Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory75.3 B

Variable types

Text2
Categorical5
Numeric2

Alerts

fond_de is highly overall correlated with item_nmHigh correlation
ctprvn_nm is highly overall correlated with afltion_group_nm and 1 other fieldsHigh correlation
item_nm is highly overall correlated with fond_de and 2 other fieldsHigh correlation
afltion_group_nm is highly overall correlated with ctprvn_nm and 2 other fieldsHigh correlation
item_cl_nm is highly overall correlated with ctprvn_nm and 2 other fieldsHigh correlation
afltion_group_nm is highly imbalanced (80.6%)Imbalance
item_cl_nm is highly imbalanced (89.8%)Imbalance

Reproduction

Analysis started2023-12-10 10:02:15.275408
Analysis finished2023-12-10 10:02:18.000762
Duration2.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

club_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:02:18.433596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length3.07
Min length1

Characters and Unicode

Total characters307
Distinct characters176
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row보디빌딩피지크
2nd row팀사랑해(海)
3rd row(사)예산스포츠클럽
4th row굿
5th row둠
ValueCountFrequency (%)
팀 6
 
4.7%
클럽 5
 
3.9%
라온 2
 
1.6%
배구 2
 
1.6%
한울 2
 
1.6%
대전 2
 
1.6%
가온 2
 
1.6%
요트 2
 
1.6%
은호 1
 
0.8%
한강 1
 
0.8%
Other values (104) 104
80.6%
2023-12-10T19:02:19.196362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
 
9.4%
팀 8
 
2.6%
스 7
 
2.3%
클 7
 
2.3%
럽 7
 
2.3%
수 5
 
1.6%
구 5
 
1.6%
사 5
 
1.6%
( 4
 
1.3%
키 4
 
1.3%
Other values (166) 226
73.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
87.9%
Space Separator 29
 
9.4%
Open Punctuation 4
 
1.3%
Close Punctuation 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
팀 8
 
3.0%
스 7
 
2.6%
클 7
 
2.6%
럽 7
 
2.6%
수 5
 
1.9%
구 5
 
1.9%
사 5
 
1.9%
키 4
 
1.5%
온 4
 
1.5%
한 4
 
1.5%
Other values (163) 214
79.3%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 266
86.6%
Common 37
 
12.1%
Han 4
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
팀 8
 
3.0%
스 7
 
2.6%
클 7
 
2.6%
럽 7
 
2.6%
수 5
 
1.9%
구 5
 
1.9%
사 5
 
1.9%
키 4
 
1.5%
온 4
 
1.5%
한 4
 
1.5%
Other values (159) 210
78.9%
Han
ValueCountFrequency (%)
秀 1
25.0%
雲 1
25.0%
白 1
25.0%
海 1
25.0%
Common
ValueCountFrequency (%)
29
78.4%
( 4
 
10.8%
) 4
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 266
86.6%
ASCII 37
 
12.1%
CJK 4
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29
78.4%
( 4
 
10.8%
) 4
 
10.8%
Hangul
ValueCountFrequency (%)
팀 8
 
3.0%
스 7
 
2.6%
클 7
 
2.6%
럽 7
 
2.6%
수 5
 
1.9%
구 5
 
1.9%
사 5
 
1.9%
키 4
 
1.5%
온 4
 
1.5%
한 4
 
1.5%
Other values (159) 210
78.9%
CJK
ValueCountFrequency (%)
秀 1
25.0%
雲 1
25.0%
白 1
25.0%
海 1
25.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
17 
대구광역시
15 
제주특별자치도
14 
서울특별시
12 
전라북도
7 
Other values (12)
35 

Length

Max length7
Median length5
Mean length4.73
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row경상북도
2nd row경기도
3rd row충청남도
4th row전라북도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 17
17.0%
대구광역시 15
15.0%
제주특별자치도 14
14.0%
서울특별시 12
12.0%
전라북도 7
7.0%
전라남도 5
 
5.0%
광주광역시 4
 
4.0%
경상남도 4
 
4.0%
충청북도 4
 
4.0%
경상북도 3
 
3.0%
Other values (7) 15
15.0%

Length

2023-12-10T19:02:19.675606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 17
17.0%
대구광역시 15
15.0%
제주특별자치도 14
14.0%
서울특별시 12
12.0%
전라북도 7
7.0%
전라남도 5
 
