Overview

Dataset statistics

Number of variables11
Number of observations87
Missing cells194
Missing cells (%)20.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory95.5 B

Variable types

Numeric4
Categorical1
Text4
Unsupported2

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
업종내역 has 87 (100.0%) missing valuesMissing
영업시간 has 87 (100.0%) missing valuesMissing
소재지우편번호 has 4 (4.6%) missing valuesMissing
소재지지번주소 has 4 (4.6%) missing valuesMissing
소재지도로명주소 has 4 (4.6%) missing valuesMissing
WGS84위도 has 4 (4.6%) missing valuesMissing
WGS84경도 has 4 (4.6%) missing valuesMissing
업종내역 is an unsupported type, check if it needs cleaning or further analysisUnsupported
영업시간 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-12 23:39:12.717340
Analysis finished2024-03-12 23:39:14.673981
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Real number (ℝ)

Distinct11
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.6552
Minimum2013
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-03-13T08:39:14.716537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2014
Q12017
median2019
Q32021
95-th percentile2022
Maximum2023
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8112216
Coefficient of variation (CV)0.001392621
Kurtosis-0.92767093
Mean2018.6552
Median Absolute Deviation (MAD)2
Skewness-0.35427641
Sum175623
Variance7.9029671
MonotonicityDecreasing
2024-03-13T08:39:14.807161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2021 13
14.9%
2022 12
13.8%
2018 12
13.8%
2020 10
11.5%
2017 9
10.3%
2019 7
8.0%
2014 7
8.0%
2016 5
 
5.7%
2015 5
 
5.7%
2023 4
 
4.6%
ValueCountFrequency (%)
2013 3
 
3.4%
2014 7
8.0%
2015 5
 
5.7%
2016 5
 
5.7%
2017 9
10.3%
2018 12
13.8%
2019 7
8.0%
2020 10
11.5%
2021 13
14.9%
2022 12
13.8%
ValueCountFrequency (%)
2023 4
 
4.6%
2022 12
13.8%
2021 13
14.9%
2020 10
11.5%
2019 7
8.0%
2018 12
13.8%
2017 9
10.3%
2016 5
 
5.7%
2015 5
 
5.7%
2014 7
8.0%

시군명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
고양시
13 
화성시
11 
용인시
10 
김포시
7 
안성시
6 
Other values (16)
40 

Length

Max length4
Median length3
Mean length3.0229885
Min length3

Unique

Unique6 ?
Unique (%)6.9%

Sample

1st row광명시
2nd row김포시
3rd row평택시
4th row포천시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 13
14.9%
화성시 11
12.6%
용인시 10
11.5%
김포시 7
 
8.0%
안성시 6
 
6.9%
파주시 5
 
5.7%
평택시 5
 
5.7%
여주시 4
 
4.6%
포천시 4
 
4.6%
양평군 4
 
4.6%
Other values (11) 18
20.7%

Length

2024-03-13T08:39:14.907271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 13
14.9%
화성시 11
12.6%
용인시 10
11.5%
김포시 7
 
8.0%
안성시 6
 
6.9%
파주시 5
 
5.7%
평택시 5
 
5.7%
여주시 4
 
4.6%
포천시 4
 
4.6%
양평군 4
 
4.6%
Other values (11) 18
20.7%

상호명
Text

Distinct86
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-03-13T08:39:15.126993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length12.034483
Min length3

Characters and Unicode

Total characters1047
Distinct characters141
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

Unique85 ?
Unique (%)97.7%

Sample

1st row광명농협 로컬푸드 직매장
2nd row고촌농협 로컬푸드직매장 고촌점
3rd row배다리직매장
4th row일동농협 로컬푸드직매장
5th row지도농협 로컬푸드직매장(3호점)
ValueCountFrequency (%)
로컬푸드직매장 47
28.7%
로컬푸드 5
 
3.0%
직매장 4
 
2.4%
일산농협 4
 
2.4%
지도농협 3
 
1.8%
부천시흥원예농협 2
 
1.2%
기흥농협 2
 
1.2%
로컬푸드직매장(2호점 2
 
1.2%
김포농협 2
 
1.2%
양평친환경 2
 
1.2%
Other values (88) 91
55.5%
2024-03-13T08:39:15.500092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
7.4%
장 75
 
