Overview

Dataset statistics

Number of variables66
Number of observations10000
Missing cells159652
Missing cells (%)24.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory580.0 B

Variable types

Categorical23
Numeric21
Text12
Boolean4
Unsupported6

Dataset

Description산림복지서비스이용권시스템에서 추출한 이용상세정보입니다.
Author한국산림복지진흥원
URLhttps://www.data.go.kr/data/15088996/fileData.do

Alerts

사업번호(BIZ_NO) has constant value ""Constant
정지사유(STOP_RSN) has constant value ""Constant
사용여부(USE_YN) has constant value ""Constant
등록일자(INPUT_DT).2 has constant value ""Constant
수정일자(UPDATE_DT).2 has constant value ""Constant
정산여부(CALC_YN) has constant value ""Constant
응답메세지(RESP_RSN).1 has constant value ""Constant
문자수신여부(SMS_REC_FLAG) has constant value ""Constant
응답메세지(RESP_RSN) is highly imbalanced (98.7%)Imbalance
정지일자(STOP_DT) is highly imbalanced (99.9%)Imbalance
카드상태(K014)(CARD_STS) is highly imbalanced (72.2%)Imbalance
등기번호_수신상태(REG_STS) is highly imbalanced (88.1%)Imbalance
이월금액(LIMIT_PREV_AMT) is highly imbalanced (78.8%)Imbalance
취소여부(CANCEL_YN) is highly imbalanced (71.1%)Imbalance
요청일(REQ_DT) is highly imbalanced (99.7%)Imbalance
정지일(STOP_DT) is highly imbalanced (99.7%)Imbalance
정지시각(STOP_TM) is highly imbalanced (99.7%)Imbalance
사용정지코드(1:정지,2:정지해제)(USE_STOP_CD) is highly imbalanced (99.7%)Imbalance
응답코드(RESP_CD) is highly imbalanced (99.9%)Imbalance
송수신작업상태(K013)(PRC_STATE) is highly imbalanced (99.7%)Imbalance
송신일시(SEND_DT) is highly imbalanced (99.5%)Imbalance
수신일시(RCV_DT) is highly imbalanced (99.9%)Imbalance
정지사유(STOP_RSN) has 9999 (> 99.9%) missing valuesMissing
수정일자(UPDATE_DT).1 has 8969 (89.7%) missing valuesMissing
정산일자(CALC_DT) has 10000 (100.0%) missing valuesMissing
수정일자(UPDATE_DT).3 has 2695 (27.0%) missing valuesMissing
원거래승인일자(ORG_APVL_DT) has 9502 (95.0%) missing valuesMissing
원거래승인시각(ORG_APVL_TM) has 9502 (95.0%) missing valuesMissing
원거래승인번호(ORG_APVL_NO) has 9502 (95.0%) missing valuesMissing
수정일자(UPDATE_DT).4 has 9493 (94.9%) missing valuesMissing
원거래매입접수번호(ORG_BUY_NO) has 10000 (100.0%) missing valuesMissing
전체결제금액(TOT_APVL_AMT) has 10000 (100.0%) missing valuesMissing
포인트잔액(BALANCE) has 10000 (100.0%) missing valuesMissing
원가맹점번호(ORIGIN_STORE_NO) has 10000 (100.0%) missing valuesMissing
원가맹점명(ORIGIN_STORE_NM) has 10000 (100.0%) missing valuesMissing
응답메세지(RESP_RSN).1 has 9999 (> 99.9%) missing valuesMissing
등록일자(INPUT_DT).5 has 9996 (> 99.9%) missing valuesMissing
수정일자(UPDATE_DT).5 has 9996 (> 99.9%) missing valuesMissing
문자수신여부(SMS_REC_FLAG) has 9999 (> 99.9%) missing valuesMissing
한도금액(LIMIT_AMT) is highly skewed (γ1 = 22.33253046)Skewed
이력순번(HIS_SEQ) has unique valuesUnique
정산일자(CALC_DT) is an unsupported type, check if it needs cleaning or further analysisUnsupported
원거래매입접수번호(ORG_BUY_NO) is an unsupported type, check if it needs cleaning or further analysisUnsupported
전체결제금액(TOT_APVL_AMT) is an unsupported type, check if it needs cleaning or further analysisUnsupported
포인트잔액(BALANCE) is an unsupported type, check if it needs cleaning or further analysisUnsupported
원가맹점번호(ORIGIN_STORE_NO) is an unsupported type, check if it needs cleaning or further analysisUnsupported
원가맹점명(ORIGIN_STORE_NM) is an unsupported type, check if it needs cleaning or further analysisUnsupported
정산결제건수(CALC_CNT) has 112 (1.1%) zerosZeros
포인트사용금액(CALC_AMT) has 112 (1.1%) zerosZeros
정산취소건수(CALC_CANCEL_CNT) has 9592 (95.9%) zerosZeros
포인트취소금액(CALC_CANCEL_AMT) has 9592 (95.9%) zerosZeros
사용금액(USE_AMT) has 2651 (26.5%) zerosZeros
잔액(BALANCE) has 6350 (63.5%) zerosZeros

Reproduction

Analysis started2023-12-11 23:03:05.643477
Analysis finished2023-12-11 23:03:07.541714
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
5254 
2018
4746 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2018
3rd row2017
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2017 5254
52.5%
2018 4746
47.5%

Length

2023-12-12T08:03:07.595989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:07.680561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 5254
52.5%
2018 4746
47.5%

사업번호(BIZ_NO)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10000
100.0%

Length

2023-12-12T08:03:07.796409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:07.901784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

일련번호(SEQ)
Real number (ℝ)

Distinct248
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.097
Minimum1
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:08.006022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median19
Q340
95-th percentile101
Maximum323
Range322
Interquartile range (IQR)36

Descriptive statistics

Standard deviation37.010636
Coefficient of variation (CV)1.2297118
Kurtosis10.240254
Mean30.097
Median Absolute Deviation (MAD)16
Skewness2.6740333
Sum300970
Variance1369.7872
MonotonicityNot monotonic
2023-12-12T08:03:08.159006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1330
 
13.3%
2 539
 
5.4%
3 377
 
3.8%
4 270
 
2.7%
5 210
 
2.1%
7 186
 
1.9%
6 184
 
1.8%
18 180
 
1.8%
13 174
 
1.7%
17 172
 
1.7%
Other values (238) 6378
63.8%
ValueCountFrequency (%)
1 1330
13.3%
2 539
5.4%
3 377
 
3.8%
4 270
 
2.7%
5 210
 
2.1%
6 184
 
1.8%
7 186
 
1.9%
8 165
 
1.7%
9 166
 
1.7%
10 169
 
1.7%
ValueCountFrequency (%)
323 1
< 0.1%
313 1
< 0.1%
311 1
< 0.1%
304 1
< 0.1%
298 1
< 0.1%
297 1
< 0.1%
290 1
< 0.1%
288 1
< 0.1%
287 1
< 0.1%
284 1
< 0.1%

발급일자(ISSUE_DT)
Real number (ℝ)

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20175038
Minimum20170302
Maximum20180228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:08.327941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170302
5-th percentile20170302
Q120170307
median20170522
Q320180228
95-th percentile20180228
Maximum20180228
Range9926
Interquartile range (IQR)9921

Descriptive statistics

Standard deviation4929.8318
Coefficient of variation (CV)0.00024435304
Kurtosis-1.9894814
Mean20175038
Median Absolute Deviation (MAD)220
Skewness0.10123194
Sum2.0175038 × 1011
Variance24303242
MonotonicityNot monotonic
2023-12-12T08:03:08.469077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
20180228 2789
27.9%
20170307 2420
24.2%
20170302 1336
13.4%
20180220 988
 
9.9%
20180219 969
 
9.7%
20170522 756
 
7.6%
20170405 705
 
7.0%
20171016 3
 
< 0.1%
20170906 2
 
< 0.1%
20170706 2
 
< 0.1%
Other values (25) 30
 
0.3%
ValueCountFrequency (%)
20170302 1336
13.4%
20170307 2420
24.2%
20170315 2
 
< 0.1%
20170317 1
 
< 0.1%
20170324 1
 
< 0.1%
20170405 705
 
7.0%
20170522 756
 
7.6%
20170613 1
 
< 0.1%
20170614 1
 
< 0.1%
20170621 1
 
< 0.1%
ValueCountFrequency (%)
20180228 2789
27.9%
20180220 988
 
9.9%
20180219 969
 
9.7%
20171122 1
 
< 0.1%
20171120 2
 
< 0.1%
20171115 1
 
< 0.1%
20171106 2
 
< 0.1%
20171102 1
 
< 0.1%
20171030 2
 
< 0.1%
20171020 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5254 
3
4746 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 5254
52.5%
3 4746
47.5%

Length

2023-12-12T08:03:08.584794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:08.660883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5254
52.5%
3 4746
47.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20170101
5254 
20180219
4746 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20170101
2nd row20180219
3rd row20170101
4th row20180219
5th row20180219

Common Values

ValueCountFrequency (%)
20170101 5254
52.5%
20180219 4746
47.5%

Length

2023-12-12T08:03:08.744583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:08.820620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20170101 5254
52.5%
20180219 4746
47.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20171231
5254 
20181231
4746 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20171231
2nd row20181231
3rd row20171231
4th row20181231
5th row20181231

Common Values

ValueCountFrequency (%)
20171231 5254
52.5%
20181231 4746
47.5%

Length

2023-12-12T08:03:08.911367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:08.992970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20171231 5254
52.5%
20181231 4746
47.5%

