Overview

Dataset statistics

Number of variables16
Number of observations259
Missing cells469
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.0 KiB
Average record size in memory130.5 B

Variable types

Numeric2
Categorical6
Text6
DateTime2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-22228/F/1/datasetView.do

Alerts

공사구분 is highly overall correlated with 종류 and 3 other fieldsHigh correlation
전원투입 is highly overall correlated with 순번 and 6 other fieldsHigh correlation
이력변경 is highly overall correlated with 종류 and 2 other fieldsHigh correlation
가로시설물 is highly overall correlated with 종류 and 1 other fieldsHigh correlation
종류 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
행정구 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
순번 is highly overall correlated with 종류 and 2 other fieldsHigh correlation
좌표(Poi_Y) is highly overall correlated with 전원투입 and 1 other fieldsHigh correlation
종류 is highly imbalanced (53.0%)Imbalance
가로시설물 is highly imbalanced (59.1%)Imbalance
공사구분 is highly imbalanced (63.4%)Imbalance
이력변경 is highly imbalanced (73.4%)Imbalance
공사날짜 has 220 (84.9%) missing valuesMissing
공사내용 has 220 (84.9%) missing valuesMissing
도로명 주소 has 28 (10.8%) missing valuesMissing
순번 has unique valuesUnique
번호 has unique valuesUnique

Reproduction

Analysis started2024-03-23 04:14:42.419660
Analysis finished2024-03-23 04:14:46.044574
Duration3.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct259
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130
Minimum1
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-23T13:14:46.159080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.9
Q165.5
median130
Q3194.5
95-th percentile246.1
Maximum259
Range258
Interquartile range (IQR)129

Descriptive statistics

Standard deviation74.911058
Coefficient of variation (CV)0.57623891
Kurtosis-1.2
Mean130
Median Absolute Deviation (MAD)65
Skewness0
Sum33670
Variance5611.6667
MonotonicityStrictly increasing
2024-03-23T13:14:46.409538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
164 1
 
0.4%
166 1
 
0.4%
167 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%
Other values (249) 249
96.1%
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 (%)
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%
252 1
0.4%
251 1
0.4%
250 1
0.4%

종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
쉘터형 택시 승차대
233 
폴형 택시승차대
26 

Length

Max length10
Median length10
Mean length9.7992278
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row쉘터형 택시 승차대
2nd row쉘터형 택시 승차대
3rd row쉘터형 택시 승차대
4th row쉘터형 택시 승차대
5th row쉘터형 택시 승차대

Common Values

ValueCountFrequency (%)
쉘터형 택시 승차대 233
90.0%
폴형 택시승차대 26
 
10.0%

Length

2024-03-23T13:14:46.682512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:14:46.871827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쉘터형 233
31.0%
택시 233
31.0%
승차대 233
31.0%
폴형 26
 
3.5%
택시승차대 26
 
3.5%

번호
Text

UNIQUE 

Distinct259
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-23T13:14:47.479362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3011583
Min length4

Characters and Unicode

Total characters1114
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)100.0%

Sample

1st rowA-02
2nd rowA-03
3rd rowA-04
4th rowA-08
5th rowA-09
ValueCountFrequency (%)
a-02 1
 
0.4%
s-24 1
 
0.4%
v-70 1
 
0.4%
v-02 1
 
0.4%
v-03 1
 
0.4%
v-04 1
 
0.4%
v-05 1
 
0.4%
v-06 1
 
0.4%
v-07 1
 
0.4%
v-09 1
 
0.4%
Other values (249) 249
96.1%
2024-03-23T13:14:48.360606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 259
23.2%
0 110
 
9.9%
1 107
 
9.6%
2 86
 
7.7%
3 51
 
4.6%
P 39
 
3.5%
4 34
 
3.1%
5 32
 
2.9%
7 31
 
2.8%
e 26
 
2.3%
Other values (29) 339
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 518
46.5%
Dash Punctuation 259
23.2%
Uppercase Letter 259
23.2%
Lowercase Letter 78
 
7.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 39
15.1%
A 24
 
9.3%
Y 22
 
8.5%
X 21
 
8.1%
S 21
 
8.1%
V 16
 
6.2%
U 14
 
5.4%
M 11
 
4.2%
T 11
 
4.2%
D 8
 
3.1%
Other values (15) 72
27.8%
Decimal Number
ValueCountFrequency (%)
0 110
21.2%
1 107
20.7%
2 86
16.6%
3 51
9.8%
4 34
 
