Overview

Dataset statistics

Number of variables11
Number of observations1761
Missing cells152
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory156.6 KiB
Average record size in memory91.1 B

Variable types

Numeric3
Categorical2
Text5
DateTime1

Dataset

Description인천광역시에서는 폭염 피해 최소화를 위해 폭염저감시설을 설치하고 있습니다.폭염저감시설 종류 중 하나인 그늘막 현황(군구, 설치장소 등)에 대한 정보를 제공합니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15036756&srcSe=7661IVAWM27C61E190

Alerts

시도 has constant value ""Constant
연번 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시군구High correlation
위도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
시군구 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
설치장소명 has 71 (4.0%) missing valuesMissing
설치일시 has 81 (4.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-18 03:01:54.892190
Analysis finished2024-03-18 03:01:57.259125
Duration2.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean881
Minimum1
Maximum1761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-18T12:01:57.321628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile89
Q1441
median881
Q31321
95-th percentile1673
Maximum1761
Range1760
Interquartile range (IQR)880

Descriptive statistics

Standard deviation508.50123
Coefficient of variation (CV)0.57718641
Kurtosis-1.2
Mean881
Median Absolute Deviation (MAD)440
Skewness0
Sum1551441
Variance258573.5
MonotonicityStrictly increasing
2024-03-18T12:01:57.464499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1171 1
 
0.1%
1182 1
 
0.1%
1181 1
 
0.1%
1180 1
 
0.1%
1179 1
 
0.1%
1178 1
 
0.1%
1177 1
 
0.1%
1176 1
 
0.1%
1175 1
 
0.1%
Other values (1751) 1751
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1761 1
0.1%
1760 1
0.1%
1759 1
0.1%
1758 1
0.1%
1757 1
0.1%
1756 1
0.1%
1755 1
0.1%
1754 1
0.1%
1753 1
0.1%
1752 1
0.1%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
인천광역시
1761 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 1761
100.0%

Length

2024-03-18T12:01:57.574843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:01:57.650757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 1761
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
연수구
356 
서구
332 
남동구
237 
중구
218 
부평구
146 
Other values (5)
472 

Length

Max length4
Median length3
Mean length2.7200454
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연수구 356
20.2%
서구 332
18.9%
남동구 237
13.5%
중구 218
12.4%
부평구 146
8.3%
미추홀구 142
 
8.1%
계양구 139
 
7.9%
강화군 91
 
5.2%
동구 85
 
4.8%
옹진군 15
 
0.9%

Length

2024-03-18T12:01:57.742619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:01:57.856328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연수구 356
20.2%
서구 332
18.9%
남동구 237
13.5%
중구 218
12.4%
부평구 146
8.3%
미추홀구 142
 
8.1%
계양구 139
 
7.9%
강화군 91
 
5.2%
동구 85
 
4.8%
옹진군 15
 
0.9%

읍면동(행정동)
Text

Distinct144
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-18T12:01:58.118886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9239069
Min length3

Characters and Unicode

Total characters6910
Distinct characters104
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row연안동
2nd row연안동
3rd row연안동
4th row연안동
5th row연안동
ValueCountFrequency (%)
영종1동 88
 
5.0%
강화읍 63
 
3.6%
아라동 51
 
2.9%
송도1동 50
 
2.8%
송도3동 49
 
2.8%
청라3동 45
 
2.6%
송도4동 44
 
2.5%
청라2동 40
 
2.3%
송도2동 39
 
2.2%
운서동 39
 
2.2%
Other values (134) 1253
71.2%
2024-03-18T12:01:58.521020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1713
24.8%
1 459
 
6.6%
2 374
 
5.4%
도 271
 
3.9%
송 269
 
3.9%
3 242
 
3.5%
라 171
 
2.5%
청 158
 
2.3%
현 130
 
1.9%
화 125
 
1.8%
Other values (94) 2998
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5523
79.9%
Decimal Number 1324
 
19.2%
Other Punctuation 63
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1713
31.0%
도 271
 
