Overview

Dataset statistics

Number of variables11
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory104.3 B

Variable types

Text1
Numeric10

Dataset

Description공무원연금기금 사업수익, 대부수익, 사업외수익, 사업외비용 등에 대한 데이터입니다. 2013년부터 시작되며 연 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15054064/fileData.do

Alerts

2013 is highly overall correlated with 2014 and 8 other fieldsHigh correlation
2014 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2015 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2016 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2017 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2018 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2019 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2020 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2021 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
2022 is highly overall correlated with 2013 and 8 other fieldsHigh correlation
구분 has unique valuesUnique
2013 has 3 (14.3%) zerosZeros
2014 has 4 (19.0%) zerosZeros
2015 has 3 (14.3%) zerosZeros
2017 has 4 (19.0%) zerosZeros
2018 has 2 (9.5%) zerosZeros
2019 has 4 (19.0%) zerosZeros
2020 has 1 (4.8%) zerosZeros
2021 has 1 (4.8%) zerosZeros
2022 has 1 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-12 22:02:00.794631
Analysis finished2023-12-12 22:02:11.020391
Duration10.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T07:02:11.149820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length9.047619
Min length6

Characters and Unicode

Total characters190
Distinct characters51
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st rowI. 사업수익
2nd row1. 금융자산운용수익
3rd row가. 보통예금이자
4th row나. 대여학자금이자
5th row2. 대부수익
ValueCountFrequency (%)
6
 
12.2%
가 4
 
8.2%
1 3
 
6.1%
2 3
 
6.1%
i 2
 
4.1%
나 2
 
4.1%
익 2
 
4.1%
ii 2
 
4.1%
복지및부동산사업비 1
 
2.0%
대손상각비 1
 
2.0%
Other values (23) 23
46.9%
2023-12-13T07:02:11.485237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
18.9%
. 18
 
9.5%
비 11
 
5.8%
I 9
 
4.7%
용 7
 
3.7%
수 7
 
3.7%
자 7
 
3.7%
익 6
 
3.2%
금 6
 
3.2%
사 5
 
2.6%
Other values (41) 78
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
60.5%
Space Separator 36
 
18.9%
Other Punctuation 18
 
9.5%
Uppercase Letter 9
 
4.7%
Decimal Number 6
 
3.2%
Close Punctuation 3
 
1.6%
Open Punctuation 3
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 11
 
9.6%
용 7
 
6.1%
수 7
 
6.1%
자 7
 
6.1%
익 6
 
5.2%
금 6
 
5.2%
사 5
 
4.3%
업 5
 
4.3%
대 4
 
3.5%
가 4
 
3.5%
Other values (34) 53
46.1%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 9
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
60.5%
Common 66
34.7%
Latin 9
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 11
 
9.6%
용 7
 
6.1%
수 7
 
6.1%
자 7
 
6.1%
익 6
 
5.2%
금 6
 
5.2%
사 5
 
4.3%
업 5
 
4.3%
대 4
 
3.5%
가 4
 
3.5%
Other values (34) 53
46.1%
Common
ValueCountFrequency (%)
36
54.5%
. 18
27.3%
] 3
 
4.5%
[ 3
 
4.5%
1 3
 
4.5%
2 3
 
4.5%
Latin
ValueCountFrequency (%)
I 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
60.5%
ASCII 75
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
48.0%
. 18
24.0%
I 9
 
12.0%
] 3
 
4.0%
[ 3
 
4.0%
1 3
 
4.0%
2 3
 
4.0%
Hangul
ValueCountFrequency (%)
비 11
 
9.6%
용 7
 
6.1%
수 7
 
6.1%
자 7
 
6.1%
익 6
 
5.2%
금 6
 
5.2%
사 5
 
4.3%
업 5
 
4.3%
대 4
 
3.5%
가 4
 
3.5%
Other values (34) 53
46.1%

2013
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59589314
Minimum0
Maximum1.7153324 × 108
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:11.635351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q166967
median887989
Q31.3069709 × 108
95-th percentile1.7151395 × 108
Maximum1.7153324 × 108
Range1.7153324 × 108
Interquartile range (IQR)1.3063012 × 108

Descriptive statistics

Standard deviation75039549
Coefficient of variation (CV)1.2592786
Kurtosis-1.6564389
Mean59589314
Median Absolute Deviation (MAD)887989
Skewness0.58624007
Sum1.2513756 × 109
Variance5.6309339 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:11.809891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3
14.3%
887989 2
 
