Overview

Dataset statistics

Number of variables8
Number of observations211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory69.6 B

Variable types

Numeric3
Text2
Categorical3

Dataset

Description과천시 cctv의 관리번호, 행정동, 주소, 계량기, 비상벨, 위도, 경도 등에 대한 정보를 기준으로 설명하는 자료입니다.
Author경기도 과천시
URLhttps://www.data.go.kr/data/15062876/fileData.do

Alerts

비상벨 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
행정동 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
계량기 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
연번 is highly overall correlated with 계량기 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 행정동 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 계량기 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:29:54.303706
Analysis finished2023-12-12 23:29:55.703630
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106
Minimum1
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T08:29:55.801986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.5
Q153.5
median106
Q3158.5
95-th percentile200.5
Maximum211
Range210
Interquartile range (IQR)105

Descriptive statistics

Standard deviation61.05462
Coefficient of variation (CV)0.57598698
Kurtosis-1.2
Mean106
Median Absolute Deviation (MAD)53
Skewness0
Sum22366
Variance3727.6667
MonotonicityStrictly increasing
2023-12-13T08:29:56.010080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
134 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
140 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
143 1
 
0.5%
Other values (201) 201
95.3%
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 (%)
211 1
0.5%
210 1
0.5%
209 1
0.5%
208 1
0.5%
207 1
0.5%
206 1
0.5%
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%

관리번호
Text

UNIQUE 

Distinct211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-13T08:29:56.444229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.492891
Min length3

Characters and Unicode

Total characters948
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)100.0%

Sample

1st rowA-1
2nd rowA-2
3rd rowA-3
4th rowA-4
5th rowA-5
ValueCountFrequency (%)
a-1 1
 
0.5%
a-109 1
 
0.5%
a-147 1
 
0.5%
a-137 1
 
0.5%
a-138 1
 
0.5%
a-139 1
 
0.5%
a-140 1
 
0.5%
a-141 1
 
0.5%
a-142 1
 
0.5%
a-143 1
 
0.5%
Other values (201) 201
95.3%
2023-12-13T08:29:57.094737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 211
22.3%
- 211
22.3%
1 145
15.3%
2 59
 
6.2%
3 42
 
4.4%
4 41
 
4.3%
7 41
 
4.3%
5 40
 
4.2%
6 40
 
4.2%
8 40
 
4.2%
Other values (2) 78
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 526
55.5%
Uppercase Letter 211
22.3%
Dash Punctuation 211
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 145
27.6%
2 59
11.2%
3 42
 
8.0%
4 41
 
7.8%
7 41
 
7.8%
5 40
 
7.6%
6 40
 
7.6%
8 40
 
7.6%
9 39
 
7.4%
0 39
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
A 211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 737
77.7%
Latin 211
 
22.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 211
28.6%
1 145
19.7%
2 59
 
8.0%
3 42
 
5.7%
4 41
 
5.6%
7 41
 
5.6%
5 40
 
5.4%
6 40
 
5.4%
8 40
 
5.4%
9 39
 
5.3%
Latin
ValueCountFrequency (%)
A 211
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 211
22.3%
- 211
22.3%
1 145
15.3%
2 59
 
6.2%
3 42
 
4.4%
4 41
 
4.3%
7 41
 
4.3%
5 40
 
4.2%
6 40
 
4.2%
8 40
 
4.2%
Other values (2) 78
 
8.2%

행정동
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
과천동
65 
문원동
45 
별양동
32 
중앙동
30 
부림동
21 
Other values (2)
18 

Length

Max length4
Median length3
Mean length3.0047393
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row별양동
2nd row과천동
3rd row과천동
4th row중앙동
5th row중앙동

Common Values

ValueCountFrequency (%)
과천동 65
30.8%
문원동 45
21.3%
별양동 32
15.2%
중앙동 30
14.2%
부림동 21
 
10.0%
갈현동 17
 
8.1%
과천동 1
 
0.5%

Length

2023-12-13T08:29:57.256287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:57.385500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과천동 66
31.3%
문원동 45
21.3%
별양동 32
15.2%
중앙동 30
14.2%
부림동 21
 
10.0%
갈현동 17
 
8.1%

주소
Text

Distinct209
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-13T08:29:57.723406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length21
Mean length14.696682
Min length6

Characters and Unicode

Total characters3101
Distinct characters193
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)98.6%

Sample

1st row별양동 2 (중앙공원 내 물놀이터)
2nd row과천동 521-4
3rd row뒷골로 62-3
4th row중앙동 67 1014동 옆 초소~상가사이
5th row중앙동 67 1007동~1002동사이
ValueCountFrequency (%)
문원동 43
 
7.4%
과천동 41
 
7.1%
과천시 27
 
4.7%
별양동 20
 
3.5%
중앙동 19
 
3.3%
주암동 16
 
2.8%
갈현동 16
 
2.8%
앞 16
 
2.8%
부림동 12
 
2.1%
입구 9
 
1.6%
Other values (311) 360
62.2%
2023-12-13T08:29:58.271483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
371
 
