Overview

Dataset statistics

Number of variables13
Number of observations21
Missing cells13
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory113.3 B

Variable types

Text7
Numeric2
Categorical4

Dataset

Description강원도 공공기관지진계측기 정보(관측소명, 관측기관, 운영기관 소재지주소, 위치 위도/경도 등) 데이터를 제공합니다
Author강원도
URLhttps://www.data.go.kr/data/15033685/fileData.do

Alerts

운영상태 has constant value ""Constant
경도(WGS84좌표) is highly overall correlated with 관측소유형High correlation
위도(WGS84좌표) is highly overall correlated with 관측개시연도High correlation
관측개시연도 is highly overall correlated with 위도(WGS84좌표)High correlation
관측소유형 is highly overall correlated with 경도(WGS84좌표)High correlation
특이사항 has 13 (61.9%) missing valuesMissing
관측소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:58:00.106418
Analysis finished2023-12-12 07:58:01.595898
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:58:01.697507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63
Distinct characters27
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

Unique13 ?
Unique (%)61.9%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row동해시
5th row동해시
ValueCountFrequency (%)
동해시 5
23.8%
양양군 3
14.3%
강릉시 1
 
4.8%
고성군 1
 
4.8%
속초시 1
 
4.8%
양구군 1
 
4.8%
원주시 1
 
4.8%
인제군 1
 
4.8%
정선군 1
 
4.8%
철원군 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T16:58:02.010486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 12
19.0%
시 9
14.3%
양 7
11.1%
동 5
 
7.9%
해 5
 
7.9%
천 2
 
3.2%
성 2
 
3.2%
원 2
 
3.2%
선 1
 
1.6%
화 1
 
1.6%
Other values (17) 17
27.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 12
19.0%
시 9
14.3%
양 7
11.1%
동 5
 
7.9%
해 5
 
7.9%
천 2
 
3.2%
성 2
 
3.2%
원 2
 
3.2%
선 1
 
1.6%
화 1
 
1.6%
Other values (17) 17
27.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 12
19.0%
시 9
14.3%
양 7
11.1%
동 5
 
7.9%
해 5
 
7.9%
천 2
 
3.2%
성 2
 
3.2%
원 2
 
3.2%
선 1
 
1.6%
화 1
 
1.6%
Other values (17) 17
27.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 12
19.0%
시 9
14.3%
양 7
11.1%
동 5
 
7.9%
해 5
 
7.9%
천 2
 
3.2%
성 2
 
3.2%
원 2
 
3.2%
선 1
 
1.6%
화 1
 
1.6%
Other values (17) 17
27.0%

관측소명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:58:02.244541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length4
Mean length6.0952381
Min length4

Characters and Unicode

Total characters128
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row강릉시청
2nd row고성군청
3rd row동해시청(지하3축센서)
4th row동해시청(최상층2축센서)
5th row동해시청(최상층1축센서)
ValueCountFrequency (%)
동해시청 2
 
8.3%
별관 2
 
8.3%
강릉시청 1
 
4.2%
원주시청 1
 
4.2%
화천군청 1
 
4.2%
홍천군청 1
 
4.2%
평창군청 1
 
4.2%
태백시청 1
 
4.2%
철원군청 1
 
4.2%
정선군청 1
 
4.2%
Other values (12) 12
50.0%
2023-12-12T16:58:02.613489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 19
 
14.8%
군 10
 
7.8%
시 9
 
7.0%
양 6
 
4.7%
동 5
 
3.9%
해 5
 
3.9%
4
 
3.1%
( 4
 
3.1%
) 4
 
3.1%
상 3
 
2.3%
Other values (45) 59
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
88.3%
Space Separator 4
 
3.1%
Open Punctuation 4
 
3.1%
Close Punctuation 4
 
3.1%
Decimal Number 3
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 19
16.8%
군 10
 
