Overview

Dataset statistics

Number of variables11
Number of observations287
Missing cells98
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.9 KiB
Average record size in memory92.5 B

Variable types

Categorical2
Text5
Numeric4

Alerts

시군명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
설립년도 has 29 (10.1%) missing valuesMissing
회원수 has 29 (10.1%) missing valuesMissing
사무실전화번호 has 10 (3.5%) missing valuesMissing
팩스번호 has 30 (10.5%) missing valuesMissing

Reproduction

Analysis started2024-04-29 12:54:03.192088
Analysis finished2024-04-29 12:54:08.023552
Duration4.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
부천시
 
11
의왕시
 
11
가평군
 
10
김포시
 
10
시흥시
 
10
Other values (26)
235 

Length

Max length4
Median length3
Mean length3.097561
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시흥시
2nd row용인시
3rd row시흥시
4th row시흥시
5th row시흥시

Common Values

ValueCountFrequency (%)
부천시 11
 
3.8%
의왕시 11
 
3.8%
가평군 10
 
3.5%
김포시 10
 
3.5%
시흥시 10
 
3.5%
구리시 10
 
3.5%
의정부시 10
 
3.5%
군포시 10
 
3.5%
안산시 10
 
3.5%
포천시 10
 
3.5%
Other values (21) 185
64.5%

Length

2024-04-29T21:54:08.096846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부천시 11
 
3.8%
의왕시 11
 
3.8%
의정부시 10
 
3.5%
안산시 10
 
3.5%
군포시 10
 
3.5%
포천시 10
 
3.5%
구리시 10
 
3.5%
시흥시 10
 
3.5%
김포시 10
 
3.5%
가평군 10
 
3.5%
Other values (21) 185
64.5%

단체명
Text

Distinct133
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-29T21:54:08.294472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length10.212544
Min length3

Characters and Unicode

Total characters2931
Distinct characters77
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)42.2%

Sample

1st row상이군경회
2nd row특수임무유공자회
3rd row전몰군경미망인회
4th row무공수훈자회
5th row고엽제전우회
ValueCountFrequency (%)
대한민국 25
 
6.0%
상이군경회 24
 
5.7%
무공수훈자회 23
 
5.5%
경기도지부 22
 
5.2%
전몰군경유족회 22
 
5.2%
고엽제전우회 21
 
5.0%
전몰군경미망인회 21
 
5.0%
특수임무유공자회 20
 
4.8%
광복회 20
 
4.8%
6.25참전유공자회 19
 
4.5%
Other values (90) 203
48.3%
2024-04-29T21:54:08.604083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 376
 
12.8%
전 182
 
6.2%
133
 
4.5%
경 125
 
4.3%
자 122
 
4.2%
지 120
 
4.1%
군 104
 
3.5%
시 95
 
3.2%
유 95
 
3.2%
공 94
 
3.2%
Other values (67) 1485
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2674
91.2%
Space Separator 133
 
4.5%
Decimal Number 93
 
3.2%
Other Punctuation 31
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 376
 
14.1%
전 182
 
6.8%
경 125
 
4.7%
자 122
 
4.6%
지 120
 
4.5%
군 104
 
3.9%
시 95
 
3.6%
유 95
 
3.6%
공 94
 
3.5%
수 74
 
2.8%
Other values (61) 1287
48.1%
Decimal Number
ValueCountFrequency (%)
5 31
33.3%
2 31
33.3%
6 31
33.3%
Other Punctuation
ValueCountFrequency (%)
. 29
93.5%
· 2
 
6.5%
Space Separator
ValueCountFrequency (%)
133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2674
91.2%
Common 257
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 376
 
14.1%
전 182
 
6.8%
경 125
 
4.7%
자 122
 
4.6%
지 120
 
4.5%
군 104
 
3.9%
시 95
 
3.6%
유 95
 
3.6%
공 94
 
3.5%
수 74
 
2.8%
Other values (61) 1287
48.1%
Common
ValueCountFrequency (%)
133
51.8%
5 31
 
12.1%
2 31
 
12.1%
6 31
 
12.1%
. 29
 
11.3%
· 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2674
91.2%
ASCII 255
 