송신일자(SEND_DT)
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20175061
Minimum20170303
Maximum20180308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:09.118992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170303
5-th percentile20170308
Q120170310
median20170524
Q320180302
95-th percentile20180302
Maximum20180308
Range10005
Interquartile range (IQR)9992

Descriptive statistics

Standard deviation4950.6948
Coefficient of variation (CV)0.00024538685
Kurtosis-1.9893544
Mean20175061
Median Absolute Deviation (MAD)221
Skewness0.10133928
Sum2.0175061 × 1011
Variance24509379
MonotonicityNot monotonic
2023-12-12T08:03:09.263869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
20180302 2392
23.9%
20170308 1006
10.1%
20180220 959
9.6%
20180221 940
 
9.4%
20170309 846
 
8.5%
20170310 844
 
8.4%
20170524 755
 
7.5%
20170406 705
 
7.0%
20170313 695
 
7.0%
20180305 439
 
4.4%
Other values (37) 419
 
4.2%
ValueCountFrequency (%)
20170303 252
 
2.5%
20170306 76
 
0.8%
20170308 1006
10.1%
20170309 846
8.5%
20170310 844
8.4%
20170313 695
7.0%
20170315 31
 
0.3%
20170316 8
 
0.1%
20170320 1
 
< 0.1%
20170327 1
 
< 0.1%
ValueCountFrequency (%)
20180308 2
 
< 0.1%
20180307 2
 
< 0.1%
20180306 1
 
< 0.1%
20180305 439
 
4.4%
20180302 2392
23.9%
20180227 1
 
< 0.1%
20180223 3
 
< 0.1%
20180222 7
 
0.1%
20180221 940
 
9.4%
20180220 959
9.6%

수신일자(RCV_DT)
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20175061
Minimum20170303
Maximum20180308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:09.398342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170303
5-th percentile20170308
Q120170310
median20170524
Q320180302
95-th percentile20180302
Maximum20180308
Range10005
Interquartile range (IQR)9992

Descriptive statistics

Standard deviation4950.6948
Coefficient of variation (CV)0.00024538685
Kurtosis-1.9893544
Mean20175061
Median Absolute Deviation (MAD)221
Skewness0.10133928
Sum2.0175061 × 1011
Variance24509379
MonotonicityNot monotonic
2023-12-12T08:03:09.523694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
20180302 2392
23.9%
20170308 1006
10.1%
20180220 959
9.6%
20180221 940
 
9.4%
20170309 846
 
8.5%
20170310 844
 
8.4%
20170524 755
 
7.5%
20170406 705
 
7.0%
20170313 695
 
7.0%
20180305 439
 
4.4%
Other values (37) 419
 
4.2%
ValueCountFrequency (%)
20170303 252
 
2.5%
20170306 76
 
0.8%
20170308 1006
10.1%
20170309 846
8.5%
20170310 844
8.4%
20170313 695
7.0%
20170315 31
 
0.3%
20170316 8
 
0.1%
20170320 1
 
< 0.1%
20170327 1
 
< 0.1%
ValueCountFrequency (%)
20180308 2
 
< 0.1%
20180307 2
 
< 0.1%
20180306 1
 
< 0.1%
20180305 439
 
4.4%
20180302 2392
23.9%
20180227 1
 
< 0.1%
20180223 3
 
< 0.1%
20180222 7
 
0.1%
20180221 940
 
9.4%
20180220 959
9.6%

응답메세지(RESP_RSN)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정상처리
9988 
재발급처리가능여부체크 모듈 =[EGC0082],[분실사고등록이 되지 않은 카드입니다. 9531에서 분실사고등록 후 거래하세요.]
 
12

Length

Max length71
Median length4
Mean length4.0804
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상처리 9988
99.9%
재발급처리가능여부체크 모듈 =[EGC0082],[분실사고등록이 되지 않은 카드입니다. 9531에서 분실사고등록 후 거래하세요.] 12
 
0.1%

Length

2023-12-12T08:03:09.635916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:09.725688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상처리 9988
98.8%
재발급처리가능여부체크 12
 
0.1%
모듈 12
 
0.1%
egc0082],[분실사고등록이 12
 
0.1%
되지 12
 
0.1%
않은 12
 
0.1%
카드입니다 12
 
0.1%
9531에서 12
 
0.1%
분실사고등록 12
 
0.1%
12
 
0.1%
Distinct488
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:03:10.036605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)0.4%

Sample

1st row52:56.0
2nd row20:39.0
3rd row14:32.0
4th row20:39.0
5th row37:22.0
ValueCountFrequency (%)
20:39.0 2237
22.4%
56:03.0 988
 
9.9%
58:05.0 969
 
9.7%
39:00.0 705
 
7.0%
37:22.0 552
 
5.5%
14:14.0 66
 
0.7%
10:06.0 58
 
0.6%
08:21.0 46
 
0.5%
14:06.0 44
 
0.4%
11:59.0 41
 
0.4%
Other values (478) 4294
42.9%
2023-12-12T08:03:10.475290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18442
26.3%
: 10000
14.3%
. 10000
14.3%
3 6346
 
9.1%
2 5010
 
7.2%
5 4794
 
6.8%
1 4002
 
5.7%
9 3852
 
5.5%
4 2455
 
3.5%
8 1824
 
2.6%
Other values (2) 3275
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
71.4%
Other Punctuation 20000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18442
36.9%
3 6346
 
12.7%
2 5010
 
10.0%
5 4794
 
9.6%
1 4002
 
8.0%
9 3852
 
7.7%
4 2455
 
4.9%
8 1824
 
3.6%
7 1660
 
3.3%
6 1615
 
3.2%
Other Punctuation
ValueCountFrequency (%)
: 10000
50.0%
. 10000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18442
26.3%
: 10000
14.3%
. 10000
14.3%
3 6346
 
9.1%
2 5010
 
7.2%
5 4794
 
6.8%
1 4002
 
5.7%
9 3852
 
5.5%
4 2455
 
3.5%
8 1824
 
2.6%
Other values (2) 3275
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18442
26.3%
: 10000
14.3%
. 10000
14.3%
3 6346
 
9.1%
2 5010
 
7.2%
5 4794
 
6.8%
1 4002
 
5.7%
9 3852
 
5.5%
4 2455
 
3.5%
8 1824
 
2.6%
Other values (2) 3275
 
4.7%
Distinct290
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:03:10.869814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st row00:17.0
2nd row27:21.0
3rd row59:48.0
4th row26:40.0
5th row24:53.0
ValueCountFrequency (%)
00:32.0 116
 
1.2%
00:37.0 112
 
1.1%
00:35.0 110
 
1.1%
00:33.0 109
 
1.1%
00:40.0 108
 
1.1%
00:29.0 101
 
1.0%
00:36.0 101
 
1.0%
00:34.0 101
 
1.0%
00:39.0 99
 
1.0%
00:38.0 98
 
1.0%
Other values (280) 8945
89.5%
2023-12-12T08:03:11.404290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20771
29.7%
: 10000
14.3%
. 10000
14.3%
2 5183
 
7.4%
4 4837
 
6.9%
5 4351
 
6.2%
3 3271
 
4.7%
6 2883
 
4.1%
1 2562
 
3.7%
7 2475
 
3.5%
Other values (2) 3667
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
71.4%
Other Punctuation 20000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20771
41.5%
2 5183
 
10.4%
4 4837
 
9.7%
5 4351
 
8.7%
3 3271
 
6.5%
6 2883
 
5.8%
1 2562
 
5.1%
7 2475
 
5.0%
9 2313
 
4.6%
8 1354
 
2.7%
Other Punctuation
ValueCountFrequency (%)
: 10000
50.0%
. 10000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20771
29.7%
: 10000
14.3%
. 10000
14.3%
2 5183
 
7.4%
4 4837
 
6.9%
5 4351
 
6.2%
3 3271
 
4.7%
6 2883
 
4.1%
1 2562
 
3.7%
7 2475
 
3.5%
Other values (2) 3667
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20771
29.7%
: 10000
14.3%
. 10000
14.3%
2 5183
 
7.4%
4 4837
 
6.9%
5 4351
 
6.2%
3 3271
 
4.7%
6 2883
 
4.1%
1 2562
 
3.7%
7 2475
 
3.5%
Other values (2) 3667
 
5.2%

이력순번(HIS_SEQ)
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19392.058
Minimum2
Maximum57214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:11.548099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1732.4
Q19081.25
median18363
Q329308.25
95-th percentile39327.25
Maximum57214
Range57212
Interquartile range (IQR)20227

Descriptive statistics

Standard deviation12032.213
Coefficient of variation (CV)0.62047119
Kurtosis-0.97592278
Mean19392.058
Median Absolute Deviation (MAD)10099.5
Skewness0.21940054
Sum1.9392058 × 108
Variance1.4477415 × 108
MonotonicityNot monotonic
2023-12-12T08:03:11.718940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4919 1
 
< 0.1%
622 1
 
< 0.1%
28440 1
 
< 0.1%
29044 1
 
< 0.1%
26796 1
 
< 0.1%
8595 1
 
< 0.1%
33477 1
 
< 0.1%
8779 1
 
< 0.1%
4496 1
 
< 0.1%
1995 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
13 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
53 1
< 0.1%
54 1
< 0.1%
55 1
< 0.1%
56 1
< 0.1%
ValueCountFrequency (%)
57214 1
< 0.1%
57212 1
< 0.1%
57208 1
< 0.1%
56473 1
< 0.1%
56444 1
< 0.1%
56237 1
< 0.1%
56093 1
< 0.1%
56086 1
< 0.1%
56036 1
< 0.1%
56032 1
< 0.1%

