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

Description공무원 인건비 비율 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=KHEAL670CYX6N3ZSXUAM22266624&infSeq=1

Alerts

공무원인건비(원) is highly overall correlated with 세출결산액(원)High correlation
세출결산액(원) is highly overall correlated with 공무원인건비(원) and 1 other fieldsHigh correlation
공무원인건비비율(%) is highly overall correlated with 세출결산액(원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
공무원인건비(원) has unique valuesUnique
세출결산액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:33:11.749041
Analysis finished2023-12-10 22:33:12.937528
Duration1.19 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

2023-12-11T07:33:12.989543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:33:13.072744image/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
2023-12-11T07:33:13.234708image/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%
2023-12-11T07:33:13.557689image/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
2023-12-11T07:33:14.149472image/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%
2023-12-11T07:33:14.638166image/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%

공무원인건비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1098126 × 1011
Minimum3.234493 × 1010
Maximum8.6144047 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:33:14.801787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.234493 × 1010
5-th percentile4.7384536 × 1010
Q15.788686 × 1010
median8.2153089 × 1010
Q31.1911751 × 1011
95-th percentile2.7653187 × 1011
Maximum8.6144047 × 1011
Range8.2909554 × 1011
Interquartile range (IQR)6.1230648 × 1010

Descriptive statistics

Standard deviation9.6886755 × 1010
Coefficient of variation (CV)0.87300107
Kurtosis18.06252
Mean1.1098126 × 1011
Median Absolute Deviation (MAD)2.609805 × 1010
Skewness3.638199
Sum3.0519845 × 1013
Variance9.3870433 × 1021
MonotonicityNot monotonic
2023-12-11T07:33:14.928431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61072739740 1
 
0.4%
396738902813 1
 
0.4%
100734160710 1
 
0.4%
93046065430 1
 
0.4%
108594028544 1
 
0.4%
130069111608 1
 
0.4%
128952846140 1
 
0.4%
196160383320 1
 
0.4%
67123810850 1
 
0.4%
33722950115 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
32344930000 1
0.4%
33722950115 1
0.4%
36141349360 1
0.4%
40609035711 1
0.4%
40757929530 1
0.4%
41825454490 1
0.4%
43341416826 1
0.4%
44357443980 1
0.4%
45736491400 1
0.4%
45890268805 1
0.4%
ValueCountFrequency (%)
861440465991 1
0.4%
616521887498 1
0.4%
498129577481 1
0.4%
494243497699 1
0.4%
454452087748 1
0.4%
448600453900 1
0.4%
420205549470 1
0.4%
414833378950 1
0.4%
410266376127 1
0.4%
396738902813 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
2023-12-11T07:33:15.054870image/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
2023-12-11T07:33:15.236420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
627701847937 1
 
0.4%
7633943887854 1
 
0.4%
935561010558 1
 
0.4%
884357039415 1
 
0.4%
966214985452 1
 
0.4%
1408664384649 1
 
0.4%
1391014917553 1
 
0.4%
2085056099749 1
 
0.4%
593011667627 1
 
0.4%
220834492812 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%

공무원인건비비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct227
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.553345
Minimum1.27
Maximum20.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:33:15.454615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile5.543
Q19.4
median10.58
Q311.74
95-th percentile14.913
Maximum20.61
Range19.34
Interquartile range (IQR)2.34

Descriptive statistics

Standard deviation2.7132261
Coefficient of variation (CV)0.25709631
Kurtosis2.8055221
Mean10.553345
Median Absolute Deviation (MAD)1.18
Skewness-0.16506774
Sum2902.17
Variance7.3615961
MonotonicityNot monotonic
2023-12-11T07:33:15.623048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.48 4
 
1.5%
9.64 4
 
1.5%
11.15 3
 
1.1%
10.43 3
 
1.1%
12.38 3
 
1.1%
10.77 3
 
1.1%
11.93 2
 
0.7%
13.68 2
 
0.7%
12.94 2
 
0.7%
10.71 2
 
0.7%
Other values (217) 247
89.8%
ValueCountFrequency (%)
1.27 1
0.4%
1.39 1
0.4%
2.35 1
0.4%
2.7 1
0.4%
2.84 1
0.4%
3.4 1
0.4%
3.56 1
0.4%
3.61 1
0.4%
3.65 1
0.4%
3.84 1
0.4%
ValueCountFrequency (%)
20.61 1
0.4%
20.54 1
0.4%
19.52 1
0.4%
17.54 1
0.4%
16.9 1
0.4%
16.31 1
0.4%
15.78 1
0.4%
15.69 1
0.4%
15.67 1
0.4%
15.66 1
0.4%

Interactions

2023-12-11T07:33:12.514298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:11.991129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.244805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.593903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.073813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.322789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.699956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.160210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:12.411210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:33:15.751400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명공무원인건비(원)세출결산액(원)공무원인건비비율(%)
회계연도1.000NaN0.2010.0000.327
시군명NaN1.0001.0001.0001.000
공무원인건비(원)0.2011.0001.0000.7780.537
세출결산액(원)0.0001.0000.7781.0000.739
공무원인건비비율(%)0.3271.0000.5370.7391.000
2023-12-11T07:33:15.868818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공무원인건비(원)세출결산액(원)공무원인건비비율(%)회계연도
공무원인건비(원)1.0000.949-0.4780.149
세출결산액(원)0.9491.000-0.6980.000
공무원인건비비율(%)-0.478-0.6981.0000.245
회계연도0.1490.0000.2451.000

Missing values

2023-12-11T07:33:12.807915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:33:12.900088image/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

회계연도시군명자치단체명공무원인건비(원)세출결산액(원)공무원인건비비율(%)
02021가평군경기가평군610727397406277018479379.73
12021경기도경기본청448600453900354144081384431.27
22021고양시경기고양시25818986380026919678172009.59
32021과천시경기과천시5211439487044468175071311.72
42021광명시경기광명시9716520616086363799505111.25
52021광주시경기광주시9886492382013223667671297.48
62021구리시경기구리시7256114614066376301352010.93
72021군포시경기군포시8486280000079483649090310.68
82021김포시경기김포시10684523556014960966039307.14
92021남양주시경기남양주시17399976876020046558214058.68
회계연도시군명자치단체명공무원인건비(원)세출결산액(원)공무원인건비비율(%)
2652020<NA>서울종로구10710434112052149752139020.54
2662020<NA>서울중구10771697900355178535278519.52
2672020<NA>서울용산구10354960450559028984199017.54
2682020<NA>서울성동구11151126597072534825192015.37
2692020<NA>서울광진구11045884722076987867781014.35
2702020<NA>서울동대문구11755742729083945910156014.0
2712020<NA>서울중랑구119322907490100040781807211.93
2722020<NA>서울성북구12288138163898722399711612.45
2732020<NA>서울강북구11152028950083924104107813.29
2742020<NA>서울도봉구10664336655782433360249112.94