Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells14
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory88.1 B

Variable types

Text6
Numeric3
Categorical1

Alerts

영업상태명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호High correlation
WGS84경도 is highly overall correlated with 소재지우편번호High correlation
소재지우편번호 has 2 (7.7%) missing valuesMissing
소재지도로명주소 has 5 (19.2%) missing valuesMissing
문화재지정사항 has 1 (3.8%) missing valuesMissing
WGS84위도 has 3 (11.5%) missing valuesMissing
WGS84경도 has 3 (11.5%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:14:54.277708
Analysis finished2023-12-10 22:14:56.009183
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T07:14:56.134086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters78
Distinct characters25
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

Unique10 ?
Unique (%)38.5%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row김포시
5th row김포시
ValueCountFrequency (%)
안성시 3
11.5%
파주시 3
11.5%
김포시 2
 
7.7%
수원시 2
 
7.7%
양평군 2
 
7.7%
평택시 2
 
7.7%
용인시 2
 
7.7%
여주군 1
 
3.8%
가평군 1
 
3.8%
하남시 1
 
3.8%
Other values (7) 7
26.9%
2023-12-11T07:14:56.427410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 21
26.9%
주 5
 
6.4%
평 5
 
6.4%
군 5
 
6.4%
성 4
 
5.1%
천 4
 
5.1%
양 4
 
5.1%
안 3
 
3.8%
파 3
 
3.8%
포 3
 
3.8%
Other values (15) 21
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
26.9%
주 5
 
6.4%
평 5
 
6.4%
군 5
 
6.4%
성 4
 
5.1%
천 4
 
5.1%
양 4
 
5.1%
안 3
 
3.8%
파 3
 
3.8%
포 3
 
3.8%
Other values (15) 21
26.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
26.9%
주 5
 
6.4%
평 5
 
6.4%
군 5
 
6.4%
성 4
 
5.1%
천 4
 
5.1%
양 4
 
5.1%
안 3
 
3.8%
파 3
 
3.8%
포 3
 
3.8%
Other values (15) 21
26.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 21
26.9%
주 5
 
6.4%
평 5
 
6.4%
군 5
 
6.4%
성 4
 
5.1%
천 4
 
5.1%
양 4
 
5.1%
안 3
 
3.8%
파 3
 
3.8%
포 3
 
3.8%
Other values (15) 21
26.9%

사업장명
Text

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T07:14:56.608200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters52
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

Unique24 ?
Unique (%)92.3%

Sample

1st row가평
2nd row고양
3rd row과천
4th row통진
5th row김포
ValueCountFrequency (%)
수원 2
 
7.7%
가평 1
 
3.8%
연천 1
 
3.8%
광주 1
 
3.8%
포천 1
 
3.8%
진위 1
 
3.8%
평택 1
 
3.8%
파주 1
 
3.8%
교하 1
 
3.8%
적성 1
 
3.8%
Other values (15) 15
57.7%
2023-12-11T07:14:56.913148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean13740.25
Minimum10024
Maximum17988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:14:57.030944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10024
5-th percentile10131.05
Q110993.5
median12592.5
Q317207.75
95-th percentile17693.1
Maximum17988
Range7964
Interquartile range (IQR)6214.25

Descriptive statistics

Standard deviation3013.7883
Coefficient of variation (CV)0.21934013
Kurtosis-1.7143759
Mean13740.25
Median Absolute Deviation (MAD)2055
Skewness0.30022673
Sum329766
Variance9082919.7
MonotonicityNot monotonic
2023-12-11T07:14:57.139227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11015 1
 
