Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory111.4 B

Variable types

Numeric11
Text1

Dataset

Description샘플 데이터
Author신한은행
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=320

Reproduction

Analysis started2023-12-10 14:58:12.533545
Analysis finished2023-12-10 14:58:40.758464
Duration28.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(BASE_YYMM)
Real number (ℝ)

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201906.7
Minimum201901
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:40.882173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201901
Q1201905
median201906.5
Q3201908.75
95-th percentile201912
Maximum201912
Range11
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.3025591
Coefficient of variation (CV)1.6356857 × 10-5
Kurtosis-0.59199284
Mean201906.7
Median Absolute Deviation (MAD)1.5
Skewness0.012800532
Sum6057201
Variance10.906897
MonotonicityNot monotonic
2023-12-10T23:58:41.135987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
201907 5
16.7%
201906 5
16.7%
201905 4
13.3%
201912 4
13.3%
201901 3
10.0%
201908 2
 
6.7%
201910 2
 
6.7%
201903 1
 
3.3%
201902 1
 
3.3%
201909 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
201901 3
10.0%
201902 1
 
3.3%
201903 1
 
3.3%
201904 1
 
3.3%
201905 4
13.3%
201906 5
16.7%
201907 5
16.7%
201908 2
 
6.7%
201909 1
 
3.3%
201910 2
 
6.7%
ValueCountFrequency (%)
201912 4
13.3%
201911 1
 
3.3%
201910 2
 
6.7%
201909 1
 
3.3%
201908 2
 
6.7%
201907 5
16.7%
201906 5
16.7%
201905 4
13.3%
201904 1
 
3.3%
201903 1
 
3.3%
Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:58:41.512272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters210
Distinct characters11
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

Unique22 ?
Unique (%)73.3%

Sample

1st row1*0*1*8
2nd row1*0*5*1
3rd row1*0*0*7
4th row1*0*1*4
5th row1*0*1*7
ValueCountFrequency (%)
1*0*1*8 2
 
6.7%
1*0*3*3 2
 
6.7%
1*0*1*7 2
 
6.7%
1*0*3*7 2
 
6.7%
1*0*0*5 1
 
3.3%
1*0*2*5 1
 
3.3%
1*0*7*1 1
 
3.3%
1*0*1*0 1
 
3.3%
1*0*8*7 1
 
3.3%
1*0*4*3 1
 
3.3%
Other values (16) 16
53.3%
2023-12-10T23:58:42.081689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 90
42.9%
1 41
19.5%
0 35
 
16.7%
3 8
 
3.8%
7 8
 
3.8%
5 7
 
3.3%
2 6
 
2.9%
8 5
 
2.4%
9 4
 
1.9%
4 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
57.1%
Other Punctuation 90
42.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 41
34.2%
0 35
29.2%
3 8
 
6.7%
7 8
 
6.7%
5 7
 
5.8%
2 6
 
5.0%
8 5
 
4.2%
9 4
 
3.3%
4 3
 
2.5%
6 3
 
2.5%
Other Punctuation
ValueCountFrequency (%)
* 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 90
42.9%
1 41
19.5%
0 35
 
16.7%
3 8
 
3.8%
7 8
 
3.8%
5 7
 
3.3%
2 6
 
2.9%
8 5
 
2.4%
9 4
 
1.9%
4 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 90
42.9%
1 41
19.5%
0 35
 
16.7%
3 8
 
3.8%
7 8
 
3.8%
5 7
 
3.3%
2 6
 
2.9%
8 5
 
2.4%
9 4
 
1.9%
4 3
 
1.4%

총수신평잔_건수(DEP_TOT_AVJN_N)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7879.3667
Minimum1711
Maximum15268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:42.339193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1711
5-th percentile2909.35
Q16178
median7829.5
Q39137
95-th percentile12582.75
Maximum15268
Range13557
Interquartile range (IQR)2959

