Overview

Dataset statistics

Number of variables11
Number of observations65
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory99.0 B

Variable types

Text1
Categorical3
Numeric7

Dataset

Description중소벤처기업진흥공단에서 관리하는 상생협력형 내일채움공제의 연도별 참여기관 및 선정 누적 인원수입니다.*상생협력형 내일채움공제 : 대외기관이 가입자의 납입금을 지원
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15073629/fileData.do

Alerts

2016년 is highly overall correlated with 2017년 and 1 other fieldsHigh correlation
2015년 is highly overall correlated with 2016년High correlation
2017년 is highly overall correlated with 2016년High correlation
2018년 is highly overall correlated with 2019년High correlation
2019년 is highly overall correlated with 2018년High correlation
2015년 is highly imbalanced (88.5%)Imbalance
2016년 is highly imbalanced (80.3%)Imbalance
참여기관명 has unique valuesUnique
2017년 has 57 (87.7%) zerosZeros
2018년 has 49 (75.4%) zerosZeros
2019년 has 45 (69.2%) zerosZeros
2020년 has 37 (56.9%) zerosZeros
2021년 has 31 (47.7%) zerosZeros
2022년 has 37 (56.9%) zerosZeros
2023년 has 34 (52.3%) zerosZeros

Reproduction

Analysis started2024-04-21 01:41:47.566020
Analysis finished2024-04-21 01:41:54.675126
Duration7.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

참여기관명
Text

UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size652.0 B
2024-04-21T10:41:54.857103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length5.7384615
Min length3

Characters and Unicode

Total characters373
Distinct characters117
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

Unique65 ?
Unique (%)100.0%

Sample

1st row강원도청
2nd row대전광역시청
3rd row울산광역시청
4th row충청북도청
5th row계룡시청
ValueCountFrequency (%)
강원도청 1
 
1.5%
한국가스공사 1
 
1.5%
cj제일제당 1
 
1.5%
한국토지주택공사 1
 
1.5%
한국동서발전 1
 
1.5%
한국수자원공사 1
 
1.5%
한국중부발전 1
 
1.5%
한국수력원자력 1
 
1.5%
한국남부발전 1
 
1.5%
인천항만공사 1
 
1.5%
Other values (55) 55
84.6%
2024-04-21T10:41:55.207061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 33
 
8.8%
시 26
 
7.0%
국 20
 
5.4%
한 19
 
5.1%
공 19
 
5.1%
사 13
 
3.5%
전 10
 
2.7%
주 9
 
2.4%
천 9
 
2.4%
산 8
 
2.1%
Other values (107) 207
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
98.1%
Uppercase Letter 4
 
1.1%
Other Symbol 2
 
0.5%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 33
 
9.0%
시 26
 
7.1%
국 20
 
5.5%
한 19
 
5.2%
공 19
 
5.2%
사 13
 
3.6%
전 10
 
2.7%
주 9
 
2.5%
천 9
 
2.5%
산 8
 
2.2%
Other values (101) 200
54.6%
Uppercase Letter
ValueCountFrequency (%)
C 1
25.0%
J 1
25.0%
K 1
25.0%
T 1
25.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 368
98.7%
Latin 4
 
1.1%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 33
 
9.0%
시 26
 
7.1%
국 20
 
5.4%
한 19
 
5.2%
공 19
 
5.2%
사 13
 
3.5%
전 10
 
2.7%
주 9
 
2.4%
천 9
 
2.4%
산 8
 
2.2%
Other values (102) 202
54.9%
Latin
ValueCountFrequency (%)
C 1
25.0%
J 1
25.0%
K 1
25.0%
T 1
25.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
98.1%
ASCII 5
 
1.3%
None 2
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 33
 
9.0%
시 26
 
7.1%
국 20
 
5.5%
한 19
 
5.2%
공 19
 
5.2%
사 13
 
3.6%
전 10
 
2.7%
주 9
 
2.5%
천 9
 
2.5%
산 8
 
2.2%
Other values (101) 200
54.6%
None
ValueCountFrequency (%)
㈜ 2
100.0%
ASCII
ValueCountFrequency (%)
C 1
20.0%
J 1
20.0%
1
20.0%
K 1
20.0%
T 1
20.0%

기관유형
Categorical

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size652.0 B
지방자치단체
31 
공공기관
24 
민간기업
9 
특수법인
 
1

Length

Max length6
Median length4
Mean length4.9538462
Min length4

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
지방자치단체 31
47.7%
공공기관 24
36.9%
민간기업 9
 
13.8%
특수법인 1
 
1.5%

Length

2024-04-21T10:41:55.344596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:41:55.463276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방자치단체 31
47.7%
공공기관 24
36.9%
민간기업 9
 
