Overview

Dataset statistics

Number of variables12
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory112.0 B

Variable types

Text1
Numeric10
DateTime1

Dataset

Description2012년부터 2022년까지의 연도별 총세입·총세출 예산 및 결산 시계열 자료입니다. *수치가 기본단위보다 적거나 수치가 없을 경우 0으로 표시하였습니다. *2017년과 2018년의 결산상 잉여금은 국가재정법 제90조 제1항에 따라 각각 국채상환 0.4조원, 4조원을 차감한 금액입니다. *세입·세출 총계 기준입니다.
URLhttps://www.data.go.kr/data/15065234/fileData.do

Alerts

데이터기준일 has constant value ""Constant
예산_본예산 is highly overall correlated with 예산_최종 and 5 other fieldsHigh correlation
예산_최종 is highly overall correlated with 예산_본예산 and 5 other fieldsHigh correlation
세입_징수액 is highly overall correlated with 예산_본예산 and 5 other fieldsHigh correlation
세입_세입초과 is highly overall correlated with 결산_결산상잉여금 and 2 other fieldsHigh correlation
세출_현액 is highly overall correlated with 예산_본예산 and 4 other fieldsHigh correlation
세출_집행액 is highly overall correlated with 예산_본예산 and 5 other fieldsHigh correlation
결산_결산상잉여금 is highly overall correlated with 세입_세입초과 and 1 other fieldsHigh correlation
결산_이월 is highly overall correlated with 예산_본예산 and 4 other fieldsHigh correlation
결산_세계잉여금 is highly overall correlated with 예산_본예산 and 7 other fieldsHigh correlation
결산_불용 is highly overall correlated with 세입_세입초과High correlation
구분 has unique valuesUnique
예산_본예산 has unique valuesUnique
세입_징수액 has unique valuesUnique
세출_집행액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:11:16.430687
Analysis finished2023-12-12 16:11:26.606966
Duration10.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T01:11:26.732510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters198
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row2012_일반회계
2nd row2012_특별회계
3rd row2013_일반회계
4th row2013_특별회계
5th row2014_일반회계
ValueCountFrequency (%)
2012_일반회계 1
 
4.5%
2012_특별회계 1
 
4.5%
2022_일반회계 1
 
4.5%
2021_특별회계 1
 
4.5%
2021_일반회계 1
 
4.5%
2020_특별회계 1
 
4.5%
2020_일반회계 1
 
4.5%
2019_특별회계 1
 
4.5%
2019_일반회계 1
 
4.5%
2018_특별회계 1
 
4.5%
Other values (12) 12
54.5%
2023-12-13T01:11:26.987028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 32
16.2%
0 24
12.1%
계 22
11.1%
_ 22
11.1%
회 22
11.1%
1 18
9.1%
별 11
 
5.6%
특 11
 
5.6%
반 11
 
5.6%
일 11
 
5.6%
Other values (7) 14
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
44.4%
Other Letter 88
44.4%
Connector Punctuation 22
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 32
36.4%
0 24
27.3%
1 18
20.5%
3 2
 
2.3%
4 2
 
2.3%
5 2
 
2.3%
6 2
 
2.3%
7 2
 
2.3%
8 2
 
2.3%
9 2
 
2.3%
Other Letter
ValueCountFrequency (%)
계 22
25.0%
회 22
25.0%
별 11
12.5%
특 11
12.5%
반 11
12.5%
일 11
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 110
55.6%
Hangul 88
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 32
29.1%
0 24
21.8%
_ 22
20.0%
1 18
16.4%
3 2
 
1.8%
4 2
 
1.8%
5 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
Hangul
ValueCountFrequency (%)
계 22
25.0%
회 22
25.0%
별 11
12.5%
특 11
12.5%
반 11
12.5%
일 11
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110
55.6%
Hangul 88
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 32
29.1%
0 24
21.8%
_ 22
20.0%
1 18
16.4%
3 2
 
1.8%
4 2
 
1.8%
5 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
Hangul
ValueCountFrequency (%)
계 22
25.0%
회 22
25.0%
별 11
12.5%
특 11
12.5%
반 11
12.5%
일 11
12.5%

