Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory106.0 B

Variable types

Numeric9
Categorical2

Dataset

Description회계연도,자치단체코드,기준일,세목코드,세목코드명,전일누계,수입액,과오납반환액,과목경정액,당일누계,출력순서
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13268/S/1/datasetView.do

Alerts

자치단체코드 has constant value ""Constant
회계연도 is highly overall correlated with 기준일High correlation
기준일 is highly overall correlated with 회계연도High correlation
세목코드 is highly overall correlated with 출력순서 and 1 other fieldsHigh correlation
전일누계 is highly overall correlated with 당일누계High correlation
수입액 is highly overall correlated with 당일누계High correlation
당일누계 is highly overall correlated with 전일누계 and 1 other fieldsHigh correlation
출력순서 is highly overall correlated with 세목코드 and 1 other fieldsHigh correlation
세목코드명 is highly overall correlated with 세목코드 and 1 other fieldsHigh correlation
과오납반환액 is highly skewed (γ1 = 53.26260326)Skewed
과목경정액 is highly skewed (γ1 = -39.71300472)Skewed
세목코드 has 177 (1.8%) zerosZeros
전일누계 has 3136 (31.4%) zerosZeros
수입액 has 6935 (69.3%) zerosZeros
과오납반환액 has 8987 (89.9%) zerosZeros
과목경정액 has 9704 (97.0%) zerosZeros
당일누계 has 3108 (31.1%) zerosZeros

Reproduction

Analysis started2024-05-10 23:15:48.559840
Analysis finished2024-05-10 23:16:23.978328
Duration35.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.1767
Minimum2019
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:16:24.362021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2019
5-th percentile2019
Q12020
median2021
Q32022
95-th percentile2024
Maximum2024
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5509731
Coefficient of variation (CV)0.00076736148
Kurtosis-1.1313827
Mean2021.1767
Median Absolute Deviation (MAD)1
Skewness0.11763327
Sum20211767
Variance2.4055177
MonotonicityNot monotonic
2024-05-10T23:16:25.045361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 1924
19.2%
2021 1914
19.1%
2019 1865
18.6%
2022 1829
18.3%
2023 1812
18.1%
2024 656
 
6.6%
ValueCountFrequency (%)
2019 1865
18.6%
2020 1924
19.2%
2021 1914
19.1%
2022 1829
18.3%
2023 1812
18.1%
2024 656
 
6.6%
ValueCountFrequency (%)
2024 656
 
6.6%
2023 1812
18.1%
2022 1829
18.3%
2021 1914
19.1%
2020 1924
19.2%
2019 1865
18.6%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-05-10T23:16:25.639067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:16:25.931480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

기준일
Real number (ℝ)

HIGH CORRELATION 

Distinct1908
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20212715
Minimum20190203
Maximum20240509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:16:26.289056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190203
5-th percentile20190505
Q120200511
median20210818
Q320221226
95-th percentile20240128
Maximum20240509
Range50306
Interquartile range (IQR)20715

Descriptive statistics

Standard deviation15223.291
Coefficient of variation (CV)0.0007531542
Kurtosis-1.1041787
Mean20212715
Median Absolute Deviation (MAD)10392.5
Skewness0.098478465
Sum2.0212715 × 1011
Variance2.317486 × 108
MonotonicityNot monotonic
2024-05-10T23:16:26.811957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210116 19
 
0.2%
20200111 16
 
0.2%
20210112 16
 
0.2%
20210119 15
 
0.1%
20200103 15
 
0.1%
20210109 14
 
0.1%
20190325 14
 
0.1%
20210522 14
 
0.1%
20200116 13
 
0.1%
20200120 13
 
0.1%
Other values (1898) 9851
98.5%
ValueCountFrequency (%)
20190203 1
 
< 0.1%
20190204 7
0.1%
20190205 7
0.1%
20190206 7
0.1%
20190207 6
0.1%
20190208 6
0.1%
20190209 4
< 0.1%
20190210 4
< 0.1%
20190211 3
< 0.1%
20190212 3
< 0.1%
ValueCountFrequency (%)
20240509 3
< 0.1%
20240508 6
0.1%
20240507 4
< 0.1%
20240506 4
< 0.1%
20240505 6
0.1%
20240504 7
0.1%
20240503 5
0.1%
20240502 7
0.1%
20240501 6
0.1%
20240430 3
< 0.1%

