Overview

Dataset statistics

Number of variables11
Number of observations262
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 KiB
Average record size in memory93.5 B

Variable types

Numeric3
Text4
Categorical4

Dataset

Description대전광역시 공영자전거(타슈)위치 현황 입니다. 타슈 무인대여시스템 개선 사업을 통해 변경된 위치에 대하여 등록할 예정입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15062798/fileData.do

Alerts

광역시도코드 has constant value ""Constant
광역시도명 has constant value ""Constant
시군구코드 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
행정동코드 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
스테이션, 성명(Station) has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:57:22.532372
Analysis finished2023-12-12 00:57:25.216011
Duration2.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct262
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.5
Minimum1
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T09:57:25.306130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.05
Q166.25
median131.5
Q3196.75
95-th percentile248.95
Maximum262
Range261
Interquartile range (IQR)130.5

Descriptive statistics

Standard deviation75.777085
Coefficient of variation (CV)0.5762516
Kurtosis-1.2
Mean131.5
Median Absolute Deviation (MAD)65.5
Skewness0
Sum34453
Variance5742.1667
MonotonicityStrictly increasing
2023-12-12T09:57:25.455089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
166 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
171 1
 
0.4%
172 1
 
0.4%
173 1
 
0.4%
174 1
 
0.4%
175 1
 
0.4%
Other values (252) 252
96.2%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
262 1
0.4%
261 1
0.4%
260 1
0.4%
259 1
0.4%
258 1
0.4%
257 1
0.4%
256 1
0.4%
255 1
0.4%
254 1
0.4%
253 1
0.4%

스테이션, 성명(Station)
Text

UNIQUE 

Distinct262
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T09:57:25.786710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length9.1374046
Min length3

Characters and Unicode

Total characters2394
Distinct characters292
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

Unique262 ?
Unique (%)100.0%

Sample

1st row무역전시관입구(택시승강장)
2nd row대전컨벤션센터
3rd row한밭수목원1
4th row초원아파트(104동 버스정류장)
5th row둔산대공원 입구(버스정류장)
ValueCountFrequency (%)
도안 12
 
3.2%
3번출구 7
 
1.9%
카이스트 7
 
1.9%
맞은편 5
 
1.4%
입구 4
 
1.1%
4번출구 4
 
1.1%
버스정류장 3
 
0.8%
노은3지구 3
 
0.8%
대전광역시 3
 
0.8%
정문 3
 
0.8%
Other values (305) 319
86.2%
2023-12-12T09:57:26.249349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
 
4.5%
) 90
 
3.8%
( 89
 
3.7%
동 77
 
3.2%
대 64
 
2.7%
리 53
 
2.2%
구 47
 
2.0%
트 42
 
1.8%
거 40
 
1.7%
1 38
 
1.6%
Other values (282) 1746
72.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1957
81.7%
Decimal Number 140
 
5.8%
Space Separator 108
 
4.5%
Close Punctuation 90
 
3.8%
Open Punctuation 89
 
3.7%
Uppercase Letter 9
 
0.4%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 77
 
3.9%
대 64
 
3.3%
리 53
 
2.7%
구 47
 
2.4%
트 42
 
2.1%
거 40
 
2.0%
교 37
 
1.9%
전 35
 
1.8%
원 32
 
1.6%
네 32
 
1.6%
Other values (262) 1498
76.5%
Decimal Number
ValueCountFrequency (%)
1 38
27.1%
0 28
20.0%
3 19
13.6%
2 19
13.6%
4 11
 
7.9%
5 9
 
6.4%
9 5
 
3.6%
6 5
 
3.6%
7 4
 
2.9%
8 2
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
K 2
22.2%
B 2
22.2%
T 2
22.2%
R 1
11.1%
G 1
11.1%
S 1
11.1%
Space Separator
ValueCountFrequency (%)
108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1957
81.7%
Common 428
 
17.9%
Latin 9
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 77
 
3.9%
대 64
 
3.3%
리 53
 
2.7%
구 47
 
2.4%
트 42
 
2.1%
거 40
 
2.0%
교 37
 
1.9%
전 35
 
1.8%
원 32
 
1.6%
네 32
 
1.6%
Other values (262) 1498
76.5%
Common
ValueCountFrequency (%)
108
25.2%
) 90
21.0%
( 89
20.8%
1 38
 
