Overview

Dataset statistics

Number of variables6
Number of observations182
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory50.7 B

Variable types

Numeric2
Text3
Categorical1

Dataset

Description충청남도 보령시 문화유통업(노래연습장, 인터넷컴퓨터게임시설제공업, 청소년게임제공업, 일반게임제공업, 게임제작업) 상호, 우편번호, 소재지, 업종 항목을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=423&beforeMenuCd=DOM_000000201001001000&publicdatapk=15037841

Alerts

연번 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:48:33.191220
Analysis finished2024-01-09 21:48:33.868811
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.5
Minimum1
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T06:48:33.924532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.05
Q146.25
median91.5
Q3136.75
95-th percentile172.95
Maximum182
Range181
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation52.683014
Coefficient of variation (CV)0.57577065
Kurtosis-1.2
Mean91.5
Median Absolute Deviation (MAD)45.5
Skewness0
Sum16653
Variance2775.5
MonotonicityStrictly increasing
2024-01-10T06:48:34.053109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
116 1
 
0.5%
118 1
 
0.5%
119 1
 
0.5%
120 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
Other values (172) 172
94.5%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%
177 1
0.5%
176 1
0.5%
175 1
0.5%
174 1
0.5%
173 1
0.5%
Distinct176
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-01-10T06:48:34.294789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length6.7032967
Min length3

Characters and Unicode

Total characters1220
Distinct characters242
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

Unique172 ?
Unique (%)94.5%

Sample

1st row신나는노래연습장
2nd row내마음의노래방
3rd row은하수노래방
4th row새천년노래방
5th row바다 필 노래연습장
ValueCountFrequency (%)
노래연습장 10
 
4.8%
토이랜드 4
 
1.9%
pc방 3
 
1.4%
시유pc방 2
 
1.0%
뽑기세상 2
 
1.0%
라이브노래연습장 2
 
1.0%
코인노래연습장 2
 
1.0%
노다지pc 1
 
0.5%
조은피씨방 1
 
0.5%
라이또pc방 1
 
0.5%
Other values (182) 182
86.7%
2024-01-10T06:48:34.607668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111
 
9.1%
109
 
8.9%
90
 
7.4%
84
 
6.9%
83
 
6.8%
C 40
 
3.3%
P 39
 
3.2%
39
 
3.2%
29
 
2.4%
28
 
2.3%
Other values (232) 568
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1076
88.2%
Uppercase Letter 109
 
8.9%
Space Separator 28
 
2.3%
Lowercase Letter 5
 
0.4%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
111
 
10.3%
109
 
10.1%
90
 
8.4%
84
 
7.8%
83
 
7.7%
39
 
3.6%
29
 
2.7%
23
 
2.1%
21
 
2.0%
21
 
2.0%
Other values (208) 466
43.3%
Uppercase Letter
ValueCountFrequency (%)
C 40
36.7%
P 39
35.8%
O 4
 
3.7%
Y 3
 
2.8%
N 3
 
2.8%
M 3
 
2.8%
D 2
 
1.8%
U 2
 
1.8%
L 2
 
1.8%
G 2
 
1.8%
Other values (8) 9
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
i 2
40.0%
g 1
20.0%
n 1
20.0%
x 1
20.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Decimal Number
ValueCountFrequency (%)
8 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1076
88.2%
Latin 114
 
9.3%
Common 30
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
111
 
10.3%
109
 
10.1%
90
 
8.4%
84
 
7.8%
83
 
7.7%
39
 
3.6%
29
 
2.7%
23
 
2.1%
21
 
2.0%
21
 
2.0%
Other values (208) 466
43.3%
Latin
ValueCountFrequency (%)
C 40
35.1%
P 39
34.2%
O 4
 
3.5%
Y 3
 
2.6%
N 3
 
2.6%
M 3
 
2.6%
D 2
 
1.8%
U 2
 
1.8%
L 2
 
1.8%
i 2
 
1.8%
Other values (12) 14
 
12.3%
Common
ValueCountFrequency (%)
28
93.3%
8 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1076
88.2%
ASCII 144
 
11.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
111
 
10.3%
109
 
10.1%
90
 
8.4%
84
 
7.8%
83
 
7.7%
39
 
3.6%
29
 
2.7%
23
 
2.1%
21
 
2.0%
21
 
2.0%
Other values (208) 466
43.3%
ASCII
ValueCountFrequency (%)
C 40
27.8%
P 39
27.1%
28
19.4%
O 4
 
2.8%
Y 3
 
2.1%
N 3
 
2.1%
M 3
 
2.1%
D 2
 
1.4%
U 2
 
1.4%
L 2
 
1.4%
Other values (14) 18
12.5%

우편번호
Real number (ℝ)

Distinct41
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35238.989
Minimum33411
Maximum355812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T06:48:34.710724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33411
5-th percentile33434
Q133455.75
median33469
Q333487
95-th percentile33507.15
Maximum355812
Range322401
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation23893.746
Coefficient of variation (CV)0.67804858
Kurtosis181.9997
Mean35238.989
Median Absolute Deviation (MAD)14
Skewness13.490721
Sum6413496
Variance5.7091112 × 108
MonotonicityNot monotonic
2024-01-10T06:48:34.806674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
33470 16
 
