Overview

Dataset statistics

Number of variables8
Number of observations71
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory73.9 B

Variable types

Numeric8

Dataset

Description사립학교교직원연금공단 5차 재정재계산 결과와 관련된 데이터로 연도별 총수입, 부담금, 운용수입, 총지출, 급여, 재정수지, 기금액 등의 항목을 제공합니다.(단위 : 억 원)
URLhttps://www.data.go.kr/data/15102552/fileData.do

Alerts

연도 is highly overall correlated with 부담금 and 5 other fieldsHigh correlation
총수입 is highly overall correlated with 부담금 and 2 other fieldsHigh correlation
부담금 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
운용수입 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
총지출 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
급여 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
재정수지 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
기금액 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
연도 has unique valuesUnique
총수입 has unique valuesUnique
부담금 has unique valuesUnique
총지출 has unique valuesUnique
급여 has unique valuesUnique
재정수지 has unique valuesUnique
운용수입 has 42 (59.2%) zerosZeros
기금액 has 42 (59.2%) zerosZeros

Reproduction

Analysis started2023-12-12 11:44:06.835138
Analysis finished2023-12-12 11:44:20.368285
Duration13.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2055
Minimum2020
Maximum2090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:20.528316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2020
5-th percentile2023.5
Q12037.5
median2055
Q32072.5
95-th percentile2086.5
Maximum2090
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.639767
Coefficient of variation (CV)0.010043682
Kurtosis-1.2
Mean2055
Median Absolute Deviation (MAD)18
Skewness0
Sum145905
Variance426
MonotonicityStrictly increasing
2023-12-12T20:44:20.873507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020 1
 
1.4%
2021 1
 
1.4%
2072 1
 
1.4%
2071 1
 
1.4%
2070 1
 
1.4%
2069 1
 
1.4%
2068 1
 
1.4%
2067 1
 
1.4%
2066 1
 
1.4%
2065 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
2020 1
1.4%
2021 1
1.4%
2022 1
1.4%
2023 1
1.4%
2024 1
1.4%
2025 1
1.4%
2026 1
1.4%
2027 1
1.4%
2028 1
1.4%
2029 1
1.4%
ValueCountFrequency (%)
2090 1
1.4%
2089 1
1.4%
2088 1
1.4%
2087 1
1.4%
2086 1
1.4%
2085 1
1.4%
2084 1
1.4%
2083 1
1.4%
2082 1
1.4%
2081 1
1.4%

총수입
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62119.775
Minimum42827
Maximum67465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:21.174602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42827
5-th percentile48526
Q160814
median64024
Q366246
95-th percentile67405
Maximum67465
Range24638
Interquartile range (IQR)5432

Descriptive statistics

Standard deviation5820.9958
Coefficient of variation (CV)0.093706004
Kurtosis2.6430605
Mean62119.775
Median Absolute Deviation (MAD)2558
Skewness-1.7464311
Sum4410504
Variance33883992
MonotonicityNot monotonic
2023-12-12T20:44:21.491776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42827 1
 
1.4%
44389 1
 
1.4%
65893 1
 
1.4%
66217 1
 
1.4%
66504 1
 
1.4%
66760 1
 
1.4%
66990 1
 
1.4%
67158 1
 
1.4%
67284 1
 
1.4%
67373 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
42827 1
1.4%
44389 1
1.4%
45956 1
1.4%
47796 1
1.4%
49256 1
1.4%
50575 1
1.4%
51929 1
1.4%
53234 1
1.4%
55263 1
1.4%
56495 1
1.4%
ValueCountFrequency (%)
67465 1
1.4%
67456 1
1.4%
67437 1
1.4%
67436 1
1.4%
67374 1
1.4%
67373 1
1.4%
67284 1
1.4%
67282 1
1.4%
67158 1
1.4%
67152 1
1.4%

부담금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59043.127
Minimum35592
Maximum67465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:21.785575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35592
5-th percentile38967
Q155263.5
median62130
Q366246
95-th percentile67405
Maximum67465
Range31873
Interquartile range (IQR)10982.5

