Overview

Dataset statistics

Number of variables15
Number of observations266
Missing cells45
Missing cells (%)1.1%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory33.1 KiB
Average record size in memory127.5 B

Variable types

Numeric6
Categorical6
Text2
DateTime1

Dataset

Description경기도 의왕시 보행안전지수 지도 시각화에 사용된 의왕시 정류장 현황에 대한 정보를 담고 있는 csv형태의 데이터를 제공합니다.
Author경기도 의왕시
URLhttps://www.data.go.kr/data/15108969/fileData.do

Alerts

지형지물부호 has constant value ""Constant
Dataset has 1 (0.4%) duplicate rowsDuplicates
정류장유형 is highly overall correlated with 관리기관코드 and 3 other fieldsHigh correlation
대장초기화여부 is highly overall correlated with 관리기관코드 and 3 other fieldsHigh correlation
공사번호 is highly overall correlated with 공간지리식별번호 and 9 other fieldsHigh correlation
관리기관코드 is highly overall correlated with 정류장 종류 and 3 other fieldsHigh correlation
정류장 종류 is highly overall correlated with 관리기관코드 and 3 other fieldsHigh correlation
공간지리식별번호 is highly overall correlated with 공사번호High correlation
관리번호 is highly overall correlated with 도로구간번호 and 1 other fieldsHigh correlation
행정읍면동코드 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 행정읍면동코드 and 2 other fieldsHigh correlation
관리기관코드 is highly imbalanced (80.5%)Imbalance
정류장 종류 is highly imbalanced (66.6%)Imbalance
정류장유형 is highly imbalanced (55.4%)Imbalance
공사번호 is highly imbalanced (84.0%)Imbalance
대장초기화여부 is highly imbalanced (82.4%)Imbalance
정류장명 has 21 (7.9%) missing valuesMissing
설치일자 has 24 (9.0%) missing valuesMissing
도로구간번호 has 62 (23.3%) zerosZeros

Reproduction

Analysis started2023-12-12 06:06:23.191220
Analysis finished2023-12-12 06:06:28.672769
Duration5.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간지리식별번호
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.27068
Minimum1
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:06:28.747678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.25
Q177.25
median160
Q3236.75
95-th percentile298.75
Maximum314
Range313
Interquartile range (IQR)159.5

Descriptive statistics

Standard deviation91.337205
Coefficient of variation (CV)0.57709493
Kurtosis-1.1928804
Mean158.27068
Median Absolute Deviation (MAD)80.5
Skewness-0.038742628
Sum42100
Variance8342.4849
MonotonicityNot monotonic
2023-12-12T15:06:28.906668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 2
 
0.8%
134 1
 
0.4%
197 1
 
0.4%
182 1
 
0.4%
183 1
 
0.4%
185 1
 
0.4%
186 1
 
0.4%
187 1
 
0.4%
188 1
 
0.4%
189 1
 
0.4%
Other values (255) 255
95.9%
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 (%)
314 1
0.4%
313 1
0.4%
312 1
0.4%
311 1
0.4%
310 1
0.4%
309 1
0.4%
308 1
0.4%
305 1
0.4%
304 1
0.4%
303 1
0.4%

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
AE260
266 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAE260
2nd rowAE260
3rd rowAE260
4th rowAE260
5th rowAE260

Common Values

ValueCountFrequency (%)
AE260 266
100.0%

Length

2023-12-12T15:06:29.046558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:29.135320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ae260 266
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct264
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5858071 × 108
Minimum4
Maximum2.00901 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:06:29.243909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile37.5
Q1120.25
median100081.5
Q3100234.75
95-th percentile2.0073 × 109
Maximum2.00901 × 109
Range2.00901 × 109
Interquartile range (IQR)100114.5

