Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory56.9 B

Variable types

Numeric4
Text2

Dataset

Description고유번호,관측소명,주소,평균기온,경도,위도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1366/S/1/datasetView.do

Alerts

고유번호 is highly overall correlated with 위도High correlation
평균기온 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
고유번호 has unique valuesUnique
관측소명 has unique valuesUnique
주소 has unique valuesUnique
평균기온 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-11 09:32:28.629357
Analysis finished2023-12-11 09:32:30.948850
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T18:32:31.033336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-11T18:32:31.192516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

관측소명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T18:32:31.418483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1851852
Min length2

Characters and Unicode

Total characters59
Distinct characters39
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

Unique27 ?
Unique (%)100.0%

Sample

1st row관악
2nd row금천
3rd row서초
4th row구로
5th row기상청
ValueCountFrequency (%)
관악 1
 
3.7%
강서 1
 
3.7%
도봉 1
 
3.7%
강북 1
 
3.7%
노원 1
 
3.7%
북한산 1
 
3.7%
성북 1
 
3.7%
은평 1
 
3.7%
중랑 1
 
3.7%
동대문 1
 
3.7%
Other values (17) 17
63.0%
2023-12-11T18:32:31.797879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T18:32:32.170243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length27.333333
Min length22

Characters and Unicode

Total characters738
Distinct characters129
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

Unique27 ?
Unique (%)100.0%

Sample

1st row서울특별시 관악구 신림동 산56-1 (서울대학교)
2nd row서울특별시 금천구 독산동 1034 (독산초등학교)
3rd row서울특별시 서초구 서초동 1650 (서울교육대학교)
4th row서울특별시 구로구 궁동 213-42 (수궁동사무소)
5th row서울특별시 동작구 신대방동 460-18 (기상청)
ValueCountFrequency (%)
서울특별시 27
 
19.9%
영등포구 2
 
1.5%
종로구 2
 
1.5%
신촌동 1
 
0.7%
551 1
 
0.7%
면목동 1
 
0.7%
중랑구 1
 
0.7%
서울시립대 1
 
0.7%
90 1
 
0.7%
전농동 1
 
0.7%
Other values (98) 98
72.1%
2023-12-11T18:32:32.632375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
14.8%
서 35
 
4.7%
동 35
 
4.7%
울 31
 
4.2%
구 31
 
4.2%
1 31
 
4.2%
시 28
 
3.8%
( 28
 
3.8%
특 27
 
3.7%
별 27
 
3.7%
Other values (119) 356
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 458
62.1%
Space Separator 109
 
14.8%
Decimal Number 99
 
13.4%
Open Punctuation 28
 
3.8%
Close Punctuation 27
 
3.7%
Dash Punctuation 17
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 35
 
7.6%
동 35
 
7.6%
울 31
 
6.8%
구 31
 
6.8%
시 28
 
6.1%
특 27
 
5.9%
별 27
 
5.9%
학 17
 
3.7%
교 15
 
3.3%
대 11
 
2.4%
Other values (105) 201
43.9%
Decimal Number
ValueCountFrequency (%)
1 31
31.3%
2 12
 
12.1%
4 10
 
10.1%
0 10
 
10.1%
3 9
 
9.1%
5 7
 
7.1%
6 6
 
6.1%
9 6
 
6.1%
8 5
 
5.1%
7 3
 
3.0%
Space Separator
ValueCountFrequency (%)
109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 458
62.1%
Common 280
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 35
 
7.6%
동 35
 
7.6%
울 31
 
6.8%
구 31
 
6.8%
시 28
 
6.1%
특 27
 
5.9%
별 27
 
5.9%
학 17
 
3.7%
교 15
 
3.3%
대 11
 
2.4%
Other values (105) 201
43.9%
Common
ValueCountFrequency (%)
109
38.9%
1 31
 
11.1%
( 28
 
10.0%
) 27
 
9.6%
- 17
 
6.1%
2 12
 
4.3%
4 10
 
3.6%
0 10
 
3.6%
3 9
 
3.2%
5 7
 
2.5%
Other values (4) 20
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 458
62.1%
ASCII 280
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
38.9%
1 31
 