Descriptive statistics

Standard deviation9142.8462
Coefficient of variation (CV)0.15485031
Kurtosis0.40197433
Mean59043.127
Median Absolute Deviation (MAD)4452
Skewness-1.227509
Sum4192062
Variance83591637
MonotonicityNot monotonic
2023-12-12T20:44:22.073973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35592 1
 
1.4%
36496 1
 
1.4%
65893 1
 
1.4%
66217 1
 
1.4%
66504 1
 
1.4%
66760 1
 
1.4%
66990 1
 
1.4%
67158 1
 
1.4%
67284 1
 
1.4%
67373 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
35592 1
1.4%
36496 1
1.4%
37335 1
1.4%
38427 1
1.4%
39507 1
1.4%
40592 1
1.4%
41760 1
1.4%
43023 1
1.4%
44888 1
1.4%
46144 1
1.4%
ValueCountFrequency (%)
67465 1
1.4%
67456 1
1.4%
67437 1
1.4%
67436 1
1.4%
67374 1
1.4%
67373 1
1.4%
67284 1
1.4%
67282 1
1.4%
67158 1
1.4%
67152 1
1.4%

운용수입
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3076.7465
Minimum0
Maximum10375
Zeros42
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:22.380907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37457
95-th percentile10219
Maximum10375
Range10375
Interquartile range (IQR)7457

Descriptive statistics

Standard deviation4174.4739
Coefficient of variation (CV)1.3567819
Kurtosis-1.1937954
Mean3076.7465
Median Absolute Deviation (MAD)0
Skewness0.79485776
Sum218449
Variance17426233
MonotonicityNot monotonic
2023-12-12T20:44:22.655115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 42
59.2%
7235 1
 
1.4%
8785 1
 
1.4%
493 1
 
1.4%
1414 1
 
1.4%
2266 1
 
1.4%
3098 1
 
1.4%
3853 1
 
1.4%
4577 1
 
1.4%
5275 1
 
1.4%
Other values (20) 20
28.2%
ValueCountFrequency (%)
0 42
59.2%
493 1
 
1.4%
1414 1
 
1.4%
2266 1
 
1.4%
3098 1
 
1.4%
3853 1
 
1.4%
4577 1
 
1.4%
5275 1
 
1.4%
5962 1
 
1.4%
6583 1
 
1.4%
ValueCountFrequency (%)
10375 1
1.4%
10352 1
1.4%
10330 1
1.4%
10227 1
1.4%
10211 1
1.4%
10168 1
1.4%
10099 1
1.4%
9983 1
1.4%
9815 1
1.4%
9750 1
1.4%

총지출
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108035.73
Minimum32997
Maximum177768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:22.921556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32997
5-th percentile42691.5
Q173318.5
median105040
Q3145022.5
95-th percentile174041
Maximum177768
Range144771
Interquartile range (IQR)71704

Descriptive statistics

Standard deviation42768.73
Coefficient of variation (CV)0.39587578
Kurtosis-1.165437
Mean108035.73
Median Absolute Deviation (MAD)36140
Skewness0.05348713
Sum7670537
Variance1.8291643 × 109
MonotonicityStrictly increasing
2023-12-12T20:44:23.237418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32997 1
 
1.4%
35766 1
 
1.4%
143750 1
 
1.4%
141180 1
 
1.4%
138629 1
 
1.4%
136088 1
 
1.4%
133578 1
 
1.4%
131110 1
 
1.4%
128711 1
 
1.4%
126315 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
32997 1
1.4%
35766 1
1.4%
38457 1
1.4%
41284 1
1.4%
44099 1
1.4%
46925 1
1.4%
49294 1
1.4%
51811 1
1.4%
54284 1
1.4%
56520 1
1.4%
ValueCountFrequency (%)
177768 1
1.4%
176877 1
1.4%
175848 1
1.4%
174680 1
1.4%
173402 1
1.4%
171984 1
1.4%
170415 1
1.4%
168686 1
1.4%
166840 1
1.4%
164909 1
1.4%