8.7%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 376
 
14.1%
전 182
 
6.8%
경 125
 
4.7%
자 122
 
4.6%
지 120
 
4.5%
군 104
 
3.9%
시 95
 
3.6%
유 95
 
3.6%
공 94
 
3.5%
수 74
 
2.8%
Other values (61) 1287
48.1%
ASCII
ValueCountFrequency (%)
133
52.2%
5 31
 
12.2%
2 31
 
12.2%
6 31
 
12.2%
. 29
 
11.4%
None
ValueCountFrequency (%)
· 2
100.0%

설립년도
Real number (ℝ)

MISSING 

Distinct49
Distinct (%)19.0%
Missing29
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean1989.3721
Minimum1951
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-29T21:54:08.740854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1951
5-th percentile1963
Q11965
median1993
Q32007
95-th percentile2012
Maximum2022
Range71
Interquartile range (IQR)42

Descriptive statistics

Standard deviation18.756092
Coefficient of variation (CV)0.0094281465
Kurtosis-1.1904009
Mean1989.3721
Median Absolute Deviation (MAD)14
Skewness-0.42898204
Sum513258
Variance351.79097
MonotonicityDecreasing
2024-04-29T21:54:08.883173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1963 55
19.2%
2008 24
 
8.4%
1989 15
 
5.2%
2007 15
 
5.2%
2001 14
 
4.9%
1992 8
 
2.8%
2009 8
 
2.8%
2000 7
 
2.4%
2012 7
 
2.4%
1993 7
 
2.4%
Other values (39) 98
34.1%
(Missing) 29
 
10.1%
ValueCountFrequency (%)
1951 5
 
1.7%
1961 1
 
0.3%
1962 2
 
0.7%
1963 55
19.2%
1965 4
 
1.4%
1968 1
 
0.3%
1972 1
 
0.3%
1973 5
 
1.7%
1975 1
 
0.3%
1978 1
 
0.3%
ValueCountFrequency (%)
2022 1
 
0.3%
2020 1
 
0.3%
2019 2
 
0.7%
2018 2
 
0.7%
2017 1
 
0.3%
2016 1
 
0.3%
2015 1
 
0.3%
2014 1
 
0.3%
2012 7
2.4%
2011 1
 
0.3%

회원수
Real number (ℝ)

MISSING 

Distinct201
Distinct (%)77.9%
Missing29
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean511.16279
Minimum7
Maximum16500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-29T21:54:09.018197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile29.7
Q194.25
median181
Q3397
95-th percentile1518.75
Maximum16500
Range16493
Interquartile range (IQR)302.75

Descriptive statistics

Standard deviation1391.6449
Coefficient of variation (CV)2.7225083
Kurtosis77.78879
Mean511.16279
Median Absolute Deviation (MAD)120
Skewness7.977992
Sum131880
Variance1936675.6
MonotonicityNot monotonic
2024-04-29T21:54:09.154040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 4
 
1.4%
80 4
 
1.4%
276 4
 
1.4%
160 4
 
1.4%
126 3
 
1.0%
165 3
 
1.0%
100 3
 
1.0%
31 3
 
1.0%
187 3
 
1.0%
256 2
 
0.7%
Other values (191) 225
78.4%
(Missing) 29
 
10.1%
ValueCountFrequency (%)
7 1
0.3%
9 1
0.3%
14 1
0.3%
17 1
0.3%
18 2
0.7%
19 1
0.3%
20 2
0.7%
21 1
0.3%
26 1
0.3%
27 1
0.3%
ValueCountFrequency (%)
16500 1
0.3%
9588 1
0.3%
7919 1
0.3%
5197 1
0.3%
4454 1
0.3%
3455 1
0.3%
3423 1
0.3%
3400 1
0.3%
1755 1
0.3%
1750 1
0.3%

소재지도로명주소
Text

Distinct70
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-29T21:54:09.396785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length18.010453
Min length14

Characters and Unicode

Total characters5169
Distinct characters121
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