7.2%
드 74
 
7.1%
컬 73
 
7.0%
푸 73
 
7.0%
로 73
 
7.0%
매 70
 
6.7%
직 69
 
6.6%
협 55
 
5.3%
농 54
 
5.2%
Other values (131) 354
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 939
89.7%
Space Separator 77
 
7.4%
Close Punctuation 13
 
1.2%
Open Punctuation 13
 
1.2%
Decimal Number 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 75
 
8.0%
드 74
 
7.9%
컬 73
 
7.8%
푸 73
 
7.8%
로 73
 
7.8%
매 70
 
7.5%
직 69
 
7.3%
협 55
 
5.9%
농 54
 
5.8%
점 21
 
2.2%
Other values (125) 302
32.2%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%
Space Separator
ValueCountFrequency (%)
77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 939
89.7%
Common 108
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 75
 
8.0%
드 74
 
7.9%
컬 73
 
7.8%
푸 73
 
7.8%
로 73
 
7.8%
매 70
 
7.5%
직 69
 
7.3%
협 55
 
5.9%
농 54
 
5.8%
점 21
 
2.2%
Other values (125) 302
32.2%
Common
ValueCountFrequency (%)
77
71.3%
) 13
 
12.0%
( 13
 
12.0%
1 2
 
1.9%
2 2
 
1.9%
3 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 939
89.7%
ASCII 108
 
10.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
71.3%
) 13
 
12.0%
( 13
 
12.0%
1 2
 
1.9%
2 2
 
1.9%
3 1
 
0.9%
Hangul
ValueCountFrequency (%)
장 75
 
8.0%
드 74
 
7.9%
컬 73
 
7.8%
푸 73
 
7.8%
로 73
 
7.8%
매 70
 
7.5%
직 69
 
7.3%
협 55
 
5.9%
농 54
 
5.8%
점 21
 
2.2%
Other values (125) 302
32.2%

전화번호
Text

Distinct84
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-03-13T08:39:15.699382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.091954
Min length11

Characters and Unicode

Total characters1052
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

Unique82 ?
Unique (%)94.3%

Sample

1st row02-2169-8092
2nd row031-961-0880
3rd row031-618-1583
4th row031-533-0877
5th row031-969-5345
ValueCountFrequency (%)
031-656-0747 3
 
3.4%
031-975-8322 2
 
2.3%
031-5180-7844 1
 
1.1%
02-2169-8092 1
 
1.1%
031-5180-7842 1
 
1.1%
031-889-8611 1
 
1.1%
031-339-2042 1
 
1.1%
031-283-9483 1
 
1.1%
031-832-8548 1
 
1.1%
031-773-6355 1
 
1.1%
Other values (74) 74
85.1%
2024-03-13T08:39:16.017233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 174
16.5%
1 147
14.0%
3 146
13.9%
0 144
13.7%
8 85
8.1%
5 66
 
6.3%
6 64
 
6.1%
2 61
 
5.8%
7 59
 
5.6%
4 54
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 878
83.5%
Dash Punctuation 174
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147
16.7%
3 146
16.6%
0 144
16.4%
8 85
9.7%
5 66
7.5%
6 64
7.3%
2 61
6.9%
7 59
6.7%
4 54
 
6.2%
9 52
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1052
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 174
16.5%
1 147
14.0%
3 146
13.9%
0 144
13.7%
8 85
8.1%
5 66
 
6.3%
6 64
 
6.1%
2 61
 
5.8%
7 59
 
5.6%
4 54
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 174
16.5%
1 147
14.0%
3 146
13.9%
0 144
13.7%
8 85
8.1%
5 66
 
6.3%
6 64
 
6.1%
2 61
 
5.8%
7 59
 
5.6%
4 54
 
5.1%

업종내역
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing87
Missing (%)100.0%
Memory size915.0 B

영업시간
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing87
Missing (%)100.0%
Memory size915.0 B

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)100.0%
Missing4
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean14151.265
Minimum10058
Maximum18597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-03-13T08:39:16.149256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10058
5-th percentile10123.3
Q110858.5
median13105
Q317377
95-th percentile18510
Maximum18597
Range8539
Interquartile range (IQR)6518.5

Descriptive statistics

Standard deviation3232.3303
Coefficient of variation (CV)0.22841281
Kurtosis-1.7199241
Mean14151.265
Median Absolute Deviation (MAD)2812
Skewness0.066025781
Sum1174555
Variance10447959
MonotonicityNot monotonic
2024-03-13T08:39:16.258743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18476 1
 
