Overview

Dataset statistics

Number of variables9
Number of observations310
Missing cells221
Missing cells (%)7.9%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory22.8 KiB
Average record size in memory75.4 B

Variable types

Categorical2
Text4
Numeric3

Dataset

Description경기도 역사관광지 현황
Author경기관광공사
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=64KXQZ4MZAUD2152FLJ431131017&infSeq=1

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.3%) duplicate rowsDuplicates
정제우편번호 is highly overall correlated with 정제WGS84위도 and 1 other fieldsHigh correlation
정제WGS84위도 is highly overall correlated with 정제우편번호 and 1 other fieldsHigh correlation
정제WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 정제우편번호 and 2 other fieldsHigh correlation
전화번호 has 73 (23.5%) missing valuesMissing
정제지번주소 has 9 (2.9%) missing valuesMissing
정제우편번호 has 55 (17.7%) missing valuesMissing
정제WGS84위도 has 42 (13.5%) missing valuesMissing
정제WGS84경도 has 42 (13.5%) missing valuesMissing

Reproduction

Analysis started2023-12-10 21:28:53.598633
Analysis finished2023-12-10 21:28:55.432663
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
안성시
 
21
수원시
 
20
용인시
 
18
광주시
 
18
안양시
 
17
Other values (26)
216 

Length

Max length4
Median length3
Mean length3.0580645
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용인시
2nd row광주시
3rd row광주시
4th row광주시
5th row구리시

Common Values

ValueCountFrequency (%)
안성시 21
 
6.8%
수원시 20
 
6.5%
용인시 18
 
5.8%
광주시 18
 
5.8%
안양시 17
 
5.5%
고양시 15
 
4.8%
파주시 14
 
4.5%
의왕시 13
 
4.2%
과천시 11
 
3.5%
연천군 11
 
3.5%
Other values (21) 152
49.0%

Length

2023-12-11T06:28:55.488369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안성시 21
 
6.8%
수원시 20
 
6.5%
용인시 18
 
5.8%
광주시 18
 
5.8%
안양시 17
 
5.5%
고양시 15
 
4.8%
파주시 14
 
4.5%
의왕시 13
 
4.2%
과천시 11
 
3.5%
연천군 11
 
3.5%
Other values (21) 152
49.0%

관광정보명
Text

Distinct306
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T06:28:55.715860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.2258065
Min length2

Characters and Unicode

Total characters1930
Distinct characters277
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)98.4%

Sample

1st row처인성
2nd row지수당
3rd row연무관
4th row숭렬전
5th row아차산일대보루군
ValueCountFrequency (%)
남한산성 7
 
1.6%
묘역 6
 
1.3%
묘 6
 
1.3%
선생 6
 
1.3%
및 5
 
1.1%
삼막사 5
 
1.1%
광주 5
 
1.1%
용인 4
 
0.9%
선생묘 3
 
0.7%
청계사 3
 
0.7%
Other values (377) 395
88.8%
2023-12-11T06:28:56.071749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135
 
7.0%
사 71
 
3.7%
지 57
 
3.0%
성 55
 
2.8%
묘 51
 
2.6%
산 50
 
2.6%
주 33
 
1.7%
정 33
 
1.7%
생 33
 
1.7%
선 32
 
1.7%
Other values (267) 1380
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1779
92.2%
Space Separator 135
 
7.0%
Decimal Number 5
 
0.3%
Open Punctuation 4
 
0.2%
Close Punctuation 4
 
0.2%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 71
 
4.0%
지 57
 
3.2%
성 55
 
3.1%
묘 51
 
2.9%
산 50
 
2.8%
주 33
 
1.9%
정 33
 
1.9%
생 33
 
1.9%
선 32
 
1.8%
원 31
 
1.7%
Other values (259) 1333
74.9%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 3
75.0%
] 1
 
25.0%
Decimal Number
ValueCountFrequency (%)
3 3
60.0%
1 2
40.0%
Space Separator
ValueCountFrequency (%)
135
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1779
92.2%
Common 151
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 71
 
