Overview

Dataset statistics

Number of variables16
Number of observations106
Missing cells71
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory133.2 B

Variable types

Text11
Numeric4
DateTime1

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름)
Author지방자치단체
URLhttps://www.data.go.kr/data/15021106/standard.do

Alerts

보관소면적 is highly overall correlated with 보관가능대수High correlation
보관가능대수 is highly overall correlated with 보관소면적High correlation
소재지도로명주소 has 17 (16.0%) missing valuesMissing
소재지지번주소 has 7 (6.6%) missing valuesMissing
보관소면적 has 13 (12.3%) missing valuesMissing
견인료추가요금 has 29 (27.4%) missing valuesMissing
관리기관전화번호 has 3 (2.8%) missing valuesMissing

Reproduction

Analysis started2024-05-11 10:20:39.447282
Analysis finished2024-05-11 10:21:18.177547
Duration38.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

견인차량보관소명
Text

Distinct96
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:18.672006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length10.09434
Min length4

Characters and Unicode

Total characters1070
Distinct characters129
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)81.1%

Sample

1st row면목견인보관소
2nd row제일견인(제3지구 견인관리소)
3rd row원주시 견인차사무소
4th row무단방치차량 견인보관소
5th row강남구 견인차량보관소
ValueCountFrequency (%)
견인차량보관소 21
 
13.0%
견인보관소 5
 
3.1%
보관소 4
 
2.5%
견인차량 4
 
2.5%
성북구견인차량보관소 2
 
1.2%
무단방치차량보관소 2
 
1.2%
계양구 2
 
1.2%
강북구견인차량보관소 2
 
1.2%
북구 2
 
1.2%
금천구 2
 
1.2%
Other values (107) 116
71.6%
2024-05-11T10:21:20.083994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 89
 
8.3%
견 87
 
8.1%
차 85
 
7.9%
관 84
 
7.9%
소 83
 
7.8%
보 75
 
7.0%
량 67
 
6.3%
56
 
5.2%
시 38
 
3.6%
구 33
 
3.1%
Other values (119) 373
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 999
93.4%
Space Separator 56
 
5.2%
Close Punctuation 6
 
0.6%
Open Punctuation 6
 
0.6%
Decimal Number 2
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 89
 
8.9%
견 87
 
8.7%
차 85
 
8.5%
관 84
 
8.4%
소 83
 
8.3%
보 75
 
7.5%
량 67
 
6.7%
시 38
 
3.8%
구 33
 
3.3%
주 19
 
1.9%
Other values (113) 339
33.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1000
93.5%
Common 70
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 89
 
8.9%
견 87
 
8.7%
차 85
 
8.5%
관 84
 
8.4%
소 83
 
8.3%
보 75
 
7.5%
량 67
 
6.7%
시 38
 
3.8%
구 33
 
3.3%
주 19
 
1.9%
Other values (114) 340
34.0%
Common
ValueCountFrequency (%)
56
80.0%
) 6
 
8.6%
( 6
 
8.6%
1 1
 
1.4%
3 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 999
93.4%
ASCII 70
 
6.5%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 89
 
8.9%
견 87
 
8.7%
차 85
 
8.5%
관 84
 
8.4%
소 83
 
8.3%
보 75
 
7.5%
량 67
 
6.7%
시 38
 
3.8%
구 33
 
3.3%
주 19
 
1.9%
Other values (113) 339
33.9%
ASCII
ValueCountFrequency (%)
56
80.0%
) 6
 
8.6%
( 6
 
8.6%
1 1
 
1.4%
3 1
 
1.4%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지도로명주소
Text

MISSING 

Distinct82
Distinct (%)92.1%
Missing17
Missing (%)16.0%
Memory size980.0 B
2024-05-11T10:21:21.194904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length19.359551
Min length11

Characters and Unicode

Total characters1723
Distinct characters169
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)84.3%

Sample

1st row서울특별시 중랑구 면목천로 33
2nd row부산광역시 동래구 중앙대로 1520
3rd row강원도 원주시 원일로 242
4th row충청남도 서산시 충의로 359-33
5th row인천광역시 서구 경명대로 265
ValueCountFrequency (%)
경기도 24
 