Unique30 ?
Unique (%)10.5%

Sample

1st row경기도 시흥시 능곡로 140
2nd row경기도 용인시 기흥구 동탄기흥로 923
3rd row경기도 시흥시 능곡로 140
4th row경기도 시흥시 능곡로 140
5th row경기도 시흥시 능곡로 140
ValueCountFrequency (%)
경기도 287
 
23.0%
40 16
 
1.3%
호국로 14
 
1.1%
의왕시 11
 
0.9%
부천시 11
 
0.9%
51 11
 
0.9%
안골로30번길 10
 
0.8%
김포시 10
 
0.8%
포천시 10
 
0.8%
가평읍 10
 
0.8%
Other values (162) 858
68.8%
2024-04-29T21:54:09.784357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
961
18.6%
1 314
 
6.1%
경 297
 
5.7%
시 292
 
5.6%
기 289
 
5.6%
도 287
 
5.6%
로 276
 
5.3%
5 110
 
2.1%
4 102
 
2.0%
3 100
 
1.9%
Other values (111) 2141
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3224
62.4%
Decimal Number 965
 
18.7%
Space Separator 961
 
18.6%
Dash Punctuation 19
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 297
 
9.2%
시 292
 
9.1%
기 289
 
9.0%
도 287
 
8.9%
로 276
 
8.6%
길 95
 
2.9%
천 86
 
2.7%
번 84
 
2.6%
양 82
 
2.5%
구 65
 
2.0%
Other values (99) 1371
42.5%
Decimal Number
ValueCountFrequency (%)
1 314
32.5%
5 110
 
11.4%
4 102
 
10.6%
3 100
 
10.4%
2 93
 
9.6%
0 75
 
7.8%
9 72
 
7.5%
7 43
 
4.5%
6 31
 
3.2%
8 25
 
2.6%
Space Separator
ValueCountFrequency (%)
961
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3224
62.4%
Common 1945
37.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 297
 
9.2%
시 292
 
9.1%
기 289
 
9.0%
도 287
 
8.9%
로 276
 
8.6%
길 95
 
2.9%
천 86
 
2.7%
번 84
 
2.6%
양 82
 
2.5%
구 65
 
2.0%
Other values (99) 1371
42.5%
Common
ValueCountFrequency (%)
961
49.4%
1 314
 
16.1%
5 110
 
5.7%
4 102
 
5.2%
3 100
 
5.1%
2 93
 
4.8%
0 75
 
3.9%
9 72
 
3.7%
7 43
 
2.2%
6 31
 
1.6%
Other values (2) 44
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3224
62.4%
ASCII 1945
37.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
961
49.4%
1 314
 
16.1%
5 110
 
5.7%
4 102
 
5.2%
3 100
 
5.1%
2 93
 
4.8%
0 75
 
3.9%
9 72
 
3.7%
7 43
 
2.2%
6 31
 
1.6%
Other values (2) 44
 
2.3%
Hangul
ValueCountFrequency (%)
경 297
 
9.2%
시 292
 
9.1%
기 289
 
9.0%
도 287
 
8.9%
로 276
 
8.6%
길 95
 
2.9%
천 86
 
2.7%
번 84
 
2.6%
양 82
 
2.5%
구 65
 
2.0%
Other values (99) 1371
42.5%

소재지지번주소
Text

Distinct71
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-29T21:54:10.056733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length21.094077
Min length16

Characters and Unicode

Total characters6054
Distinct characters123
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)10.8%

Sample

1st row경기도 시흥시 능곡동 710번지 4층
2nd row경기도 용인시 기흥구 공세동 377-21번지 조정경기장 내
3rd row경기도 시흥시 능곡동 710번지 4층
4th row경기도 시흥시 능곡동 710번지 4층
5th row경기도 시흥시 능곡동 710번지 4층
ValueCountFrequency (%)
경기도 287
 
22.1%
부천시 11
 
0.8%
의왕시 11
 
0.8%
군포시 10
 
0.8%
시흥시 10
 
0.8%
4층 10
 
0.8%
의정부시 10
 
0.8%
김포시 10
 
0.8%
가평군 10
 
0.8%
1096-6번지 10
 
0.8%
Other values (173) 918
70.8%
2024-04-29T21:54:10.465228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1010
 