발급일자(ISSUE_DT).1
Real number (ℝ)

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20181652
Minimum20160727
Maximum20190416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:11.862800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160727
5-th percentile20170307
Q120180220
median20180228
Q320180328
95-th percentile20190305
Maximum20190416
Range29689
Interquartile range (IQR)108

Descriptive statistics

Standard deviation5458.1942
Coefficient of variation (CV)0.00027045329
Kurtosis0.17013672
Mean20181652
Median Absolute Deviation (MAD)9
Skewness0.080720763
Sum2.0181652 × 1011
Variance29791884
MonotonicityNot monotonic
2023-12-12T08:03:11.996595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180228 3714
37.1%
20190305 2260
22.6%
20180219 1061
 
10.6%
20180220 1027
 
10.3%
20170307 794
 
7.9%
20180315 393
 
3.9%
20180304 201
 
2.0%
20180720 138
 
1.4%
20180320 137
 
1.4%
20180328 96
 
1.0%
Other values (49) 179
 
1.8%
ValueCountFrequency (%)
20160727 1
 
< 0.1%
20160803 1
 
< 0.1%
20160809 2
 
< 0.1%
20160824 1
 
< 0.1%
20170302 56
 
0.6%
20170307 794
7.9%
20170405 3
 
< 0.1%
20170522 9
 
0.1%
20170809 1
 
< 0.1%
20170811 2
 
< 0.1%
ValueCountFrequency (%)
20190416 1
 
< 0.1%
20190325 7
 
0.1%
20190305 2260
22.6%
20181218 1
 
< 0.1%
20181217 1
 
< 0.1%
20181122 1
 
< 0.1%
20181112 6
 
0.1%
20181105 1
 
< 0.1%
20181102 1
 
< 0.1%
20181029 1
 
< 0.1%

정지일자(STOP_DT)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9999 
20181220
 
1

Length

Max length8
Median length4
Mean length4.0004
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> 9999
> 99.9%
20181220 1
 
< 0.1%

Length

2023-12-12T08:03:12.140396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:12.240279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9999
> 99.9%
20181220 1
 
< 0.1%

정지사유(STOP_RSN)
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-12T08:03:12.320404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
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 row퇴사자
ValueCountFrequency (%)
퇴사자 1
100.0%
2023-12-12T08:03:12.518239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

카드상태(K014)(CARD_STS)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정상
9091 
재발급
 
908
정치요청
 
1

Length

Max length4
Median length2
Mean length2.091
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정상 9091
90.9%
재발급 908
 
9.1%
정치요청 1
 
< 0.1%

Length

2023-12-12T08:03:12.642365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:12.744121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 9091
90.9%
재발급 908
 
9.1%
정치요청 1
 
< 0.1%

사용여부(USE_YN)
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
10000 
ValueCountFrequency (%)
True 10000
100.0%
2023-12-12T08:03:12.817956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct1834
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:03:13.106819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1161 ?
Unique (%)11.6%

Sample

1st row46:24.0
2nd row00:45.0
3rd row26:39.0
4th row08:13.0
5th row19:06.0
ValueCountFrequency (%)
01:37.0 78
 
0.8%
00:36.0 76
 
0.8%
01:36.0 69
 
0.7%
00:37.0 64
 
0.6%
01:35.0 61
 
0.6%
01:34.0 59
 
0.6%
27:34.0 55
 
0.5%
27:59.0 53
 
0.5%
26:41.0 49
 
0.5%
46:24.0 49
 
0.5%
Other values (1824) 9387
93.9%
2023-12-12T08:03:13.525754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16914
24.2%
: 10000
14.3%
. 10000
14.3%
2 6959
9.9%
4 4933
 
7.0%
1 4040
 
5.8%
5 3960
 
5.7%
3 3674
 
5.2%
7 3492
 
5.0%
6 3328
 
4.8%
Other values (2) 2700
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
71.4%
Other Punctuation 20000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16914
33.8%
2 6959
13.9%
4 4933
 
9.9%
1 4040
 
8.1%
5 3960
 
7.9%
3 3674
 
7.3%
7 3492
 
7.0%
6 3328
 
6.7%
9 1401
 
2.8%
8 1299
 
2.6%
Other Punctuation
ValueCountFrequency (%)
: 10000
50.0%
. 10000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16914
24.2%
: 10000
14.3%
. 10000
14.3%
2 6959
9.9%
4 4933
 
7.0%
1 4040
 
5.8%
5 3960
 
5.7%
3 3674
 
5.2%
7 3492
 
5.0%
6 3328
 
4.8%
Other values (2) 2700
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16914
24.2%
: 10000
14.3%
. 10000
14.3%
2 6959
9.9%
4 4933
 
7.0%
1 4040
 
5.8%
5 3960
 
5.7%
3 3674
 
5.2%
7 3492
 
5.0%
6 3328
 
4.8%
Other values (2) 2700
 
3.9%
Distinct204
Distinct (%)19.8%
Missing8969
Missing (%)89.7%
Memory size156.2 KiB
2023-12-12T08:03:13.866450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7217
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)16.6%

Sample

1st row50:28.0
2nd row00:47.0
3rd row00:42.0
4th row32:59.0
5th row32:59.0
ValueCountFrequency (%)
32:59.0 763
74.0%
01:59.0 8
 
0.8%
01:03.0 7
 
0.7%
01:47.0 5
 
0.5%
01:25.0 5
 
0.5%
02:41.0 5
 
0.5%
29:51.0 4
 
0.4%
01:13.0 4
 
0.4%
00:53.0 4
 
0.4%
01:49.0 4
 
0.4%
Other values (194) 222
 
21.5%
2023-12-12T08:03:14.330946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1273
17.6%
: 1031
14.3%
. 1031
14.3%
2 897
12.4%
5 876
12.1%
3 866
12.0%
9 818
11.3%
1 158
 
2.2%
4 148
 
2.1%
8 45
 
0.6%
Other values (2) 74
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5155
71.4%
Other Punctuation 2062
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1273
24.7%
2 897
17.4%
5 876
17.0%
3 866
16.8%
9 818
15.9%
1 158
 
3.1%
4 148
 
2.9%
8 45
 
0.9%
6 38
 
0.7%
7 36
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 1031
50.0%
. 1031
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1273
17.6%
: 1031
14.3%
. 1031
14.3%
2 897
12.4%
5 876
12.1%
3 866
12.0%
9 818
11.3%
1 158
 
2.2%
4 148
 
2.1%
8 45
 
0.6%
Other values (2) 74
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1273
17.6%
: 1031
14.3%
. 1031
14.3%
2 897
12.4%
5 876
12.1%
3 866
12.0%
9 818
11.3%
1 158
 
2.2%
4 148
 
2.1%
8 45
 
0.6%
Other values (2) 74
 
1.0%

등기번호_수신상태(REG_STS)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미수신
9745 
수신완료
 
210
99
 
45

Length

Max length4
Median length3
Mean length3.0165
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미수신 9745
97.5%
수신완료 210
 
2.1%
99 45
 
0.4%

Length

2023-12-12T08:03:14.504374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:14.626501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미수신 9745
97.5%
수신완료 210
 
2.1%
99 45
 
0.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
4197 
2018
3016 
2017
1867 
2016
920 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2018
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2019 4197
42.0%
2018 3016
30.2%
2017 1867
18.7%
2016 920
 
9.2%

Length

2023-12-12T08:03:14.751294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:14.849436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 4197
42.0%
2018 3016
30.2%
2017 1867
18.7%
2016 920
 
9.2%

정산월(CALC_MONTH)
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0408
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:14.961234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median9
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5172543
Coefficient of variation (CV)0.31306018
Kurtosis-1.1748213
Mean8.0408
Median Absolute Deviation (MAD)2
Skewness-0.20031308
Sum80408
Variance6.336569
MonotonicityNot monotonic
2023-12-12T08:03:15.091312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 1873
18.7%
6 1287
12.9%
9 1276
12.8%
11 1257
12.6%
5 1225
12.2%
7 811
8.1%
4 795
8.0%
8 724
 
7.2%
12 628
 
6.3%
3 105
 
1.1%
Other values (2) 19
 
0.2%
ValueCountFrequency (%)
1 18
 
0.2%
2 1
 
< 0.1%
3 105
 
1.1%
4 795
8.0%
5 1225
12.2%
6 1287
12.9%
7 811
8.1%
8 724
 
7.2%
9 1276
12.8%
10 1873
18.7%
ValueCountFrequency (%)
12 628
 
6.3%
11 1257
12.6%
10 1873
18.7%
9 1276
12.8%
8 724
 
7.2%
7 811
8.1%
6 1287
12.9%
5 1225
12.2%
4 795
8.0%
3 105
 
1.1%

정산결제건수(CALC_CNT)
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0409
Minimum0
Maximum11
Zeros112
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:15.456550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33650931
Coefficient of variation (CV)0.32328687
Kurtosis130.87782
Mean1.0409
Median Absolute Deviation (MAD)0
Skewness8.1255708
Sum10409
Variance0.11323851
MonotonicityNot monotonic
2023-12-12T08:03:15.583188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 9520
95.2%
2 273
 
2.7%
0 112
 
1.1%
3 59
 
0.6%
4 23
 
0.2%
5 10
 
0.1%
11 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 112
 
1.1%
1 9520
95.2%
2 273
 
2.7%
3 59
 
0.6%
4 23
 
0.2%
5 10
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 10
 
0.1%
4 23
 
0.2%
3 59
 
0.6%
2 273
 
2.7%
1 9520
95.2%
0 112
 
1.1%

포인트사용금액(CALC_AMT)
Real number (ℝ)