1.1%
18316 1
 
1.1%
18341 1
 
1.1%
16867 1
 
1.1%
17168 1
 
1.1%
16920 1
 
1.1%
11027 1
 
1.1%
12523 1
 
1.1%
12426 1
 
1.1%
18597 1
 
1.1%
Other values (73) 73
83.9%
(Missing) 4
 
4.6%
ValueCountFrequency (%)
10058 1
1.1%
10064 1
1.1%
10065 1
1.1%
10096 1
1.1%
10122 1
1.1%
10135 1
1.1%
10287 1
1.1%
10293 1
1.1%
10294 1
1.1%
10308 1
1.1%
ValueCountFrequency (%)
18597 1
1.1%
18567 1
1.1%
18531 1
1.1%
18517 1
1.1%
18511 1
1.1%
18501 1
1.1%
18476 1
1.1%
18421 1
1.1%
18341 1
1.1%
18336 1
1.1%

소재지지번주소
Text

MISSING 

Distinct83
Distinct (%)100.0%
Missing4
Missing (%)4.6%
Memory size828.0 B
2024-03-13T08:39:16.463786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length21.831325
Min length17

Characters and Unicode

Total characters1812
Distinct characters144
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

Unique83 ?
Unique (%)100.0%

Sample

1st row경기도 광명시 소하동 1340-3번지
2nd row경기도 평택시 죽백동 794번지
3rd row경기도 포천시 일동면 화대리 797-9번지
4th row경기도 고양시 덕양구 화정동 808-17번지
5th row경기도 김포시 양촌읍 구래리 202-1번지
ValueCountFrequency (%)
경기도 83
 
20.8%
고양시 13
 
3.2%
화성시 11
 
2.8%
용인시 10
 
2.5%
덕양구 8
 
2.0%
김포시 6
 
1.5%
파주시 5
 
1.2%
처인구 5
 
1.2%
평택시 5
 
1.2%
안성시 4
 
1.0%
Other values (219) 250
62.5%
2024-03-13T08:39:16.772367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
317
 
17.5%
기 88
 
4.9%
지 87
 
4.8%
도 86
 
4.7%
번 84
 
4.6%
경 83
 
4.6%
시 81
 
4.5%
동 53
 
2.9%
1 53
 
2.9%
- 53
 
2.9%
Other values (134) 827
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1129
62.3%
Space Separator 317
 
17.5%
Decimal Number 313
 
17.3%
Dash Punctuation 53
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 88
 
7.8%
지 87
 
7.7%
도 86
 
7.6%
번 84
 
7.4%
경 83
 
7.4%
시 81
 
7.2%
동 53
 
4.7%
리 39
 
3.5%
구 30
 
2.7%
양 30
 
2.7%
Other values (122) 468
41.5%
Decimal Number
ValueCountFrequency (%)
1 53
16.9%
2 39
12.5%
5 38
12.1%
4 38
12.1%
3 34
10.9%
0 26
8.3%
7 24
7.7%
6 23
7.3%
8 19
 
6.1%
9 19
 
6.1%
Space Separator
ValueCountFrequency (%)
317
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1129
62.3%
Common 683
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 88
 
7.8%
지 87
 
7.7%
도 86
 
7.6%
번 84
 
7.4%
경 83
 
7.4%
시 81
 
7.2%
동 53
 
4.7%
리 39
 
3.5%
구 30
 
2.7%
양 30
 
2.7%
Other values (122) 468
41.5%
Common
ValueCountFrequency (%)
317
46.4%
1 53
 
7.8%
- 53
 
7.8%
2 39
 
5.7%
5 38
 
5.6%
4 38
 
5.6%
3 34
 
5.0%
0 26
 
3.8%
7 24
 
3.5%
6 23
 
3.4%
Other values (2) 38
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1129
62.3%
ASCII 683
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317
46.4%
1 53
 
7.8%
- 53
 
7.8%
2 39
 
5.7%
5 38
 
5.6%
4 38
 
5.6%
3 34
 
5.0%
0 26
 
3.8%
7 24
 
3.5%
6 23
 
3.4%
Other values (2) 38
 
5.6%
Hangul
ValueCountFrequency (%)
기 88
 
7.8%
지 87
 
7.7%
도 86
 
7.6%
번 84
 
7.4%
경 83
 
7.4%
시 81
 
7.2%
동 53
 
4.7%
리 39
 
3.5%
구 30
 
2.7%
양 30
 
2.7%
Other values (122) 468
41.5%

소재지도로명주소
Text

MISSING 

Distinct83
Distinct (%)100.0%
Missing4
Missing (%)4.6%
Memory size828.0 B
2024-03-13T08:39:16.972868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length19.626506
Min length14