6.3%
서울특별시 21
 
5.5%
부산광역시 15
 
4.0%
인천광역시 5
 
1.3%
남구 5
 
1.3%
전라남도 4
 
1.1%
강원도 4
 
1.1%
10 3
 
0.8%
충청북도 3
 
0.8%
서구 3
 
0.8%
Other values (244) 292
77.0%
2024-05-11T10:21:22.575993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
 
16.8%
시 87
 
5.0%
로 83
 
4.8%
1 64
 
3.7%
구 61
 
3.5%
2 47
 
2.7%
도 45
 
2.6%
0 33
 
1.9%
서 32
 
1.9%
광 31
 
1.8%
Other values (159) 950
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1090
63.3%
Decimal Number 302
 
17.5%
Space Separator 290
 
16.8%
Dash Punctuation 21
 
1.2%
Close Punctuation 10
 
0.6%
Open Punctuation 10
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 87
 
8.0%
로 83
 
7.6%
구 61
 
5.6%
도 45
 
4.1%
서 32
 
2.9%
광 31
 
2.8%
경 31
 
2.8%
산 30
 
2.8%
길 28
 
2.6%
대 27
 
2.5%
Other values (145) 635
58.3%
Decimal Number
ValueCountFrequency (%)
1 64
21.2%
2 47
15.6%
0 33
10.9%
3 29
9.6%
6 28
9.3%
9 23
 
7.6%
5 22
 
7.3%
4 20
 
6.6%
7 19
 
6.3%
8 17
 
5.6%
Space Separator
ValueCountFrequency (%)
290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1090
63.3%
Common 633
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 87
 
8.0%
로 83
 
7.6%
구 61
 
5.6%
도 45
 
4.1%
서 32
 
2.9%
광 31
 
2.8%
경 31
 
2.8%
산 30
 
2.8%
길 28
 
2.6%
대 27
 
2.5%
Other values (145) 635
58.3%
Common
ValueCountFrequency (%)
290
45.8%
1 64
 
10.1%
2 47
 
7.4%
0 33
 
5.2%
3 29
 
4.6%
6 28
 
4.4%
9 23
 
3.6%
5 22
 
3.5%
- 21
 
3.3%
4 20
 
3.2%
Other values (4) 56
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1090
63.3%
ASCII 633
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
45.8%
1 64
 
10.1%
2 47
 
7.4%
0 33
 
5.2%
3 29
 
4.6%
6 28
 
4.4%
9 23
 
3.6%
5 22
 
3.5%
- 21
 
3.3%
4 20
 
3.2%
Other values (4) 56
 
8.8%
Hangul
ValueCountFrequency (%)
시 87
 
8.0%
로 83
 
7.6%
구 61
 
5.6%
도 45
 
4.1%
서 32
 
2.9%
광 31
 
2.8%
경 31
 
2.8%
산 30
 
2.8%
길 28
 
2.6%
대 27
 
2.5%
Other values (145) 635
58.3%

소재지지번주소
Text

MISSING 

Distinct92
Distinct (%)92.9%
Missing7
Missing (%)6.6%
Memory size980.0 B
2024-05-11T10:21:23.684280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length19.212121
Min length15

Characters and Unicode

Total characters1902
Distinct characters162
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)85.9%

Sample

1st row서울특별시 중랑구 면목동168-2
2nd row부산광역시 동래구 온천동 1750-1
3rd row강원도 원주시 학성동 469-1
4th row충청남도 서산시 성연면 일람리 809-3
5th row서울특별시 강남구 대치동 78-29
ValueCountFrequency (%)
경기도 29
 
6.9%
서울특별시 23
 
5.5%
부산광역시 13
 
3.1%
인천광역시 6
 
1.4%
강원도 4
 
1.0%
서구 4
 
1.0%
광주광역시 3
 
0.7%
시흥시 3
 
0.7%
용인시 3
 
0.7%
고양시 3
 
0.7%
Other values (274) 327
78.2%
2024-05-11T10:21:25.491975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
319
 
16.8%
동 101
 
5.3%
시 99
 
5.2%
1 82
 
4.3%
- 79
 
4.2%
구 67
 
3.5%
도 52
 
2.7%
2 52
 
2.7%
3 47
 
2.5%
4 47
 
2.5%
Other values (152) 957
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1099
57.8%
Decimal Number 403
 