Descriptive statistics

Standard deviation3172.2043
Coefficient of variation (CV)0.40259636
Kurtosis-0.031912736
Mean7879.3667
Median Absolute Deviation (MAD)1593.5
Skewness0.16268541
Sum236381
Variance10062880
MonotonicityNot monotonic
2023-12-10T23:58:42.592163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3769 1
 
3.3%
7205 1
 
3.3%
12909 1
 
3.3%
2629 1
 
3.3%
4520 1
 
3.3%
7515 1
 
3.3%
10472 1
 
3.3%
15268 1
 
3.3%
6352 1
 
3.3%
6120 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1711 1
3.3%
2629 1
3.3%
3252 1
3.3%
3769 1
3.3%
4520 1
3.3%
4842 1
3.3%
4862 1
3.3%
6120 1
3.3%
6352 1
3.3%
7205 1
3.3%
ValueCountFrequency (%)
15268 1
3.3%
12909 1
3.3%
12184 1
3.3%
11986 1
3.3%
11852 1
3.3%
10871 1
3.3%
10472 1
3.3%
9187 1
3.3%
8987 1
3.3%
8850 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1243129 × 1010
Minimum1.2036649 × 1010
Maximum7.6715268 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:42.856791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2036649 × 1010
5-th percentile1.3346679 × 1010
Q12.3300447 × 1010
median4.1010145 × 1010
Q35.6002578 × 1010
95-th percentile6.8168372 × 1010
Maximum7.6715268 × 1010
Range6.4678619 × 1010
Interquartile range (IQR)3.2702131 × 1010

Descriptive statistics

Standard deviation1.8896561 × 1010
Coefficient of variation (CV)0.45817477
Kurtosis-1.1689776
Mean4.1243129 × 1010
Median Absolute Deviation (MAD)1.7038843 × 1010
Skewness0.064072594
Sum1.2372939 × 1012
Variance3.5708002 × 1020
MonotonicityNot monotonic
2023-12-10T23:58:43.128664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
23073729186.0 1
 
3.3%
48784938462.0 1
 
3.3%
39298895366.1199 1
 
3.3%
53765596892.0 1
 
3.3%
50175097864.1255 1
 
3.3%
12036649238.0 1
 
3.3%
64434948910.0439 1
 
3.3%
65766581882.0 1
 
3.3%
23430305592.3689 1
 
3.3%
39327884578.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
12036649238.0 1
3.3%
12778261617.6508 1
3.3%
14041410274.9991 1
3.3%
17038924767.0 1
3.3%
21591718683.0 1
3.3%
21891809921.0 1
3.3%
23073729186.0 1
3.3%
23257160971.0 1
3.3%
23430305592.3689 1
3.3%
27739864263.4784 1
3.3%
ValueCountFrequency (%)
76715267975.0839 1
3.3%
69156668704.0 1
3.3%
66960452940.0 1
3.3%
65766581882.0 1
3.3%
64434948910.0439 1
3.3%
59629699507.0075 1
3.3%
57507991447.0 1
3.3%
56546298145.1687 1
3.3%
54371419373.0 1
3.3%
53765596892.0 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094.8667
Minimum36
Maximum2504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:43.378228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile334.1
Q1645
median868
Q31635.5
95-th percentile2224.2
Maximum2504
Range2468
Interquartile range (IQR)990.5

Descriptive statistics

Standard deviation663.26599
Coefficient of variation (CV)0.60579613
Kurtosis-0.66891379
Mean1094.8667
Median Absolute Deviation (MAD)348.5
Skewness0.66135466
Sum32846
Variance439921.77
MonotonicityNot monotonic
2023-12-10T23:58:43.640409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
36 1
 