6.6%
5 32
 
6.2%
7 31
 
6.0%
6 26
 
5.0%
9 22
 
4.2%
8 19
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
e 26
33.3%
l 26
33.3%
o 26
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 259
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 777
69.7%
Latin 337
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 39
 
11.6%
e 26
 
7.7%
l 26
 
7.7%
o 26
 
7.7%
A 24
 
7.1%
Y 22
 
6.5%
X 21
 
6.2%
S 21
 
6.2%
V 16
 
4.7%
U 14
 
4.2%
Other values (18) 102
30.3%
Common
ValueCountFrequency (%)
- 259
33.3%
0 110
14.2%
1 107
13.8%
2 86
 
11.1%
3 51
 
6.6%
4 34
 
4.4%
5 32
 
4.1%
7 31
 
4.0%
6 26
 
3.3%
9 22
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 259
23.2%
0 110
 
9.9%
1 107
 
9.6%
2 86
 
7.7%
3 51
 
4.6%
P 39
 
3.5%
4 34
 
3.1%
5 32
 
2.9%
7 31
 
2.8%
e 26
 
2.3%
Other values (29) 339
30.4%

가로시설물
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
표준형TS
205 
폴형TS
26 
TS
22 
TS(시범)
 
4
스마트TS
 
1

Length

Max length9
Median length5
Mean length4.6756757
Min length2

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row표준형TS
2nd row표준형TS
3rd row스마트TS
4th row표준형TS
5th row표준형TS

Common Values

ValueCountFrequency (%)
표준형TS 205
79.2%
폴형TS 26
 
10.0%
TS 22
 
8.5%
TS(시범) 4
 
1.5%
스마트TS 1
 
0.4%
TS(Stern) 1
 
0.4%

Length

2024-03-23T13:14:48.588398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:14:48.744075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표준형ts 205
79.2%
폴형ts 26
 
10.0%
ts 22
 
8.5%
ts(시범 4
 
1.5%
스마트ts 1
 
0.4%
ts(stern 1
 
0.4%

설치일
Date

Distinct132
Distinct (%)51.2%
Missing1
Missing (%)0.4%
Memory size2.2 KiB
Minimum2002-01-01 00:00:00
Maximum2020-04-14 00:00:00
2024-03-23T13:14:49.249887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T13:14:49.711197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

공사구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
<NA>
214 
이설
 
14
교체
 
13
신설
 
13
철거
 
2
Other values (2)
 
3

Length

Max length5
Median length4
Mean length3.6795367
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row이설
2nd row<NA>
3rd row교체
4th row<NA>
5th row교체

Common Values

ValueCountFrequency (%)
<NA> 214
82.6%
이설 14
 
5.4%
교체 13
 
5.0%
신설 13
 
5.0%
철거 2
 
0.8%
임시철거 2
 
0.8%
1
 
0.4%

Length

2024-03-23T13:14:49.919679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:14:50.077223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 214
82.9%
이설 14
 
5.4%
교체 13
 
5.0%
신설 13
 
5.0%
철거 2
 
0.8%
임시철거 2
 
0.8%

공사날짜
Date

MISSING 

Distinct29
Distinct (%)74.4%
Missing220
Missing (%)84.9%
Memory size2.2 KiB
Minimum2016-05-20 00:00:00
Maximum2021-06-02 00:00:00
2024-03-23T13:14:50.260757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T13:14:50.475210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

공사내용
Text

MISSING 

Distinct24
Distinct (%)61.5%
Missing220
Missing (%)84.9%
Memory size2.2 KiB
2024-03-23T13:14:50.881632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length18.25641
Min length4

Characters and Unicode

Total characters712
Distinct characters119
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)56.4%

Sample

1st row신분당선 공사로 이설
2nd row표준형TS => 스마트TS 교체 설치
3rd row21년도 택시승차대 5단계 개선정비
4th row21년도 택시승차대 5단계 개선정비
5th row21년도 택시승차대 5단계 개선정비
ValueCountFrequency (%)
택시승차대 15
 
8.8%
개선정비 13
 
7.6%
21년도 13
 
7.6%
5단계 13
 
7.6%
이설 8
 
4.7%
및 7
 
4.1%
관련 5
 
2.9%
종로brt 4
 
2.3%
자전거도로 4
 
2.3%
긴급 4
 
2.3%
Other values (61) 85
49.7%
2024-03-23T13:14:51.492928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132
 
18.5%
설 20
 
2.8%
승 20
 
2.8%
도 19
 
2.7%
차 19
 
2.7%
대 19
 
2.7%
선 18
 
2.5%
로 18
 
2.5%
시 16
 
2.2%
1 16
 
2.2%
Other values (109) 415
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 488
68.5%
Space Separator 132
 
