Overview

Dataset statistics

Number of variables7
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.9 KiB
Average record size in memory61.3 B

Variable types

Numeric5
Categorical2

Dataset

Description샘플 데이터
Author신한카드
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=51

Alerts

기준년월(TS_YM) is highly overall correlated with 일별(TS_YMD)High correlation
일별(TS_YMD) is highly overall correlated with 기준년월(TS_YM)High correlation

Reproduction

Analysis started2023-12-10 14:54:14.899017
Analysis finished2023-12-10 14:54:19.206913
Duration4.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가맹점블록코드(BLCK_CD)
Real number (ℝ)

Distinct451
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205613.17
Minimum1311
Maximum502544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:19.302136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1311
5-th percentile20455
Q1153538.75
median213421
Q3269022.75
95-th percentile416883.1
Maximum502544
Range501233
Interquartile range (IQR)115484

Descriptive statistics

Standard deviation121955.35
Coefficient of variation (CV)0.59313007
Kurtosis-0.50100478
Mean205613.17
Median Absolute Deviation (MAD)57129
Skewness0.027261777
Sum1.0280658 × 108
Variance1.4873108 × 1010
MonotonicityNot monotonic
2023-12-10T23:54:19.455971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355358 5
 
1.0%
270459 4
 
0.8%
23776 4
 
0.8%
154322 3
 
0.6%
222264 3
 
0.6%
206799 3
 
0.6%
223825 2
 
0.4%
223538 2
 
0.4%
231328 2
 
0.4%
152543 2
 
0.4%
Other values (441) 470
94.0%
ValueCountFrequency (%)
1311 1
0.2%
5507 1
0.2%
8238 1
0.2%
8364 1
0.2%
8667 1
0.2%
10072 1
0.2%
10173 1
0.2%
11015 1
0.2%
11603 1
0.2%
11636 1
0.2%
ValueCountFrequency (%)
502544 1
0.2%
502147 2
0.4%
502139 1
0.2%
501958 1
0.2%
501500 1
0.2%
500591 1
0.2%
422411 1
0.2%
421655 2
0.4%
421588 1
0.2%
421271 1
0.2%

외국인관광업종코드(SF_UPJONG_CD)
Categorical

Distinct43
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
SF020816
85 
SF010408
75 
SF010101
56 
SF020713
33 
SF010203
24 
Other values (38)
227 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique11 ?
Unique (%)2.2%

Sample

1st rowSF020816
2nd rowSF020816
3rd rowSF082044
4th rowSF051330
5th rowSF041224

Common Values

ValueCountFrequency (%)
SF020816 85
17.0%
SF010408 75
15.0%
SF010101 56
11.2%
SF020713 33
 
6.6%
SF010203 24
 
4.8%
SF010305 22
 
4.4%
SF031020 18
 
3.6%
SF082148 17
 
3.4%
SF082045 16
 
3.2%
SF041121 16
 
3.2%
Other values (33) 138
27.6%

Length

2023-12-10T23:54:19.600225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf020816 85
17.0%
sf010408 75
15.0%
sf010101 56
11.2%
sf020713 33
 
6.6%
sf010203 24
 
4.8%
sf010305 22
 
4.4%
sf031020 18
 
3.6%
sf082148 17
 
3.4%
sf082045 16
 
3.2%
sf041121 16
 
3.2%
Other values (33) 138
27.6%

기준년월(TS_YM)
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201887.2
Minimum201701
Maximum202107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:19.738227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201701
5-th percentile201703
Q1201804
median201903.5
Q3202005
95-th percentile202105
Maximum202107
Range406
Interquartile range (IQR)201

Descriptive statistics

Standard deviation129.8154
Coefficient of variation (CV)0.00064300954
Kurtosis-1.1117484
Mean201887.2
Median Absolute Deviation (MAD)101.5
Skewness0.12051724
Sum1.009436 × 108
Variance16852.037
MonotonicityNot monotonic
2023-12-10T23:54:20.132464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201901 16
 
3.2%
201910 16
 
3.2%
201805 15
 
3.0%
201804 14
 
2.8%
202005 14
 
2.8%
201806 13
 
2.6%
202001 13
 
2.6%
201710 13
 
2.6%
201909 12
 
2.4%
201911 12
 
2.4%
Other values (45) 362
72.4%
ValueCountFrequency (%)
201701 11
2.2%
201702 11
2.2%
201703 9
1.8%
201704 5
 
1.0%
201705 8
1.6%
201706 7
1.4%
201707 4
 
0.8%
201708 10
2.0%
201709 5
 
1.0%
201710 13
2.6%
ValueCountFrequency (%)
202107 9
1.8%
202106 7
1.4%
202105 11
2.2%
202104 7
1.4%
202103 10
2.0%
202102 9
1.8%
202101 7
1.4%
202012 9
1.8%
202011 7
1.4%
202010 7
1.4%

