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

Description행정 운영경비 비중 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=2V8QAN0XT6IGU2DR3FAD22982202&infSeq=1

Alerts

행정운영경비비중비율 is highly overall correlated with 일반회계예산액(원)High correlation
행정운영경비(원) is highly overall correlated with 일반회계예산액(원)High correlation
일반회계예산액(원) is highly overall correlated with 행정운영경비비중비율 and 1 other fieldsHigh correlation
시군명 has 243 (88.4%) missing valuesMissing
행정운영경비(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:27:28.737213
Analysis finished2023-12-10 22:27:29.931785
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
2022
243 
2023
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2022 243
88.4%
2023 32
 
11.6%

Length

2023-12-11T07:27:29.990181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:30.094262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 243
88.4%
2023 32
 
11.6%

시군명
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing243
Missing (%)88.4%
Memory size2.3 KiB
2023-12-11T07:27:30.298767image/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%
2023-12-11T07:27:30.708798image/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
2023-12-11T07:27:31.044783image/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%
2023-12-11T07:27:31.596076image/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%

행정운영경비비중비율
Real number (ℝ)

HIGH CORRELATION 

Distinct238
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.345782
Minimum1.8
Maximum27.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:27:31.759767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile4.686
Q111.64
median13.24
Q315.005
95-th percentile18.938
Maximum27.63
Range25.83
Interquartile range (IQR)3.365

Descriptive statistics

Standard deviation3.7537573
Coefficient of variation (CV)0.28126919
Kurtosis2.5520979
Mean13.345782
Median Absolute Deviation (MAD)1.7
Skewness-0.21117777
Sum3670.09
Variance14.090694
MonotonicityNot monotonic
2023-12-11T07:27:31.902188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.02 3
 
1.1%
12.59 3
 
1.1%
14.12 3
 
1.1%
13.71 3
 
1.1%
11.4 3
 
1.1%
11.05 2
 
0.7%
12.66 2
 
0.7%
11.37 2
 
0.7%
4.05 2
 
0.7%
14.06 2
 
0.7%
Other values (228) 250
90.9%
ValueCountFrequency (%)
1.8 1
0.4%
1.83 1
0.4%
2.89 1
0.4%
2.91 1
0.4%
2.96 1
0.4%
3.16 2
0.7%
3.18 1
0.4%
3.76 1
0.4%
4.05 2
0.7%
4.14 1
0.4%
ValueCountFrequency (%)
27.63 1
0.4%
25.84 1
0.4%
25.67 1
0.4%
23.09 1
0.4%
22.59 1
0.4%
21.86 1
0.4%
20.82 1
0.4%
20.55 1
0.4%
19.76 1
0.4%
19.7 1
0.4%

행정운영경비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2786985 × 1011
Minimum3.7305404 × 1010
Maximum9.900145 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:27:32.064429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7305404 × 1010
5-th percentile5.7471032 × 1010
Q16.8516725 × 1010
median9.7655725 × 1010
Q31.4139819 × 1011
95-th percentile2.9954866 × 1011
Maximum9.900145 × 1011
Range9.5270909 × 1011
Interquartile range (IQR)7.2881465 × 1010

Descriptive statistics

Standard deviation1.01061 × 1011
Coefficient of variation (CV)0.79034269
Kurtosis22.442986
Mean1.2786985 × 1011
Median Absolute Deviation (MAD)3.2059599 × 1010
Skewness3.7878629
Sum3.5164209 × 1013
Variance1.0213326 × 1022
MonotonicityNot monotonic
2023-12-11T07:27:32.217444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76980922000 1
 
0.4%
292110975000 1
 
0.4%
202383720000 1
 
0.4%
230559434000 1
 
0.4%
212340362000 1
 
0.4%
217992254000 1
 
0.4%
246001718000 1
 
0.4%
274537506000 1
 
0.4%
307199550000 1
 
0.4%
193453956000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
37305404000 1
0.4%
41191313000 1
0.4%
42854768000 1
0.4%
46126871000 1
0.4%
49522509000 1
0.4%
51684412000 1
0.4%
51797888000 1
0.4%
52384323000 1
0.4%
52934414000 1
0.4%
54831483000 1
0.4%
ValueCountFrequency (%)
990014496000 1
0.4%
599887570000 1
0.4%
549522450000 1
0.4%
538644653000 1
0.4%
462550540000 1
0.4%
450162224000 1
0.4%
386093193000 1
0.4%
359185675000 1
0.4%
355148720000 1
0.4%
349247261000 1
0.4%