16.7%
경 289
 
4.8%
기 289
 
4.8%
번 287
 
4.7%
도 287
 
4.7%
지 287
 
4.7%
시 279
 
4.6%
동 260
 
4.3%
1 257
 
4.2%
- 214
 
3.5%
Other values (113) 2595
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3667
60.6%
Decimal Number 1162
 
19.2%
Space Separator 1010
 
16.7%
Dash Punctuation 214
 
3.5%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 289
 
7.9%
기 289
 
7.9%
번 287
 
7.8%
도 287
 
7.8%
지 287
 
7.8%
시 279
 
7.6%
동 260
 
7.1%
양 80
 
2.2%
구 74
 
2.0%
천 67
 
1.8%
Other values (100) 1468
40.0%
Decimal Number
ValueCountFrequency (%)
1 257
22.1%
3 146
12.6%
2 121
10.4%
7 109
9.4%
6 107
9.2%
5 99
 
8.5%
0 89
 
7.7%
8 83
 
7.1%
4 77
 
6.6%
9 74
 
6.4%
Space Separator
ValueCountFrequency (%)
1010
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 214
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3667
60.6%
Common 2386
39.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 289
 
7.9%
기 289
 
7.9%
번 287
 
7.8%
도 287
 
7.8%
지 287
 
7.8%
시 279
 
7.6%
동 260
 
7.1%
양 80
 
2.2%
구 74
 
2.0%
천 67
 
1.8%
Other values (100) 1468
40.0%
Common
ValueCountFrequency (%)
1010
42.3%
1 257
 
10.8%
- 214
 
9.0%
3 146
 
6.1%
2 121
 
5.1%
7 109
 
4.6%
6 107
 
4.5%
5 99
 
4.1%
0 89
 
3.7%
8 83
 
3.5%
Other values (2) 151
 
6.3%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3667
60.6%
ASCII 2387
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1010
42.3%
1 257
 
10.8%
- 214
 
9.0%
3 146
 
6.1%
2 121
 
5.1%
7 109
 
4.6%
6 107
 
4.5%
5 99
 
4.1%
0 89
 
3.7%
8 83
 
3.5%
Other values (3) 152
 
6.4%
Hangul
ValueCountFrequency (%)
경 289
 
7.9%
기 289
 
7.9%
번 287
 
7.8%
도 287
 
7.8%
지 287
 
7.8%
시 279
 
7.6%
동 260
 
7.1%
양 80
 
2.2%
구 74
 
2.0%
천 67
 
1.8%
Other values (100) 1468
40.0%

사무실전화번호
Text

MISSING 

Distinct253
Distinct (%)91.3%
Missing10
Missing (%)3.5%
Memory size2.4 KiB
2024-04-29T21:54:10.700652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.974729
Min length11

Characters and Unicode

Total characters3317
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

Unique242 ?
Unique (%)87.4%

Sample

1st row031-404-2323
2nd row031-275-9797
3rd row031-404-6258
4th row031-315-4200
5th row031-404-7314
ValueCountFrequency (%)
031-585-9799 6
 
2.2%
031-582-3492 4
 
1.4%
031-772-7816 4
 
1.4%
031-635-2579 4
 
1.4%
031-764-1398 3
 
1.1%
031-834-0161 3
 
1.1%
031-865-3072 3
 
1.1%
031-457-1351 2
 
0.7%
031-867-4516 2
 
0.7%
031-531-0625 2
 
0.7%
Other values (243) 244
88.1%
2024-04-29T21:54:11.060089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 554
16.7%
3 468
14.1%
0 447
13.5%
1 403
12.1%
5 260
7.8%
2 229
6.9%
6 206
 
6.2%
7 204
 
6.2%
8 196
 
5.9%
4 177
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2763
83.3%
Dash Punctuation 554
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 468
16.9%
0 447
16.2%
1 403
14.6%
5 260
9.4%
2 229
8.3%
6 206
7.5%
7 204
7.4%
8 196
7.1%
4 177
 
6.4%
9 173
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3317
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 554
16.7%
3 468
14.1%
0 447
13.5%
1 403
12.1%
5 260
7.8%
2 229
6.9%
6 206
 