5.0%
경상남도 4
 
4.0%
충청북도 4
 
4.0%
광주광역시 4
 
4.0%
경상북도 3
 
3.0%
Other values (7) 15
15.0%

signgu_nm
Text

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:02:20.138327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.82
Min length7

Characters and Unicode

Total characters882
Distinct characters79
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)42.0%

Sample

1st row경상북도 구미시
2nd row경기도 시전체
3rd row충청남도 예산군
4th row전라북도 전주시
5th row경기도 과천시
ValueCountFrequency (%)
경기도 17
 
8.3%
대구광역시 15
 
7.3%
제주특별자치도 14
 
6.8%
제주시 12
 
5.8%
서울특별시 12
 
5.8%
전체 9
 
4.4%
전라북도 7
 
3.4%
서구 5
 
2.4%
전라남도 5
 
2.4%
달서구 4
 
1.9%
Other values (64) 106
51.5%
2023-12-10T19:02:20.981517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106
 
12.0%
시 96
 
10.9%
도 57
 
6.5%
구 56
 
6.3%
주 37
 
4.2%
광 34
 
3.9%
특 29
 
3.3%
별 29
 
3.3%
역 28
 
3.2%
제 27
 
3.1%
Other values (69) 383
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 776
88.0%
Space Separator 106
 
12.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 96
 
12.4%
도 57
 
7.3%
구 56
 
7.2%
주 37
 
4.8%
광 34
 
4.4%
특 29
 
3.7%
별 29
 
3.7%
역 28
 
3.6%
제 27
 
3.5%
전 27
 
3.5%
Other values (68) 356
45.9%
Space Separator
ValueCountFrequency (%)
106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 776
88.0%
Common 106
 
12.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
12.4%
도 57
 
7.3%
구 56
 
7.2%
주 37
 
4.8%
광 34
 
4.4%
특 29
 
3.7%
별 29
 
3.7%
역 28
 
3.6%
제 27
 
3.5%
전 27
 
3.5%
Other values (68) 356
45.9%
Common
ValueCountFrequency (%)
106
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 776
88.0%
ASCII 106
 
12.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
106
100.0%
Hangul
ValueCountFrequency (%)
시 96
 
12.4%
도 57
 
7.3%
구 56
 
7.2%
주 37
 
4.8%
광 34
 
4.4%
특 29
 
3.7%
별 29
 
3.7%
역 28
 
3.6%
제 27
 
3.5%
전 27
 
3.5%
Other values (68) 356
45.9%

item_nm
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
농구
38 
볼링
16 
배구
11 
요트
7 
보디빌딩
4 
Other values (15)
24 

Length

Max length4
Median length2
Mean length2.23
Min length2

Unique

Unique11 ?
Unique (%)11.0%

Sample

1st row보디빌딩
2nd row요트
3rd row조정
4th row농구
5th row농구

Common Values

ValueCountFrequency (%)
농구 38
38.0%
볼링 16
16.0%
배구 11
 
11.0%
요트 7
 
7.0%
보디빌딩 4
 
4.0%
스쿼시 4
 
4.0%
테니스 4
 
4.0%
탁구 3
 
3.0%
게이트볼 2
 
2.0%
수영 1
 
1.0%
Other values (10) 10
 
10.0%

Length

2023-12-10T19:02:21.454539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
농구 38
38.0%
볼링 16
16.0%
배구 11
 
11.0%
요트 7
 
7.0%
보디빌딩 4
 
4.0%
스쿼시 4
 
4.0%
테니스 4
 
4.0%
탁구 3
 
3.0%
게이트볼 2
 
2.0%
하키 1
 
1.0%
Other values (10) 10
 
10.0%

afltion_group_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
체육동호인조직
97 
공공스포츠클럽
 
3

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row체육동호인조직
2nd row체육동호인조직
3rd row공공스포츠클럽
4th row체육동호인조직
5th row체육동호인조직

Common Values

ValueCountFrequency (%)
체육동호인조직 97
97.0%
공공스포츠클럽 3
 
3.0%

Length

2023-12-10T19:02:21.804124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:22.149716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육동호인조직 97
97.0%
공공스포츠클럽 3
 