12.0%
동 227
 
7.3%
1 203
 
6.5%
- 160
 
5.2%
2 99
 
3.2%
4 96
 
3.1%
( 93
 
3.0%
5 92
 
3.0%
) 91
 
2.9%
3 85
 
2.7%
Other values (183) 1584
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1513
48.8%
Decimal Number 854
27.5%
Space Separator 371
 
12.0%
Dash Punctuation 160
 
5.2%
Open Punctuation 94
 
3.0%
Close Punctuation 92
 
3.0%
Math Symbol 13
 
0.4%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 227
 
15.0%
과 78
 
5.2%
천 78
 
5.2%
원 76
 
5.0%
문 71
 
4.7%
양 36
 
2.4%
별 34
 
2.2%
중 33
 
2.2%
앙 33
 
2.2%
시 30
 
2.0%
Other values (164) 817
54.0%
Decimal Number
ValueCountFrequency (%)
1 203
23.8%
2 99
11.6%
4 96
11.2%
5 92
10.8%
3 85
10.0%
0 63
 
7.4%
7 60
 
7.0%
6 57
 
6.7%
9 55
 
6.4%
8 44
 
5.2%
Open Punctuation
ValueCountFrequency (%)
( 93
98.9%
[ 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 91
98.9%
] 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
~ 12
92.3%
> 1
 
7.7%
Space Separator
ValueCountFrequency (%)
371
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1588
51.2%
Hangul 1513
48.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 227
 
15.0%
과 78
 
5.2%
천 78
 
5.2%
원 76
 
5.0%
문 71
 
4.7%
양 36
 
2.4%
별 34
 
2.2%
중 33
 
2.2%
앙 33
 
2.2%
시 30
 
2.0%
Other values (164) 817
54.0%
Common
ValueCountFrequency (%)
371
23.4%
1 203
12.8%
- 160
10.1%
2 99
 
6.2%
4 96
 
6.0%
( 93
 
5.9%
5 92
 
5.8%
) 91
 
5.7%
3 85
 
5.4%
0 63
 
4.0%
Other values (9) 235
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1588
51.2%
Hangul 1513
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
371
23.4%
1 203
12.8%
- 160
10.1%
2 99
 
6.2%
4 96
 
6.0%
( 93
 
5.9%
5 92
 
5.8%
) 91
 
5.7%
3 85
 
5.4%
0 63
 
4.0%
Other values (9) 235
14.8%
Hangul
ValueCountFrequency (%)
동 227
 
15.0%
과 78
 
5.2%
천 78
 
5.2%
원 76
 
5.0%
문 71
 
4.7%
양 36
 
2.4%
별 34
 
2.2%
중 33
 
2.2%
앙 33
 
2.2%
시 30
 
2.0%
Other values (164) 817
54.0%

계량기
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
1
137 
<NA>
74 

Length

Max length4
Median length1
Mean length2.0521327
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row1
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
1 137
64.9%
<NA> 74
35.1%

Length

2023-12-13T08:29:58.436347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:58.566283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 137
64.9%
na 74
35.1%

비상벨
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
1
176 
<NA>
35 

Length

Max length4
Median length1
Mean length1.4976303
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 176
83.4%
<NA> 35
 
16.6%

Length

2023-12-13T08:29:58.694768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:59.151618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 176
83.4%
na 35
 
16.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.434792
Minimum37.40405
Maximum37.463468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T08:29:59.297605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.40405
5-th percentile37.416373
Q137.424716
median37.432503
Q337.44649
95-th percentile37.459306
Maximum37.463468
Range0.059418
Interquartile range (IQR)0.021775

Descriptive statistics

Standard deviation0.013439944
Coefficient of variation (CV)0.00035902281
Kurtosis-0.66251934
Mean37.434792
Median Absolute Deviation (MAD)0.008566
Skewness0.35280123
Sum7898.7411
Variance0.00018063209
MonotonicityNot monotonic
2023-12-13T08:29:59.457546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.450355 2
 
0.9%
37.453237 2
 
0.9%
37.460261 2
 
0.9%
37.433763 1
 
0.5%
37.417867 1
 
0.5%
37.428103 1
 
0.5%
37.424501 1
 
0.5%
37.417504 1
 
0.5%
37.421942 1
 
0.5%
37.458219 1
 
0.5%
Other values (198) 198
93.8%
ValueCountFrequency (%)
37.40405 1
0.5%
37.404463 1
0.5%
37.410242 1
0.5%
37.411159 1
0.5%
37.411291 1
0.5%
37.412163 1
0.5%
37.413015 1
0.5%
37.414653 1
0.5%
37.415478 1
0.5%
37.415838 1
0.5%
ValueCountFrequency (%)
37.463468 1
0.5%
37.463224 1
0.5%
37.462784 1
0.5%
37.462343 1
0.5%
37.462042 1
0.5%
37.461074 1
0.5%
37.460926 1
0.5%
37.460261 2
0.9%
37.459904 1
0.5%
37.45945 1
0.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct206
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.0005
Minimum126.9793
Maximum127.03549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T08:29:59.631934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.9793
5-th percentile126.98622
Q1126.99404
median126.99793
Q3127.00455
95-th percentile127.03174
Maximum127.03549
Range0.05619
Interquartile range (IQR)0.01051