8.8%
시 9
 
8.0%
양 6
 
5.3%
동 5
 
4.4%
해 5
 
4.4%
상 3
 
2.7%
센 3
 
2.7%
관 3
 
2.7%
서 3
 
2.7%
Other values (39) 47
41.6%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
88.3%
Common 15
 
11.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 19
16.8%
군 10
 
8.8%
시 9
 
8.0%
양 6
 
5.3%
동 5
 
4.4%
해 5
 
4.4%
상 3
 
2.7%
센 3
 
2.7%
관 3
 
2.7%
서 3
 
2.7%
Other values (39) 47
41.6%
Common
ValueCountFrequency (%)
4
26.7%
( 4
26.7%
) 4
26.7%
3 1
 
6.7%
2 1
 
6.7%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
88.3%
ASCII 15
 
11.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 19
16.8%
군 10
 
8.8%
시 9
 
8.0%
양 6
 
5.3%
동 5
 
4.4%
해 5
 
4.4%
상 3
 
2.7%
센 3
 
2.7%
관 3
 
2.7%
서 3
 
2.7%
Other values (39) 47
41.6%
ASCII
ValueCountFrequency (%)
4
26.7%
( 4
26.7%
) 4
26.7%
3 1
 
6.7%
2 1
 
6.7%
1 1
 
6.7%

경도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.48696
Minimum127.31254
Maximum129.11449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T16:58:02.782758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.31254
5-th percentile127.70813
Q1127.99239
median128.54221
Q3128.98687
95-th percentile129.11449
Maximum129.11449
Range1.8019491
Interquartile range (IQR)0.9944802

Descriptive statistics

Standard deviation0.53426293
Coefficient of variation (CV)0.00415811
Kurtosis-0.60871849
Mean128.48696
Median Absolute Deviation (MAD)0.5498236
Skewness-0.48611089
Sum2698.2262
Variance0.28543688
MonotonicityNot monotonic
2023-12-12T16:58:02.891294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
129.114494 5
23.8%
128.5422133 2
 
9.5%
128.8760583 1
 
4.8%
127.3125449 1
 
4.8%
127.9850466 1
 
4.8%
127.7081308 1
 
4.8%
127.8888694 1
 
4.8%
128.3901368 1
 
4.8%
128.9868699 1
 
4.8%
128.169734 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
127.3125449 1
4.8%
127.7081308 1
4.8%
127.8888694 1
4.8%
127.9187 1
4.8%
127.9850466 1
4.8%
127.9923897 1
4.8%
128.169734 1
4.8%
128.3901368 1
4.8%
128.4680551 1
4.8%
128.5422133 2
9.5%
ValueCountFrequency (%)
129.114494 5
23.8%
128.9868699 1
 
4.8%
128.8760583 1
 
4.8%
128.6613918 1
 
4.8%
128.6191583 1
 
4.8%
128.5922597 1
 
4.8%
128.5422133 2
 
9.5%
128.4680551 1
 
4.8%
128.3901368 1
 
4.8%
128.169734 1
 
4.8%

위도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.75898
Minimum37.162661
Maximum38.380563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T16:58:02.999322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.162661
5-th percentile37.340921
Q137.525025
median37.696768
Q338.07565
95-th percentile38.207371
Maximum38.380563
Range1.2179024
Interquartile range (IQR)0.5506254

Descriptive statistics

Standard deviation0.3522386
Coefficient of variation (CV)0.0093286048
Kurtosis-1.3570261
Mean37.75898
Median Absolute Deviation (MAD)0.3185185
Skewness0.10856147
Sum792.93857
Variance0.12407203
MonotonicityNot monotonic
2023-12-12T16:58:03.094638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
37.5250249 5
23.8%
38.0119521 2
 
9.5%
37.7518458 1
 
4.8%
38.1465863 1
 
4.8%
37.4917027 1
 
4.8%
38.1062847 1
 
4.8%
37.6967679 1
 
4.8%
37.3709597 1
 
4.8%
37.1626608 1
 
4.8%
38.0695361 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
37.1626608 1
 