18.5%
Decimal Number 51
 
7.2%
Uppercase Letter 22
 
3.1%
Lowercase Letter 7
 
1.0%
Math Symbol 4
 
0.6%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%
Dash Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 20
 
4.1%
승 20
 
4.1%
도 19
 
3.9%
차 19
 
3.9%
대 19
 
3.9%
선 18
 
3.7%
로 18
 
3.7%
시 16
 
3.3%
이 15
 
3.1%
택 15
 
3.1%
Other values (86) 309
63.3%
Decimal Number
ValueCountFrequency (%)
1 16
31.4%
2 14
27.5%
5 13
25.5%
0 5
 
9.8%
4 2
 
3.9%
3 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
T 8
36.4%
R 5
22.7%
B 4
18.2%
C 2
 
9.1%
S 2
 
9.1%
V 1
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
m 4
57.1%
e 1
 
14.3%
p 1
 
14.3%
y 1
 
14.3%
Math Symbol
ValueCountFrequency (%)
> 2
50.0%
= 2
50.0%
Space Separator
ValueCountFrequency (%)
132
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 488
68.5%
Common 195
 
27.4%
Latin 29
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 20
 
4.1%
승 20
 
4.1%
도 19
 
3.9%
차 19
 
3.9%
대 19
 
3.9%
선 18
 
3.7%
로 18
 
3.7%
시 16
 
3.3%
이 15
 
3.1%
택 15
 
3.1%
Other values (86) 309
63.3%
Common
ValueCountFrequency (%)
132
67.7%
1 16
 
8.2%
2 14
 
7.2%
5 13
 
6.7%
0 5
 
2.6%
( 3
 
1.5%
) 3
 
1.5%
4 2
 
1.0%
> 2
 
1.0%
= 2
 
1.0%
Other values (3) 3
 
1.5%
Latin
ValueCountFrequency (%)
T 8
27.6%
R 5
17.2%
B 4
13.8%
m 4
13.8%
C 2
 
6.9%
S 2
 
6.9%
e 1
 
3.4%
p 1
 
3.4%
y 1
 
3.4%
V 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 488
68.5%
ASCII 224
31.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132
58.9%
1 16
 
7.1%
2 14
 
6.2%
5 13
 
5.8%
T 8
 
3.6%
R 5
 
2.2%
0 5
 
2.2%
B 4
 
1.8%
m 4
 
1.8%
( 3
 
1.3%
Other values (13) 20
 
8.9%
Hangul
ValueCountFrequency (%)
설 20
 
4.1%
승 20
 
4.1%
도 19
 
3.9%
차 19
 
3.9%
대 19
 
3.9%
선 18
 
3.7%
로 18
 
3.7%
시 16
 
3.3%
이 15
 
3.1%
택 15
 
3.1%
Other values (86) 309
63.3%

이력변경
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
<NA>
223 
5월 교체
 
11
4월 이설
 
6
3월 이설
 
2
6월 교체
 
2
Other values (10)
 
15

Length

Max length6
Median length4
Mean length4.1505792
Min length4

Unique

Unique5 ?
Unique (%)1.9%

Sample

1st row3월 이설
2nd row<NA>
3rd row6월 교체
4th row<NA>
5th row5월 교체

Common Values

ValueCountFrequency (%)
<NA> 223
86.1%
5월 교체 11
 
4.2%
4월 이설 6
 
2.3%
3월 이설 2
 
0.8%
6월 교체 2
 
0.8%
5월 이설 2
 
0.8%
6월 이설 2
 
0.8%
3월 설치 2
 
0.8%
4월 설치 2
 
0.8%
5월 설치 2
 
0.8%
Other values (5) 5
 
1.9%

Length

2024-03-23T13:14:51.830535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 223
76.1%
5월 15
 
5.1%
교체 13
 
4.4%
이설 13
 
4.4%
4월 8
 
2.7%
설치 8
 
2.7%
6월 5
 
1.7%
3월 4
 
1.4%
10월 2
 
0.7%
신설 1
 
0.3%

좌표(Poi_X)
Text

Distinct252
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-23T13:14:52.271271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0849421
Min length7

Characters and Unicode

Total characters2353
Distinct characters12
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

Unique246 ?
Unique (%)95.0%

Sample

1st row127.02229
2nd row127.02127
3rd row127.022959
4th row127.03552
5th row127.05136
ValueCountFrequency (%)
126.96917 3
 
