Overview

Dataset statistics

Number of variables6
Number of observations275
Missing cells243
Missing cells (%)14.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.1 KiB
Average record size in memory52.5 B

Variable types

Categorical1
Text2
Numeric3

Dataset

Description기관운영 기본경비 비율 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=51T8P1HM4AQ15YOIBFLD22321898&infSeq=1

Alerts

기본경비(원) is highly overall correlated with 세출결산액(원)High correlation
세출결산액(원) is highly overall correlated with 기본경비(원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
기본경비(원) has unique valuesUnique
세출결산액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:03:42.679326
Analysis finished2023-12-10 21:03:44.641079
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2020
243 
2021
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 243
88.4%
2021 32
 
11.6%

Length

2023-12-11T06:03:44.723405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:03:44.844806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 243
88.4%
2021 32
 
11.6%

시군명
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing243
Missing (%)88.4%
Memory size2.3 KiB
2023-12-11T06:03:45.025999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.09375
Min length3

Characters and Unicode

Total characters99
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row경기도
3rd row고양시
4th row과천시
5th row광명시
ValueCountFrequency (%)
경기도 1
 
3.1%
고양시 1
 
3.1%
화성시 1
 
3.1%
하남시 1
 
3.1%
포천시 1
 
3.1%
평택시 1
 
3.1%
파주시 1
 
3.1%
이천시 1
 
3.1%
의정부시 1
 
3.1%
의왕시 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T06:03:45.337292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

자치단체명
Text

Distinct243
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-11T06:03:45.653528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8872727
Min length4

Characters and Unicode

Total characters1344
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)76.7%

Sample

1st row경기가평군
2nd row경기본청
3rd row경기고양시
4th row경기과천시
5th row경기광명시
ValueCountFrequency (%)
경기가평군 2
 
0.7%
경기평택시 2
 
0.7%
경기안성시 2
 
0.7%
경기여주시 2
 
0.7%
경기용인시 2
 
0.7%
경기연천군 2
 
0.7%
경기양평군 2
 
0.7%
경기의왕시 2
 
0.7%
경기하남시 2
 
0.7%
경기이천시 2
 
0.7%
Other values (233) 255
92.7%
2023-12-11T06:03:46.101436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1344
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1344
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1344
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

기본경비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.674304 × 109
Minimum6.375251 × 108
Maximum3.7131501 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:03:46.275296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.375251 × 108
5-th percentile1.3678143 × 109
Q12.2552277 × 109
median3.2859918 × 109
Q35.7421456 × 109
95-th percentile1.2089308 × 1010
Maximum3.7131501 × 1010
Range3.6493976 × 1010
Interquartile range (IQR)3.4869179 × 109

Descriptive statistics

Standard deviation4.0522637 × 109
Coefficient of variation (CV)0.86692344
Kurtosis16.911518
Mean4.674304 × 109
Median Absolute Deviation (MAD)1.3575952 × 109
Skewness3.2243772
Sum1.2854336 × 1012
Variance1.6420841 × 1019
MonotonicityNot monotonic
2023-12-11T06:03:46.416174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1777839140 1
 
0.4%
3961357720 1
 
0.4%
2377963310 1
 
0.4%
2134173060 1
 
0.4%
1796456794 1
 
0.4%
1398778000 1
 
0.4%
1311097450 1
 
0.4%
3094141600 1
 
0.4%
2297327640 1
 
0.4%
4235197847 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
637525095 1
0.4%
734407270 1
0.4%
975821300 1
0.4%
1078371750 1
0.4%
1136329672 1
0.4%
1149345671 1
0.4%
1183122420 1
0.4%
1273071621 1
0.4%
1305462940 1
0.4%
1311097450 1
0.4%
ValueCountFrequency (%)
37131500687 1
0.4%
21680471884 1
0.4%
18976906762 1
0.4%
18349436888 1
0.4%
18111882211 1
0.4%
16838315639 1
0.4%
16519190876 1
0.4%
16425239854 1
0.4%
16320208520 1
0.4%
15748108802 1
0.4%

세출결산액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.589749 × 1012
Minimum1.9776779 × 1011
Maximum3.5414408 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:03:46.556736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9776779 × 1011
5-th percentile3.5484197 × 1011
Q15.2326295 × 1011
median7.738949 × 1011
Q31.214491 × 1012
95-th percentile5.2740029 × 1012
Maximum3.5414408 × 1013
Range3.521664 × 1013
Interquartile range (IQR)6.9122808 × 1011

Descriptive statistics

Standard deviation3.6604453 × 1012
Coefficient of variation (CV)2.3025304
Kurtosis56.592463
Mean1.589749 × 1012
Median Absolute Deviation (MAD)2.7304387 × 1011
Skewness7.0752606
Sum4.3718097 × 1014
Variance1.339886 × 1025
MonotonicityNot monotonic
2023-12-11T06:03:46.688198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
627701847937 1
 