4.0%
지 57
 
3.2%
성 55
 
3.1%
묘 51
 
2.9%
산 50
 
2.8%
주 33
 
1.9%
정 33
 
1.9%
생 33
 
1.9%
선 32
 
1.8%
원 31
 
1.7%
Other values (259) 1333
74.9%
Common
ValueCountFrequency (%)
135
89.4%
( 3
 
2.0%
) 3
 
2.0%
3 3
 
2.0%
· 3
 
2.0%
1 2
 
1.3%
] 1
 
0.7%
[ 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1778
92.1%
ASCII 148
 
7.7%
None 3
 
0.2%
Compat Jamo 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135
91.2%
( 3
 
2.0%
) 3
 
2.0%
3 3
 
2.0%
1 2
 
1.4%
] 1
 
0.7%
[ 1
 
0.7%
Hangul
ValueCountFrequency (%)
사 71
 
4.0%
지 57
 
3.2%
성 55
 
3.1%
묘 51
 
2.9%
산 50
 
2.8%
주 33
 
1.9%
정 33
 
1.9%
생 33
 
1.9%
선 32
 
1.8%
원 31
 
1.7%
Other values (258) 1332
74.9%
None
ValueCountFrequency (%)
· 3
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

전화번호
Text

MISSING 

Distinct146
Distinct (%)61.6%
Missing73
Missing (%)23.5%
Memory size2.6 KiB
2023-12-11T06:28:56.257272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.075949
Min length11

Characters and Unicode

Total characters2862
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

Unique115 ?
Unique (%)48.5%

Sample

1st row031-324-3049
2nd row031-760-4821
3rd row031-760-4821
4th row031-550-2546
5th row031-760-4821
ValueCountFrequency (%)
031-481-3437 10
 
4.2%
031-678-2502 9
 
3.8%
031-8024-3213 7
 
3.0%
031-940-5831 7
 
3.0%
031-839-2061 7
 
3.0%
031-644-2114 7
 
3.0%
031-538-2106 5
 
2.1%
031-677-1330 5
 
2.1%
031-345-2533 5
 
2.1%
031-324-2147 5
 
2.1%
Other values (136) 170
71.7%
2023-12-11T06:28:56.541803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 474
16.6%
0 431
15.1%
3 423
14.8%
1 369
12.9%
2 235
8.2%
4 199
7.0%
8 180
 
6.3%
6 158
 
5.5%
7 148
 
5.2%
5 140
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2388
83.4%
Dash Punctuation 474
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 431
18.0%
3 423
17.7%
1 369
15.5%
2 235
9.8%
4 199
8.3%
8 180
7.5%
6 158
 
6.6%
7 148
 
6.2%
5 140
 
5.9%
9 105
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2862
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 474
16.6%
0 431
15.1%
3 423
14.8%
1 369
12.9%
2 235
8.2%
4 199
7.0%
8 180
 
6.3%
6 158
 
5.5%
7 148
 
5.2%
5 140
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 474
16.6%
0 431
15.1%
3 423
14.8%
1 369
12.9%
2 235
8.2%
4 199
7.0%
8 180
 
6.3%
6 158
 
5.5%
7 148
 
5.2%
5 140
 
4.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2021-03-05
310 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-05
2nd row2021-03-05
3rd row2021-03-05
4th row2021-03-05
5th row2021-03-05

Common Values

ValueCountFrequency (%)
2021-03-05 310
100.0%

Length

2023-12-11T06:28:56.658638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:28:56.750283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-05 310
100.0%

정제도로명주소
Text

Distinct292
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T06:28:56.943942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length31
Mean length20.480645
Min length11

Characters and Unicode

Total characters6349
Distinct characters231
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique279 ?
Unique (%)90.0%

Sample

1st row경기도 용인시 남사면 아곡리 산43
2nd row경기도 광주시 중부면 산성리 124-1
3rd row경기도 광주시 중부면 산성리 400-1
4th row경기도 광주시 중부면 남한산성로780번길 41-20
5th row경기도 구리시 우미내길 50-140
ValueCountFrequency (%)
경기도 310
 
20.3%
산 25
 
1.6%
안성시 21
 
1.4%
수원시 20
 
1.3%
용인시 18
 
1.2%
광주시 18
 
1.2%
안양시 17
 
1.1%
팔달구 15
 
1.0%
고양시 15
 
1.0%
만안구 14
 
0.9%
Other values (667) 1055
69.0%
2023-12-11T06:28:57.327163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1218
 