1.2%
126.97914 2
 
0.8%
127.04885 2
 
0.8%
126.83706 2
 
0.8%
126.83977 2
 
0.8%
126.91033 2
 
0.8%
126.96918 1
 
0.4%
126.98043 1
 
0.4%
126.95927 1
 
0.4%
126.9641 1
 
0.4%
Other values (242) 242
93.4%
2024-03-23T13:14:53.116250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 391
16.6%
2 371
15.8%
. 258
11.0%
7 246
10.5%
6 238
10.1%
9 215
9.1%
0 195
8.3%
8 137
 
5.8%
4 114
 
4.8%
3 96
 
4.1%
Other values (2) 92
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2094
89.0%
Other Punctuation 259
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 391
18.7%
2 371
17.7%
7 246
11.7%
6 238
11.4%
9 215
10.3%
0 195
9.3%
8 137
 
6.5%
4 114
 
5.4%
3 96
 
4.6%
5 91
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 258
99.6%
/ 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 391
16.6%
2 371
15.8%
. 258
11.0%
7 246
10.5%
6 238
10.1%
9 215
9.1%
0 195
8.3%
8 137
 
5.8%
4 114
 
4.8%
3 96
 
4.1%
Other values (2) 92
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 391
16.6%
2 371
15.8%
. 258
11.0%
7 246
10.5%
6 238
10.1%
9 215
9.1%
0 195
8.3%
8 137
 
5.8%
4 114
 
4.8%
3 96
 
4.1%
Other values (2) 92
 
3.9%

좌표(Poi_Y)
Real number (ℝ)

HIGH CORRELATION 

Distinct250
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.543146
Minimum37.45341
Maximum37.670621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-23T13:14:53.396680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.45341
5-th percentile37.484464
Q137.511715
median37.53796
Q337.5666
95-th percentile37.631627
Maximum37.670621
Range0.217211
Interquartile range (IQR)0.054885

Descriptive statistics

Standard deviation0.042757549
Coefficient of variation (CV)0.0011388909
Kurtosis0.32618492
Mean37.543146
Median Absolute Deviation (MAD)0.02808
Skewness0.6712723
Sum9723.6748
Variance0.001828208
MonotonicityNot monotonic
2024-03-23T13:14:53.615802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.554384 3
 
1.2%
37.5585 2
 
0.8%
37.50988 2
 
0.8%
37.49484 2
 
0.8%
37.56434 2
 
0.8%
37.55583 2
 
0.8%
37.55854 2
 
0.8%
37.51746 2
 
0.8%
37.553739 1
 
0.4%
37.5302 1
 
0.4%
Other values (240) 240
92.7%
ValueCountFrequency (%)
37.45341 1
0.4%
37.45626 1
0.4%
37.46718 1
0.4%
37.47054 1
0.4%
37.4706 1
0.4%
37.47638 1
0.4%
37.47843 1
0.4%
37.47941 1
0.4%
37.480168 1
0.4%
37.481054 1
0.4%
ValueCountFrequency (%)
37.670621 1
0.4%
37.66137 1
0.4%
37.6582 1
0.4%
37.65806 1
0.4%
37.65475 1
0.4%
37.653504 1
0.4%
37.65194 1
0.4%
37.649 1
0.4%
37.64792 1
0.4%
37.644993 1
0.4%

전원투입
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
OK
149 
<NA>
110 

Length

Max length4
Median length2
Mean length2.8494208
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOK
2nd row<NA>
3rd rowOK
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK 149
57.5%
<NA> 110
42.5%

Length

2024-03-23T13:14:53.887893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:14:54.119982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ok 149
57.5%
na 110
42.5%

행정구
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
강남구
25 
중구
23 
송파구
22 
종로구
21 
용산구
19 
Other values (20)
149 

Length

Max length4
Median length3
Mean length3.019305
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 25
 
9.7%
중구 23
 
8.9%
송파구 22
 
8.5%
종로구 21
 
8.1%
용산구 19
 
7.3%
서초구 15
 
5.8%
영등포구 15
 
5.8%
양천구 11
 
4.2%
마포구 11
 
4.2%
노원구 9
 
3.5%
Other values (15) 88
34.0%

Length

2024-03-23T13:14:54.356139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 25
 
9.7%
중구 23
 
8.9%
송파구 22
 
8.5%
종로구 21
 
8.1%
용산구 19
 
7.3%
서초구 15
 
5.8%
영등포구 15
 
5.8%
양천구 11
 
4.2%
마포구 11
 
4.2%
노원구 9
 
3.5%
Other values (15) 88
34.0%

지번 주소
Text

Distinct256
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-23T13:14:54.727450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length8.3281853
Min length5