Characters and Unicode

Total characters1629
Distinct characters148
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

Unique83 ?
Unique (%)100.0%

Sample

1st row경기도 광명시 금하로 450
2nd row경기도 평택시 죽백5로 40
3rd row경기도 포천시 일동면 화동로 1092
4th row경기도 고양시 덕양구 호국로 573
5th row경기도 김포시 양촌읍 구래로26번길 65-20
ValueCountFrequency (%)
경기도 83
 
20.9%
고양시 13
 
3.3%
화성시 11
 
2.8%
용인시 10
 
2.5%
덕양구 8
 
2.0%
김포시 6
 
1.5%
처인구 5
 
1.3%
평택시 5
 
1.3%
파주시 5
 
1.3%
호국로 4
 
1.0%
Other values (205) 248
62.3%
2024-03-13T08:39:17.293090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
315
19.3%
도 88
 
5.4%
기 87
 
5.3%
경 85
 
5.2%
시 82
 
5.0%
로 74
 
4.5%
1 46
 
2.8%
2 34
 
2.1%
양 33
 
2.0%
3 32
 
2.0%
Other values (138) 753
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1036
63.6%
Space Separator 315
 
19.3%
Decimal Number 268
 
16.5%
Dash Punctuation 10
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 88
 
8.5%
기 87
 
8.4%
경 85
 
8.2%
시 82
 
7.9%
로 74
 
7.1%
양 33
 
3.2%
구 31
 
3.0%
길 24
 
2.3%
고 22
 
2.1%
읍 22
 
2.1%
Other values (126) 488
47.1%
Decimal Number
ValueCountFrequency (%)
1 46
17.2%
2 34
12.7%
3 32
11.9%
0 30
11.2%
5 25
9.3%
6 25
9.3%
4 25
9.3%
9 22
8.2%
7 15
 
5.6%
8 14
 
5.2%
Space Separator
ValueCountFrequency (%)
315
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1036
63.6%
Common 593
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 88
 
8.5%
기 87
 
8.4%
경 85
 
8.2%
시 82
 
7.9%
로 74
 
7.1%
양 33
 
3.2%
구 31
 
3.0%
길 24
 
2.3%
고 22
 
2.1%
읍 22
 
2.1%
Other values (126) 488
47.1%
Common
ValueCountFrequency (%)
315
53.1%
1 46
 
7.8%
2 34
 
5.7%
3 32
 
5.4%
0 30
 
5.1%
5 25
 
4.2%
6 25
 
4.2%
4 25
 
4.2%
9 22
 
3.7%
7 15
 
2.5%
Other values (2) 24
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1036
63.6%
ASCII 593
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
315
53.1%
1 46
 
7.8%
2 34
 
5.7%
3 32
 
5.4%
0 30
 
5.1%
5 25
 
4.2%
6 25
 
4.2%
4 25
 
4.2%
9 22
 
3.7%
7 15
 
2.5%
Other values (2) 24
 
4.0%
Hangul
ValueCountFrequency (%)
도 88
 
8.5%
기 87
 
8.4%
경 85
 
8.2%
시 82
 
7.9%
로 74
 
7.1%
양 33
 
3.2%
구 31
 
3.0%
길 24
 
2.3%
고 22
 
2.1%
읍 22
 
2.1%
Other values (126) 488
47.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)100.0%
Missing4
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean37.432218
Minimum36.976333
Maximum38.020566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-03-13T08:39:17.411354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.976333
5-th percentile37.011517
Q137.222633
median37.393408
Q337.651573
95-th percentile37.847197
Maximum38.020566
Range1.0442331
Interquartile range (IQR)0.42893925

Descriptive statistics

Standard deviation0.26640371
Coefficient of variation (CV)0.007116963
Kurtosis-0.99876123
Mean37.432218
Median Absolute Deviation (MAD)0.22862928
Skewness0.10168159
Sum3106.8741
Variance0.070970936
MonotonicityNot monotonic
2024-03-13T08:39:17.523595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.199839868 1
 