4.8%
37.3409214 1
 
4.8%
37.3709597 1
 
4.8%
37.3782494 1
 
4.8%
37.4917027 1
 
4.8%
37.5250249 5
23.8%
37.6967679 1
 
4.8%
37.7518458 1
 
4.8%
38.0119521 2
 
9.5%
38.0695361 1
 
4.8%
ValueCountFrequency (%)
38.3805632 1
4.8%
38.2073706 1
4.8%
38.1465863 1
4.8%
38.1104458 1
4.8%
38.1062847 1
4.8%
38.0756503 1
4.8%
38.0695361 1
4.8%
38.0119521 2
9.5%
37.7518458 1
4.8%
37.6967679 1
4.8%

관측기관
Text

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:58:03.220486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2857143
Min length3

Characters and Unicode

Total characters69
Distinct characters32
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

Unique11 ?
Unique (%)52.4%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row동해시
5th row동해시
ValueCountFrequency (%)
동해시 5
23.8%
기상청 3
14.3%
행정안전부 2
 
9.5%
강릉시 1
 
4.8%
고성군 1
 
4.8%
속초시 1
 
4.8%
인제군 1
 
4.8%
정선군 1
 
4.8%
국민안전처 1
 
4.8%
태백시 1
 
4.8%
Other values (4) 4
19.0%
2023-12-12T16:58:03.486680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 8
 
11.6%
군 7
 
10.1%
동 5
 
7.2%
해 5
 
7.2%
상 3
 
4.3%
청 3
 
4.3%
정 3
 
4.3%
전 3
 
4.3%
기 3
 
4.3%
안 3
 
4.3%
Other values (22) 26
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8
 
11.6%
군 7
 
10.1%
동 5
 
7.2%
해 5
 
7.2%
상 3
 
4.3%
청 3
 
4.3%
정 3
 
4.3%
전 3
 
4.3%
기 3
 
4.3%
안 3
 
4.3%
Other values (22) 26
37.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8
 
11.6%
군 7
 
10.1%
동 5
 
7.2%
해 5
 
7.2%
상 3
 
4.3%
청 3
 
4.3%
정 3
 
4.3%
전 3
 
4.3%
기 3
 
4.3%
안 3
 
4.3%
Other values (22) 26
37.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 8
 
11.6%
군 7
 
10.1%
동 5
 
7.2%
해 5
 
7.2%
상 3
 
4.3%
청 3
 
4.3%
정 3
 
4.3%
전 3
 
4.3%
기 3
 
4.3%
안 3
 
4.3%
Other values (22) 26
37.7%

운영기관
Text

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:58:03.649124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.4285714
Min length3

Characters and Unicode

Total characters72
Distinct characters35
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

Unique14 ?
Unique (%)66.7%

Sample

1st row강릉시
2nd row행정안전부
3rd row동해시
4th row동해시
5th row동해시
ValueCountFrequency (%)
동해시 5
23.8%
행정안전부 2
 
9.5%
강릉시 1
 
4.8%
국민안전처 1
 
4.8%
양구군 1
 
4.8%
양양군 1
 
4.8%
양수발전소 1
 
4.8%
양양공항 1
 
4.8%
인제군 1
 
4.8%
정선군 1
 
4.8%
Other values (6) 6
28.6%
2023-12-12T16:58:03.948560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 8
 
11.1%
시 7
 
9.7%
양 6
 
8.3%
동 5
 
6.9%
해 5
 
6.9%
전 4
 
5.6%
정 3
 
4.2%
안 3
 
4.2%
행 2
 
2.8%
부 2
 
2.8%
Other values (25) 27
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 8
 
11.1%
시 7
 
9.7%
양 6
 
8.3%
동 5
 
6.9%
해 5
 
6.9%
전 4
 
5.6%
정 3
 
4.2%
안 3
 
4.2%
행 2
 
2.8%
부 2
 
2.8%
Other values (25) 27
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 8
 