Characters and Unicode

Total characters2157
Distinct characters143
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

Unique254 ?
Unique (%)98.1%

Sample

1st row논현동 164
2nd row논현동 2-15
3rd row신사동 537-8
4th row논현동 248
5th row삼성동 52-17
ValueCountFrequency (%)
신정동 7
 
1.5%
방이동 6
 
1.3%
서초동 6
 
1.3%
잠실동 6
 
1.3%
구로동 6
 
1.3%
삼성동 5
 
1.0%
논현동 5
 
1.0%
역삼동 5
 
1.0%
목동 4
 
0.8%
반포동 4
 
0.8%
Other values (378) 423
88.7%
2024-03-23T13:14:55.303396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 229
 
10.6%
222
 
10.3%
1 189
 
8.8%
- 148
 
6.9%
3 126
 
5.8%
2 109
 
5.1%
4 89
 
4.1%
6 85
 
3.9%
5 82
 
3.8%
7 74
 
3.4%
Other values (133) 804
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 963
44.6%
Other Letter 824
38.2%
Space Separator 222
 
10.3%
Dash Punctuation 148
 
6.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 229
27.8%
가 45
 
5.5%
로 38
 
4.6%
신 27
 
3.3%
계 12
 
1.5%
서 12
 
1.5%
정 11
 
1.3%
삼 11
 
1.3%
한 10
 
1.2%
포 10
 
1.2%
Other values (121) 419
50.8%
Decimal Number
ValueCountFrequency (%)
1 189
19.6%
3 126
13.1%
2 109
11.3%
4 89
9.2%
6 85
8.8%
5 82
8.5%
7 74
 
7.7%
9 72
 
7.5%
0 72
 
7.5%
8 65
 
6.7%
Space Separator
ValueCountFrequency (%)
222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1333
61.8%
Hangul 824
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 229
27.8%
가 45
 
5.5%
로 38
 
4.6%
신 27
 
3.3%
계 12
 
1.5%
서 12
 
1.5%
정 11
 
1.3%
삼 11
 
1.3%
한 10
 
1.2%
포 10
 
1.2%
Other values (121) 419
50.8%
Common
ValueCountFrequency (%)
222
16.7%
1 189
14.2%
- 148
11.1%
3 126
9.5%
2 109
8.2%
4 89
6.7%
6 85
 
6.4%
5 82
 
6.2%
7 74
 
5.6%
9 72
 
5.4%
Other values (2) 137
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1333
61.8%
Hangul 824
38.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 229
27.8%
가 45
 
5.5%
로 38
 
4.6%
신 27
 
3.3%
계 12
 
1.5%
서 12
 
1.5%
정 11
 
1.3%
삼 11
 
1.3%
한 10
 
1.2%
포 10
 
1.2%
Other values (121) 419
50.8%
ASCII
ValueCountFrequency (%)
222
16.7%
1 189
14.2%
- 148
11.1%
3 126
9.5%
2 109
8.2%
4 89
6.7%
6 85
 
6.4%
5 82
 
6.2%
7 74
 
5.6%
9 72
 
5.4%
Other values (2) 137
10.3%

도로명 주소
Text

MISSING 

Distinct226
Distinct (%)97.8%
Missing28
Missing (%)10.8%
Memory size2.2 KiB
2024-03-23T13:14:55.794922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.4069264
Min length3

Characters and Unicode

Total characters1711
Distinct characters151
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

Unique221 ?
Unique (%)95.7%

Sample

1st row강남대로 518
2nd row도산대로 114
3rd row도산대로 133
4th row언주로 640
5th row삼성로 646
ValueCountFrequency (%)
올림픽로 11
 
2.5%
남부순환로 9
 
2.0%
종로 6
 
1.3%
을지로 6
 
1.3%
마포대로 5
 
1.1%
목동서로 4
 
0.9%
테헤란로 4
 
0.9%
한강대로 4
 
0.9%
언주로 4
 
0.9%
동일로 4
 
0.9%
Other values (300) 388
87.2%
2024-03-23T13:14:56.559973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 229
 
13.4%
215
 
12.6%
1 126
 
7.4%
2 109
 
6.4%
3 82
 
4.8%
4 68
 
4.0%
0 57
 
3.3%
5 55
 
3.2%
대 51
 
3.0%
6 50
 
2.9%
Other values (141) 669
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 828
48.4%
Decimal Number 659
38.5%
Space Separator 215
 