ZEROS 

Distinct452
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96047.849
Minimum0
Maximum1195000
Zeros112
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:15.702606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40000
Q1100000
median100000
Q3100000
95-th percentile100000
Maximum1195000
Range1195000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38705.627
Coefficient of variation (CV)0.40298275
Kurtosis102.19705
Mean96047.849
Median Absolute Deviation (MAD)0
Skewness6.1556696
Sum9.6047849 × 108
Variance1.4981255 × 109
MonotonicityNot monotonic
2023-12-12T08:03:15.831558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 7450
74.5%
50000 162
 
1.6%
0 112
 
1.1%
85000 88
 
0.9%
91000 77
 
0.8%
60000 58
 
0.6%
200000 46
 
0.5%
40000 44
 
0.4%
67000 42
 
0.4%
80000 39
 
0.4%
Other values (442) 1882
 
18.8%
ValueCountFrequency (%)
0 112
1.1%
1000 1
 
< 0.1%
1200 1
 
< 0.1%
1500 2
 
< 0.1%
2000 4
 
< 0.1%
2300 1
 
< 0.1%
2630 1
 
< 0.1%
3000 3
 
< 0.1%
4000 1
 
< 0.1%
4300 3
 
< 0.1%
ValueCountFrequency (%)
1195000 1
 
< 0.1%
690400 1
 
< 0.1%
600000 1
 
< 0.1%
599900 1
 
< 0.1%
590000 1
 
< 0.1%
520000 1
 
< 0.1%
500000 4
< 0.1%
495000 1
 
< 0.1%
493600 1
 
< 0.1%
480000 1
 
< 0.1%

정산취소건수(CALC_CANCEL_CNT)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0504
Minimum0
Maximum9
Zeros9592
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:15.943666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27868909
Coefficient of variation (CV)5.5295454
Kurtosis155.78846
Mean0.0504
Median Absolute Deviation (MAD)0
Skewness9.1285764
Sum504
Variance0.077667607
MonotonicityNot monotonic
2023-12-12T08:03:16.032935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9592
95.9%
1 338
 
3.4%
2 55
 
0.5%
3 10
 
0.1%
4 3
 
< 0.1%
9 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 9592
95.9%
1 338
 
3.4%
2 55
 
0.5%
3 10
 
0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
5 1
 
< 0.1%
4 3
 
< 0.1%
3 10
 
0.1%
2 55
 
0.5%
1 338
 
3.4%
0 9592
95.9%
Distinct166
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3223.4678
Minimum0
Maximum955000
Zeros9592
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:16.153196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum955000
Range955000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21302.814
Coefficient of variation (CV)6.6086634
Kurtosis453.18894
Mean3223.4678
Median Absolute Deviation (MAD)0
Skewness14.899715
Sum32234678
Variance4.5380987 × 108
MonotonicityNot monotonic
2023-12-12T08:03:16.322042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9592
95.9%
100000 36
 
0.4%
91000 19
 
0.2%
85000 19
 
0.2%
33500 17
 
0.2%
68000 13
 
0.1%
29000 10
 
0.1%
50000 10
 
0.1%
42500 8
 
0.1%
25000 8
 
0.1%
Other values (156) 268
 
2.7%
ValueCountFrequency (%)
0 9592
95.9%
36 1
 
< 0.1%
100 2
 
< 0.1%
1000 2
 
< 0.1%
3300 4
 
< 0.1%
6300 1
 
< 0.1%
6700 1
 
< 0.1%
8500 2
 
< 0.1%
9872 1
 
< 0.1%
10000 3
 
< 0.1%
ValueCountFrequency (%)
955000 1
 
< 0.1%
345000 1
 
< 0.1%
293000 1
 
< 0.1%
289000 1
 
< 0.1%
282000 1
 
< 0.1%
274000 1
 
< 0.1%
270000 3
< 0.1%
269000 1
 
< 0.1%
250000 1
 
< 0.1%
240000 1
 
< 0.1%

등록일자(INPUT_DT).2
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
45:13.0
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45:13.0
2nd row45:13.0
3rd row45:13.0
4th row45:13.0
5th row45:13.0

Common Values

ValueCountFrequency (%)
45:13.0 10000
100.0%

Length

2023-12-12T08:03:16.447374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:16.534949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45:13.0 10000
100.0%

수정일자(UPDATE_DT).2
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
45:13.0
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45:13.0
2nd row45:13.0
3rd row45:13.0
4th row45:13.0
5th row45:13.0

Common Values

ValueCountFrequency (%)
45:13.0 10000
100.0%

Length

2023-12-12T08:03:16.637669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:16.725816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45:13.0 10000
100.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
5071 
2018
2475 
2016
2453 
2015
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016
2nd row2016
3rd row2017
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2017 5071
50.7%
2018 2475
24.8%
2016 2453
24.5%
2015 1
 
< 0.1%

Length

2023-12-12T08:03:16.811632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:16.898095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 5071
50.7%
2018 2475
24.8%
2016 2453
24.5%
2015 1
 
< 0.1%

한도금액(LIMIT_AMT)
Real number (ℝ)

SKEWED 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101125
Minimum100000
Maximum900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:16.995660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile100000
Q1100000
median100000
Q3100000
95-th percentile100000
Maximum900000
Range800000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18735.514
Coefficient of variation (CV)0.18527084
Kurtosis628.34987
Mean101125
Median Absolute Deviation (MAD)0
Skewness22.33253
Sum1.01125 × 109
Variance3.5101948 × 108
MonotonicityNot monotonic
2023-12-12T08:03:17.113083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
100000 9949
99.5%
200000 20
 
0.2%
400000 12
 
0.1%
300000 11
 
0.1%
500000 5
 
0.1%
700000 1
 
< 0.1%
150000 1
 
< 0.1%
900000 1
 
< 0.1%
ValueCountFrequency (%)
100000 9949
99.5%
150000 1
 
< 0.1%
200000 20
 
0.2%
300000 11
 
0.1%
400000 12
 
0.1%
500000 5
 
0.1%
700000 1
 
< 0.1%
900000 1
 
< 0.1%
ValueCountFrequency (%)
900000 1
 
< 0.1%
700000 1
 
< 0.1%
500000 5
 
0.1%
400000 12
 
0.1%
300000 11
 
0.1%
200000 20
 
0.2%
150000 1
 
< 0.1%
100000 9949
99.5%

사용금액(USE_AMT)
Real number (ℝ)

ZEROS 

Distinct164
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71399.195
Minimum0
Maximum633400
Zeros2651
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:17.260572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100000
Q3100000
95-th percentile100000
Maximum633400
Range633400
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation45375.637
Coefficient of variation (CV)0.63552028
Kurtosis3.9367583
Mean71399.195
Median Absolute Deviation (MAD)0
Skewness-0.31199034
Sum7.1399195 × 108
Variance2.0589484 × 109
MonotonicityNot monotonic
2023-12-12T08:03:17.376995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 6342
63.4%
0 2651
26.5%
85000 49
 
0.5%
91000 48
 
0.5%
98000 29
 
0.3%
81200 28
 
0.3%
99000 26
 
0.3%
96070 26
 
0.3%
47400 25
 
0.2%
50000 25
 
0.2%
Other values (154) 751
 
7.5%
ValueCountFrequency (%)
0 2651
26.5%
1500 1
 
< 0.1%
3000 1
 
< 0.1%
5800 1
 
< 0.1%
7000 2
 
< 0.1%
7500 1
 
< 0.1%
7800 1
 
< 0.1%
9000 1
 
< 0.1%
10000 4
 
< 0.1%
12000 1
 
< 0.1%
ValueCountFrequency (%)
633400 1
 
< 0.1%
500000 1
 
< 0.1%
467000 1
 
< 0.1%
420200 1
 
< 0.1%
400000 3
< 0.1%
305000 1
 
< 0.1%
300000 2
< 0.1%
290000 1
 
< 0.1%
200000 2
< 0.1%
196000 1
 
< 0.1%

잔액(BALANCE)
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29725.805
Minimum0
Maximum900000
Zeros6350
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:17.488494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100000
95-th percentile100000
Maximum900000
Range900000
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation46667.621
Coefficient of variation (CV)1.5699363
Kurtosis14.850975
Mean29725.805
Median Absolute Deviation (MAD)0
Skewness1.9788111
Sum2.9725805 × 108
Variance2.1778668 × 109
MonotonicityNot monotonic
2023-12-12T08:03:17.598187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6350
63.5%
100000 2626
26.3%
15000 49
 
0.5%
9000 48
 
0.5%
2000 29
 
0.3%
18800 28
 
0.3%
1000 26
 
0.3%
3930 26
 
0.3%
50000 26
 
0.3%
52600 25
 
0.2%
Other values (151) 767
 
7.7%
ValueCountFrequency (%)
0 6350
63.5%
100 1
 
< 0.1%
140 16
 
0.2%
300 5
 
0.1%
370 6
 
0.1%
440 11
 
0.1%
590 13
 
0.1%
600 1
 
< 0.1%
720 4
 
< 0.1%
880 13
 
0.1%
ValueCountFrequency (%)
900000 1
 
< 0.1%
500000 1
 
< 0.1%
400000 5
0.1%
359800 1
 
< 0.1%
300000 6
0.1%
234000 1
 
< 0.1%
230000 1
 
< 0.1%
226000 1
 
< 0.1%
200000 12
0.1%
195000 1
 
< 0.1%

정산일자(CALC_DT)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

정산여부(CALC_YN)
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
10000 
ValueCountFrequency (%)
False 10000
100.0%
2023-12-12T08:03:17.677874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct253
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:03:17.918828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row03:56.0
2nd row03:59.0
3rd row00:29.0
4th row46:44.0
5th row46:30.0
ValueCountFrequency (%)
00:34.0 122
 