13.8%
특수법인 1
 
1.5%

2015년
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
64 
28
 
1

Length

Max length2
Median length1
Mean length1.0153846
Min length1

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64
98.5%
28 1
 
1.5%

Length

2024-04-21T10:41:55.568477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:41:55.664351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 64
98.5%
28 1
 
1.5%

2016년
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
61 
60
 
1
8
 
1
23
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.0461538
Min length1

Unique

Unique4 ?
Unique (%)6.2%

Sample

1st row60
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61
93.8%
60 1
 
1.5%
8 1
 
1.5%
23 1
 
1.5%
10 1
 
1.5%

Length

2024-04-21T10:41:55.764943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:41:55.862190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 61
93.8%
60 1
 
1.5%
8 1
 
1.5%
23 1
 
1.5%
10 1
 
1.5%

2017년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5846154
Minimum0
Maximum159
Zeros57
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:55.940449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum159
Range159
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.557491
Coefficient of variation (CV)4.920258
Kurtosis38.068312
Mean4.5846154
Median Absolute Deviation (MAD)0
Skewness6.0418122
Sum298
Variance508.84038
MonotonicityNot monotonic
2024-04-21T10:41:56.043996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 57
87.7%
10 2
 
3.1%
159 1
 
1.5%
90 1
 
1.5%
6 1
 
1.5%
17 1
 
1.5%
1 1
 
1.5%
5 1
 
1.5%
ValueCountFrequency (%)
0 57
87.7%
1 1
 
1.5%
5 1
 
1.5%
6 1
 
1.5%
10 2
 
3.1%
17 1
 
1.5%
90 1
 
1.5%
159 1
 
1.5%
ValueCountFrequency (%)
159 1
 
1.5%
90 1
 
1.5%
17 1
 
1.5%
10 2
 
3.1%
6 1
 
1.5%
5 1
 
1.5%
1 1
 
1.5%
0 57
87.7%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.246154
Minimum0
Maximum311
Zeros49
Zeros (%)75.4%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:56.144288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile74.2
Maximum311
Range311
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.403135
Coefficient of variation (CV)3.5031403
Kurtosis28.770504
Mean13.246154
Median Absolute Deviation (MAD)0
Skewness5.0874627
Sum861
Variance2153.251
MonotonicityNot monotonic
2024-04-21T10:41:56.251814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 49
75.4%
13 2
 
3.1%
11 1
 
1.5%
7 1
 
1.5%
9 1
 
1.5%
18 1
 
1.5%
6 1
 
1.5%
29 1
 
1.5%
8 1
 
1.5%
99 1
 
1.5%
Other values (6) 6
 
9.2%
ValueCountFrequency (%)
0 49
75.4%
5 1
 
1.5%
6 1
 
1.5%
7 1
 
1.5%
8 1
 
1.5%
9 1
 
1.5%
11 1
 
1.5%
13 2
 
3.1%
17 1
 
1.5%
18 1
 
1.5%
ValueCountFrequency (%)
311 1
1.5%
175 1
1.5%
99 1
1.5%
77 1
1.5%
63 1
1.5%
29 1
1.5%
18 1
1.5%
17 1
1.5%
13 2
3.1%
11 1
1.5%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.015385
Minimum0
Maximum276
Zeros45
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:56.344631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile47.8
Maximum276
Range276
Interquartile range (IQR)5

Descriptive statistics

Standard deviation39.226702
Coefficient of variation (CV)3.5610833
Kurtosis34.504323
Mean11.015385
Median Absolute Deviation (MAD)0
Skewness5.5744372
Sum716
Variance1538.7341
MonotonicityNot monotonic
2024-04-21T10:41:56.433160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 45
69.2%
10 2
 
3.1%
12 2
 
3.1%
2 2
 
3.1%
5 2
 
3.1%
14 2
 
3.1%
27 1
 
1.5%
22 1
 
1.5%
81 1
 
1.5%
276 1
 
1.5%
Other values (6) 6
 
9.2%
ValueCountFrequency (%)
0 45
69.2%
1 1
 
1.5%
2 2
 
3.1%
5 2
 
3.1%
7 1
 
1.5%
10 2
 
3.1%
12 2
 
3.1%
13 1
 
1.5%
14 2
 
3.1%
15 1
 
1.5%
ValueCountFrequency (%)
276 1
1.5%
135 1
1.5%
81 1
1.5%
53 1
1.5%
27 1
1.5%
22 1
1.5%
15 1
1.5%
14 2
3.1%
13 1
1.5%
12 2
3.1%

