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

Description지방의회경비 비율 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=93LRHI72ZBC3L6J74Q4A22878585&infSeq=1

Alerts

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

Reproduction

Analysis started2023-12-10 22:54:05.574115
Analysis finished2023-12-10 22:54:06.958662
Duration1.38 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

2023-12-11T07:54:07.011934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:54:07.113416image/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
2023-12-11T07:54:07.285678image/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%
2023-12-11T07:54:07.578860image/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
2023-12-11T07:54:07.849548image/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%
2023-12-11T07:54:08.242562image/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%

지방의회경비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7835672 × 108
Minimum2.892908 × 108
Maximum1.2644588 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:54:08.365441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.892908 × 108
5-th percentile3.2833008 × 108
Q14.111119 × 108
median6.1466106 × 108
Q31.009615 × 109
95-th percentile2.4739616 × 109
Maximum1.2644588 × 1010
Range1.2355297 × 1010
Interquartile range (IQR)5.9850307 × 108

Descriptive statistics

Standard deviation1.337495 × 109
Coefficient of variation (CV)1.3670831
Kurtosis45.064557
Mean9.7835672 × 108
Median Absolute Deviation (MAD)2.3776893 × 108
Skewness6.0243865
Sum2.690481 × 1011
Variance1.7888928 × 1018
MonotonicityNot monotonic
2023-12-11T07:54:08.484488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360209120 1
 
0.4%
676318850 1
 
0.4%
434786420 1
 
0.4%
442600160 1
 
0.4%
398951540 1
 
0.4%
409663230 1
 
0.4%
398244610 1
 
0.4%
619364120 1
 
0.4%
759436068 1
 
0.4%
543703170 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
289290800 1
0.4%
296319600 1
0.4%
296921740 1
0.4%
297099671 1
0.4%
305471750 1
0.4%
311860480 1
0.4%
312085610 1
0.4%
316613940 1
0.4%
317619530 1
0.4%
317888500 1
0.4%
ValueCountFrequency (%)
12644587900 1
0.4%
12294800212 1
0.4%
9608320427 1
0.4%
4690729310 1
0.4%
4610194270 1
0.4%
4360112970 1
0.4%
3733621070 1
0.4%
3589694650 1
0.4%
3544144905 1
0.4%
3426776387 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
2023-12-11T07:54:08.597429image/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
2023-12-11T07:54:08.713532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
627701847937 1
 
0.4%
935561010558 1
 
0.4%
435510239630 1
 
0.4%
457096571521 1
 
0.4%
391505170176 1
 
0.4%
385797306667 1
 
0.4%
417607802550 1
 
0.4%
751559629840 1
 
0.4%
884357039415 1
 
0.4%
667975190999 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%

지방의회경비비율(%)
Real number (ℝ)

Distinct14
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082981818
Minimum0.03
Maximum0.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:54:08.808831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.04
Q10.06
median0.08
Q30.1
95-th percentile0.13
Maximum0.19
Range0.16
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.026578276
Coefficient of variation (CV)0.32029036
Kurtosis0.72288116
Mean0.082981818
Median Absolute Deviation (MAD)0.02
Skewness0.55792142
Sum22.82
Variance0.00070640478
MonotonicityNot monotonic
2023-12-11T07:54:08.894510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.09 43
15.6%
0.08 43
15.6%
0.07 37
13.5%
0.1 35
12.7%
0.06 30
10.9%
0.05 22
8.0%
0.04 16
 
5.8%
0.12 15
 
5.5%
0.13 13
 
4.7%
0.11 11
 
4.0%
Other values (4) 10
 
3.6%
ValueCountFrequency (%)
0.03 3
 
1.1%
0.04 16
 
5.8%
0.05 22
8.0%
0.06 30
10.9%
0.07 37
13.5%
0.08 43
15.6%
0.09 43
15.6%
0.1 35
12.7%
0.11 11
 
4.0%
0.12 15
 
5.5%
ValueCountFrequency (%)
0.19 1
 
0.4%
0.17 2
 
0.7%
0.14 4
 
1.5%
0.13 13
 
4.7%
0.12 15
 
5.5%
0.11 11
 
4.0%
0.1 35
12.7%
0.09 43
15.6%
0.08 43
15.6%
0.07 37
13.5%

Interactions

2023-12-11T07:54:06.335393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:05.803010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:06.068922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:06.417020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:05.887828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:06.159396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:06.725279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:05.983074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:54:06.248076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:54:08.965527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명지방의회경비(원)세출결산액(원)지방의회경비비율(%)
회계연도1.000NaN0.1420.0000.295
시군명NaN1.0001.0001.0001.000
지방의회경비(원)0.1421.0001.0000.9490.410
세출결산액(원)0.0001.0000.9491.0000.450
지방의회경비비율(%)0.2951.0000.4100.4501.000
2023-12-11T07:54:09.048230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방의회경비(원)세출결산액(원)지방의회경비비율(%)회계연도
지방의회경비(원)1.0000.8630.0030.102
세출결산액(원)0.8631.000-0.4400.000
지방의회경비비율(%)0.003-0.4401.0000.291
회계연도0.1020.0000.2911.000

Missing values

2023-12-11T07:54:06.833477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:54:06.922798image/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

회계연도시군명자치단체명지방의회경비(원)세출결산액(원)지방의회경비비율(%)
02021가평군경기가평군3602091206277018479370.06
12021경기도경기본청12644587900354144081384430.04
22021고양시경기고양시229305967026919678172000.09
32021과천시경기과천시4452152204446817507130.1
42021광명시경기광명시6941738508636379950510.08
52021광주시경기광주시61589333413223667671290.05
62021구리시경기구리시4387303806637630135200.07
72021군포시경기군포시4975968807948364909030.06
82021김포시경기김포시75739425014960966039300.05
92021남양주시경기남양주시111777918020046558214050.06
회계연도시군명자치단체명지방의회경비(원)세출결산액(원)지방의회경비비율(%)
2652020<NA>서울중랑구107203100510004078180720.11
2662020<NA>서울성북구13078748759872239971160.13
2672020<NA>서울강북구8641518208392410410780.1
2682020<NA>서울도봉구8357807108243336024910.1
2692020<NA>서울노원구128920807012962255414050.1
2702020<NA>서울은평구112246621010700077597450.1
2712020<NA>서울서대문구9848453507727180188600.13
2722020<NA>서울마포구10706017538189418921600.13
2732020<NA>서울양천구10720369709159743409130.12
2742020<NA>서울강서구137220276012627487051700.11