11.1%
시 7
 
9.7%
양 6
 
8.3%
동 5
 
6.9%
해 5
 
6.9%
전 4
 
5.6%
정 3
 
4.2%
안 3
 
4.2%
행 2
 
2.8%
부 2
 
2.8%
Other values (25) 27
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 8
 
11.1%
시 7
 
9.7%
양 6
 
8.3%
동 5
 
6.9%
해 5
 
6.9%
전 4
 
5.6%
정 3
 
4.2%
안 3
 
4.2%
행 2
 
2.8%
부 2
 
2.8%
Other values (25) 27
37.5%

관측개시연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
2014
6 
2016
5 
2015
4 
2012
3 
2013
3 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2014 6
28.6%
2016 5
23.8%
2015 4
19.0%
2012 3
14.3%
2013 3
14.3%

Length

2023-12-12T16:58:04.079898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:04.213512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 6
28.6%
2016 5
23.8%
2015 4
19.0%
2012 3
14.3%
2013 3
14.3%

관측소유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
지진관측소
13 
지자체 재난상황실
5 
지진가속도계측기
2 
지진계측기
 
1

Length

Max length9
Median length5
Mean length6.2380952
Min length5

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row지진관측소
2nd row지진관측소
3rd row지자체 재난상황실
4th row지자체 재난상황실
5th row지자체 재난상황실

Common Values

ValueCountFrequency (%)
지진관측소 13
61.9%
지자체 재난상황실 5
 
23.8%
지진가속도계측기 2
 
9.5%
지진계측기 1
 
4.8%

Length

2023-12-12T16:58:04.354074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:04.510855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지진관측소 13
50.0%
지자체 5
 
19.2%
재난상황실 5
 
19.2%
지진가속도계측기 2
 
7.7%
지진계측기 1
 
3.8%

운영상태
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
운영
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영 21
100.0%

Length

2023-12-12T16:58:04.633585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:04.741200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영 21
100.0%

소재지지번주소
Text

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:58:04.936852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length17.571429
Min length13

Characters and Unicode

Total characters369
Distinct characters67
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row강원도 강릉시 홍제동 1001
2nd row강원도 고성군 간성읍 하리 12
3rd row강원도 동해시 천곡동 806
4th row강원도 동해시 천곡동 806
5th row강원도 동해시 천곡동 806
ValueCountFrequency (%)
강원도 21
22.1%
천곡동 5
 
5.3%
806 5
 
5.3%
동해시 5
 
5.3%
하리 3
 
3.2%
양양군 3
 
3.2%
서면 2
 
2.1%
영덕리 2
 
2.1%
산74-1 2
 
2.1%
평창군 1
 
1.1%
Other values (46) 46
48.4%
2023-12-12T16:58:05.389476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
74
20.1%
원 24
 
6.5%
강 22
 
6.0%
도 21
 
5.7%
동 15
 
4.1%
군 13
 
3.5%
양 11
 
3.0%
읍 11
 
3.0%
1 11
 
3.0%
리 11
 
3.0%
Other values (57) 156
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 224
60.7%
Space Separator 74
 
20.1%
Decimal Number 63
 
17.1%
Dash Punctuation 8
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 24
 
10.7%
강 22
 
9.8%
도 21
 
9.4%
동 15
 
6.7%
군 13
 
5.8%
양 11
 
4.9%
읍 11
 
4.9%
리 11
 
4.9%
천 9
 
4.0%
시 9
 
4.0%
Other values (45) 78
34.8%
Decimal Number
ValueCountFrequency (%)
1 11
17.5%
6 10
15.9%
4 8
12.7%
0 8
12.7%
8 7
11.1%
2 6
9.5%
9 4
 
6.3%
3 4
 
6.3%
7 3
 
4.8%
5 2
 
3.2%
Space Separator
ValueCountFrequency (%)
74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 224
60.7%
Common 145
39.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 24
 