1.2%
00:38.0 119
 
1.2%
00:31.0 116
 
1.2%
03:56.0 114
 
1.1%
00:37.0 108
 
1.1%
00:40.0 107
 
1.1%
00:33.0 107
 
1.1%
03:50.0 106
 
1.1%
00:32.0 106
 
1.1%
03:44.0 104
 
1.0%
Other values (243) 8891
88.9%
2023-12-12T08:03:18.289077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23103
33.0%
: 10000
14.3%
. 10000
14.3%
4 5488
 
7.8%
3 4888
 
7.0%
5 4129
 
5.9%
2 2915
 
4.2%
6 2563
 
3.7%
1 2404
 
3.4%
9 2134
 
3.0%
Other values (2) 2376
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
71.4%
Other Punctuation 20000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23103
46.2%
4 5488
 
11.0%
3 4888
 
9.8%
5 4129
 
8.3%
2 2915
 
5.8%
6 2563
 
5.1%
1 2404
 
4.8%
9 2134
 
4.3%
8 1260
 
2.5%
7 1116
 
2.2%
Other Punctuation
ValueCountFrequency (%)
: 10000
50.0%
. 10000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23103
33.0%
: 10000
14.3%
. 10000
14.3%
4 5488
 
7.8%
3 4888
 
7.0%
5 4129
 
5.9%
2 2915
 
4.2%
6 2563
 
3.7%
1 2404
 
3.4%
9 2134
 
3.0%
Other values (2) 2376
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23103
33.0%
: 10000
14.3%
. 10000
14.3%
4 5488
 
7.8%
3 4888
 
7.0%
5 4129
 
5.9%
2 2915
 
4.2%
6 2563
 
3.7%
1 2404
 
3.4%
9 2134
 
3.0%
Other values (2) 2376
 
3.4%
Distinct249
Distinct (%)3.4%
Missing2695
Missing (%)27.0%
Memory size156.2 KiB
2023-12-12T08:03:18.582288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters51135
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.2%

Sample

1st row50:08.0
2nd row50:13.0
3rd row20:37.0
4th row21:19.0
5th row21:42.0
ValueCountFrequency (%)
21:11.0 120
 
1.6%
21:24.0 117
 
1.6%
21:17.0 106
 
1.5%
21:04.0 99
 
1.4%
21:26.0 97
 
1.3%
21:03.0 95
 
1.3%
21:21.0 90
 
1.2%
21:14.0 89
 
1.2%
20:47.0 86
 
1.2%
20:43.0 85
 
1.2%
Other values (239) 6321
86.5%
2023-12-12T08:03:18.967526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12244
23.9%
: 7305
14.3%
. 7305
14.3%
2 6934
13.6%
1 5496
10.7%
5 3380
 
6.6%
4 2470
 
4.8%
3 1862
 
3.6%
9 1614
 
3.2%
8 917
 
1.8%
Other values (2) 1608
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36525
71.4%
Other Punctuation 14610
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12244
33.5%
2 6934
19.0%
1 5496
15.0%
5 3380
 
9.3%
4 2470
 
6.8%
3 1862
 
5.1%
9 1614
 
4.4%
8 917
 
2.5%
7 882
 
2.4%
6 726
 
2.0%
Other Punctuation
ValueCountFrequency (%)
: 7305
50.0%
. 7305
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51135
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12244
23.9%
: 7305
14.3%
. 7305
14.3%
2 6934
13.6%
1 5496
10.7%
5 3380
 
6.6%
4 2470
 
4.8%
3 1862
 
3.6%
9 1614
 
3.2%
8 917
 
1.8%
Other values (2) 1608
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12244
23.9%
: 7305
14.3%
. 7305
14.3%
2 6934
13.6%
1 5496
10.7%
5 3380
 
6.6%
4 2470
 
4.8%
3 1862
 
3.6%
9 1614
 
3.2%
8 917
 
1.8%
Other values (2) 1608
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
시설
8345 
개인
1655 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시설
2nd row시설
3rd row개인
4th row시설
5th row시설

Common Values

ValueCountFrequency (%)
시설 8345
83.5%
개인 1655
 
16.6%

Length

2023-12-12T08:03:19.076726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:19.158648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시설 8345
83.5%
개인 1655
 
16.6%

이월금액(LIMIT_PREV_AMT)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9144 
100000
 
854
300000
 
1
50000
 
1

Length

Max length6
Median length1
Mean length1.4279
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 9144
91.4%
100000 854
 
8.5%
300000 1
 
< 0.1%
50000 1
 
< 0.1%

Length

2023-12-12T08:03:19.252857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:19.328938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9144
91.4%
100000 854
 
8.5%
300000 1
 
< 0.1%
50000 1
 
< 0.1%

승인일자(APVL_DT)
Real number (ℝ)

Distinct734
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20175696
Minimum20160719
Maximum20190526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:19.421506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160719
5-th percentile20161007
Q120170704
median20180426
Q320180912
95-th percentile20190419
Maximum20190526
Range29807
Interquartile range (IQR)10208

Descriptive statistics

Standard deviation8470.9627
Coefficient of variation (CV)0.00041985976
Kurtosis-0.68855223
Mean20175696
Median Absolute Deviation (MAD)9319
Skewness-0.27638673
Sum2.0175696 × 1011
Variance71757209
MonotonicityNot monotonic
2023-12-12T08:03:19.534743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190404 96
 
1.0%
20180426 82
 
0.8%
20181030 78
 
0.8%
20170517 67
 
0.7%
20180619 63
 
0.6%
20180523 63
 
0.6%
20180913 60
 
0.6%
20180615 60
 
0.6%
20181023 58
 
0.6%
20160928 57
 
0.6%
Other values (724) 9316
93.2%
ValueCountFrequency (%)
20160719 18
0.2%
20160723 8
0.1%
20160726 14
0.1%
20160729 5
 
0.1%
20160803 10
0.1%
20160813 2
 
< 0.1%
20160816 13
0.1%
20160818 16
0.2%
20160822 3
 
< 0.1%
20160823 6
 
0.1%
ValueCountFrequency (%)
20190526 1
 
< 0.1%
20190525 5
 
0.1%
20190524 14
 
0.1%
20190523 24
0.2%
20190522 56
0.6%
20190521 28
0.3%
20190518 6
 
0.1%
20190517 47
0.5%
20190516 2
 
< 0.1%
20190513 10
 
0.1%

승인시각(APVL_TM)
Real number (ℝ)

Distinct8902
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137227.06
Minimum531
Maximum235813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:19.645828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum531
5-th percentile84116.5
Q1113033.75
median135014.5
Q3161244.25
95-th percentile201907.2
Maximum235813
Range235282
Interquartile range (IQR)48210.5

Descriptive statistics

Standard deviation37573.604
Coefficient of variation (CV)0.27380609
Kurtosis0.53535243
Mean137227.06
Median Absolute Deviation (MAD)23108
Skewness-0.16915342
Sum1.3722706 × 109
Variance1.4117757 × 109
MonotonicityNot monotonic
2023-12-12T08:03:19.749665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141111 7
 
0.1%
141432 5
 
0.1%
165435 4
 
< 0.1%
131942 4
 
< 0.1%
132313 4
 
< 0.1%
134354 4
 
< 0.1%
135224 4
 
< 0.1%
133446 3
 
< 0.1%
133413 3
 
< 0.1%
164947 3
 
< 0.1%
Other values (8892) 9959
99.6%
ValueCountFrequency (%)
531 1
< 0.1%
604 1
< 0.1%
1039 1
< 0.1%
1229 1
< 0.1%
1647 1
< 0.1%
1653 1
< 0.1%
1800 1
< 0.1%
1819 1
< 0.1%
2408 1
< 0.1%
2437 1
< 0.1%
ValueCountFrequency (%)
235813 1
< 0.1%
235727 1
< 0.1%
235638 1
< 0.1%
235024 1
< 0.1%
235006 1
< 0.1%
234936 1
< 0.1%
234930 1
< 0.1%
234900 1
< 0.1%
234827 1
< 0.1%
234723 1
< 0.1%

원거래승인일자(ORG_APVL_DT)
Real number (ℝ)

MISSING 

Distinct300
Distinct (%)60.2%
Missing9502
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean20177440
Minimum20160826
Maximum20190521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:19.857585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160826
5-th percentile20161111
Q120170911
median20180610
Q320181107
95-th percentile20190404
Maximum20190521
Range29695
Interquartile range (IQR)10195.75

Descriptive statistics

Standard deviation7429.933
Coefficient of variation (CV)0.00036822971
Kurtosis-0.06519078
Mean20177440
Median Absolute Deviation (MAD)601.5
Skewness-0.49497373
Sum1.0048365 × 1010
Variance55203904
MonotonicityNot monotonic
2023-12-12T08:03:19.982637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190404 16
 