Descriptive statistics

Standard deviation5.4229211 × 108
Coefficient of variation (CV)3.4196601
Kurtosis7.9229986
Mean1.5858071 × 108
Median Absolute Deviation (MAD)99924
Skewness3.1406177
Sum4.2182468 × 1010
Variance2.9408073 × 1017
MonotonicityNot monotonic
2023-12-12T15:06:29.400081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100079 2
 
0.8%
100001 2
 
0.8%
911005 1
 
0.4%
100172 1
 
0.4%
138 1
 
0.4%
135 1
 
0.4%
100063 1
 
0.4%
103 1
 
0.4%
94 1
 
0.4%
20008 1
 
0.4%
Other values (254) 254
95.5%
ValueCountFrequency (%)
4 1
0.4%
5 1
0.4%
6 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
11 1
0.4%
18 1
0.4%
23 1
0.4%
24 1
0.4%
ValueCountFrequency (%)
2009010001 1
0.4%
2007300019 1
0.4%
2007300018 1
0.4%
2007300017 1
0.4%
2007300016 1
0.4%
2007300015 1
0.4%
2007300014 1
0.4%
2007300013 1
0.4%
2007300012 1
0.4%
2007300011 1
0.4%

행정읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1430106 × 109
Minimum4.1430101 × 109
Maximum4.1430111 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:06:29.541633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1430101 × 109
5-th percentile4.1430101 × 109
Q14.1430104 × 109
median4.1430106 × 109
Q34.1430108 × 109
95-th percentile4.143011 × 109
Maximum4.1430111 × 109
Range1000
Interquartile range (IQR)400

Descriptive statistics

Standard deviation256.20294
Coefficient of variation (CV)6.18398 × 10-8
Kurtosis-0.75941998
Mean4.1430106 × 109
Median Absolute Deviation (MAD)200
Skewness-0.18622609
Sum1.1020408 × 1012
Variance65639.949
MonotonicityNot monotonic
2023-12-12T15:06:29.671815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4143010700 48
18.0%
4143010500 39
14.7%
4143010300 33
12.4%
4143010800 31
11.7%
4143010600 30
11.3%
4143010900 24
9.0%
4143010100 17
 
6.4%
4143011000 15
 
5.6%
4143010400 13
 
4.9%
4143010200 11
 
4.1%
ValueCountFrequency (%)
4143010100 17
 
6.4%
4143010200 11
 
4.1%
4143010300 33
12.4%
4143010400 13
 
4.9%
4143010500 39
14.7%
4143010600 30
11.3%
4143010700 48
18.0%
4143010800 31
11.7%
4143010900 24
9.0%
4143011000 15
 
5.6%
ValueCountFrequency (%)
4143011100 5
 
1.9%
4143011000 15
 
5.6%
4143010900 24
9.0%
4143010800 31
11.7%
4143010700 48
18.0%
4143010600 30
11.3%
4143010500 39
14.7%
4143010400 13
 
4.9%
4143010300 33
12.4%
4143010200 11
 
4.1%

도엽번호
Text

Distinct141
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T15:06:29.951822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2660
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)19.9%

Sample

1st row376122005B
2nd row376121519D
3rd row376121520D
4th row376122005A
5th row376121596C
ValueCountFrequency (%)
376121520d 5
 
1.9%
376122071b 4
 
1.5%
376121960d 4
 
1.5%
376121536b 4
 
1.5%
376121584a 4
 
1.5%
376122053d 4
 
1.5%
376121596d 4
 
1.5%
376121527a 4
 
1.5%
377091111c 4
 
1.5%
376122005b 4
 
1.5%
Other values (131) 225
84.6%
2023-12-12T15:06:30.381180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 516
19.4%
2 368
13.8%
7 360
13.5%
3 319
12.0%
6 289
10.9%
0 188
 
7.1%
5 183
 
6.9%
9 87
 
3.3%
D 70
 
2.6%
B 70
 
2.6%
Other values (4) 210
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2394
90.0%
Uppercase Letter 266
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 516
21.6%
2 368
15.4%
7 360
15.0%
3 319
13.3%
6 289
12.1%
0 188
 