10.7%
강 22
 
9.8%
도 21
 
9.4%
동 15
 
6.7%
군 13
 
5.8%
양 11
 
4.9%
읍 11
 
4.9%
리 11
 
4.9%
천 9
 
4.0%
시 9
 
4.0%
Other values (45) 78
34.8%
Common
ValueCountFrequency (%)
74
51.0%
1 11
 
7.6%
6 10
 
6.9%
4 8
 
5.5%
- 8
 
5.5%
0 8
 
5.5%
8 7
 
4.8%
2 6
 
4.1%
9 4
 
2.8%
3 4
 
2.8%
Other values (2) 5
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 224
60.7%
ASCII 145
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74
51.0%
1 11
 
7.6%
6 10
 
6.9%
4 8
 
5.5%
- 8
 
5.5%
0 8
 
5.5%
8 7
 
4.8%
2 6
 
4.1%
9 4
 
2.8%
3 4
 
2.8%
Other values (2) 5
 
3.4%
Hangul
ValueCountFrequency (%)
원 24
 
10.7%
강 22
 
9.8%
도 21
 
9.4%
동 15
 
6.7%
군 13
 
5.8%
양 11
 
4.9%
읍 11
 
4.9%
리 11
 
4.9%
천 9
 
4.0%
시 9
 
4.0%
Other values (45) 78
34.8%

특이사항
Text

MISSING 

Distinct4
Distinct (%)50.0%
Missing13
Missing (%)61.9%
Memory size300.0 B
2023-12-12T16:58:05.588748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8.5
Mean length5.25
Min length2

Characters and Unicode

Total characters42
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)25.0%

Sample

1st row기록계 고장으로 교체 예정
2nd row기록계
3rd row기록계
4th row없음
5th row없음
ValueCountFrequency (%)
없음 4
28.6%
기록계 3
21.4%
고장으로 1
 
7.1%
교체 1
 
7.1%
예정 1
 
7.1%
안전성 1
 
7.1%
평가 1
 
7.1%
결과 1
 
7.1%
이상발생 1
 
7.1%
2023-12-12T16:58:05.888989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
14.3%
없 4
 
9.5%
음 4
 
9.5%
기 3
 
7.1%
록 3
 
7.1%
계 3
 
7.1%
전 1
 
2.4%
발 1
 
2.4%
상 1
 
2.4%
이 1
 
2.4%
Other values (15) 15
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36
85.7%
Space Separator 6
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
없 4
 
11.1%
음 4
 
11.1%
기 3
 
8.3%
록 3
 
8.3%
계 3
 
8.3%
전 1
 
2.8%
발 1
 
2.8%
상 1
 
2.8%
이 1
 
2.8%
과 1
 
2.8%
Other values (14) 14
38.9%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36
85.7%
Common 6
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
없 4
 
11.1%
음 4
 
11.1%
기 3
 
8.3%
록 3
 
8.3%
계 3
 
8.3%
전 1
 
2.8%
발 1
 
2.8%
상 1
 
2.8%
이 1
 
2.8%
과 1
 
2.8%
Other values (14) 14
38.9%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36
85.7%
ASCII 6
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6
100.0%
Hangul
ValueCountFrequency (%)
없 4
 
11.1%
음 4
 
11.1%
기 3
 
8.3%
록 3
 
8.3%
계 3
 
8.3%
전 1
 
2.8%
발 1
 
2.8%
상 1
 
2.8%
이 1
 
2.8%
과 1
 
2.8%
Other values (14) 14
38.9%

관측장비
Categorical

Distinct9
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
가속도계
7 
센서, 자유장, 기록계
5 
기록계
3 
기록계 geosig GMS+
1 
가속도계
1 
Other values (4)
4 