12.6%
Dash Punctuation 9
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 229
27.7%
대 51
 
6.2%
동 28
 
3.4%
남 17
 
2.1%
길 16
 
1.9%
강 16
 
1.9%
서 14
 
1.7%
림 14
 
1.7%
산 12
 
1.4%
올 12
 
1.4%
Other values (129) 419
50.6%
Decimal Number
ValueCountFrequency (%)
1 126
19.1%
2 109
16.5%
3 82
12.4%
4 68
10.3%
0 57
8.6%
5 55
8.3%
6 50
 
7.6%
7 40
 
6.1%
8 36
 
5.5%
9 36
 
5.5%
Space Separator
ValueCountFrequency (%)
215
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 883
51.6%
Hangul 828
48.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 229
27.7%
대 51
 
6.2%
동 28
 
3.4%
남 17
 
2.1%
길 16
 
1.9%
강 16
 
1.9%
서 14
 
1.7%
림 14
 
1.7%
산 12
 
1.4%
올 12
 
1.4%
Other values (129) 419
50.6%
Common
ValueCountFrequency (%)
215
24.3%
1 126
14.3%
2 109
12.3%
3 82
 
9.3%
4 68
 
7.7%
0 57
 
6.5%
5 55
 
6.2%
6 50
 
5.7%
7 40
 
4.5%
8 36
 
4.1%
Other values (2) 45
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 883
51.6%
Hangul 828
48.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 229
27.7%
대 51
 
6.2%
동 28
 
3.4%
남 17
 
2.1%
길 16
 
1.9%
강 16
 
1.9%
서 14
 
1.7%
림 14
 
1.7%
산 12
 
1.4%
올 12
 
1.4%
Other values (129) 419
50.6%
ASCII
ValueCountFrequency (%)
215
24.3%
1 126
14.3%
2 109
12.3%
3 82
 
9.3%
4 68
 
7.7%
0 57
 
6.5%
5 55
 
6.2%
6 50
 
5.7%
7 40
 
4.5%
8 36
 
4.1%
Other values (2) 45
 
5.1%

위치명
Text

Distinct257
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-23T13:14:56.892934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length21
Mean length13.930502
Min length5

Characters and Unicode

Total characters3608
Distinct characters386
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)98.8%

Sample

1st row어반테일러 앞
2nd row수일빌딩 앞(수협은행 앞)
3rd row가로수길 영동빌딩 (올리브영 가로수길 중앙점 앞)
4th row임페리얼팰리스호텔 앞
5th row한국건설기술인협회별관 좌측(상아아파트 건너)
ValueCountFrequency (%)
앞 139
 
19.5%
건너 26
 
3.7%
옆 18
 
2.5%
출구 7
 
1.0%
5번출구 6
 
0.8%
1번출구 6
 
0.8%
우측 5
 
0.7%
좌측 5
 
0.7%
방향 5
 
0.7%
101동 5
 
0.7%
Other values (446) 489
68.8%
2024-03-23T13:14:57.394999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
453
 
12.6%
앞 229
 
6.3%
) 105
 
2.9%
( 105
 
2.9%
동 76
 
2.1%
구 65
 
1.8%
역 59
 
1.6%
빌 51
 
1.4%
딩 50
 
1.4%
출 48
 
1.3%
Other values (376) 2367
65.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2716
75.3%
Space Separator 453
 
12.6%
Decimal Number 147
 
4.1%
Close Punctuation 105
 
2.9%
Open Punctuation 105
 
2.9%
Uppercase Letter 64
 
1.8%
Lowercase Letter 9
 
0.2%
Other Punctuation 6
 
0.2%
Math Symbol 1
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 229
 
8.4%
동 76
 
2.8%
구 65
 
2.4%
역 59
 
2.2%
빌 51
 
1.9%
딩 50
 
1.8%
출 48
 
1.8%
점 47
 
1.7%
번 46
 
1.7%
아 44
 
1.6%
Other values (331) 2001
73.7%
Uppercase Letter
ValueCountFrequency (%)
K 13
20.3%
S 9
14.1%
C 6
9.4%
E 6
9.4%
B 5
 
7.8%
G 4
 
6.2%
I 4
 
6.2%
T 3
 
4.7%
L 3
 
4.7%
O 2
 
3.1%
Other values (9) 9
14.1%
Decimal Number
ValueCountFrequency (%)
1 44
29.9%
2 22
15.0%
0 18
12.2%
3 16
 