3.8%
13020 1
 
3.8%
11152 1
 
3.8%
17712 1
 
3.8%
17988 1
 
3.8%
10835 1
 
3.8%
10929 1
 
3.8%
10802 1
 
3.8%
17357 1
 
3.8%
17158 1
 
3.8%
Other values (14) 14
53.8%
(Missing) 2
 
7.7%
ValueCountFrequency (%)
10024 1
3.8%
10106 1
3.8%
10273 1
3.8%
10802 1
3.8%
10835 1
3.8%
10929 1
3.8%
11015 1
3.8%
11152 1
3.8%
11498 1
3.8%
12417 1
3.8%
ValueCountFrequency (%)
17988 1
3.8%
17712 1
3.8%
17586 1
3.8%
17519 1
3.8%
17502 1
3.8%
17357 1
3.8%
17158 1
3.8%
16917 1
3.8%
16463 1
3.8%
13800 1
3.8%

소재지도로명주소
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing5
Missing (%)19.2%
Memory size340.0 B
2023-12-11T07:14:57.363664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length23.380952
Min length18

Characters and Unicode

Total characters491
Distinct characters80
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

Unique21 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 향교로 23-1
2nd row경기도 고양시 덕양구 대양로285번길 33-13 (고양동)
3rd row경기도 과천시 자하동길 18 (중앙동)
4th row경기도 김포시 월곶면 군하로 288-21
5th row경기도 김포시 북변중로25번길 38 (북변동)
ValueCountFrequency (%)
경기도 21
 
18.9%
안성시 3
 
2.7%
김포시 2
 
1.8%
20 2
 
1.8%
양평군 2
 
1.8%
용인시 2
 
1.8%
파주시 2
 
1.8%
평택시 2
 
1.8%
0 2
 
1.8%
향교길 2
 
1.8%
Other values (70) 71
64.0%
2023-12-11T07:14:57.699102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
 
18.3%
기 22
 
4.5%
경 21
 
4.3%
도 21
 
4.3%
시 17
 
3.5%
3 16
 
3.3%
1 15
 
3.1%
길 14
 
2.9%
로 14
 
2.9%
2 14
 
2.9%
Other values (70) 247
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 284
57.8%
Space Separator 90
 
18.3%
Decimal Number 85
 
17.3%
Close Punctuation 11
 
2.2%
Open Punctuation 11
 
2.2%
Dash Punctuation 10
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 22
 
7.7%
경 21
 
7.4%
도 21
 
7.4%
시 17
 
6.0%
길 14
 
4.9%
로 14
 
4.9%
동 11
 
3.9%
교 11
 
3.9%
양 10
 
3.5%
면 8
 
2.8%
Other values (56) 135
47.5%
Decimal Number
ValueCountFrequency (%)
3 16
18.8%
1 15
17.6%
2 14
16.5%
5 7
8.2%
8 7
8.2%
4 7
8.2%
7 7
8.2%
0 6
 
7.1%
9 3
 
3.5%
6 3
 
3.5%
Space Separator
ValueCountFrequency (%)
90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 284
57.8%
Common 207
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 22
 
7.7%
경 21
 
7.4%
도 21
 
7.4%
시 17
 
6.0%
길 14
 
4.9%
로 14
 
4.9%
동 11
 
3.9%
교 11
 
3.9%
양 10
 
3.5%
면 8
 
2.8%
Other values (56) 135
47.5%
Common
ValueCountFrequency (%)
90
43.5%
3 16
 
7.7%
1 15
 
7.2%
2 14
 
6.8%
) 11
 
5.3%
( 11
 
5.3%
- 10
 
4.8%
5 7
 
3.4%
8 7
 
3.4%
4 7
 
3.4%
Other values (4) 19
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 284
57.8%
ASCII 207
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
43.5%
3 16
 
7.7%
1 15
 
7.2%
2 14
 
6.8%
) 11
 
5.3%
( 11
 
5.3%
- 10
 
4.8%
5 7
 
3.4%
8 7
 
3.4%
4 7
 
3.4%
Other values (4) 19
 
9.2%
Hangul
ValueCountFrequency (%)
기 22
 
7.7%
경 21
 
7.4%
도 21
 
7.4%
시 17
 
6.0%
길 14
 
4.9%
로 14
 
4.9%
동 11
 
3.9%
교 11
 
3.9%
양 10
 
3.5%
면 8
 
2.8%
Other values (56) 135
47.5%

소재지지번주소
Text

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T07:14:57.945109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length20
Min length16