11.1%
( 28
 
10.0%
) 27
 
9.6%
- 17
 
6.1%
2 12
 
4.3%
4 10
 
3.6%
0 10
 
3.6%
3 9
 
3.2%
5 7
 
2.5%
Other values (4) 20
 
7.1%
Hangul
ValueCountFrequency (%)
서 35
 
7.6%
동 35
 
7.6%
울 31
 
6.8%
구 31
 
6.8%
시 28
 
6.1%
특 27
 
5.9%
별 27
 
5.9%
학 17
 
3.7%
교 15
 
3.3%
대 11
 
2.4%
Other values (105) 201
43.9%

평균기온
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13902289
Minimum-2.605182
Maximum1.547382
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)37.0%
Memory size375.0 B
2023-12-11T18:32:32.794807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.605182
5-th percentile-1.2383707
Q1-0.45235
median0.434751
Q30.7119615
95-th percentile1.0291336
Maximum1.547382
Range4.152564
Interquartile range (IQR)1.1643115

Descriptive statistics

Standard deviation0.90840888
Coefficient of variation (CV)6.5342397
Kurtosis1.7473685
Mean0.13902289
Median Absolute Deviation (MAD)0.442307
Skewness-1.1917192
Sum3.753618
Variance0.82520669
MonotonicityNot monotonic
2023-12-11T18:32:32.917344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-1.065771 1
 
3.7%
1.547382 1
 
3.7%
0.038765 1
 
3.7%
-0.652582 1
 
3.7%
0.434751 1
 
3.7%
-0.941102 1
 
3.7%
-2.605182 1
 
3.7%
-0.413166 1
 
3.7%
-0.052481 1
 
3.7%
0.3828 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
-2.605182 1
3.7%
-1.312342 1
3.7%
-1.065771 1
3.7%
-0.941102 1
3.7%
-0.652582 1
3.7%
-0.525573 1
3.7%
-0.491534 1
3.7%
-0.413166 1
3.7%
-0.124186 1
3.7%
-0.052481 1
3.7%
ValueCountFrequency (%)
1.547382 1
3.7%
1.045957 1
3.7%
0.989879 1
3.7%
0.877058 1
3.7%
0.860559 1
3.7%
0.852671 1
3.7%
0.715912 1
3.7%
0.708011 1
3.7%
0.697345 1
3.7%
0.69474 1
3.7%

경도
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9833
Minimum126.83123
Maximum127.1463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T18:32:33.059446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.83123
5-th percentile126.85672
Q1126.93287
median126.97542
Q3127.04246
95-th percentile127.09501
Maximum127.1463
Range0.3150733
Interquartile range (IQR)0.10958895

Descriptive statistics

Standard deviation0.079407578
Coefficient of variation (CV)0.00062533874
Kurtosis-0.53620192
Mean126.9833
Median Absolute Deviation (MAD)0.0576934
Skewness0.0879341
Sum3428.5492
Variance0.0063055635
MonotonicityNot monotonic
2023-12-11T18:32:33.230228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
126.9502188 1
 
3.7%
126.9141209 1
 
3.7%
126.9635149 1
 
3.7%
127.0331096 1
 
3.7%
126.9996128 1
 
3.7%
127.0873044 1
 
3.7%
126.9544188 1
 
3.7%
126.9972197 1
 
3.7%
126.9336226 1
 
3.7%
127.0868052 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
126.8312293 1
3.7%
126.8479308 1
3.7%
126.8772291 1
3.7%
126.9059224 1
3.7%
126.9141209 1
3.7%
126.920722 1
3.7%
126.9321232 1
3.7%
126.9336226 1
3.7%
126.9373233 1
3.7%
126.9445225 1
3.7%
ValueCountFrequency (%)
127.1463026 1
3.7%
127.0983106 1
3.7%
127.0873044 1
3.7%
127.0868052 1
3.7%
127.0770084 1
3.7%
127.0480106 1
3.7%
127.0464123 1
3.7%
127.0385114 1
3.7%
127.0331096 1
3.7%
127.0179133 1
3.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.551016
Minimum37.452863
Maximum37.666091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T18:32:33.387378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.452863
5-th percentile37.467064
Q137.515906
median37.547803
Q337.583149
95-th percentile37.631924
Maximum37.666091
Range0.2132273
Interquartile range (IQR)0.0672425

Descriptive statistics

Standard deviation0.054194593
Coefficient of variation (CV)0.0014432257
Kurtosis-0.45590083
Mean37.551016
Median Absolute Deviation (MAD)0.0355952
Skewness0.18460134
Sum1013.8774
Variance0.0029370539
MonotonicityNot monotonic
2023-12-11T18:32:33.532125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
37.4528634 1
 