6.2%
7 204
 
6.2%
8 196
 
5.9%
4 177
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 554
16.7%
3 468
14.1%
0 447
13.5%
1 403
12.1%
5 260
7.8%
2 229
6.9%
6 206
 
6.2%
7 204
 
6.2%
8 196
 
5.9%
4 177
 
5.3%

팩스번호
Text

MISSING 

Distinct227
Distinct (%)88.3%
Missing30
Missing (%)10.5%
Memory size2.4 KiB
2024-04-29T21:54:11.295179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.976654
Min length11

Characters and Unicode

Total characters3078
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

Unique211 ?
Unique (%)82.1%

Sample

1st row031-317-0094
2nd row031-404-6257
3rd row031-315-4218
4th row031-404-7315
5th row031-311-0626
ValueCountFrequency (%)
031-584-2799 6
 
2.3%
031-772-7836 4
 
1.6%
031-582-6888 4
 
1.6%
031-631-4623 4
 
1.6%
031-593-3072 3
 
1.2%
031-764-6398 3
 
1.2%
031-858-3072 3
 
1.2%
031-834-2077 3
 
1.2%
031-653-9551 2
 
0.8%
031-886-3689 2
 
0.8%
Other values (217) 223
86.8%
2024-04-29T21:54:11.893177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 514
16.7%
3 431
14.0%
0 382
12.4%
1 362
11.8%
5 234
7.6%
6 233
7.6%
2 214
7.0%
8 198
 
6.4%
7 185
 
6.0%
4 181
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2564
83.3%
Dash Punctuation 514
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 431
16.8%
0 382
14.9%
1 362
14.1%
5 234
9.1%
6 233
9.1%
2 214
8.3%
8 198
7.7%
7 185
7.2%
4 181
7.1%
9 144
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 514
16.7%
3 431
14.0%
0 382
12.4%
1 362
11.8%
5 234
7.6%
6 233
7.6%
2 214
7.0%
8 198
 
6.4%
7 185
 
6.0%
4 181
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 514
16.7%
3 431
14.0%
0 382
12.4%
1 362
11.8%
5 234
7.6%
6 233
7.6%
2 214
7.0%
8 198
 
6.4%
7 185
 
6.0%
4 181
 
5.9%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-06
20 
2024-04-01
20 
2024-02-27
 
18
2024-02-28
 
17
2024-02-29
 
17
Other values (21)
195 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-09
2nd row2020-03-13
3rd row2023-06-09
4th row2023-06-09
5th row2023-06-09

Common Values

ValueCountFrequency (%)
2024-03-06 20
 
7.0%
2024-04-01 20
 
7.0%
2024-02-27 18
 
6.3%
2024-02-28 17
 
5.9%
2024-02-29 17
 
5.9%
2023-01-10 11
 
3.8%
2020-03-02 10
 
3.5%
2023-04-19 10
 
3.5%
2023-02-07 10
 
3.5%
2023-06-09 10
 
3.5%
Other values (16) 144
50.2%

Length

2024-04-29T21:54:12.034062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-03-06 20
 
7.0%
2024-04-01 20
 
7.0%
2024-02-27 18
 
6.3%
2024-02-28 17
 
5.9%
2024-02-29 17
 
5.9%
2023-01-10 11
 
3.8%
2024-02-23 10
 
3.5%
2024-02-26 10
 
3.5%
2023-06-09 10
 
3.5%
2023-02-07 10
 
3.5%
Other values (16) 144
50.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.488031
Minimum36.996133
Maximum38.0943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-29T21:54:12.160301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.996133
5-th percentile37.001053
Q137.301253
median37.445327
Q337.654742
95-th percentile37.90383
Maximum38.0943
Range1.0981665
Interquartile range (IQR)0.35348851

Descriptive statistics

Standard deviation0.25744191
Coefficient of variation (CV)0.0068673095
Kurtosis-0.40632323
Mean37.488031
Median Absolute Deviation (MAD)0.17192043
Skewness0.25038342
Sum10759.065
Variance0.066276335
MonotonicityNot monotonic
2024-04-29T21:54:12.288472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.59658595 10
 
