Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells1
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory108.7 B

Variable types

Categorical7
Numeric5

Dataset

Description부산광역시해운대구_지방세미환급금현황_20201231
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15078943

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
미환급유형 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액 and 3 other fieldsHigh correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 4 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 당해미환급건수 and 3 other fieldsHigh correlation
누적미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액증감 is highly overall correlated with 당해미환급금액High correlation
납세자유형 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액증감 has 1 (4.3%) missing valuesMissing
당해미환급금액 has unique valuesUnique
누적미환급금액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:39:28.263763
Analysis finished2023-12-10 16:39:32.193733
Duration3.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
부산광역시
23 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 23
100.0%

Length

2023-12-11T01:39:32.279027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:32.430776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 23
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
해운대구
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 23
100.0%

Length

2023-12-11T01:39:32.576945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:32.699374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 23
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
26350
23 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 23
100.0%

Length

2023-12-11T01:39:32.828898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:32.942875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 23
100.0%

세목명
Categorical

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
자동차세
8 
지방소득세
8 
등록면허세
4 
재산세
3 

Length

Max length5
Median length5
Mean length4.3913043
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자동차세
2nd row자동차세
3rd row지방소득세
4th row지방소득세
5th row자동차세

Common Values

ValueCountFrequency (%)
자동차세 8
34.8%
지방소득세 8
34.8%
등록면허세 4
17.4%
재산세 3
 
13.0%

Length

2023-12-11T01:39:33.081335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:33.197388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 8
34.8%
지방소득세 8
34.8%
등록면허세 4
17.4%
재산세 3
 
13.0%

과세년도
Categorical

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
2020
7 
2018
6 
2019
6 
2017
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020 7
30.4%
2018 6
26.1%
2019 6
26.1%
2017 4
17.4%

Length

2023-12-11T01:39:33.334137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:33.513938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 7
30.4%
2018 6
26.1%
2019 6
26.1%
2017 4
17.4%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
신규
23 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 23
100.0%

Length

2023-12-11T01:39:33.679332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:33.812709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 23
100.0%

납세자유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
개인
12 
법인
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 12
52.2%
법인 11
47.8%

Length

2023-12-11T01:39:33.948283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:39:34.070414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 12
52.2%
법인 11
47.8%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.652174
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T01:39:34.201605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q370.5
95-th percentile176.7
Maximum185
Range184
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation61.499896
Coefficient of variation (CV)1.3773102
Kurtosis0.55619249
Mean44.652174
Median Absolute Deviation (MAD)8
Skewness1.3746679
Sum1027
Variance3782.2372
MonotonicityNot monotonic
2023-12-11T01:39:34.391428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 4
17.4%
7 2
 
8.7%
78 1
 
4.3%
180 1
 
4.3%
9 1
 
4.3%
147 1
 
4.3%
12 1
 
4.3%
185 1
 
4.3%
3 1
 
4.3%
2 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
1 4
17.4%
2 1
 
4.3%
3 1
 
4.3%
5 1
 
4.3%
6 1
 
4.3%
7 2
8.7%
8 1
 
4.3%
9 1
 
4.3%
12 1
 
4.3%
13 1
 
4.3%
ValueCountFrequency (%)
185 1
4.3%
180 1
4.3%
147 1
4.3%
142 1
4.3%
78 1
4.3%
71 1
4.3%
70 1
4.3%
61 1
4.3%
17 1
4.3%
13 1
4.3%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean925240.87
Minimum160
Maximum6043710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T01:39:34.592140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile3732
Q140230
median224550
Q31145295
95-th percentile2777652
Maximum6043710
Range6043550
Interquartile range (IQR)1105065

Descriptive statistics

Standard deviation1429041.9
Coefficient of variation (CV)1.5445079
Kurtosis6.8141482
Mean925240.87
Median Absolute Deviation (MAD)220440
Skewness2.407036
Sum21280540
Variance2.0421606 × 1012
MonotonicityNot monotonic
2023-12-11T01:39:34.784839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
613030 1
 