0.2%
20160826 8
 
0.1%
20180404 7
 
0.1%
20180425 7
 
0.1%
20161111 6
 
0.1%
20161024 6
 
0.1%
20170904 6
 
0.1%
20170612 6
 
0.1%
20181213 5
 
0.1%
20170607 5
 
0.1%
Other values (290) 426
 
4.3%
(Missing) 9502
95.0%
ValueCountFrequency (%)
20160826 8
0.1%
20160928 2
 
< 0.1%
20161024 6
0.1%
20161104 2
 
< 0.1%
20161105 2
 
< 0.1%
20161108 2
 
< 0.1%
20161110 1
 
< 0.1%
20161111 6
0.1%
20161115 1
 
< 0.1%
20161125 3
 
< 0.1%
ValueCountFrequency (%)
20190521 1
< 0.1%
20190520 1
< 0.1%
20190514 1
< 0.1%
20190507 1
< 0.1%
20190503 1
< 0.1%
20190425 1
< 0.1%
20190419 1
< 0.1%
20190417 1
< 0.1%
20190416 1
< 0.1%
20190414 1
< 0.1%

원거래승인시각(ORG_APVL_TM)
Real number (ℝ)

MISSING 

Distinct497
Distinct (%)99.8%
Missing9502
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean145398.91
Minimum356
Maximum235543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:20.134514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum356
5-th percentile83001.75
Q1112256
median144263.5
Q3175196
95-th percentile222684.8
Maximum235543
Range235187
Interquartile range (IQR)62940

Descriptive statistics

Standard deviation47555.861
Coefficient of variation (CV)0.32707165
Kurtosis0.4940698
Mean145398.91
Median Absolute Deviation (MAD)31284
Skewness-0.42889467
Sum72408656
Variance2.2615599 × 109
MonotonicityNot monotonic
2023-12-12T08:03:20.279218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133346 2
 
< 0.1%
111041 1
 
< 0.1%
195136 1
 
< 0.1%
123637 1
 
< 0.1%
135251 1
 
< 0.1%
222848 1
 
< 0.1%
154115 1
 
< 0.1%
150352 1
 
< 0.1%
140305 1
 
< 0.1%
90507 1
 
< 0.1%
Other values (487) 487
 
4.9%
(Missing) 9502
95.0%
ValueCountFrequency (%)
356 1
< 0.1%
1229 1
< 0.1%
2223 1
< 0.1%
4303 1
< 0.1%
5602 1
< 0.1%
10432 1
< 0.1%
11008 1
< 0.1%
12311 1
< 0.1%
12542 1
< 0.1%
13242 1
< 0.1%
ValueCountFrequency (%)
235543 1
< 0.1%
234320 1
< 0.1%
234219 1
< 0.1%
234111 1
< 0.1%
233836 1
< 0.1%
233746 1
< 0.1%
233111 1
< 0.1%
232839 1
< 0.1%
232743 1
< 0.1%
232511 1
< 0.1%

원거래승인번호(ORG_APVL_NO)
Real number (ℝ)

MISSING 

Distinct498
Distinct (%)100.0%
Missing9502
Missing (%)95.0%
Infinite0
Infinite (%)0.0%
Mean52145859
Minimum30103846
Maximum78489713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:20.417509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30103846
5-th percentile32320616
Q139500637
median51584872
Q363459712
95-th percentile75733978
Maximum78489713
Range48385867
Interquartile range (IQR)23959075

Descriptive statistics

Standard deviation13591312
Coefficient of variation (CV)0.26064029
Kurtosis-1.1852987
Mean52145859
Median Absolute Deviation (MAD)11923784
Skewness0.14551638
Sum2.5968638 × 1010
Variance1.8472376 × 1014
MonotonicityNot monotonic
2023-12-12T08:03:20.556678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62010223 1
 
< 0.1%
41458178 1
 
< 0.1%
38439470 1
 
< 0.1%
32986446 1
 
< 0.1%
34629813 1
 
< 0.1%
76438541 1
 
< 0.1%
41883769 1
 
< 0.1%
37507275 1
 
< 0.1%
43924408 1
 
< 0.1%
55112535 1
 
< 0.1%
Other values (488) 488
 
4.9%
(Missing) 9502
95.0%
ValueCountFrequency (%)
30103846 1
< 0.1%
30153368 1
< 0.1%
30238930 1
< 0.1%
30288758 1
< 0.1%
30297083 1
< 0.1%
30448381 1
< 0.1%
30552414 1
< 0.1%
31185885 1
< 0.1%
31438216 1
< 0.1%
31529240 1
< 0.1%
ValueCountFrequency (%)
78489713 1
< 0.1%
78330196 1
< 0.1%
77490510 1
< 0.1%
77460522 1
< 0.1%
77368742 1
< 0.1%
77295891 1
< 0.1%
77292830 1
< 0.1%
76906194 1
< 0.1%
76438541 1
< 0.1%
76329193 1
< 0.1%
Distinct339
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92558.731
Minimum10
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:20.733337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile37000
Q199000
median100000
Q3100000
95-th percentile100000
Maximum1000000
Range999990
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation31977.939
Coefficient of variation (CV)0.34548809
Kurtosis111.45205
Mean92558.731
Median Absolute Deviation (MAD)0
Skewness5.6553726
Sum9.255873 × 108
Variance1.0225886 × 109
MonotonicityNot monotonic
2023-12-12T08:03:21.267713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 7186
71.9%
50000 213
 
2.1%
85000 195
 
1.9%
91000 191
 
1.9%
46000 77
 
0.8%
37000 60
 
0.6%
68000 59
 
0.6%
58000 55
 
0.5%
67000 50
 
0.5%
77000 42
 
0.4%
Other values (329) 1872
 
18.7%
ValueCountFrequency (%)
10 1
 
< 0.1%
99 1
 
< 0.1%
100 4
 
< 0.1%
300 2
 
< 0.1%
1000 10
0.1%
1200 12
0.1%
1500 5
0.1%
2000 2
 
< 0.1%
2301 1
 
< 0.1%
3000 8
0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
700000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 5
0.1%
456000 1
 
< 0.1%
400000 6
0.1%
372000 1
 
< 0.1%
349200 1
 
< 0.1%
333000 1
 
< 0.1%
325000 1
 
< 0.1%

취소여부(CANCEL_YN)
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9494 
True
 
506
ValueCountFrequency (%)
False 9494
94.9%
True 506
 
5.1%
2023-12-12T08:03:21.385524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4647294 × 108
Minimum7.0014273 × 108
Maximum7.9905233 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:03:21.499764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0014273 × 108
5-th percentile7.0014273 × 108
Q17.0129575 × 108
median7.3869534 × 108
Q37.9868724 × 108
95-th percentile7.9905233 × 108
Maximum7.9905233 × 108
Range98909605
Interquartile range (IQR)97391496

Descriptive statistics

Standard deviation41064275
Coefficient of variation (CV)0.05501107
Kurtosis-1.693448
Mean7.4647294 × 108
Median Absolute Deviation (MAD)37644533
Skewness0.1490073
Sum7.4647294 × 1012
Variance1.6862747 × 1015
MonotonicityNot monotonic
2023-12-12T08:03:21.665942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799052332 1508
15.1%
738695341 1159
11.6%
798687245 1120
11.2%
701050808 1028
10.3%
704613245 820
8.2%
778789130 729
7.3%
700142727 691
6.9%
785061543 620
6.2%
701295749 597
 
6.0%
730623298 337
 
3.4%
Other values (69) 1391
13.9%
ValueCountFrequency (%)
700142727 691
6.9%
701024891 149
 
1.5%
701050808 1028
10.3%
701050816 143
 
1.4%
701295731 1
 
< 0.1%
701295749 597
6.0%
703761221 3
 
< 0.1%
704613237 1
 
< 0.1%
704613245 820
8.2%
704622421 10
 
0.1%
ValueCountFrequency (%)
799052332 1508
15.1%
799052195 6
 
0.1%
798687245 1120
11.2%
793903271 3
 
< 0.1%
793609341 1
 
< 0.1%
793089360 1
 
< 0.1%
785061543 620
6.2%
778789130 729
7.3%
774878974 9
 
0.1%
774126829 1
 
< 0.1%
Distinct276
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:03:22.093898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.2%

Sample

1st row50:31.0
2nd row20:44.0
3rd row20:46.0
4th row21:17.0
5th row21:20.0
ValueCountFrequency (%)
21:17.0 181
 
1.8%
21:24.0 159
 
1.6%
21:18.0 150
 
1.5%
21:23.0 142
 
1.4%
20:45.0 142
 
1.4%
21:13.0 135
 
1.4%
21:19.0 132
 
1.3%
20:55.0 128
 
1.3%
21:22.0 124
 
1.2%
21:20.0 122
 
1.2%
Other values (266) 8585
85.9%
2023-12-12T08:03:22.640841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16275
23.2%
2 10858
15.5%
: 10000
14.3%
. 10000
14.3%
1 8140
11.6%
5 3903
 
5.6%
4 3103
 
4.4%
3 2573
 
3.7%
9 1688
 
2.4%
7 1216
 
1.7%
Other values (2) 2244
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
71.4%
Other Punctuation 20000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16275
32.6%
2 10858
21.7%
1 8140
16.3%
5 3903
 
7.8%
4 3103
 
6.2%
3 2573
 
5.1%
9 1688
 
3.4%
7 1216
 
2.4%
8 1174
 
2.3%
6 1070
 
2.1%
Other Punctuation
ValueCountFrequency (%)
: 10000
50.0%
. 10000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16275
23.2%
2 10858
15.5%
: 10000
14.3%
. 10000
14.3%
1 8140
11.6%
5 3903
 
5.6%
4 3103
 
4.4%
3 2573
 
3.7%
9 1688
 
2.4%
7 1216
 
1.7%
Other values (2) 2244
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16275
23.2%
2 10858
15.5%
: 10000
14.3%
. 10000
14.3%
1 8140
11.6%
5 3903
 