3.3%
578 1
 
3.3%
848 1
 
3.3%
1247 1
 
3.3%
962 1
 
3.3%
290 1
 
3.3%
513 1
 
3.3%
1445 1
 
3.3%
907 1
 
3.3%
888 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
36 1
3.3%
290 1
3.3%
388 1
3.3%
422 1
3.3%
513 1
3.3%
526 1
3.3%
578 1
3.3%
643 1
3.3%
651 1
3.3%
696 1
3.3%
ValueCountFrequency (%)
2504 1
3.3%
2235 1
3.3%
2211 1
3.3%
2082 1
3.3%
1952 1
3.3%
1919 1
3.3%
1844 1
3.3%
1699 1
3.3%
1445 1
3.3%
1247 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1470925 × 109
Minimum2.0084157 × 108
Maximum8.7616143 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:43.906742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0084157 × 108
5-th percentile4.6560638 × 108
Q11.6032266 × 109
median2.6915417 × 109
Q34.2989539 × 109
95-th percentile6.3100257 × 109
Maximum8.7616143 × 109
Range8.5607727 × 109
Interquartile range (IQR)2.6957273 × 109

Descriptive statistics

Standard deviation2.0402423 × 109
Coefficient of variation (CV)0.64829436
Kurtosis0.44865923
Mean3.1470925 × 109
Median Absolute Deviation (MAD)1.3276926 × 109
Skewness0.7582842
Sum9.4412776 × 1010
Variance4.1625888 × 1018
MonotonicityNot monotonic
2023-12-10T23:58:44.174021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
590162439.0079 1
 
3.3%
4005841410.0 1
 
3.3%
6740149280.0 1
 
3.3%
1927887725.0 1
 
3.3%
5076948363.0434 1
 
3.3%
1386412936.0469 1
 
3.3%
1368182177.0457 1
 
3.3%
3942432228.0 1
 
3.3%
4851646552.0 1
 
3.3%
1748694886.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
200841570.999 1
3.3%
363696871.0 1
3.3%
590162439.0079 1
3.3%
972488161.0 1
3.3%
1057048356.0 1
3.3%
1368182177.0457 1
3.3%
1386412936.0469 1
3.3%
1554737198.962 1
3.3%
1748694886.0 1
3.3%
1785804307.969 1
3.3%
ValueCountFrequency (%)
8761614256.0 1
3.3%
6740149280.0 1
3.3%
5784319146.0 1
3.3%
5749952973.0 1
3.3%
5076948363.0434 1
3.3%
4851646552.0 1
3.3%
4851300133.0 1
3.3%
4307801591.0 1
3.3%
4272411007.0 1
3.3%
4023567287.015 1
3.3%
Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1233.9333
Minimum238
Maximum2945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:44.419557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum238
5-th percentile315.35
Q1714.75
median1114
Q31445.75
95-th percentile2552.15
Maximum2945
Range2707
Interquartile range (IQR)731

Descriptive statistics

Standard deviation733.91881
Coefficient of variation (CV)0.59477995
Kurtosis-0.1893742
Mean1233.9333
Median Absolute Deviation (MAD)395.5
Skewness0.75646653
Sum37018
Variance538636.82
MonotonicityNot monotonic
2023-12-10T23:58:44.711310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1388 2
 
6.7%
1185 1
 
3.3%
726 1
 
3.3%
238 1
 
3.3%
2152 1
 
3.3%
2945 1
 
3.3%
1372 1
 
3.3%
1143 1
 
3.3%
502 1
 
3.3%
824 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
238 1
3.3%
296 1
3.3%
339 1
3.3%
385 1
3.3%
483 1
3.3%
502 1
3.3%
703 1
3.3%
711 1
3.3%
726 1
3.3%
824 1
3.3%
ValueCountFrequency (%)
2945 1
3.3%
2603 1
3.3%
2490 1
3.3%
2380 1
3.3%
2152 1
3.3%
2136 1
3.3%
1671 1
3.3%
1465 1
3.3%
1388 2
6.7%
1384 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1900946 × 109
Minimum27669588
Maximum5.8359492 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:44.965599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27669588
5-th percentile2.7836439 × 108
Q19.5978134 × 108
median2.2522025 × 109
Q33.0981025 × 109
95-th percentile4.1945681 × 109
Maximum5.8359492 × 109
Range5.8082797 × 109
Interquartile range (IQR)2.1383212 × 109

