Overview

Dataset statistics

Number of variables5
Number of observations338
Missing cells1127
Missing cells (%)66.7%
Duplicate rows11
Duplicate rows (%)3.3%
Total size in memory14.7 KiB
Average record size in memory44.4 B

Variable types

Text1
Numeric4

Dataset

Description한국장학재단의 재무상태표에 대한 데이터로 특정시점(12월31일)의 재무상태(자산, 부채, 자본)에 대한 정보 제공(당기: 데이터 기준 연도, 전기: 데이터 기준 전년도)
URLhttps://www.data.go.kr/data/15100137/fileData.do

Alerts

Dataset has 11 (3.3%) duplicate rowsDuplicates
제 14 (당)기_1 is highly overall correlated with 제 13 (전)기_1High correlation
제 14 (당)기_2 is highly overall correlated with 제 13 (전)기_2High correlation
제 13 (전)기_1 is highly overall correlated with 제 14 (당)기_1High correlation
제 13 (전)기_2 is highly overall correlated with 제 14 (당)기_2High correlation
제 14 (당)기_1 has 253 (74.9%) missing valuesMissing
제 14 (당)기_2 has 310 (91.7%) missing valuesMissing
제 13 (전)기_1 has 253 (74.9%) missing valuesMissing
제 13 (전)기_2 has 311 (92.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:33:17.249922
Analysis finished2023-12-12 04:33:19.492968
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과목
Text

Distinct151
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-12T13:33:19.697598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length8.1035503
Min length2

Characters and Unicode

Total characters2739
Distinct characters136
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)40.5%

Sample

1st row자산
2nd rowI.유동자산
3rd row(1)현금및현금성자산
4th row현금
5th row(정부보조금)
ValueCountFrequency (%)
정부보조금 47
 
12.9%
수탁사업자금 44
 
12.1%
현재가치할인차금 27
 
7.4%
대손충당금 21
 
5.8%
손상차손누계액 18
 
4.9%
공사부담금 13
 
3.6%
감가상각누계액 13
 
3.6%
현재가치할증차금 7
 
1.9%
및 4
 
1.1%
상각누계액 3
 
0.8%
Other values (155) 167
45.9%
2023-12-12T13:33:20.120042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
금 221
 
8.1%
( 214
 
7.8%
) 214
 
7.8%
부 95
 
3.5%
기 86
 
3.1%
자 85
 
3.1%
사 68
 
2.5%
정 67
 
2.4%
손 66
 
2.4%
수 64
 
2.3%
Other values (126) 1559
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2224
81.2%
Open Punctuation 214
 
7.8%
Close Punctuation 214
 
7.8%
Space Separator 27
 
1.0%
Decimal Number 26
 
0.9%
Uppercase Letter 15
 
0.5%
Other Punctuation 12
 
0.4%
Dash Punctuation 7
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 221
 
9.9%
부 95
 
4.3%
기 86
 
3.9%
자 85
 
3.8%
사 68
 
3.1%
정 67
 
3.0%
손 66
 
3.0%
수 64
 
2.9%
차 58
 
2.6%
채 57
 
2.6%
Other values (110) 1357
61.0%
Decimal Number
ValueCountFrequency (%)
1 5
19.2%
5 5
19.2%
4 4
15.4%
3 4
15.4%
2 4
15.4%
6 2
 
7.7%
7 1
 
3.8%
8 1
 
3.8%
Space Separator
ValueCountFrequency (%)
26
96.3%
　 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 8
66.7%
, 4
33.3%
Open Punctuation
ValueCountFrequency (%)
( 214
100.0%
Close Punctuation
ValueCountFrequency (%)
) 214
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2224
81.2%
Common 500
 
18.3%
Latin 15
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 221
 
9.9%
부 95
 
4.3%
기 86
 
3.9%
자 85
 
3.8%
사 68
 
3.1%
정 67
 
3.0%
손 66
 
3.0%
수 64
 
2.9%
차 58
 
2.6%
채 57
 
2.6%
Other values (110) 1357
61.0%
Common
ValueCountFrequency (%)
( 214
42.8%
) 214
42.8%
26
 