7.9%
5 183
 
7.6%
9 87
 
3.6%
4 43
 
1.8%
8 41
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
D 70
26.3%
B 70
26.3%
A 67
25.2%
C 59
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2394
90.0%
Latin 266
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 516
21.6%
2 368
15.4%
7 360
15.0%
3 319
13.3%
6 289
12.1%
0 188
 
7.9%
5 183
 
7.6%
9 87
 
3.6%
4 43
 
1.8%
8 41
 
1.7%
Latin
ValueCountFrequency (%)
D 70
26.3%
B 70
26.3%
A 67
25.2%
C 59
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 516
19.4%
2 368
13.8%
7 360
13.5%
3 319
12.0%
6 289
10.9%
0 188
 
7.1%
5 183
 
6.9%
9 87
 
3.3%
D 70
 
2.6%
B 70
 
2.6%
Other values (4) 210
7.9%

관리기관코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
MNG001
258 
MNG000
 
8

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMNG001
2nd rowMNG001
3rd rowMNG001
4th rowMNG001
5th rowMNG001

Common Values

ValueCountFrequency (%)
MNG001 258
97.0%
MNG000 8
 
3.0%

Length

2023-12-12T15:06:30.537490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:30.665920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mng001 258
97.0%
mng000 8
 
3.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5099875 × 108
Minimum0
Maximum2.0073002 × 109
Zeros62
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:06:30.816289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median744
Q3100018
95-th percentile2.0073 × 109
Maximum2.0073002 × 109
Range2.0073002 × 109
Interquartile range (IQR)100014

Descriptive statistics

Standard deviation5.3028877 × 108
Coefficient of variation (CV)3.5118753
Kurtosis8.5638998
Mean1.5099875 × 108
Median Absolute Deviation (MAD)744
Skewness3.2403028
Sum4.0165666 × 1010
Variance2.8120618 × 1017
MonotonicityNot monotonic
2023-12-12T15:06:30.982009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
 
23.3%
2002 6
 
2.3%
30012 6
 
2.3%
744 6
 
2.3%
81 6
 
2.3%
911041 5
 
1.9%
742 4
 
1.5%
568 4
 
1.5%
2 4
 
1.5%
1014 4
 
1.5%
Other values (91) 159
59.8%
ValueCountFrequency (%)
0 62
23.3%
2 4
 
1.5%
4 2
 
0.8%
41 2
 
0.8%
59 3
 
1.1%
80 3
 
1.1%
81 6
 
2.3%
230 1
 
0.4%
360 2
 
0.8%
362 2
 
0.8%
ValueCountFrequency (%)
2007300191 2
0.8%
2007300187 2
0.8%
2007300177 2
0.8%
2007300114 2
0.8%
2007300088 2
0.8%
2007300029 2
0.8%
2007300018 2
0.8%
2007300009 2
0.8%
2007300008 2
0.8%
2007240003 1
0.4%

정류장 종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
AEE003
233 
AEE004
25 
AEE000
 
5
AEE010
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAEE003
2nd rowAEE003
3rd rowAEE003
4th rowAEE003
5th rowAEE003

Common Values

ValueCountFrequency (%)
AEE003 233
87.6%
AEE004 25
 
9.4%
AEE000 5
 
1.9%
AEE010 3
 
1.1%

Length

2023-12-12T15:06:31.147546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:31.244379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
aee003 233
87.6%
aee004 25
 
9.4%
aee000 5
 
1.9%
aee010 3
 
1.1%

정류장명
Text

MISSING 

Distinct156
Distinct (%)63.7%
Missing21
Missing (%)7.9%
Memory size2.2 KiB
2023-12-12T15:06:31.474200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.0326531
Min length2

Characters and Unicode

Total characters1478
Distinct characters220
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

Unique88 ?
Unique (%)35.9%

Sample

1st row의왕고천
2nd row청계마을(덕장교회)
3rd row청계마을 2단지앞
4th row무명
5th row선병원.KT아파트
ValueCountFrequency (%)
명칭없음 18
 
