Overview

Dataset statistics

Number of variables10
Number of observations149
Missing cells45
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 KiB
Average record size in memory83.9 B

Variable types

Categorical3
Text4
Numeric3

Alerts

우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 우편번호 and 3 other fieldsHigh correlation
사업수행기관 is highly overall correlated with 우편번호 and 3 other fieldsHigh correlation
유형 is highly imbalanced (56.4%)Imbalance
도로명주소 has 16 (10.7%) missing valuesMissing
우편번호 has 13 (8.7%) missing valuesMissing
WGS84위도 has 8 (5.4%) missing valuesMissing
WGS84경도 has 8 (5.4%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:27:52.356913
Analysis finished2023-12-10 22:27:54.174594
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
화성시
50 
성남시
15 
남양주시
10 
안양시
9 
부천시
7 
Other values (20)
58 

Length

Max length4
Median length3
Mean length3.0872483
Min length3

Unique

Unique6 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
화성시 50
33.6%
성남시 15
 
10.1%
남양주시 10
 
6.7%
안양시 9
 
6.0%
부천시 7
 
4.7%
용인시 7
 
4.7%
구리시 6
 
4.0%
오산시 5
 
3.4%
수원시 5
 
3.4%
고양시 5
 
3.4%
Other values (15) 30
20.1%

Length

2023-12-11T07:27:54.235428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 50
33.6%
성남시 15
 
10.1%
남양주시 10
 
6.7%
안양시 9
 
6.0%
부천시 7
 
4.7%
용인시 7
 
4.7%
구리시 6
 
4.0%
오산시 5
 
3.4%
수원시 5
 
3.4%
고양시 5
 
3.4%
Other values (15) 30
20.1%

매장명
Text

Distinct147
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T07:27:54.437096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length10.731544
Min length2

Characters and Unicode

Total characters1599
Distinct characters269
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)97.3%

Sample

1st row마카롱세차사업
2nd row카페아르젠또 덕양점
3rd row카페아르젠또 어울림점
4th row대화실버카페
5th row카페(다향당)
ValueCountFrequency (%)
노노카페 51
 
18.5%
스팀세차 13
 
4.7%
카페 10
 
3.6%
커플데이 5
 
1.8%
2호점 5
 
1.8%
실버카페 4
 
1.4%
1호점 4
 
1.4%
카페휴 3
 
1.1%
휴 3
 
1.1%
마망베이커리&카페 3
 
1.1%
Other values (162) 175
63.4%
2023-12-11T07:27:54.790353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
7.9%
노 106
 
6.6%
페 103
 
6.4%
카 102
 
6.4%
점 93
 
5.8%
사 29
 
1.8%
호 23
 
1.4%
시 20
 
1.3%
스 19
 
1.2%
동 18
 
1.1%
Other values (259) 959
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1384
86.6%
Space Separator 127
 
7.9%
Decimal Number 23
 
1.4%
Other Punctuation 21
 
1.3%
Close Punctuation 16
 
1.0%
Open Punctuation 16
 
1.0%
Uppercase Letter 11
 
0.7%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
노 106
 
7.7%
페 103
 
7.4%
카 102
 
7.4%
점 93
 
6.7%
사 29
 
2.1%
호 23
 
1.7%
시 20
 
1.4%
스 19
 
1.4%
동 18
 
1.3%
세 18
 
1.3%
Other values (238) 853
61.6%
Uppercase Letter
ValueCountFrequency (%)
K 3
27.3%
B 2
18.2%
I 1
 
9.1%
C 1
 
9.1%
T 1
 
9.1%
E 1
 
9.1%
F 1
 
9.1%
A 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
2 9
39.1%
1 8
34.8%
3 4
17.4%
4 2
 
8.7%
Other Punctuation
ValueCountFrequency (%)
' 14
66.7%
& 5
 
23.8%
" 2
 
9.5%
Close Punctuation
ValueCountFrequency (%)
) 15
93.8%
] 1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 15
93.8%
[ 1
 
6.2%
Space Separator
ValueCountFrequency (%)
127
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1382
86.4%
Common 204
 