9.5%
130697089 2
 
9.5%
171513952 1
 
4.8%
740976 1
 
4.8%
39920413 1
 
4.8%
131612826 1
 
4.8%
174761 1
 
4.8%
57654 1
 
4.8%
683322 1
 
4.8%
Other values (7) 7
33.3%
ValueCountFrequency (%)
0 3
14.3%
19287 1
 
4.8%
57654 1
 
4.8%
66967 1
 
4.8%
75428 1
 
4.8%
174761 1
 
4.8%
683322 1
 
4.8%
740976 1
 
4.8%
887989 2
9.5%
39920413 1
 
4.8%
ValueCountFrequency (%)
171533239 1
4.8%
171513952 1
4.8%
170625963 1
4.8%
170550535 1
4.8%
131612826 1
4.8%
130697089 2
9.5%
130630122 1
4.8%
39920413 1
4.8%
887989 2
9.5%
740976 1
4.8%

2014
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52962383
Minimum0
Maximum1.5684604 × 108
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:11.955429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133667
median807389
Q31.0883611 × 108
95-th percentile1.5683133 × 108
Maximum1.5684604 × 108
Range1.5684604 × 108
Interquartile range (IQR)1.0880244 × 108

Descriptive statistics

Standard deviation66605519
Coefficient of variation (CV)1.2576005
Kurtosis-1.482826
Mean52962383
Median Absolute Deviation (MAD)807389
Skewness0.63179804
Sum1.11221 × 109
Variance4.4362952 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:12.110925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4
19.0%
807389 2
 
9.5%
108836112 2
 
9.5%
108730815 1
 
4.8%
47259459 1
 
4.8%
109586580 1
 
4.8%
64155 1
 
4.8%
686313 1
 
4.8%
750468 1
 
4.8%
156831329 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 4
19.0%
14710 1
 
4.8%
33667 1
 
4.8%
64155 1
 
4.8%
105297 1
 
4.8%
686313 1
 
4.8%
750468 1
 
4.8%
807389 2
9.5%
47259459 1
 
4.8%
108730815 1
 
4.8%
ValueCountFrequency (%)
156846039 1
4.8%
156831329 1
4.8%
156023940 1
4.8%
155990273 1
4.8%
109586580 1
4.8%
108836112 2
9.5%
108730815 1
4.8%
47259459 1
4.8%
807389 2
9.5%
750468 1
4.8%

2015
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41879510
Minimum0
Maximum1.4100648 × 108
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:12.239214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q169559
median744374
Q358469145
95-th percentile1.409925 × 108
Maximum1.4100648 × 108
Range1.4100648 × 108
Interquartile range (IQR)58399586

Descriptive statistics

Standard deviation55883078
Coefficient of variation (CV)1.3343776
Kurtosis-0.60658007
Mean41879510
Median Absolute Deviation (MAD)744374
Skewness0.97115339
Sum8.794697 × 108
Variance3.1229184 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:12.351770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3
14.3%
744374 2
 
9.5%
57698685 2
 
9.5%
140992498 1
 
4.8%
598756 1
 
4.8%
82537334 1
 
4.8%
58469145 1
 
4.8%
171704 1
 
4.8%
63547 1
 
4.8%
535209 1
 
4.8%
Other values (7) 7
33.3%
ValueCountFrequency (%)
0 3
14.3%
13981 1
 
4.8%
63547 1
 
4.8%
69559 1
 
4.8%
81664 1
 
4.8%
171704 1
 
4.8%
535209 1
 
4.8%
598756 1
 
4.8%
744374 2
9.5%
57629126 1
 
4.8%
ValueCountFrequency (%)
141006479 1
4.8%
140992498 1
4.8%
140248124 1
4.8%
140166460 1
4.8%
82537334 1
4.8%
58469145 1
4.8%
57698685 2
9.5%
57629126 1
4.8%
744374 2
9.5%
598756 1
4.8%

2016
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41341018
Minimum170
Maximum1.4172286 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:12.459126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170
5-th percentile170
Q163729
median1438097
Q353841741
95-th percentile1.4166829 × 108
Maximum1.4172286 × 108
Range1.4172269 × 108
Interquartile range (IQR)53778012

Descriptive statistics

Standard deviation55868182
Coefficient of variation (CV)1.3513983
Kurtosis-0.51258302
Mean41341018
Median Absolute Deviation (MAD)1437927
Skewness1.0269385
Sum8.6816138 × 108
Variance3.1212538 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:12.597943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1438097 2
 