8.9%
0 28
 
6.5%
3 19
 
4.4%
2 19
 
4.4%
4 11
 
2.6%
5 9
 
2.1%
9 5
 
1.2%
Other values (4) 12
 
2.8%
Latin
ValueCountFrequency (%)
K 2
22.2%
B 2
22.2%
T 2
22.2%
R 1
11.1%
G 1
11.1%
S 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1957
81.7%
ASCII 437
 
18.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
24.7%
) 90
20.6%
( 89
20.4%
1 38
 
8.7%
0 28
 
6.4%
3 19
 
4.3%
2 19
 
4.3%
4 11
 
2.5%
5 9
 
2.1%
9 5
 
1.1%
Other values (10) 21
 
4.8%
Hangul
ValueCountFrequency (%)
동 77
 
3.9%
대 64
 
3.3%
리 53
 
2.7%
구 47
 
2.4%
트 42
 
2.1%
거 40
 
2.0%
교 37
 
1.9%
전 35
 
1.8%
원 32
 
1.6%
네 32
 
1.6%
Other values (262) 1498
76.5%

위치
Text

Distinct258
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T09:57:26.811977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length17.881679
Min length15

Characters and Unicode

Total characters4685
Distinct characters100
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)97.7%

Sample

1st row대전광역시 유성구 도룡동 3-8
2nd row대전광역시 유성구 도룡동 4-19
3rd row대전광역시 서구 만년동 396
4th row대전광역시 서구 만년동 401
5th row대전광역시 서구 둔산2동 1521-10
ValueCountFrequency (%)
대전광역시 262
25.0%
유성구 76
 
7.3%
서구 73
 
7.0%
중구 39
 
3.7%
대덕구 38
 
3.6%
동구 36
 
3.4%
봉명동 9
 
0.9%
둔산동 8
 
0.8%
관저동 8
 
0.8%
지족동 7
 
0.7%
Other values (344) 492
46.9%
2023-12-12T09:57:27.519706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
786
16.8%
대 318
 
6.8%
동 297
 
6.3%
전 271
 
5.8%
구 270
 
5.8%
광 262
 
5.6%
역 262
 
5.6%
시 262
 
5.6%
1 222
 
4.7%
- 164
 
3.5%
Other values (90) 1571
33.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2729
58.2%
Decimal Number 1006
 
21.5%
Space Separator 786
 
16.8%
Dash Punctuation 164
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 318
11.7%
동 297
10.9%
전 271
9.9%
구 270
9.9%
광 262
9.6%
역 262
9.6%
시 262
9.6%
성 92
 
3.4%
유 83
 
3.0%
서 73
 
2.7%
Other values (78) 539
19.8%
Decimal Number
ValueCountFrequency (%)
1 222
22.1%
2 140
13.9%
4 116
11.5%
3 115
11.4%
5 103
10.2%
6 69
 
6.9%
9 65
 
6.5%
0 64
 
6.4%
8 63
 
6.3%
7 49
 
4.9%
Space Separator
ValueCountFrequency (%)
786
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2729
58.2%
Common 1956
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 318
11.7%
동 297
10.9%
전 271
9.9%
구 270
9.9%
광 262
9.6%
역 262
9.6%
시 262
9.6%
성 92
 
3.4%
유 83
 
3.0%
서 73
 
2.7%
Other values (78) 539
19.8%
Common
ValueCountFrequency (%)
786
40.2%
1 222
 
11.3%
- 164
 
8.4%
2 140
 
7.2%
4 116
 
5.9%
3 115
 
5.9%
5 103
 
5.3%
6 69
 
3.5%
9 65
 
3.3%
0 64
 
3.3%
Other values (2) 112
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2729
58.2%
ASCII 1956
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
786
40.2%
1 222
 
11.3%
- 164
 
8.4%
2 140
 
7.2%
4 116
 
5.9%
3 115
 
5.9%
5 103
 
5.3%
6 69
 
3.5%
9 65
 
3.3%
0 64
 
3.3%
Other values (2) 112
 
5.7%
Hangul
ValueCountFrequency (%)
대 318
11.7%
동 297
10.9%
전 271
9.9%
구 270
9.9%
광 262
9.6%
역 262
9.6%
시 262
9.6%
성 92
 
3.4%
유 83
 
3.0%
서 73
 
2.7%
Other values (78) 539
19.8%

광역시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
30
262 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 262
100.0%

Length

2023-12-12T09:57:27.700017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:27.814195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 262
100.0%

광역시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
대전광역시
262 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 262
100.0%

Length

2023-12-12T09:57:27.943766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:28.076102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 262
100.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
30200
76 
30170
73 
30140
39 
30230
38 
30110
36 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30200
2nd row30200
3rd row30170
4th row30170
5th row30170