Length

Max length33
Median length28
Mean length9.6666667
Min length3

Unique

Unique6 ?
Unique (%)28.6%

Sample

1st row가속도계
2nd row기록계 geosig GMS+
3rd row가속도계
4th row가속도계
5th row가속도계

Common Values

ValueCountFrequency (%)
가속도계 7
33.3%
센서, 자유장, 기록계 5
23.8%
기록계 3
14.3%
기록계 geosig GMS+ 1
 
4.8%
가속도계 1
 
4.8%
지진기록계1식 지진관측시스템 부대장비 및 설치 1식 1
 
4.8%
자유장센서, 기록계, 가속도계 1
 
4.8%
가속도계, 기록계 1
 
4.8%
지진가속도센서, 지진계측기, 지진신호모니터링, 자유장계측장치 1
 
4.8%

Length

2023-12-12T16:58:06.070080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:06.219706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기록계 11
25.0%
가속도계 10
22.7%
자유장 5
11.4%
센서 5
11.4%
설치 1
 
2.3%
지진신호모니터링 1
 
2.3%
지진계측기 1
 
2.3%
지진가속도센서 1
 
2.3%
자유장센서 1
 
2.3%
1식 1
 
2.3%
Other values (7) 7
15.9%

관측내용
Text

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:58:06.443422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.4761905
Min length2

Characters and Unicode

Total characters157
Distinct characters43
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)42.9%

Sample

1st row지진가속도
2nd row지진가속도 계측데이터 관측
3rd row지진신호 3축(x,y,z)
4th row지진신호 2축(남-북, 동-서)
5th row지진신호 1축(수직U-D)
ValueCountFrequency (%)
지진가속도 6
18.2%
지진신호 4
12.1%
지진계측 3
 
9.1%
계측 2
 
6.1%
3축(x,y,z 2
 
6.1%
지진발생 2
 
6.1%
지진 2
 
6.1%
관측 2
 
6.1%
지진진호 1
 
3.0%
발생 1
 
3.0%
Other values (8) 8
24.2%
2023-12-12T16:58:06.784339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
진 20
 
12.7%
지 19
 
12.1%
12
 
7.6%
측 8
 
5.1%
신 6
 
3.8%
호 6
 
3.8%
계 6
 
3.8%
도 6
 
3.8%
속 6
 
3.8%
가 6
 
3.8%
Other values (33) 62
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117
74.5%
Space Separator 12
 
7.6%
Lowercase Letter 6
 
3.8%
Other Punctuation 5
 
3.2%
Close Punctuation 4
 
2.5%
Open Punctuation 4
 
2.5%
Decimal Number 4
 
2.5%
Dash Punctuation 3
 
1.9%
Uppercase Letter 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
진 20
17.1%
지 19
16.2%
측 8
 
6.8%
신 6
 
5.1%
호 6
 
5.1%
계 6
 
5.1%
도 6
 
5.1%
속 6
 
5.1%
가 6
 
5.1%
축 4
 
3.4%
Other values (20) 30
25.6%
Lowercase Letter
ValueCountFrequency (%)
z 2
33.3%
x 2
33.3%
y 2
33.3%
Decimal Number
ValueCountFrequency (%)
3 2
50.0%
1 1
25.0%
2 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
50.0%
U 1
50.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 117
74.5%
Common 32
 
20.4%
Latin 8
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
진 20
17.1%
지 19
16.2%
측 8
 
6.8%
신 6
 
5.1%
호 6
 
5.1%
계 6
 
5.1%
도 6
 
5.1%
속 6
 
5.1%
가 6
 
5.1%
축 4
 
3.4%
Other values (20) 30
25.6%
Common
ValueCountFrequency (%)
12
37.5%
, 5
15.6%
) 4
 
12.5%
( 4
 
12.5%
- 3
 
9.4%
3 2
 
6.2%
1 1
 
3.1%
2 1
 
3.1%
Latin
ValueCountFrequency (%)
z 2
25.0%
x 2
25.0%
y 2
25.0%
D 1
12.5%
U 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 117
74.5%
ASCII 40
 