3.7%
37.4603141 1
 
3.7%
37.5741861 1
 
3.7%
37.6660907 1
 
3.7%
37.6360933 1
 
3.7%
37.6221956 1
 
3.7%
37.6182946 1
 
3.7%
37.613296 1
 
3.7%
37.6111958 1
 
3.7%
37.5862996 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
37.4528634 1
3.7%
37.4603141 1
3.7%
37.4828143 1
3.7%
37.4861128 1
3.7%
37.4963142 1
3.7%
37.5110096 1
3.7%
37.5122075 1
3.7%
37.5196055 1
3.7%
37.5241055 1
3.7%
37.5268081 1
3.7%
ValueCountFrequency (%)
37.6660907 1
3.7%
37.6360933 1
3.7%
37.6221956 1
3.7%
37.6182946 1
3.7%
37.613296 1
3.7%
37.6111958 1
3.7%
37.5862996 1
3.7%
37.5799984 1
3.7%
37.5741861 1
3.7%
37.5703995 1
3.7%

Interactions

2023-12-11T18:32:30.375812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:28.899563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.266135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.678049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:30.475660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:28.981027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.398457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:30.067003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:30.551207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.061791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.519351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:30.163033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:30.640208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.153739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:29.607749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:32:30.275259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:32:33.636477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호관측소명주소평균기온경도위도
고유번호1.0001.0001.0000.2020.0000.933
관측소명1.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.000
평균기온0.2021.0001.0001.0000.0000.344
경도0.0001.0001.0000.0001.0000.000
위도0.9331.0001.0000.3440.0001.000
2023-12-11T18:32:33.756643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호평균기온경도위도
고유번호1.000-0.4950.3020.978
평균기온-0.4951.000-0.015-0.501
경도0.302-0.0151.0000.326
위도0.978-0.5010.3261.000

Missing values

2023-12-11T18:32:30.777952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:32:30.896100image/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

고유번호관측소명주소평균기온경도위도
01관악서울특별시 관악구 신림동 산56-1 (서울대학교)-1.065771126.95021937.452863
12금천서울특별시 금천구 독산동 1034 (독산초등학교)1.547382126.91412137.460314
23서초서울특별시 서초구 서초동 1650 (서울교육대학교)0.989879127.01791337.482814
34구로서울특별시 구로구 궁동 213-42 (수궁동사무소)-0.124186126.83122937.486113
45기상청서울특별시 동작구 신대방동 460-18 (기상청)0.653926126.92072237.496314
56송파서울특별시 송파구 잠실동 40-1 (롯데월드)1.045957127.09831137.51101
67강남서울특별시 강남구 삼성동 42 (삼릉초등학교)0.69474127.04641237.512208
78용산서울특별시 용산구 이촌동 301-75 (신용산초등학교)0.697345126.97541637.519605
89한강서울특별시 영등포구 여의도동 85-1 (세모유람선)0.632861126.93732337.524105
910양천서울특별시 양천구 목동 915 (목동주차장)0.708011126.87722937.526808
고유번호관측소명주소평균기온경도위도
1718서대문서울특별시 서대문구 신촌동 134 (연세대학교)-0.525573126.94452337.5704
1819동대문서울특별시 동대문구 전농동 90 (서울시립대)0.877058127.04801137.579998
1920중랑서울특별시 중랑구 면목동 551 (면동초등학교)0.3828127.08680537.5863
2021은평서울특별시 은평구 불광동 280-17 (국립환경연구원)-0.052481126.93362337.611196
2122성북서울특별시 성북구 정릉동 861-1 (국민대학교)-0.413166126.9972237.613296
2223북한산서울특별시 종로구 구기동 산1 (승가사)-2.605182126.95441937.618295
2324노원서울특별시 노원구 공릉동 230-3 (육군사관학교)-0.941102127.08730437.622196
2425강북서울특별시 강북구 수유동 192-49 (강북구청 본관)0.434751126.99961337.636093
2526도봉서울특별시 도봉구 방학동 310 (신방학초등학교)-0.652582127.0331137.666091
2627서울서울특별시 종로구 송월동 1번지 (서울기상대)0.038765126.96351537.574186