21.2%
Space Separator 319
 
16.8%
Dash Punctuation 79
 
4.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
 
9.2%
시 99
 
9.0%
구 67
 
6.1%
도 52
 
4.7%
서 35
 
3.2%
경 35
 
3.2%
광 33
 
3.0%
산 30
 
2.7%
기 30
 
2.7%
별 28
 
2.5%
Other values (138) 589
53.6%
Decimal Number
ValueCountFrequency (%)
1 82
20.3%
2 52
12.9%
3 47
11.7%
4 47
11.7%
5 36
8.9%
7 33
8.2%
6 32
 
7.9%
8 27
 
6.7%
9 24
 
6.0%
0 23
 
5.7%
Space Separator
ValueCountFrequency (%)
319
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1099
57.8%
Common 803
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
 
9.2%
시 99
 
9.0%
구 67
 
6.1%
도 52
 
4.7%
서 35
 
3.2%
경 35
 
3.2%
광 33
 
3.0%
산 30
 
2.7%
기 30
 
2.7%
별 28
 
2.5%
Other values (138) 589
53.6%
Common
ValueCountFrequency (%)
319
39.7%
1 82
 
10.2%
- 79
 
9.8%
2 52
 
6.5%
3 47
 
5.9%
4 47
 
5.9%
5 36
 
4.5%
7 33
 
4.1%
6 32
 
4.0%
8 27
 
3.4%
Other values (4) 49
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1099
57.8%
ASCII 803
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319
39.7%
1 82
 
10.2%
- 79
 
9.8%
2 52
 
6.5%
3 47
 
5.9%
4 47
 
5.9%
5 36
 
4.5%
7 33
 
4.1%
6 32
 
4.0%
8 27
 
3.4%
Other values (4) 49
 
6.1%
Hangul
ValueCountFrequency (%)
동 101
 
9.2%
시 99
 
9.0%
구 67
 
6.1%
도 52
 
4.7%
서 35
 
3.2%
경 35
 
3.2%
광 33
 
3.0%
산 30
 
2.7%
기 30
 
2.7%
별 28
 
2.5%
Other values (138) 589
53.6%

위도
Real number (ℝ)

Distinct96
Distinct (%)91.4%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean36.709374
Minimum34.760148
Maximum37.876534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T10:21:26.400061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.760148
5-th percentile35.080827
Q135.538246
median37.321062
Q337.505237
95-th percentile37.66337
Maximum37.876534
Range3.116386
Interquartile range (IQR)1.9669912

Descriptive statistics

Standard deviation1.0344471
Coefficient of variation (CV)0.028179372
Kurtosis-1.2277215
Mean36.709374
Median Absolute Deviation (MAD)0.28488795
Skewness-0.72497402
Sum3854.4843
Variance1.0700808
MonotonicityNot monotonic
2024-05-11T10:21:27.085137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.16324019 2
 
1.9%
37.35760541 2
 
1.9%
35.19129473 2
 
1.9%
37.48330365 2
 
1.9%
35.22028597 2
 
1.9%
37.16421677 2
 
1.9%
37.5908337 2
 
1.9%
35.93822469 2
 
1.9%
35.14457728 2
 
1.9%
37.5792085 1
 
0.9%
Other values (86) 86
81.1%
ValueCountFrequency (%)
34.7601484061 1
0.9%
34.80503084 1
0.9%
34.89696576 1
0.9%
34.94226905 1
0.9%
35.08060134 1
0.9%
35.08061701 1
0.9%
35.08166819 1
0.9%
35.11153803 1
0.9%
35.12959476 1
0.9%
35.12974704 1
0.9%
ValueCountFrequency (%)
37.87653439 1
0.9%
37.84197 1
0.9%
37.76684766 1
0.9%
37.755765 1
0.9%
37.6779725 1
0.9%
37.66337052 1
0.9%
37.66336998 1
0.9%
37.65559958 1
0.9%
37.65408443 1
0.9%
37.6089213 1
0.9%

경도
Real number (ℝ)

Distinct96
Distinct (%)91.4%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean127.49737
Minimum126.4037
Maximum129.34563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T10:21:28.032545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.4037
5-th percentile126.7001
Q1126.88505
median127.06036
Q3127.94313
95-th percentile129.08638
Maximum129.34563
Range2.9419293
Interquartile range (IQR)1.0580811

Descriptive statistics

Standard deviation0.88059768
Coefficient of variation (CV)0.0069067909
Kurtosis-0.62803933
Mean127.49737
Median Absolute Deviation (MAD)0.2613326
Skewness0.9988389
Sum13387.224
Variance0.77545227
MonotonicityNot monotonic
2024-05-11T10:21:28.960801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0930137 2
 