6.9%
청계마을 5
 
1.9%
의왕역 4
 
1.5%
청계체육시설 3
 
1.2%
의왕경찰서 3
 
1.2%
숲속마을4단지 3
 
1.2%
앞 3
 
1.2%
5단지 2
 
0.8%
부고차량사무소 2
 
0.8%
의왕역(택시 2
 
0.8%
Other values (152) 214
82.6%
2023-12-12T15:06:31.873295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 46
 
3.1%
파 43
 
2.9%
지 42
 
2.8%
트 42
 
2.8%
교 40
 
2.7%
단 32
 
2.2%
을 32
 
2.2%
마 32
 
2.2%
) 26
 
1.8%
( 26
 
1.8%
Other values (210) 1117
75.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1349
91.3%
Decimal Number 39
 
2.6%
Close Punctuation 26
 
1.8%
Open Punctuation 26
 
1.8%
Uppercase Letter 15
 
1.0%
Space Separator 14
 
0.9%
Other Punctuation 8
 
0.5%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 46
 
3.4%
파 43
 
3.2%
지 42
 
3.1%
트 42
 
3.1%
교 40
 
3.0%
단 32
 
2.4%
을 32
 
2.4%
마 32
 
2.4%
학 24
 
1.8%
청 23
 
1.7%
Other values (190) 993
73.6%
Decimal Number
ValueCountFrequency (%)
2 9
23.1%
4 9
23.1%
1 8
20.5%
6 5
12.8%
3 4
10.3%
5 3
 
7.7%
0 1
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
A 3
20.0%
G 2
13.3%
T 2
13.3%
L 2
13.3%
K 2
13.3%
C 2
13.3%
I 2
13.3%
Other Punctuation
ValueCountFrequency (%)
, 4
50.0%
. 4
50.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1349
91.3%
Common 114
 
7.7%
Latin 15
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 46
 
3.4%
파 43
 
3.2%
지 42
 
3.1%
트 42
 
3.1%
교 40
 
3.0%
단 32
 
2.4%
을 32
 
2.4%
마 32
 
2.4%
학 24
 
1.8%
청 23
 
1.7%
Other values (190) 993
73.6%
Common
ValueCountFrequency (%)
) 26
22.8%
( 26
22.8%
14
12.3%
2 9
 
7.9%
4 9
 
7.9%
1 8
 
7.0%
6 5
 
4.4%
, 4
 
3.5%
. 4
 
3.5%
3 4
 
3.5%
Other values (3) 5
 
4.4%
Latin
ValueCountFrequency (%)
A 3
20.0%
G 2
13.3%
T 2
13.3%
L 2
13.3%
K 2
13.3%
C 2
13.3%
I 2
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1349
91.3%
ASCII 129
 
8.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 46
 
3.4%
파 43
 
3.2%
지 42
 
3.1%
트 42
 
3.1%
교 40
 
3.0%
단 32
 
2.4%
을 32
 
2.4%
마 32
 
2.4%
학 24
 
1.8%
청 23
 
1.7%
Other values (190) 993
73.6%
ASCII
ValueCountFrequency (%)
) 26
20.2%
( 26
20.2%
14
10.9%
2 9
 
7.0%
4 9
 
7.0%
1 8
 
6.2%
6 5
 
3.9%
, 4
 
3.1%
. 4
 
3.1%
3 4
 
3.1%
Other values (10) 20
15.5%

정류장유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
STT002
222 
STT001
41 
STT000
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTT002
2nd rowSTT002
3rd rowSTT002
4th rowSTT001
5th rowSTT002

Common Values

ValueCountFrequency (%)
STT002 222
83.5%
STT001 41
 
15.4%
STT000 3
 
1.1%

Length

2023-12-12T15:06:32.012125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:32.117664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
stt002 222
83.5%
stt001 41
 