12.8%
Latin 11
 
0.7%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
노 106
 
7.7%
페 103
 
7.5%
카 102
 
7.4%
점 93
 
6.7%
사 29
 
2.1%
호 23
 
1.7%
시 20
 
1.4%
스 19
 
1.4%
동 18
 
1.3%
세 18
 
1.3%
Other values (236) 851
61.6%
Common
ValueCountFrequency (%)
127
62.3%
) 15
 
7.4%
( 15
 
7.4%
' 14
 
6.9%
2 9
 
4.4%
1 8
 
3.9%
& 5
 
2.5%
3 4
 
2.0%
4 2
 
1.0%
" 2
 
1.0%
Other values (3) 3
 
1.5%
Latin
ValueCountFrequency (%)
K 3
27.3%
B 2
18.2%
I 1
 
9.1%
C 1
 
9.1%
T 1
 
9.1%
E 1
 
9.1%
F 1
 
9.1%
A 1
 
9.1%
Han
ValueCountFrequency (%)
情 1
50.0%
茶 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1382
86.4%
ASCII 215
 
13.4%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
59.1%
) 15
 
7.0%
( 15
 
7.0%
' 14
 
6.5%
2 9
 
4.2%
1 8
 
3.7%
& 5
 
2.3%
3 4
 
1.9%
K 3
 
1.4%
4 2
 
0.9%
Other values (11) 13
 
6.0%
Hangul
ValueCountFrequency (%)
노 106
 
7.7%
페 103
 
7.5%
카 102
 
7.4%
점 93
 
6.7%
사 29
 
2.1%
호 23
 
1.7%
시 20
 
1.4%
스 19
 
1.4%
동 18
 
1.3%
세 18
 
1.3%
Other values (236) 851
61.6%
CJK
ValueCountFrequency (%)
情 1
50.0%
茶 1
50.0%

유형
Categorical

IMBALANCE 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
카페
115 
세차
16 
식당
 
10
도시락 판매
 
3
반찬가게
 
2
Other values (2)
 
3

Length

Max length6
Median length2
Mean length2.114094
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row세차
2nd row카페
3rd row카페
4th row카페
5th row카페

Common Values

ValueCountFrequency (%)
카페 115
77.2%
세차 16
 
10.7%
식당 10
 
6.7%
도시락 판매 3
 
2.0%
반찬가게 2
 
1.3%
매점 2
 
1.3%
미용실 1
 
0.7%

Length

2023-12-11T07:27:54.932187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:55.046951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
카페 115
75.7%
세차 16
 
10.5%
식당 10
 
6.6%
도시락 3
 
2.0%
판매 3
 
2.0%
반찬가게 2
 
1.3%
매점 2
 
1.3%
미용실 1
 
0.7%

사업수행기관
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
화성시니어클럽
50 
안양시니어클럽
9 
대한노인회 경기 남양주시지회
8 
용인시니어클럽
 
6
구리시니어클럽
 
6
Other values (42)
70 

Length

Max length15
Median length7
Mean length8.1744966
Min length7

Unique

Unique26 ?
Unique (%)17.4%

Sample

1st row고양시니어클럽
2nd row덕양노인종합복지관
3rd row덕양노인종합복지관
4th row고양시대화노인종합복지관
5th row일산노인종합복지관

Common Values

ValueCountFrequency (%)
화성시니어클럽 50
33.6%
안양시니어클럽 9
 
6.0%
대한노인회 경기 남양주시지회 8
 
5.4%
용인시니어클럽 6
 
4.0%
구리시니어클럽 6
 
4.0%
의왕시니어클럽 5
 
3.4%
오산노인종합복지관 5
 
3.4%
부천시니어클럽 4
 
2.7%
수원시니어클럽 4
 
2.7%
수정노인종합복지관 3
 
2.0%
Other values (37) 49
32.9%

Length

2023-12-11T07:27:55.166366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시니어클럽 50
30.1%
안양시니어클럽 9
 
5.4%
대한노인회 8
 
4.8%
경기 8
 
4.8%
남양주시지회 8
 
4.8%
용인시니어클럽 6
 
3.6%
구리시니어클럽 6
 
3.6%
오산노인종합복지관 5
 
3.0%
의왕시니어클럽 5
 
3.0%
부천시니어클럽 4
 
2.4%
Other values (40) 57
34.3%

도로명주소
Text

MISSING 

Distinct127
Distinct (%)95.5%
Missing16
Missing (%)10.7%
Memory size1.3 KiB
2023-12-11T07:27:55.383011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length37
Mean length29.526316
Min length20