급여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107465
Minimum32733
Maximum176715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:23.579975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32733
5-th percentile42410
Q172946.5
median104514
Q3144278
95-th percentile173059
Maximum176715
Range143982
Interquartile range (IQR)71331.5

Descriptive statistics

Standard deviation42542.26
Coefficient of variation (CV)0.39587084
Kurtosis-1.1654255
Mean107465
Median Absolute Deviation (MAD)35944
Skewness0.051151073
Sum7630015
Variance1.8098438 × 109
MonotonicityStrictly increasing
2023-12-12T20:44:23.937627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32733 1
 
1.4%
35497 1
 
1.4%
143013 1
 
1.4%
140458 1
 
1.4%
137921 1
 
1.4%
135394 1
 
1.4%
132897 1
 
1.4%
130443 1
 
1.4%
128056 1
 
1.4%
125673 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
32733 1
1.4%
35497 1
1.4%
38183 1
1.4%
41005 1
1.4%
43815 1
1.4%
46635 1
1.4%
48998 1
1.4%
51508 1
1.4%
53976 1
1.4%
56205 1
1.4%
ValueCountFrequency (%)
176715 1
1.4%
175845 1
1.4%
174837 1
1.4%
173688 1
1.4%
172430 1
1.4%
171031 1
1.4%
169480 1
1.4%
167770 1
1.4%
165942 1
1.4%
164028 1
1.4%

재정수지
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-45915.887
Minimum-118728
Maximum9830
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)87.3%
Memory size771.0 B
2023-12-12T20:44:24.315957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-118728
5-th percentile-113612.5
Q1-79303
median-38653
Q3-10083
95-th percentile5835
Maximum9830
Range128558
Interquartile range (IQR)69220

Descriptive statistics

Standard deviation40059.645
Coefficient of variation (CV)-0.87245716
Kurtosis-1.1604759
Mean-45915.887
Median Absolute Deviation (MAD)33373
Skewness-0.36703873
Sum-3260028
Variance1.6047751 × 109
MonotonicityStrictly decreasing
2023-12-12T20:44:24.600877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9830 1
 
1.4%
8623 1
 
1.4%
-77857 1
 
1.4%
-74963 1
 
1.4%
-72125 1
 
1.4%
-69328 1
 
1.4%
-66587 1
 
1.4%
-63952 1
 
1.4%
-61426 1
 
1.4%
-58941 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
-118728 1
1.4%
-117448 1
1.4%
-116023 1
1.4%
-114453 1
1.4%
-112772 1
1.4%
-110986 1
1.4%
-109045 1
1.4%
-106941 1
1.4%
-104724 1
1.4%
-102426 1
1.4%
ValueCountFrequency (%)
9830 1
1.4%
8623 1
1.4%
7500 1
1.4%
6513 1
1.4%
5157 1
1.4%
3650 1
1.4%
2634 1
1.4%
1424 1
1.4%
979 1
1.4%
-24 1
1.4%

기금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78043.535
Minimum0
Maximum251248
Zeros42
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T20:44:25.458973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3206815.5
95-th percentile249519
Maximum251248
Range251248
Interquartile range (IQR)206815.5

Descriptive statistics

Standard deviation105375.95
Coefficient of variation (CV)1.3502201
Kurtosis-1.331102
Mean78043.535
Median Absolute Deviation (MAD)0
Skewness0.74390345
Sum5541091
Variance1.1104091 × 1010
MonotonicityNot monotonic
2023-12-12T20:44:25.705486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 42
59.2%
214769 1
 