Descriptive statistics

Standard deviation0.011277523
Coefficient of variation (CV)8.8799044 × 10-5
Kurtosis2.1245755
Mean127.0005
Median Absolute Deviation (MAD)0.004781
Skewness1.3467377
Sum26797.106
Variance0.00012718253
MonotonicityNot monotonic
2023-12-13T08:29:59.816272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.995546 2
 
0.9%
127.011507 2
 
0.9%
127.00198 2
 
0.9%
126.994759 2
 
0.9%
127.000528 2
 
0.9%
126.997251 1
 
0.5%
126.991301 1
 
0.5%
127.009831 1
 
0.5%
127.003718 1
 
0.5%
127.009149 1
 
0.5%
Other values (196) 196
92.9%
ValueCountFrequency (%)
126.979304 1
0.5%
126.979858 1
0.5%
126.98082 1
0.5%
126.981945 1
0.5%
126.982755 1
0.5%
126.983951 1
0.5%
126.98396 1
0.5%
126.984384 1
0.5%
126.985189 1
0.5%
126.986083 1
0.5%
ValueCountFrequency (%)
127.035494 1
0.5%
127.034043 1
0.5%
127.033571 1
0.5%
127.033414 1
0.5%
127.033251 1
0.5%
127.0331 1
0.5%
127.032843 1
0.5%
127.032566 1
0.5%
127.032175 1
0.5%
127.032103 1
0.5%

Interactions

2023-12-13T08:29:55.172633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:54.651887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:54.915426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:55.253601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:54.724327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:55.000039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:55.355368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:54.828410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:55.092796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:29:59.925006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동위도경도
연번1.0000.5470.4880.430
행정동0.5471.0000.8100.719
위도0.4880.8101.0000.698
경도0.4300.7190.6981.000
2023-12-13T08:30:00.037183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비상벨행정동계량기
비상벨1.0001.0001.000
행정동1.0001.0001.000
계량기1.0001.0001.000
2023-12-13T08:30:00.160026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도행정동계량기비상벨
연번1.0000.0890.1950.3171.0001.000
위도0.0891.0000.3900.5851.0001.000
경도0.1950.3901.0000.4871.0001.000
행정동0.3170.5850.4871.0001.0001.000
계량기1.0001.0001.0001.0001.0001.000
비상벨1.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T08:29:55.486319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:29:55.641179image/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

연번관리번호행정동주소계량기비상벨위도경도
01A-1별양동별양동 2 (중앙공원 내 물놀이터)<NA>137.433763126.997251
12A-2과천동과천동 521-41137.449155126.996415
23A-3과천동뒷골로 62-31137.457511127.000202
34A-4중앙동중앙동 67 1014동 옆 초소~상가사이<NA>137.434781126.995984
45A-5중앙동중앙동 67 1007동~1002동사이<NA>137.433414126.994506
56A-6중앙동희망3길 41 (중앙동 19-7번지앞)1137.433361126.991332
67A-7중앙동중앙동 29 (교동경로당)<NA>137.432503126.990868
78A-8중앙동희망1길 1(중앙동 33-13번지 앞)1137.431294126.99053
89A-9중앙동중앙동 9-1 과천외국어고등학교 앞1137.431637126.987886
910A-10중앙동향교말길 7-4(중앙동 25-7)1137.429931126.989297
연번관리번호행정동주소계량기비상벨위도경도
201202A-207과천동과천동 645-91137.441028127.009682
202203A-208과천동과천동 378-40(뒷골)1<NA>37.456241126.999979
203204A-209과천동막계동 324-22(대공원 나들길)1137.434043127.003809
204205A-210문원동문원동 15-1701<NA>37.429304127.00212
205206A-211문원동문원동 902-11(세곡마을 입구)1137.414653126.992916
206207A-212문원동문원동 1058(매봉 버스정류장)1137.412163126.996377
207208A-213과천동과천동 303-1(외곽)<NA><NA>37.441026127.004854
208209A-214중앙동중앙로 129(고려빌딩 앞)1137.428468126.990883
209210A-215별양동별양상가2로 20(새서울쇼핑 앞)<NA>137.427069126.992345
210211A-216문원동참마을로 10-6(예지유치원 앞)<NA>137.427511127.001852