5.2%
. 8
 
1.6%
- 7
 
1.4%
1 5
 
1.0%
5 5
 
1.0%
, 4
 
0.8%
4 4
 
0.8%
3 4
 
0.8%
Other values (5) 9
 
1.8%
Latin
ValueCountFrequency (%)
I 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2224
81.2%
ASCII 514
 
18.8%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
금 221
 
9.9%
부 95
 
4.3%
기 86
 
3.9%
자 85
 
3.8%
사 68
 
3.1%
정 67
 
3.0%
손 66
 
3.0%
수 64
 
2.9%
차 58
 
2.6%
채 57
 
2.6%
Other values (110) 1357
61.0%
ASCII
ValueCountFrequency (%)
( 214
41.6%
) 214
41.6%
26
 
5.1%
I 15
 
2.9%
. 8
 
1.6%
- 7
 
1.4%
1 5
 
1.0%
5 5
 
1.0%
, 4
 
0.8%
4 4
 
0.8%
Other values (5) 12
 
2.3%
None
ValueCountFrequency (%)
　 1
100.0%

제 14 (당)기_1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)98.8%
Missing253
Missing (%)74.9%
Infinite0
Infinite (%)0.0%
Mean2.5751525 × 1011
Minimum-6.8091699 × 1011
Maximum1.0413369 × 1013
Zeros0
Zeros (%)0.0%
Negative40
Negative (%)11.8%
Memory size3.1 KiB
2023-12-12T13:33:20.276836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.8091699 × 1011
5-th percentile-9.7386261 × 1010
Q1-4.4976925 × 109
median2.5328814 × 108
Q32.3782286 × 1010
95-th percentile6.1387038 × 1011
Maximum1.0413369 × 1013
Range1.1094286 × 1013
Interquartile range (IQR)2.8279978 × 1010

Descriptive statistics

Standard deviation1.4483137 × 1012
Coefficient of variation (CV)5.6241861
Kurtosis39.856881
Mean2.5751525 × 1011
Median Absolute Deviation (MAD)5.7905346 × 109
Skewness6.2755424
Sum2.1888796 × 1013
Variance2.0976125 × 1024
MonotonicityNot monotonic
2023-12-12T13:33:20.414524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1146875578 2
 
0.6%
3236038998 1
 
0.3%
-290554600 1
 
0.3%
31028050999 1
 
0.3%
-70343732357 1
 
0.3%
-950724070 1
 
0.3%
-5240734805 1
 
0.3%
76535323899 1
 
0.3%
-943103211 1
 
0.3%
278355030 1
 
0.3%
Other values (74) 74
 
21.9%
(Missing) 253
74.9%
ValueCountFrequency (%)
-680916993108 1
0.3%
-365185936372 1
0.3%
-170745687180 1
0.3%
-147510824020 1
0.3%
-99029292342 1
0.3%
-90814134869 1
0.3%
-79029544378 1
0.3%
-70343732357 1
0.3%
-55427805306 1
0.3%
-43683067103 1
0.3%
ValueCountFrequency (%)
10413369479382 1
0.3%
8280533246147 1
0.3%
1540000000000 1
0.3%
798659974555 1
0.3%
622489872846 1
0.3%
579392415306 1
0.3%
486001077918 1
0.3%
330308555103 1
0.3%
170745687180 1
0.3%
76535323899 1
0.3%

제 14 (당)기_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)96.4%
Missing310
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean2.7036405 × 1012
Minimum2.5328814 × 108
Maximum1.0949573 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T13:33:20.541762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5328814 × 108
5-th percentile7.6019393 × 108
Q11.1352166 × 1010
median2.9853154 × 1011
Q33.3275512 × 1012
95-th percentile1.0652965 × 1013
Maximum1.0949573 × 1013
Range1.094932 × 1013
Interquartile range (IQR)3.316199 × 1012

Descriptive statistics

Standard deviation4.2025332 × 1012
Coefficient of variation (CV)1.5543979
Kurtosis-0.37419664
Mean2.7036405 × 1012
Median Absolute Deviation (MAD)2.9816462 × 1011
Skewness1.232636
Sum7.5701935 × 1013
Variance1.7661285 × 1025
MonotonicityNot monotonic
2023-12-12T13:33:20.666234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10949573287911 2
 