19.2%
도 315
 
5.0%
기 314
 
4.9%
경 313
 
4.9%
시 290
 
4.6%
1 220
 
3.5%
2 158
 
2.5%
산 149
 
2.3%
동 135
 
2.1%
- 133
 
2.1%
Other values (221) 3104
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3902
61.5%
Space Separator 1219
 
19.2%
Decimal Number 1056
 
16.6%
Dash Punctuation 133
 
2.1%
Open Punctuation 17
 
0.3%
Close Punctuation 17
 
0.3%
Other Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 315
 
8.1%
기 314
 
8.0%
경 313
 
8.0%
시 290
 
7.4%
산 149
 
3.8%
동 135
 
3.5%
로 131
 
3.4%
면 101
 
2.6%
구 94
 
2.4%
양 85
 
2.2%
Other values (205) 1975
50.6%
Decimal Number
ValueCountFrequency (%)
1 220
20.8%
2 158
15.0%
3 110
10.4%
5 96
9.1%
4 92
8.7%
7 90
8.5%
9 83
 
7.9%
8 75
 
7.1%
0 69
 
6.5%
6 63
 
6.0%
Space Separator
ValueCountFrequency (%)
1218
99.9%
　 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 133
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3901
61.4%
Common 2447
38.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 315
 
8.1%
기 314
 
8.0%
경 313
 
8.0%
시 290
 
7.4%
산 149
 
3.8%
동 135
 
3.5%
로 131
 
3.4%
면 101
 
2.6%
구 94
 
2.4%
양 85
 
2.2%
Other values (204) 1974
50.6%
Common
ValueCountFrequency (%)
1218
49.8%
1 220
 
9.0%
2 158
 
6.5%
- 133
 
5.4%
3 110
 
4.5%
5 96
 
3.9%
4 92
 
3.8%
7 90
 
3.7%
9 83
 
3.4%
8 75
 
3.1%
Other values (6) 172
 
7.0%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3901
61.4%
ASCII 2446
38.5%
None 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1218
49.8%
1 220
 
9.0%
2 158
 
6.5%
- 133
 
5.4%
3 110
 
4.5%
5 96
 
3.9%
4 92
 
3.8%
7 90
 
3.7%
9 83
 
3.4%
8 75
 
3.1%
Other values (5) 171
 
7.0%
Hangul
ValueCountFrequency (%)
도 315
 
8.1%
기 314
 
8.0%
경 313
 
8.0%
시 290
 
7.4%
산 149
 
3.8%
동 135
 
3.5%
로 131
 
3.4%
면 101
 
2.6%
구 94
 
2.4%
양 85
 
2.2%
Other values (204) 1974
50.6%
None
ValueCountFrequency (%)
　 1
100.0%
CJK
ValueCountFrequency (%)
內 1
100.0%

정제지번주소
Text

MISSING 

Distinct279
Distinct (%)92.7%
Missing9
Missing (%)2.9%
Memory size2.6 KiB
2023-12-11T06:28:57.621542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length29
Mean length20.634551
Min length11

Characters and Unicode

Total characters6211
Distinct characters212
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique264 ?
Unique (%)87.7%

Sample

1st row경기도 용인시 처인구 남사읍 아곡리 산43
2nd row경기도 광주시 남한산성면 산성리 124-1
3rd row경기도 광주시 남한산성면 산성리 400-1
4th row경기도 광주시 남한산성면 산성리 717번지
5th row경기도 구리시 아천동 57번지
ValueCountFrequency (%)
경기도 301
 
20.5%
산 25
 
1.7%
안성시 21
 
1.4%
수원시 19
 
1.3%
안양시 17
 
1.2%
용인시 17
 
1.2%
고양시 15
 
1.0%
광주시 14
 
1.0%
파주시 14
 
1.0%
만안구 14
 
1.0%
Other values (619) 1013
68.9%
2023-12-11T06:28:58.095783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1169
 
18.8%
도 308
 
5.0%
기 305
 
4.9%
경 302
 
4.9%
시 281
 
4.5%
1 217
 
3.5%
동 194
 
3.1%
지 180
 
2.9%
산 166
 
2.7%
번 163
 
2.6%
Other values (202) 2926
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3947
63.5%
Space Separator 1169
 