세목코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39056166
Minimum0
Maximum90000000
Zeros177
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:16:27.231452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10100003
Q110135000
median20000013
Q380000004
95-th percentile80000014
Maximum90000000
Range90000000
Interquartile range (IQR)69865004

Descriptive statistics

Standard deviation31958684
Coefficient of variation (CV)0.81827501
Kurtosis-1.6756596
Mean39056166
Median Absolute Deviation (MAD)9896013
Skewness0.45798341
Sum3.9056166 × 1011
Variance1.0213575 × 1015
MonotonicityNot monotonic
2024-05-10T23:16:27.883347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
20000020 241
 
2.4%
10200002 235
 
2.4%
10200001 231
 
2.3%
80000010 228
 
2.3%
80000002 226
 
2.3%
10100005 224
 
2.2%
20000000 218
 
2.2%
10129999 217
 
2.2%
80000013 216
 
2.2%
80000006 215
 
2.1%
Other values (39) 7749
77.5%
ValueCountFrequency (%)
0 177
1.8%
10000000 185
1.8%
10100003 206
2.1%
10100005 224
2.2%
10101000 208
2.1%
10104000 186
1.9%
10106000 197
2.0%
10109000 188
1.9%
10110000 212
2.1%
10113000 185
1.8%
ValueCountFrequency (%)
90000000 206
2.1%
80000015 192
1.9%
80000014 204
2.0%
80000013 216
2.2%
80000012 198
2.0%
80000011 202
2.0%
80000010 228
2.3%
80000009 191
1.9%
80000008 206
2.1%
80000007 205
2.1%

세목코드명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지역개발기금
 
433
임시적세외수입
 
235
경상적세외수입
 
231
체육진흥기금
 
228
중소기업육성기금
 
226
Other values (43)
8647 

Length

Max length9
Median length7
Mean length5.1692
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남북교류협력기금
2nd row도로굴착복구기금
3rd row보통세
4th row지방세
5th row재난관리기금

Common Values

ValueCountFrequency (%)
지역개발기금 433
 
4.3%
임시적세외수입 235
 
2.4%
경상적세외수입 231
 
2.3%
체육진흥기금 228
 
2.3%
중소기업육성기금 226
 
2.3%
보통세 224
 
2.2%
특별회계 218
 
2.2%
목적세 217
 
2.2%
남북교류협력기금 216
 
2.2%
도로굴착복구기금 215
 
2.1%
Other values (38) 7557
75.6%

Length

2024-05-10T23:16:28.337884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지역개발기금 433
 
4.3%
임시적세외수입 235
 
2.4%
경상적세외수입 231
 
2.3%
체육진흥기금 228
 
2.3%
중소기업육성기금 226
 
2.3%
보통세 224
 
2.2%
특별회계 218
 
2.2%
목적세 217
 
2.2%
남북교류협력기금 216
 
2.2%
도로굴착복구기금 215
 
2.1%
Other values (38) 7557
75.6%

전일누계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5500
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3833822 × 1012
Minimum-2.0938012 × 1013
Maximum6.1030868 × 1013
Zeros3136
Zeros (%)31.4%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-10T23:16:28.862302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.0938012 × 1013
5-th percentile0
Q10
median2.8293088 × 1010
Q34.8318529 × 1011
95-th percentile6.7199526 × 1012
Maximum6.1030868 × 1013
Range8.1968879 × 1013
Interquartile range (IQR)4.8318529 × 1011