10.9%
4 13
 
8.8%
5 11
 
7.5%
7 6
 
4.1%
6 6
 
4.1%
9 6
 
4.1%
8 5
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
e 2
22.2%
a 1
11.1%
s 1
11.1%
n 1
11.1%
t 1
11.1%
v 1
11.1%
i 1
11.1%
w 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 5
83.3%
. 1
 
16.7%
Space Separator
ValueCountFrequency (%)
453
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2717
75.3%
Common 818
 
22.7%
Latin 73
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 229
 
8.4%
동 76
 
2.8%
구 65
 
2.4%
역 59
 
2.2%
빌 51
 
1.9%
딩 50
 
1.8%
출 48
 
1.8%
점 47
 
1.7%
번 46
 
1.7%
아 44
 
1.6%
Other values (332) 2002
73.7%
Latin
ValueCountFrequency (%)
K 13
17.8%
S 9
12.3%
C 6
 
8.2%
E 6
 
8.2%
B 5
 
6.8%
G 4
 
5.5%
I 4
 
5.5%
T 3
 
4.1%
L 3
 
4.1%
e 2
 
2.7%
Other values (17) 18
24.7%
Common
ValueCountFrequency (%)
453
55.4%
) 105
 
12.8%
( 105
 
12.8%
1 44
 
5.4%
2 22
 
2.7%
0 18
 
2.2%
3 16
 
2.0%
4 13
 
1.6%
5 11
 
1.3%
7 6
 
0.7%
Other values (7) 25
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2716
75.3%
ASCII 891
 
24.7%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
453
50.8%
) 105
 
11.8%
( 105
 
11.8%
1 44
 
4.9%
2 22
 
2.5%
0 18
 
2.0%
3 16
 
1.8%
K 13
 
1.5%
4 13
 
1.5%
5 11
 
1.2%
Other values (34) 91
 
10.2%
Hangul
ValueCountFrequency (%)
앞 229
 
8.4%
동 76
 
2.8%
구 65
 
2.4%
역 59
 
2.2%
빌 51
 
1.9%
딩 50
 
1.8%
출 48
 
1.8%
점 47
 
1.7%
번 46
 
1.7%
아 44
 
1.6%
Other values (331) 2001
73.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

Interactions

2024-03-23T13:14:44.780813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T13:14:44.473397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T13:14:44.923738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T13:14:44.634011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T13:14:57.530360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번종류가로시설물공사구분공사날짜공사내용이력변경좌표(Poi_Y)행정구
순번1.0001.0000.6980.2690.9430.7880.5050.7930.973
종류1.0001.0001.0000.9180.8001.000NaN0.2510.376
가로시설물0.6981.0001.0000.8090.9571.0000.5610.1110.366
공사구분0.2690.9180.8091.0000.9560.9900.9710.4710.861
공사날짜0.9430.8000.9570.9561.0000.9770.9950.9851.000
공사내용0.7881.0001.0000.9900.9771.0000.9760.9530.923
이력변경0.505NaN0.5610.9710.9950.9761.0000.5310.577
좌표(Poi_Y)0.7930.2510.1110.4710.9850.9530.5311.0000.944
행정구0.9730.3760.3660.8611.0000.9230.5770.9441.000
2024-03-23T13:14:58.110900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사구분전원투입이력변경가로시설물종류행정구
공사구분1.0001.0000.7970.4710.7090.549
전원투입1.0001.0001.0001.0001.0001.000
이력변경0.7971.0001.0000.3431.0000.218
가로시설물0.4711.0000.3431.0000.9920.165
종류0.7091.0001.0000.9921.0000.310
행정구0.5491.0000.2180.1650.3101.000
2024-03-23T13:14:58.250884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번좌표(Poi_Y)종류가로시설물공사구분이력변경전원투입행정구
순번1.0000.3070.9840.4590.1140.1921.0000.792
좌표(Poi_Y)0.3071.0000.1890.0570.2470.2031.0000.689
종류0.9840.1891.0000.9920.7091.0001.0000.310
가로시설물0.4590.0570.9921.0000.4710.3431.0000.165
공사구분0.1140.2470.7090.4711.0000.7971.0000.549
이력변경0.1920.2031.0000.3430.7971.0001.0000.218
전원투입1.0001.0001.0001.0001.0001.0001.0001.000
행정구0.7920.6890.3100.1650.5490.2181.0001.000

Missing values

2024-03-23T13:14:45.119377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T13:14:45.639100image/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.
2024-03-23T13:14:45.881052image/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