15.4%
stt000 3
 
1.1%

공사번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
<NA>
251 
RD20190002
 
12
RD20170008
 
1
RD20170001
 
1
RD20160010
 
1

Length

Max length10
Median length4
Mean length4.3383459
Min length4

Unique

Unique3 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 251
94.4%
RD20190002 12
 
4.5%
RD20170008 1
 
0.4%
RD20170001 1
 
0.4%
RD20160010 1
 
0.4%

Length

2023-12-12T15:06:32.237592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:32.339079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 251
94.4%
rd20190002 12
 
4.5%
rd20170008 1
 
0.4%
rd20170001 1
 
0.4%
rd20160010 1
 
0.4%

대장초기화여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
1
259 
0
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 259
97.4%
0 7
 
2.6%

Length

2023-12-12T15:06:32.450854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:32.540493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 259
97.4%
0 7
 
2.6%

설치일자
Date

MISSING 

Distinct51
Distinct (%)21.1%
Missing24
Missing (%)9.0%
Memory size2.2 KiB
Minimum1990-01-01 00:00:00
Maximum2020-01-01 00:00:00
2023-12-12T15:06:32.651436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:33.157293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198094.18
Minimum194599.49
Maximum202268.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:06:33.309510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum194599.49
5-th percentile195587.04
Q1196843.94
median198048.14
Q3199297.98
95-th percentile201000.97
Maximum202268.8
Range7669.3082
Interquartile range (IQR)2454.0372

Descriptive statistics

Standard deviation1672.0676
Coefficient of variation (CV)0.0084407713
Kurtosis-0.51083578
Mean198094.18
Median Absolute Deviation (MAD)1222.5072
Skewness0.24398466
Sum52693051
Variance2795810.2
MonotonicityNot monotonic
2023-12-12T15:06:33.467986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199821.8381 2
 
0.8%
197681.4978 1
 
0.4%
196801.4421 1
 
0.4%
198199.1711 1
 
0.4%
200198.6 1
 
0.4%
196278.368 1
 
0.4%
198113.0525 1
 
0.4%
198328.9135 1
 
0.4%
197067.447 1
 
0.4%
200214.0818 1
 
0.4%
Other values (255) 255
95.9%
ValueCountFrequency (%)
194599.49 1
0.4%
194609.064 1
0.4%
194611.421 1
0.4%
194665.2197 1
0.4%
194673.856 1
0.4%
195186.961 1
0.4%
195215.487 1
0.4%
195437.223 1
0.4%
195452.3974 1
0.4%
195455.636 1
0.4%
ValueCountFrequency (%)
202268.7982 1
0.4%
202055.9085 1
0.4%
201997.946 1
0.4%
201990.451 1
0.4%
201603.75 1
0.4%
201588.42 1
0.4%
201561.6304 1
0.4%
201360.279 1
0.4%
201353.5193 1
0.4%
201265.75 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529098.16
Minimum522577.5
Maximum534066.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:06:33.630903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum522577.5
5-th percentile523767.85
Q1526669.21
median529769.08
Q3532090.43
95-th percentile533063.18
Maximum534066.41
Range11488.902
Interquartile range (IQR)5421.2275

Descriptive statistics

Standard deviation3189.743
Coefficient of variation (CV)0.0060286413
Kurtosis-1.1430368
Mean529098.16
Median Absolute Deviation (MAD)2420.7411
Skewness-0.39730549
Sum1.4074011 × 108
Variance10174461
MonotonicityNot monotonic
2023-12-12T15:06:33.770148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
531908.7342 2
 
