Overview

Dataset statistics

Number of variables9
Number of observations125
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory76.1 B

Variable types

Categorical1
Text5
Numeric3

Alerts

소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
음식점명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique
WGS84위도 has unique valuesUnique
WGS84경도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:03:24.418775
Analysis finished2023-12-10 21:03:26.448455
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
수원시
17 
안산시
8 
용인시
8 
포천시
8 
부천시
 
7
Other values (22)
77 

Length

Max length4
Median length3
Mean length3.12
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row고양시
5th row과천시

Common Values

ValueCountFrequency (%)
수원시 17
 
13.6%
안산시 8
 
6.4%
용인시 8
 
6.4%
포천시 8
 
6.4%
부천시 7
 
5.6%
광주시 6
 
4.8%
파주시 6
 
4.8%
양주시 5
 
4.0%
남양주시 5
 
4.0%
동두천시 5
 
4.0%
Other values (17) 50
40.0%

Length

2023-12-11T06:03:26.540778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 17
 
13.6%
용인시 8
 
6.4%
포천시 8
 
6.4%
안산시 8
 
6.4%
부천시 7
 
5.6%
광주시 6
 
4.8%
파주시 6
 
4.8%
양주시 5
 
4.0%
남양주시 5
 
4.0%
동두천시 5
 
4.0%
Other values (17) 50
40.0%

음식점명
Text

UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T06:03:26.897439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.536
Min length2

Characters and Unicode

Total characters692
Distinct characters221
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)100.0%

Sample

1st row청정바지락칼국수
2nd row쥐눈이 콩마을
3rd row정통중화요리 남궁
4th row야구장농원
5th row경마장 오리집
ValueCountFrequency (%)
청정바지락칼국수 1
 
0.7%
여울쌈밥 1
 
0.7%
어부나라생선구이 1
 
0.7%
콩마당 1
 
0.7%
예찬 1
 
0.7%
세마오리농원 1
 
0.7%
주식회사 1
 
0.7%
청와정 1
 
0.7%
주식회사시골농장가든 1
 
0.7%
옛터 1
 
0.7%
Other values (135) 135
93.1%
2023-12-11T06:03:27.402848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
2.9%
탕 19
 
2.7%
한 13
 
1.9%
정 12
 
1.7%
수 12
 
1.7%
이 11
 
1.6%
갈 11
 
1.6%
국 10
 
1.4%
원 10
 
1.4%
장 10
 
1.4%
Other values (211) 564
81.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 665
96.1%
Space Separator 20
 
2.9%
Other Symbol 2
 
0.3%
Decimal Number 2
 
0.3%
Other Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 19
 
2.9%
한 13
 
2.0%
정 12
 
1.8%
수 12
 
1.8%
이 11
 
1.7%
갈 11
 
1.7%
국 10
 
1.5%
원 10
 
1.5%
장 10
 
1.5%
대 10
 
1.5%
Other values (204) 547
82.3%
Decimal Number
ValueCountFrequency (%)
6 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 667
96.4%
Common 25
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 19
 
2.8%
한 13
 
1.9%
정 12
 
1.8%
수 12
 
1.8%
이 11
 
1.6%
갈 11
 
1.6%
국 10
 
1.5%
원 10
 
1.5%
장 10
 
1.5%
대 10
 
1.5%
Other values (205) 549
82.3%
Common
ValueCountFrequency (%)
20
80.0%
, 1
 
4.0%
6 1
 
4.0%
3 1
 
4.0%
) 1
 
4.0%
( 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 665
96.1%
ASCII 25
 
3.6%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
80.0%
, 1
 
4.0%
6 1
 
4.0%
3 1
 
4.0%
) 1
 
4.0%
( 1
 
4.0%
Hangul
ValueCountFrequency (%)
탕 19
 
2.9%
한 13
 
2.0%
정 12
 
1.8%
수 12
 
1.8%
이 11
 
1.7%
갈 11
 
1.7%
국 10
 
1.5%
원 10
 
1.5%
장 10
 
1.5%
대 10
 
1.5%
Other values (204) 547
82.3%
None
ValueCountFrequency (%)
㈜ 2
100.0%

맛집전화번호
Text

Distinct124
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2023-12-11T06:03:27.702223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12
Min length11

