Overview

Dataset statistics

Number of variables3
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory27.3 B

Variable types

Numeric2
Text1

Dataset

Description충청남도 보령시의 법정 리에 대한 코드로 웅천읍 13개 리, 주포면 5개 리, 오천면 10개 리, 천북면 8개 리, 청소면 8개 리, 청라면 11개 리, 남포면 13개 리, 주산면 11개 리, 미산면 14개 리, 성주면 2개 리, 주교면 6개 리의 법정코드를 공개합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=400&beforeMenuCd=DOM_000000201001001000&publicdatapk=15041711

Alerts

순번 has unique valuesUnique
리코드 has unique valuesUnique
리명칭 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:34:55.790398
Analysis finished2024-01-09 21:34:56.258151
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T06:34:56.316269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median51
Q376
95-th percentile96
Maximum101
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.300171
Coefficient of variation (CV)0.57451315
Kurtosis-1.2
Mean51
Median Absolute Deviation (MAD)25
Skewness0
Sum5151
Variance858.5
MonotonicityStrictly increasing
2024-01-10T06:34:56.424514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%

리코드
Real number (ℝ)

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4180345 × 109
Minimum4.418025 × 109
Maximum4.418041 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T06:34:56.530512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.418025 × 109
5-th percentile4.418025 × 109
Q14.418032 × 109
median4.418035 × 109
Q34.418038 × 109
95-th percentile4.418041 × 109
Maximum4.418041 × 109
Range16005
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation4561.5945
Coefficient of variation (CV)1.0324941 × 10-6
Kurtosis-0.018078246
Mean4.4180345 × 109
Median Absolute Deviation (MAD)3000
Skewness-0.79845175
Sum4.4622148 × 1011
Variance20808144
MonotonicityNot monotonic
2024-01-10T06:34:56.652847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4418039030 1
 
1.0%
4418038022 1
 
1.0%
4418035027 1
 
1.0%
4418033025 1
 
1.0%
4418025026 1
 
1.0%
4418032026 1
 
1.0%
4418036024 1
 
1.0%
4418032028 1
 
1.0%
4418032027 1
 
1.0%
4418038024 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
4418025021 1
1.0%
4418025022 1
1.0%
4418025023 1
1.0%
4418025024 1
1.0%
4418025025 1
1.0%
4418025026 1
1.0%
4418025027 1
1.0%
4418025028 1
1.0%
4418025029 1
1.0%
4418025030 1
1.0%
ValueCountFrequency (%)
4418041026 1
1.0%
4418041025 1
1.0%
4418041024 1
1.0%
4418041023 1
1.0%
4418041022 1
1.0%
4418041021 1
1.0%
4418040022 1
1.0%
4418040021 1
1.0%
4418039034 1
1.0%
4418039033 1
1.0%

리명칭
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-01-10T06:34:56.890059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length16.029703
Min length15

Characters and Unicode

Total characters1619
Distinct characters102
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)100.0%

Sample

1st row충청남도 보령시 미산면 내평리
2nd row충청남도 보령시 청소면 정전리
3rd row충청남도 보령시 남포면 옥서리
4th row충청남도 보령시 청소면 성연리
5th row충청남도 보령시 청소면 재정리
ValueCountFrequency (%)
충청남도 101
25.1%
보령시 101
25.1%
미산면 16
 
4.0%
남포면 13
 
3.2%
웅천읍 13
 
3.2%
청라면 11
 
2.7%
오천면 10
 
2.5%
주산면 9
 
2.2%
천북면 8
 
2.0%
청소면 7
 
1.7%
Other values (104) 114
28.3%
2024-01-10T06:34:57.222957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
302
18.7%
청 120
 
7.4%
남 115
 
7.1%
도 108
 
6.7%
보 103
 
6.4%
령 102
 
6.3%
시 102
 
6.3%
충 101
 
6.2%
리 101
 
6.2%
면 88
 
5.4%
Other values (92) 377
23.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1317
81.3%
Space Separator 302
 
18.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 120
 
9.1%
남 115
 
8.7%
도 108
 
8.2%
보 103
 
7.8%
령 102
 
7.7%
시 102
 
7.7%
충 101
 
7.7%
리 101
 
7.7%
면 88
 
6.7%
천 35
 
2.7%
Other values (91) 342
26.0%
Space Separator
ValueCountFrequency (%)
302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1317
81.3%
Common 302
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 120
 
9.1%
남 115
 
8.7%
도 108
 
8.2%
보 103
 
7.8%
령 102
 
7.7%
시 102
 
7.7%
충 101
 
7.7%
리 101
 
7.7%
면 88
 
6.7%
천 35
 
2.7%
Other values (91) 342
26.0%
Common
ValueCountFrequency (%)
302
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1317
81.3%
ASCII 302
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
302
100.0%
Hangul
ValueCountFrequency (%)
청 120
 
9.1%
남 115
 
8.7%
도 108
 
8.2%
보 103
 
7.8%
령 102
 
7.7%
시 102
 
7.7%
충 101
 
7.7%
리 101
 
7.7%
면 88
 
6.7%
천 35
 
2.7%
Other values (91) 342
26.0%

Interactions

2024-01-10T06:34:56.016035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:55.887594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:56.079869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:55.946849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:34:57.298098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번리코드
순번1.0000.431
리코드0.4311.000
2024-01-10T06:34:57.360119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번리코드
순번1.000-0.037
리코드-0.0371.000

Missing values

2024-01-10T06:34:56.178925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:34:56.235369image/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.

Sample

순번리코드리명칭
014418039030충청남도 보령시 미산면 내평리
124418034026충청남도 보령시 청소면 정전리
234418036028충청남도 보령시 남포면 옥서리
344418034025충청남도 보령시 청소면 성연리
454418034023충청남도 보령시 청소면 재정리
564418036029충청남도 보령시 남포면 달산리
674418034027충청남도 보령시 청소면 야현리
784418034022충청남도 보령시 청소면 신송리
894418032024충청남도 보령시 오천면 갈현리
9104418032021충청남도 보령시 오천면 소성리
순번리코드리명칭
91924418039025충청남도 보령시 미산면 늑전리
92934418034024충청남도 보령시 청소면 죽림리
93944418032030충청남도 보령시 오천면 외연도리
94954418025033충청남도 보령시 웅천읍 노천리
95964418036022충청남도 보령시 남포면 옥동리
96974418025025충청남도 보령시 웅천읍 대천리
97984418032029충청남도 보령시 오천면 녹도리
98994418039027충청남도 보령시 미산면 봉성리
991004418036021충청남도 보령시 남포면 읍내리
1001014418039022충청남도 보령시 미산면 도화담리