0.8%
527624.9925 1
 
0.4%
524650.3283 1
 
0.4%
527439.2331 1
 
0.4%
530831.563 1
 
0.4%
523042.8705 1
 
0.4%
528101.1713 1
 
0.4%
527847.2707 1
 
0.4%
526498.2985 1
 
0.4%
530838.5417 1
 
0.4%
Other values (255) 255
95.9%
ValueCountFrequency (%)
522577.5029 1
0.4%
522812.104 1
0.4%
522971.0255 1
0.4%
522974.1182 1
0.4%
523037.5472 1
0.4%
523042.8705 1
0.4%
523246.195 1
0.4%
523426.8219 1
0.4%
523446.1385 1
0.4%
523446.6888 1
0.4%
ValueCountFrequency (%)
534066.405 1
0.4%
534023.5609 1
0.4%
533829.769 1
0.4%
533752.858 1
0.4%
533687.068 1
0.4%
533544.085 1
0.4%
533543.665 1
0.4%
533509.16 1
0.4%
533445.038 1
0.4%
533293.1473 1
0.4%

Interactions

2023-12-12T15:06:27.611720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.059731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.729984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.431162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.032315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.965733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.713947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.158250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.881425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.520118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.144915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.075897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.804766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.281799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.007763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.644145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.242542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.208529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.905663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.390375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.104031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.732092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.647709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.310323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:28.024203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.508463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.220542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.847555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.765607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.434681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:28.127066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:24.604817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.329825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:25.942296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:26.869368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:27.530414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:06:33.890677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간지리식별번호관리번호행정읍면동코드관리기관코드도로구간번호정류장 종류정류장유형공사번호대장초기화여부설치일자위도경도
공간지리식별번호1.0000.9760.8300.0840.9670.3380.1441.0000.1470.8230.7590.800
관리번호0.9761.0000.8460.0000.9970.1440.021NaN0.0000.9810.7780.879
행정읍면동코드0.8300.8461.0000.1320.8670.4820.4331.0000.1940.9280.9260.963
관리기관코드0.0840.0000.1321.0000.0000.8160.384NaN0.9770.6510.3060.117
도로구간번호0.9670.9970.8670.0001.0000.0230.012NaN0.0000.9800.8000.899
정류장 종류0.3380.1440.4820.8160.0231.0000.554NaN0.8560.9060.4660.241
정류장유형0.1440.0210.4330.3840.0120.5541.000NaN0.4160.6640.5320.147
공사번호1.000NaN1.000NaNNaNNaNNaN1.000NaN1.0001.0001.000
대장초기화여부0.1470.0000.1940.9770.0000.8560.416NaN1.0000.8620.3500.164
설치일자0.8230.9810.9280.6510.9800.9060.6641.0000.8621.0000.8680.912
위도0.7590.7780.9260.3060.8000.4660.5321.0000.3500.8681.0000.859
경도0.8000.8790.9630.1170.8990.2410.1471.0000.1640.9120.8591.000
2023-12-12T15:06:34.017259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류장유형대장초기화여부공사번호관리기관코드정류장 종류
정류장유형1.0000.6511.0000.6060.560
대장초기화여부0.6511.0001.0000.8640.652
공사번호1.0001.0001.0001.0001.000
관리기관코드0.6060.8641.0001.0000.607
정류장 종류0.5600.6521.0000.6071.000
2023-12-12T15:06:34.130601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간지리식별번호관리번호행정읍면동코드도로구간번호위도경도관리기관코드정류장 종류정류장유형공사번호대장초기화여부
공간지리식별번호1.0000.149-0.2200.161-0.010-0.3320.0620.2060.0840.9570.110
관리번호0.1491.0000.1580.5060.2770.1750.0000.0950.0341.0000.000
행정읍면동코드-0.2200.1581.0000.1020.3610.5290.1190.3120.2770.9570.159
도로구간번호0.1610.5060.1021.0000.1640.0030.0000.0140.0201.0000.000
위도-0.0100.2770.3610.1641.0000.7660.2310.3080.3820.9200.264
경도-0.3320.1750.5290.0030.7661.0000.0890.1420.0830.9570.124
관리기관코드0.0620.0000.1190.0000.2310.0891.0000.6070.6061.0000.864
정류장 종류0.2060.0950.3120.0140.3080.1420.6071.0000.5601.0000.652
정류장유형0.0840.0340.2770.0200.3820.0830.6060.5601.0001.0000.651
공사번호0.9571.0000.9571.0000.9200.9571.0001.0001.0001.0001.000
대장초기화여부0.1100.0000.1590.0000.2640.1240.8640.6520.6511.0001.000