순번종류번호가로시설물설치일공사구분공사날짜공사내용이력변경좌표(Poi_X)좌표(Poi_Y)전원투입행정구지번 주소도로명 주소위치명
01쉘터형 택시 승차대A-02표준형TS2010-10-02이설2017-03-25신분당선 공사로 이설3월 이설127.0222937.50828OK강남구논현동 164강남대로 518어반테일러 앞
12쉘터형 택시 승차대A-03표준형TS2010-10-14<NA><NA><NA><NA>127.0212737.51668<NA>강남구논현동 2-15도산대로 114수일빌딩 앞(수협은행 앞)
23쉘터형 택시 승차대A-04스마트TS2010-10-29교체2019-06-05표준형TS => 스마트TS 교체 설치6월 교체127.02295937.518136OK강남구신사동 537-8도산대로 133가로수길 영동빌딩 (올리브영 가로수길 중앙점 앞)
34쉘터형 택시 승차대A-08표준형TS2010-10-07<NA><NA><NA><NA>127.0355237.5137OK강남구논현동 248언주로 640임페리얼팰리스호텔 앞
45쉘터형 택시 승차대A-09표준형TS2002-01-01교체2021-05-2821년도 택시승차대 5단계 개선정비5월 교체127.0513637.51688OK강남구삼성동 52-17삼성로 646한국건설기술인협회별관 좌측(상아아파트 건너)
56쉘터형 택시 승차대A-10표준형TS2010-09-29<NA><NA><NA><NA>127.0509337.51729<NA>강남구삼성동 21삼성로 649상아아파트 4동 앞
67쉘터형 택시 승차대A-11표준형TS2010-10-29<NA><NA><NA>127.0331137.525OK강남구신사동 623-5언주로 835삼원가든 앞
78쉘터형 택시 승차대A-12표준형TS2010-10-07<NA><NA><NA><NA>127.0354737.51807OK강남구논현동 105-7언주로 726논현삼계탕 앞(서울세관 건너)
89쉘터형 택시 승차대A-13TS2002-01-01<NA><NA><NA><NA>127.0374637.50988<NA>강남구논현동 275언주로 116길논현동부센트레빌 103동 앞
910쉘터형 택시 승차대A-14표준형TS2010-09-30<NA><NA><NA><NA>127.0488537.51868OK강남구청담동 46-16학동로 435언고신관빌딩(삼성동롯데아파트 101동 건너)
순번종류번호가로시설물설치일공사구분공사날짜공사내용이력변경좌표(Poi_X)좌표(Poi_Y)전원투입행정구지번 주소도로명 주소위치명
249250폴형 택시승차대Pole-17폴형TS2016-09-01<NA><NA><NA><NA>127.0674637.623201<NA>노원구월계동 17<NA>월계미성아파트 15동 옆
250251폴형 택시승차대Pole-18폴형TS2016-10-12임시철거2021-01-18신안산선<NA>126.9035937.484531<NA>관악구신림동 1677-5<NA>관악,동작 견인차량보관소 입구(공중화장실 앞)
251252폴형 택시승차대Pole-19폴형TS2016-10-12임시철거2021-01-18복선전철 공사<NA>126.48441237.484412<NA>관악구신림동 1643<NA>24시공단사우나 옆
252253폴형 택시승차대Pole-20폴형TS2016-10-13<NA><NA><NA><NA>126.88892737.50966<NA>구로구신도림동 337<NA>홈플러스 앞
253254폴형 택시승차대Pole-21폴형TS2016-10-13철거2017-04-10신분당선 공사<NA>127.02468937.503527<NA>서초구서초동 1303-31<NA>신논현역 6번출구 교보타워 앞
254255폴형 택시승차대Pole-22폴형TS2017-04-21신설<NA><NA><NA>126.88601937.525558<NA>영등포구양평동2가 33-1<NA>양평역 1번출구 건너
255256폴형 택시승차대Pole-23폴형TS2018-06-03신설<NA><NA><NA>126.98651137.56088<NA>중구퇴계로134<NA>명동역 2번출구 앞(CU편의점 앞)
256257폴형 택시승차대Pole-24폴형TS2019-04-23신설<NA><NA><NA>127.09257237.616654<NA>중랑구신내로211<NA>봉화산역3번출구 앞
257258폴형 택시승차대Pole-25폴형TS2019-08-29신설<NA><NA><NA>127.07977837.670621<NA>노원구상계로309<NA>당고개역 1번출구
258259폴형 택시승차대Pole-26폴형TS2020-04-14신설<NA><NA><NA>127.09122237.598008<NA>중랑구망우로353<NA>프리미어엠코 앞