일별(TS_YMD)
Real number (ℝ)

HIGH CORRELATION 

Distinct436
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20188736
Minimum20170101
Maximum20210728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:20.307850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170101
5-th percentile20170312
Q120180403
median20190364
Q320200523
95-th percentile20210514
Maximum20210728
Range40627
Interquartile range (IQR)20120.25

Descriptive statistics

Standard deviation12981.032
Coefficient of variation (CV)0.00064298388
Kurtosis-1.1117322
Mean20188736
Median Absolute Deviation (MAD)10140.5
Skewness0.12067225
Sum1.0094368 × 1010
Variance1.6850718 × 108
MonotonicityNot monotonic
2023-12-10T23:54:20.478216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191026 3
 
0.6%
20201102 3
 
0.6%
20181228 3
 
0.6%
20210215 2
 
0.4%
20170628 2
 
0.4%
20201008 2
 
0.4%
20210314 2
 
0.4%
20191129 2
 
0.4%
20210708 2
 
0.4%
20181112 2
 
0.4%
Other values (426) 477
95.4%
ValueCountFrequency (%)
20170101 2
0.4%
20170106 1
0.2%
20170109 1
0.2%
20170115 1
0.2%
20170117 1
0.2%
20170122 1
0.2%
20170123 1
0.2%
20170125 1
0.2%
20170126 1
0.2%
20170128 1
0.2%
ValueCountFrequency (%)
20210728 1
0.2%
20210727 1
0.2%
20210722 1
0.2%
20210719 1
0.2%
20210715 1
0.2%
20210709 1
0.2%
20210708 2
0.4%
20210701 1
0.2%
20210624 1
0.2%
20210623 1
0.2%

시간대구간(TM_CD)
Categorical

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
T5
137 
T3
117 
T4
116 
T6
65 
T2
43 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT5
2nd rowT5
3rd rowT6
4th rowT3
5th rowT6

Common Values

ValueCountFrequency (%)
T5 137
27.4%
T3 117
23.4%
T4 116
23.2%
T6 65
13.0%
T2 43
 
8.6%
T1 22
 
4.4%

Length

2023-12-10T23:54:20.655334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:20.795646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t5 137
27.4%
t3 117
23.4%
t4 116
23.2%
t6 65
13.0%
t2 43
 
8.6%
t1 22
 
4.4%

카드이용금액계(AMT_CORR)
Real number (ℝ)

Distinct329
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193753.94
Minimum500
Maximum19373737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:21.014893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q16800
median17550
Q350000
95-th percentile351595
Maximum19373737
Range19373237
Interquartile range (IQR)43200

Descriptive statistics

Standard deviation1186168
Coefficient of variation (CV)6.1220328
Kurtosis160.72786
Mean193753.94
Median Absolute Deviation (MAD)14050
Skewness11.673074
Sum96876972
Variance1.4069945 × 1012
MonotonicityNot monotonic
2023-12-10T23:54:21.181293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500.0 10
 
2.0%
5000.0 8
 
1.6%
4000.0 8
 
1.6%
9000.0 7
 
1.4%
2000.0 7
 
1.4%
24000.0 6
 
1.2%
2500.0 6
 
1.2%
3000.0 6
 
1.2%
14000.0 5
 
1.0%
45000.0 5
 
1.0%
Other values (319) 432
86.4%
ValueCountFrequency (%)
500.0 1
 
0.2%
900.0 1
 
0.2%
950.0 2
 
0.4%
1000.0 2
 
0.4%
1200.0 1
 
0.2%
1500.0 1
 
0.2%
1600.0 2
 
0.4%
1800.0 3
0.6%
1900.0 2
 
0.4%
2000.0 7
1.4%
ValueCountFrequency (%)
19373737.06 1
0.2%
11675630.65 1
0.2%
7177087.76 1
0.2%
6925210.0 1
0.2%
6879680.0 1
0.2%
4984000.0 1
0.2%
3361986.01 1
0.2%
2550000.0 1
0.2%
1967022.37 1
0.2%
1783600.0 1
0.2%

카드이용건수(USECT_CORR)
Real number (ℝ)

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.93894
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:21.354016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum58
Range57
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5368691
Coefficient of variation (CV)1.8241251
Kurtosis138.03856
Mean1.93894
Median Absolute Deviation (MAD)0
Skewness10.171275
Sum969.47
Variance12.509443
MonotonicityNot monotonic
2023-12-10T23:54:21.495654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.0 347
69.4%
2.0 83
 
