Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.2 KiB
Average record size in memory80.3 B

Variable types

Numeric7
Categorical2

Dataset

Description샘플 데이터
Author서울시(신용보증재단)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=324

Reproduction

Analysis started2024-04-16 19:18:53.175102
Analysis finished2024-04-16 19:18:58.409251
Duration5.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(STD_YM)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201858.3
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:58.470474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201807
median201901
Q3201907
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.260631
Coefficient of variation (CV)0.00024898967
Kurtosis-1.9844396
Mean201858.3
Median Absolute Deviation (MAD)11
Skewness-0.07204636
Sum1.0092915 × 108
Variance2526.1311
MonotonicityNot monotonic
2024-04-17T04:18:58.604493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201809 29
 
5.8%
201802 28
 
5.6%
201807 27
 
5.4%
201908 27
 
5.4%
201903 26
 
5.2%
201910 25
 
5.0%
201904 22
 
4.4%
201911 22
 
4.4%
201907 21
 
4.2%
201906 21
 
4.2%
Other values (14) 252
50.4%
ValueCountFrequency (%)
201801 21
4.2%
201802 28
5.6%
201803 16
3.2%
201804 15
3.0%
201805 18
3.6%
201806 19
3.8%
201807 27
5.4%
201808 19
3.8%
201809 29
5.8%
201810 12
2.4%
ValueCountFrequency (%)
201912 20
4.0%
201911 22
4.4%
201910 25
5.0%
201909 19
3.8%
201908 27
5.4%
201907 21
4.2%
201906 21
4.2%
201905 21
4.2%
201904 22
4.4%
201903 26
5.2%
Distinct269
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11446110
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:58.723045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140570
Q111290685
median11470575
Q311620565
95-th percentile11710690
Maximum11740700
Range630185
Interquartile range (IQR)329880

Descriptive statistics

Standard deviation189125.76
Coefficient of variation (CV)0.016523148
Kurtosis-1.2276861
Mean11446110
Median Absolute Deviation (MAD)164965
Skewness-0.12567943
Sum5.7230548 × 109
Variance3.5768553 × 1010
MonotonicityNot monotonic
2024-04-17T04:18:58.849971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11710710 6
 
1.2%
11710690 6
 
1.2%
11650530 6
 
1.2%
11410700 6
 
1.2%
11320690 6
 
1.2%
11680650 5
 
1.0%
11560610 5
 
1.0%
11500510 5
 
1.0%
11590620 4
 
0.8%
11230740 4
 
0.8%
Other values (259) 447
89.4%
ValueCountFrequency (%)
11110515 2
0.4%
11110530 1
 
0.2%
11110550 3
0.6%
11110560 1
 
0.2%
11110580 1
 
0.2%
11110640 3
0.6%
11110670 1
 
0.2%
11110710 3
0.6%
11140520 2
0.4%
11140540 4
0.8%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740685 3
0.6%
11740660 2
 
0.4%
11740610 3
0.6%
11740600 1
 
0.2%
11740580 1
 
0.2%
11740550 2
 
0.4%
11740515 2
 
0.4%
11710710 6
1.2%
11710690 6
1.2%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
135 
B
126 
E
90 
L
53 
J
32 
Other values (4)
64 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowB
4th rowE
5th rowL

Common Values

ValueCountFrequency (%)
A 135
27.0%
B 126
25.2%
E 90
18.0%
L 53
 
10.6%
J 32
 
6.4%
C 24
 
4.8%
F 23
 
4.6%
I 11
 
2.2%
G 6
 
1.2%

Length

2024-04-17T04:18:58.975200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:18:59.079344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 135
27.0%
b 126
25.2%
e 90
18.0%
l 53
 
10.6%
j 32
 
6.4%
c 24
 
4.8%
f 23
 
4.6%
i 11
 
2.2%
g 6
 
1.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
264 
1
236 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 264
52.8%
1 236
47.2%

Length

2024-04-17T04:18:59.198875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:18:59.289239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 264
52.8%
1 236
47.2%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.158
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:59.370744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.614388
Coefficient of variation (CV)0.38826069
Kurtosis-0.93529629
Mean4.158
Median Absolute Deviation (MAD)1
Skewness0.13513298
Sum2079
Variance2.6062485
MonotonicityNot monotonic
2024-04-17T04:18:59.472985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 116
23.2%
3 93
18.6%
2 81
16.2%
5 78
15.6%
6 74
14.8%
7 47
9.4%
1 11
 
