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

Description행사축제경비 비율 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=ZWCB4FQ16NK5Q3EDD7HV22965560&infSeq=1

Alerts

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

Reproduction

Analysis started2023-12-10 21:41:36.796749
Analysis finished2023-12-10 21:41:38.199425
Duration1.4 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

2023-12-11T06:41:38.253661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:41:38.339127image/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
2023-12-11T06:41:38.501179image/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%
2023-12-11T06:41:38.823057image/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
2023-12-11T06:41:39.123676image/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%
2023-12-11T06:41:39.569650image/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%

행사축제경비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7759628 × 109
Minimum32569290
Maximum3.4280593 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:41:39.707357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32569290
5-th percentile3.9976403 × 108
Q11.1504993 × 109
median2.0231443 × 109
Q33.4669766 × 109
95-th percentile7.2260785 × 109
Maximum3.4280593 × 1010
Range3.4248024 × 1010
Interquartile range (IQR)2.3164774 × 109

Descriptive statistics

Standard deviation3.035361 × 109
Coefficient of variation (CV)1.0934444
Kurtosis44.376874
Mean2.7759628 × 109
Median Absolute Deviation (MAD)1.0152857 × 109
Skewness5.1911133
Sum7.6338978 × 1011
Variance9.2134164 × 1018
MonotonicityNot monotonic
2023-12-11T06:41:39.867326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3049713140 1
 
0.4%
3106259400 1
 
0.4%
2047113120 1
 
0.4%
2160943410 1
 
0.4%
2422314240 1
 
0.4%
2350496440 1
 
0.4%
1057478200 1
 
0.4%
685785790 1
 
0.4%
2426243900 1
 
0.4%
2673511670 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
32569290 1
0.4%
71216810 1
0.4%
105417680 1
0.4%
143192860 1
0.4%
161000770 1
0.4%
170057840 1
0.4%
178819130 1
0.4%
239494090 1
0.4%
242176490 1
0.4%
336655910 1
0.4%
ValueCountFrequency (%)
34280593324 1
0.4%
16221935815 1
0.4%
15748528059 1
0.4%
12999026810 1
0.4%
11179029330 1
0.4%
10594868640 1
0.4%
9745889540 1
0.4%
9006123440 1
0.4%
8840935465 1
0.4%
8297610410 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
2023-12-11T06:41:40.012818image/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
2023-12-11T06:41:40.151147image/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%

행사축제경비비율(%)
Real number (ℝ)

Distinct70
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27621818
Minimum0.01
Maximum2.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:41:40.276076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.14
median0.23
Q30.36
95-th percentile0.613
Maximum2.12
Range2.11
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.21150716
Coefficient of variation (CV)0.76572499
Kurtosis20.940895
Mean0.27621818
Median Absolute Deviation (MAD)0.11
Skewness3.1163295
Sum75.96
Variance0.044735281
MonotonicityNot monotonic
2023-12-11T06:41:40.413405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19 14
 
5.1%
0.18 11
 
4.0%
0.25 9
 
3.3%
0.14 9
 
3.3%
0.31 9
 
3.3%
0.16 8
 
2.9%
0.08 8
 
2.9%
0.17 8
 
2.9%
0.15 8
 
2.9%
0.12 8
 
2.9%
Other values (60) 183
66.5%
ValueCountFrequency (%)
0.01 1
 
0.4%
0.02 6
2.2%
0.03 3
 
1.1%
0.04 3
 
1.1%
0.05 4
1.5%
0.06 6
2.2%
0.07 3
 
1.1%
0.08 8
2.9%
0.09 6
2.2%
0.1 2
 
0.7%
ValueCountFrequency (%)
2.12 1
0.4%
0.97 1
0.4%
0.94 1
0.4%
0.86 1
0.4%
0.85 1
0.4%
0.82 1
0.4%
0.78 2
0.7%
0.76 1
0.4%
0.74 1
0.4%
0.72 1
0.4%

Interactions

2023-12-11T06:41:37.797221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.276722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.537661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.875753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.368692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.626741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.959643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.462925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:37.717480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:41:40.502817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명행사축제경비(원)세출결산액(원)행사축제경비비율(%)
회계연도1.000NaN0.3170.0000.190
시군명NaN1.0001.0001.0001.000
행사축제경비(원)0.3171.0001.0000.8590.660
세출결산액(원)0.0001.0000.8591.0000.000
행사축제경비비율(%)0.1901.0000.6600.0001.000
2023-12-11T06:41:40.597511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행사축제경비(원)세출결산액(원)행사축제경비비율(%)회계연도
행사축제경비(원)1.0000.5900.4790.227
세출결산액(원)0.5901.000-0.3120.000
행사축제경비비율(%)0.479-0.3121.0000.136
회계연도0.2270.0000.1361.000

Missing values

2023-12-11T06:41:38.055187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:41:38.161045image/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

회계연도시군명자치단체명행사축제경비(원)세출결산액(원)행사축제경비비율(%)
02021가평군경기가평군30497131406277018479370.49
12021경기도경기본청8186054560354144081384430.02
22021고양시경기고양시716482571026919678172000.27
32021과천시경기과천시13321447404446817507130.3
42021광명시경기광명시26333388508636379950510.3
52021광주시경기광주시245158474813223667671290.19
62021구리시경기구리시27559667106637630135200.42
72021군포시경기군포시20231442697948364909030.25
82021김포시경기김포시312548743014960966039300.21
92021남양주시경기남양주시494956661720046558214050.25
회계연도시군명자치단체명행사축제경비(원)세출결산액(원)행사축제경비비율(%)
2652020<NA>서울중랑구188321043010004078180720.19
2662020<NA>서울성북구20228738409872239971160.2
2672020<NA>서울강북구12794494508392410410780.15
2682020<NA>서울도봉구39230233268243336024910.48
2692020<NA>서울노원구89435494012962255414050.07
2702020<NA>서울은평구295091235010700077597450.28
2712020<NA>서울서대문구15069737007727180188600.2
2722020<NA>서울마포구18355293588189418921600.22
2732020<NA>서울양천구16470687309159743409130.18
2742020<NA>서울강서구106118472012627487051700.08