18.8%
Decimal Number 936
 
15.1%
Dash Punctuation 145
 
2.3%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 308
 
7.8%
기 305
 
7.7%
경 302
 
7.7%
시 281
 
7.1%
동 194
 
4.9%
지 180
 
4.6%
산 166
 
4.2%
번 163
 
4.1%
리 139
 
3.5%
구 96
 
2.4%
Other values (187) 1813
45.9%
Decimal Number
ValueCountFrequency (%)
1 217
23.2%
2 150
16.0%
3 88
9.4%
4 84
 
9.0%
5 81
 
8.7%
6 69
 
7.4%
9 67
 
7.2%
7 67
 
7.2%
8 64
 
6.8%
0 49
 
5.2%
Space Separator
ValueCountFrequency (%)
1169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3946
63.5%
Common 2264
36.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 308
 
7.8%
기 305
 
7.7%
경 302
 
7.7%
시 281
 
7.1%
동 194
 
4.9%
지 180
 
4.6%
산 166
 
4.2%
번 163
 
4.1%
리 139
 
3.5%
구 96
 
2.4%
Other values (186) 1812
45.9%
Common
ValueCountFrequency (%)
1169
51.6%
1 217
 
9.6%
2 150
 
6.6%
- 145
 
6.4%
3 88
 
3.9%
4 84
 
3.7%
5 81
 
3.6%
6 69
 
3.0%
9 67
 
3.0%
7 67
 
3.0%
Other values (5) 127
 
5.6%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3946
63.5%
ASCII 2264
36.5%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1169
51.6%
1 217
 
9.6%
2 150
 
6.6%
- 145
 
6.4%
3 88
 
3.9%
4 84
 
3.7%
5 81
 
3.6%
6 69
 
3.0%
9 67
 
3.0%
7 67
 
3.0%
Other values (5) 127
 
5.6%
Hangul
ValueCountFrequency (%)
도 308
 
7.8%
기 305
 
7.7%
경 302
 
7.7%
시 281
 
7.1%
동 194
 
4.9%
지 180
 
4.6%
산 166
 
4.2%
번 163
 
4.1%
리 139
 
3.5%
구 96
 
2.4%
Other values (186) 1812
45.9%
CJK
ValueCountFrequency (%)
內 1
100.0%

정제우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct196
Distinct (%)76.9%
Missing55
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean13890.286
Minimum10000
Maximum18595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T06:28:58.287618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10287
Q111515
median13027
Q316258
95-th percentile17975.3
Maximum18595
Range8595
Interquartile range (IQR)4743

Descriptive statistics

Standard deviation2612.7559
Coefficient of variation (CV)0.1880995
Kurtosis-1.3217508
Mean13890.286
Median Absolute Deviation (MAD)2025
Skewness0.26412092
Sum3542023
Variance6826493.5
MonotonicityNot monotonic
2023-12-11T06:28:58.443806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12700 15
 
4.8%
13910 5
 
1.6%
13800 4
 
1.3%
13841 3
 
1.0%
13912 3
 
1.0%
10825 3
 
1.0%
11498 3
 
1.0%
12283 3
 
1.0%
16258 3
 
1.0%
16000 3
 
1.0%
Other values (186) 210
67.7%
(Missing) 55
 
17.7%
ValueCountFrequency (%)
10000 2
0.6%
10004 1
0.3%
10024 2
0.6%
10039 1
0.3%
10041 1
0.3%
10103 1
0.3%
10106 1
0.3%
10118 1
0.3%
10273 1
0.3%
10274 1
0.3%
ValueCountFrequency (%)
18595 1
0.3%
18591 1
0.3%
18555 1
0.3%
18347 1
0.3%
18345 1
0.3%
18260 1
0.3%
18235 1
0.3%
18120 1
0.3%
18109 1
0.3%
18102 2
0.6%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct245
Distinct (%)91.4%
Missing42
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean37.468727
Minimum36.908937
Maximum38.128397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T06:28:58.597054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.908937
5-th percentile37.045616
Q137.287492
median37.430366
Q337.66451
95-th percentile37.971818
Maximum38.128397
Range1.2194601
Interquartile range (IQR)0.37701777