3.5%
37.8323886 10
 
3.5%
37.301253 10
 
3.5%
37.3647608229 10
 
3.5%
37.481934 9
 
3.1%
37.62683899 9
 
3.1%
37.49580658 9
 
3.1%
36.99613303 9
 
3.1%
37.75845539 9
 
3.1%
37.20917533 9
 
3.1%
Other values (62) 193
67.2%
ValueCountFrequency (%)
36.99613303 9
3.1%
37.00105341 9
3.1%
37.17758288 8
2.8%
37.20917533 9
3.1%
37.23140767 3
 
1.0%
37.23339764 4
1.4%
37.23796415 1
 
0.3%
37.24081598 1
 
0.3%
37.265386 9
3.1%
37.26811836 1
 
0.3%
ValueCountFrequency (%)
38.0942995378 7
2.4%
38.0222199338 1
 
0.3%
37.91174696 1
 
0.3%
37.9106305 3
 
1.0%
37.90588665 1
 
0.3%
37.90408101 2
 
0.7%
37.9032435 2
 
0.7%
37.89713836 1
 
0.3%
37.89157994 8
2.8%
37.89033181 1
 
0.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.06989
Minimum126.70271
Maximum127.63828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-29T21:54:12.414577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70271
5-th percentile126.77636
Q1126.86221
median127.05317
Q3127.2026
95-th percentile127.51591
Maximum127.63828
Range0.935566
Interquartile range (IQR)0.3403906

Descriptive statistics

Standard deviation0.22983555
Coefficient of variation (CV)0.0018087333
Kurtosis-0.21360273
Mean127.06989
Median Absolute Deviation (MAD)0.1529466
Skewness0.61128714
Sum36469.059
Variance0.052824378
MonotonicityNot monotonic
2024-04-29T21:54:12.563368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1345164 10
 
3.5%
127.5159145 10
 
3.5%
126.8604125 10
 
3.5%
126.933577905 10
 
3.5%
126.8622118 9
 
3.1%
127.2003627 9
 
3.1%
127.5053468 9
 
3.1%
127.0866971 9
 
3.1%
126.7774308 9
 
3.1%
126.8149846 9
 
3.1%
Other values (62) 193
67.2%
ValueCountFrequency (%)
126.7027143024 1
 
0.3%
126.7114941908 2
0.7%
126.7150750397 3
1.0%
126.716745267 2
0.7%
126.7231040142 1
 
0.3%
126.723137948 1
 
0.3%
126.7495216 1
 
0.3%
126.7559623 1
 
0.3%
126.7749894 1
 
0.3%
126.775066 1
 
0.3%
ValueCountFrequency (%)
127.6382803 1
 
0.3%
127.6319679 1
 
0.3%
127.6318542048 1
 
0.3%
127.6318015 4
 
1.4%
127.6285876 1
 
0.3%
127.5159145 10
3.5%
127.5053468 9
3.1%
127.4605358 1
 
0.3%
127.4413625 4
 
1.4%
127.438127 4
 
1.4%

Interactions

2024-04-29T21:54:07.284941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.061081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.509043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.886356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:07.384329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.219579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.604440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.975325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:07.474784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.316887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.696116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:07.099105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:07.554853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.405185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:06.791814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:54:07.195402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T21:54:12.649282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명설립년도회원수소재지도로명주소소재지지번주소데이터기준일자위도경도
시군명1.0000.4020.1441.0001.0001.0000.9990.998
설립년도0.4021.0000.0000.2980.2670.2970.1650.252
회원수0.1440.0001.0000.9510.9520.0000.1830.000
소재지도로명주소1.0000.2980.9511.0001.0001.0001.0001.000
소재지지번주소1.0000.2670.9521.0001.0001.0001.0001.000
데이터기준일자1.0000.2970.0001.0001.0001.0000.9750.989
위도0.9990.1650.1831.0001.0000.9751.0000.892
경도0.9980.2520.0001.0001.0000.9890.8921.000
2024-04-29T21:54:12.763453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명데이터기준일자
시군명1.0000.990
데이터기준일자0.9901.000
2024-04-29T21:54:12.845178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설립년도회원수위도경도시군명데이터기준일자
설립년도1.000-0.339-0.0480.0830.1360.091
회원수-0.3391.000-0.024-0.2480.0480.000
위도-0.048-0.0241.000-0.0100.9520.829
경도0.083-0.248-0.0101.0000.9440.903
시군명0.1360.0480.9520.9441.0000.990
데이터기준일자0.0910.0000.8290.9030.9901.000