2020년
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.830769
Minimum0
Maximum277
Zeros37
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:56.527864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile63.2
Maximum277
Range277
Interquartile range (IQR)8

Descriptive statistics

Standard deviation43.418231
Coefficient of variation (CV)2.9275778
Kurtosis24.147931
Mean14.830769
Median Absolute Deviation (MAD)0
Skewness4.684391
Sum964
Variance1885.1428
MonotonicityNot monotonic
2024-04-21T10:41:56.631883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 37
56.9%
8 3
 
4.6%
3 2
 
3.1%
1 2
 
3.1%
6 2
 
3.1%
12 1
 
1.5%
2 1
 
1.5%
10 1
 
1.5%
9 1
 
1.5%
5 1
 
1.5%
Other values (14) 14
 
21.5%
ValueCountFrequency (%)
0 37
56.9%
1 2
 
3.1%
2 1
 
1.5%
3 2
 
3.1%
5 1
 
1.5%
6 2
 
3.1%
7 1
 
1.5%
8 3
 
4.6%
9 1
 
1.5%
10 1
 
1.5%
ValueCountFrequency (%)
277 1
1.5%
186 1
1.5%
102 1
1.5%
65 1
1.5%
56 1
1.5%
40 1
1.5%
32 1
1.5%
31 1
1.5%
23 1
1.5%
21 1
1.5%

2021년
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.769231
Minimum0
Maximum238
Zeros31
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:56.730011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q313
95-th percentile42.8
Maximum238
Range238
Interquartile range (IQR)13

Descriptive statistics

Standard deviation36.230067
Coefficient of variation (CV)2.6312339
Kurtosis26.511459
Mean13.769231
Median Absolute Deviation (MAD)1
Skewness4.8677119
Sum895
Variance1312.6178
MonotonicityNot monotonic
2024-04-21T10:41:56.826362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 31
47.7%
1 4
 
6.2%
2 4
 
6.2%
3 3
 
4.6%
32 2
 
3.1%
5 2
 
3.1%
11 2
 
3.1%
9 1
 
1.5%
6 1
 
1.5%
13 1
 
1.5%
Other values (14) 14
21.5%
ValueCountFrequency (%)
0 31
47.7%
1 4
 
6.2%
2 4
 
6.2%
3 3
 
4.6%
5 2
 
3.1%
6 1
 
1.5%
9 1
 
1.5%
11 2
 
3.1%
13 1
 
1.5%
14 1
 
1.5%
ValueCountFrequency (%)
238 1
1.5%
159 1
1.5%
48 1
1.5%
44 1
1.5%
38 1
1.5%
35 1
1.5%
34 1
1.5%
33 1
1.5%
32 2
3.1%
28 1
1.5%

2022년
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.538462
Minimum0
Maximum195
Zeros37
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:56.925160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile95.8
Maximum195
Range195
Interquartile range (IQR)8

Descriptive statistics

Standard deviation38.508553
Coefficient of variation (CV)2.4782732
Kurtosis11.439466
Mean15.538462
Median Absolute Deviation (MAD)0
Skewness3.3639492
Sum1010
Variance1482.9087
MonotonicityNot monotonic
2024-04-21T10:41:57.036198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 37
56.9%
2 4
 
6.2%
6 3
 
4.6%
1 2
 
3.1%
25 2
 
3.1%
3 2
 
3.1%
13 2
 
3.1%
20 2
 
3.1%
99 1
 
1.5%
8 1
 
1.5%
Other values (9) 9
 
13.8%
ValueCountFrequency (%)
0 37
56.9%
1 2
 
3.1%
2 4
 
6.2%
3 2
 
3.1%
6 3
 
4.6%
8 1
 
1.5%
13 2
 
3.1%
20 2
 
3.1%
22 1
 
1.5%
25 2
 
3.1%
ValueCountFrequency (%)
195 1
1.5%
167 1
1.5%
139 1
1.5%
99 1
1.5%
83 1
1.5%
47 1
1.5%
39 1
1.5%
32 1
1.5%
29 1
1.5%
25 2
3.1%

2023년
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.246154
Minimum0
Maximum156
Zeros34
Zeros (%)52.3%
Negative0
Negative (%)0.0%
Memory size717.0 B
2024-04-21T10:41:57.144396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile70
Maximum156
Range156
Interquartile range (IQR)13

Descriptive statistics

Standard deviation29.120456
Coefficient of variation (CV)2.1984084
Kurtosis12.118768
Mean13.246154
Median Absolute Deviation (MAD)0
Skewness3.336485
Sum861
Variance848.00096
MonotonicityNot monotonic
2024-04-21T10:41:57.260659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 34
52.3%
1 5
 