4.9%
송 269
 
4.9%
라 171
 
3.1%
청 158
 
2.9%
현 130
 
2.4%
화 125
 
2.3%
산 109
 
2.0%
영 101
 
1.8%
종 99
 
1.8%
Other values (85) 2377
43.0%
Decimal Number
ValueCountFrequency (%)
1 459
34.7%
2 374
28.2%
3 242
18.3%
4 124
 
9.4%
5 75
 
5.7%
6 34
 
2.6%
8 10
 
0.8%
7 6
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5523
79.9%
Common 1387
 
20.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1713
31.0%
도 271
 
4.9%
송 269
 
4.9%
라 171
 
3.1%
청 158
 
2.9%
현 130
 
2.4%
화 125
 
2.3%
산 109
 
2.0%
영 101
 
1.8%
종 99
 
1.8%
Other values (85) 2377
43.0%
Common
ValueCountFrequency (%)
1 459
33.1%
2 374
27.0%
3 242
17.4%
4 124
 
8.9%
5 75
 
5.4%
. 63
 
4.5%
6 34
 
2.5%
8 10
 
0.7%
7 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5523
79.9%
ASCII 1387
 
20.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1713
31.0%
도 271
 
4.9%
송 269
 
4.9%
라 171
 
3.1%
청 158
 
2.9%
현 130
 
2.4%
화 125
 
2.3%
산 109
 
2.0%
영 101
 
1.8%
종 99
 
1.8%
Other values (85) 2377
43.0%
ASCII
ValueCountFrequency (%)
1 459
33.1%
2 374
27.0%
3 242
17.4%
4 124
 
8.9%
5 75
 
5.4%
. 63
 
4.5%
6 34
 
2.5%
8 10
 
0.7%
7 6
 
0.4%

관리번호
Text

Distinct1318
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-18T12:01:58.856893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.0795003
Min length1

Characters and Unicode

Total characters7184
Distinct characters51
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1172 ?
Unique (%)66.6%

Sample

1st row연안-1
2nd row연안-2
3rd row연안-3
4th row연안-4
5th row연안-5
ValueCountFrequency (%)
42 5
 
0.3%
70 5
 
0.3%
16 5
 
0.3%
37 5
 
0.3%
36 5
 
0.3%
49 5
 
0.3%
34 5
 
0.3%
25 5
 
0.3%
53 5
 
0.3%
58 5
 
0.3%
Other values (1308) 1711
97.2%
2024-03-18T12:01:59.346972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1087
15.1%
- 1074
14.9%
2 951
13.2%
3 568
 
7.9%
0 562
 
7.8%
4 398
 
5.5%
5 339
 
4.7%
6 268
 
3.7%
9 254
 
3.5%
7 238
 
3.3%
Other values (41) 1445
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4902
68.2%
Other Letter 1203
 
16.7%
Dash Punctuation 1074
 
14.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 223
18.5%
도 221
18.4%
영 105
 
8.7%
종 103
 
8.6%
동 58
 
4.8%
춘 54
 
4.5%
연 52
 
4.3%
수 39
 
3.2%
신 39
 
3.2%
운 35
 
2.9%
Other values (29) 274
22.8%
Decimal Number
ValueCountFrequency (%)
1 1087
22.2%
2 951
19.4%
3 568
11.6%
0 562
11.5%
4 398
 
8.1%
5 339
 
6.9%
6 268
 
5.5%
9 254
 
5.2%
7 238
 
4.9%
8 237
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 1074
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5976
83.2%
Hangul 1203
 
16.7%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 223
18.5%
도 221
18.4%
영 105
 
8.7%
종 103
 
8.6%
동 58
 
4.8%
춘 54
 
4.5%
연 52
 
4.3%
수 39
 
3.2%
신 39
 
3.2%
운 35
 
2.9%
Other values (29) 274
22.8%
Common
ValueCountFrequency (%)
1 1087
18.2%
- 1074
18.0%
2 951
15.9%
3 568
9.5%
0 562
9.4%
4 398
 
6.7%
5 339
 
5.7%
6 268
 
4.5%
9 254
 
4.3%
7 238
 
4.0%
Latin
ValueCountFrequency (%)
E 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5981
83.3%
Hangul 1203
 