Characters and Unicode

Total characters520
Distinct characters82
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

Unique24 ?
Unique (%)92.3%

Sample

1st row경기도 가평군 가평읍 읍내리 551번지 2호
2nd row경기도 고양시 덕양구 고양동 306번지
3rd row경기도 과천시 중앙동 81번지
4th row경기도 김포시 월곶면 군하리 220번지
5th row경기도 김포시 북변동 371번지
ValueCountFrequency (%)
경기도 26
 
20.6%
파주시 3
 
2.4%
안성시 3
 
2.4%
용인시 2
 
1.6%
양평군 2
 
1.6%
구읍리 2
 
1.6%
팔달구 2
 
1.6%
수원시 2
 
1.6%
김포시 2
 
1.6%
평택시 2
 
1.6%
Other values (76) 80
63.5%
2023-12-11T07:14:58.283906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
19.2%
지 30
 
5.8%
기 27
 
5.2%
경 26
 
5.0%
도 26
 
5.0%
번 26
 
5.0%
시 21
 
4.0%
1 18
 
3.5%
3 14
 
2.7%
5 13
 
2.5%
Other values (72) 219
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 343
66.0%
Space Separator 100
 
19.2%
Decimal Number 77
 
14.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 30
 
8.7%
기 27
 
7.9%
경 26
 
7.6%
도 26
 
7.6%
번 26
 
7.6%
시 21
 
6.1%
동 13
 
3.8%
리 13
 
3.8%
양 11
 
3.2%
면 8
 
2.3%
Other values (61) 142
41.4%
Decimal Number
ValueCountFrequency (%)
1 18
23.4%
3 14
18.2%
5 13
16.9%
2 8
10.4%
6 7
 
9.1%
8 5
 
6.5%
7 4
 
5.2%
0 3
 
3.9%
4 3
 
3.9%
9 2
 
2.6%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 343
66.0%
Common 177
34.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 30
 
8.7%
기 27
 
7.9%
경 26
 
7.6%
도 26
 
7.6%
번 26
 
7.6%
시 21
 
6.1%
동 13
 
3.8%
리 13
 
3.8%
양 11
 
3.2%
면 8
 
2.3%
Other values (61) 142
41.4%
Common
ValueCountFrequency (%)
100
56.5%
1 18
 
10.2%
3 14
 
7.9%
5 13
 
7.3%
2 8
 
4.5%
6 7
 
4.0%
8 5
 
2.8%
7 4
 
2.3%
0 3
 
1.7%
4 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 343
66.0%
ASCII 177
34.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
56.5%
1 18
 
10.2%
3 14
 
7.9%
5 13
 
7.3%
2 8
 
4.5%
6 7
 
4.0%
8 5
 
2.8%
7 4
 
2.3%
0 3
 
1.7%
4 3
 
1.7%
Hangul
ValueCountFrequency (%)
지 30
 
8.7%
기 27
 
7.9%
경 26
 
7.6%
도 26
 
7.6%
번 26
 
7.6%
시 21
 
6.1%
동 13
 
3.8%
리 13
 
3.8%
양 11
 
3.2%
면 8
 
2.3%
Other values (61) 142
41.4%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
운영중
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 26
100.0%

Length

2023-12-11T07:14:58.427809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:14:58.504889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 26
100.0%

문화재지정사항
Text

MISSING 

Distinct24
Distinct (%)96.0%
Missing1
Missing (%)3.8%
Memory size340.0 B
2023-12-11T07:14:58.648042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.28
Min length7

Characters and Unicode

Total characters232
Distinct characters21
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

Unique23 ?
Unique (%)92.0%

Sample

1st row향토유적 제2호
2nd row문화재자료 제69호
3rd row문화재자료 제9호
4th row문화재자료 제30호
5th row문화재자료 제29호
ValueCountFrequency (%)
문화재자료 19
39.6%
향토유적 4
 