0.6%
1541089736352 1
 
0.3%
945077857911 1
 
0.3%
36635477256 1
 
0.3%
895741380655 1
 
0.3%
12701000000 1
 
0.3%
10004495430000 1
 
0.3%
253288141 1
 
0.3%
8349341523214 1
 
0.3%
1279547260 1
 
0.3%
Other values (17) 17
 
5.0%
(Missing) 310
91.7%
ValueCountFrequency (%)
253288141 1
0.3%
480542144 1
0.3%
1279547260 1
0.3%
3044764050 1
0.3%
6472638911 1
0.3%
6676303314 1
0.3%
7305664139 1
0.3%
12701000000 1
0.3%
16239702834 1
0.3%
21900598851 1
0.3%
ValueCountFrequency (%)
10949573287911 2
0.6%
10102120067894 1
0.3%
10004495430000 1
0.3%
9589544801178 1
0.3%
8350874358615 1
0.3%
8349341523214 1
0.3%
1653621071385 1
0.3%
1541089736352 1
0.3%
945077857911 1
0.3%
895741380655 1
0.3%

제 13 (전)기_1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)98.8%
Missing253
Missing (%)74.9%
Infinite0
Infinite (%)0.0%
Mean2.6411003 × 1011
Minimum-6.8848372 × 1011
Maximum1.0511219 × 1013
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)11.5%
Memory size3.1 KiB
2023-12-12T13:33:20.805700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.8848372 × 1011
5-th percentile-7.2432152 × 1010
Q1-3.5159568 × 109
median3.1064017 × 108
Q31.2701 × 1010
95-th percentile5.9209622 × 1011
Maximum1.0511219 × 1013
Range1.1199702 × 1013
Interquartile range (IQR)1.6216957 × 1010

Descriptive statistics

Standard deviation1.4880496 × 1012
Coefficient of variation (CV)5.6342031
Kurtosis39.442959
Mean2.6411003 × 1011
Median Absolute Deviation (MAD)5.5997437 × 109
Skewness6.2738163
Sum2.2449353 × 1013
Variance2.2142915 × 1024
MonotonicityNot monotonic
2023-12-12T13:33:20.952911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1049829136 2
 
0.6%
-59554600 1
 
0.3%
-7300155395 1
 
0.3%
-606000000 1
 
0.3%
31403050999 1
 
0.3%
-67715494590 1
 
0.3%
-951361676 1
 
0.3%
-5763370446 1
 
0.3%
74430447379 1
 
0.3%
-2005233989 1
 
0.3%
Other values (74) 74
 
21.9%
(Missing) 253
74.9%
ValueCountFrequency (%)
-688483721686 1
0.3%
-174191582682 1
0.3%
-166563127084 1
0.3%
-120989348842 1
0.3%
-73611316786 1
0.3%
-67715494590 1
0.3%
-55136157612 1
0.3%
-44971302048 1
0.3%
-42034374405 1
0.3%
-34721726202 1
0.3%
ValueCountFrequency (%)
10511218631141 1
0.3%
8791828393840 1
0.3%
1460000000000 1
0.3%
746827633488 1
0.3%
611328347997 1
0.3%
515167715088 1
0.3%
351130435630 1
0.3%
219517846247 1
0.3%
218637648082 1
0.3%
84370230428 1
0.3%

제 13 (전)기_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)96.3%
Missing311
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean2.8779153 × 1012
Minimum1.5905351 × 108
Maximum1.1226221 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T13:33:21.079926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5905351 × 108
5-th percentile3.7240337 × 108
Q19.5121373 × 109
median4.6153648 × 1011
Q35.1726545 × 1012
95-th percentile1.0962272 × 1013
Maximum1.1226221 × 1013
Range1.1226062 × 1013
Interquartile range (IQR)5.1631424 × 1012

Descriptive statistics

Standard deviation4.3506876 × 1012
Coefficient of variation (CV)1.5117497
Kurtosis-0.55101934
Mean2.8779153 × 1012
Median Absolute Deviation (MAD)4.6101691 × 1011
Skewness1.169067
Sum7.7703714 × 1013
Variance1.8928483 × 1025
MonotonicityNot monotonic
2023-12-12T13:33:21.222699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11226220591962 2
 