Descriptive statistics

Standard deviation0.27250203
Coefficient of variation (CV)0.0072727858
Kurtosis-0.48449507
Mean37.468727
Median Absolute Deviation (MAD)0.16868998
Skewness0.25756576
Sum10041.619
Variance0.074257357
MonotonicityNot monotonic
2023-12-11T06:28:58.754712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4781620524 5
 
1.6%
37.4346097296 3
 
1.0%
37.4120725443 3
 
1.0%
37.5162760421 3
 
1.0%
37.8661350846 3
 
1.0%
37.4227894787 3
 
1.0%
37.7856760794 2
 
0.6%
37.4176916311 2
 
0.6%
37.2807973662 2
 
0.6%
37.2818848306 2
 
0.6%
Other values (235) 240
77.4%
(Missing) 42
 
13.5%
ValueCountFrequency (%)
36.9089372694 1
0.3%
36.9132573531 1
0.3%
36.927963923 1
0.3%
36.9397947684 1
0.3%
36.9496660565 1
0.3%
36.9648846827 1
0.3%
36.9689622612 1
0.3%
36.9951357502 1
0.3%
37.0138597704 1
0.3%
37.0150005358 1
0.3%
ValueCountFrequency (%)
38.1283973615 1
0.3%
38.1015059159 1
0.3%
38.0963042686 1
0.3%
38.0438379055 1
0.3%
38.023712603 1
0.3%
38.0235350328 1
0.3%
38.0157274657 1
0.3%
38.015648742 1
0.3%
38.011592569 1
0.3%
38.0094809079 1
0.3%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct245
Distinct (%)91.4%
Missing42
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean127.08222
Minimum126.52673
Maximum127.68344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T06:28:58.907888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.52673
5-th percentile126.75196
Q1126.93208
median127.05361
Q3127.19316
95-th percentile127.5233
Maximum127.68344
Range1.1567048
Interquartile range (IQR)0.26108005

Descriptive statistics

Standard deviation0.23248438
Coefficient of variation (CV)0.0018294013
Kurtosis0.085637528
Mean127.08222
Median Absolute Deviation (MAD)0.13580428
Skewness0.31774706
Sum34058.035
Variance0.054048988
MonotonicityNot monotonic
2023-12-11T06:28:59.047941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1812262803 5
 
1.6%
126.9361430665 3
 
1.0%
127.0348717747 3
 
1.0%
127.2990516016 3
 
1.0%
126.8709890093 3
 
1.0%
126.98478394 3
 
1.0%
127.0294511072 2
 
0.6%
126.9177070084 2
 
0.6%
127.0151841956 2
 
0.6%
127.0143988553 2
 
0.6%
Other values (235) 240
77.4%
(Missing) 42
 
13.5%
ValueCountFrequency (%)
126.5267318856 1
0.3%
126.5308936298 1
0.3%
126.5342103363 1
0.3%
126.535947134 1
0.3%
126.5501928022 1
0.3%
126.585552606 1
0.3%
126.5943192116 1
0.3%
126.67699057 1
0.3%
126.6939256881 1
0.3%
126.7095635949 1
0.3%
ValueCountFrequency (%)
127.6834366955 1
0.3%
127.6554257107 1
0.3%
127.6524637426 1
0.3%
127.6476557365 1
0.3%
127.6371525651 1
0.3%
127.6281620527 1
0.3%
127.6036444223 1
0.3%
127.5861587505 1
0.3%
127.571062054 1
0.3%
127.5604116819 1
0.3%

Interactions

2023-12-11T06:28:54.798430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.106409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.341531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.870530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.188302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.657164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.957738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.267065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:28:54.723224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:28:59.140074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명정제우편번호정제WGS84위도정제WGS84경도
시군명1.0000.9950.9470.924
정제우편번호0.9951.0000.9070.846
정제WGS84위도0.9470.9071.0000.686
정제WGS84경도0.9240.8460.6861.000
2023-12-11T06:28:59.232677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정제우편번호정제WGS84위도정제WGS84경도시군명
정제우편번호1.000-0.9140.2320.912
정제WGS84위도-0.9141.000-0.1860.694
정제WGS84경도0.232-0.1861.0000.628
시군명0.9120.6940.6281.000