7.7%
24 2
 
3.1%
5 2
 
3.1%
3 2
 
3.1%
8 2
 
3.1%
10 1
 
1.5%
38 1
 
1.5%
26 1
 
1.5%
6 1
 
1.5%
Other values (14) 14
21.5%
ValueCountFrequency (%)
0 34
52.3%
1 5
 
7.7%
3 2
 
3.1%
5 2
 
3.1%
6 1
 
1.5%
8 2
 
3.1%
9 1
 
1.5%
10 1
 
1.5%
13 1
 
1.5%
17 1
 
1.5%
ValueCountFrequency (%)
156 1
1.5%
127 1
1.5%
92 1
1.5%
74 1
1.5%
54 1
1.5%
38 1
1.5%
35 1
1.5%
34 1
1.5%
26 1
1.5%
25 1
1.5%

Interactions

2024-04-21T10:41:53.665066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:49.834028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.606327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.324131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.908703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.495345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.046935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.746853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:49.993802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.709189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.406358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.992999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.582483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.145243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.823712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.100031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.824627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.497630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.078218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.652808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.227228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.894361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.189166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.920911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.586175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.156838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.731105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.313521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:54.168548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.318255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.004473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.663468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.232938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.803372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.405126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:54.253377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.418303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.082741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.731633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.305863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.867486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.493176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:54.334507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:50.513254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.206521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:51.817017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.397194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:52.960088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:41:53.580368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:41:57.350593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
참여기관명기관유형2015년2016년2017년2018년2019년2020년2021년2022년2023년
참여기관명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기관유형1.0001.0000.0000.0000.0000.0000.0000.0000.0410.0000.000
2015년1.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.000
2016년1.0000.0001.0001.0000.8320.0000.0000.0000.0000.0000.000
2017년1.0000.0000.0000.8321.0000.6110.6110.0000.0000.0000.000
2018년1.0000.0000.0000.0000.6111.0000.9980.8730.9520.8420.880
2019년1.0000.0000.0000.0000.6110.9981.0000.8360.9500.8630.911
2020년1.0000.0000.0000.0000.0000.8730.8361.0000.8680.8050.832
2021년1.0000.0410.0000.0000.0000.9520.9500.8681.0000.8500.824
2022년1.0000.0000.0000.0000.0000.8420.8630.8050.8501.0000.963
2023년1.0000.0000.0000.0000.0000.8800.9110.8320.8240.9631.000
2024-04-21T10:41:57.473806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016년기관유형2015년
2016년1.0000.0000.976
기관유형0.0001.0000.000
2015년0.9760.0001.000
2024-04-21T10:41:57.565431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년2018년2019년2020년2021년2022년2023년기관유형2015년2016년
2017년1.0000.2160.1800.035-0.007-0.082-0.1100.0000.0000.796
2018년0.2161.0000.6150.2830.2190.1970.2880.0000.0000.000
2019년0.1800.6151.0000.3090.2720.0750.2240.0000.0000.000
2020년0.0350.2830.3091.0000.3620.0660.1200.0000.0000.000
2021년-0.0070.2190.2720.3621.0000.3690.1700.0110.0000.000
2022년-0.0820.1970.0750.0660.3691.0000.3760.0000.0000.000
2023년-0.1100.2880.2240.1200.1700.3761.0000.0000.0000.000
기관유형0.0000.0000.0000.0000.0110.0000.0001.0000.0000.000
2015년0.0000.0000.0000.0000.0000.0000.0000.0001.0000.976
2016년0.7960.0000.0000.0000.0000.0000.0000.0000.9761.000

Missing values

2024-04-21T10:41:54.460423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:41:54.618649image/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

참여기관명기관유형2015년2016년2017년2018년2019년2020년2021년2022년2023년
0강원도청지방자치단체060159000000
1대전광역시청지방자치단체009099810000
2울산광역시청지방자치단체000635332144754
3충청북도청지방자치단체00031113518623813992
4계룡시청지방자치단체000570000
5제주특별자치도청지방자치단체00017527627715919574
6산청군청지방자치단체006000000
7부천시청지방자치단체00077031353234
8김해시청지방자치단체0000270000
9포항시청지방자치단체00002256482517
참여기관명기관유형2015년2016년2017년2018년2019년2020년2021년2022년2023년
55농업기술실용화재단공공기관000000100
56국가철도공단공공기관0000001100
57건강보험심사평가원공공기관000000100
58한국장애인고용공단공공기관000000511
59한일시멘트민간기업00000001326
60한화시스템민간기업0000000200
61현대일렉트릭앤에너지시스템민간기업000000080
62한국농어촌공사공공기관000000060
63롯데알미늄민간기업000000001
64순천상공회의소특수법인000000020