302840<NA><NA><NA><NA>20190600000000<NA>KRW1906040000003001<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023010500대전광역시대덕구신대동<NA>023153020000000000<NA><NA>302000000000000000000000013023052500회덕동306080<NA><NA><NA>02012-10-26 15:43Y
12561<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>173000<NA><NA>2013-06-05 14:3030012013-06-05 14:30<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD1278000302840<NA><NA><NA><NA>20210700000000<NA>KRW2107220000003001.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023010100대전광역시대덕구오정동<NA>038733020000000000<NA><NA>302000000000000000000000013023051000오정동306821<NA><NA><NA>02012-10-26 15:43Y
28389<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>10<NA><NA>2015-06-22 21:35CASH2015-06-22 21:35<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011011600대전광역시동구성남동<NA>0507353010000000000<NA><NA>301000000000000000000000033011066500성남동300819<NA><NA><NA>02012-10-26 15:43Y
18316<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2730000<NA>2013-06-14 11:0320012013-06-14 11:03<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023011700대전광역시대덕구평촌동<NA>085103020000000000<NA><NA>302000000000000000000000013023057000덕암동306130<NA><NA><NA>02012-10-26 15:43Y
26492<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>30<NA><NA>2014-02-26 16:05CASH2014-02-26 16:05<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011010100대전광역시동구원동<NA>03513010000000000대륙기계사<NA>301000000000000000000000013011051500중앙동300010<NA><NA><NA>02012-10-26 15:43Y
27372<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2400000<NA>2014-02-27 10:5120012014-02-27 10:51<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011010100대전광역시동구원동<NA>0101513010000000000<NA>평양식당301000000000000000000000013011051500중앙동300010<NA><NA><NA>02012-10-26 15:43Y
5926<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>19000<NA><NA>2013-05-15 10:3830012013-05-15 9:01<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD190000302840<NA><NA><NA><NA>20190900000000<NA>KRW1909120000003001<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023011400대전광역시대덕구신일동<NA>0168223020000000000한일제관(주)<NA>302000000000000000000000023023058000목상동306230<NA><NA><NA>02012-10-26 15:43Y
9840<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>173000<NA><NA>2013-05-29 23:1230012013-05-29 9:05<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD1237000302840<NA><NA><NA><NA>20210200000000<NA>KRW2102090000003001.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023010200대전광역시대덕구대화동<NA>0275233020000000000<NA><NA>302000000000000000000000013023052000대화동306801<NA><NA><NA>02012-10-26 15:43Y
15129<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>140000<NA><NA>2013-06-07 13:3830012013-06-07 13:39<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023010700대전광역시대덕구송촌동<NA>045213020000000000<NA><NA>302000000000000000000000013023054300송촌동306813<NA><NA><NA>02012-10-26 15:43Y
912<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>419000취소 성공Y2001<NA>158000<NA><NA>2013-05-24 21:3941102013-05-24 21:25<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>CARD1186000302840<NA><NA><NA><NA>20161100000000<NA>KRW16112100000030011<NA><NA><NA><NA><NA><NA><NA><NA><NA>107F010702012중형(무료주차)무료주차시설/중형22012<NA>11<NA><NA>00.01999992000-01-012999-12-31 23:59NN012009-11-30 18:15<NA>Y<NA><NA><NA><NA>3023010400대전광역시대덕구연축동<NA>019913020000000000<NA><NA>302000000000000000000000013023052500회덕동306090<NA><NA><NA>02012-10-26 15:43Y