3.0%

item_cl_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
13세이하부,16세이하부,19세이하부,20세이상부
98 
19세이하부,20세이상부
 
1
20세이상부
 
1

Length

Max length27
Median length27
Mean length26.65
Min length6

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row13세이하부,16세이하부,19세이하부,20세이상부
2nd row13세이하부,16세이하부,19세이하부,20세이상부
3rd row19세이하부,20세이상부
4th row13세이하부,16세이하부,19세이하부,20세이상부
5th row13세이하부,16세이하부,19세이하부,20세이상부

Common Values

ValueCountFrequency (%)
13세이하부,16세이하부,19세이하부,20세이상부 98
98.0%
19세이하부,20세이상부 1
 
1.0%
20세이상부 1
 
1.0%

Length

2023-12-10T19:02:22.332303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:22.495035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13세이하부,16세이하부,19세이하부,20세이상부 98
98.0%
19세이하부,20세이상부 1
 
1.0%
20세이상부 1
 
1.0%

sexdstn_flag_nm
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
혼성
55 
남성
43 
여성
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남성
2nd row혼성
3rd row남성
4th row남성
5th row혼성

Common Values

ValueCountFrequency (%)
혼성 55
55.0%
남성 43
43.0%
여성 2
 
2.0%

Length

2023-12-10T19:02:22.734620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:22.969315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
혼성 55
55.0%
남성 43
43.0%
여성 2
 
2.0%

mber_co
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:02:23.177027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median8.5
Q317.25
95-th percentile26.05
Maximum43
Range42
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.1842806
Coefficient of variation (CV)0.90485523
Kurtosis0.56832215
Mean10.15
Median Absolute Deviation (MAD)7.5
Skewness0.9535549
Sum1015
Variance84.35101
MonotonicityNot monotonic
2023-12-10T19:02:23.395258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 25
25.0%
5 7
 
7.0%
2 7
 
7.0%
19 6
 
6.0%
9 5
 
5.0%
14 5
 
5.0%
20 5
 
5.0%
8 4
 
4.0%
11 4
 
4.0%
3 3
 
3.0%
Other values (18) 29
29.0%
ValueCountFrequency (%)
1 25
25.0%
2 7
 
7.0%
3 3
 
3.0%
4 3
 
3.0%
5 7
 
7.0%
6 1
 
1.0%
8 4
 
4.0%
9 5
 
5.0%
10 3
 
3.0%
11 4
 
4.0%
ValueCountFrequency (%)
43 1
 
1.0%
34 1
 
1.0%
30 1
 
1.0%
29 1
 
1.0%
27 1
 
1.0%
26 2
 
2.0%
24 2
 
2.0%
23 1
 
1.0%
21 2
 
2.0%
20 5
5.0%

fond_de
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20204425
Minimum20190410
Maximum20211128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:02:23.666369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190410
5-th percentile20190610
Q120200108
median20201210
Q320210703
95-th percentile20211104
Maximum20211128
Range20718
Interquartile range (IQR)10595.25

Descriptive statistics

Standard deviation6575.0284
Coefficient of variation (CV)0.00032542516
Kurtosis-0.75961582
Mean20204425
Median Absolute Deviation (MAD)9202.5
Skewness-0.50752097
Sum2.0204425 × 109
Variance43230998
MonotonicityNot monotonic
2023-12-10T19:02:23.921749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211022 6
 
6.0%
20200101 5
 
5.0%
20200102 4
 
4.0%
20210616 3
 
3.0%
20200108 3
 
3.0%
20200103 3
 
3.0%
20211024 2
 
2.0%
20210412 2
 
2.0%
20201214 2
 
2.0%
20211025 2
 
2.0%
Other values (65) 68
68.0%
ValueCountFrequency (%)
20190410 2
 
2.0%
20190503 1
 
1.0%
20190509 1
 
1.0%
20190607 1
 
1.0%
20190610 1
 
1.0%
20191024 1
 
1.0%
20191224 1
 
1.0%
20191226 1
 
1.0%
20200101 5
5.0%
20200102 4
4.0%
ValueCountFrequency (%)
20211128 1
1.0%
20211124 1
1.0%
20211120 1
1.0%
20211118 1
1.0%
20211105 1
1.0%
20211104 1
1.0%
20211029 1
1.0%
20211028 1
1.0%
20211025 2
2.0%
20211024 2
2.0%