Descriptive statistics

Standard deviation1.4119367 × 109
Coefficient of variation (CV)0.64469211
Kurtosis-0.02831889
Mean2.1900946 × 109
Median Absolute Deviation (MAD)9.6390483 × 108
Skewness0.40659897
Sum6.5702837 × 1010
Variance1.9935652 × 1018
MonotonicityNot monotonic
2023-12-10T23:58:45.219307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
297418100.0 1
 
3.3%
804438363.995 1
 
3.3%
4448665753.0 1
 
3.3%
27669588.0 1
 
3.3%
2615783051.0 1
 
3.3%
2601558618.0 1
 
3.3%
2998821369.0 1
 
3.3%
3317587708.0 1
 
3.3%
3815291551.9642 1
 
3.3%
2433829148.978 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
27669588.0 1
3.3%
266980875.0 1
3.3%
292277564.0 1
3.3%
297418100.0 1
3.3%
496410769.0 1
3.3%
694730343.0 1
3.3%
765804040.0 1
3.3%
804438363.995 1
3.3%
1425810284.0 1
3.3%
1514156867.0 1
3.3%
ValueCountFrequency (%)
5835949239.0 1
3.3%
4448665753.0 1
3.3%
3884004375.0 1
3.3%
3815291551.9642 1
3.3%
3522155683.052 1
3.3%
3418466158.0 1
3.3%
3317587708.0 1
3.3%
3114626977.0 1
3.3%
3048529156.018 1
3.3%
2998821369.0 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4072.8333
Minimum947
Maximum8914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:45.473828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum947
5-th percentile2022.15
Q12767.75
median3492.5
Q35120.25
95-th percentile7509.85
Maximum8914
Range7967
Interquartile range (IQR)2352.5

Descriptive statistics

Standard deviation1865.0379
Coefficient of variation (CV)0.4579215
Kurtosis0.82355346
Mean4072.8333
Median Absolute Deviation (MAD)1063
Skewness0.94758945
Sum122185
Variance3478366.5
MonotonicityNot monotonic
2023-12-10T23:58:45.699251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4396 1
 
3.3%
2368 1
 
3.3%
2301 1
 
3.3%
5163 1
 
3.3%
3984 1
 
3.3%
4992 1
 
3.3%
5906 1
 
3.3%
4417 1
 
3.3%
947 1
 
3.3%
2950 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
947 1
3.3%
1794 1
3.3%
2301 1
3.3%
2368 1
3.3%
2402 1
3.3%
2457 1
3.3%
2476 1
3.3%
2707 1
3.3%
2950 1
3.3%
3039 1
3.3%
ValueCountFrequency (%)
8914 1
3.3%
8530 1
3.3%
6263 1
3.3%
6198 1
3.3%
5906 1
3.3%
5785 1
3.3%
5698 1
3.3%
5163 1
3.3%
4992 1
3.3%
4720 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6139915 × 109
Minimum1.5050952 × 109
Maximum1.0206783 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:45.922757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5050952 × 109
5-th percentile2.2108625 × 109
Q14.3102539 × 109
median5.6520034 × 109
Q36.6202621 × 109
95-th percentile9.6436702 × 109
Maximum1.0206783 × 1010
Range8.7016878 × 109
Interquartile range (IQR)2.3100082 × 109

Descriptive statistics

Standard deviation2.2155714 × 109
Coefficient of variation (CV)0.39465172
Kurtosis-0.21496775
Mean5.6139915 × 109
Median Absolute Deviation (MAD)1.3035727 × 109
Skewness0.23024867
Sum1.6841975 × 1011
Variance4.9087567 × 1018
MonotonicityNot monotonic
2023-12-10T23:58:46.562874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7215547950.1702 1
 
