Overview

Dataset statistics

Number of variables5
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory46.7 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description가스안전의 정책방향과 대국민 의식 전황에 참고를 위해 매년 실시하고 있는 가스안전 의식조사 결과 현황을 구분(분야별, 성별, 거주지역, 직종 등)하여 제공하는 데이터입니다.
Author한국가스안전공사
URLhttps://www.data.go.kr/data/15001652/fileData.do

Alerts

2020년 is highly overall correlated with 2019년High correlation
2019년 is highly overall correlated with 2020년 and 1 other fieldsHigh correlation
2018년 is highly overall correlated with 2019년High correlation
세부구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:54:41.754786
Analysis finished2023-12-12 20:54:42.953406
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct5
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
4. 거주지역
17 
5. 직종
9 
3. 연령별
5 
1. 분야별
3 
2. 성별
2 

Length

Max length7
Median length6
Mean length6.1666667
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1. 분야별
2nd row1. 분야별
3rd row1. 분야별
4th row2. 성별
5th row2. 성별

Common Values

ValueCountFrequency (%)
4. 거주지역 17
47.2%
5. 직종 9
25.0%
3. 연령별 5
 
13.9%
1. 분야별 3
 
8.3%
2. 성별 2
 
5.6%

Length

2023-12-13T05:54:43.044977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:43.177563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 17
23.6%
거주지역 17
23.6%
5 9
12.5%
직종 9
12.5%
3 5
 
6.9%
연령별 5
 
6.9%
1 3
 
4.2%
분야별 3
 
4.2%
2 2
 
2.8%
성별 2
 
2.8%

세부구분
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T05:54:43.385671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length3.1944444
Min length2

Characters and Unicode

Total characters115
Distinct characters60
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

Unique36 ?
Unique (%)100.0%

Sample

1st row인식/공감
2nd row경험
3rd row실천
4th row남자
5th row여자
ValueCountFrequency (%)
인식/공감 1
 
2.7%
경기 1
 
2.7%
제주 1
 
2.7%
충북 1
 
2.7%
충남 1
 
2.7%
전북 1
 
2.7%
전남 1
 
2.7%
경북 1
 
2.7%
경남 1
 
2.7%
자영업 1
 
2.7%
Other values (27) 27
73.0%
2023-12-13T05:54:43.778555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 8
 
7.0%
/ 6
 
5.2%
직 5
 
4.3%
경 5
 
4.3%
0 5
 
4.3%
전 5
 
4.3%
남 4
 
3.5%
자 4
 
3.5%
기 3
 
2.6%
영 3
 
2.6%
Other values (50) 67
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
83.5%
Decimal Number 10
 
8.7%
Other Punctuation 6
 
5.2%
Close Punctuation 1
 
0.9%
Open Punctuation 1
 
0.9%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 8
 
8.3%
직 5
 
5.2%
경 5
 
5.2%
전 5
 
5.2%
남 4
 
4.2%
자 4
 
4.2%
기 3
 
3.1%
영 3
 
3.1%
북 3
 
3.1%
원 3
 
3.1%
Other values (40) 53
55.2%
Decimal Number
ValueCountFrequency (%)
0 5
50.0%
4 1
 
10.0%
5 1
 
10.0%
2 1
 
10.0%
3 1
 
10.0%
6 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
83.5%
Common 19
 
16.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 8
 
8.3%
직 5
 
5.2%
경 5
 
5.2%
전 5
 
5.2%
남 4
 
4.2%
자 4
 
4.2%
기 3
 
3.1%
영 3
 
3.1%
북 3
 
3.1%
원 3
 
3.1%
Other values (40) 53
55.2%
Common
ValueCountFrequency (%)
/ 6
31.6%
0 5
26.3%
) 1
 
5.3%
( 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%
2 1
 
5.3%
3 1
 
5.3%
6 1
 
5.3%
1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
83.5%
ASCII 19
 
16.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 8
 
8.3%
직 5
 
5.2%
경 5
 
5.2%
전 5
 
5.2%
남 4
 
4.2%
자 4
 
4.2%
기 3
 
3.1%
영 3
 
3.1%
북 3
 
3.1%
원 3
 
3.1%
Other values (40) 53
55.2%
ASCII
ValueCountFrequency (%)
/ 6
31.6%
0 5
26.3%
) 1
 
5.3%
( 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%
2 1
 
5.3%
3 1
 
5.3%
6 1
 
5.3%
1
 
5.3%

2020년
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.080556
Minimum76.7
Maximum92.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T05:54:43.905043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.7
5-th percentile78.875
Q184.475
median85.4
Q386.75
95-th percentile88.925
Maximum92.1
Range15.4
Interquartile range (IQR)2.275

Descriptive statistics

Standard deviation3.1364055
Coefficient of variation (CV)0.036863952
Kurtosis1.1324368
Mean85.080556
Median Absolute Deviation (MAD)1.2
Skewness-0.73020401
Sum3062.9
Variance9.8370397
MonotonicityNot monotonic
2023-12-13T05:54:44.043347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
86.0 3
 