예산_본예산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.5
Minimum59.5
Maximum421.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:27.084831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.5
5-th percentile62.205
Q165.325
median151.05
Q3273.35
95-th percentile379.685
Maximum421.4
Range361.9
Interquartile range (IQR)208.025

Descriptive statistics

Standard deviation127.40198
Coefficient of variation (CV)0.69428875
Kurtosis-1.4269142
Mean183.5
Median Absolute Deviation (MAD)88.8
Skewness0.40438211
Sum4037
Variance16231.266
MonotonicityNot monotonic
2023-12-13T01:11:27.178339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
223.1 1
 
4.5%
301.4 1
 
4.5%
76.3 1
 
4.5%
421.4 1
 
4.5%
79.0 1
 
4.5%
380.9 1
 
4.5%
70.5 1
 
4.5%
356.6 1
 
4.5%
68.0 1
 
4.5%
331.8 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
59.5 1
4.5%
62.2 1
4.5%
62.3 1
4.5%
62.5 1
4.5%
64.2 1
4.5%
64.7 1
4.5%
67.2 1
4.5%
68.0 1
4.5%
70.5 1
4.5%
76.3 1
4.5%
ValueCountFrequency (%)
421.4 1
4.5%
380.9 1
4.5%
356.6 1
4.5%
331.8 1
4.5%
301.4 1
4.5%
275.0 1
4.5%
268.4 1
4.5%
258.6 1
4.5%
247.2 1
4.5%
236.2 1
4.5%

예산_최종
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.34545
Minimum59.5
Maximum495.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:27.273903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.5
5-th percentile62.5
Q165.95
median152.7
Q3283.525
95-th percentile422.64
Maximum495.2
Range435.7
Interquartile range (IQR)217.575

Descriptive statistics

Standard deviation140.64602
Coefficient of variation (CV)0.73121573
Kurtosis-0.8503207
Mean192.34545
Median Absolute Deviation (MAD)90.2
Skewness0.61897616
Sum4231.6
Variance19781.304
MonotonicityNot monotonic
2023-12-13T01:11:27.367850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
62.5 2
 
9.1%
223.1 1
 
4.5%
67.3 1
 
4.5%
78.3 1
 
4.5%
495.2 1
 
4.5%
82.3 1
 
4.5%
424.4 1
 
4.5%
70.8 1
 
4.5%
389.2 1
 
4.5%
69.4 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
59.5 1
4.5%
62.5 2
9.1%
63.2 1
4.5%
65.0 1
4.5%
65.5 1
4.5%
67.3 1
4.5%
69.4 1
4.5%
70.8 1
4.5%
78.3 1
4.5%
82.3 1
4.5%
ValueCountFrequency (%)
495.2 1
4.5%
424.4 1
4.5%
389.2 1
4.5%
334.7 1
4.5%
304.0 1
4.5%
284.9 1
4.5%
279.4 1
4.5%
262.5 1
4.5%
247.2 1
4.5%
240.7 1
4.5%

세입_징수액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.50909
Minimum58.7
Maximum493.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:27.457571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58.7
5-th percentile59.55
Q167.15
median154.75
Q3290.1
95-th percentile436.1
Maximum493.9
Range435.2
Interquartile range (IQR)222.95

Descriptive statistics

Standard deviation142.06484
Coefficient of variation (CV)0.73415071
Kurtosis-0.8585504
Mean193.50909
Median Absolute Deviation (MAD)92.85
Skewness0.63078829
Sum4257.2
Variance20182.418
MonotonicityNot monotonic
2023-12-13T01:11:27.546091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
223.7 1
 
4.5%
316.2 1
 
4.5%
80.1 1
 
4.5%
493.9 1
 
4.5%
85.8 1
 
4.5%
438.4 1
 
4.5%
73.1 1
 
4.5%
392.4 1
 
4.5%
69.7 1
 
4.5%
332.2 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
58.7 1
4.5%
59.5 1
4.5%
60.5 1
4.5%
63.3 1
4.5%
66.2 1
4.5%
66.6 1
4.5%
68.8 1
4.5%
69.7 1
4.5%
73.1 1
4.5%
80.1 1
4.5%
ValueCountFrequency (%)
493.9 1
4.5%
438.4 1
4.5%
392.4 1
4.5%
332.2 1
4.5%
316.2 1
4.5%
292.9 1
4.5%
281.7 1
4.5%
261.9 1
4.5%
239.2 1
4.5%
232.4 1
4.5%

