Overview

Dataset statistics

Number of variables8
Number of observations201
Missing cells16
Missing cells (%)1.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory13.1 KiB
Average record size in memory66.7 B

Variable types

Numeric2
Text2
Categorical4

Dataset

Description전라남도 목포시 경로당 현황에 관한 데이터로 경로당 명칭, 관리기관, 행정동 소재지, 관리기관전화번호 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15089771/fileData.do

Alerts

Dataset has 1 (0.5%) duplicate rowsDuplicates
행정동 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
관리기관전화번호 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
관리기관 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
연번 is highly overall correlated with 행정동 and 3 other fieldsHigh correlation
회원수 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
관리기관 is highly imbalanced (85.9%)Imbalance
관리기관전화번호 is highly imbalanced (85.9%)Imbalance
데이터기준일자 is highly imbalanced (85.9%)Imbalance
연번 has 4 (2.0%) missing valuesMissing
경로당 명칭 has 4 (2.0%) missing valuesMissing
회원수 has 4 (2.0%) missing valuesMissing
소재지 has 4 (2.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:22:04.901086
Analysis finished2023-12-12 19:22:06.421708
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct197
Distinct (%)100.0%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean99
Minimum1
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:22:06.504672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.8
Q150
median99
Q3148
95-th percentile187.2
Maximum197
Range196
Interquartile range (IQR)98

Descriptive statistics

Standard deviation57.013156
Coefficient of variation (CV)0.57589047
Kurtosis-1.2
Mean99
Median Absolute Deviation (MAD)49
Skewness0
Sum19503
Variance3250.5
MonotonicityStrictly increasing
2023-12-13T04:22:06.678070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
Other values (187) 187
93.0%
(Missing) 4
 
2.0%
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 (%)
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%

경로당 명칭
Text

MISSING 

Distinct197
Distinct (%)100.0%
Missing4
Missing (%)2.0%
Memory size1.7 KiB
2023-12-13T04:22:06.957379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length14
Mean length8.7614213
Min length5

Characters and Unicode

Total characters1726
Distinct characters195
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

Unique197 ?
Unique (%)100.0%

Sample

1st row용당1동경로당
2nd row산정경로당
3rd row산정2동경로당
4th row청호경로당
5th row동목포경로당
ValueCountFrequency (%)
아파트경로당 45
 
17.4%
신흥동 3
 
1.2%
신안비치 2
 
0.8%
근화 2
 
0.8%
동명동 2
 
0.8%
연동육거리경로당 1
 
0.4%
우진아트빌 1
 
0.4%
상동주공3단지경로당 1
 
0.4%
상동현대 1
 
0.4%
도룡마을경로당 1
 
0.4%
Other values (200) 200
77.2%
2023-12-13T04:22:07.392493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 208
 
12.1%
당 206
 
11.9%
경 203
 
11.8%
아 68
 
3.9%
파 65
 
3.8%
트 63
 
3.7%
62
 
3.6%
동 44
 
2.5%
차 37
 
2.1%
산 23
 
1.3%
Other values (185) 747
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1588
92.0%
Space Separator 62
 
3.6%
Decimal Number 62
 
3.6%
Open Punctuation 5
 
0.3%
Close Punctuation 5
 
0.3%
Uppercase Letter 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 208
 
13.1%
당 206
 
13.0%
경 203
 
12.8%
아 68
 
4.3%
파 65
 
4.1%
트 63
 
4.0%
동 44
 
2.8%
차 37
 
2.3%
산 23
 
1.4%
빌 17
 
1.1%
Other values (170) 654
41.2%
Decimal Number
ValueCountFrequency (%)
2 22
35.5%
1 19
30.6%
3 8
 
12.9%
5 5
 
8.1%
4 4
 
6.5%
6 2
 
3.2%
7 1
 
1.6%
9 1
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
H 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1588
92.0%
Common 135
 