Characters and Unicode

Total characters1488
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st row031-912-7676
2nd row031-965-5990
3rd row031-911-3702
4th row031-964-2884
5th row02-502-7500
ValueCountFrequency (%)
031-912-7676 1
 
0.8%
031-401-1778 1
 
0.8%
031-335-0201 1
 
0.8%
031-889-2233 1
 
0.8%
031-334-9258 1
 
0.8%
031-339-6630 1
 
0.8%
031-339-6600 1
 
0.8%
031-896-9876 1
 
0.8%
031-375-5299 1
 
0.8%
031-375-2552 1
 
0.8%
Other values (114) 114
91.9%
2023-12-11T06:03:28.161210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 248
16.7%
3 220
14.8%
0 217
14.6%
1 181
12.2%
2 110
7.4%
5 103
6.9%
8 96
 
6.5%
7 87
 
5.8%
9 82
 
5.5%
6 79
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1240
83.3%
Dash Punctuation 248
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 220
17.7%
0 217
17.5%
1 181
14.6%
2 110
8.9%
5 103
8.3%
8 96
7.7%
7 87
 
7.0%
9 82
 
6.6%
6 79
 
6.4%
4 65
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 248
16.7%
3 220
14.8%
0 217
14.6%
1 181
12.2%
2 110
7.4%
5 103
6.9%
8 96
 
6.5%
7 87
 
5.8%
9 82
 
5.5%
6 79
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 248
16.7%
3 220
14.8%
0 217
14.6%
1 181
12.2%
2 110
7.4%
5 103
6.9%
8 96
 
6.5%
7 87
 
5.8%
9 82
 
5.5%
6 79
 
5.3%

대표음식명
Text

Distinct111
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T06:03:28.467624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length6.368
Min length2

Characters and Unicode

Total characters796
Distinct characters182
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

Unique104 ?
Unique (%)83.2%

Sample

1st row천년초들깨수제비
2nd row한정식
3rd row해물고추짬뽕, 양장피잡채
4th row오리진흙구이
5th row오리구이
ValueCountFrequency (%)
한정식 8
 
4.8%
양념갈비 4
 
2.4%
생갈비 3
 
1.8%
등심 3
 
1.8%
설렁탕 3
 
1.8%
막국수 3
 
1.8%
추어탕 3
 
1.8%
한우 3
 
1.8%
갈비탕 2
 
1.2%
순두부 2
 
1.2%
Other values (124) 131
79.4%
2023-12-11T06:03:28.955205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
5.0%
탕 36
 
4.5%
, 36
 
4.5%
비 28
 
3.5%
갈 27
 
3.4%
한 24
 
3.0%
식 19
 
2.4%
정 19
 
2.4%
리 18
 
2.3%
이 18
 
2.3%
Other values (172) 531
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 716
89.9%
Space Separator 40
 
5.0%
Other Punctuation 36
 
4.5%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 36
 
5.0%
비 28
 
3.9%
갈 27
 
3.8%
한 24
 
3.4%
식 19
 
2.7%
정 19
 
2.7%
리 18
 
2.5%
이 18
 
2.5%
어 16
 
2.2%
구 14
 
2.0%
Other values (168) 497
69.4%
Space Separator
ValueCountFrequency (%)
40
100.0%
Other Punctuation
ValueCountFrequency (%)
, 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 716
89.9%
Common 80
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 36
 
5.0%
비 28
 
3.9%
갈 27
 
3.8%
한 24
 
3.4%
식 19
 
2.7%
정 19
 
2.7%
리 18
 
2.5%
이 18
 
2.5%
어 16
 
2.2%
구 14
 
2.0%
Other values (168) 497
69.4%
Common
ValueCountFrequency (%)
40
50.0%
, 36
45.0%
) 2
 
2.5%
( 2
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 716
89.9%
ASCII 80
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
50.0%
, 36
45.0%
) 2
 
2.5%
( 2
 
2.5%
Hangul
ValueCountFrequency (%)
탕 36
 
5.0%
비 28
 
3.9%
갈 27
 
3.8%
한 24
 
3.4%
식 19
 
2.7%
정 19
 
2.7%
리 18
 
2.5%
이 18
 
2.5%
어 16
 
2.2%
구 14
 
2.0%
Other values (168) 497
69.4%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13951.784
Minimum10020
Maximum18593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T06:03:29.114364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10020
5-th percentile10458.8
Q111518
median13823
Q316405
95-th percentile17902.4
Maximum18593
Range8573
Interquartile range (IQR)4887