3.3%
9551466967.0458 1
 
3.3%
4890765091.0 1
 
3.3%
3527725331.0368 1
 
3.3%
5237656545.8039 1
 
3.3%
2935366949.0497 1
 
3.3%
5065293250.976 1
 
3.3%
4298583660.1018 1
 
3.3%
4361250957.0 1
 
3.3%
5755251332.0205 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1505095181.0 1
3.3%
1634819436.0 1
3.3%
2914915026.8994 1
3.3%
2935366949.0497 1
3.3%
3491554674.0 1
3.3%
3527725331.0368 1
3.3%
3833492251.0 1
3.3%
4298583660.1018 1
3.3%
4345264716.8621 1
3.3%
4351596730.0136 1
3.3%
ValueCountFrequency (%)
10206783005.049 1
3.3%
9719109145.9309 1
3.3%
9551466967.0458 1
3.3%
8261725874.0 1
3.3%
8165121710.0 1
3.3%
7215547950.1702 1
3.3%
6689866244.0 1
3.3%
6632355427.1296 1
3.3%
6583982296.0087 1
3.3%
6578283675.187 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2847.2667
Minimum518
Maximum7688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:46.809906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum518
5-th percentile599.85
Q11769.75
median2407
Q33770.25
95-th percentile6339.25
Maximum7688
Range7170
Interquartile range (IQR)2000.5

Descriptive statistics

Standard deviation1795.973
Coefficient of variation (CV)0.63077093
Kurtosis0.8544482
Mean2847.2667
Median Absolute Deviation (MAD)1120.5
Skewness1.0021048
Sum85418
Variance3225519.2
MonotonicityNot monotonic
2023-12-10T23:58:47.053269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2728 1
 
3.3%
5078 1
 
3.3%
2036 1
 
3.3%
2280 1
 
3.3%
1002 1
 
3.3%
3280 1
 
3.3%
5803 1
 
3.3%
2029 1
 
3.3%
6778 1
 
3.3%
1752 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
518 1
3.3%
549 1
3.3%
662 1
3.3%
726 1
3.3%
1002 1
3.3%
1126 1
3.3%
1603 1
3.3%
1752 1
3.3%
1823 1
3.3%
2021 1
3.3%
ValueCountFrequency (%)
7688 1
3.3%
6778 1
3.3%
5803 1
3.3%
5078 1
3.3%
4439 1
3.3%
4061 1
3.3%
3975 1
3.3%
3799 1
3.3%
3684 1
3.3%
3371 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8035261 × 109
Minimum3.6350494 × 108
Maximum7.4786045 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:47.297547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.6350494 × 108
5-th percentile7.6345438 × 108
Q12.0785597 × 109
median3.820845 × 109
Q35.2567036 × 109
95-th percentile7.0070494 × 109
Maximum7.4786045 × 109
Range7.1150996 × 109
Interquartile range (IQR)3.1781439 × 109

Descriptive statistics

Standard deviation2.0320796 × 109
Coefficient of variation (CV)0.53426205
Kurtosis-1.0000722
Mean3.8035261 × 109
Median Absolute Deviation (MAD)1.6099484 × 109
Skewness0.031977513
Sum1.1410578 × 1011
Variance4.1293476 × 1018
MonotonicityNot monotonic
2023-12-10T23:58:47.539097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2071095111.0267 1
 