Interactions

2023-12-10T19:02:16.857170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:16.522408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:16.991701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:16.712792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:02:24.114642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
club_nmctprvn_nmsigngu_nmitem_nmafltion_group_nmitem_cl_nmsexdstn_flag_nmmber_cofond_de
club_nm1.0000.9720.9960.9991.0001.0001.0000.9020.977
ctprvn_nm0.9721.0001.0000.7680.8900.8040.4550.2370.542
signgu_nm0.9961.0001.0000.9730.8501.0000.6710.3690.890
item_nm0.9990.7680.9731.0000.7740.8950.4490.0000.869
afltion_group_nm1.0000.8900.8500.7741.0000.5410.2270.0000.195
item_cl_nm1.0000.8041.0000.8950.5411.0000.8190.0000.218
sexdstn_flag_nm1.0000.4550.6710.4490.2270.8191.0000.0000.293
mber_co0.9020.2370.3690.0000.0000.0000.0001.0000.307
fond_de0.9770.5420.8900.8690.1950.2180.2930.3071.000
2023-12-10T19:02:24.343777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_nmafltion_group_nmitem_cl_nmsexdstn_flag_nmitem_nm
ctprvn_nm1.0000.7850.5880.2530.342
afltion_group_nm0.7851.0000.8040.3690.570
item_cl_nm0.5880.8041.0000.4890.694
sexdstn_flag_nm0.2530.3690.4891.0000.238
item_nm0.3420.5700.6940.2381.000
2023-12-10T19:02:24.535928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mber_cofond_dectprvn_nmitem_nmafltion_group_nmitem_cl_nmsexdstn_flag_nm
mber_co1.000-0.1930.0820.0000.0000.0000.000
fond_de-0.1931.0000.3470.5190.0790.1640.258
ctprvn_nm0.0820.3471.0000.3420.7850.5880.253
item_nm0.0000.5190.3421.0000.5700.6940.238
afltion_group_nm0.0000.0790.7850.5701.0000.8040.369
item_cl_nm0.0000.1640.5880.6940.8041.0000.489
sexdstn_flag_nm0.0000.2580.2530.2380.3690.4891.000

Missing values

2023-12-10T19:02:17.673369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:02:17.907623image/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

club_nmctprvn_nmsigngu_nmitem_nmafltion_group_nmitem_cl_nmsexdstn_flag_nmmber_cofond_de
0보디빌딩피지크경상북도경상북도 구미시보디빌딩체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성120211105
1팀사랑해(海)경기도경기도 시전체요트체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성1120210412
2(사)예산스포츠클럽충청남도충청남도 예산군조정공공스포츠클럽19세이하부,20세이상부남성1920210708
3굿전라북도전라북도 전주시농구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성2120200106
4둠경기도경기도 과천시농구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성2920200109
5맥대구광역시대구광역시 중구농구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성520200101
6수제주특별자치도제주특별자치도 서귀포시볼링체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성1020211022
7백운 (白雲)부산광역시부산광역시 사상구배구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성120210702
8흙광주광역시광주광역시 전체농구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성220201228
9샵 수사랑경기도경기도 수원시 권선구산악체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성120210311
club_nmctprvn_nmsigngu_nmitem_nmafltion_group_nmitem_cl_nmsexdstn_flag_nmmber_cofond_de
90안인 유닛경기도경기도 광명시배구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성820211028
91여수 한울전라남도전라남도 여수시농구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성1820210616
92용인 사격경기도경기도 용인시 처인구사격체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성120210625
93청주 샛별충청북도충청북도 청주시테니스체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성220201214
94클럽 몽키경상남도경상남도 창원시 마산합포구역도체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성120210420
95김제 하키 클럽전라북도전라북도 김제시하키체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성120210707
96오창 배구 클럽충청북도충청북도 청주시 청원구배구체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성1720200309
97은호 펜싱 클럽서울특별시서울특별시 서초구펜싱체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성1920201021
98제주 요트 클럽제주특별자치도제주특별자치도 서귀포시요트체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부혼성120200625
99대전 서구 요트 동호회대전광역시대전광역시 서구요트체육동호인조직13세이하부,16세이하부,19세이하부,20세이상부남성120210125