16.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1087
18.2%
- 1074
18.0%
2 951
15.9%
3 568
9.5%
0 562
9.4%
4 398
 
6.7%
5 339
 
5.7%
6 268
 
4.5%
9 254
 
4.2%
7 238
 
4.0%
Other values (2) 242
 
4.0%
Hangul
ValueCountFrequency (%)
송 223
18.5%
도 221
18.4%
영 105
 
8.7%
종 103
 
8.6%
동 58
 
4.8%
춘 54
 
4.5%
연 52
 
4.3%
수 39
 
3.2%
신 39
 
3.2%
운 35
 
2.9%
Other values (29) 274
22.8%

설치장소명
Text

MISSING 

Distinct1594
Distinct (%)94.3%
Missing71
Missing (%)4.0%
Memory size13.9 KiB
2024-03-18T12:01:59.659482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length12.892308
Min length1

Characters and Unicode

Total characters21788
Distinct characters561
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1536 ?
Unique (%)90.9%

Sample

1st row국민은행 앞
2nd row종합어시장 사거리(공중화장실 앞)
3rd row라이프3차 아파트 정문 앞
4th row연안자율방범대 초소 앞
5th row인천항여객터미널 앞 삼거리
ValueCountFrequency (%)
앞 750
 
16.8%
횡단보도 321
 
7.2%
건너편 129
 
2.9%
교통섬 122
 
2.7%
맞은편 75
 
1.7%
사거리 73
 
1.6%
정문 52
 
1.2%
입구 34
 
0.8%
인근 30
 
0.7%
101동 28
 
0.6%
Other values (1893) 2861
63.9%
2024-03-18T12:02:00.108381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2821
 
12.9%
앞 829
 
3.8%
동 576
 
2.6%
도 503
 
2.3%
리 471
 
2.2%
1 463
 
2.1%
단 448
 
2.1%
보 408
 
1.9%
교 382
 
1.8%
횡 381
 
1.7%
Other values (551) 14506
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16331
75.0%
Space Separator 2821
 
12.9%
Decimal Number 1561
 
7.2%
Close Punctuation 334
 
1.5%
Open Punctuation 334
 
1.5%
Uppercase Letter 211
 
1.0%
Lowercase Letter 72
 
0.3%
Dash Punctuation 60
 
0.3%
Other Punctuation 54
 
0.2%
Math Symbol 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 829
 
5.1%
동 576
 
3.5%
도 503
 
3.1%
리 471
 
2.9%
단 448
 
2.7%
보 408
 
2.5%
교 382
 
2.3%
횡 381
 
2.3%
거 353
 
2.2%
사 332
 
2.0%
Other values (495) 11648
71.3%
Uppercase Letter
ValueCountFrequency (%)
C 31
14.7%
K 30
14.2%
S 28
13.3%
L 22
10.4%
G 21
10.0%
U 14
6.6%
A 14
6.6%
H 14
6.6%
B 10
 
4.7%
T 8
 
3.8%
Other values (9) 19
9.0%
Lowercase Letter
ValueCountFrequency (%)
e 18
25.0%
s 12
16.7%
k 8
11.1%
t 6
 
8.3%
l 5
 
6.9%
a 3
 
4.2%
c 3
 
4.2%
u 3
 
4.2%
o 3
 
4.2%
g 3
 
4.2%
Other values (5) 8
11.1%
Decimal Number
ValueCountFrequency (%)
1 463
29.7%
0 270
17.3%
2 225
14.4%
3 150
 
9.6%
4 105
 
6.7%
5 94
 
6.0%
6 79
 
5.1%
9 67
 
4.3%
7 61
 
3.9%
8 47
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 41
75.9%
? 4
 
7.4%
/ 4
 
7.4%
. 3
 
5.6%
& 1
 
1.9%
@ 1
 
1.9%
Math Symbol
ValueCountFrequency (%)
~ 7
70.0%
+ 3
30.0%
Space Separator
ValueCountFrequency (%)
2821
100.0%
Close Punctuation
ValueCountFrequency (%)
) 334
100.0%
Open Punctuation
ValueCountFrequency (%)
( 334
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16331
75.0%
Common 5174
 
