Overview

Dataset statistics

Number of variables4
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory35.2 B

Variable types

Numeric2
Categorical1
Text1

Dataset

Description지역별 보행환경개선지구 지정 통계정보를 제공하는 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2990

Alerts

보행환경개선지구(개소) has 3 (2.8%) zerosZeros

Reproduction

Analysis started2024-01-09 19:51:24.301966
Analysis finished2024-01-09 19:51:24.857906
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T04:51:24.896986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018.5
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7157871
Coefficient of variation (CV)0.00085003075
Kurtosis-1.2716396
Mean2018.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum217998
Variance2.9439252
MonotonicityIncreasing
2024-01-10T04:51:24.977826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 18
16.7%
2017 18
16.7%
2018 18
16.7%
2019 18
16.7%
2020 18
16.7%
2021 18
16.7%
ValueCountFrequency (%)
2016 18
16.7%
2017 18
16.7%
2018 18
16.7%
2019 18
16.7%
2020 18
16.7%
2021 18
16.7%
ValueCountFrequency (%)
2021 18
16.7%
2020 18
16.7%
2019 18
16.7%
2018 18
16.7%
2017 18
16.7%
2016 18
16.7%

지역
Categorical

Distinct18
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size996.0 B
합계
 
6
서울
 
6
부산
 
6
대구
 
6
인천
 
6
Other values (13)
78 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row합계
2nd row서울
3rd row부산
4th row대구
5th row인천

Common Values

ValueCountFrequency (%)
합계 6
 
5.6%
서울 6
 
5.6%
부산 6
 
5.6%
대구 6
 
5.6%
인천 6
 
5.6%
광주 6
 
5.6%
대전 6
 
5.6%
울산 6
 
5.6%
세종 6
 
5.6%
경기 6
 
5.6%
Other values (8) 48
44.4%

Length

2024-01-10T04:51:25.073660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합계 6
 
5.6%
서울 6
 
5.6%
경남 6
 
5.6%
경북 6
 
5.6%
전남 6
 
5.6%
전북 6
 
5.6%
충남 6
 
5.6%
충북 6
 
5.6%
강원 6
 
5.6%
경기 6
 
5.6%
Other values (8) 48
44.4%

보행환경개선지구(개소)
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.222222
Minimum0
Maximum294
Zeros3
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T04:51:25.158540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.35
Q14
median9
Q317
95-th percentile118.6
Maximum294
Range294
Interquartile range (IQR)13

Descriptive statistics

Standard deviation46.972717
Coefficient of variation (CV)2.1137723
Kurtosis17.41605
Mean22.222222
Median Absolute Deviation (MAD)6
Skewness4.0646771
Sum2400
Variance2206.4361
MonotonicityNot monotonic
2024-01-10T04:51:25.464195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 13
 
12.0%
9 12
 
11.1%
4 7
 
6.5%
6 6
 
5.6%
7 5
 
4.6%
5 5
 
4.6%
17 5
 
4.6%
8 5
 
4.6%
3 5
 
4.6%
15 4
 
3.7%
Other values (25) 41
38.0%
ValueCountFrequency (%)
0 3
 
2.8%
1 3
 
2.8%
2 13
12.0%
3 5
 
4.6%
4 7
6.5%
5 5
 
4.6%
6 6
5.6%
7 5
 
4.6%
8 5
 
4.6%
9 12
11.1%
ValueCountFrequency (%)
294 1
0.9%
246 1
0.9%
214 1
0.9%
150 1
0.9%
148 2
1.9%
64 1
0.9%
57 2
1.9%
52 1
0.9%
48 1
0.9%
40 1
0.9%

시군구
Text

Distinct63
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size996.0 B
2024-01-10T04:51:25.700390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length245
Median length88
Mean length51.759259
Min length1

Characters and Unicode

Total characters5590
Distinct characters113
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)35.2%

Sample

1st row0
2nd row광진구, 용산구, 성북구, 중구(2)
3rd row북구, 수영구, 동구(2), 남구, 영도구, 동래구
4th row북구, 달성군, 남구, 서구(2), 달서구
5th row강화군, 계양구, 남구, 남동구, 동구, 부평구, 서구, 연수구, 중구
ValueCountFrequency (%)
0 12
 
2.0%
동구&#44 9
 
1.5%
중구(1)&#44 9
 
1.5%
동구(1)&#44 9
 
1.5%
서구(2)&#44 9
 
1.5%
남구(1)&#44 7
 
1.2%
남구&#44 7
 
1.2%
대덕구(2 6
 
1.0%
유성구(3)&#44 6
 
1.0%
북구&#44 6
 
1.0%
Other values (219) 520
86.7%
2024-01-10T04:51:26.112887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 998
17.9%
492
8.8%
; 490
8.8%
& 490
8.8%
# 490
8.8%
( 423
 