7.8%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 208
 
13.1%
당 206
 
13.0%
경 203
 
12.8%
아 68
 
4.3%
파 65
 
4.1%
트 63
 
4.0%
동 44
 
2.8%
차 37
 
2.3%
산 23
 
1.4%
빌 17
 
1.1%
Other values (170) 654
41.2%
Common
ValueCountFrequency (%)
62
45.9%
2 22
 
16.3%
1 19
 
14.1%
3 8
 
5.9%
( 5
 
3.7%
) 5
 
3.7%
5 5
 
3.7%
4 4
 
3.0%
6 2
 
1.5%
7 1
 
0.7%
Other values (2) 2
 
1.5%
Latin
ValueCountFrequency (%)
L 1
33.3%
H 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1588
92.0%
ASCII 138
 
8.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 208
 
13.1%
당 206
 
13.0%
경 203
 
12.8%
아 68
 
4.3%
파 65
 
4.1%
트 63
 
4.0%
동 44
 
2.8%
차 37
 
2.3%
산 23
 
1.4%
빌 17
 
1.1%
Other values (170) 654
41.2%
ASCII
ValueCountFrequency (%)
62
44.9%
2 22
 
15.9%
1 19
 
13.8%
3 8
 
5.8%
( 5
 
3.6%
) 5
 
3.6%
5 5
 
3.6%
4 4
 
2.9%
6 2
 
1.4%
7 1
 
0.7%
Other values (5) 5
 
3.6%

행정동
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상동
18 
용해동
15 
유달동
 
13
용당1동
 
12
삼향동
 
12
Other values (19)
131 

Length

Max length4
Median length3
Mean length2.9751244
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용당1동
2nd row용당1동
3rd row용당1동
4th row용당1동
5th row용당1동

Common Values

ValueCountFrequency (%)
상동 18
 
9.0%
용해동 15
 
7.5%
유달동 13
 
6.5%
용당1동 12
 
6.0%
삼향동 12
 
6.0%
원산동 12
 
6.0%
옥암동 11
 
5.5%
부주동 11
 
5.5%
산정동 10
 
5.0%
이로동 10
 
5.0%
Other values (14) 77
38.3%

Length

2023-12-13T04:22:07.556256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상동 18
 
9.0%
용해동 15
 
7.5%
유달동 13
 
6.5%
용당1동 12
 
6.0%
삼향동 12
 
6.0%
원산동 12
 
6.0%
옥암동 11
 
5.5%
부주동 11
 
5.5%
산정동 10
 
5.0%
이로동 10
 
5.0%
Other values (14) 77
38.3%

회원수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)36.0%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean46.604061
Minimum11
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T04:22:07.716210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile22.8
Q134
median43
Q355
95-th percentile85.4
Maximum131
Range120
Interquartile range (IQR)21

Descriptive statistics

Standard deviation19.777992
Coefficient of variation (CV)0.42438344
Kurtosis2.1956401
Mean46.604061
Median Absolute Deviation (MAD)10
Skewness1.290645
Sum9181
Variance391.16896
MonotonicityNot monotonic
2023-12-13T04:22:07.905436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 9
 
4.5%
43 9
 
4.5%
41 9
 
4.5%
40 8
 
4.0%
52 7
 
3.5%
31 7
 
3.5%
38 7
 
3.5%
35 6
 
3.0%
23 6
 
3.0%
34 6
 
3.0%
Other values (61) 123
61.2%
ValueCountFrequency (%)
11 1
 
0.5%
15 1
 
0.5%
20 4
2.0%
21 2
 
1.0%
22 2
 
1.0%
23 6
3.0%
24 4
2.0%
25 1
 
0.5%
26 3
1.5%
27 4
2.0%
ValueCountFrequency (%)
131 1
0.5%
109 1
0.5%
106 2
1.0%
103 1
0.5%
99 1
0.5%
98 1
0.5%
90 2
1.0%
87 1
0.5%
85 1
0.5%
84 1
0.5%

소재지
Text

MISSING 

Distinct196
Distinct (%)99.5%
Missing4
Missing (%)2.0%
Memory size1.7 KiB
2023-12-13T04:22:08.197745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length33
Mean length28.203046
Min length14

Characters and Unicode

Total characters5556
Distinct characters209
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