1.9%
129.1336287 2
 
1.9%
128.986551 2
 
1.9%
126.9410088 2
 
1.9%
126.9771882 2
 
1.9%
129.0656127 2
 
1.9%
129.076018 2
 
1.9%
127.943134 2
 
1.9%
129.0863784 2
 
1.9%
126.72532 1
 
0.9%
Other values (86) 86
81.1%
ValueCountFrequency (%)
126.4036997 1
0.9%
126.4382303 1
0.9%
126.6054713 1
0.9%
126.638884 1
0.9%
126.6521587 1
0.9%
126.6974287 1
0.9%
126.7107667 1
0.9%
126.7175304 1
0.9%
126.72532 1
0.9%
126.7452458 1
0.9%
ValueCountFrequency (%)
129.345629 1
0.9%
129.1939384 1
0.9%
129.1336287 2
1.9%
129.1050057 1
0.9%
129.0863784 2
1.9%
129.076018 2
1.9%
129.0656127 2
1.9%
129.0544179 1
0.9%
129.0544159 1
0.9%
129.0243117 1
0.9%

보관소전화번호
Text

Distinct93
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:29.689134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.915094
Min length11

Characters and Unicode

Total characters1263
Distinct characters11
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

Unique80 ?
Unique (%)75.5%

Sample

1st row02-492-8185
2nd row051-517-0507
3rd row000-0000-0000
4th row041-660-2318
5th row02-558-7230
ValueCountFrequency (%)
02-854-5273 2
 
1.9%
063-211-8246 2
 
1.9%
051-744-0421 2
 
1.9%
063-843-8422 2
 
1.9%
02-6925-2004 2
 
1.9%
051-254-6441 2
 
1.9%
033-550-2105 2
 
1.9%
02-360-8542 2
 
1.9%
051-517-0507 2
 
1.9%
051-817-1994 2
 
1.9%
Other values (83) 86
81.1%
2024-05-11T10:21:30.549113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 220
17.4%
- 212
16.8%
2 132
10.5%
3 117
9.3%
5 112
8.9%
1 106
8.4%
4 95
7.5%
6 87
 
6.9%
8 79
 
6.3%
7 58
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1051
83.2%
Dash Punctuation 212
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 220
20.9%
2 132
12.6%
3 117
11.1%
5 112
10.7%
1 106
10.1%
4 95
9.0%
6 87
 
8.3%
8 79
 
7.5%
7 58
 
5.5%
9 45
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 220
17.4%
- 212
16.8%
2 132
10.5%
3 117
9.3%
5 112
8.9%
1 106
8.4%
4 95
7.5%
6 87
 
6.9%
8 79
 
6.3%
7 58
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 220
17.4%
- 212
16.8%
2 132
10.5%
3 117
9.3%
5 112
8.9%
1 106
8.4%
4 95
7.5%
6 87
 
6.9%
8 79
 
6.3%
7 58
 
4.6%

보관소면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)88.2%
Missing13
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean2685.3062
Minimum36
Maximum36178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T10:21:30.998864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile352.9
Q1852
median1609
Q32600
95-th percentile9640
Maximum36178
Range36142
Interquartile range (IQR)1748

Descriptive statistics

Standard deviation4260.8068
Coefficient of variation (CV)1.5867117
Kurtosis41.745345
Mean2685.3062
Median Absolute Deviation (MAD)838
Skewness5.6715121
Sum249733.48
Variance18154474
MonotonicityNot monotonic
2024-05-11T10:21:31.530343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660.0 3
 
2.8%
400.0 2
 
1.9%
2514.0 2
 
1.9%
2000.0 2
 
1.9%
2860.0 2
 
1.9%
1640.0 2
 
1.9%
758.5 2
 
1.9%
852.0 2
 
1.9%
9640.0 2
 
1.9%
890.0 2
 
1.9%
Other values (72) 72
67.9%
(Missing) 13
 
12.3%
ValueCountFrequency (%)
36.0 1
0.9%
101.0 1
0.9%
210.0 1
0.9%
235.0 1
0.9%
295.0 1
0.9%
391.5 1
0.9%
400.0 2
1.9%
407.0 1
0.9%
407.38 1
0.9%
540.0 1
0.9%
ValueCountFrequency (%)
36178.0 1
0.9%
9980.0 1
0.9%
9900.0 1
0.9%
9865.0 1
0.9%
9640.0 2
1.9%
8057.0 1
0.9%
7310.0 1
0.9%
6782.0 1
0.9%
6099.0 1
0.9%
6079.0 1
0.9%