5.6%
4 3103
 
4.4%
3 2573
 
3.7%
9 1688
 
2.4%
7 1216
 
1.7%
Other values (2) 2244
 
3.2%
Distinct151
Distinct (%)29.8%
Missing9493
Missing (%)94.9%
Memory size156.2 KiB
2023-12-12T08:03:23.043988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3549
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)9.9%

Sample

1st row20:48.0
2nd row20:50.0
3rd row21:45.0
4th row21:36.0
5th row20:51.0
ValueCountFrequency (%)
21:17.0 28
 
5.5%
21:06.0 16
 
3.2%
21:13.0 11
 
2.2%
49:56.0 10
 
2.0%
21:19.0 10
 
2.0%
21:23.0 10
 
2.0%
20:42.0 9
 
1.8%
21:54.0 9
 
1.8%
21:31.0 8
 
1.6%
21:18.0 8
 
1.6%
Other values (141) 388
76.5%
2023-12-12T08:03:23.637995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 792
22.3%
2 580
16.3%
: 507
14.3%
. 507
14.3%
1 453
12.8%
5 163
 
4.6%
4 143
 
4.0%
3 120
 
3.4%
9 85
 
2.4%
7 81
 
2.3%
Other values (2) 118
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2535
71.4%
Other Punctuation 1014
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 792
31.2%
2 580
22.9%
1 453
17.9%
5 163
 
6.4%
4 143
 
5.6%
3 120
 
4.7%
9 85
 
3.4%
7 81
 
3.2%
6 59
 
2.3%
8 59
 
2.3%
Other Punctuation
ValueCountFrequency (%)
: 507
50.0%
. 507
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 792
22.3%
2 580
16.3%
: 507
14.3%
. 507
14.3%
1 453
12.8%
5 163
 
4.6%
4 143
 
4.0%
3 120
 
3.4%
9 85
 
2.4%
7 81
 
2.3%
Other values (2) 118
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 792
22.3%
2 580
16.3%
: 507
14.3%
. 507
14.3%
1 453
12.8%
5 163
 
4.6%
4 143
 
4.0%
3 120
 
3.4%
9 85
 
2.4%
7 81
 
2.3%
Other values (2) 118
 
3.3%

원거래매입접수번호(ORG_BUY_NO)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

전체결제금액(TOT_APVL_AMT)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

포인트잔액(BALANCE)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

원가맹점번호(ORIGIN_STORE_NO)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

원가맹점명(ORIGIN_STORE_NM)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

요청일(REQ_DT)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
20191217
 
2
20181220
 
1
20181129
 
1

Length

Max length8
Median length4
Mean length4.0016
Min length4

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> 9996
> 99.9%
20191217 2
 
< 0.1%
20181220 1
 
< 0.1%
20181129 1
 
< 0.1%

Length

2023-12-12T08:03:23.801908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:23.924215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
20191217 2
 
< 0.1%
20181220 1
 
< 0.1%
20181129 1
 
< 0.1%

정지일(STOP_DT)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
20191217
 
2
20181220
 
1
20181129
 
1

Length

Max length8
Median length4
Mean length4.0016
Min length4

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> 9996
> 99.9%
20191217 2
 
< 0.1%
20181220 1
 
< 0.1%
20181129 1
 
< 0.1%

Length

2023-12-12T08:03:24.046817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:24.157008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
20191217 2
 
< 0.1%
20181220 1
 
< 0.1%
20181129 1
 
< 0.1%

정지시각(STOP_TM)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
143138
 
2
171116
 
1
170724
 
1

Length

Max length6
Median length4
Mean length4.0008
Min length4

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> 9996
> 99.9%
143138 2
 
< 0.1%
171116 1
 
< 0.1%
170724 1
 
< 0.1%

Length

2023-12-12T08:03:24.313543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:24.458221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
143138 2
 
< 0.1%
171116 1
 
< 0.1%
170724 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
2
 
2
3
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.9988
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> 9996
> 99.9%
2 2
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%

Length

2023-12-12T08:03:24.596169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:24.728863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
2 2
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%

응답코드(RESP_CD)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9999 
0
 
1

Length

Max length4
Median length4
Mean length3.9997
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> 9999
> 99.9%
0 1
 
< 0.1%

Length

2023-12-12T08:03:24.875320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:24.994435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9999
> 99.9%
0 1
 
< 0.1%

응답메세지(RESP_RSN).1
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-12T08:03:25.096502image/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 row정상처리
ValueCountFrequency (%)
정상처리 1
100.0%
2023-12-12T08:03:25.348044image/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%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
2
 
3
9
 
1

Length

Max length4
Median length4
Mean length3.9988
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> 9996
> 99.9%
2 3
 
< 0.1%
9 1
 
< 0.1%

Length

2023-12-12T08:03:25.522093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:25.651087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
2 3
 
< 0.1%
9 1
 
< 0.1%

송신일시(SEND_DT)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
20200000000000
 
4

Length

Max length14
Median length4
Mean length4.004
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> 9996
> 99.9%
20200000000000 4
 
< 0.1%

Length

2023-12-12T08:03:25.779857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:25.879292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
20200000000000 4
 
< 0.1%

수신일시(RCV_DT)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9999 
20200000000000
 
1

Length

Max length14
Median length4
Mean length4.001
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> 9999
> 99.9%
20200000000000 1
 
< 0.1%

Length

2023-12-12T08:03:25.987396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:03:26.092278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9999
> 99.9%
20200000000000 1
 
< 0.1%
Distinct3
Distinct (%)75.0%
Missing9996
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-12T08:03:26.193549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters28
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row31:38.0
2nd row11:16.0
3rd row07:24.0
4th row31:38.0
ValueCountFrequency (%)
31:38.0 2
50.0%
11:16.0 1
25.0%
07:24.0 1
25.0%
2023-12-12T08:03:26.467344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
17.9%
0 5
17.9%
3 4
14.3%
: 4
14.3%
. 4
14.3%
8 2
 
7.1%
6 1
 
3.6%
7 1
 
3.6%
2 1
 
3.6%
4 1
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
71.4%
Other Punctuation 8
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
25.0%
0 5
25.0%
3 4
20.0%
8 2
 
10.0%
6 1
 
5.0%
7 1
 
5.0%
2 1
 
5.0%
4 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
: 4
50.0%
. 4
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
17.9%
0 5
17.9%
3 4
14.3%
: 4
14.3%
. 4
14.3%
8 2
 
7.1%
6 1
 
3.6%
7 1
 
3.6%
2 1
 
3.6%
4 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
17.9%
0 5
17.9%
3 4
14.3%
: 4
14.3%
. 4
14.3%
8 2
 
7.1%
6 1
 
3.6%
7 1
 
3.6%
2 1
 
3.6%
4 1
 
3.6%
Distinct4
Distinct (%)100.0%
Missing9996
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-12T08:03:26.638896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters28
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row31:49.0
2nd row11:17.0
3rd row07:25.0
4th row31:39.0
ValueCountFrequency (%)
31:49.0 1
25.0%
11:17.0 1
25.0%
07:25.0 1
25.0%
31:39.0 1
25.0%
2023-12-12T08:03:26.958192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
17.9%
0 5
17.9%
: 4
14.3%
. 4
14.3%
3 3
10.7%
9 2
 
7.1%
7 2
 
7.1%
4 1
 
3.6%
2 1
 
3.6%
5 1
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
71.4%
Other Punctuation 8
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
25.0%
0 5
25.0%
3 3
15.0%
9 2
 
10.0%
7 2
 
10.0%
4 1
 
5.0%
2 1
 
5.0%
5 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
: 4
50.0%
. 4
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
17.9%
0 5
17.9%
: 4
14.3%
. 4
14.3%
3 3
10.7%
9 2
 
7.1%
7 2
 
7.1%
4 1
 
3.6%
2 1
 
3.6%
5 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
17.9%
0 5
17.9%
: 4
14.3%
. 4
14.3%
3 3
10.7%
9 2
 
7.1%
7 2
 
7.1%
4 1
 
3.6%
2 1
 
3.6%
5 1
 
3.6%

문자수신여부(SMS_REC_FLAG)
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size97.7 KiB
True
 
1
(Missing)
9999 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 9999
> 99.9%
2023-12-12T08:03:27.075940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