1.1%
37.2211809193 1
 
1.1%
37.2240858679 1
 
1.1%
37.3319431785 1
 
1.1%
37.1647788507 1
 
1.1%
37.2942492566 1
 
1.1%
38.0205662112 1
 
1.1%
37.4839597857 1
 
1.1%
37.7948694337 1
 
1.1%
37.1291004558 1
 
1.1%
Other values (73) 73
83.9%
(Missing) 4
 
4.6%
ValueCountFrequency (%)
36.9763331411 1
1.1%
36.9945235041 1
1.1%
37.0035808118 1
1.1%
37.0089164022 1
1.1%
37.011250685 1
1.1%
37.0139109782 1
1.1%
37.0149817581 1
1.1%
37.0389308052 1
1.1%
37.0660526248 1
1.1%
37.0856063316 1
1.1%
ValueCountFrequency (%)
38.0205662112 1
1.1%
37.9604678893 1
1.1%
37.9039096853 1
1.1%
37.8585808333 1
1.1%
37.8474005204 1
1.1%
37.8453680722 1
1.1%
37.7948694337 1
1.1%
37.7771417773 1
1.1%
37.7667366737 1
1.1%
37.7635629083 1
1.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)100.0%
Missing4
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean127.05277
Minimum126.61991
Maximum127.73632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-03-13T08:39:17.841331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.61991
5-th percentile126.71662
Q1126.82905
median127.01413
Q3127.20854
95-th percentile127.57862
Maximum127.73632
Range1.1164119
Interquartile range (IQR)0.37949191

Descriptive statistics

Standard deviation0.27121599
Coefficient of variation (CV)0.0021346721
Kurtosis-0.38818998
Mean127.05277
Median Absolute Deviation (MAD)0.19182192
Skewness0.63064437
Sum10545.38
Variance0.073558116
MonotonicityNot monotonic
2024-03-13T08:39:17.945882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1122843061 1
 
1.1%
126.9515546944 1
 
1.1%
126.9841999469 1
 
1.1%
127.1051947625 1
 
1.1%
127.3097737601 1
 
1.1%
127.1140568204 1
 
1.1%
127.0678056502 1
 
1.1%
127.5947528398 1
 
1.1%
127.4764769865 1
 
1.1%
126.9098564576 1
 
1.1%
Other values (73) 73
83.9%
(Missing) 4
 
4.6%
ValueCountFrequency (%)
126.6199103324 1
1.1%
126.643480722 1
1.1%
126.6589039113 1
1.1%
126.6844825722 1
1.1%
126.7129923547 1
1.1%
126.7492193177 1
1.1%
126.7530977905 1
1.1%
126.7582861371 1
1.1%
126.7675699635 1
1.1%
126.7713868104 1
1.1%
ValueCountFrequency (%)
127.7363222241 1
1.1%
127.6539289238 1
1.1%
127.6298805484 1
1.1%
127.5947528398 1
1.1%
127.5789271087 1
1.1%
127.5758694881 1
1.1%
127.4946966025 1
1.1%
127.4925566554 1
1.1%
127.4764769865 1
1.1%
127.4486166797 1
1.1%

Interactions

2024-03-13T08:39:14.142759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.355446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.598350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.879884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:14.203215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.419720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.664266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.951509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:14.263072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.476671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.726961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:14.015357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:14.326658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.538625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:13.793314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:39:14.076843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:39:18.025390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도시군명상호명전화번호소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
집계년도1.0000.2850.9700.9010.3881.0001.0000.0890.000
시군명0.2851.0000.0001.0000.9851.0001.0000.9400.876
상호명0.9700.0001.0000.9961.0001.0001.0001.0001.000
전화번호0.9011.0000.9961.0001.0001.0001.0001.0001.000
소재지우편번호0.3880.9851.0001.0001.0001.0001.0000.8930.810
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
WGS84위도0.0890.9401.0001.0000.8931.0001.0001.0000.575
WGS84경도0.0000.8761.0001.0000.8101.0001.0000.5751.000
2024-03-13T08:39:18.125515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도소재지우편번호WGS84위도WGS84경도시군명
집계년도1.0000.025-0.0440.0190.065
소재지우편번호0.0251.000-0.8690.4310.836
WGS84위도-0.044-0.8691.000-0.3240.665
WGS84경도0.0190.431-0.3241.0000.522
시군명0.0650.8360.6650.5221.000