Common Values

ValueCountFrequency (%)
30200 76
29.0%
30170 73
27.9%
30140 39
14.9%
30230 38
14.5%
30110 36
13.7%

Length

2023-12-12T09:57:28.193733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:28.338877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30200 76
29.0%
30170 73
27.9%
30140 39
14.9%
30230 38
14.5%
30110 36
13.7%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
유성구
76 
서구
73 
중구
39 
대덕구
38 
동구
36 

Length

Max length3
Median length2
Mean length2.4351145
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유성구
2nd row유성구
3rd row서구
4th row서구
5th row서구

Common Values

ValueCountFrequency (%)
유성구 76
29.0%
서구 73
27.9%
중구 39
14.9%
대덕구 38
14.5%
동구 36
13.7%

Length

2023-12-12T09:57:28.501738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:57:28.658352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 76
29.0%
서구 73
27.9%
중구 39
14.9%
대덕구 38
14.5%
동구 36
13.7%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0174809 × 109
Minimum3.0110101 × 109
Maximum3.0230126 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T09:57:28.823534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0110101 × 109
5-th percentile3.0110109 × 109
Q13.0140115 × 109
median3.0170114 × 109
Q33.0200124 × 109
95-th percentile3.023011 × 109
Maximum3.0230126 × 109
Range12002500
Interquartile range (IQR)6000900

Descriptive statistics

Standard deviation3735294.1
Coefficient of variation (CV)0.0012378849
Kurtosis-0.87206547
Mean3.0174809 × 109
Median Absolute Deviation (MAD)3000600
Skewness-0.27603986
Sum7.9058 × 1011
Variance1.3952422 × 1013
MonotonicityNot monotonic
2023-12-12T09:57:29.036423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3017011200 20
 
7.6%
3017011300 11
 
4.2%
3020011100 9
 
3.4%
3017011600 8
 
3.1%
3020012000 7
 
2.7%
3017010600 7
 
2.7%
3014011500 7
 
2.7%
3011011500 7
 
2.7%
3011010500 6
 
2.3%
3020012400 6
 
2.3%
Other values (77) 174
66.4%
ValueCountFrequency (%)
3011010100 1
 
0.4%
3011010200 1
 
0.4%
3011010400 1
 
0.4%
3011010500 6
2.3%
3011010600 1
 
0.4%
3011010700 3
1.1%
3011010900 3
1.1%
3011011000 1
 
0.4%
3011011100 1
 
0.4%
3011011400 4
1.5%
ValueCountFrequency (%)
3023012600 5
1.9%
3023012400 1
 
0.4%
3023011700 1
 
0.4%
3023011500 1
 
0.4%
3023011300 2
 
0.8%
3023011200 1
 
0.4%
3023011100 2
 
0.8%
3023011000 3
1.1%
3023010900 4
1.5%
3023010800 5
1.9%

법정동명
Text

Distinct87
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T09:57:29.427673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9732824
Min length2

Characters and Unicode

Total characters779
Distinct characters84
Distinct categories1 ?
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 (%)11.8%

Sample

1st row도룡동
2nd row도룡동
3rd row만년동
4th row만년동
5th row둔산동
ValueCountFrequency (%)
둔산동 20
 
7.6%
월평동 11
 
4.2%
봉명동 9
 
3.4%
관저동 8
 
3.1%
지족동 7
 
2.7%
탄방동 7
 
2.7%
유천동 7
 
2.7%
용전동 7
 
2.7%
가오동 6
 
2.3%
구성동 6
 
2.3%
Other values (77) 174
66.4%
2023-12-12T09:57:29.935967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 262
33.6%
산 22
 
2.8%
평 22
 
2.8%
둔 20
 
2.6%
가 20
 
2.6%
대 18
 
2.3%
성 16
 
2.1%
용 15
 
1.9%
오 14
 
1.8%
신 14
 
1.8%
Other values (74) 356
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 779
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 262
33.6%
산 22
 
2.8%
평 22
 
2.8%
둔 20
 
2.6%
가 20
 
2.6%
대 18
 
2.3%
성 16
 
2.1%
용 15
 
1.9%
오 14
 
1.8%
신 14
 
1.8%
Other values (74) 356
45.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 779
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 262
33.6%
산 22
 
2.8%
평 22
 
2.8%
둔 20
 
2.6%
가 20
 
2.6%
대 18
 
2.3%
성 16
 
2.1%
용 15
 
1.9%
오 14
 
1.8%
신 14
 
1.8%
Other values (74) 356
45.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 779
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 262
33.6%
산 22
 