세입_세입초과
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1636364
Minimum-8.3
Maximum14
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)36.4%
Memory size330.0 B
2023-12-13T01:11:27.636959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.3
5-th percentile-7.75
Q1-1.2
median0.7
Q32.275
95-th percentile12.085
Maximum14
Range22.3
Interquartile range (IQR)3.475

Descriptive statistics

Standard deviation5.265028
Coefficient of variation (CV)4.5246334
Kurtosis1.5193108
Mean1.1636364
Median Absolute Deviation (MAD)1.8
Skewness0.72297908
Sum25.6
Variance27.720519
MonotonicityNot monotonic
2023-12-13T01:11:27.725053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.6 2
 
9.1%
-0.9 1
 
4.5%
1.8 1
 
4.5%
-1.3 1
 
4.5%
3.5 1
 
4.5%
14.0 1
 
4.5%
2.3 1
 
4.5%
3.2 1
 
4.5%
0.3 1
 
4.5%
-2.5 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
-8.3 1
4.5%
-8.0 1
4.5%
-3.0 1
4.5%
-2.7 1
4.5%
-2.5 1
4.5%
-1.3 1
4.5%
-0.9 1
4.5%
-0.5 1
4.5%
0.3 1
4.5%
0.6 2
9.1%
ValueCountFrequency (%)
14.0 1
4.5%
12.3 1
4.5%
8.0 1
4.5%
3.5 1
4.5%
3.2 1
4.5%
2.3 1
4.5%
2.2 1
4.5%
1.8 1
4.5%
1.7 1
4.5%
1.5 1
4.5%

세출_현액
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195
Minimum62.8
Maximum497.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:27.815649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.8
5-th percentile67
Q170.65
median154.35
Q3285.2
95-th percentile424.125
Maximum497.9
Range435.1
Interquartile range (IQR)214.55

Descriptive statistics

Standard deviation140.00204
Coefficient of variation (CV)0.71795917
Kurtosis-0.83200344
Mean195
Median Absolute Deviation (MAD)87.95
Skewness0.6255166
Sum4290
Variance19600.57
MonotonicityNot monotonic
2023-12-13T01:11:27.907378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
67.0 2
 
9.1%
225.4 1
 
4.5%
70.6 1
 
4.5%
79.8 1
 
4.5%
497.9 1
 
4.5%
83.3 1
 
4.5%
425.9 1
 
4.5%
72.4 1
 
4.5%
390.4 1
 
4.5%
71.5 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
62.8 1
4.5%
67.0 2
9.1%
68.3 1
4.5%
68.9 1
4.5%
70.6 1
4.5%
70.8 1
4.5%
71.5 1
4.5%
72.4 1
4.5%
79.8 1
4.5%
83.3 1
4.5%
ValueCountFrequency (%)
497.9 1
4.5%
425.9 1
4.5%
390.4 1
4.5%
336.3 1
4.5%
305.9 1
4.5%
286.6 1
4.5%
281.0 1
4.5%
265.3 1
4.5%
250.0 1
4.5%
242.9 1
4.5%

세출_집행액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.24545
Minimum54.1
Maximum485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:27.999844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54.1
5-th percentile55.285
Q162.95
median149.9
Q3278.875
95-th percentile416.075
Maximum485
Range430.9
Interquartile range (IQR)215.925

Descriptive statistics

Standard deviation139.57508
Coefficient of variation (CV)0.74541239
Kurtosis-0.87804544
Mean187.24545
Median Absolute Deviation (MAD)92.35
Skewness0.61465361
Sum4119.4
Variance19481.204
MonotonicityNot monotonic
2023-12-13T01:11:28.099798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
220.7 1
 