8.3%
85.1 2
 
5.6%
84.8 2
 
5.6%
85.2 2
 
5.6%
86.9 2
 
5.6%
82.9 2
 
5.6%
84.5 2
 
5.6%
86.3 2
 
5.6%
86.7 1
 
2.8%
88.8 1
 
2.8%
Other values (17) 17
47.2%
ValueCountFrequency (%)
76.7 1
2.8%
78.8 1
2.8%
78.9 1
2.8%
79.4 1
2.8%
81.3 1
2.8%
82.6 1
2.8%
82.9 2
5.6%
84.4 1
2.8%
84.5 2
5.6%
84.7 1
2.8%
ValueCountFrequency (%)
92.1 1
2.8%
89.3 1
2.8%
88.8 1
2.8%
88.7 1
2.8%
87.9 1
2.8%
87.7 1
2.8%
87.6 1
2.8%
86.9 2
5.6%
86.7 1
2.8%
86.5 1
2.8%

2019년
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.4
Minimum76.4
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T05:54:44.237932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.4
5-th percentile77.625
Q181.1
median82.45
Q383.425
95-th percentile87.7
Maximum90
Range13.6
Interquartile range (IQR)2.325

Descriptive statistics

Standard deviation2.8137418
Coefficient of variation (CV)0.034147352
Kurtosis1.1089662
Mean82.4
Median Absolute Deviation (MAD)1.3
Skewness0.32185641
Sum2966.4
Variance7.9171429
MonotonicityNot monotonic
2023-12-13T05:54:44.399466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
83.4 3
 
8.3%
81.1 3
 
8.3%
79.2 2
 
5.6%
87.7 2
 
5.6%
80.3 2
 
5.6%
81.6 2
 
5.6%
83.3 1
 
2.8%
82.5 1
 
2.8%
85.6 1
 
2.8%
77.9 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
76.4 1
 
2.8%
76.8 1
 
2.8%
77.9 1
 
2.8%
79.2 2
5.6%
80.3 2
5.6%
80.5 1
 
2.8%
81.1 3
8.3%
81.2 1
 
2.8%
81.6 2
5.6%
82.0 1
 
2.8%
ValueCountFrequency (%)
90.0 1
 
2.8%
87.7 2
5.6%
85.6 1
 
2.8%
85.3 1
 
2.8%
84.8 1
 
2.8%
84.1 1
 
2.8%
83.7 1
 
2.8%
83.5 1
 
2.8%
83.4 3
8.3%
83.3 1
 
2.8%

2018년
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.463889
Minimum73.3
Maximum86.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T05:54:44.556520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73.3
5-th percentile74
Q177.825
median79.8
Q381.525
95-th percentile83.125
Maximum86.2
Range12.9
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.9511405
Coefficient of variation (CV)0.037138133
Kurtosis-0.060267314
Mean79.463889
Median Absolute Deviation (MAD)1.85
Skewness-0.30797439
Sum2860.7
Variance8.7092302
MonotonicityNot monotonic
2023-12-13T05:54:44.728337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
81.2 3
 
8.3%
78.8 2
 
5.6%
79.9 2
 
5.6%
83.2 1
 
2.8%
76.2 1
 
2.8%
76.8 1
 
2.8%
78.0 1
 
2.8%
78.3 1
 
2.8%
79.7 1
 
2.8%
82.1 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
73.3 1
2.8%
73.4 1
2.8%
74.2 1
2.8%
75.1 1
2.8%
75.7 1
2.8%
76.2 1
2.8%
76.8 1
2.8%
77.1 1
2.8%
77.6 1
2.8%
77.9 1
2.8%
ValueCountFrequency (%)
86.2 1
2.8%
83.2 1
2.8%
83.1 1
2.8%
83.0 1
2.8%
82.4 1
2.8%
82.1 1
2.8%
81.9 1
2.8%
81.7 1
2.8%
81.6 1
2.8%
81.5 1
2.8%

Interactions

2023-12-13T05:54:42.516420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:41.959350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.233072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.609873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.050017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.327009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.697716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.142157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:42.425822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:54:44.900244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세부구분2020년2019년2018년
구분1.0001.0000.1670.3640.436
세부구분1.0001.0001.0001.0001.000
2020년0.1671.0001.0000.7840.759
2019년0.3641.0000.7841.0000.872
2018년0.4361.0000.7590.8721.000
2023-12-13T05:54:45.065367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020년2019년2018년구분
2020년1.0000.7440.4800.070
2019년0.7441.0000.5480.188
2018년0.4800.5481.0000.238
구분0.0700.1880.2381.000

Missing values

2023-12-13T05:54:42.829216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:54:42.917713image/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

구분세부구분2020년2019년2018년
01. 분야별인식/공감85.183.381.2
11. 분야별경험79.476.473.4
21. 분야별실천88.785.383.0
32. 성별남자85.282.680.5
42. 성별여자86.082.879.9
53. 연령별20대78.976.875.1
63. 연령별30대82.679.277.9
73. 연령별40대84.781.279.9
83. 연령별50대89.387.783.1
93. 연령별60대 이상92.190.086.2
구분세부구분2020년2019년2018년
264. 거주지역제주85.182.978.3
275. 직종자영업87.683.581.2
285. 직종일반사무/기술직84.581.679.7
295. 직종경영/관리직87.983.783.2
305. 직종공무원84.587.782.1
315. 직종영엽/판매/서비스직86.582.180.8
325. 직종자유전문직85.884.181.9
335. 직종대학(원)생78.877.975.7
345. 직종전업주부88.885.681.5
355. 직종무직/기타86.782.578.9