9.5%
170 2
 
9.5%
141668286 1
 
4.8%
52285338 1
 
4.8%
87881122 1
 
4.8%
53841741 1
 
4.8%
406392 1
 
4.8%
61605 1
 
4.8%
994619 1
 
4.8%
1056224 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
170 2
9.5%
887 1
4.8%
54577 1
4.8%
61605 1
4.8%
63729 1
4.8%
92730 1
4.8%
406392 1
4.8%
994619 1
4.8%
1056224 1
4.8%
1438097 2
9.5%
ValueCountFrequency (%)
141722863 1
4.8%
141668286 1
4.8%
140230189 1
4.8%
140166460 1
4.8%
87881122 1
4.8%
53841741 1
4.8%
52379125 1
4.8%
52378955 1
4.8%
52285338 1
4.8%
1438097 2
9.5%

2017
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38207575
Minimum0
Maximum1.2177918 × 108
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:12.746215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160441
median856956
Q363936325
95-th percentile1.2176423 × 108
Maximum1.2177918 × 108
Range1.2177918 × 108
Interquartile range (IQR)63875884

Descriptive statistics

Standard deviation48950574
Coefficient of variation (CV)1.2811746
Kurtosis-0.9480924
Mean38207575
Median Absolute Deviation (MAD)856956
Skewness0.81005689
Sum8.0235907 × 108
Variance2.3961587 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:12.868929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4
19.0%
856956 2
 
9.5%
63936325 2
 
9.5%
63846014 1
 
4.8%
57000818 1
 
4.8%
64778367 1
 
4.8%
67502 1
 
4.8%
774540 1
 
4.8%
842042 1
 
4.8%
121764230 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 4
19.0%
14955 1
 
4.8%
60441 1
 
4.8%
67502 1
 
4.8%
90311 1
 
4.8%
774540 1
 
4.8%
842042 1
 
4.8%
856956 2
9.5%
57000818 1
 
4.8%
63846014 1
 
4.8%
ValueCountFrequency (%)
121779185 1
4.8%
121764230 1
4.8%
120907274 1
4.8%
120846833 1
4.8%
64778367 1
4.8%
63936325 2
9.5%
63846014 1
4.8%
57000818 1
4.8%
856956 2
9.5%
842042 1
4.8%

2018
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35462386
Minimum0
Maximum1.0511904 × 108
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:13.002076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q163045
median652281
Q372599684
95-th percentile1.0509112 × 108
Maximum1.0511904 × 108
Range1.0511904 × 108
Interquartile range (IQR)72536639

Descriptive statistics

Standard deviation44535798
Coefficient of variation (CV)1.2558601
Kurtosis-1.4755178
Mean35462386
Median Absolute Deviation (MAD)652281
Skewness0.63409608
Sum7.4471011 × 108
Variance1.9834373 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:13.123145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2
 
9.5%
652281 2
 
9.5%
19 2
 
9.5%
79053 1
 
4.8%
31833504 1
 
4.8%
73285534 1
 
4.8%
63045 1
 
4.8%
622805 1
 
4.8%
685850 1
 
4.8%
72520612 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
0 2
9.5%
19 2
9.5%
27915 1
4.8%
63045 1
4.8%
79053 1
4.8%
84726 1
4.8%
622805 1
4.8%
652281 2
9.5%
685850 1
4.8%
31833504 1
4.8%
ValueCountFrequency (%)
105119038 1
4.8%
105091123 1
4.8%
104438842 1
4.8%
104354116 1
4.8%
73285534 1
4.8%
72599684 1
4.8%
72599665 1
4.8%
72520612 1
4.8%
31833504 1
4.8%
685850 1
4.8%

2019
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27777365
Minimum0
Maximum91772429
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:13.248899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180220
median736094
Q341750392
95-th percentile91764263
Maximum91772429
Range91772429
Interquartile range (IQR)41670172

Descriptive statistics

Standard deviation36377844
Coefficient of variation (CV)1.3096218
Kurtosis-0.70589782
Mean27777365
Median Absolute Deviation (MAD)736094
Skewness0.91768598
Sum5.8332467 × 108
Variance1.3233476 × 1015
MonotonicityNot monotonic
2023-12-13T07:02:13.413978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4
19.0%
736094 2
 