Descriptive statistics

Standard deviation2471.5928
Coefficient of variation (CV)0.17715245
Kurtosis-1.3469532
Mean13951.784
Median Absolute Deviation (MAD)2430
Skewness0.15087601
Sum1743973
Variance6108770.9
MonotonicityNot monotonic
2023-12-11T06:03:29.265038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18102 3
 
2.4%
12018 2
 
1.6%
12708 2
 
1.6%
11330 2
 
1.6%
10858 2
 
1.6%
10359 1
 
0.8%
16804 1
 
0.8%
17052 1
 
0.8%
16879 1
 
0.8%
17170 1
 
0.8%
Other values (109) 109
87.2%
ValueCountFrequency (%)
10020 1
0.8%
10072 1
0.8%
10111 1
0.8%
10292 1
0.8%
10313 1
0.8%
10359 1
0.8%
10367 1
0.8%
10826 1
0.8%
10836 1
0.8%
10858 2
1.6%
ValueCountFrequency (%)
18593 1
 
0.8%
18271 1
 
0.8%
18102 3
2.4%
18101 1
 
0.8%
17904 1
 
0.8%
17896 1
 
0.8%
17548 1
 
0.8%
17517 1
 
0.8%
17346 1
 
0.8%
17301 1
 
0.8%

소재지도로명주소
Text

UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T06:03:29.511501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length19.88
Min length13

Characters and Unicode

Total characters2485
Distinct characters182
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

Unique125 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산동구 일산로463번길 7
2nd row경기도 고양시 덕양구 신촌길 81-15
3rd row경기도 고양시 일산서구 일산로 682
4th row경기도 고양시 일산동구 견달산로 351
5th row경기도 과천시 궁말로 20-4
ValueCountFrequency (%)
경기도 125
 
21.4%
수원시 17
 
2.9%
포천시 8
 
1.4%
안산시 8
 
1.4%
용인시 8
 
1.4%
부천시 7
 
1.2%
팔달구 6
 
1.0%
광주시 6
 
1.0%
파주시 6
 
1.0%
의정부시 5
 
0.9%
Other values (294) 389
66.5%
2023-12-11T06:03:29.905355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
460
18.5%
도 129
 
5.2%
기 128
 
5.2%
경 126
 
5.1%
시 125
 
5.0%
로 111
 
4.5%
1 89
 
3.6%
2 65
 
2.6%
길 56
 
2.3%
4 47
 
1.9%
Other values (172) 1149
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1548
62.3%
Space Separator 460
 
18.5%
Decimal Number 453
 
18.2%
Dash Punctuation 24
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 129
 
8.3%
기 128
 
8.3%
경 126
 
8.1%
시 125
 
8.1%
로 111
 
7.2%
길 56
 
3.6%
구 47
 
3.0%
번 41
 
2.6%
산 35
 
2.3%
수 35
 
2.3%
Other values (160) 715
46.2%
Decimal Number
ValueCountFrequency (%)
1 89
19.6%
2 65
14.3%
4 47
10.4%
5 45
9.9%
6 43
9.5%
3 40
8.8%
8 35
 
7.7%
7 34
 
7.5%
9 30
 
6.6%
0 25
 
5.5%
Space Separator
ValueCountFrequency (%)
460
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1548
62.3%
Common 937
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 129
 
8.3%
기 128
 
8.3%
경 126
 
8.1%
시 125
 
8.1%
로 111
 
7.2%
길 56
 
3.6%
구 47
 
3.0%
번 41
 
2.6%
산 35
 
2.3%
수 35
 
2.3%
Other values (160) 715
46.2%
Common
ValueCountFrequency (%)
460
49.1%
1 89
 
9.5%
2 65
 
6.9%
4 47
 
5.0%
5 45
 
4.8%
6 43
 
4.6%
3 40
 
4.3%
8 35
 
3.7%
7 34
 
3.6%
9 30
 
3.2%
Other values (2) 49
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1548
62.3%
ASCII 937
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
460
49.1%
1 89
 