Characters and Unicode

Total characters3927
Distinct characters235
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)91.0%

Sample

1st row경기도 고양시 일산동구 무궁화로 106 (정발산동)
2nd row경기도 고양시 덕양구 어울림로 49 고양덕양노인복지관 내 (화정동)
3rd row경기도 고양시 덕양구 어울림로 33 내 (성사동,고양어울림누리)
4th row경기도 고양시 일산서구 일산로 778 대화노인복지관 내 (대화동)
5th row경기도 고양시 일산동구 호수로 731 고양일산노인종합복지관 내 (장항동)
ValueCountFrequency (%)
경기도 133
 
16.2%
화성시 41
 
5.0%
1층 22
 
2.7%
내 21
 
2.6%
성남시 11
 
1.3%
향남읍 10
 
1.2%
동안구 9
 
1.1%
남양주시 9
 
1.1%
안양시 9
 
1.1%
용인시 7
 
0.9%
Other values (398) 550
66.9%
2023-12-11T07:27:55.785152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
689
 
17.5%
시 151
 
3.8%
도 146
 
3.7%
경 143
 
3.6%
기 137
 
3.5%
로 133
 
3.4%
( 133
 
3.4%
) 133
 
3.4%
동 131
 
3.3%
1 128
 
3.3%
Other values (225) 2003
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2430
61.9%
Space Separator 689
 
17.5%
Decimal Number 484
 
12.3%
Open Punctuation 133
 
3.4%
Close Punctuation 133
 
3.4%
Other Punctuation 40
 
1.0%
Dash Punctuation 16
 
0.4%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 151
 
6.2%
도 146
 
6.0%
경 143
 
5.9%
기 137
 
5.6%
로 133
 
5.5%
동 131
 
5.4%
성 75
 
3.1%
화 57
 
2.3%
남 55
 
2.3%
구 53
 
2.2%
Other values (207) 1349
55.5%
Decimal Number
ValueCountFrequency (%)
1 128
26.4%
2 67
13.8%
3 53
11.0%
9 47
 
9.7%
4 43
 
8.9%
5 41
 
8.5%
7 35
 
7.2%
0 28
 
5.8%
6 21
 
4.3%
8 21
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 38
95.0%
. 2
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
689
100.0%
Open Punctuation
ValueCountFrequency (%)
( 133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2430
61.9%
Common 1495
38.1%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 151
 
6.2%
도 146
 
6.0%
경 143
 
5.9%
기 137
 
5.6%
로 133
 
5.5%
동 131
 
5.4%
성 75
 
3.1%
화 57
 
2.3%
남 55
 
2.3%
구 53
 
2.2%
Other values (207) 1349
55.5%
Common
ValueCountFrequency (%)
689
46.1%
( 133
 
8.9%
) 133
 
8.9%
1 128
 
8.6%
2 67
 
4.5%
3 53
 
3.5%
9 47
 
3.1%
4 43
 
2.9%
5 41
 
2.7%
, 38
 
2.5%
Other values (6) 123
 
8.2%
Latin
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2430
61.9%
ASCII 1497
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
689
46.0%
( 133
 
8.9%
) 133
 
8.9%
1 128
 
8.6%
2 67
 
4.5%
3 53
 
3.5%
9 47
 
3.1%
4 43
 
2.9%
5 41
 
2.7%
, 38
 
2.5%
Other values (8) 125
 
8.4%
Hangul
ValueCountFrequency (%)
시 151
 
6.2%
도 146
 
6.0%
경 143
 
5.9%
기 137
 
5.6%
로 133
 
5.5%
동 131
 
5.4%
성 75
 
3.1%
화 57
 
2.3%
남 55
 
2.3%
구 53
 
2.2%
Other values (207) 1349
55.5%

지번주소
Text

Distinct143
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T07:27:56.024127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length24.946309
Min length14

Characters and Unicode

Total characters3717
Distinct characters211
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

Unique137 ?
Unique (%)91.9%

Sample

1st row경기도 고양시 일산동구 정발산동 816번지
2nd row경기도 고양시 덕양구 화정동 846번지 고양덕양노인복지관 내
3rd row경기도 고양시 덕양구 성사동 826번지 고양어울림누리 내
4th row경기도 고양시 일산서구 대화동 2237번지 대화노인복지관 내
5th row경기도 고양시 일산동구 장항동 906번지 고양일산노인종합복지관 내
ValueCountFrequency (%)
경기도 149
 