Descriptive statistics

Standard deviation4.9652104 × 1012
Coefficient of variation (CV)3.589182
Kurtosis44.314225
Mean1.3833822 × 1012
Median Absolute Deviation (MAD)2.8293088 × 1010
Skewness6.0831014
Sum1.3833822 × 1016
Variance2.4653314 × 1025
MonotonicityNot monotonic
2024-05-10T23:16:29.341622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3136
31.4%
6226623520 41
 
0.4%
6627748506 16
 
0.2%
899393380 12
 
0.1%
6839646287 11
 
0.1%
88425965347 10
 
0.1%
140440 10
 
0.1%
14019575110 10
 
0.1%
96497538547 10
 
0.1%
1055621182194 9
 
0.1%
Other values (5490) 6735
67.3%
ValueCountFrequency (%)
-20938011638396 1
 
< 0.1%
-4935685285020 1
 
< 0.1%
0 3136
31.4%
3941 1
 
< 0.1%
4713 1
 
< 0.1%
8420 2
 
< 0.1%
18490 3
 
< 0.1%
41038 1
 
< 0.1%
60970 1
 
< 0.1%
140000 7
 
0.1%
ValueCountFrequency (%)
61030867590285 1
< 0.1%
59605440417110 1
< 0.1%
59587738056330 1
< 0.1%
58067097330014 1
< 0.1%
57686088429911 1
< 0.1%
55255094359813 1
< 0.1%
55200363341838 1
< 0.1%
54887557572820 1
< 0.1%
53714341390644 1
< 0.1%
53538858008360 1
< 0.1%

수입액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2999
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5098178 × 109
Minimum-7.4329337 × 1010
Maximum1.5985862 × 1012
Zeros6935
Zeros (%)69.3%
Negative17
Negative (%)0.2%
Memory size166.0 KiB
2024-05-10T23:16:29.717452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.4329337 × 1010
5-th percentile0
Q10
median0
Q38098125
95-th percentile1.3655897 × 1010
Maximum1.5985862 × 1012
Range1.6729156 × 1012
Interquartile range (IQR)8098125

Descriptive statistics

Standard deviation6.5761695 × 1010
Coefficient of variation (CV)7.7277442
Kurtosis212.95284
Mean8.5098178 × 109
Median Absolute Deviation (MAD)0
Skewness13.258978
Sum8.5098178 × 1013
Variance4.3246006 × 1021
MonotonicityNot monotonic
2024-05-10T23:16:30.114100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6935
69.3%
14490 6
 
0.1%
100000000 5
 
0.1%
10399999 3
 
< 0.1%
1000000000 3
 
< 0.1%
26833333 2
 
< 0.1%
20000000 2
 
< 0.1%
24534820 2
 
< 0.1%
21782080 2
 
< 0.1%
1920000 2
 
< 0.1%
Other values (2989) 3038
30.4%
ValueCountFrequency (%)
-74329336777 1
< 0.1%
-57618903230 1
< 0.1%
-53377764310 1
< 0.1%
-36435660720 1
< 0.1%
-32901159670 1
< 0.1%
-29450566020 1
< 0.1%
-16630674930 1
< 0.1%
-8224831630 1
< 0.1%
-7709682690 1
< 0.1%
-6097722720 1
< 0.1%
ValueCountFrequency (%)
1598586221414 1
< 0.1%
1457613562300 1
< 0.1%
1437175110210 1
< 0.1%
1316201162788 1
< 0.1%
1277478567796 1
< 0.1%
1144987174575 1
< 0.1%
1137578829542 1
< 0.1%
1092049876310 1
< 0.1%
1091936587230 1
< 0.1%
1016774724560 1
< 0.1%

과오납반환액
Real number (ℝ)