보관가능대수
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.160377
Minimum3
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T10:21:32.227201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q130.25
median42
Q369.25
95-th percentile237.5
Maximum500
Range497
Interquartile range (IQR)39

Descriptive statistics

Standard deviation84.514877
Coefficient of variation (CV)1.222013
Kurtosis10.726761
Mean69.160377
Median Absolute Deviation (MAD)17
Skewness3.1499386
Sum7331
Variance7142.7645
MonotonicityNot monotonic
2024-05-11T10:21:32.682192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 10
 
9.4%
50 8
 
7.5%
15 6
 
5.7%
39 5
 
4.7%
30 3
 
2.8%
8 3
 
2.8%
55 3
 
2.8%
25 3
 
2.8%
200 3
 
2.8%
29 2
 
1.9%
Other values (47) 60
56.6%
ValueCountFrequency (%)
3 1
 
0.9%
7 1
 
0.9%
8 3
2.8%
10 2
 
1.9%
12 1
 
0.9%
13 1
 
0.9%
15 6
5.7%
16 1
 
0.9%
17 1
 
0.9%
21 1
 
0.9%
ValueCountFrequency (%)
500 1
 
0.9%
400 2
1.9%
360 1
 
0.9%
279 1
 
0.9%
250 1
 
0.9%
200 3
2.8%
180 1
 
0.9%
130 1
 
0.9%
123 1
 
0.9%
120 1
 
0.9%

견인료기본요금
Text

Distinct72
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:33.219615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length223
Median length174
Mean length49.084906
Min length1

Characters and Unicode

Total characters5203
Distinct characters73
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)52.8%

Sample

1st row승용(경형 40000원+소형 45000원+중형 50000원+대형 60000원)+승합(경형 40000원+소형 60000원+중형 80000원+대형 140000원)+화물·특수(2.5톤미만 40000원+2.5톤이상 6.5톤미만 60000원+6.5톤이상 10톤미만 80000원+10톤이상 140000원)
2nd row2.5톤 미만 40000원+2.5톤 이상 45000원
3rd row20,000원~40,000원
4th row60000원
5th row승용(경형 40000원+소형 45000원+중형 50000원+대형 60000원)+승합(경형 40000원+소형 60000원+중형 80000원+대형 140000원)+이륜 40000원+개인형 이동장치 40000원+화물 및 특수(2.5톤 미만 40000원+2.5톤 이상 6.5톤 미만 60000원+6.5톤 이상 10톤 미만 80000원+10톤 이상 140000원)
ValueCountFrequency (%)
미만 48
 
8.5%
이상 38
 
6.7%
2.5톤미만 23
 
4.0%
6.5톤미만 23
 
4.0%
40000원 22
 
3.9%
2.5톤 22
 
3.9%
6.5톤 19
 
3.3%
18
 
3.2%
40000원+소형 16
 
2.8%
50000원 15
 
2.6%
Other values (162) 324
57.0%
2024-05-11T10:21:34.462171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1393
26.8%
463
 
8.9%
원 337
 
6.5%
5 318
 
6.1%
톤 242
 
4.7%
. 218
 
4.2%
+ 214
 
4.1%
6 162
 
3.1%
4 152
 
2.9%
2 151
 
2.9%
Other values (63) 1553
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2310
44.4%
Other Letter 1778
34.2%
Space Separator 463
 
8.9%
Other Punctuation 330
 
6.3%
Math Symbol 232
 
4.5%
Open Punctuation 41
 
0.8%
Close Punctuation 40
 
0.8%
Lowercase Letter 8
 
0.2%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 337
19.0%
톤 242
13.6%
형 135
 
7.6%
만 133
 
7.5%
미 131
 
7.4%
이 125
 
7.0%
상 115
 
6.5%
승 60
 
3.4%
차 58
 
3.3%
동 38
 
2.1%
Other values (38) 404
22.7%
Decimal Number
ValueCountFrequency (%)
0 1393
60.3%
5 318
 