일반회계예산액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5095293 × 1012
Minimum1.6694544 × 1011
Maximum3.134246 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:27:32.347385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6694544 × 1011
5-th percentile3.5384416 × 1011
Q15.0052025 × 1011
median7.2 × 1011
Q31.0712129 × 1012
95-th percentile5.4103729 × 1012
Maximum3.134246 × 1013
Range3.1175514 × 1013
Interquartile range (IQR)5.7069266 × 1011

Descriptive statistics

Standard deviation3.4509361 × 1012
Coefficient of variation (CV)2.2861008
Kurtosis53.190142
Mean1.5095293 × 1012
Median Absolute Deviation (MAD)2.521893 × 1011
Skewness6.8566682
Sum4.1512056 × 1014
Variance1.190896 × 1025
MonotonicityNot monotonic
2023-12-11T07:27:32.494009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720000000000 2
 
0.7%
462298820000 1
 
0.4%
2515721349000 1
 
0.4%
1693115718000 1
 
0.4%
2575005687000 1
 
0.4%
1813406305000 1
 
0.4%
1717423441000 1
 
0.4%
1813531925000 1
 
0.4%
2597624002000 1
 
0.4%
2593956137000 1
 
0.4%
Other values (264) 264
96.0%
ValueCountFrequency (%)
166945436000 1
0.4%
196476971000 1
0.4%
215500000000 1
0.4%
243024278000 1
0.4%
280435723000 1
0.4%
287000000000 1
0.4%
301251647000 1
0.4%
308000000000 1
0.4%
314807864000 1
0.4%
324724497000 1
0.4%
ValueCountFrequency (%)
31342459847000 1
0.4%
29977017979000 1
0.4%
29975489088000 1
0.4%
11128166851000 1
0.4%
10108293256000 1
0.4%
9757400000000 1
0.4%
9326396434000 1
0.4%
9058346252000 1
0.4%
8027600000000 1
0.4%
7820000000000 1
0.4%

Interactions

2023-12-11T07:27:29.491943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:28.986307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.239869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.578632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.068814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.324216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.661207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.150065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:29.402178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:27:32.849527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명행정운영경비비중비율행정운영경비(원)일반회계예산액(원)
회계연도1.000NaN0.1110.1340.000
시군명NaN1.0001.0001.0001.000
행정운영경비비중비율0.1111.0001.0000.4520.840
행정운영경비(원)0.1341.0000.4521.0000.709
일반회계예산액(원)0.0001.0000.8400.7091.000
2023-12-11T07:27:32.983514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정운영경비비중비율행정운영경비(원)일반회계예산액(원)회계연도
행정운영경비비중비율1.000-0.365-0.6370.083
행정운영경비(원)-0.3651.0000.9350.142
일반회계예산액(원)-0.6370.9351.0000.000
회계연도0.0830.1420.0001.000

Missing values

2023-12-11T07:27:29.772098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:27:29.886663image/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

회계연도시군명자치단체명행정운영경비비중비율행정운영경비(원)일반회계예산액(원)
02023가평군경기가평군16.6576980922000462298820000
12023경기도경기본청1.8354952245000029977017979000
22023고양시경기고양시12.83285879970002567500410000
32023과천시경기과천시16.8763729513000377702837000
42023광명시경기광명시14.72130532141000886744415000
52023광주시경기광주시10.731170046450001090002336000
62023구리시경기구리시14.1284292358000596801550000
72023군포시경기군포시13.7899671992000723320973000
82023김포시경기김포시10.231438852220001406265935000
92023남양주시경기남양주시11.462185864320001907535437000
회계연도시군명자치단체명행정운영경비비중비율행정운영경비(원)일반회계예산액(원)
2652022<NA>전북진안군14.763870334000434554662000
2662022<NA>전북무주군14.1258674205000415642919000
2672022<NA>전북장수군14.7559091773000400605265000
2682022<NA>전북순창군13.9462192992000446013000000
2692022<NA>전북고창군11.9183891159000704472750000
2702022<NA>전북부안군12.7285943187000675507363000
2712022<NA>전남본청3.162859780060009058346252000
2722022<NA>전남목포시16.17130371533000806474498000
2732022<NA>전남여수시14.441767947650001224140080000
2742022<NA>전남순천시12.871518519100001179535399000