4.3%
132380 1
 
4.3%
39150 1
 
4.3%
2476980 1
 
4.3%
13220 1
 
4.3%
216370 1
 
4.3%
2147520 1
 
4.3%
224550 1
 
4.3%
7580 1
 
4.3%
391560 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
160 1
4.3%
3690 1
4.3%
4110 1
4.3%
7580 1
4.3%
13220 1
4.3%
39150 1
4.3%
41310 1
4.3%
80260 1
4.3%
132380 1
4.3%
206890 1
4.3%
ValueCountFrequency (%)
6043710 1
4.3%
2811060 1
4.3%
2476980 1
4.3%
2276000 1
4.3%
2147520 1
4.3%
1283740 1
4.3%
1006850 1
4.3%
863150 1
4.3%
613030 1
4.3%
397270 1
4.3%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.26087
Minimum1
Maximum361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T01:39:34.932479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median31
Q3148.5
95-th percentile359.7
Maximum361
Range360
Interquartile range (IQR)140.5

Descriptive statistics

Standard deviation130.53325
Coefficient of variation (CV)1.3284357
Kurtosis0.18268619
Mean98.26087
Median Absolute Deviation (MAD)28
Skewness1.3083106
Sum2260
Variance17038.929
MonotonicityNot monotonic
2023-12-11T01:39:35.437043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 3
 
13.0%
8 2
 
8.7%
117 1
 
4.3%
40 1
 
4.3%
30 1
 
4.3%
361 1
 
4.3%
31 1
 
4.3%
357 1
 
4.3%
4 1
 
4.3%
33 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
1 1
 
4.3%
3 3
13.0%
4 1
 
4.3%
8 2
8.7%
13 1
 
4.3%
20 1
 
4.3%
26 1
 
4.3%
30 1
 
4.3%
31 1
 
4.3%
33 1
 
4.3%
ValueCountFrequency (%)
361 1
4.3%
360 1
4.3%
357 1
4.3%
330 1
4.3%
188 1
4.3%
180 1
4.3%
117 1
4.3%
110 1
4.3%
40 1
4.3%
34 1
4.3%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1687910
Minimum7740
Maximum6105490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T01:39:35.602611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7740
5-th percentile27401
Q1233675
median422660
Q32649350
95-th percentile6068046
Maximum6105490
Range6097750
Interquartile range (IQR)2415675

Descriptive statistics

Standard deviation2157060.8
Coefficient of variation (CV)1.2779478
Kurtosis-0.087292879
Mean1687910
Median Absolute Deviation (MAD)391560
Skewness1.2079954
Sum38821930
Variance4.6529114 × 1012
MonotonicityNot monotonic
2023-12-11T01:39:35.769581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1033970 1
 
4.3%
247240 1
 
4.3%
435300 1
 
4.3%
5759940 1
 
4.3%
220110 1
 
4.3%
413410 1
 
4.3%
4665570 1
 
4.3%
621820 1
 
4.3%
7740 1
 
4.3%
422660 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
7740 1
4.3%
26990 1
4.3%
31100 1
4.3%
49080 1
4.3%
216710 1
4.3%
220110 1
4.3%
247240 1
4.3%
327500 1
4.3%
368810 1
4.3%
397270 1
4.3%
ValueCountFrequency (%)
6105490 1
4.3%
6102280 1
4.3%
5759940 1
4.3%
4665570 1
4.3%
4173120 1
4.3%
3291220 1
4.3%
2007480 1
4.3%
1897120 1
4.3%
1033970 1
4.3%
621820 1
4.3%

누적미환급금액증감
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1672.9932
Minimum1.02
Maximum30575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T01:39:35.947026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile2.242
Q184.205
median118.52
Q3550.5925
95-th percentile1537.325
Maximum30575
Range30573.98
Interquartile range (IQR)466.3875

Descriptive statistics

Standard deviation6467.6252
Coefficient of variation (CV)3.8659005
Kurtosis21.80815
Mean1672.9932
Median Absolute Deviation (MAD)84.49
Skewness4.6615778
Sum36805.85
Variance41830176
MonotonicityNot monotonic
2023-12-11T01:39:36.122920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
68.67 1
 