Unique195 ?
Unique (%)99.0%

Sample

1st row전라남도 목포시소영길 27-6 (용당동)
2nd row전라남도 목포시호정로37번길 25 (산정동)
3rd row전라남도 목포시산정언덕로 9 (산정동)
4th row전라남도 목포시동부로58번길 6 (용당동)
5th row전라남도 목포시동부로46번길 36 (용당동)
ValueCountFrequency (%)
전라남도 197
 
22.1%
산정동 37
 
4.2%
옥암동 27
 
3.0%
용해동 21
 
2.4%
상동 19
 
2.1%
석현동 13
 
1.5%
죽교동 9
 
1.0%
용당동 9
 
1.0%
25 7
 
0.8%
6 7
 
0.8%
Other values (396) 545
61.2%
2023-12-13T04:22:08.694302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
694
 
12.5%
남 214
 
3.9%
포 212
 
3.8%
동 209
 
3.8%
목 207
 
3.7%
시 207
 
3.7%
도 206
 
3.7%
라 203
 
3.7%
전 197
 
3.5%
) 189
 
3.4%
Other values (199) 3018
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3600
64.8%
Decimal Number 723
 
13.0%
Space Separator 694
 
12.5%
Close Punctuation 189
 
3.4%
Open Punctuation 188
 
3.4%
Other Punctuation 105
 
1.9%
Dash Punctuation 55
 
1.0%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 214
 
5.9%
포 212
 
5.9%
동 209
 
5.8%
목 207
 
5.8%
시 207
 
5.8%
도 206
 
5.7%
라 203
 
5.6%
전 197
 
5.5%
로 174
 
4.8%
길 123
 
3.4%
Other values (182) 1648
45.8%
Decimal Number
ValueCountFrequency (%)
1 159
22.0%
2 118
16.3%
3 102
14.1%
5 61
 
8.4%
4 60
 
8.3%
6 55
 
7.6%
7 45
 
6.2%
0 44
 
6.1%
8 43
 
5.9%
9 36
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
H 1
50.0%
Space Separator
ValueCountFrequency (%)
694
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 188
100.0%
Other Punctuation
ValueCountFrequency (%)
, 105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3600
64.8%
Common 1954
35.2%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 214
 
5.9%
포 212
 
5.9%
동 209
 
5.8%
목 207
 
5.8%
시 207
 
5.8%
도 206
 
5.7%
라 203
 
5.6%
전 197
 
5.5%
로 174
 
4.8%
길 123
 
3.4%
Other values (182) 1648
45.8%
Common
ValueCountFrequency (%)
694
35.5%
) 189
 
9.7%
( 188
 
9.6%
1 159
 
8.1%
2 118
 
6.0%
, 105
 
5.4%
3 102
 
5.2%
5 61
 
3.1%
4 60
 
3.1%
6 55
 
2.8%
Other values (5) 223
 
11.4%
Latin
ValueCountFrequency (%)
L 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3600
64.8%
ASCII 1956
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
694
35.5%
) 189
 
9.7%
( 188
 
9.6%
1 159
 
8.1%
2 118
 
6.0%
, 105
 
5.4%
3 102
 
5.2%
5 61
 
3.1%
4 60
 
3.1%
6 55
 
2.8%
Other values (7) 225
 
11.5%
Hangul
ValueCountFrequency (%)
남 214
 
5.9%
포 212
 
5.9%
동 209
 
5.8%
목 207
 
5.8%
시 207
 
5.8%
도 206
 
5.7%
라 203
 
5.6%
전 197
 
5.5%
로 174
 
4.8%
길 123
 
3.4%
Other values (182) 1648
45.8%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
목포시
197 
<NA>
 
4

Length

Max length4
Median length3
Mean length3.0199005
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목포시 197
98.0%
<NA> 4
 
2.0%

Length

2023-12-13T04:22:08.840472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:22:08.962538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목포시 197
98.0%
na 4
 
2.0%

관리기관전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
061-270-3341
197 
<NA>
 