23.7%
Latin 283
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 829
 
5.1%
동 576
 
3.5%
도 503
 
3.1%
리 471
 
2.9%
단 448
 
2.7%
보 408
 
2.5%
교 382
 
2.3%
횡 381
 
2.3%
거 353
 
2.2%
사 332
 
2.0%
Other values (495) 11648
71.3%
Latin
ValueCountFrequency (%)
C 31
 
11.0%
K 30
 
10.6%
S 28
 
9.9%
L 22
 
7.8%
G 21
 
7.4%
e 18
 
6.4%
U 14
 
4.9%
A 14
 
4.9%
H 14
 
4.9%
s 12
 
4.2%
Other values (24) 79
27.9%
Common
ValueCountFrequency (%)
2821
54.5%
1 463
 
8.9%
) 334
 
6.5%
( 334
 
6.5%
0 270
 
5.2%
2 225
 
4.3%
3 150
 
2.9%
4 105
 
2.0%
5 94
 
1.8%
6 79
 
1.5%
Other values (12) 299
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16331
75.0%
ASCII 5457
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2821
51.7%
1 463
 
8.5%
) 334
 
6.1%
( 334
 
6.1%
0 270
 
4.9%
2 225
 
4.1%
3 150
 
2.7%
4 105
 
1.9%
5 94
 
1.7%
6 79
 
1.4%
Other values (46) 582
 
10.7%
Hangul
ValueCountFrequency (%)
앞 829
 
5.1%
동 576
 
3.5%
도 503
 
3.1%
리 471
 
2.9%
단 448
 
2.7%
보 408
 
2.5%
교 382
 
2.3%
횡 381
 
2.3%
거 353
 
2.2%
사 332
 
2.0%
Other values (495) 11648
71.3%

도로명주소
Text

Distinct955
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-18T12:02:00.370243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length49
Mean length19.90176
Min length1

Characters and Unicode

Total characters35047
Distinct characters353
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

Unique804 ?
Unique (%)45.7%

Sample

1st row인천광역시 중구 연안부두로33번길 27 (항동7가)
2nd row인천광역시 중구 연안부두로33번길 37 (항동7가)
3rd row인천광역시 중구 축항대로86번길 47 (항동7가, 비취맨숀)
4th row인천광역시 중구 연안부두로33번길 3 (항동7가)
5th row인천광역시 중구 연안부두로 68 (항동7가)
ValueCountFrequency (%)
인천광역시 1167
 
18.3%
연수구 261
 
4.1%
남동구 197
 
3.1%
서구 172
 
2.7%
중구 166
 
2.6%
송도동 148
 
2.3%
부평구 108
 
1.7%
미추홀구 107
 
1.7%
동구 73
 
1.1%
중산동 71
 
1.1%
Other values (1167) 3898
61.2%
2024-03-18T12:02:00.977671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8213
23.4%
동 1519
 
4.3%
인 1315
 
3.8%
시 1258
 
3.6%
구 1238
 
3.5%
천 1232
 
3.5%
로 1191
 
3.4%
역 1176
 
3.4%
광 1172
 
3.3%
( 1153
 
3.3%
Other values (343) 15580
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20342
58.0%
Space Separator 8213
23.4%
Decimal Number 3678
 
10.5%
Open Punctuation 1153
 
3.3%
Close Punctuation 1153
 
3.3%
Other Punctuation 324
 
0.9%
Uppercase Letter 86
 
0.2%
Dash Punctuation 75
 
0.2%
Lowercase Letter 23
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1519
 
7.5%
인 1315
 
6.5%
시 1258
 
6.2%
구 1238
 
6.1%
천 1232
 
6.1%
로 1191
 
5.9%
역 1176
 
5.8%
광 1172
 
5.8%
수 397
 
2.0%
송 333
 
1.6%
Other values (307) 9511
46.8%
Decimal Number
ValueCountFrequency (%)
1 689
18.7%
2 496
13.5%
3 435
11.8%
5 357
9.7%
4 340
9.2%
7 310
8.4%
8 289
7.9%
6 265
 
7.2%
0 260
 
7.1%
9 237
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
B 14
16.3%
L 14
16.3%
C 12
14.0%
K 11
12.8%
F 8
9.3%
S 7
8.1%
I 5
 