2.8%
평 22
 
2.8%
둔 20
 
2.6%
가 20
 
2.6%
대 18
 
2.3%
성 16
 
2.1%
용 15
 
1.9%
오 14
 
1.8%
신 14
 
1.8%
Other values (74) 356
45.7%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0175281 × 109
Minimum3.0110515 × 109
Maximum3.023061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T09:57:30.168208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0110515 × 109
5-th percentile3.0110551 × 109
Q13.0140702 × 109
median3.017064 × 109
Q33.020055 × 109
95-th percentile3.023055 × 109
Maximum3.023061 × 109
Range12009500
Interquartile range (IQR)5984750

Descriptive statistics

Standard deviation3732556.9
Coefficient of variation (CV)0.0012369584
Kurtosis-0.86974245
Mean3.0175281 × 109
Median Absolute Deviation (MAD)2991500
Skewness-0.27662984
Sum7.9059237 × 1011
Variance1.3931981 × 1013
MonotonicityNot monotonic
2023-12-12T09:57:30.350339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3020055000 14
 
5.3%
3020054000 14
 
5.3%
3020061000 12
 
4.6%
3017064000 11
 
4.2%
3020053000 11
 
4.2%
3011064000 7
 
2.7%
3020054800 7
 
2.7%
3011053000 7
 
2.7%
3017055500 7
 
2.7%
3023055000 6
 
2.3%
Other values (60) 166
63.4%
ValueCountFrequency (%)
3011051500 2
 
0.8%
3011053000 7
2.7%
3011054500 2
 
0.8%
3011055100 3
1.1%
3011056000 3
1.1%
3011058500 1
 
0.4%
3011059000 1
 
0.4%
3011062000 2
 
0.8%
3011063000 2
 
0.8%
3011064000 7
2.7%
ValueCountFrequency (%)
3023061000 4
1.5%
3023060000 1
 
0.4%
3023058000 3
1.1%
3023057000 2
 
0.8%
3023056000 2
 
0.8%
3023055000 6
2.3%
3023054600 4
1.5%
3023054300 5
1.9%
3023053300 3
1.1%
3023052500 4
1.5%

행정동명
Text

Distinct70
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T09:57:30.638526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.4770992
Min length2

Characters and Unicode

Total characters911
Distinct characters76
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

Unique11 ?
Unique (%)4.2%

Sample

1st row신성동
2nd row신성동
3rd row만년동
4th row만년동
5th row둔산2동
ValueCountFrequency (%)
신성동 14
 
5.3%
온천2동 14
 
5.3%
원신흥동 12
 
4.6%
둔산2동 11
 
4.2%
온천1동 11
 
4.2%
용전동 7
 
2.7%
노은3동 7
 
2.7%
효동 7
 
2.7%
탄방동 7
 
2.7%
신탄진동 6
 
2.3%
Other values (60) 166
63.4%
2023-12-12T09:57:31.033427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 262
28.8%
2 52
 
5.7%
1 47
 
5.2%
신 34
 
3.7%
천 32
 
3.5%
온 25
 
2.7%
산 25
 
2.7%
둔 20
 
2.2%
평 20
 
2.2%
성 19
 
2.1%
Other values (66) 375
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 800
87.8%
Decimal Number 111
 
12.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 262
32.8%
신 34
 
4.2%
천 32
 
4.0%
온 25
 
3.1%
산 25
 
3.1%
둔 20
 
2.5%
평 20
 
2.5%
성 19
 
2.4%
원 18
 
2.2%
은 16
 
2.0%
Other values (63) 329
41.1%
Decimal Number
ValueCountFrequency (%)
2 52
46.8%
1 47
42.3%
3 12
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 800
87.8%
Common 111
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 262
32.8%
신 34
 
4.2%
천 32
 
4.0%
온 25
 
3.1%
산 25
 
3.1%
둔 20
 
2.5%
평 20
 
2.5%
성 19
 
2.4%
원 18
 
2.2%
은 16
 
2.0%
Other values (63) 329
41.1%
Common
ValueCountFrequency (%)
2 52
46.8%
1 47
42.3%
3 12
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 800
87.8%
ASCII 111
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 262
32.8%
신 34
 
4.2%
천 32
 
4.0%
온 25
 
3.1%
산 25
 
3.1%
둔 20
 
2.5%
평 20
 
2.5%
성 19
 
2.4%
원 18
 
2.2%
은 16
 
2.0%
Other values (63) 329
41.1%
ASCII
ValueCountFrequency (%)
2 52
46.8%
1 47
42.3%
3 12
 