0.6%
6323274612 1
 
0.3%
879829966953 1
 
0.3%
35873836404 1
 
0.3%
831255130549 1
 
0.3%
12701000000 1
 
0.3%
10346390625009 1
 
0.3%
519564874 1
 
0.3%
403006125 1
 
0.3%
8794143559385 1
 
0.3%
Other values (16) 16
 
4.7%
(Missing) 311
92.0%
ValueCountFrequency (%)
159053514 1
0.3%
359287910 1
0.3%
403006125 1
0.3%
519564874 1
0.3%
3088624675 1
0.3%
6276813450 1
0.3%
6323274612 1
0.3%
12701000000 1
0.3%
23437561671 1
0.3%
35873836404 1
0.3%
ValueCountFrequency (%)
11226220591962 2
0.6%
10346390625009 1
0.3%
9870737625341 1
0.3%
9795492165375 1
0.3%
8795225183898 1
0.3%
8794143559385 1
0.3%
1551165441111 1
0.3%
1460373988016 1
0.3%
1355482966621 1
0.3%
879829966953 1
0.3%

Interactions

2023-12-12T13:33:18.708239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:17.450033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:17.874181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.283923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.828156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:17.560639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:17.973540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.390587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.937225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:17.652188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.074353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.492822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:19.022717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:17.773124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.161153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:33:18.603589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:33:21.337740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제 14 (당)기_1제 14 (당)기_2제 13 (전)기_1제 13 (전)기_2
제 14 (당)기_11.000NaN0.912NaN
제 14 (당)기_2NaN1.000NaN0.987
제 13 (전)기_10.912NaN1.000NaN
제 13 (전)기_2NaN0.987NaN1.000
2023-12-12T13:33:21.464451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제 14 (당)기_1제 14 (당)기_2제 13 (전)기_1제 13 (전)기_2
제 14 (당)기_11.000NaN0.969NaN
제 14 (당)기_2NaN1.000NaN0.984
제 13 (전)기_10.969NaN1.000NaN
제 13 (전)기_2NaN0.984NaN1.000

Missing values

2023-12-12T13:33:19.179773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:33:19.299361image/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.
2023-12-12T13:33:19.421784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

과목제 14 (당)기_1제 14 (당)기_2제 13 (전)기_1제 13 (전)기_2
0자산<NA><NA><NA><NA>
1I.유동자산<NA>847453220017<NA>1355482966621
2(1)현금및현금성자산<NA>166489801605<NA>196548620768
3현금<NA><NA><NA><NA>
4(정부보조금)<NA><NA><NA><NA>
5(수탁사업자금)<NA><NA><NA><NA>
6현금성자산622489872846<NA>218637648082<NA>
7(정부보조금)-365185936372<NA>-18404013577<NA>
8(수탁사업자금)-90814134869<NA>-3685013737<NA>
9(2)유동금융자산<NA>661198409384<NA>693949955563
과목제 14 (당)기_1제 14 (당)기_2제 13 (전)기_1제 13 (전)기_2
328기본재산12701000000<NA>12701000000<NA>
329II. 이익잉여금(결손금)<NA>895741380655<NA>831255130549
330기타법정적립금798659974555<NA>746827633488<NA>
331미처분이익잉여금(미처리결손금)32595155994<NA>32859472016<NA>
332(당기순이익)64486250106<NA>51568025045<NA>
333III. 기타자본구성요소<NA>36635477256<NA>35873836404
334기타포괄손익누계액-5537246464<NA>-6298887316<NA>
335기타자본42172723720<NA>42172723720<NA>
336자본총계<NA>945077857911<NA>879829966953
337부채와자본총계<NA>10949573287911<NA>11226220591962

Duplicate rows

Most frequently occurring

과목제 14 (당)기_1제 14 (당)기_2제 13 (전)기_1제 13 (전)기_2# duplicates
5(수탁사업자금)<NA><NA><NA><NA>32
6(정부보조금)<NA><NA><NA><NA>31
7(현재가치할인차금)<NA><NA><NA><NA>25
4(손상차손누계액)<NA><NA><NA><NA>17
2(대손충당금)<NA><NA><NA><NA>16
1(공사부담금)<NA><NA><NA><NA>13
0(감가상각누계액)<NA><NA><NA><NA>8
10현재가치할증차금<NA><NA><NA><NA>7
3(사채할인발행차금)<NA><NA><NA><NA>2
8사채할증발행차금<NA><NA><NA><NA>2