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 19:17:59.858753
Analysis finished2024-04-16 19:18:04.924815
Duration5.07 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%
Mean201853
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:04.981071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201806
median201812
Q3201906
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.13233
Coefficient of variation (CV)0.00024836058
Kurtosis-1.970743
Mean201853
Median Absolute Deviation (MAD)11
Skewness0.13514378
Sum1.009265 × 108
Variance2513.2505
MonotonicityNot monotonic
2024-04-17T04:18:05.090656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201803 32
 
6.4%
201810 29
 
5.8%
201802 26
 
5.2%
201805 25
 
5.0%
201907 23
 
4.6%
201908 23
 
4.6%
201812 23
 
4.6%
201910 22
 
4.4%
201806 21
 
4.2%
201901 21
 
4.2%
Other values (14) 255
51.0%
ValueCountFrequency (%)
201801 21
4.2%
201802 26
5.2%
201803 32
6.4%
201804 18
3.6%
201805 25
5.0%
201806 21
4.2%
201807 15
3.0%
201808 20
4.0%
201809 20
4.0%
201810 29
5.8%
ValueCountFrequency (%)
201912 16
3.2%
201911 17
3.4%
201910 22
4.4%
201909 19
3.8%
201908 23
4.6%
201907 23
4.6%
201906 19
3.8%
201905 20
4.0%
201904 21
4.2%
201903 14
2.8%
Distinct294
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11435948
Minimum11110530
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:05.216687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110530
5-th percentile11140644
Q111290605
median11440570
Q311590665
95-th percentile11710642
Maximum11740700
Range630170
Interquartile range (IQR)300060

Descriptive statistics

Standard deviation185625.46
Coefficient of variation (CV)0.01623175
Kurtosis-1.2228394
Mean11435948
Median Absolute Deviation (MAD)150060
Skewness-0.025754672
Sum5.7179742 × 109
Variance3.445681 × 1010
MonotonicityNot monotonic
2024-04-17T04:18:05.346227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11410565 6
 
1.2%
11680690 6
 
1.2%
11710647 5
 
1.0%
11650530 5
 
1.0%
11230705 5
 
1.0%
11710650 4
 
0.8%
11410655 4
 
0.8%
11200560 4
 
0.8%
11380530 4
 
0.8%
11530540 4
 
0.8%
Other values (284) 453
90.6%
ValueCountFrequency (%)
11110530 1
 
0.2%
11110540 2
0.4%
11110560 1
 
0.2%
11110600 2
0.4%
11110630 1
 
0.2%
11110650 3
0.6%
11110690 2
0.4%
11110710 2
0.4%
11140540 2
0.4%
11140550 1
 
0.2%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740650 1
 
0.2%
11740640 2
0.4%
11740600 1
 
0.2%
11740560 1
 
0.2%
11740550 2
0.4%
11740540 3
0.6%
11740530 1
 
0.2%
11710690 1
 
0.2%
11710680 1
 
0.2%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
99 
E
95 
B
87 
L
79 
I
39 
Other values (4)
101 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowJ
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
A 99
19.8%
E 95
19.0%
B 87
17.4%
L 79
15.8%
I 39
 
7.8%
J 35
 
7.0%
C 30
 
6.0%
G 19
 
3.8%
F 17
 
3.4%

Length

2024-04-17T04:18:05.472931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:18:05.591096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 99
19.8%
e 95
19.0%
b 87
17.4%
l 79
15.8%
i 39
 
7.8%
j 35
 
7.0%
c 30
 
6.0%
g 19
 
3.8%
f 17
 
3.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
263 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 263
52.6%
1 237
47.4%

Length

2024-04-17T04:18:05.709121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:18:06.142116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 263
52.6%
1 237
47.4%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.074
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:06.211465image/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 deviation1.606763
Coefficient of variation (CV)0.39439446
Kurtosis-0.86229552
Mean4.074
Median Absolute Deviation (MAD)1
Skewness0.11195062
Sum2037
Variance2.5816874
MonotonicityNot monotonic
2024-04-17T04:18:06.323273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 108
21.6%
4 105
21.0%
5 85
17.0%
6 72
14.4%
2 72
14.4%
7 39
 