9.5%
2 65
 
6.9%
4 47
 
5.0%
5 45
 
4.8%
6 43
 
4.6%
3 40
 
4.3%
8 35
 
3.7%
7 34
 
3.6%
9 30
 
3.2%
Other values (2) 49
 
5.2%
Hangul
ValueCountFrequency (%)
도 129
 
8.3%
기 128
 
8.3%
경 126
 
8.1%
시 125
 
8.1%
로 111
 
7.2%
길 56
 
3.6%
구 47
 
3.0%
번 41
 
2.6%
산 35
 
2.3%
수 35
 
2.3%
Other values (160) 715
46.2%

소재지지번주소
Text

UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T06:03:30.174392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length25
Mean length21.784
Min length17

Characters and Unicode

Total characters2723
Distinct characters172
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산동구 정발산동 1148번지
2nd row경기도 고양시 덕양구 원당동 293-1번지
3rd row경기도 고양시 일산서구 대화동 2101번지
4th row경기도 고양시 일산동구 문봉동 102-1번지
5th row경기도 과천시 과천동 647-2번지
ValueCountFrequency (%)
경기도 125
 
21.3%
수원시 17
 
2.9%
안산시 8
 
1.4%
포천시 8
 
1.4%
용인시 8
 
1.4%
부천시 7
 
1.2%
파주시 6
 
1.0%
팔달구 6
 
1.0%
광주시 6
 
1.0%
남양주시 5
 
0.9%
Other values (298) 390
66.6%
2023-12-11T06:03:30.550967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
461
 
16.9%
지 135
 
5.0%
도 131
 
4.8%
기 128
 
4.7%
시 126
 
4.6%
번 125
 
4.6%
경 125
 
4.6%
1 104
 
3.8%
동 98
 
3.6%
- 95
 
3.5%
Other values (162) 1195
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1659
60.9%
Decimal Number 495
 
18.2%
Space Separator 461
 
16.9%
Dash Punctuation 95
 
3.5%
Lowercase Letter 9
 
0.3%
Uppercase Letter 3
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 135
 
8.1%
도 131
 
7.9%
기 128
 
7.7%
시 126
 
7.6%
번 125
 
7.5%
경 125
 
7.5%
동 98
 
5.9%
구 48
 
2.9%
리 39
 
2.4%
원 32
 
1.9%
Other values (138) 672
40.5%
Decimal Number
ValueCountFrequency (%)
1 104
21.0%
2 67
13.5%
3 62
12.5%
4 47
9.5%
5 46
9.3%
7 45
9.1%
6 39
 
7.9%
9 31
 
6.3%
8 27
 
5.5%
0 27
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
m 2
22.2%
l 1
11.1%
a 1
11.1%
c 1
11.1%
e 1
11.1%
t 1
11.1%
i 1
11.1%
u 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
C 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
461
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1659
60.9%
Common 1052
38.6%
Latin 12
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 135
 
8.1%
도 131
 
7.9%
기 128
 
7.7%
시 126
 
7.6%
번 125
 
7.5%
경 125
 
7.5%
동 98
 
5.9%
구 48
 
2.9%
리 39
 
2.4%
원 32
 
1.9%
Other values (138) 672
40.5%
Common
ValueCountFrequency (%)
461
43.8%
1 104
 
9.9%
- 95
 
9.0%
2 67
 
6.4%
3 62
 
5.9%
4 47
 
4.5%
5 46
 
4.4%
7 45
 
4.3%
6 39
 
3.7%
9 31
 
2.9%
Other values (3) 55
 
5.2%
Latin
ValueCountFrequency (%)
m 2
16.7%
P 1
8.3%
C 1
8.3%
l 1
8.3%
a 1
8.3%
c 1
8.3%
e 1
8.3%
t 1
8.3%
i 1
8.3%
u 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1659
60.9%
ASCII 1064
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
461
43.3%
1 104
 