Duplicate rows

Most frequently occurring

상품(ID)시설대분류코드(FCLT_LRCLS_CD)시설용도구분코드(FCLT_PRPSE_TPCD)시즌구분코드(SSN_TPCD)일자구분코드(DT_TPCD)위약규정일수(PNLT_RGLTN_DCNT)공제율비(DDCTN_RATE)등록일시(RGTER_ID)유효여부(VLDTY_YN)취소금액(CANCELAMT)취소사유(CANCELMSG)부분취소구분(PARTIALCANCELCODE)취소결과코드(CANCELRESULTCODE)결제순번(STTLM_SEQ)결제순번(STTLM_SEQ).1결제금액(STTLM_AMT)위약금금액(PNLT_AMT)결제만기일시(STTLM_MT_DTM)결제완료일시(STTLM_CMPLE_DTM)결제상태코드(STTLM_STCD)등록일시(RGSTN_DTM)최종수정일시(LAST_UPDT_DTM).1유효여부(VLDTY_YN).1환불테이블순번(RFNDM_TABLE_SEQ)환불유형코드(RFNDM_TPE_CD)부서ID(DPRTM_ID)예약순번(RSRVT_SEQ)결제순번(STTLM_SEQ).2환불금액(RFNDM_AMT)위약금금액(PNLT_AMT).1환불사유(RFNDM_RSN)환불요청일시(RFNDM_RQUST_DTM)환불완료일시(RFNDM_CMPLE_DTM)등록일시(RGSTN_DTM).1최종수정일시(LAST_UPDT_DTM).2유효여부(VLDTY_YN).2결제수단(PAYMETHOD)결제상품개수(GOODSCNT)결제상품금액(AMT)우편번호(BUYERPOSTNO)가상계좌입금만료일(VBANKEXPDATE)전문생성일시코드(EDIDATECODE)통화구분(CURRENCY)승인일자코드(AUTHDATECODE)결제순번(STTLM_SEQ).3시설대분류코드(FCLT_LRCLS_CD).1시설용도구분코드(FCLT_PRPSE_TPCD).1시즌구분코드(SSN_TPCD).1일자구분코드(DT_TPCD).1위약규정일수(PNLT_RGLTN_DCNT).1공제율비(DDCTN_RATE).1등록일시(RGTER_ID).1유효여부(VLDTY_YN).3부서ID(DPRTM_ID).1시설ID(FCLT_ID)시설명(FCLT_NM)시설설명(FCLT_DSCRT)시설대분류코드(FCLT_LRCLS_CD).2시설중분류코드(FCLT_MDCLS_CD)시설소분류코드(FCLT_SMCLS_CD)시설용도구분코드(FCLT_PRPSE_TPCD).2시설예약구분코드(FCLT_RSRVT_TPCD)시설이미지맵좌표(FCLT_IMAGE_MAP_CRD)배치물품내용(PSTNG_THING_CONT)구역명(ZONE_NM)면적단위(AREA_UNIT)면적합계(AREA_SUM)최소수용수(MNMM_ACCPT_CNT)최대수용수(MXMM_ACCPT_CNT)시설오픈일자(FCLT_OPEN_DTM)시설폐쇄일자(FCLT_CLSNG_DTM)전기시설여부(ELCTY_FCLT_YN)난방시설여부(HEAT_FCLT_YN)난방시설유형코드(HEAT_FCLT_TPE_CD)시설상태코드(FCLT_STCD)등록일시(RGSTN_DTM).2최종수정일시(LAST_UPDT_DTM).4유효여부(VLDTY_YN).4AS-IS 자료(PRODUCT_CODE)가로크기(FCLT_SIZE_W)세로크기(FCLT_SIZE_H)(PPL_CNT)법정동코드(LGDNG_CD)시도명(CTPRV_NM)시군구명(SGNG_NM)읍면동명(EMNDN_NM)산여부(MNTN_YN)시작주번지명(BGN_MIN_LTNMB_NM)시작부번지명(BGN_SUB_LTNMB_NM)도로코드(ROAD_CD)지하여부(UNDRG_YN)건물명(BLDNG_NM)상세건물명(DTL_BLDNG_NM)건물관리명(BLDNG_MNGME_NM)읍면동ID(EMNDN_ID)행정코드(ADMNS_CD)행정코드명(ADMNS_CD_NM)우편번호ID(ZIPCD_ID)기타명(ETC_NM)공동건물여부(CPRTI_BLDNG_YN)유효여부(RGSTN_DTM)등록일시(VLDTY_YN)# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>30<NA><NA>2014-03-01 14:58CASH2014-03-01 14:58<NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3011011000대전광역시동구대동020693010000000000<NA><NA>301000000000000000000000043011058500대동300809<NA>02012-10-26 15:43Y2