18.3%
화성시 52
 
6.4%
내 24
 
2.9%
1층 23
 
2.8%
성남시 15
 
1.8%
향남읍 10
 
1.2%
남양주시 10
 
1.2%
안양시 9
 
1.1%
동안구 9
 
1.1%
2층 8
 
1.0%
Other values (368) 506
62.1%
2023-12-11T07:27:56.659540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
670
 
18.0%
도 163
 
4.4%
시 161
 
4.3%
지 157
 
4.2%
경 152
 
4.1%
기 151
 
4.1%
번 131
 
3.5%
1 128
 
3.4%
동 127
 
3.4%
2 87
 
2.3%
Other values (201) 1790
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2398
64.5%
Space Separator 670
 
18.0%
Decimal Number 576
 
15.5%
Dash Punctuation 69
 
1.9%
Uppercase Letter 2
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 163
 
6.8%
시 161
 
6.7%
지 157
 
6.5%
경 152
 
6.3%
기 151
 
6.3%
번 131
 
5.5%
동 127
 
5.3%
성 87
 
3.6%
화 63
 
2.6%
구 56
 
2.3%
Other values (185) 1150
48.0%
Decimal Number
ValueCountFrequency (%)
1 128
22.2%
2 87
15.1%
3 66
11.5%
7 52
9.0%
6 51
 
8.9%
0 44
 
7.6%
4 41
 
7.1%
5 39
 
6.8%
9 36
 
6.2%
8 32
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
670
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2398
64.5%
Common 1317
35.4%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 163
 
6.8%
시 161
 
6.7%
지 157
 
6.5%
경 152
 
6.3%
기 151
 
6.3%
번 131
 
5.5%
동 127
 
5.3%
성 87
 
3.6%
화 63
 
2.6%
구 56
 
2.3%
Other values (185) 1150
48.0%
Common
ValueCountFrequency (%)
670
50.9%
1 128
 
9.7%
2 87
 
6.6%
- 69
 
5.2%
3 66
 
5.0%
7 52
 
3.9%
6 51
 
3.9%
0 44
 
3.3%
4 41
 
3.1%
5 39
 
3.0%
Other values (4) 70
 
5.3%
Latin
ValueCountFrequency (%)
T 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2398
64.5%
ASCII 1319
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
670
50.8%
1 128
 
9.7%
2 87
 
6.6%
- 69
 
5.2%
3 66
 
5.0%
7 52
 
3.9%
6 51
 
3.9%
0 44
 
3.3%
4 41
 
3.1%
5 39
 
3.0%
Other values (6) 72
 
5.5%
Hangul
ValueCountFrequency (%)
도 163
 
6.8%
시 161
 
6.7%
지 157
 
6.5%
경 152
 
6.3%
기 151
 
6.3%
번 131
 
5.5%
동 127
 
5.3%
성 87
 
3.6%
화 63
 
2.6%
구 56
 
2.3%
Other values (185) 1150
48.0%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct116
Distinct (%)85.3%
Missing13
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean15536.846
Minimum10068
Maximum18633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:27:56.793411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10068
5-th percentile11003.75
Q113333
median15940
Q318360
95-th percentile18591
Maximum18633
Range8565
Interquartile range (IQR)5027

Descriptive statistics

Standard deviation2705.1086
Coefficient of variation (CV)0.17410926
Kurtosis-1.3106912
Mean15536.846
Median Absolute Deviation (MAD)2434.5
Skewness-0.31802728
Sum2113011
Variance7317612.6
MonotonicityNot monotonic
2023-12-11T07:27:56.932460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18274 3
 