9.8%
- 95
 
8.9%
2 67
 
6.3%
3 62
 
5.8%
4 47
 
4.4%
5 46
 
4.3%
7 45
 
4.2%
6 39
 
3.7%
9 31
 
2.9%
Other values (14) 67
 
6.3%
Hangul
ValueCountFrequency (%)
지 135
 
8.1%
도 131
 
7.9%
기 128
 
7.7%
시 126
 
7.6%
번 125
 
7.5%
경 125
 
7.5%
동 98
 
5.9%
구 48
 
2.9%
리 39
 
2.4%
원 32
 
1.9%
Other values (138) 672
40.5%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.487323
Minimum36.988622
Maximum37.989704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T06:03:30.687604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.988622
5-th percentile37.177652
Q137.291564
median37.421231
Q337.739038
95-th percentile37.90342
Maximum37.989704
Range1.0010818
Interquartile range (IQR)0.44747439

Descriptive statistics

Standard deviation0.24725087
Coefficient of variation (CV)0.0065955862
Kurtosis-0.96322471
Mean37.487323
Median Absolute Deviation (MAD)0.14943429
Skewness0.28463346
Sum4685.9154
Variance0.061132994
MonotonicityNot monotonic
2023-12-11T06:03:31.032756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6737966 1
 
0.8%
37.21457228 1
 
0.8%
37.23642217 1
 
0.8%
37.3303507 1
 
0.8%
37.13821673 1
 
0.8%
37.32747395 1
 
0.8%
37.26086243 1
 
0.8%
37.32806393 1
 
0.8%
37.18680461 1
 
0.8%
37.1819471 1
 
0.8%
Other values (115) 115
92.0%
ValueCountFrequency (%)
36.98862248 1
0.8%
36.9958963 1
0.8%
37.01499591 1
0.8%
37.03674194 1
0.8%
37.13273371 1
0.8%
37.13821673 1
0.8%
37.17741589 1
0.8%
37.17859824 1
0.8%
37.1819471 1
0.8%
37.18680461 1
0.8%
ValueCountFrequency (%)
37.9897043 1
0.8%
37.97202868 1
0.8%
37.96592242 1
0.8%
37.90763724 1
0.8%
37.9064955 1
0.8%
37.90524384 1
0.8%
37.90353541 1
0.8%
37.90295879 1
0.8%
37.89645635 1
0.8%
37.89080184 1
0.8%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.04524
Minimum126.52603
Maximum127.65319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T06:03:31.176105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.52603
5-th percentile126.7338
Q1126.88962
median127.03701
Q3127.16895
95-th percentile127.44573
Maximum127.65319
Range1.1271552
Interquartile range (IQR)0.279328

Descriptive statistics

Standard deviation0.21358066
Coefficient of variation (CV)0.0016811386
Kurtosis0.34658572
Mean127.04524
Median Absolute Deviation (MAD)0.1367709
Skewness0.45108489
Sum15880.655
Variance0.0456167
MonotonicityNot monotonic
2023-12-11T06:03:31.301729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7754243 1
 
0.8%
127.5802138 1
 
0.8%
127.2039908 1
 
0.8%
127.1283772 1
 
0.8%
127.3114504 1
 
0.8%
127.1737785 1
 
0.8%
127.2799287 1
 
0.8%
127.0962076 1
 
0.8%
127.012034 1
 
0.8%
127.0370076 1
 
0.8%
Other values (115) 115
92.0%
ValueCountFrequency (%)
126.5260318 1
0.8%
126.6656657 1
0.8%
126.6858912 1
0.8%
126.6877763 1
0.8%
126.7226239 1
0.8%
126.7229956 1
0.8%
126.7329946 1
0.8%
126.7369971 1
0.8%
126.7515325 1
0.8%
126.7535463 1
0.8%
ValueCountFrequency (%)
127.653187 1
0.8%
127.6485946 1
0.8%
127.5802138 1
0.8%
127.5489582 1
0.8%
127.4734269 1
0.8%
127.4671755 1
0.8%
127.4513294 1
0.8%
127.4233289 1
0.8%
127.3714092 1
0.8%
127.3511585 1
0.8%

Interactions

2023-12-11T06:03:25.768321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.084839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.428285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.883773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.188781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.556411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:26.000455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.304713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:03:25.660487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:03:31.404528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호WGS84위도WGS84경도
시군명1.0000.9930.9620.944
소재지우편번호0.9931.0000.9320.738
WGS84위도0.9620.9321.0000.527
WGS84경도0.9440.7380.5271.000
2023-12-11T06:03:31.510886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명
소재지우편번호1.000-0.8970.0650.879
WGS84위도-0.8971.000-0.0680.733
WGS84경도0.065-0.0681.0000.672
시군명0.8790.7330.6721.000