9.5%
40978074 2
 
9.5%
40883561 1
 
4.8%
50022037 1
 
4.8%
41750392 1
 
4.8%
80220 1
 
4.8%
692098 1
 
4.8%
772318 1
 
4.8%
91764263 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 4
19.0%
8166 1
 
4.8%
80220 1
 
4.8%
94513 1
 
4.8%
122571 1
 
4.8%
692098 1
 
4.8%
736094 2
9.5%
772318 1
 
4.8%
40883561 1
 
4.8%
40978074 2
9.5%
ValueCountFrequency (%)
91772429 1
4.8%
91764263 1
4.8%
91028169 1
4.8%
90905598 1
4.8%
50022037 1
4.8%
41750392 1
4.8%
40978074 2
9.5%
40883561 1
4.8%
772318 1
4.8%
736094 2
9.5%

2020
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23389171
Minimum0
Maximum77925222
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:13.540613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q144512
median543691
Q334037007
95-th percentile77913732
Maximum77925222
Range77925222
Interquartile range (IQR)33992495

Descriptive statistics

Standard deviation30873048
Coefficient of variation (CV)1.3199719
Kurtosis-0.65976762
Mean23389171
Median Absolute Deviation (MAD)543685
Skewness0.94163643
Sum4.9117259 × 108
Variance9.5314507 × 1014
MonotonicityNot monotonic
2023-12-13T07:02:13.672856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
543691 2
 
9.5%
6 2
 
9.5%
77913732 1
 
4.8%
33414751 1
 
4.8%
43888215 1
 
4.8%
34037007 1
 
4.8%
0 1
 
4.8%
56511 1
 
4.8%
485047 1
 
4.8%
541558 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
0 1
4.8%
6 2
9.5%
11490 1
4.8%
36180 1
4.8%
44512 1
4.8%
56511 1
4.8%
67075 1
4.8%
485047 1
4.8%
541558 1
4.8%
543691 2
9.5%
ValueCountFrequency (%)
77925222 1
4.8%
77913732 1
4.8%
77370041 1
4.8%
77302966 1
4.8%
43888215 1
4.8%
34037007 1
4.8%
33495449 1
4.8%
33495443 1
4.8%
33414751 1
4.8%
543691 2
9.5%

2021
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20932925
Minimum0
Maximum65015433
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:13.823313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q150218
median501938
Q337848258
95-th percentile64979846
Maximum65015433
Range65015433
Interquartile range (IQR)37798040

Descriptive statistics

Standard deviation26483045
Coefficient of variation (CV)1.2651383
Kurtosis-1.137408
Mean20932925
Median Absolute Deviation (MAD)501881
Skewness0.73964706
Sum4.3959143 × 108
Variance7.0135169 × 1014
MonotonicityNot monotonic
2023-12-13T07:02:13.967559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
501938 2
 
9.5%
57 2
 
9.5%
64979846 1
 
4.8%
37759674 1
 
4.8%
26646842 1
 
4.8%
38368591 1
 
4.8%
0 1
 
4.8%
50218 1
 
4.8%
470115 1
 
4.8%
520333 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
0 1
4.8%
57 2
9.5%
30224 1
4.8%
35587 1
4.8%
50218 1
4.8%
58303 1
4.8%
71393 1
4.8%
470115 1
4.8%
501938 2
9.5%
520333 1
4.8%
ValueCountFrequency (%)
65015433 1
4.8%
64979846 1
4.8%
64477908 1
4.8%
64406515 1
4.8%
38368591 1
4.8%
37848258 1
4.8%
37848201 1
4.8%
37759674 1
4.8%
26646842 1
4.8%
520333 1
4.8%

2022
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20977279
Minimum0
Maximum55378047
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T07:02:14.112378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q141508
median436833
Q354269430
95-th percentile55352910
Maximum55378047
Range55378047
Interquartile range (IQR)54227922

Descriptive statistics

Standard deviation27145064
Coefficient of variation (CV)1.2940222
Kurtosis-1.911886
Mean20977279
Median Absolute Deviation (MAD)436824
Skewness0.52904221
Sum4.4052286 × 108
Variance7.3685449 × 1014
MonotonicityNot monotonic
2023-12-13T07:02:14.217704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
436833 2
 