8.3%
제2호 3
 
6.2%
제1호 2
 
4.2%
제3호 2
 
4.2%
제20호 1
 
2.1%
제13호 1
 
2.1%
제16호 1
 
2.1%
제4호 1
 
2.1%
향토유적제2호 1
 
2.1%
Other values (13) 13
27.1%
2023-12-11T07:14:58.909686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 25
10.8%
호 25
10.8%
23
9.9%
재 20
8.6%
자 20
8.6%
문 20
8.6%
화 20
8.6%
료 20
8.6%
2 13
 
5.6%
1 7
 
3.0%
Other values (11) 39
16.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
73.3%
Decimal Number 39
 
16.8%
Space Separator 23
 
9.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 25
14.7%
호 25
14.7%
재 20
11.8%
자 20
11.8%
문 20
11.8%
화 20
11.8%
료 20
11.8%
향 5
 
2.9%
토 5
 
2.9%
적 5
 
2.9%
Decimal Number
ValueCountFrequency (%)
2 13
33.3%
1 7
17.9%
3 6
15.4%
9 4
 
10.3%
6 3
 
7.7%
0 2
 
5.1%
4 2
 
5.1%
7 1
 
2.6%
8 1
 
2.6%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
73.3%
Common 62
 
26.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 25
14.7%
호 25
14.7%
재 20
11.8%
자 20
11.8%
문 20
11.8%
화 20
11.8%
료 20
11.8%
향 5
 
2.9%
토 5
 
2.9%
적 5
 
2.9%
Common
ValueCountFrequency (%)
23
37.1%
2 13
21.0%
1 7
 
11.3%
3 6
 
9.7%
9 4
 
6.5%
6 3
 
4.8%
0 2
 
3.2%
4 2
 
3.2%
7 1
 
1.6%
8 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
73.3%
ASCII 62
 
26.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 25
14.7%
호 25
14.7%
재 20
11.8%
자 20
11.8%
문 20
11.8%
화 20
11.8%
료 20
11.8%
향 5
 
2.9%
토 5
 
2.9%
적 5
 
2.9%
ASCII
ValueCountFrequency (%)
23
37.1%
2 13
21.0%
1 7
 
11.3%
3 6
 
9.7%
9 4
 
6.5%
6 3
 
4.8%
0 2
 
3.2%
4 2
 
3.2%
7 1
 
1.6%
8 1
 
1.6%

향교명
Text

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T07:14:59.070740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters52
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

Unique24 ?
Unique (%)92.3%

Sample

1st row가평
2nd row고양
3rd row과천
4th row통진
5th row김포
ValueCountFrequency (%)
수원 2
 
7.7%
가평 1
 
3.8%
연천 1
 
3.8%
광주 1
 
3.8%
포천 1
 
3.8%
진위 1
 
3.8%
평택 1
 
3.8%
파주 1
 
3.8%
교하 1
 
3.8%
적성 1
 
3.8%
Other values (15) 15
57.7%
2023-12-11T07:14:59.329835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 6
 
11.5%
주 4
 
7.7%
천 4
 
7.7%
평 3
 
5.8%
성 3
 
5.8%
수 2
 
3.8%
포 2
 
3.8%
원 2
 
3.8%
지 2
 
3.8%
진 2
 
3.8%
Other values (22) 22
42.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing3
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean37.473889
Minimum36.965471
Maximum38.096304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:14:59.445741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.965471
5-th percentile37.019143
Q137.223243
median37.48098
Q337.739091
95-th percentile37.956257
Maximum38.096304
Range1.1308334
Interquartile range (IQR)0.51584823

Descriptive statistics

Standard deviation0.33361784
Coefficient of variation (CV)0.0089026747
Kurtosis-1.1484209
Mean37.473889
Median Absolute Deviation (MAD)0.27315626
Skewness0.15277925
Sum861.89944
Variance0.11130086
MonotonicityNot monotonic
2023-12-11T07:14:59.559623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
37.7037363723 1
 