25.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
진 20
17.1%
지 19
16.2%
측 8
 
6.8%
신 6
 
5.1%
호 6
 
5.1%
계 6
 
5.1%
도 6
 
5.1%
속 6
 
5.1%
가 6
 
5.1%
축 4
 
3.4%
Other values (20) 30
25.6%
ASCII
ValueCountFrequency (%)
12
30.0%
, 5
12.5%
) 4
 
10.0%
( 4
 
10.0%
- 3
 
7.5%
z 2
 
5.0%
x 2
 
5.0%
3 2
 
5.0%
y 2
 
5.0%
D 1
 
2.5%
Other values (3) 3
 
7.5%

Interactions

2023-12-12T16:58:00.960506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:00.721746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:01.067839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:00.831993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:58:06.887109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명관측소명경도(WGS84좌표)위도(WGS84좌표)관측기관운영기관관측개시연도관측소유형소재지지번주소특이사항관측장비관측내용
시군구명1.0001.0000.9840.9731.0000.9810.9591.0001.0001.0000.9870.881
관측소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경도(WGS84좌표)0.9841.0001.0000.6630.9810.9840.6470.8881.0000.3460.0000.557
위도(WGS84좌표)0.9731.0000.6631.0000.9550.9870.7530.8051.0000.8060.7230.731
관측기관1.0001.0000.9810.9551.0000.9840.8411.0001.0000.8060.9250.926
운영기관0.9811.0000.9840.9870.9841.0000.9631.0000.9970.5460.8820.000
관측개시연도0.9591.0000.6470.7530.8410.9631.0000.5880.9480.0000.3200.490
관측소유형1.0001.0000.8880.8051.0001.0000.5881.0001.0000.6410.0000.883
소재지지번주소1.0001.0001.0001.0001.0000.9970.9481.0001.0001.0000.9790.809
특이사항1.0001.0000.3460.8060.8060.5460.0000.6411.0001.0001.0001.000
관측장비0.9871.0000.0000.7230.9250.8820.3200.0000.9791.0001.0000.850
관측내용0.8811.0000.5570.7310.9260.0000.4900.8830.8091.0000.8501.000
2023-12-12T16:58:07.010504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측장비관측개시연도관측소유형
관측장비1.0000.0670.000
관측개시연도0.0671.0000.491
관측소유형0.0000.4911.000
2023-12-12T16:58:07.094478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도(WGS84좌표)위도(WGS84좌표)관측개시연도관측소유형관측장비
경도(WGS84좌표)1.000-0.3320.3600.6680.000
위도(WGS84좌표)-0.3321.0000.5220.3940.418
관측개시연도0.3600.5221.0000.4910.067
관측소유형0.6680.3940.4911.0000.000
관측장비0.0000.4180.0670.0001.000

Missing values

2023-12-12T16:58:01.257646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:58:01.511594image/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

시군구명관측소명경도(WGS84좌표)위도(WGS84좌표)관측기관운영기관관측개시연도관측소유형운영상태소재지지번주소특이사항관측장비관측내용
0강릉시강릉시청128.87605837.751846강릉시강릉시2014지진관측소운영강원도 강릉시 홍제동 1001<NA>가속도계지진가속도
1고성군고성군청128.46805538.380563고성군행정안전부2014지진관측소운영강원도 고성군 간성읍 하리 12기록계 고장으로 교체 예정기록계 geosig GMS+지진가속도 계측데이터 관측
2동해시동해시청(지하3축센서)129.11449437.525025동해시동해시2016지자체 재난상황실운영강원도 동해시 천곡동 806<NA>가속도계지진신호 3축(x,y,z)
3동해시동해시청(최상층2축센서)129.11449437.525025동해시동해시2016지자체 재난상황실운영강원도 동해시 천곡동 806<NA>가속도계지진신호 2축(남-북, 동-서)
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