3.3%
4982085478.1411 1
 
3.3%
2958616704.0691 1
 
3.3%
5279186612.0 1
 
3.3%
7478604496.0486 1
 
3.3%
1113718087.0 1
 
3.3%
5307939340.0 1
 
3.3%
6635769828.2157 1
 
3.3%
4595270901.0 1
 
3.3%
5424145501.0812 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
363504941.0 1
3.3%
476874991.0 1
3.3%
1113718087.0 1
3.3%
1340979256.0 1
3.3%
1391979271.0 1
3.3%
1723069066.0 1
3.3%
1993491117.0 1
3.3%
2071095111.0267 1
3.3%
2100953354.0 1
3.3%
2204248608.0 1
3.3%
ValueCountFrequency (%)
7478604496.0486 1
3.3%
7296519008.0 1
3.3%
6653253233.0 1
3.3%
6635769828.2157 1
3.3%
5549950594.0 1
3.3%
5424145501.0812 1
3.3%
5307939340.0 1
3.3%
5279186612.0 1
3.3%
5189254494.0 1
3.3%
5158190777.2143 1
3.3%

Interactions

2023-12-10T23:58:37.725935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.055578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:15.312115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:17.432150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:19.697662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:23.270305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:25.982012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:28.053771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:29.980838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:33.187415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:35.161345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:37.902764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.228156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:15.487840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:17.661810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:19.920563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:23.650476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:26.165385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:28.223361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:30.119113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:33.374405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:35.326646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:38.158765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.411180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:15.668302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:17.897855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:20.072271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:24.074600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:26.360833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:28.409507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:30.292018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:33.544638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:35.901336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:38.422864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.633129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:15.864265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:18.105057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:20.268880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:24.408892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:26.553649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:28.585219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:30.536973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:33.746426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:36.166894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:38.711149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.787261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:16.023647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:18.306990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:20.431805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:24.581954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:26.724604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:28.753923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:31.121588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:33.925174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:36.373785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:38.892063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.960674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:16.212886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:18.495636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:20.650917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:24.794657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:26.884035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:28.898550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:31.496660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:34.083572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:36.555389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.082727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:14.118175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:16.384909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:18.677259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:20.885576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:24.982967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:27.065979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:29.057706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:31.896731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:34.265268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:36.759546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.265456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:14.277926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:16.576724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:18.943059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:21.067761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:25.168025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:27.303514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:29.231030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:32.143685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:34.448975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:36.962056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.483641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:14.481155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:16.841326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:19.134292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:21.286790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:25.375308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:27.514456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:29.439257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:32.428538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:34.650413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:37.176192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.668327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:14.730292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:16.991836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:19.311285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:21.674424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:25.577462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:27.700462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:29.623067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:32.747923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:34.795167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:37.362006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.891703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:15.094625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:17.234036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:19.528721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:22.700876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:25.793479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:27.874503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:29.817161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:32.960298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:34.966055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:37.534483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:47.733185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(BASE_YYMM)상권코드(TRDAR_NO)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
기준년월(BASE_YYMM)1.0000.9290.0000.2370.0000.0000.6010.4670.5970.1680.5950.000
상권코드(TRDAR_NO)0.9291.0000.9260.8850.0000.9140.9280.0000.6580.0000.7810.709
총수신평잔_건수(DEP_TOT_AVJN_N)0.0000.9261.0000.4290.5650.6680.5700.5400.3200.0000.0000.670
총수신평잔_총액(DEP_TOT_AVJN_TOT)0.2370.8850.4291.0000.0000.0000.4490.0000.0000.5590.0000.027
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)0.0000.0000.5650.0001.0000.3190.0000.6260.0000.6030.0000.000
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)0.0000.9140.6680.0000.3191.0000.3950.7920.0000.0000.0000.000
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)0.6010.9280.5700.4490.0000.3951.0000.0000.2800.0000.0000.459
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.4670.0000.5400.0000.6260.7920.0001.0000.0000.3040.6610.000
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)0.5970.6580.3200.0000.0000.0000.2800.0001.0000.0000.0000.342
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)0.1680.0000.0000.5590.6030.0000.0000.3040.0001.0000.5470.342
신용카드_체크카드_건수(C_CARDSUM_AMT_N)0.5950.7810.0000.0000.0000.0000.0000.6610.0000.5471.0000.000
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)0.0000.7090.6700.0270.0000.0000.4590.0000.3420.3420.0001.000
2023-12-10T23:58:48.017725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(BASE_YYMM)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
기준년월(BASE_YYMM)1.0000.0330.095-0.067-0.1220.1190.017-0.015-0.0860.193-0.034
총수신평잔_건수(DEP_TOT_AVJN_N)0.0331.0000.0310.225-0.088-0.0860.118-0.156-0.1240.091-0.094
총수신평잔_총액(DEP_TOT_AVJN_TOT)0.0950.0311.0000.001-0.2600.178-0.0470.048-0.0980.0080.006
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)-0.0670.2250.0011.0000.178-0.162-0.019-0.011-0.038-0.1730.316
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)-0.122-0.088-0.2600.1781.000-0.2250.067-0.1120.0860.1090.237
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)0.119-0.0860.178-0.162-0.2251.000-0.0510.363-0.4380.0050.352
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.0170.118-0.047-0.0190.067-0.0511.0000.024-0.338-0.2040.070
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)-0.015-0.1560.048-0.011-0.1120.3630.0241.000-0.148-0.0740.018
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)-0.086-0.124-0.098-0.0380.086-0.438-0.338-0.1481.0000.129-0.255
신용카드_체크카드_건수(C_CARDSUM_AMT_N)0.1930.0910.008-0.1730.1090.005-0.204-0.0740.1291.000-0.022
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)-0.034-0.0940.0060.3160.2370.3520.0700.018-0.255-0.0221.000