4.5%
299.9 1
 
4.5%
74.7 1
 
4.5%
485.0 1
 
4.5%
79.1 1
 
4.5%
417.7 1
 
4.5%
68.6 1
 
4.5%
385.2 1
 
4.5%
66.4 1
 
4.5%
330.9 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
54.1 1
4.5%
55.2 1
4.5%
56.9 1
4.5%
58.2 1
4.5%
61.5 1
4.5%
62.4 1
4.5%
64.6 1
4.5%
66.4 1
4.5%
68.6 1
4.5%
74.7 1
4.5%
ValueCountFrequency (%)
485.0 1
4.5%
417.7 1
4.5%
385.2 1
4.5%
330.9 1
4.5%
299.9 1
4.5%
280.5 1
4.5%
274.0 1
4.5%
257.9 1
4.5%
236.4 1
4.5%
229.5 1
4.5%

결산_결산상잉여금
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0863636
Minimum1.3
Maximum20.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:28.193502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.805
Q13.725
median4.55
Q37.075
95-th percentile12.395
Maximum20.6
Range19.3
Interquartile range (IQR)3.35

Descriptive statistics

Standard deviation4.3066168
Coefficient of variation (CV)0.70758453
Kurtosis5.4594075
Mean6.0863636
Median Absolute Deviation (MAD)1.35
Skewness2.16127
Sum133.9
Variance18.546948
MonotonicityNot monotonic
2023-12-13T01:11:28.309993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.2 2
 
9.1%
4.6 1
 
4.5%
5.4 1
 
4.5%
8.8 1
 
4.5%
6.7 1
 
4.5%
20.6 1
 
4.5%
4.5 1
 
4.5%
7.2 1
 
4.5%
3.4 1
 
4.5%
1.3 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
1.3 1
4.5%
2.8 1
4.5%
2.9 1
4.5%
3.0 1
4.5%
3.4 1
4.5%
3.6 1
4.5%
4.1 1
4.5%
4.2 2
9.1%
4.4 1
4.5%
4.5 1
4.5%
ValueCountFrequency (%)
20.6 1
4.5%
12.4 1
4.5%
12.3 1
4.5%
8.8 1
4.5%
7.7 1
4.5%
7.2 1
4.5%
6.7 1
4.5%
5.4 1
4.5%
5.1 1
4.5%
4.7 1
4.5%

결산_이월
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5409091
Minimum0.9
Maximum5.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:28.428430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.3
Q11.525
median2.25
Q32.95
95-th percentile5.165
Maximum5.6
Range4.7
Interquartile range (IQR)1.425

Descriptive statistics

Standard deviation1.3333306
Coefficient of variation (CV)0.52474551
Kurtosis0.20953755
Mean2.5409091
Median Absolute Deviation (MAD)0.75
Skewness1.0268593
Sum55.9
Variance1.7777706
MonotonicityNot monotonic
2023-12-13T01:11:28.550601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2.8 3
13.6%
1.3 3
13.6%
1.6 2
 
9.1%
2.2 1
 
4.5%
3.0 1
 
4.5%
2.3 1
 
4.5%
2.6 1
 
4.5%
0.9 1
 
4.5%
1.4 1
 
4.5%
1.7 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0.9 1
 
4.5%
1.3 3
13.6%
1.4 1
 
4.5%
1.5 1
 
4.5%
1.6 2
9.1%
1.7 1
 
4.5%
1.9 1
 
4.5%
2.2 1
 
4.5%
2.3 1
 
4.5%
2.6 1
 
4.5%
ValueCountFrequency (%)
5.6 1
 
4.5%
5.2 1
 
4.5%
4.5 1
 
4.5%
4.4 1
 
4.5%
3.2 1
 
4.5%
3.0 1
 
4.5%
2.8 3
13.6%
2.6 1
 
4.5%
2.3 1
 
4.5%
2.2 1
 
4.5%

결산_세계잉여금
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.55
Minimum-1
Maximum18
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)13.6%
Memory size330.0 B
2023-12-13T01:11:28.648805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.895
Q10.15
median2.3
Q35.6
95-th percentile10.69
Maximum18
Range19
Interquartile range (IQR)5.45

Descriptive statistics

Standard deviation4.6491935
Coefficient of variation (CV)1.309632
Kurtosis3.3672448
Mean3.55
Median Absolute Deviation (MAD)2.2
Skewness1.7207958
Sum78.1
Variance21.615
MonotonicityNot monotonic
2023-12-13T01:11:28.760119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.1 3
 