1.4%
221554 1
 
1.4%
516 1
 
1.4%
29305 1
 
1.4%
56011 1
 
1.4%
80598 1
 
1.4%
103107 1
 
1.4%
123717 1
 
1.4%
142556 1
 
1.4%
Other values (20) 20
28.2%
ValueCountFrequency (%)
0 42
59.2%
516 1
 
1.4%
29305 1
 
1.4%
56011 1
 
1.4%
80598 1
 
1.4%
103107 1
 
1.4%
123717 1
 
1.4%
142556 1
 
1.4%
159756 1
 
1.4%
175210 1
 
1.4%
ValueCountFrequency (%)
251248 1
1.4%
251224 1
1.4%
250269 1
1.4%
250192 1
1.4%
248846 1
1.4%
248084 1
1.4%
246212 1
1.4%
244999 1
1.4%
242562 1
1.4%
241018 1
1.4%

Interactions

2023-12-12T20:44:18.213891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:07.208218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.309353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:09.915056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.524279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:12.673779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:15.232995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:16.777233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:18.405706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:07.342965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.450131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:10.129566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.649826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:13.055015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:15.429574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:16.991936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:18.670829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:07.465427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.577619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:10.344820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.786884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:13.335279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:15.594903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:17.161539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:18.906255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:07.610502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.753405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:10.662328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.916064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:13.544546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:15.819492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:17.339204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:19.085683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:07.745077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.986188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:10.993155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:12.050726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:13.742330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:15.991509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:17.487822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:19.296494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:07.881073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:09.195042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.122742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:12.194881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:13.958652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:16.200014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:17.669613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:19.508944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.026326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:09.445764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.269233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:12.357772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:14.158545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:16.396955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:17.856028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:19.700440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:08.158293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:09.696732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:11.395200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:12.507248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:15.038240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:16.584699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:18.051063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:44:25.916131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도총수입부담금운용수입총지출급여재정수지기금액
연도1.0000.8720.9390.8420.9840.9840.9880.844
총수입0.8721.0000.9430.6880.8920.8920.7770.435
부담금0.9390.9431.0000.8850.9160.9160.8790.794
운용수입0.8420.6880.8851.0000.8150.8150.7790.985
총지출0.9840.8920.9160.8151.0001.0000.9880.823
급여0.9840.8920.9160.8151.0001.0000.9880.823
재정수지0.9880.7770.8790.7790.9880.9881.0000.809
기금액0.8440.4350.7940.9850.8230.8230.8091.000
2023-12-12T20:44:26.172274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도총수입부담금운용수입총지출급여재정수지기금액
연도1.0000.3590.587-0.8691.0001.000-1.000-0.874
총수입0.3591.0000.938-0.6450.3590.359-0.359-0.653
부담금0.5870.9381.000-0.8300.5870.587-0.587-0.835
운용수입-0.869-0.645-0.8301.000-0.869-0.8690.8690.999
총지출1.0000.3590.587-0.8691.0001.000-1.000-0.874
급여1.0000.3590.587-0.8691.0001.000-1.000-0.874
재정수지-1.000-0.359-0.5870.869-1.000-1.0001.0000.874
기금액-0.874-0.653-0.8350.999-0.874-0.8740.8741.000

Missing values

2023-12-12T20:44:19.967770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:44:20.249838image/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

연도총수입부담금운용수입총지출급여재정수지기금액
020204282735592723532997327339830214769
120214438936496789335766354978623223393
220224595637335862138457381837500230892
320234779638427936941284410056513237405
420244925639507975044099438155157242562
520255057540592998346925466353650246212
6202651929417601016849294489982634248846
7202753234430231021151811515081424250269
820285526344888103755428453976979251248
920295649546144103525652056205-24251224
연도총수입부담금운용수입총지출급여재정수지기금액
61208162483624830164909164028-1024260
62208262116621160166840165942-1047240
63208361746617460168686167770-1069410
64208461370613700170415169480-1090450
65208560998609980171984171031-1109860
66208660630606300173402172430-1127720
67208760227602270174680173688-1144530
68208859825598250175848174837-1160230
69208959429594290176877175845-1174480
70209059040590400177768176715-1187280