4

Length

Max length12
Median length12
Mean length11.840796
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row061-270-3341
2nd row061-270-3341
3rd row061-270-3341
4th row061-270-3341
5th row061-270-3341

Common Values

ValueCountFrequency (%)
061-270-3341 197
98.0%
<NA> 4
 
2.0%

Length

2023-12-13T04:22:09.096499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:22:09.256517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
061-270-3341 197
98.0%
na 4
 
2.0%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-06-09
197 
<NA>
 
4

Length

Max length10
Median length10
Mean length9.880597
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2023-06-09 197
98.0%
<NA> 4
 
2.0%

Length

2023-12-13T04:22:09.427507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:22:09.557595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-09 197
98.0%
na 4
 
2.0%

Interactions

2023-12-13T04:22:05.507508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:22:05.313558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:22:05.600761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:22:05.416260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:22:09.636456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동회원수
연번1.0000.9810.405
행정동0.9811.0000.464
회원수0.4050.4641.000
2023-12-13T04:22:09.750767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동데이터기준일자관리기관전화번호관리기관
행정동1.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.000
관리기관1.0001.0001.0001.000
2023-12-13T04:22:09.871754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번회원수행정동관리기관관리기관전화번호데이터기준일자
연번1.000-0.2830.8521.0001.0001.000
회원수-0.2831.0000.1581.0001.0001.000
행정동0.8520.1581.0001.0001.0001.000
관리기관1.0001.0001.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T04:22:05.725623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:22:06.158917image/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.
2023-12-13T04:22:06.304292image/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

연번경로당 명칭행정동회원수소재지관리기관관리기관전화번호데이터기준일자
01용당1동경로당용당1동70전라남도 목포시소영길 27-6 (용당동)목포시061-270-33412023-06-09
12산정경로당용당1동56전라남도 목포시호정로37번길 25 (산정동)목포시061-270-33412023-06-09
23산정2동경로당용당1동35전라남도 목포시산정언덕로 9 (산정동)목포시061-270-33412023-06-09
34청호경로당용당1동61전라남도 목포시동부로58번길 6 (용당동)목포시061-270-33412023-06-09
45동목포경로당용당1동80전라남도 목포시동부로46번길 36 (용당동)목포시061-270-33412023-06-09
56용당 아파트경로당용당1동56전라남도 목포시송림로 5 (용당동, 용당아파트)목포시061-270-33412023-06-09
67미주경로당용당1동58전라남도 목포시산정로 248 (용당동)목포시061-270-33412023-06-09
78용인경로당용당1동57전라남도 목포시용당로 243 (용당동), 2층목포시061-270-33412023-06-09
89현대경로당용당1동27전라남도 목포시신촌로13번길 11 (산정동)목포시061-270-33412023-06-09
910용산경로당용당1동54전라남도 목포시산정언덕로36번길 10 (산정동)목포시061-270-33412023-06-09
연번경로당 명칭행정동회원수소재지관리기관관리기관전화번호데이터기준일자
191192옥암2차휴먼시아경로당부주동57전라남도 목포시남악1로52번길 35-8 (옥암동, 옥암2휴먼시아)목포시061-270-33412023-06-09
192193골드클래스경로당부주동41전라남도 목포시남악1로16번길 10 (옥암동, 옥암골드클래스)목포시061-270-33412023-06-09
193194모아엘가경로당부주동21전라남도 목포시남악2로22번길 58 (옥암동, 모아엘가아파트)목포시061-270-33412023-06-09
194195우미파렌하이트아파트경로당부주동46전라남도 목포시남악2로22번길 15 (옥암동, 우미파렌하이트)목포시061-270-33412023-06-09
195196코아루천년가아파트경로당부주동35전라남도 목포시남악1로16번길 43-14 (부주동,코아루천년가)목포시061-270-33412023-06-09
196197옥암베아채경로당부주동34전라남도 목포시남악1로52번길 83(옥암동, 근화옥암베아채)목포시061-270-33412023-06-09
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Duplicate rows

Most frequently occurring

연번경로당 명칭행정동회원수소재지관리기관관리기관전화번호데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>4