5.8%
W 5
 
5.8%
E 5
 
5.8%
V 5
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
e 7
30.4%
n 2
 
8.7%
a 2
 
8.7%
i 2
 
8.7%
s 2
 
8.7%
r 2
 
8.7%
o 2
 
8.7%
k 2
 
8.7%
y 2
 
8.7%
Other Punctuation
ValueCountFrequency (%)
, 321
99.1%
. 2
 
0.6%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
8213
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1153
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20342
58.0%
Common 14596
41.6%
Latin 109
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1519
 
7.5%
인 1315
 
6.5%
시 1258
 
6.2%
구 1238
 
6.1%
천 1232
 
6.1%
로 1191
 
5.9%
역 1176
 
5.8%
광 1172
 
5.8%
수 397
 
2.0%
송 333
 
1.6%
Other values (307) 9511
46.8%
Latin
ValueCountFrequency (%)
B 14
12.8%
L 14
12.8%
C 12
11.0%
K 11
10.1%
F 8
 
7.3%
S 7
 
6.4%
e 7
 
6.4%
I 5
 
4.6%
W 5
 
4.6%
E 5
 
4.6%
Other values (9) 21
19.3%
Common
ValueCountFrequency (%)
8213
56.3%
( 1153
 
7.9%
) 1153
 
7.9%
1 689
 
4.7%
2 496
 
3.4%
3 435
 
3.0%
5 357
 
2.4%
4 340
 
2.3%
, 321
 
2.2%
7 310
 
2.1%
Other values (7) 1129
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20342
58.0%
ASCII 14705
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8213
55.9%
( 1153
 
7.8%
) 1153
 
7.8%
1 689
 
4.7%
2 496
 
3.4%
3 435
 
3.0%
5 357
 
2.4%
4 340
 
2.3%
, 321
 
2.2%
7 310
 
2.1%
Other values (26) 1238
 
8.4%
Hangul
ValueCountFrequency (%)
동 1519
 
7.5%
인 1315
 
6.5%
시 1258
 
6.2%
구 1238
 
6.1%
천 1232
 
6.1%
로 1191
 
5.9%
역 1176
 
5.8%
광 1172
 
5.8%
수 397
 
2.0%
송 333
 
1.6%
Other values (307) 9511
46.8%

지번주소
Text

Distinct1433
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
2024-03-18T12:02:01.299910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length19.679727
Min length14

Characters and Unicode

Total characters34656
Distinct characters137
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

Unique1204 ?
Unique (%)68.4%

Sample

1st row인천광역시 중구 항동7가 27-111
2nd row인천광역시 중구 항동7가 27-69
3rd row인천광역시 중구 항동7가 27-107
4th row인천광역시 중구 항동7가 27-94
5th row인천광역시 중구 항동7가 86
ValueCountFrequency (%)
인천광역시 1761
24.6%
연수구 356
 
5.0%
서구 332
 
4.6%
남동구 237
 
3.3%
중구 218
 
3.0%
송도동 217
 
3.0%
부평구 146
 
2.0%
계양구 139
 
1.9%
미추홀구 135
 
1.9%
강화군 91
 
1.3%
Other values (1482) 3538
49.3%
2024-03-18T12:02:01.756108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7170
20.7%
동 2037
 
5.9%
천 1785
 
5.2%
시 1763
 
5.1%
인 1762
 
5.1%
역 1761
 
5.1%
광 1761
 
5.1%
구 1713
 
4.9%
1 1520
 
4.4%
- 1324
 
3.8%
Other values (127) 12060
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19215
55.4%
Space Separator 7170
 
20.7%
Decimal Number 6946
 
20.0%
Dash Punctuation 1324
 
3.8%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2037
 
10.6%
천 1785
 
9.3%
시 1763
 
9.2%
인 1762
 
9.2%
역 1761
 
9.2%
광 1761
 
9.2%
구 1713
 
8.9%
수 441
 
2.3%
서 436
 
2.3%
연 412
 
2.1%
Other values (114) 5344
27.8%
Decimal Number
ValueCountFrequency (%)
1 1520
21.9%
2 842
12.1%
3 745
10.7%
4 603
 