4.3%
792.79 1
 
4.3%
1011.88 1
 
4.3%
132.54 1
 
4.3%
1564.98 1
 
4.3%
91.07 1
 
4.3%
117.25 1
 
4.3%
176.92 1
 
4.3%
2.11 1
 
4.3%
7.94 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
1.02 1
4.3%
2.11 1
4.3%
4.75 1
4.3%
7.94 1
4.3%
68.67 1
4.3%
83.35 1
4.3%
86.77 1
4.3%
91.07 1
4.3%
99.38 1
4.3%
117.08 1
4.3%
ValueCountFrequency (%)
30575.0 1
4.3%
1564.98 1
4.3%
1011.88 1
4.3%
792.79 1
4.3%
656.69 1
4.3%
631.44 1
4.3%
308.05 1
4.3%
176.92 1
4.3%
156.38 1
4.3%
132.54 1
4.3%

Interactions

2023-12-11T01:39:31.192173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:28.671934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.373347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.074311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.574424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:31.314526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:28.780611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.611631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.180488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.689237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:31.422248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:28.884941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.758098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.287772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.829387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:31.550992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.016628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.872322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.381759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.952818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:31.672000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.208312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:29.974224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:30.470999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:39:31.065650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:39:36.272491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.3510.0000.0000.629
과세년도0.0001.0000.0000.0470.0000.5020.0000.259
납세자유형0.0000.0001.0000.5090.8670.7990.3570.000
당해미환급건수0.0000.0470.5091.0000.7500.7970.8480.000
당해미환급금액0.3510.0000.8670.7501.0000.9580.909NaN
누적미환급건수0.0000.5020.7990.7970.9581.0000.7880.000
누적미환급금액0.0000.0000.3570.8480.9090.7881.0000.000
누적미환급금액증감0.6290.2590.0000.000NaN0.0000.0001.000
2023-12-11T01:39:36.449032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도납세자유형세목명
과세년도1.0000.0000.000
납세자유형0.0001.0000.000
세목명0.0000.0001.000
2023-12-11T01:39:36.561611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도납세자유형
당해미환급건수1.0000.6080.9340.617-0.2490.0000.0000.567
당해미환급금액0.6081.0000.5750.939-0.5160.2420.0000.537
누적미환급건수0.9340.5751.0000.630-0.0370.0000.3120.537
누적미환급금액0.6170.9390.6301.000-0.2740.0000.0000.208
누적미환급금액증감-0.249-0.516-0.037-0.2741.0000.4080.1410.000
세목명0.0000.2420.0000.0000.4081.0000.0000.000
과세년도0.0000.0000.3120.0000.1410.0001.0000.000
납세자유형0.5670.5370.5370.2080.0000.0000.0001.000

Missing values

2023-12-11T01:39:31.843087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:39:32.084846image/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

시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
0부산광역시해운대구26350자동차세2017신규개인78613030117103397068.67
1부산광역시해운대구26350자동차세2017신규법인171323802624724086.77
2부산광역시해운대구26350지방소득세2017신규개인611006850110200748099.38
3부산광역시해운대구26350지방소득세2017신규법인536901326990631.44
4부산광역시해운대구26350자동차세2018신규개인718631501881897120119.79
5부산광역시해운대구26350자동차세2018신규법인88026034327500308.05
6부산광역시해운대구26350재산세2018신규개인720689082167104.75
7부산광역시해운대구26350재산세2018신규법인16043710361054901.02
8부산광역시해운대구26350지방소득세2018신규개인7012837401803291220156.38
9부산광역시해운대구26350지방소득세2018신규법인741102031100656.69
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
13부산광역시해운대구26350자동차세2019신규법인64131040368810792.79
14부산광역시해운대구26350지방소득세2019신규개인18028110603606102280117.08
15부산광역시해운대구26350지방소득세2019신규법인13391560334226607.94
16부산광역시해운대구26350등록면허세2020신규개인27580377402.11
17부산광역시해운대구26350등록면허세2020신규법인32245504621820176.92
18부산광역시해운대구26350자동차세2020신규개인18521475203574665570117.25
19부산광역시해운대구26350자동차세2020신규법인122163703141341091.07
20부산광역시해운대구26350재산세2020신규개인11322082201101564.98
21부산광역시해운대구26350지방소득세2020신규개인14724769803615759940132.54
22부산광역시해운대구26350지방소득세2020신규법인939150304353001011.88