SKEWED  ZEROS 

Distinct982
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5414088 × 108
Minimum0
Maximum2.1964721 × 1011
Zeros8987
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:16:30.592500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9979670.5
Maximum2.1964721 × 1011
Range2.1964721 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.998107 × 109
Coefficient of variation (CV)19.450434
Kurtosis3432.157
Mean1.5414088 × 108
Median Absolute Deviation (MAD)0
Skewness53.262603
Sum1.5414088 × 1012
Variance8.9886454 × 1018
MonotonicityNot monotonic
2024-05-10T23:16:31.105447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8987
89.9%
3184930 2
 
< 0.1%
868860 2
 
< 0.1%
4098730 2
 
< 0.1%
1994070 2
 
< 0.1%
43710040 2
 
< 0.1%
5452084150 2
 
< 0.1%
19814190 2
 
< 0.1%
96815870 2
 
< 0.1%
1130490 2
 
< 0.1%
Other values (972) 995
 
10.0%
ValueCountFrequency (%)
0 8987
89.9%
520 1
 
< 0.1%
720 1
 
< 0.1%
2870 1
 
< 0.1%
4670 1
 
< 0.1%
9000 1
 
< 0.1%
9440 1
 
< 0.1%
11370 1
 
< 0.1%
15880 1
 
< 0.1%
16160 1
 
< 0.1%
ValueCountFrequency (%)
219647214620 1
< 0.1%
140198704930 1
< 0.1%
85751652810 1
< 0.1%
48677811730 1
< 0.1%
43998013800 1
< 0.1%
30981566540 1
< 0.1%
29739852480 1
< 0.1%
29481583010 1
< 0.1%
29018318180 1
< 0.1%
26932093980 1
< 0.1%

과목경정액
Real number (ℝ)

SKEWED  ZEROS 

Distinct276
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-14843822
Minimum-8.4699021 × 1010
Maximum4.4701678 × 1010
Zeros9704
Zeros (%)97.0%
Negative211
Negative (%)2.1%
Memory size166.0 KiB
2024-05-10T23:16:31.592820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.4699021 × 1010
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.4701678 × 1010
Range1.294007 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1834909 × 109
Coefficient of variation (CV)-79.729524
Kurtosis3140.3959
Mean-14843822
Median Absolute Deviation (MAD)0
Skewness-39.713005
Sum-1.4843822 × 1011
Variance1.4006506 × 1018
MonotonicityNot monotonic
2024-05-10T23:16:32.090900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9704
97.0%
-687539270 3
 
< 0.1%
-3662400 2
 
< 0.1%
-112000000 2
 
< 0.1%
8317520 2
 
< 0.1%
2626870 2
 
< 0.1%
-7545060 2
 
< 0.1%
-4216530 2
 
< 0.1%
-132320 2
 
< 0.1%
-6020380 2
 
< 0.1%
Other values (266) 277
 
2.8%
ValueCountFrequency (%)
-84699021000 1
< 0.1%
-44916272260 1
< 0.1%
-37038678010 1
< 0.1%
-16839271890 1
< 0.1%
-12777489610 1
< 0.1%
-9155071030 1
< 0.1%
-6238263490 1
< 0.1%
-5647800000 1
< 0.1%
-4896782880 1
< 0.1%
-4296893000 1
< 0.1%
ValueCountFrequency (%)
44701678021 1
< 0.1%
16835417240 1
< 0.1%
12658000000 1
< 0.1%
12108690410 1
< 0.1%
9175874620 1
< 0.1%
3619094350 1
< 0.1%
3470982000 1
< 0.1%
3365717960 1
< 0.1%
2337989950 1
< 0.1%
1716421560 1
< 0.1%

당일누계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5557
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3917227 × 1012
Minimum-2.0491385 × 1013
Maximum6.2343849 × 1013
Zeros3108
Zeros (%)31.1%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-10T23:16:32.653555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.0491385 × 1013
5-th percentile0
Q10
median2.8773459 × 1010
Q34.8812608 × 1011
95-th percentile6.7384535 × 1012
Maximum6.2343849 × 1013
Range8.2835233 × 1013
Interquartile range (IQR)4.8812608 × 1011