13.6%
2.5 2
 
9.1%
0.9 1
 
4.5%
10.7 1
 
4.5%
3.1 1
 
4.5%
6.0 1
 
4.5%
5.3 1
 
4.5%
18.0 1
 
4.5%
3.7 1
 
4.5%
5.7 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
-1.0 1
 
4.5%
-0.9 1
 
4.5%
-0.8 1
 
4.5%
0.1 3
13.6%
0.3 1
 
4.5%
0.9 1
 
4.5%
1.2 1
 
4.5%
1.9 1
 
4.5%
2.1 1
 
4.5%
2.5 2
9.1%
ValueCountFrequency (%)
18.0 1
4.5%
10.7 1
4.5%
10.5 1
4.5%
6.1 1
4.5%
6.0 1
4.5%
5.7 1
4.5%
5.3 1
4.5%
3.7 1
4.5%
3.1 1
4.5%
2.5 2
9.1%

결산_불용
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2136364
Minimum2.6
Maximum10.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T01:11:28.926690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile2.805
Q13.275
median4.3
Q35.825
95-th percentile10.48
Maximum10.9
Range8.3
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation2.5234699
Coefficient of variation (CV)0.48401341
Kurtosis0.63325797
Mean5.2136364
Median Absolute Deviation (MAD)1.35
Skewness1.2282338
Sum114.7
Variance6.3679004
MonotonicityNot monotonic
2023-12-13T01:11:29.062432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2.9 3
 
13.6%
3.8 2
 
9.1%
4.3 2
 
9.1%
2.6 1
 
4.5%
4.2 1
 
4.5%
2.8 1
 
4.5%
10.1 1
 
4.5%
5.5 1
 
4.5%
4.1 1
 
4.5%
5.6 1
 
4.5%
Other values (8) 8
36.4%
ValueCountFrequency (%)
2.6 1
 
4.5%
2.8 1
 
4.5%
2.9 3
13.6%
3.1 1
 
4.5%
3.8 2
9.1%
4.1 1
 
4.5%
4.2 1
 
4.5%
4.3 2
9.1%
4.9 1
 
4.5%
5.4 1
 
4.5%
ValueCountFrequency (%)
10.9 1
4.5%
10.5 1
4.5%
10.1 1
4.5%
7.6 1
4.5%
6.6 1
4.5%
5.9 1
4.5%
5.6 1
4.5%
5.5 1
4.5%
5.4 1
4.5%
4.9 1
4.5%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2023-07-17 00:00:00
Maximum2023-07-17 00:00:00
2023-12-13T01:11:29.175002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:29.287082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T01:11:25.683083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:16.794188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.004456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.901738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.834468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.739933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.686693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.680703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.701137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.909906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.768950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:16.883740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.087904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.004817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.936935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.857364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.795547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.805693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.791894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.991626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.839440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:16.964377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.164116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.117913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.024606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.950434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.896727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.906379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.878237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.061589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.901832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.039026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.250734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.213973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.107152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.028898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.008964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.015688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.965616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.133256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.961115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.442777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.338685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.308842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.205648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.131143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.093340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.111593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.047059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.213612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:26.033490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.523378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.426169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.383457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.285451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.228927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.177554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.209905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.467376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.295684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:26.097196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.609715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.527643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.461188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.373983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.310064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.269458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.298672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.561692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.378411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:26.166156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.716027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.622360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.580326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.476569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.397534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.382533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.401839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.661254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.457678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:26.223095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.809796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.701858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.668694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.560598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.486924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.481083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.487010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.750131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.531740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:26.291301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:17.910153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:18.810561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:19.759291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:20.657725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:21.596059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:22.592013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:23.602020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:24.841110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:25.607332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:11:29.374170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분예산_본예산예산_최종세입_징수액세입_세입초과세출_현액세출_집행액결산_결산상잉여금결산_이월결산_세계잉여금결산_불용
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
예산_본예산1.0001.0000.9290.9570.6540.9290.9290.7330.0000.3990.000
예산_최종1.0000.9291.0000.9980.6481.0001.0000.8540.5580.7180.000
세입_징수액1.0000.9570.9981.0000.5670.9980.9980.8610.4480.6900.000
세입_세입초과1.0000.6540.6480.5671.0000.6480.6480.6560.2440.7360.634
세출_현액1.0000.9291.0000.9980.6481.0001.0000.8540.5580.7180.000
세출_집행액1.0000.9291.0000.9980.6481.0001.0000.8540.5580.7180.000
결산_결산상잉여금1.0000.7330.8540.8610.6560.8540.8541.0000.5680.9670.000
결산_이월1.0000.0000.5580.4480.2440.5580.5580.5681.0000.5910.825
결산_세계잉여금1.0000.3990.7180.6900.7360.7180.7180.9670.5911.0000.000
결산_불용1.0000.0000.0000.0000.6340.0000.0000.0000.8250.0001.000
2023-12-13T01:11:29.526424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산_본예산예산_최종세입_징수액세입_세입초과세출_현액세출_집행액결산_결산상잉여금결산_이월결산_세계잉여금결산_불용
예산_본예산1.0000.9950.9970.3110.9880.9970.341-0.5110.6610.123
예산_최종0.9951.0000.9970.3040.9980.9970.335-0.5100.6570.137
세입_징수액0.9970.9971.0000.3220.9911.0000.341-0.5160.6710.122
세입_세입초과0.3110.3040.3221.0000.2900.3220.753-0.4460.829-0.515
세출_현액0.9880.9980.9910.2901.0000.9910.337-0.4950.6440.156
세출_집행액0.9970.9971.0000.3220.9911.0000.341-0.5160.6710.122
결산_결산상잉여금0.3410.3350.3410.7530.3370.3411.000-0.0300.777-0.083
결산_이월-0.511-0.510-0.516-0.446-0.495-0.516-0.0301.000-0.5370.406
결산_세계잉여금0.6610.6570.6710.8290.6440.6710.777-0.5371.000-0.158
결산_불용0.1230.1370.122-0.5150.1560.122-0.0830.406-0.1581.000