10.8%

Interactions

2023-12-12T09:57:24.330739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:23.581161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:23.921669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:24.447268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:23.681116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:24.057436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:24.574310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:23.815901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:57:24.206528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:57:31.140915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구코드시군구명법정동코드법정동명행정동코드행정동명
연번1.0000.7610.7610.6340.9320.6230.930
시군구코드0.7611.0001.0001.0001.0001.0001.000
시군구명0.7611.0001.0001.0001.0001.0001.000
법정동코드0.6341.0001.0001.0001.0000.9961.000
법정동명0.9321.0001.0001.0001.0001.0000.999
행정동코드0.6231.0001.0000.9961.0001.0001.000
행정동명0.9301.0001.0001.0000.9991.0001.000
2023-12-12T09:57:31.237011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시군구명
시군구코드1.0001.000
시군구명1.0001.000
2023-12-12T09:57:31.317580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동코드행정동코드시군구코드시군구명
연번1.000-0.030-0.0770.4130.413
법정동코드-0.0301.0000.9760.9980.998
행정동코드-0.0770.9761.0000.9980.998
시군구코드0.4130.9980.9981.0001.000
시군구명0.4130.9980.9981.0001.000

Missing values

2023-12-12T09:57:24.714777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:57:25.151330image/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

연번스테이션, 성명(Station)위치광역시도코드광역시도명시군구코드시군구명법정동코드법정동명행정동코드행정동명
01무역전시관입구(택시승강장)대전광역시 유성구 도룡동 3-830대전광역시30200유성구3020012700도룡동3020055000신성동
12대전컨벤션센터대전광역시 유성구 도룡동 4-1930대전광역시30200유성구3020012700도룡동3020055000신성동
23한밭수목원1대전광역시 서구 만년동 39630대전광역시30170서구3017012800만년동3017065000만년동
34초원아파트(104동 버스정류장)대전광역시 서구 만년동 40130대전광역시30170서구3017012800만년동3017065000만년동
45둔산대공원 입구(버스정류장)대전광역시 서구 둔산2동 1521-1030대전광역시30170서구3017011200둔산동3017064000둔산2동
56백합네거리(농협)대전광역시 서구 월평2동 26630대전광역시30170서구3017011300월평동3017058700월평2동
67정부청사 입구(대덕대로)대전광역시 서구 둔산2동 920-230대전광역시30170서구3017011200둔산동3017064000둔산2동
78정부청사 입구(샘머리)대전광역시 서구 둔산2동 151830대전광역시30170서구3017011200둔산동3017064000둔산2동
89(운영중지)황실아파트(성룡초등학교)대전광역시 서구 월평2동 30430대전광역시30170서구3017011300월평동3017058800월평3동
910만년동 KBS 부근(기업은행)대전광역시 서구 만년동 30030대전광역시30170서구3017012800만년동3017065000만년동
연번스테이션, 성명(Station)위치광역시도코드광역시도명시군구코드시군구명법정동코드법정동명행정동코드행정동명
252253가수원파출소(건너편)대전광역시 서구 가수원동 137330대전광역시30170서구3017011400가수원동3017059000가수원동
253254대전과학기술대학교 정문대전광역시 서구 복수동 257-530대전광역시30170서구3017010100복수동3017051000복수동
254255용운국제수영장대전광역시 동구 용운동 301-1230대전광역시30110동구3011010900용운동3011056000용운동
255256신성동 수천이들 근린공원대전광역시 유성구 신성동 49430대전광역시30200유성구3020012500신성동3020055000신성동
256257대전테크노파크대전광역시 유성구 용산동 60530대전광역시30200유성구3020014400용산동3020060000관평동
257258천문대입구대전광역시 유성구 신성동 45830대전광역시30200유성구3020012500신성동3020055000신성동
258259대덕대학교대전광역시 유성구 장동 4830대전광역시30200유성구3020012800장동3020055000신성동
259260오정농수산물 도매시장대전광역시 대덕구 오정동 45-130대전광역시30230대덕구3023010100오정동3023051000오정동
260261도로교통공단(건너편 라도무스)대전광역시 유성구 원신흥동 60830대전광역시30200유성구3020011400원신흥동3020061000원신흥동
261262반석 더샵대전광역시 유성구 반석동 70430대전광역시30200유성구3020013900반석동3020054800노은3동