9.5%
9 2
 
9.5%
55352910 1
 
4.8%
54194295 1
 
4.8%
671313 1
 
4.8%
54706734 1
 
4.8%
0 1
 
4.8%
41508 1
 
4.8%
395796 1
 
4.8%
437304 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
0 1
4.8%
9 2
9.5%
25137 1
4.8%
31002 1
4.8%
41508 1
4.8%
44124 1
4.8%
50468 1
4.8%
395796 1
4.8%
436833 2
9.5%
437304 1
4.8%
ValueCountFrequency (%)
55378047 1
4.8%
55352910 1
4.8%
54916077 1
4.8%
54865609 1
4.8%
54706734 1
4.8%
54269430 1
4.8%
54269421 1
4.8%
54194295 1
4.8%
671313 1
4.8%
437304 1
4.8%

Interactions

2023-12-13T07:02:09.723197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.091973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.985402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.040253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.952324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.902598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.825557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.704674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.466796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.688646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.819464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.173636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.072772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.126863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.065658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.014282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.940995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.788550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.549392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.794103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.919763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.269264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.156646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.210628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.160150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.103781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.030191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.869998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.639493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.919436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.028084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.354508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.236569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.329028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.254069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.190811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.111864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.943625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.725363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.054788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.140875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.452917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.320718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.411027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.344678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.284910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.202523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.018500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.116569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.149275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.276860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.554108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.629924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.489656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.438778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.380408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.283561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.106783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.220135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.243479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.379259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.640665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.705842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.570246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.517726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.469768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.361352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.183024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.323934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.330210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.475423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.728538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.773845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.646030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.609611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.554101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.433702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.245781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.418242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.424062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.584214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.820544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.858006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.736519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.709444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.652620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.518339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.321744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.507785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.523073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:10.683700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:01.912723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:02.942587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:03.846133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:04.800207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:05.740767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:06.610399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:07.396694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:08.603750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:02:09.627673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:02:14.312395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2013201420152016201720182019202020212022
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20131.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20141.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20151.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20161.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20171.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20181.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20191.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20201.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20211.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20221.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T07:02:14.833694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013201420152016201720182019202020212022
20131.0000.9730.9870.9840.9730.9630.9600.9450.9580.956
20140.9731.0000.9600.9621.0000.9890.9790.9750.9890.986
20150.9870.9601.0000.9970.9600.9500.9730.9580.9450.943
20160.9840.9620.9971.0000.9620.9450.9710.9580.9450.944
20170.9731.0000.9600.9621.0000.9890.9790.9750.9890.986
20180.9630.9890.9500.9450.9891.0000.9840.9720.9940.990
20190.9600.9790.9730.9710.9790.9841.0000.9880.9840.980
20200.9450.9750.9580.9580.9750.9720.9881.0000.9800.982
20210.9580.9890.9450.9450.9890.9940.9840.9801.0000.999
20220.9560.9860.9430.9440.9860.9900.9800.9820.9991.000

Missing values

2023-12-13T07:02:10.810079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:02:10.963254image/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

구분2013201420152016201720182019202020212022
0I. 사업수익17151395215683132914099249814166828612176423010509112391764263779137326497984655352910
11. 금융자산운용수익17062596315602394014024812414023018912090727410443884291028169773700416447790854916077
2가. 보통예금이자754283366781664637296044184726122571670757139350468
3나. 대여학자금이자17055053515599027314016646014016646012084683310435411690905598773029666440651554865609
42. 대부수익8879898073897443741438097856956652281736094543691501938436833
5가. 대여학자금운영부담금8879898073897443741438097856956652281736094543691501938436833
6II. 사업외수익1928714710139815457714955279158166114903558725137
7[ 수 익 ]17153323915684603914100647914172286312177918510511903891772429779252226501543355378047
8I. 사업비용1306970891088361125769868552379125639363257259968440978074334954493784825854269430
91. 금융부문비용1306970891088361125769868552378955639363257259966540978074334954433784820154269421
구분2013201420152016201720182019202020212022
11나. 자산운용비669671052976955992730903117905394513445125830344124
12다. 융자보조비용1306301221087308155762912652285338638460147252061240883561334147513775967454194295
132. 복지및부동산사업비00017001906579
14가. 대손상각비00017001906579
15II. 관리운영비7409767504685987561056224842042685850772318541558520333437304
161. 인건비683322686313535209994619774540622805692098485047470115395796
172. 일반경비57654641556354761605675026304580220565115021841508
18III. 사업외비용1747610171704406392000000
19[ 비 용 ]1316128261095865805846914553841741647783677328553441750392340370073836859154706734
20[ 순 이 익 ]399204134725945982537334878811225700081831833504500220374388821526646842671313