7.8%
1 19
 
3.8%
ValueCountFrequency (%)
1 19
 
3.8%
2 72
14.4%
3 108
21.6%
4 105
21.0%
5 85
17.0%
6 72
14.4%
7 39
 
7.8%
ValueCountFrequency (%)
7 39
 
7.8%
6 72
14.4%
5 85
17.0%
4 105
21.0%
3 108
21.6%
2 72
14.4%
1 19
 
3.8%

시간대코드(TIME_CD)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.932
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:06.427096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4026085
Coefficient of variation (CV)0.3567163
Kurtosis-0.85896816
Mean3.932
Median Absolute Deviation (MAD)1
Skewness-0.14115221
Sum1966
Variance1.9673106
MonotonicityNot monotonic
2024-04-17T04:18:06.535172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 123
24.6%
4 110
22.0%
5 104
20.8%
6 83
16.6%
2 59
11.8%
1 21
 
4.2%
ValueCountFrequency (%)
1 21
 
4.2%
2 59
11.8%
3 123
24.6%
4 110
22.0%
5 104
20.8%
6 83
16.6%
ValueCountFrequency (%)
6 83
16.6%
5 104
20.8%
4 110
22.0%
3 123
24.6%
2 59
11.8%
1 21
 
4.2%

구매_고객수(ACC_CNT)
Real number (ℝ)

Distinct117
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.024
Minimum1
Maximum2529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:06.678639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q327.25
95-th percentile194.35
Maximum2529
Range2528
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation178.35805
Coefficient of variation (CV)3.6381783
Kurtosis106.93029
Mean49.024
Median Absolute Deviation (MAD)6
Skewness9.254239
Sum24512
Variance31811.595
MonotonicityNot monotonic
2024-04-17T04:18:06.816290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 107
21.4%
2 55
 
11.0%
3 34
 
6.8%
5 24
 
4.8%
7 20
 
4.0%
4 15
 
3.0%
9 13
 
2.6%
8 12
 
2.4%
14 11
 
2.2%
6 10
 
2.0%
Other values (107) 199
39.8%
ValueCountFrequency (%)
1 107
21.4%
2 55
11.0%
3 34
 
6.8%
4 15
 
3.0%
5 24
 
4.8%
6 10
 
2.0%
7 20
 
4.0%
8 12
 
2.4%
9 13
 
2.6%
10 9
 
1.8%
ValueCountFrequency (%)
2529 1
0.2%
1964 1
0.2%
1304 1
0.2%
804 1
0.2%
750 1
0.2%
746 1
0.2%
572 1
0.2%
504 1
0.2%
498 1
0.2%
460 1
0.2%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct135
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.12
Minimum1
Maximum4760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:06.942680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q337
95-th percentile438.2
Maximum4760
Range4759
Interquartile range (IQR)35

Descriptive statistics

Standard deviation373.80955
Coefficient of variation (CV)3.7712828
Kurtosis79.718571
Mean99.12
Median Absolute Deviation (MAD)9
Skewness8.0449229
Sum49560
Variance139733.58
MonotonicityNot monotonic
2024-04-17T04:18:07.062978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 72
 
14.4%
2 55
 
11.0%
4 35
 
7.0%
3 26
 
5.2%
6 15
 
3.0%
7 14
 
2.8%
5 14
 
2.8%
14 13
 
2.6%
10 13
 
2.6%
18 11
 
2.2%
Other values (125) 232
46.4%
ValueCountFrequency (%)
1 72
14.4%
2 55
11.0%
3 26
 
5.2%
4 35
7.0%
5 14
 
2.8%
6 15
 
3.0%
7 14
 
2.8%
8 4
 
0.8%
9 10
 
2.0%
10 13
 
2.6%
ValueCountFrequency (%)
4760 1
0.2%
3608 1
0.2%
3547 1
0.2%
1893 1
0.2%
1776 1
0.2%
1419 1
0.2%
1298 1
0.2%
1261 1
0.2%
1260 1
0.2%
1075 1
0.2%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct273
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean722868
Minimum1000
Maximum64841000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:07.192552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2000
Q114000
median63000
Q3307250
95-th percentile2335950
Maximum64841000
Range64840000
Interquartile range (IQR)293250

Descriptive statistics

Standard deviation3561238.1
Coefficient of variation (CV)4.92654
Kurtosis221.48796
Mean722868
Median Absolute Deviation (MAD)58000
Skewness13.429494
Sum3.61434 × 108
Variance1.2682417 × 1013
MonotonicityNot monotonic
2024-04-17T04:18:07.320404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 20
 