Missing values

2024-03-13T08:39:14.416059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:39:14.529260image/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.
2024-03-13T08:39:14.621170image/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

집계년도시군명상호명전화번호업종내역영업시간소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
02023광명시광명농협 로컬푸드 직매장02-2169-8092<NA><NA>14316경기도 광명시 소하동 1340-3번지경기도 광명시 금하로 45037.448321126.882294
12023김포시고촌농협 로컬푸드직매장 고촌점031-961-0880<NA><NA><NA><NA><NA><NA><NA>
22023평택시배다리직매장031-618-1583<NA><NA>17862경기도 평택시 죽백동 794번지경기도 평택시 죽백5로 4037.003581127.120184
32023포천시일동농협 로컬푸드직매장031-533-0877<NA><NA>11117경기도 포천시 일동면 화대리 797-9번지경기도 포천시 일동면 화동로 109237.960468127.320751
42022고양시지도농협 로컬푸드직매장(3호점)031-969-5345<NA><NA>10455경기도 고양시 덕양구 화정동 808-17번지경기도 고양시 덕양구 호국로 57337.63915126.827298
52022김포시엘리트농부 로컬푸드 직매장031-981-8456<NA><NA>10058경기도 김포시 양촌읍 구래리 202-1번지경기도 김포시 양촌읍 구래로26번길 65-2037.649133126.61991
62022남양주시와부농협 로컬푸드직매장031-577-4008<NA><NA>12203경기도 남양주시 와부읍 월문리 1249-1번지경기도 남양주시 와부읍 수레로 15037.59413127.224952
72022성남시성남농협 로컬푸드직매장 대왕점031-758-9421<NA><NA>13105경기도 성남시 수정구 고등동 583번지경기도 성남시 수정구 대왕판교로 98937.428558127.10132
82022시흥시부천시흥원예농협 로컬푸드직매장031-311-8600<NA><NA>14931경기도 시흥시 매화동 39-1번지경기도 시흥시 수인로 296637.420938126.816948
92022시흥시안양원예농협 로컬푸드직매장031-402-1600<NA><NA>14982경기도 시흥시 논곡동 179-15번지경기도 시흥시 수인로 241137.393408126.85667
집계년도시군명상호명전화번호업종내역영업시간소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
772014고양시일산농협 로컬푸드직매장(풍산점)031-975-8322<NA><NA>10308경기도 고양시 일산동구 풍동 349-13번지경기도 고양시 일산동구 숲속마을1로 3437.663956126.799602
782014고양시벽제농협 로컬푸드직매장031-962-1873<NA><NA>10287경기도 고양시 덕양구 관산동 226-2번지경기도 고양시 덕양구 통일로 77537.687223126.863849
792014고양시원당농협 로컬푸드직매장031-966-6252<NA><NA>10463경기도 고양시 덕양구 성사동 505번지경기도 고양시 덕양구 고양대로1369번길 6937.657476126.838685
802014이천시이천시직매장031-631-8555<NA><NA>17391경기도 이천시 율현동 265번지경기도 이천시 이섭대천로941번길 637.254888127.443921
812014파주시조리농협 로컬푸드직매장031-944-6556<NA><NA>10937경기도 파주시 조리읍 봉일천리 125-8번지경기도 파주시 조리읍 봉천로 1237.742875126.810289
822014포천시포천로컬푸드 파머스마켓031-538-3722<NA><NA>11162경기도 포천시 설운동 72-2번지경기도 포천시 호국로 88637.845368127.159849
832014화성시봉담 본점031-5180-7815<NA><NA>18336경기도 화성시 봉담읍 덕리 7번지경기도 화성시 봉담읍 서봉산길 1037.173741126.938166
842013김포시김포농협 로컬푸드직매장(1호점)031-984-8522<NA><NA>10096경기도 김포시 걸포동 1588번지경기도 김포시 홍도평로 2037.631557126.712992
852013안성시대덕농협 로컬푸드직매장031-676-1773<NA><NA>17572경기도 안성시 당왕동 534-9번지경기도 안성시 고수1로 4337.013911127.257632
862013양평군양평친환경 로컬푸드직매장031-775-4093<NA><NA>12556경기도 양평군 양평읍 양근리 166-8번지경기도 양평군 양평읍 양평장터길 1537.490938127.492557