Missing values

2023-12-12T15:06:28.269362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:06:28.463257image/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.
2023-12-12T15:06:28.614816image/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

공간지리식별번호지형지물부호관리번호행정읍면동코드도엽번호관리기관코드도로구간번호정류장 종류정류장명정류장유형공사번호대장초기화여부설치일자위도경도
0134AE260944143010100376122005BMNG001518AEE003의왕고천STT002<NA>12002-01-01197681.4978527624.9925
1135AE2601001644143010800376121519DMNG0010AEE003청계마을(덕장교회)STT002<NA>12011-10-31199504.9738532333.7488
2136AE2601000164143010800376121520DMNG0012039AEE003청계마을 2단지앞STT002<NA>12011-10-31199898.1626532474.8037
3138AE2602005014143010100376122005AMNG001595006AEE003무명STT001<NA>12014-01-01197535.338527833.246
4139AE260914143010500376121596CMNG001660AEE003선병원.KT아파트STT002<NA>12009-06-15197871.9919527867.658
5140AE260784143010500376121589AMNG001653AEE004뒷골STT002<NA>12009-04-19199298.7311528794.5425
6141AE2601811014143010500376122005BMNG001181102AEE003서해그랑블STT002<NA>12018-01-01197691.9025527838.0777
7142AE2609110014143010800377091113AMNG001911028AEE003새터마을STT002<NA>12018-01-01201078.5877532583.9988
8143AE2609110024143010600377091151BMNG001911041AEE003의일마을.물레방아간STT002<NA>12018-01-01200311.2512530523.1232
9144AE2609110034143010600377091151BMNG001911041AEE003의일마을.물레방아간STT002<NA>12018-01-01200326.124530535.796
공간지리식별번호지형지물부호관리번호행정읍면동코드도엽번호관리기관코드도로구간번호정류장 종류정류장명정류장유형공사번호대장초기화여부설치일자위도경도
256281AE2601194143010200376122023DMNG001744AEE003교동STT002<NA>12011-10-31196841.748526315.6779
257282AE2601434143011000376122072BMNG001766AEE003부곡중STT002<NA>12002-01-24196249.8556523868.3509
258283AE2601000154143010800376121520DMNG0012039AEE003청계마을 2단지앞STT002<NA>12011-10-31199905.204532483.294
259284AE2601544143011000376122072DMNG001767AEE003도룡마을STT002<NA>12002-01-24196289.4933523446.6888
260285AE2601114143010500376122006BMNG001660AEE003이삭아파트STT002<NA>12000-01-01198151.3049527810.3612
261287AE2601001184143010500376121596CMNG0010AEE004선병원(마을버스)STT001<NA>12000-01-01197942.2192527875.9188
262288AE260584143010700376121545DMNG001362AEE003갈뫼초등학교STT002<NA>12006-01-01197619.7017530756.6891
263289AE2601001304143010700376121536BMNG0010AEE003내손공영주차장(맞은편)STT002<NA>12011-10-31198059.22531578.57
264290AE26020072400014143010900376121518AMNG0012007240001AEE003포일삼거리STT002<NA>12019-01-01198713.4576532575.1791
265291AE26020072400024143010900376121518AMNG0012007240003AEE003의왕고용복지플러스센터STT002<NA>12019-01-01198696.0456532821.7509

Duplicate rows

Most frequently occurring

공간지리식별번호지형지물부호관리번호행정읍면동코드도엽번호관리기관코드도로구간번호정류장 종류정류장명정류장유형공사번호대장초기화여부설치일자위도경도# duplicates
0170AE2601000794143010900376121530DMNG0010AEE004청계체육시설STT002<NA>12011-10-31199821.8381531908.73422