Missing values

2023-12-13T01:11:26.379033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:11:26.543863image/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

구분예산_본예산예산_최종세입_징수액세입_세입초과세출_현액세출_집행액결산_결산상잉여금결산_이월결산_세계잉여금결산_불용데이터기준일
02012_일반회계223.1223.1223.70.6225.4220.73.02.20.92.62023-07-17
12012_특별회계59.559.558.7-0.962.854.14.65.6-1.03.12023-07-17
22013_일반회계236.2240.7232.4-8.3242.9229.52.82.80.110.52023-07-17
32013_특별회계62.263.260.5-2.768.956.93.64.5-0.87.62023-07-17
42014_일반회계247.2247.2239.2-8.0250.0236.42.92.80.110.92023-07-17
52014_특별회계62.562.559.5-3.067.055.24.45.2-0.96.62023-07-17
62015_일반회계258.6262.5261.9-0.5265.3257.94.11.52.55.92023-07-17
72015_특별회계64.265.566.20.670.861.54.74.40.34.92023-07-17
82016_일반회계268.4279.4281.72.2281.0274.07.71.66.15.42023-07-17
92016_특별회계62.362.563.30.867.058.25.13.21.95.62023-07-17
구분예산_본예산예산_최종세입_징수액세입_세입초과세출_현액세출_집행액결산_결산상잉여금결산_이월결산_세계잉여금결산_불용데이터기준일
122018_일반회계301.4304.0316.212.3305.9299.912.31.610.74.32023-07-17
132018_특별회계67.267.368.81.570.664.64.21.72.54.32023-07-17
142019_일반회계331.8334.7332.2-2.5336.3330.91.31.30.14.12023-07-17
152019_특별회계68.069.469.70.371.566.43.41.32.13.82023-07-17
162020_일반회계356.6389.2392.43.2390.4385.27.21.45.73.82023-07-17
172020_특별회계70.570.873.12.372.468.64.50.93.72.92023-07-17
182021_일반회계380.9424.4438.414.0425.9417.720.62.618.05.52023-07-17
192021_특별회계79.082.385.83.583.379.16.71.35.32.92023-07-17
202022_일반회계421.4495.2493.9-1.3497.9485.08.82.86.010.12023-07-17
212022_특별회계76.378.380.11.879.874.75.42.33.12.82023-07-17