Descriptive statistics

Standard deviation4.9852319 × 1012
Coefficient of variation (CV)3.5820583
Kurtosis44.254335
Mean1.3917227 × 1012
Median Absolute Deviation (MAD)2.8773459 × 1010
Skewness6.0772697
Sum1.3917227 × 1016
Variance2.4852538 × 1025
MonotonicityNot monotonic
2024-05-10T23:16:33.205876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3108
31.1%
6226623520 41
 
0.4%
6627748506 16
 
0.2%
140440 11
 
0.1%
6839646287 11
 
0.1%
899393380 11
 
0.1%
96497538547 10
 
0.1%
14019575110 10
 
0.1%
10000000 10
 
0.1%
88425965347 10
 
0.1%
Other values (5547) 6762
67.6%
ValueCountFrequency (%)
-20491384641836 1
 
< 0.1%
-4911034956830 1
 
< 0.1%
0 3108
31.1%
4713 1
 
< 0.1%
8420 2
 
< 0.1%
18490 2
 
< 0.1%
47420 1
 
< 0.1%
64570 1
 
< 0.1%
101470 1
 
< 0.1%
140000 7
 
0.1%
ValueCountFrequency (%)
62343848523853 1
< 0.1%
59627185549480 1
< 0.1%
59609368206680 1
< 0.1%
58182712930117 1
< 0.1%
57607939755894 1
< 0.1%
55723539113109 1
< 0.1%
55200362016398 1
< 0.1%
55049394426600 1
< 0.1%
53788013327414 1
< 0.1%
53570314781170 1
< 0.1%

출력순서
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean771.7983
Minimum1
Maximum1272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T23:16:33.776813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q196
median1025
Q31260
95-th percentile1270
Maximum1272
Range1271
Interquartile range (IQR)1164

Descriptive statistics

Standard deviation523.65002
Coefficient of variation (CV)0.6784804
Kurtosis-1.5972912
Mean771.7983
Median Absolute Deviation (MAD)241
Skewness-0.4838549
Sum7717983
Variance274209.34
MonotonicityNot monotonic
2024-05-10T23:16:34.401134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
907 424
 
4.2%
1197 241
 
2.4%
293 235
 
2.4%
249 231
 
2.3%
1267 228
 
2.3%
1259 226
 
2.3%
5 224
 
2.2%
96 217
 
2.2%
1270 216
 
2.2%
1263 215
 
2.1%
Other values (38) 7543
75.4%
ValueCountFrequency (%)
1 177
1.8%
2 185
1.8%
4 206
2.1%
5 224
2.2%
6 208
2.1%
31 186
1.9%
43 190
1.9%
75 197
2.0%
81 188
1.9%
86 212
2.1%
ValueCountFrequency (%)
1272 192
1.9%
1271 204
2.0%
1270 216
2.2%
1269 198
2.0%
1268 202
2.0%
1267 228
2.3%
1266 191
1.9%
1265 206
2.1%
1264 205
2.1%
1263 215
2.1%