Missing values

2024-04-29T21:54:07.672392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T21:54:07.833845image/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.
2024-04-29T21:54:07.961788image/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

시군명단체명설립년도회원수소재지도로명주소소재지지번주소사무실전화번호팩스번호데이터기준일자위도경도
0시흥시상이군경회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-404-2323031-317-00942023-06-0937.366284126.815746
1용인시특수임무유공자회<NA>61경기도 용인시 기흥구 동탄기흥로 923경기도 용인시 기흥구 공세동 377-21번지 조정경기장 내031-275-9797<NA>2020-03-1337.240816127.1004
2시흥시전몰군경미망인회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-404-6258031-404-62572023-06-0937.366284126.815746
3시흥시무공수훈자회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-315-4200031-315-42182023-06-0937.366284126.815746
4시흥시고엽제전우회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-404-7314031-404-73152023-06-0937.366284126.815746
5시흥시6.25참전유공자회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-404-0625031-311-06262023-06-0937.366284126.815746
6시흥시월남전참전자회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-318-0050031-499-04752023-06-0937.366284126.815746
7시흥시광복회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-318-0815031-318-08142023-06-0937.366284126.815746
8시흥시특수임무유공자회<NA><NA>경기도 시흥시 능곡로 140경기도 시흥시 능곡동 710번지 4층031-317-0476031-318-00502023-06-0937.366284126.815746
9시흥시시흥시재향군인회<NA><NA>경기도 시흥시 능곡로 78경기도 시흥시 화정동 651-6번지 2층031-312-2600031-312-26262023-06-0937.36147126.816012
시군명단체명설립년도회원수소재지도로명주소소재지지번주소사무실전화번호팩스번호데이터기준일자위도경도
277안산시전몰군경유족회1963360경기도 안산시 상록구 본삼로 1경기도 안산시 상록구 본오동 878-2번지031-502-8373031-480-83732024-03-0637.301253126.860412
278구리시대한민국 전몰군경 미망인회 경기도지부 구리시지회1963132경기도 구리시 안골로30번길 13경기도 구리시 교문동 736-4번지031-552-6772031-569-59882023-02-0737.596586127.134516
279연천군재향군인회19623400경기도 연천군 전곡읍 온골로 7경기도 연천군 전곡읍 전곡리 266-26번지031-832-2852031-832-28702024-02-2938.02222127.070275
280포천시포천시재향군인회19625197경기도 포천시 군내면 호국로 1523경기도 포천시 군내면 구읍리 688-11번지031-535-2667031-535-26632024-03-0637.890332127.200132
281부천시부천시재향군인회196116500경기도 부천시 부일로 411경기도 부천시 심곡동 351-12번지032-668-1598032-656-69732023-01-1037.487088126.775897
282수원시상이군경회수원시지회1951<NA>경기도 수원시 권선구 호매실로 225경기도 수원시 권선구 호매실동 1366번지 수원시보훈회관<NA><NA>2024-02-2837.265386126.95678
283포천시상이군경회포천시지회1951314경기도 포천시 왕방로184번길 19경기도 포천시 신읍동 137-83번지031-534-7636031-536-76392024-03-0637.897138127.199317
284평택시상이군경회1951398경기도 평택시 통복시장로16번길 28경기도 평택시 통복동 89-1번지031-654-3395031-653-95512024-02-1636.996133127.086697
285동두천시상이군경회1951172경기도 동두천시 삼육사로 1051경기도 동두천시 생연동 294-2번지031-865-3072031-858-30722024-04-0137.910631127.054622
286이천시상이군경회1951220경기도 이천시 중리천로6번길 16경기도 이천시 중리동 2-5번지031-635-2579031-631-46232023-09-1537.279575127.438127