2.2%
ValueCountFrequency (%)
1 11
 
2.2%
2 81
16.2%
3 93
18.6%
4 116
23.2%
5 78
15.6%
6 74
14.8%
7 47
9.4%
ValueCountFrequency (%)
7 47
9.4%
6 74
14.8%
5 78
15.6%
4 116
23.2%
3 93
18.6%
2 81
16.2%
1 11
 
2.2%

시간대코드(TIME_CD)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.77
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:59.578947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6365992
Coefficient of variation (CV)0.43411119
Kurtosis-1.1511421
Mean3.77
Median Absolute Deviation (MAD)1
Skewness-0.16647809
Sum1885
Variance2.6784569
MonotonicityNot monotonic
2024-04-17T04:18:59.683237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 98
19.6%
4 97
19.4%
5 90
18.0%
3 83
16.6%
2 78
15.6%
1 54
10.8%
ValueCountFrequency (%)
1 54
10.8%
2 78
15.6%
3 83
16.6%
4 97
19.4%
5 90
18.0%
6 98
19.6%
ValueCountFrequency (%)
6 98
19.6%
5 90
18.0%
4 97
19.4%
3 83
16.6%
2 78
15.6%
1 54
10.8%

구매_고객수(ACC_CNT)
Real number (ℝ)

Distinct114
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.638
Minimum1
Maximum13884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:59.797912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q323
95-th percentile377.3
Maximum13884
Range13883
Interquartile range (IQR)21

Descriptive statistics

Standard deviation956.34705
Coefficient of variation (CV)6.3486441
Kurtosis121.47736
Mean150.638
Median Absolute Deviation (MAD)4
Skewness10.382158
Sum75319
Variance914599.69
MonotonicityNot monotonic
2024-04-17T04:18:59.926182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 115
23.0%
2 55
 
11.0%
3 42
 
8.4%
4 33
 
6.6%
5 22
 
4.4%
6 17
 
3.4%
7 11
 
2.2%
8 10
 
2.0%
9 9
 
1.8%
16 9
 
1.8%
Other values (104) 177
35.4%
ValueCountFrequency (%)
1 115
23.0%
2 55
11.0%
3 42
 
8.4%
4 33
 
6.6%
5 22
 
4.4%
6 17
 
3.4%
7 11
 
2.2%
8 10
 
2.0%
9 9
 
1.8%
10 8
 
1.6%
ValueCountFrequency (%)
13884 1
0.2%
9855 1
0.2%
8251 1
0.2%
6438 1
0.2%
5423 1
0.2%
3824 1
0.2%
3015 1
0.2%
1781 1
0.2%
1452 1
0.2%
1168 1
0.2%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct131
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.97
Minimum1
Maximum26428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:00.064649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8.5
Q336
95-th percentile356.2
Maximum26428
Range26427
Interquartile range (IQR)34

Descriptive statistics

Standard deviation1463.6984
Coefficient of variation (CV)8.3654248
Kurtosis253.94449
Mean174.97
Median Absolute Deviation (MAD)7.5
Skewness15.445573
Sum87485
Variance2142413
MonotonicityNot monotonic
2024-04-17T04:19:00.202965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 93
18.6%
2 41
 
8.2%
3 36
 
7.2%
4 30
 
6.0%
7 17
 
3.4%
6 15
 
3.0%
17 10
 
2.0%
5 10
 
2.0%
10 8
 
1.6%
8 8
 
1.6%
Other values (121) 232
46.4%
ValueCountFrequency (%)
1 93
18.6%
2 41
8.2%
3 36
 
7.2%
4 30
 
6.0%
5 10
 
2.0%
6 15
 
3.0%
7 17
 
3.4%
8 8
 
1.6%
9 4
 
0.8%
10 8
 
1.6%
ValueCountFrequency (%)
26428 1
0.2%
18229 1
0.2%
3360 1
0.2%
2794 1
0.2%
2513 1
0.2%
2326 1
0.2%
2117 1
0.2%
2050 1
0.2%
1730 1
0.2%
1402 1
0.2%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct233
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1820754
Minimum1000
Maximum2.56325 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:00.348809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2000
Q19000
median38500
Q3198500
95-th percentile4936350
Maximum2.56325 × 108
Range2.56324 × 108
Interquartile range (IQR)189500

Descriptive statistics

Standard deviation14074180
Coefficient of variation (CV)7.7298633
Kurtosis239.20342
Mean1820754
Median Absolute Deviation (MAD)34500
Skewness14.598483
Sum9.10377 × 108
Variance1.9808253 × 1014
MonotonicityNot monotonic
2024-04-17T04:19:00.524247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 24
 