Missing values

2023-12-11T06:03:26.161837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:03:26.373065image/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

시군명음식점명맛집전화번호대표음식명소재지우편번호소재지도로명주소소재지지번주소WGS84위도WGS84경도
0고양시청정바지락칼국수031-912-7676천년초들깨수제비10359경기도 고양시 일산동구 일산로463번길 7경기도 고양시 일산동구 정발산동 1148번지37.673797126.775424
1고양시쥐눈이 콩마을031-965-5990한정식10292경기도 고양시 덕양구 신촌길 81-15경기도 고양시 덕양구 원당동 293-1번지37.669953126.855459
2고양시정통중화요리 남궁031-911-3702해물고추짬뽕, 양장피잡채10367경기도 고양시 일산서구 일산로 682경기도 고양시 일산서구 대화동 2101번지37.682115126.753546
3고양시야구장농원031-964-2884오리진흙구이10313경기도 고양시 일산동구 견달산로 351경기도 고양시 일산동구 문봉동 102-1번지37.697152126.819685
4과천시경마장 오리집02-502-7500오리구이13823경기도 과천시 궁말로 20-4경기도 과천시 과천동 647-2번지37.441079127.010455
5과천시해원복집02-504-1626복지리13837경기도 과천시 별양상가로 2경기도 과천시 별양동 1-15번지37.427751126.992051
6광주시고향매운탕031-767-9693붕어찜, 매운탕12708경기도 광주시 남종면 산수로 1686경기도 광주시 남종면 분원리 244-5번지37.499292127.302729
7광주시수와연031-768-6446산야초마늘밥12708경기도 광주시 남종면 산수로 1680경기도 광주시 남종면 분원리 255-1번지37.49885127.302571
8광주시능골한우가031-797-0255갈비탕, 한우12773경기도 광주시 오포로 147경기도 광주시 능평동 146-6번지37.345659127.18175
9광주시맛있는 발효밥상궁뜰031-766-0987간장제육발효밥상, 코다리발효밥상12811경기도 광주시 도척면 궁평하천길 38경기도 광주시 도척면 궁평리 2-5번지37.339834127.335649
시군명음식점명맛집전화번호대표음식명소재지우편번호소재지도로명주소소재지지번주소WGS84위도WGS84경도
115포천시상황버섯향촌031-535-0005상황버섯 삼계탕,상황버섯 닭오리백숙11151경기도 포천시 군내면 포천로 1540경기도 포천시 군내면 하성북리 673번지37.896456127.207657
116포천시허브아일랜드 아테네홀031-353-1174허브돈까스,허브비빔밥11137경기도 포천시 신북면 청신로947번길 35경기도 포천시 신북면 삼정리 517-2번지37.965922127.131709
117포천시대대손손묵집031-542-6898대대손손묵집 정식11185경기도 포천시 소흘읍 죽엽산로447번길 11-3경기도 포천시 소흘읍 고모리 221-6번지37.794427127.166659
118포천시대복 복전문점031-535-0303복매운탕11162경기도 포천시 호국로964번길 12경기도 포천시 선단동 505-9번지37.850241127.165158
119포천시동이손만두031-541-6870만두전골11186경기도 포천시 소흘읍 광릉수목원로 700-5경기도 포천시 소흘읍 직동리 376-2번지37.773718127.15844
120하남시하남미소 명품한우031-699-0002꽃등심12947경기도 하남시 대청로 27경기도 하남시 신장동 522-4번지37.541017127.215208
121하남시한채당031-792-8880궁중한정식12900경기도 하남시 미사동로 38경기도 하남시 미사동 297번지37.570665127.201575
122하남시지호 한방 삼계탕031-795-9996삼계탕12984경기도 하남시 하남대로 995경기도 하남시 풍산동 234-2번지37.547629127.188648
123화성시홍천덤바우록계탕031-366-7880록계탕18271경기도 화성시 남양읍 역골중앙로41번길 42경기도 화성시 남양읍 남양리 2033-2번지37.206364126.827823
124화성시소담뜰031-8059-7667한우구이, 한우갈비탕18593경기도 화성시 향남읍 배터길 14경기도 화성시 향남읍 평리 94-2번지37.132734126.906361