16.6%
3.0 19
 
3.8%
2.86 16
 
3.2%
4.0 9
 
1.8%
8.0 3
 
0.6%
12.0 2
 
0.4%
3.86 2
 
0.4%
5.71 2
 
0.4%
8.86 2
 
0.4%
Other values (13) 15
 
3.0%
ValueCountFrequency (%)
1.0 347
69.4%
2.0 83
 
16.6%
2.86 16
 
3.2%
3.0 19
 
3.8%
3.86 2
 
0.4%
4.0 9
 
1.8%
4.86 1
 
0.2%
5.71 2
 
0.4%
5.86 1
 
0.2%
6.0 2
 
0.4%
ValueCountFrequency (%)
58.0 1
0.2%
29.86 1
0.2%
21.42 1
0.2%
20.0 1
0.2%
17.86 1
0.2%
15.0 1
0.2%
12.42 1
0.2%
12.0 2
0.4%
10.0 2
0.4%
8.86 2
0.4%

Interactions

2023-12-10T23:54:18.311271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:15.553480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.258647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.884470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:17.602885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:18.441434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:15.675252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.388796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.998686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:17.723101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:18.591201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:15.830405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.496912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:17.123369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:17.864190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:18.720504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:15.983516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.645403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:17.313895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:18.032781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:18.874165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.129104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:16.786817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:17.452048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:18.196315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:54:21.596377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점블록코드(BLCK_CD)외국인관광업종코드(SF_UPJONG_CD)기준년월(TS_YM)일별(TS_YMD)시간대구간(TM_CD)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)
가맹점블록코드(BLCK_CD)1.0000.3000.0000.0000.0000.0000.000
외국인관광업종코드(SF_UPJONG_CD)0.3001.0000.0000.0000.1930.3820.000
기준년월(TS_YM)0.0000.0001.0001.0000.1380.0000.108
일별(TS_YMD)0.0000.0001.0001.0000.1210.0000.122
시간대구간(TM_CD)0.0000.1930.1380.1211.0000.0000.000
카드이용금액계(AMT_CORR)0.0000.3820.0000.0000.0001.0000.000
카드이용건수(USECT_CORR)0.0000.0000.1080.1220.0000.0001.000
2023-12-10T23:54:21.716418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인관광업종코드(SF_UPJONG_CD)시간대구간(TM_CD)
외국인관광업종코드(SF_UPJONG_CD)1.0000.079
시간대구간(TM_CD)0.0791.000
2023-12-10T23:54:21.805157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점블록코드(BLCK_CD)기준년월(TS_YM)일별(TS_YMD)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)외국인관광업종코드(SF_UPJONG_CD)시간대구간(TM_CD)
가맹점블록코드(BLCK_CD)1.0000.0640.0640.0060.0390.1040.000
기준년월(TS_YM)0.0641.0001.0000.0200.0230.0000.051
일별(TS_YMD)0.0641.0001.0000.0200.0230.0000.047
카드이용금액계(AMT_CORR)0.0060.0200.0201.000-0.0670.1680.000
카드이용건수(USECT_CORR)0.0390.0230.023-0.0671.0000.0000.000
외국인관광업종코드(SF_UPJONG_CD)0.1040.0000.0000.1680.0001.0000.079
시간대구간(TM_CD)0.0000.0510.0470.0000.0000.0791.000

Missing values

2023-12-10T23:54:19.023090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:54:19.140359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

가맹점블록코드(BLCK_CD)외국인관광업종코드(SF_UPJONG_CD)기준년월(TS_YM)일별(TS_YMD)시간대구간(TM_CD)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)
0358632SF02081620210420210401T514200.017.86
120455SF02081620200720200714T51600.01.0
218875SF08204420180220180204T626800.03.0
3207925SF05133020180520180502T3133610.01.0
424568SF04122420180720180728T63500.03.0
5192958SF02071320210220210211T49000.02.0
6107009SF02071320170120170123T65000.01.0
727826SF02081620171220171215T2230958.461.0
8206774SF01010120190420190410T346000.01.0
9343434SF01040820180820180820T525000.03.0
가맹점블록코드(BLCK_CD)외국인관광업종코드(SF_UPJONG_CD)기준년월(TS_YM)일별(TS_YMD)시간대구간(TM_CD)카드이용금액계(AMT_CORR)카드이용건수(USECT_CORR)
490225855SF02071320171120171123T6158610.01.0
491225847SF02081620171220171201T317500.01.0
49231468SF01040820181220181231T449000.01.0
493414758SF05132920200120200129T333629.651.0
494213421SF11255520181020181002T434500.01.0
495154322SF07194320171120171105T3690000.02.0
49629020SF07194320180520180520T57600.010.0
49723935SF01020320190320190327T349000.02.0
498271819SF02071320190220190204T35000.01.0
499229648SF01010120171020171013T315400.01.0