8.7%
5 590
 
8.5%
7 567
 
8.2%
8 545
 
7.8%
6 532
 
7.7%
9 527
 
7.6%
0 475
 
6.8%
Space Separator
ValueCountFrequency (%)
7170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1324
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19215
55.4%
Common 15441
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2037
 
10.6%
천 1785
 
9.3%
시 1763
 
9.2%
인 1762
 
9.2%
역 1761
 
9.2%
광 1761
 
9.2%
구 1713
 
8.9%
수 441
 
2.3%
서 436
 
2.3%
연 412
 
2.1%
Other values (114) 5344
27.8%
Common
ValueCountFrequency (%)
7170
46.4%
1 1520
 
9.8%
- 1324
 
8.6%
2 842
 
5.5%
3 745
 
4.8%
4 603
 
3.9%
5 590
 
3.8%
7 567
 
3.7%
8 545
 
3.5%
6 532
 
3.4%
Other values (3) 1003
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19215
55.4%
ASCII 15441
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7170
46.4%
1 1520
 
9.8%
- 1324
 
8.6%
2 842
 
5.5%
3 745
 
4.8%
4 603
 
3.9%
5 590
 
3.8%
7 567
 
3.7%
8 545
 
3.5%
6 532
 
3.4%
Other values (3) 1003
 
6.5%
Hangul
ValueCountFrequency (%)
동 2037
 
10.6%
천 1785
 
9.3%
시 1763
 
9.2%
인 1762
 
9.2%
역 1761
 
9.2%
광 1761
 
9.2%
구 1713
 
8.9%
수 441
 
2.3%
서 436
 
2.3%
연 412
 
2.1%
Other values (114) 5344
27.8%

설치일시
Date

MISSING 

Distinct142
Distinct (%)8.5%
Missing81
Missing (%)4.6%
Memory size13.9 KiB
Minimum2012-12-01 00:00:00
Maximum2023-07-18 00:00:00
2024-03-18T12:02:01.865498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:02:01.988324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1719
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.63771
Minimum124.69997
Maximum126.75952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-18T12:02:02.102697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.69997
5-th percentile126.48477
Q1126.63337
median126.66624
Q3126.70844
95-th percentile126.73869
Maximum126.75952
Range2.0595485
Interquartile range (IQR)0.0750701

Descriptive statistics

Standard deviation0.17893552
Coefficient of variation (CV)0.0014129718
Kurtosis69.188357
Mean126.63771
Median Absolute Deviation (MAD)0.0375841
Skewness-7.5156171
Sum223009.01
Variance0.032017922
MonotonicityNot monotonic
2024-03-18T12:02:02.218886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.0 15
 
0.9%
125.0 8
 
0.5%
126.714 4
 
0.2%
126.48252 3
 
0.2%
126.7075 2
 
0.1%
126.37829 2
 
0.1%
126.668691 2
 
0.1%
126.49171 2
 
0.1%
126.6319799 2
 
0.1%
126.48729 2
 
0.1%
Other values (1709) 1719
97.6%
ValueCountFrequency (%)
124.6999741 1
 
0.1%
124.7005777 1
 
0.1%
124.7012604 1
 
0.1%
124.7170529 1
 
0.1%
124.7176766 1
 
0.1%
125.0 8
0.5%
126.0 15
0.9%
126.143 1
 
0.1%
126.1550188 1
 
0.1%
126.242761 1
 
0.1%
ValueCountFrequency (%)
126.7595226 1
0.1%
126.758644 1
0.1%
126.7578598 1
0.1%
126.7575668 1
0.1%
126.7573559 1
0.1%
126.7560966 1
0.1%
126.7560668 1
0.1%
126.7560102 1
0.1%
126.7548883 1
0.1%
126.7517075 1
0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1717
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.472684
Minimum35
Maximum37.941634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2024-03-18T12:02:02.344305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile37.383568
Q137.423619
median37.479301
Q337.528634
95-th percentile37.636836
Maximum37.941634
Range2.9416338
Interquartile range (IQR)0.1050152