사업년도(BIZ_YEAR)사업번호(BIZ_NO)일련번호(SEQ)발급일자(ISSUE_DT)발급회차(ISSUE_SEQ)사용시작일(USE_START_DT)사용종료일(USE_END_DT)송신일자(SEND_DT)수신일자(RCV_DT)응답메세지(RESP_RSN)등록일자(INPUT_DT)수정일자(UPDATE_DT)이력순번(HIS_SEQ)발급일자(ISSUE_DT).1정지일자(STOP_DT)정지사유(STOP_RSN)카드상태(K014)(CARD_STS)사용여부(USE_YN)등록일자(INPUT_DT).1수정일자(UPDATE_DT).1등기번호_수신상태(REG_STS)정산년도(CALC_YEAR)정산월(CALC_MONTH)정산결제건수(CALC_CNT)포인트사용금액(CALC_AMT)정산취소건수(CALC_CANCEL_CNT)포인트취소금액(CALC_CANCEL_AMT)등록일자(INPUT_DT).2수정일자(UPDATE_DT).2사업년도(BIZ_YEAR).1한도금액(LIMIT_AMT)사용금액(USE_AMT)잔액(BALANCE)정산일자(CALC_DT)정산여부(CALC_YN)등록일자(INPUT_DT).3수정일자(UPDATE_DT).3기관개인구분 (K004)(ORG_PER_GB)이월금액(LIMIT_PREV_AMT)승인일자(APVL_DT)승인시각(APVL_TM)원거래승인일자(ORG_APVL_DT)원거래승인시각(ORG_APVL_TM)원거래승인번호(ORG_APVL_NO)포인트결제금액(APVL_AMT)취소여부(CANCEL_YN)가맹점번호(PG사PK)(STORE_NO)등록일자(INPUT_DT).4수정일자(UPDATE_DT).4원거래매입접수번호(ORG_BUY_NO)전체결제금액(TOT_APVL_AMT)포인트잔액(BALANCE)원가맹점번호(ORIGIN_STORE_NO)원가맹점명(ORIGIN_STORE_NM)요청일(REQ_DT)정지일(STOP_DT)정지시각(STOP_TM)사용정지코드(1:정지,2:정지해제)(USE_STOP_CD)응답코드(RESP_CD)응답메세지(RESP_RSN).1송수신작업상태(K013)(PRC_STATE)송신일시(SEND_DT)수신일시(RCV_DT)등록일자(INPUT_DT).5수정일자(UPDATE_DT).5문자수신여부(SMS_REC_FLAG)
428720171220170302220170101201712312017030820170308정상처리52:56.000:17.0491920180219<NA><NA>정상Y46:24.0<NA>미수신2019101400000045:13.045:13.020161000001000000<NA>N03:56.050:08.0시설020161128165724<NA><NA><NA>3000N70105081650:31.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3516420181520180228320180219201812312018030220180302정상처리20:39.027:21.03857220190305<NA><NA>정상Y00:45.0<NA>미수신2019711000000045:13.045:13.020161000001000000<NA>N03:59.050:13.0시설02019052135121201905071720184337832985000N71228912320:44.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16906201713020170522220170101201712312017052420170524정상처리14:32.059:48.01708220180228<NA><NA>정상Y26:39.0<NA>미수신2018611000000045:13.045:13.020171000001000000<NA>N00:29.020:37.0개인020180427205449<NA><NA><NA>100000N77878913020:46.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29387201813620180228320180219201812312018030220180302정상처리20:39.026:40.03080520190305<NA><NA>정상Y08:13.0<NA>미수신2017611000000045:13.045:13.020181000001000000<NA>N46:44.021:19.0시설02018110743223201810181115415277162023000N70105080821:17.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2761320181120180228320180219201812312018030520180305정상처리37:22.024:53.03479120190305<NA><NA>정상Y19:06.0<NA>미수신2017711000000045:13.045:13.020181000001000000<NA>N46:30.021:42.0시설020180904115348<NA><NA><NA>100000N70129574921:20.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
26013201813020180228320180219201812312018030220180302정상처리20:39.027:59.03434120190305<NA><NA>정상Y22:46.0<NA>미수신20181113700013700045:13.045:13.020171000001000000<NA>N00:12.020:45.0시설02018091481937<NA><NA><NA>91000N70105080820:39.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
886820171820170307220170101201712312017031020170310정상처리10:06.000:51.0749520180220<NA><NA>정상Y02:00.0<NA>미수신2019911000000045:13.045:13.0201710000099560440<NA>N00:41.025:34.0시설10000020161211130002<NA><NA><NA>89500N70014272720:10.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7496201714020170307220170101201712312017030920170309정상처리10:51.000:47.0624720180219<NA><NA>정상Y46:36.0<NA>미수신2018811000000045:13.045:13.020161000001000000<NA>N04:07.050:26.0시설020170622133515<NA><NA><NA>100000N73869534119:16.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
24350201819120180220320180219201812312018022120180221정상처리56:03.001:53.02951020180228<NA><NA>재발급Y01:35.050:28.0수신완료20191011000000045:13.045:13.020171000001000000<NA>N59:59.019:51.0시설10000020180913131455<NA><NA><NA>100000N73869534121:19.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33058201814920180228320180219201812312018030220180302정상처리20:39.026:38.03714720190305<NA><NA>정상Y26:01.0<NA>미수신2019911000000045:13.045:13.020181000001000000<NA>N26:42.021:28.0시설020190427175125<NA><NA><NA>100000N77878913021:02.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
사업년도(BIZ_YEAR)사업번호(BIZ_NO)일련번호(SEQ)발급일자(ISSUE_DT)발급회차(ISSUE_SEQ)사용시작일(USE_START_DT)사용종료일(USE_END_DT)송신일자(SEND_DT)수신일자(RCV_DT)응답메세지(RESP_RSN)등록일자(INPUT_DT)수정일자(UPDATE_DT)이력순번(HIS_SEQ)발급일자(ISSUE_DT).1정지일자(STOP_DT)정지사유(STOP_RSN)카드상태(K014)(CARD_STS)사용여부(USE_YN)등록일자(INPUT_DT).1수정일자(UPDATE_DT).1등기번호_수신상태(REG_STS)정산년도(CALC_YEAR)정산월(CALC_MONTH)정산결제건수(CALC_CNT)포인트사용금액(CALC_AMT)정산취소건수(CALC_CANCEL_CNT)포인트취소금액(CALC_CANCEL_AMT)등록일자(INPUT_DT).2수정일자(UPDATE_DT).2사업년도(BIZ_YEAR).1한도금액(LIMIT_AMT)사용금액(USE_AMT)잔액(BALANCE)정산일자(CALC_DT)정산여부(CALC_YN)등록일자(INPUT_DT).3수정일자(UPDATE_DT).3기관개인구분 (K004)(ORG_PER_GB)이월금액(LIMIT_PREV_AMT)승인일자(APVL_DT)승인시각(APVL_TM)원거래승인일자(ORG_APVL_DT)원거래승인시각(ORG_APVL_TM)원거래승인번호(ORG_APVL_NO)포인트결제금액(APVL_AMT)취소여부(CANCEL_YN)가맹점번호(PG사PK)(STORE_NO)등록일자(INPUT_DT).4수정일자(UPDATE_DT).4원거래매입접수번호(ORG_BUY_NO)전체결제금액(TOT_APVL_AMT)포인트잔액(BALANCE)원가맹점번호(ORIGIN_STORE_NO)원가맹점명(ORIGIN_STORE_NM)요청일(REQ_DT)정지일(STOP_DT)정지시각(STOP_TM)사용정지코드(1:정지,2:정지해제)(USE_STOP_CD)응답코드(RESP_CD)응답메세지(RESP_RSN).1송수신작업상태(K013)(PRC_STATE)송신일시(SEND_DT)수신일시(RCV_DT)등록일자(INPUT_DT).5수정일자(UPDATE_DT).5문자수신여부(SMS_REC_FLAG)
33673201815320180228320180219201812312018030220180302정상처리20:39.027:45.03729720190305<NA><NA>정상Y29:14.0<NA>미수신2019611000000045:13.045:13.020181000001000000<NA>N01:25.020:57.0시설020170922162223<NA><NA><NA>100000N70461324520:44.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3140820181820180228320180219201812312018030220180302정상처리20:39.026:43.04102820190305<NA><NA>정상Y09:15.0<NA>미수신2017911000000045:13.045:13.020181000001000000<NA>N01:33.020:58.0시설020190404125135<NA><NA><NA>100000N70700360821:18.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9864201712420170307220170101201712312017031020170310정상처리12:00.000:38.0665420180219<NA><NA>정상Y46:39.0<NA>미수신20161011000000045:13.045:13.020171000000100000<NA>N00:48.0<NA>시설020171111204351<NA><NA><NA>100000N73797037521:23.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9821201715520170307220170101201712312017031020170310정상처리12:13.000:39.0574420180219<NA><NA>정상Y46:35.0<NA>미수신20161111000000045:13.045:13.020171000001000000<NA>N00:48.020:55.0시설020171027102845<NA><NA><NA>100000N79905233221:51.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2779520181120180228320180219201812312018030520180305정상처리37:22.024:54.0539620180219<NA><NA>정상Y46:21.0<NA>미수신2017611000000045:13.045:13.020181000001000000<NA>N26:48.021:24.0시설020180801202742<NA><NA><NA>100000N77878913021:34.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
31955201814220180228320180219201812312018030220180302정상처리20:39.026:36.01127520180228<NA><NA>정상Y27:39.0<NA>미수신2019411000000045:13.045:13.020181000001000000<NA>N26:36.021:36.0시설020181216185521<NA><NA><NA>37500N70461324521:53.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20120171220170302220170101201712312017030820170308정상처리46:48.000:40.015520170307<NA><NA>재발급Y59:54.032:59.0미수신20161011000000045:13.045:13.020161000001000000<NA>N03:43.049:47.0시설02016102593017<NA><NA><NA>100000N79868724549:51.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23919201811320180219320180219201812312018022020180220정상처리58:05.046:30.02655720180315<NA><NA>정상Y01:08.0<NA>미수신2019511000000045:13.045:13.020161000000100000<NA>N03:53.0<NA>시설020181001124054<NA><NA><NA>100000N73869534120:18.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11447201711320170307220170101201712312017031320170313정상처리13:57.000:07.01405020180228<NA><NA>정상Y27:26.0<NA>미수신2017811000000045:13.045:13.020171000001000000<NA>N48:51.021:03.0개인020171109185653<NA><NA><NA>100000N79905233221:03.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19960201815520180220320180219201812312018022120180221정상처리56:03.001:29.02168520180228<NA><NA>정상Y27:17.0<NA>미수신2018811000000045:13.045:13.020171000001000000<NA>N59:32.021:03.0시설020180630121420<NA><NA><NA>96500N70129574920:47.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>