3.8%
37.2078234573 1
 
3.8%
37.5220740395 1
 
3.8%
37.8897027254 1
 
3.8%
37.0983279169 1
 
3.8%
36.9654708211 1
 
3.8%
37.834634356 1
 
3.8%
37.7612263231 1
 
3.8%
37.9636517563 1
 
3.8%
37.2386630072 1
 
3.8%
Other values (13) 13
50.0%
(Missing) 3
 
11.5%
ValueCountFrequency (%)
36.9654708211 1
3.8%
37.0137764724 1
3.8%
37.0674427191 1
3.8%
37.0824896617 1
3.8%
37.0983279169 1
3.8%
37.2078234573 1
3.8%
37.2386630072 1
3.8%
37.2728525821 1
3.8%
37.2746046199 1
3.8%
37.295388954 1
3.8%
ValueCountFrequency (%)
38.0963042686 1
3.8%
37.9636517563 1
3.8%
37.8897027254 1
3.8%
37.834634356 1
3.8%
37.8299869168 1
3.8%
37.7612263231 1
3.8%
37.7169565963 1
3.8%
37.7037363723 1
3.8%
37.6246481239 1
3.8%
37.5247771845 1
3.8%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing3
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean127.08765
Minimum126.55019
Maximum127.63715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T07:14:59.665148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.55019
5-th percentile126.71604
Q1126.90861
median127.06888
Q3127.24885
95-th percentile127.50267
Maximum127.63715
Range1.0869598
Interquartile range (IQR)0.34024169

Descriptive statistics

Standard deviation0.27173689
Coefficient of variation (CV)0.0021381849
Kurtosis-0.32403785
Mean127.08765
Median Absolute Deviation (MAD)0.17392653
Skewness0.1222835
Sum2923.016
Variance0.073840939
MonotonicityNot monotonic
2023-12-11T07:14:59.792219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
126.8949502796 1
 
3.8%
126.7981058917 1
 
3.8%
127.1984184636 1
 
3.8%
127.2213425922 1
 
3.8%
127.0950105478 1
 
3.8%
127.0560487934 1
 
3.8%
126.8113217385 1
 
3.8%
126.7743111763 1
 
3.8%
126.9222693066 1
 
3.8%
127.2860137085 1
 
3.8%
Other values (13) 13
50.0%
(Missing) 3
 
11.5%
ValueCountFrequency (%)
126.5501928022 1
3.8%
126.7095635949 1
3.8%
126.7743111763 1
3.8%
126.7981058917 1
3.8%
126.8113217385 1
3.8%
126.8949502796 1
3.8%
126.9222693066 1
3.8%
126.9873187204 1
3.8%
127.009556773 1
3.8%
127.0121333387 1
3.8%
ValueCountFrequency (%)
127.6371525651 1
3.8%
127.5076136997 1
3.8%
127.4581715568 1
3.8%
127.4235260158 1
3.8%
127.2860137085 1
3.8%
127.2763603795 1
3.8%
127.2213425922 1
3.8%
127.1984184636 1
3.8%
127.1968870136 1
3.8%
127.1208544693 1
3.8%

Interactions

2023-12-11T07:14:55.432376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:54.655780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:54.929750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:55.510530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:54.743235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:55.015119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:55.577919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:54.829968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:55.097181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:14:59.877398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명소재지우편번호소재지도로명주소소재지지번주소문화재지정사항향교명WGS84위도WGS84경도
시군명1.0001.0000.9681.0001.0000.8691.0000.8550.421
사업장명1.0001.0001.0001.0000.9880.9871.0001.0001.000
소재지우편번호0.9681.0001.0001.0001.0000.9311.0000.8910.584
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0000.9881.0001.0001.0000.9870.9880.8970.932
문화재지정사항0.8690.9870.9311.0000.9871.0000.9870.6130.957
향교명1.0001.0001.0001.0000.9880.9871.0001.0001.000
WGS84위도0.8551.0000.8911.0000.8970.6131.0001.0000.717
WGS84경도0.4211.0000.5841.0000.9320.9571.0000.7171.000
2023-12-11T07:14:59.977235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도
소재지우편번호1.000-0.8640.536
WGS84위도-0.8641.000-0.246
WGS84경도0.536-0.2461.000