7.6%
) 423
 
7.6%
구 265
 
4.7%
1 230
 
4.1%
2 101
 
1.8%
Other values (103) 1188
21.3%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 1473
26.4%
Decimal Number 1424
25.5%
Other Letter 1355
24.2%
Space Separator 492
 
8.8%
Open Punctuation 423
 
7.6%
Close Punctuation 423
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 265
 
19.6%
주 63
 
4.6%
동 55
 
4.1%
성 49
 
3.6%
서 43
 
3.2%
남 40
 
3.0%
영 38
 
2.8%
양 33
 
2.4%
안 32
 
2.4%
중 29
 
2.1%
Other values (86) 708
52.3%
Decimal Number
ValueCountFrequency (%)
4 998
70.1%
1 230
 
16.2%
2 101
 
7.1%
3 40
 
2.8%
0 15
 
1.1%
5 12
 
0.8%
6 11
 
0.8%
7 7
 
0.5%
9 6
 
0.4%
8 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
; 490
33.3%
& 490
33.3%
# 490
33.3%
. 3
 
0.2%
Space Separator
ValueCountFrequency (%)
492
100.0%
Open Punctuation
ValueCountFrequency (%)
( 423
100.0%
Close Punctuation
ValueCountFrequency (%)
) 423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4235
75.8%
Hangul 1355
 
24.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 265
 
19.6%
주 63
 
4.6%
동 55
 
4.1%
성 49
 
3.6%
서 43
 
3.2%
남 40
 
3.0%
영 38
 
2.8%
양 33
 
2.4%
안 32
 
2.4%
중 29
 
2.1%
Other values (86) 708
52.3%
Common
ValueCountFrequency (%)
4 998
23.6%
492
11.6%
; 490
11.6%
& 490
11.6%
# 490
11.6%
( 423
10.0%
) 423
10.0%
1 230
 
5.4%
2 101
 
2.4%
3 40
 
0.9%
Other values (7) 58
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4235
75.8%
Hangul 1355
 
24.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 998
23.6%
492
11.6%
; 490
11.6%
& 490
11.6%
# 490
11.6%
( 423
10.0%
) 423
10.0%
1 230
 
5.4%
2 101
 
2.4%
3 40
 
0.9%
Other values (7) 58
 
1.4%
Hangul
ValueCountFrequency (%)
구 265
 
19.6%
주 63
 
4.6%
동 55
 
4.1%
성 49
 
3.6%
서 43
 
3.2%
남 40
 
3.0%
영 38
 
2.8%
양 33
 
2.4%
안 32
 
2.4%
중 29
 
2.1%
Other values (86) 708
52.3%

Interactions

2024-01-10T04:51:24.620663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:24.470580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:24.686759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:51:24.546423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T04:51:26.189433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지역보행환경개선지구(개소)시군구
기준년도1.0000.0000.2190.000
지역0.0001.0000.5850.997
보행환경개선지구(개소)0.2190.5851.0000.000
시군구0.0000.9970.0001.000
2024-01-10T04:51:26.258079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도보행환경개선지구(개소)지역
기준년도1.0000.1260.000
보행환경개선지구(개소)0.1261.0000.288
지역0.0000.2881.000

Missing values

2024-01-10T04:51:24.771100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:51:24.832288image/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

기준년도지역보행환경개선지구(개소)시군구
02016합계1500
12016서울5광진구, 용산구, 성북구, 중구(2)
22016부산7북구, 수영구, 동구(2), 남구, 영도구, 동래구
32016대구6북구, 달성군, 남구, 서구(2), 달서구
42016인천9강화군, 계양구, 남구, 남동구, 동구, 부평구, 서구, 연수구, 중구
52016광주2동구, 북구
62016대전9동구, 중구, 서구(2), 유성구(3), 대덕구(2)
72016울산1중구
82016세종00
92016경기17용인(4), 여주(2), 양평, 양주(2)
기준년도지역보행환경개선지구(개소)시군구
982021세종20
992021경기15부천(2), 광주(1), 의왕(5), 여주(1), 양주(2), 용인(4)
1002021강원4원주(1), 강릉(1), 영월(1), 양구(1)
1012021충북17청주(8), 충주(1), 보은(1), 영동(1), 진천(1), 음성(5)
1022021충남7천안(1), 서산(1), 논산(1), 당진(1), 홍성(2), 예산(1)
1032021전북16전주(2), 군산(1), 정읍(1), 남원(2), 김제(3), 완주(2), 진안(1), 임실(1), 고창(1), 부안(2)
1042021전남14목포(1), 순천(1), 곡성(1), 구례(1), 고흥(1), 보성(1), 화순(2), 무안(3), 장흥(1), 해남(1), 신안(1)
1052021경북12포항(1), 경주(1), 영주(1), 경산(1), 군위(1), 영덕(1), 청도(2), 고령(1), 상주(3)
1062021경남13창원(6), 김해(1), 밀양(1), 거제(1), 창녕(1), 고성(1), 함안(1), 합천(1)
1072021제주4제주(2), 서귀포(2)