0.4%
751559629840 1
 
0.4%
703716050428 1
 
0.4%
435510239630 1
 
0.4%
457096571521 1
 
0.4%
391505170176 1
 
0.4%
385797306667 1
 
0.4%
417607802550 1
 
0.4%
935561010558 1
 
0.4%
725887670568 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
197767788320 1
0.4%
206093632978 1
0.4%
220834492812 1
0.4%
241683889120 1
0.4%
271540996093 1
0.4%
314267279691 1
0.4%
328353120047 1
0.4%
336872668680 1
0.4%
341956394407 1
0.4%
343712572400 1
0.4%
ValueCountFrequency (%)
35414408138443 1
0.4%
31952141756841 1
0.4%
30265897238043 1
0.4%
11495800974570 1
0.4%
10551719887982 1
0.4%
10360701614267 1
0.4%
8919629759110 1
0.4%
8894496711282 1
0.4%
8251316785631 1
0.4%
7990852530568 1
0.4%

기관운영기본경비비율(%)
Real number (ℝ)

Distinct85
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45850909
Minimum0.05
Maximum1.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:03:46.822491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.177
Q10.3
median0.42
Q30.59
95-th percentile0.873
Maximum1.21
Range1.16
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.21750651
Coefficient of variation (CV)0.47437775
Kurtosis1.0646225
Mean0.45850909
Median Absolute Deviation (MAD)0.14
Skewness0.91575758
Sum126.09
Variance0.047309083
MonotonicityNot monotonic
2023-12-11T06:03:46.957592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 9
 
3.3%
0.4 8
 
2.9%
0.34 8
 
2.9%
0.35 8
 
2.9%
0.28 7
 
2.5%
0.31 7
 
2.5%
0.26 7
 
2.5%
0.43 6
 
2.2%
0.44 6
 
2.2%
0.5 6
 
2.2%
Other values (75) 203
73.8%
ValueCountFrequency (%)
0.05 1
 
0.4%
0.06 1
 
0.4%
0.09 1
 
0.4%
0.1 4
1.5%
0.11 1
 
0.4%
0.12 2
0.7%
0.13 1
 
0.4%
0.16 2
0.7%
0.17 1
 
0.4%
0.18 3
1.1%
ValueCountFrequency (%)
1.21 1
0.4%
1.2 1
0.4%
1.19 1
0.4%
1.16 1
0.4%
1.11 1
0.4%
1.08 1
0.4%
0.97 1
0.4%
0.96 2
0.7%
0.92 1
0.4%
0.9 1
0.4%

Interactions

2023-12-11T06:03:44.021722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:43.007551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:43.649350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:44.145791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:43.417881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:43.746354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:44.266036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:43.546399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:43.904822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:03:47.045138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명기본경비(원)세출결산액(원)기관운영기본경비비율(%)
회계연도1.000NaN0.0000.0000.403
시군명NaN1.0001.0001.0001.000
기본경비(원)0.0001.0001.0000.7520.455
세출결산액(원)0.0001.0000.7521.0000.526
기관운영기본경비비율(%)0.4031.0000.4550.5261.000
2023-12-11T06:03:47.146502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본경비(원)세출결산액(원)기관운영기본경비비율(%)회계연도
기본경비(원)1.0000.7640.2240.000
세출결산액(원)0.7641.000-0.3790.000
기관운영기본경비비율(%)0.224-0.3791.0000.304
회계연도0.0000.0000.3041.000

Missing values

2023-12-11T06:03:44.419462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:03:44.580083image/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

회계연도시군명자치단체명기본경비(원)세출결산액(원)기관운영기본경비비율(%)
02021가평군경기가평군17778391406277018479370.28
12021경기도경기본청18111882211354144081384430.05
22021고양시경기고양시1207199476026919678172000.45
32021과천시경기과천시16023809164446817507130.36
42021광명시경기광명시46471769608636379950510.54
52021광주시경기광주시324272305013223667671290.25
62021구리시경기구리시21199095706637630135200.32
72021군포시경기군포시38886394757948364909030.49
82021김포시경기김포시340273244014960966039300.23
92021남양주시경기남양주시711203302620046558214050.35
회계연도시군명자치단체명기본경비(원)세출결산액(원)기관운영기본경비비율(%)
2652020<NA>서울성북구68650744249872239971160.7
2662020<NA>서울강북구66461939068392410410780.79
2672020<NA>서울도봉구80231001328243336024910.97
2682020<NA>서울노원구1045602420312962255414050.81
2692020<NA>서울은평구531919722310700077597450.5
2702020<NA>서울서대문구51765204707727180188600.67
2712020<NA>서울마포구53381568348189418921600.65
2722020<NA>서울양천구64133003509159743409130.7
2732020<NA>서울강서구826154932012627487051700.65
2742020<NA>서울구로구42299408729583253128020.44