2.0%
18360 2
 
1.3%
18391 2
 
1.3%
14427 2
 
1.3%
18476 2
 
1.3%
14434 2
 
1.3%
18262 2
 
1.3%
18591 2
 
1.3%
18336 2
 
1.3%
18588 2
 
1.3%
Other values (106) 115
77.2%
(Missing) 13
 
8.7%
ValueCountFrequency (%)
10068 1
0.7%
10382 1
0.7%
10400 1
0.7%
10405 1
0.7%
10470 1
0.7%
10471 1
0.7%
10934 1
0.7%
11027 1
0.7%
11329 1
0.7%
11697 1
0.7%
ValueCountFrequency (%)
18633 1
0.7%
18600 1
0.7%
18598 1
0.7%
18597 1
0.7%
18596 1
0.7%
18592 1
0.7%
18591 2
1.3%
18590 1
0.7%
18588 2
1.3%
18577 1
0.7%

전화번호
Text

Distinct53
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T07:27:57.161160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.355705
Min length11

Characters and Unicode

Total characters1841
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

Unique31 ?
Unique (%)20.8%

Sample

1st row031-904-2611
2nd row031-969-7781
3rd row031-969-7781
4th row031-917-1352
5th row031-918-4177
ValueCountFrequency (%)
031-8059-4348 47
31.5%
031-455-0558 9
 
6.0%
031-511-3223 8
 
5.4%
070-4657-2222 6
 
4.0%
031-372-2144 5
 
3.4%
031-426-7988 5
 
3.4%
031-567-0431 5
 
3.4%
032-668-4107 3
 
2.0%
031-739-2936 3
 
2.0%
031-454-2077 3
 
2.0%
Other values (43) 55
36.9%
2023-12-11T07:27:57.570063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 296
16.1%
0 276
15.0%
3 250
13.6%
1 200
10.9%
4 171
9.3%
8 157
8.5%
5 147
8.0%
2 111
 
6.0%
9 93
 
5.1%
7 78
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1545
83.9%
Dash Punctuation 296
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 276
17.9%
3 250
16.2%
1 200
12.9%
4 171
11.1%
8 157
10.2%
5 147
9.5%
2 111
7.2%
9 93
 
6.0%
7 78
 
5.0%
6 62
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1841
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 296
16.1%
0 276
15.0%
3 250
13.6%
1 200
10.9%
4 171
9.3%
8 157
8.5%
5 147
8.0%
2 111
 
6.0%
9 93
 
5.1%
7 78
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 296
16.1%
0 276
15.0%
3 250
13.6%
1 200
10.9%
4 171
9.3%
8 157
8.5%
5 147
8.0%
2 111
 
6.0%
9 93
 
5.1%
7 78
 
4.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)94.3%
Missing8
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean37.349947
Minimum36.985716
Maximum38.027503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:27:57.727106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.985716
5-th percentile37.129933
Q137.205855
median37.332141
Q337.447942
95-th percentile37.672773
Maximum38.027503
Range1.0417872
Interquartile range (IQR)0.2420871

Descriptive statistics

Standard deviation0.19308935
Coefficient of variation (CV)0.0051697356
Kurtosis0.3011144
Mean37.349947
Median Absolute Deviation (MAD)0.1258689
Skewness0.78701193
Sum5266.3426
Variance0.037283498
MonotonicityNot monotonic
2023-12-11T07:27:57.895304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2030768 2
 
1.3%
37.2407685 2
 
1.3%
37.3820883 2
 
1.3%
37.1378629 2
 
1.3%
37.217268 2
 
1.3%
37.1992982 2
 
1.3%
37.5279763 2
 
1.3%
37.1775828 2
 
1.3%
37.2051071 1
 
0.7%
37.2053872 1
 
0.7%
Other values (123) 123
82.6%
(Missing) 8
 
5.4%
ValueCountFrequency (%)
36.9857157 1
0.7%
36.9957716 1
0.7%
37.0328427 1
0.7%
37.0817051 1
0.7%
37.1165627 1
0.7%
37.1281404 1
0.7%
37.1291724 1
0.7%
37.129933 1
0.7%
37.1302492 1
0.7%
37.1304092 1
0.7%
ValueCountFrequency (%)
38.0275029 1
0.7%
37.905026 1
0.7%
37.7533966 1
0.7%
37.7367684 1
0.7%
37.7363453 1
0.7%
37.7097179 1
0.7%
37.6744622 1
0.7%
37.6727725 1
0.7%
37.6667953 1
0.7%
37.6577996 1
0.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)94.3%
Missing8
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean127.01563
Minimum126.64431
Maximum127.64122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:27:58.070013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.64431
5-th percentile126.77514
Q1126.92038
median127.01494
Q3127.1296
95-th percentile127.24962
Maximum127.64122
Range0.9969055
Interquartile range (IQR)0.2092179