Descriptive statistics

Standard deviation0.1926317
Coefficient of variation (CV)0.0051405899
Kurtosis121.42095
Mean37.472684
Median Absolute Deviation (MAD)0.0508771
Skewness-9.5933151
Sum65989.397
Variance0.037106972
MonotonicityNot monotonic
2024-03-18T12:02:02.455359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.0 15
 
0.9%
35.0 8
 
0.5%
37.5983 3
 
0.2%
37.74594 3
 
0.2%
37.4145332 3
 
0.2%
37.74581 2
 
0.1%
37.5426565 2
 
0.1%
37.5888 2
 
0.1%
37.74499 2
 
0.1%
37.53773 2
 
0.1%
Other values (1707) 1719
97.6%
ValueCountFrequency (%)
35.0 8
0.5%
37.0 15
0.9%
37.1699904 1
 
0.1%
37.1700028 1
 
0.1%
37.1772 1
 
0.1%
37.181503 1
 
0.1%
37.2272445 1
 
0.1%
37.256016 1
 
0.1%
37.259147 1
 
0.1%
37.3712781 1
 
0.1%
ValueCountFrequency (%)
37.9416338 1
0.1%
37.9400098 1
0.1%
37.9397394 1
0.1%
37.8264136 1
0.1%
37.8264041 1
0.1%
37.7510014 1
0.1%
37.75087 1
0.1%
37.750565 1
0.1%
37.75043 2
0.1%
37.75036 1
0.1%

Interactions

2024-03-18T12:01:56.618075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.064991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.374466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.706617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.187701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.454288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.803047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.284959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:01:56.527717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:02:02.530723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구경도위도
연번1.0000.9740.5250.719
시군구0.9741.0000.7500.900
경도0.5250.7501.0000.920
위도0.7190.9000.9201.000
2024-03-18T12:02:02.642324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번경도위도시군구
연번1.0000.2960.6140.720
경도0.2961.0000.0140.520
위도0.6140.0141.0000.588
시군구0.7200.5200.5881.000

Missing values

2024-03-18T12:01:56.985828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:01:57.122710image/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.
2024-03-18T12:01:57.216468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번시도시군구읍면동(행정동)관리번호설치장소명도로명주소지번주소설치일시경도위도
01인천광역시중구연안동연안-1국민은행 앞인천광역시 중구 연안부두로33번길 27 (항동7가)인천광역시 중구 항동7가 27-1112018-08-02125.035.0
12인천광역시중구연안동연안-2종합어시장 사거리(공중화장실 앞)인천광역시 중구 연안부두로33번길 37 (항동7가)인천광역시 중구 항동7가 27-692018-08-02125.035.0
23인천광역시중구연안동연안-3라이프3차 아파트 정문 앞인천광역시 중구 축항대로86번길 47 (항동7가, 비취맨숀)인천광역시 중구 항동7가 27-1072018-08-02125.035.0
34인천광역시중구연안동연안-4연안자율방범대 초소 앞인천광역시 중구 연안부두로33번길 3 (항동7가)인천광역시 중구 항동7가 27-942018-08-02125.035.0
45인천광역시중구연안동연안-5인천항여객터미널 앞 삼거리인천광역시 중구 연안부두로 68 (항동7가)인천광역시 중구 항동7가 862018-08-02125.035.0
56인천광역시중구연안동연안-6인천 연안초등학교 앞인천광역시 중구 연안부두로33번길 36 (항동7가)인천광역시 중구 항동7가 27-1312019-05-06125.035.0
67인천광역시중구연안동연안-7라이프아파트 16동 앞 횡단보도인천광역시 중구 연안부두로33번길 36 (항동7가)인천광역시 중구 항동7가 27-1312019-05-01125.035.0
78인천광역시중구연안동연안-8롯데팩토로리 아울렛 앞 횡단보도인천광역시 중구 서해대로209번길 2 (항동7가)인천광역시 중구 항동7가 76-22019-09-16125.035.0
89인천광역시중구연안동연안-9인천해양센터 입구 앞 횡단보도인천광역시 중구 연안부두로 16 (항동7가)인천광역시 중구 항동7가 602020-05-26126.60366137.456384
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