4.8%
5000 19
 
3.8%
4000 15
 
3.0%
10000 15
 
3.0%
3000 15
 
3.0%
1000 15
 
3.0%
6000 14
 
2.8%
14000 10
 
2.0%
9000 10
 
2.0%
12000 8
 
1.6%
Other values (223) 355
71.0%
ValueCountFrequency (%)
1000 15
3.0%
2000 24
4.8%
3000 15
3.0%
4000 15
3.0%
5000 19
3.8%
6000 14
2.8%
7000 8
 
1.6%
8000 8
 
1.6%
9000 10
2.0%
10000 15
3.0%
ValueCountFrequency (%)
256325000 1
0.2%
146115000 1
0.2%
87281000 1
0.2%
40373000 1
0.2%
27579000 1
0.2%
27235000 1
0.2%
22776000 1
0.2%
19564000 1
0.2%
17139000 1
0.2%
15510000 1
0.2%

Interactions

2024-04-17T04:18:57.526459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:53.504351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.101472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.798334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.399842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.974441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.914900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.612871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:53.579754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.201260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.882679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.483196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.062961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.003582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.702178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:53.663092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.297576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.972703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.568952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.163300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.104696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.803241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:53.740272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.435362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.061943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.651540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.270210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.190894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.907727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:53.818943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.535448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.139721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.728235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.361002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.270230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:58.015586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:53.903922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.629168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.226307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.813498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.746528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.360348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:58.102890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.005625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:54.712267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.303283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:55.890205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:56.828127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:57.440979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T04:19:00.635905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
기준년월(STD_YM)1.0000.0000.1770.0000.0430.0000.0280.0000.000
행정동코드(ADSTRD_CD)0.0001.0000.0000.0000.0000.1560.0000.0000.139
통계청상품코드(STAT_CD)0.1770.0001.0000.1080.0000.0520.1620.0000.047
성별코드(SEX_CD)0.0000.0000.1081.0000.0720.0000.0250.0000.000
연령대코드(AGE_CD)0.0430.0000.0000.0721.0000.0000.1890.0000.000
시간대코드(TIME_CD)0.0000.1560.0520.0000.0001.0000.0000.0000.000
구매_고객수(ACC_CNT)0.0280.0000.1620.0250.1890.0001.0000.0000.000
구매건수(PURH_CNT)0.0000.0000.0000.0000.0000.0000.0001.0000.000
구매금액(PURH_AMT)0.0000.1390.0470.0000.0000.0000.0000.0001.000
2024-04-17T04:19:00.779958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계청상품코드(STAT_CD)성별코드(SEX_CD)
통계청상품코드(STAT_CD)1.0000.107
성별코드(SEX_CD)0.1071.000
2024-04-17T04:19:00.866940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADSTRD_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)통계청상품코드(STAT_CD)성별코드(SEX_CD)
기준년월(STD_YM)1.000-0.012-0.1050.004-0.0090.0000.0210.1600.000
행정동코드(ADSTRD_CD)-0.0121.000-0.028-0.063-0.092-0.034-0.0960.0000.000
연령대코드(AGE_CD)-0.105-0.0281.0000.056-0.013-0.007-0.0090.0000.077
시간대코드(TIME_CD)0.004-0.0630.0561.000-0.038-0.0460.0430.0250.000
구매_고객수(ACC_CNT)-0.009-0.092-0.013-0.0381.0000.006-0.0530.0800.018
구매건수(PURH_CNT)0.000-0.034-0.007-0.0460.0061.000-0.0190.0000.000
구매금액(PURH_AMT)0.021-0.096-0.0090.043-0.053-0.0191.0000.0340.000
통계청상품코드(STAT_CD)0.1600.0000.0000.0250.0800.0000.0341.0000.107
성별코드(SEX_CD)0.0000.0000.0770.0000.0180.0000.0000.1071.000

Missing values

2024-04-17T04:18:58.214173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T04:18:58.354223image/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

기준년월(STD_YM)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
020180711545700B23416614000
120191111680531A116111143000
220190911200690B1357414000
320190511650540E13291549000
420180911110670L1313015822776000
520180311650530G23213557000
620181111170660E14511074159000
720180711350600G265122000
820180911470680B251271000
920190711140540F2344440000
기준년월(STD_YM)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
49020190411470540B12113276000
49120191011380625F17215540000
49220180711215870L16454234776000
49320190311500611L27555114000
49420191211545680B24614274928000
49520190611410585A16421467000
49620190811740685B23116112000
49720180711260630E1754242000
49820180211590620B175102411000
49920180911500510A2731101000