8.8%
33466 16
 
8.8%
33469 16
 
8.8%
33489 16
 
8.8%
33488 10
 
5.5%
33443 10
 
5.5%
33487 9
 
4.9%
33455 7
 
3.8%
33471 6
 
3.3%
33435 6
 
3.3%
Other values (31) 70
38.5%
ValueCountFrequency (%)
33411 2
 
1.1%
33415 1
 
0.5%
33430 1
 
0.5%
33432 1
 
0.5%
33433 4
 
2.2%
33434 6
3.3%
33435 6
3.3%
33436 2
 
1.1%
33438 1
 
0.5%
33443 10
5.5%
ValueCountFrequency (%)
355812 1
 
0.5%
33521 2
 
1.1%
33520 4
 
2.2%
33509 2
 
1.1%
33508 1
 
0.5%
33491 1
 
0.5%
33490 4
 
2.2%
33489 16
8.8%
33488 10
5.5%
33487 9
4.9%
Distinct175
Distinct (%)96.7%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T06:48:35.092656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length37
Mean length26.093923
Min length19

Characters and Unicode

Total characters4723
Distinct characters180
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

Unique169 ?
Unique (%)93.4%

Sample

1st row충청남도 보령시 작은오랏5길 37 (동대동)
2nd row충청남도 보령시 웅천읍 장터중앙길 252
3rd row충청남도 보령시 대천항2길 67 (신흑동)
4th row충청남도 보령시 대해로 897-5 (신흑동)
5th row충청남도 보령시 해수욕장4길 46 (신흑동)
ValueCountFrequency (%)
충청남도 181
18.1%
보령시 181
18.1%
동대동 67
 
6.7%
신흑동 40
 
4.0%
대천동 36
 
3.6%
1층 29
 
2.9%
죽정동 10
 
1.0%
2층 10
 
1.0%
작은오랏2길 9
 
0.9%
웅천읍 9
 
0.9%
Other values (225) 430
42.9%
2024-01-10T06:48:35.498608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
823
 
17.4%
247
 
5.2%
190
 
4.0%
187
 
4.0%
187
 
4.0%
185
 
3.9%
185
 
3.9%
185
 
3.9%
181
 
3.8%
) 174
 
3.7%
Other values (170) 2179
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2833
60.0%
Space Separator 823
 
17.4%
Decimal Number 597
 
12.6%
Close Punctuation 174
 
3.7%
Open Punctuation 174
 
3.7%
Other Punctuation 84
 
1.8%
Dash Punctuation 32
 
0.7%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
247
 
8.7%
190
 
6.7%
187
 
6.6%
187
 
6.6%
185
 
6.5%
185
 
6.5%
185
 
6.5%
181
 
6.4%
135
 
4.8%
116
 
4.1%
Other values (150) 1035
36.5%
Decimal Number
ValueCountFrequency (%)
1 150
25.1%
2 78
13.1%
6 61
10.2%
3 59
 
9.9%
5 51
 
8.5%
4 49
 
8.2%
7 47
 
7.9%
8 40
 
6.7%
0 35
 
5.9%
9 27
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
G 1
16.7%
L 1
16.7%
M 1
16.7%
F 1
16.7%
Space Separator
ValueCountFrequency (%)
823
100.0%
Close Punctuation
ValueCountFrequency (%)
) 174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Other Punctuation
ValueCountFrequency (%)
, 84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2833
60.0%
Common 1884
39.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
247
 
8.7%
190
 
6.7%
187
 
6.6%
187
 
6.6%
185
 
6.5%
185
 
6.5%
185
 
6.5%
181
 
6.4%
135
 
4.8%
116
 
4.1%
Other values (150) 1035
36.5%
Common
ValueCountFrequency (%)
823
43.7%
) 174
 
9.2%
( 174
 
9.2%
1 150
 
8.0%
, 84
 
4.5%
2 78
 
4.1%
6 61
 
3.2%
3 59
 
3.1%
5 51
 
2.7%
4 49
 
2.6%
Other values (5) 181
 
9.6%
Latin
ValueCountFrequency (%)
A 2
33.3%
G 1
16.7%
L 1
16.7%
M 1
16.7%
F 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2833
60.0%
ASCII 1890
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
823
43.5%
) 174
 
9.2%
( 174
 
9.2%
1 150
 
7.9%
, 84
 
4.4%
2 78
 
4.1%
6 61
 
3.2%
3 59
 
3.1%
5 51
 
2.7%
4 49
 
2.6%
Other values (10) 187
 
9.9%
Hangul
ValueCountFrequency (%)
247
 
8.7%
190
 
6.7%
187
 
6.6%
187
 
6.6%
185
 
6.5%
185
 
6.5%
185
 
6.5%
181
 
6.4%
135
 
4.8%
116
 
4.1%
Other values (150) 1035
36.5%
Distinct166
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-01-10T06:48:35.798872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length19.901099
Min length17

Characters and Unicode

Total characters3622
Distinct characters81
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

Unique155 ?
Unique (%)85.2%

Sample

1st row충청남도 보령시 동대동 1781
2nd row충청남도 보령시 웅천읍 대창리 682
3rd row충청남도 보령시 신흑동 950-19
4th row충청남도 보령시 신흑동 1996
5th row충청남도 보령시 신흑동 1928
ValueCountFrequency (%)
충청남도 182
23.7%
보령시 182
23.7%
동대동 67
 
8.7%
신흑동 40
 
5.2%
대천동 37
 
4.8%
죽정동 10
 
1.3%
웅천읍 9
 
1.2%
명천동 8
 
1.0%
궁촌동 7
 
0.9%
시티타워 6
 
0.8%
Other values (187) 220
28.6%
2024-01-10T06:48:36.202202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/