Missing values

2023-12-11T06:28:55.088468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:28:55.241000image/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.
2023-12-11T06:28:55.354087image/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

시군명관광정보명전화번호데이터기준일자정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
0용인시처인성031-324-30492021-03-05경기도 용인시 남사면 아곡리 산43경기도 용인시 처인구 남사읍 아곡리 산43<NA>37.147587127.168549
1광주시지수당031-760-48212021-03-05경기도 광주시 중부면 산성리 124-1경기도 광주시 남한산성면 산성리 124-11270037.475969127.189316
2광주시연무관031-760-48212021-03-05경기도 광주시 중부면 산성리 400-1경기도 광주시 남한산성면 산성리 400-11270037.478217127.186427
3광주시숭렬전<NA>2021-03-05경기도 광주시 중부면 남한산성로780번길 41-20경기도 광주시 남한산성면 산성리 717번지1270037.481285127.180395
4구리시아차산일대보루군031-550-25462021-03-05경기도 구리시 우미내길 50-140경기도 구리시 아천동 57번지1195837.564197127.104731
5광주시남한산성 행궁031-760-48212021-03-05경기도 광주시 중부면 남한산성로 784-29<NA>12700<NA><NA>
6김포시덕포진031-980-29652021-03-05경기도 김포시 대곶면 신안리 산105경기도 김포시 대곶면 신안리 산1051004137.652102126.53421
7광주시수어장대<NA>2021-03-05경기도 광주시 남한산성면 남한산성로780번길 107-65<NA><NA><NA><NA>
8안성시칠장사031-673-07762021-03-05경기도 안성시 죽산면 칠장로 399-18경기도 안성시 죽산면 칠장리 764번지1752437.026328127.396791
9군포시정난종선생묘및신도비외묘역일원<NA>2021-03-05경기도 군포시 속달동 산3번지경기도 군포시 속달동 산3번지<NA><NA><NA>
시군명관광정보명전화번호데이터기준일자정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
300양주시양주관아지031-845-31342021-03-05경기도 양주시 부흥로 1399번길 15경기도 양주시 유양동 141-1번지1149837.785676127.029451
301안성시박두진시비031-677-13302021-03-05경기도 안성시 보개면 종합운동장로 203경기도 안성시 보개면 양복리 238-2번지1750837.015001127.323345
302안성시서흥김씨삼강정여문031-678-25042021-03-05경기도 안성시 고삼면 월향리 산96경기도 안성시 고삼면 월향리 산961750437.084068127.269564
303시흥시관곡지031-310-62232021-03-05경기도 시흥시 하중동 208경기도 시흥시 하중동 2081497337.402248126.80449
304가평군조종암031-585-12812021-03-05경기도 가평군 하면 대보간선로 399경기도 가평군 조종면 대보리 195-1번지1243937.800914127.373072
305과천시온온사02-3677-20662021-03-05경기도 과천시 관악산길 58경기도 과천시 관문동 107-5번지1380137.436083126.992696
306안산시사세충렬문031-481-34372021-03-05경기도 안산시 단원구 와동 151경기도 안산시 단원구 와동 1511525537.337335126.830547
307고양시국립여성사전시관031-819-22882021-03-05경기도 고양시 덕양구 화중로 104번길 50 (정부고양지방합동청사 1, 2층)경기도 고양시 덕양구 화정동 964번지 (정부고양지방합동청사 1, 2층)1049737.635818126.834038
308여주시영월루031-887-35632021-03-05경기도 여주시 주내로 13경기도 여주시 상동 132번지1263437.294417127.647656
309군포시방짜유기 전수교육관031-390-06662021-03-05경기도 군포시 송부로 12경기도 군포시 도마교동 499-2번지1588537.314465126.91552

Duplicate rows

Most frequently occurring

시군명관광정보명전화번호데이터기준일자정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도# duplicates
0광주시광주 남한산성<NA>2021-03-05경기도 광주시 남한산성면 산성리 935-9경기도 광주시 남한산성면 산성리 935-91270037.478162127.1812265