Missing values

2023-12-10T23:58:40.162107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:40.586863image/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(BASE_YYMM)상권코드(TRDAR_NO)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
02019071*0*1*8376923073729186.036590162439.00791185297418100.043967215547950.170227282071095111.0267
12019031*0*5*1885021591718683.06434307801591.0726765804040.033865890354125.96125842100953354.0
22019081*0*0*7325276715267975.0838935261057048356.021363522155683.05289144351596730.0136518363504941.0
32019051*0*1*4898756546298145.16870111693239508155.09631810135327.035999719109145.9309017261391979271.0
42019121*0*1*7820666960452940.022352350315803.06703266980875.024766689866244.044391723069066.0
52019061*0*1*7808023257160971.016991785804307.9697115835949239.031388261725874.016032959211034.0
62019121*0*9*5484227739864263.4784016965749952973.024903418466158.062633491554674.024127296519008.0
72019061*0*2*21198612778261617.65080122118761614256.014651514156867.057856179679145.040615158190777.2143
82019101*0*3*7918749399682460.0839972488161.013671543356113.061988165121710.037993809637919.099
92019061*0*6*1738117038924767.04224023567287.0154833114626977.027076471832543.155311262204248608.0
기준년월(BASE_YYMM)상권코드(TRDAR_NO)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
202019111*0*4*81087130916435640.01844363696871.016712281232441.017943833492251.05495549950594.0
212019121*0*7*6486231983397053.07955784319146.03391425810284.0569810206783005.04939751993491117.0
222019051*0*4*3612039327884578.08881748694886.08242433829148.97829505755251332.020517525424145501.0812
232019081*0*3*7635223430305592.36899074851646552.05023815291551.96429474361250957.067784595270901.0
242019071*0*3*31526865766581882.014453942432228.011433317587708.044174298583660.101820296635769828.2157
252019011*0*8*71047264434948910.04395131368182177.045713722998821369.059065065293250.97658035307939340.0
262019071*0*1*8751512036649238.02901386412936.046913882601558618.049922935366949.049732801113718087.0
272019041*0*1*0452050175097864.1255049625076948363.043429452615783051.039845237656545.803910027478604496.0486
282019061*0*7*1262953765596892.012471927887725.0215227669588.051633527725331.036822805279186612.0
292019071*0*5*21290939298895366.1199048486740149280.02384448665753.023014890765091.020362958616704.0691