Descriptive statistics

Standard deviation0.16418976
Coefficient of variation (CV)0.0012926737
Kurtosis1.7806623
Mean127.01563
Median Absolute Deviation (MAD)0.101626
Skewness0.71067622
Sum17909.203
Variance0.026958278
MonotonicityNot monotonic
2023-12-11T07:27:58.210585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0332844 2
 
1.3%
127.1774648 2
 
1.3%
126.9590387 2
 
1.3%
126.922411 2
 
1.3%
127.0339774 2
 
1.3%
126.8312782 2
 
1.3%
126.7959895 2
 
1.3%
127.0449514 2
 
1.3%
127.0515565 1
 
0.7%
127.1064721 1
 
0.7%
Other values (123) 123
82.6%
(Missing) 8
 
5.4%
ValueCountFrequency (%)
126.64431 1
0.7%
126.7322321 1
0.7%
126.7430892 1
0.7%
126.7480209 1
0.7%
126.7634563 1
0.7%
126.7745461 1
0.7%
126.7749943 1
0.7%
126.775143 1
0.7%
126.7798744 1
0.7%
126.7862132 1
0.7%
ValueCountFrequency (%)
127.6412155 1
0.7%
127.640197 1
0.7%
127.4411031 1
0.7%
127.3204294 1
0.7%
127.3085465 1
0.7%
127.3006586 1
0.7%
127.2498976 1
0.7%
127.2496202 1
0.7%
127.2325101 1
0.7%
127.2288964 1
0.7%

Interactions

2023-12-11T07:27:53.530733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.029931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.287385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.629030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.119723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.359417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.730559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.209885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:53.447724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:27:58.317065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명유형사업수행기관우편번호전화번호WGS84위도WGS84경도
시군명1.0000.8021.0000.9911.0000.9730.939
유형0.8021.0000.7830.6430.7780.5370.483
사업수행기관1.0000.7831.0000.9981.0000.9750.956
우편번호0.9910.6430.9981.0000.9970.9290.718
전화번호1.0000.7781.0000.9971.0000.9750.947
WGS84위도0.9730.5370.9750.9290.9751.0000.462
WGS84경도0.9390.4830.9560.7180.9470.4621.000
2023-12-11T07:27:58.419559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형사업수행기관시군명
유형1.0000.3840.470
사업수행기관0.3841.0000.897
시군명0.4700.8971.000
2023-12-11T07:27:58.511027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호WGS84위도WGS84경도시군명유형사업수행기관
우편번호1.000-0.924-0.2100.8820.3910.811
WGS84위도-0.9241.0000.1600.7710.3040.702
WGS84경도-0.2100.1601.0000.6890.2770.637
시군명0.8820.7710.6891.0000.4700.897
유형0.3910.3040.2770.4701.0000.384
사업수행기관0.8110.7020.6370.8970.3841.000

Missing values

2023-12-11T07:27:53.849604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:27:53.975272image/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.
2023-12-11T07:27:54.097059image/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