Missing values

2023-12-11T07:14:55.671660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:14:55.788140image/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.
2023-12-11T07:14:55.935695image/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

시군명사업장명소재지우편번호소재지도로명주소소재지지번주소영업상태명문화재지정사항향교명WGS84위도WGS84경도
0가평군가평12417경기도 가평군 가평읍 향교로 23-1경기도 가평군 가평읍 읍내리 551번지 2호운영중향토유적 제2호가평37.829987127.507614
1고양시고양10273경기도 고양시 덕양구 대양로285번길 33-13 (고양동)경기도 고양시 덕양구 고양동 306번지운영중문화재자료 제69호고양37.703736126.89495
2과천시과천13800경기도 과천시 자하동길 18 (중앙동)경기도 과천시 중앙동 81번지운영중문화재자료 제9호과천37.433923126.987319
3김포시통진10024경기도 김포시 월곶면 군하로 288-21경기도 김포시 월곶면 군하리 220번지운영중문화재자료 제30호통진37.716957126.550193
4김포시김포10106경기도 김포시 북변중로25번길 38 (북변동)경기도 김포시 북변동 371번지운영중문화재자료 제29호김포37.624648126.709564
5수원시수원<NA><NA>경기도 수원시 팔달구 매산로3가 1번지운영중문화재자료제2호수원37.274605127.009557
6수원시수원16463경기도 수원시 팔달구 향교로 107-9 (교동)경기도 수원시 팔달구 교동 43번지운영중문화재자료 제1호수원37.272853127.012133
7안성시안성17586경기도 안성시 향교길 75 (명륜동)경기도 안성시 명륜동 118번지운영중문화재자료 제27호안성37.013776127.27636
8안성시죽산17519경기도 안성시 죽산면 죽산향교길 54-45경기도 안성시 명륜동 118번지운영중문화재자료 제26호죽산37.08249127.423526
9안성시양성17502경기도 안성시 양성면 교동길 39경기도 안성시 양성면 동항리 91번지운영중문화재자료 제28호양성37.067443127.196887
시군명사업장명소재지우편번호소재지도로명주소소재지지번주소영업상태명문화재지정사항향교명WGS84위도WGS84경도
16용인시양지17158경기도 용인시 처인구 양지면 향교로13번길 20경기도 용인시 처인구 양지면 양지리 379번지운영중문화재자료 제23호양지37.238663127.286014
17이천시이천17357<NA>경기도 이천시 창전동 10번지 1호운영중문화재자료 제22호이천<NA><NA>
18파주시적성10802<NA>경기도 파주시 적성면 구읍리 516번지운영중향토유적 제3호적성37.963652126.922269
19파주시교하10929경기도 파주시 금정18길 7-2 (금촌동)경기도 파주시 금능동 356번지운영중문화재자료 제11호교하37.761226126.774311
20파주시파주10835경기도 파주시 파주읍 향교말길 56-83경기도 파주시 파주읍 파주리 335번지운영중향토유적제2호파주37.834634126.811322
21평택시평택17988경기도 평택시 팽성읍 부용로17번길 40 (0)경기도 평택시 팽성읍 객사리 185번지운영중문화재자료 제4호평택36.965471127.056049
22평택시진위17712경기도 평택시 진위면 진위로 49경기도 평택시 진위면 봉남리 167번지운영중<NA>진위37.098328127.095011
23포천시포천11152경기도 포천시 군내면 청군로3274번길 37 (0)경기도 포천시 군내면 구읍리 165번지운영중문화재자료 제16호포천37.889703127.221343
24하남시광주13020경기도 하남시 대성로 126-13 (교산동)경기도 하남시 교산동 227번지 3호운영중문화재자료 제13호광주37.522074127.198418
25화성시남양<NA><NA>경기도 화성시 남양동 355번지운영중문화재자료 제34호남양37.207823126.798106