4.0%
5000 17
 
3.4%
1000 16
 
3.2%
2000 14
 
2.8%
3000 11
 
2.2%
8000 11
 
2.2%
15000 8
 
1.6%
13000 7
 
1.4%
4000 6
 
1.2%
11000 5
 
1.0%
Other values (263) 385
77.0%
ValueCountFrequency (%)
1000 16
3.2%
2000 14
2.8%
3000 11
2.2%
4000 6
 
1.2%
5000 17
3.4%
6000 4
 
0.8%
7000 5
 
1.0%
8000 11
2.2%
9000 4
 
0.8%
10000 20
4.0%
ValueCountFrequency (%)
64841000 1
0.2%
30178000 1
0.2%
15859000 1
0.2%
14573000 1
0.2%
12066000 1
0.2%
11924000 1
0.2%
11720000 1
0.2%
9094000 1
0.2%
8363000 1
0.2%
6421000 1
0.2%

Interactions

2024-04-17T04:18:04.076039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.204993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.859300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.613917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.243350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.847497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.486999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:04.163983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.298688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.973173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.706628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.327472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.932210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.571754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:04.256939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.394467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.095735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.796552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.422920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.030047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.671865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:04.355932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.479930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.220309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.883559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.504399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.118598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.755962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:04.454451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.562465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.323124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.963546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.592450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.200227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.835267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:04.540865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.652873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.420214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.052378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.680295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.289729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.915091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:04.620376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:00.752150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:01.502445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.131801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:02.758188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.372406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:03.986508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T04:18:07.425757image/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)1.0000.1220.1550.0000.0000.0000.0000.0000.014
행정동코드(ADSTRD_CD)0.1221.0000.0000.0870.1150.1060.0000.0470.150
통계청상품코드(STAT_CD)0.1550.0001.0000.0270.1500.0000.0000.0550.000
성별코드(SEX_CD)0.0000.0870.0271.0000.0890.0610.1170.0000.014
연령대코드(AGE_CD)0.0000.1150.1500.0891.0000.0000.1890.1300.000
시간대코드(TIME_CD)0.0000.1060.0000.0610.0001.0000.0000.0000.092
구매_고객수(ACC_CNT)0.0000.0000.0000.1170.1890.0001.0000.0000.000
구매건수(PURH_CNT)0.0000.0470.0550.0000.1300.0000.0001.0000.172
구매금액(PURH_AMT)0.0140.1500.0000.0140.0000.0920.0000.1721.000
2024-04-17T04:18:07.550051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계청상품코드(STAT_CD)성별코드(SEX_CD)
통계청상품코드(STAT_CD)1.0000.027
성별코드(SEX_CD)0.0271.000
2024-04-17T04:18:07.646128image/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)1.000-0.094-0.0520.0050.034-0.1100.0150.1400.000
행정동코드(ADSTRD_CD)-0.0941.0000.0240.063-0.0060.1020.0360.0000.061
연령대코드(AGE_CD)-0.0520.0241.0000.040-0.000-0.003-0.0170.0790.094
시간대코드(TIME_CD)0.0050.0630.0401.000-0.039-0.045-0.0110.0000.043
구매_고객수(ACC_CNT)0.034-0.006-0.000-0.0391.0000.044-0.0430.0000.125
구매건수(PURH_CNT)-0.1100.102-0.003-0.0450.0441.0000.0050.0260.000
구매금액(PURH_AMT)0.0150.036-0.017-0.011-0.0430.0051.0000.0000.016
통계청상품코드(STAT_CD)0.1400.0000.0790.0000.0000.0260.0001.0000.027
성별코드(SEX_CD)0.0000.0610.0940.0430.1250.0000.0160.0271.000

Missing values

2024-04-17T04:18:04.730372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T04:18:04.870682image/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)
020190711200720E145516436000
120180911140635E17417918110000
220180511380632J235521000
320190111215830B16433168123000
420180211140635B1619122000
520190911350710L2721845246000
620181111590630A265114103000
720180411260630A1443131000
820181111530760E2343200220000
920180611500560E14639293000
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)
49020181211740540J234331152000
49120190811110630J153482811000
49220181211230705L14341378000
49320181211140625I2637110000
49420180311680650A2451404000
49520180311170520L2441363000
49620190911440730A12438316000
49720190411530520B1655041510000
49820190811350580E2453853000
49920190811350670F151750215176000