시군명매장명유형사업수행기관도로명주소지번주소우편번호전화번호WGS84위도WGS84경도
0고양시마카롱세차사업세차고양시니어클럽경기도 고양시 일산동구 무궁화로 106 (정발산동)경기도 고양시 일산동구 정발산동 816번지10405031-904-261137.666795126.774546
1고양시카페아르젠또 덕양점카페덕양노인종합복지관경기도 고양시 덕양구 어울림로 49 고양덕양노인복지관 내 (화정동)경기도 고양시 덕양구 화정동 846번지 고양덕양노인복지관 내10470031-969-778137.648348126.836528
2고양시카페아르젠또 어울림점카페덕양노인종합복지관경기도 고양시 덕양구 어울림로 33 내 (성사동,고양어울림누리)경기도 고양시 덕양구 성사동 826번지 고양어울림누리 내10471031-969-778137.648803126.833649
3고양시대화실버카페카페고양시대화노인종합복지관경기도 고양시 일산서구 일산로 778 대화노인복지관 내 (대화동)경기도 고양시 일산서구 대화동 2237번지 대화노인복지관 내10382031-917-135237.674462126.748021
4고양시카페(다향당)카페일산노인종합복지관경기도 고양시 일산동구 호수로 731 고양일산노인종합복지관 내 (장항동)경기도 고양시 일산동구 장항동 906번지 고양일산노인종합복지관 내10400031-918-417737.6578126.763456
5과천시실버카페 페이지카페과천시실버인력뱅크경기도 과천시 중앙로 24 과천시 정보과학도서관 1층 (갈현동)경기도 과천시 갈현동 677번지 과천시 정보과학도서관 1층1383502-509-761037.418496126.989473
6과천시카페나루카페과천시실버인력뱅크경기도 과천시 문원로 57 1층 (문원동,과천노인복지관)경기도 과천시 문원동 15-168번지 과천노인복지관 1층1382802-509-761037.428075127.004306
7광명시추억의찻집 '희희낙락'카페광명시노인종합복지관경기도 광명시 소하로 25 광명시노인종합복지관 내 (소하동)경기도 광명시 소하동 1291번지 광명시노인종합복지관 내1431502-6925-74437.452127126.884905
8광주시씨밀레베이커리&카페카페대한노인회광주시지회경기도 광주시 순암로 192 내 (중대동,중대공원)경기도 광주시 중대동 46-3번지 중대공원 내12768031-766-511537.397558127.228896
9광주시실버카페 '세잎클로버'카페광주시실버인력뱅크경기도 광주시 파발로 202 내 (탄벌동,광주시노인종합복지회관)경기도 광주시 탄벌동 18-1번지 광주시노인종합복지회관 내12739031-769-912937.416861127.249898
시군명매장명유형사업수행기관도로명주소지번주소우편번호전화번호WGS84위도WGS84경도
139화성시노노카페 화성시청민원동점카페화성시니어클럽경기도 화성시 남양읍 시청로 159 (남양리)경기도 화성시 남양읍 남양리 2000번지18274031-8059-434837.199298126.831278
140화성시노노카페 나래울점카페화성시니어클럽경기도 화성시 여울로2길 33 지하1층 (능동,화성시복합복지타운)경기도 화성시 능동 1130번지 화성시복합복지타운 지하1층18427031-8059-434837.205107127.051557
141화성시노노카페 정남면사무소점카페화성시니어클럽경기도 화성시 정남면 서봉로 998 (신리)경기도 화성시 정남면 신리 260-1번지18519031-8059-434837.160025126.971371
142화성시노노카페 유앤아이센터2호점카페화성시니어클럽경기도 화성시 태안로 145 1층 (병점동,화성시 여성청소년 수련관)경기도 화성시 병점동 734번지 화성시 여성청소년 수련관 1층18372031-8059-434837.203077127.033284
143화성시노노카페 유앤아이센터점카페화성시니어클럽경기도 화성시 태안로 145 지하2층 (병점동,화성시 여성청소년 수련관)경기도 화성시 병점동 734번지 화성시 여성청소년 수련관 지하2층18372031-8059-434837.203077127.033284
144화성시노노카페 화성시보건소점카페화성시니어클럽경기도 화성시 향남읍 3.1만세로 1055 (발안리)경기도 화성시 향남읍 발안리 30918596031-8059-434837.129172126.904662
145화성시노노카페 향남환승터미널점카페화성시니어클럽경기도 화성시 향남읍 향남로 424 1층 (행정리)경기도 화성시 향남읍 행정리 472-1번지 1층18591031-8059-434837.132808126.92218
146화성시노노카페 화성시남부노인복지관점카페화성시니어클럽경기도 화성시 향남읍 토성로 37-22 (행정리)경기도 화성시 향남읍 행정리 29-2번지18590031-8059-434837.12814126.9366
147화성시노노카페 화성시남부종합사회복지관점카페화성시니어클럽경기도 화성시 향남읍 행정서로3길 50 1층 (행정리,화성남부 종합사회복지관)경기도 화성시 향남읍 행정리 437-3번지 화성남부 종합사회복지관 1층18598031-8059-434837.130409126.919459
148화성시노노카페 KB국민은행향남점카페화성시니어클럽경기도 화성시 향남읍 향남로 392 1층 (행정리,우성메디피아)경기도 화성시 향남읍 행정리 487-6번지 우성메디피아 1층18600031-8059-434837.129933126.921429