Interactions

2024-05-10T23:16:19.126742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:54.737345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:57.273890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:00.529595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:03.730650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:07.076113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:10.231949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:13.248685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:16.359814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:19.482744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:54.979621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:57.679075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:00.932931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:04.102743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:07.400107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:10.708162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:13.543578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:16.636075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:19.862466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:55.252507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:57.962925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:01.246660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:04.493882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:07.757531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:10.985085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:13.881784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:16.951071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:20.267001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:55.514380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:58.304523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:01.625529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:04.848695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:08.148449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:11.296225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:14.192621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:17.222634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:20.639060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:55.787327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:58.760185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:02.077285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:05.143312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:08.471102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:11.563097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:14.590311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:17.497685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:21.048553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:56.109409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:59.118268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:02.386417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:05.438605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:08.834941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:11.872583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:15.080870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:17.803129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:21.404499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:56.378145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:59.420006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:02.656183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:05.725565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:09.206912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:12.290363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:15.385119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:18.082361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:21.797724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:56.683879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:59.788993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:02.958480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:06.133752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:09.496975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:12.628001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:15.688193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:18.382610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:22.256719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:15:56.959531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:00.162258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:03.365399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:06.452694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:09.839548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:12.898366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:16.000982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:16:18.799973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:16:34.807431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도기준일세목코드세목코드명전일누계수입액과오납반환액과목경정액당일누계출력순서
회계연도1.0000.9860.0000.0000.0840.0560.0000.0200.0870.000
기준일0.9861.0000.0220.0000.1260.0460.0000.0330.1240.000
세목코드0.0000.0221.0000.9990.5230.2960.0930.1130.5130.800
세목코드명0.0000.0000.9991.0000.7060.3810.1110.0900.6951.000
전일누계0.0840.1260.5230.7061.0000.3850.2330.0670.9980.353
수입액0.0560.0460.2960.3810.3851.0000.4560.5210.3940.122
과오납반환액0.0000.0000.0930.1110.2330.4561.0000.0000.2450.000
과목경정액0.0200.0330.1130.0900.0670.5210.0001.0000.0710.012
당일누계0.0870.1240.5130.6950.9980.3940.2450.0711.0000.337
출력순서0.0000.0000.8001.0000.3530.1220.0000.0120.3371.000
2024-05-10T23:16:35.299909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도기준일세목코드전일누계수입액과오납반환액과목경정액당일누계출력순서세목코드명
회계연도1.0000.983-0.0020.2290.1390.047-0.0160.231-0.0020.000
기준일0.9831.000-0.0020.2380.1280.043-0.0140.240-0.0020.000
세목코드-0.002-0.0021.000-0.072-0.127-0.1020.035-0.0730.9130.992
전일누계0.2290.238-0.0721.0000.4960.436-0.1080.998-0.1330.319
수입액0.1390.128-0.1270.4961.0000.416-0.1370.505-0.1560.139
과오납반환액0.0470.043-0.1020.4360.4161.000-0.2020.438-0.1940.045
과목경정액-0.016-0.0140.035-0.108-0.137-0.2021.000-0.1090.0600.036
당일누계0.2310.240-0.0730.9980.5050.438-0.1091.000-0.1340.311
출력순서-0.002-0.0020.913-0.133-0.156-0.1940.060-0.1341.0000.998
세목코드명0.0000.0000.9920.3190.1390.0450.0360.3110.9981.000

Missing values

2024-05-10T23:16:22.941278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:16:23.580052image/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

회계연도자치단체코드기준일세목코드세목코드명전일누계수입액과오납반환액과목경정액당일누계출력순서
64383202002020110280000013남북교류협력기금39303570541000393035705411270
86622201902019090480000006도로굴착복구기금000001263
84528201902019101610100005보통세622662352000062266235205
52187202102021060810100003지방세28431626293019241590002843355045204
12344202302023083180000012재난관리기금3890972599295251612543003943488724721269
21501202302023022580000006도로굴착복구기금631457440700063145744071263
67688202002020082690000000기타수입00000907
80086201902020010810140000지방소득세0000088
70880202002020062220000010교통사업4180830154492632709102626579004183200205691070
10326202302023101180000003식품진흥기금40068976481811100040070787581260
회계연도자치단체코드기준일세목코드세목코드명전일누계수입액과오납반환액과목경정액당일누계출력순서
39389202202022022580000009사회복지기금20685637985000206856379851266
9504202302023102880000014대외협력기금379314253900037931425391271
44114202102021112010135000지역자원시설세00000170
47850202102021090520000010교통사업3638757388660003638757388661070
76454202002020022910135000지역자원시설세00000170
7932202302023112980000010체육진흥기금1161023979890537000116111451681267
72379202002020052250105000재산세0000043
47879202102021090450105000재산세1989920701070000198992070107043
59363202102021012280000003식품진흥기금40128000256000000426880001260
42184202102021123080000011감채기금105745387819400010574538781941268