13.8%
6 162
 
7.0%
4 152
 
6.6%
2 151
 
6.5%
1 63
 
2.7%
3 42
 
1.8%
8 25
 
1.1%
7 4
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 218
66.1%
, 50
 
15.2%
: 38
 
11.5%
· 13
 
3.9%
/ 10
 
3.0%
* 1
 
0.3%
Math Symbol
ValueCountFrequency (%)
+ 214
92.2%
~ 17
 
7.3%
→ 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
t 4
50.0%
m 2
25.0%
k 2
25.0%
Space Separator
ValueCountFrequency (%)
463
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Other Symbol
ValueCountFrequency (%)
㎞ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3417
65.7%
Hangul 1778
34.2%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 337
19.0%
톤 242
13.6%
형 135
 
7.6%
만 133
 
7.5%
미 131
 
7.4%
이 125
 
7.0%
상 115
 
6.5%
승 60
 
3.4%
차 58
 
3.3%
동 38
 
2.1%
Other values (38) 404
22.7%
Common
ValueCountFrequency (%)
0 1393
40.8%
463
 
13.5%
5 318
 
9.3%
. 218
 
6.4%
+ 214
 
6.3%
6 162
 
4.7%
4 152
 
4.4%
2 151
 
4.4%
1 63
 
1.8%
, 50
 
1.5%
Other values (12) 233
 
6.8%
Latin
ValueCountFrequency (%)
t 4
50.0%
m 2
25.0%
k 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3410
65.5%
Hangul 1770
34.0%
None 13
 
0.2%
Compat Jamo 8
 
0.2%
Arrows 1
 
< 0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1393
40.9%
463
 
13.6%
5 318
 
9.3%
. 218
 
6.4%
+ 214
 
6.3%
6 162
 
4.8%
4 152
 
4.5%
2 151
 
4.4%
1 63
 
1.8%
, 50
 
1.5%
Other values (12) 226
 
6.6%
Hangul
ValueCountFrequency (%)
원 337
19.0%
톤 242
13.7%
형 135
 
7.6%
만 133
 
7.5%
미 131
 
7.4%
이 125
 
7.1%
상 115
 
6.5%
승 60
 
3.4%
차 58
 
3.3%
동 38
 
2.1%
Other values (37) 396
22.4%
None
ValueCountFrequency (%)
· 13
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 8
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%
CJK Compat
ValueCountFrequency (%)
㎞ 1
100.0%

견인료추가요금
Text

MISSING 

Distinct41
Distinct (%)53.2%
Missing29
Missing (%)27.4%
Memory size980.0 B
2024-05-11T10:21:34.922680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length54
Mean length28.597403
Min length1

Characters and Unicode

Total characters2202
Distinct characters83
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)39.0%

Sample

1st row1000원(매km 추가시)
2nd row없음
3rd row없음
4th row2.5톤 미만 1000원+2.5톤 이상 6.5톤 미만 1400원+6.5톤 이상 2500원
5th row1일 10000
ValueCountFrequency (%)
2500원 27
 
8.4%
2.5톤미만 25
 
7.8%
6.5톤미만 21
 
6.5%
1400원+6.5톤이상 21
 
6.5%
미만 21
 
6.5%
1000원+2.5톤이상 18
 
5.6%
이상 18
 
5.6%
2.5톤 13
 
4.0%
1000원 10
 
3.1%
6.5톤 9
 
2.8%
Other values (79) 138
43.0%
2024-05-11T10:21:36.072633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 363
16.5%
244
11.1%
5 191
 
8.7%
. 149
 
6.8%
톤 148
 
6.7%
원 130
 
5.9%
2 115
 
5.2%
1 112
 
5.1%
만 80
 
3.6%
미 80
 
3.6%
Other values (73) 590
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 901
40.9%
Other Letter 743
33.7%
Space Separator 244
 
11.1%
Other Punctuation 163
 
7.4%
Math Symbol 82
 
3.7%
Lowercase Letter 47
 
2.1%
Open Punctuation 10
 
0.5%
Close Punctuation 10
 
0.5%
Other Symbol 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
톤 148
19.9%
원 130
17.5%
만 80
10.8%
미 80
10.8%
이 73
9.8%
상 73
9.8%
시 14
 
1.9%
매 14
 
1.9%
가 13
 
1.7%
없 10
 
1.3%
Other values (51) 108
14.5%
Decimal Number
ValueCountFrequency (%)
0 363
40.3%
5 191
21.2%
2 115
 
12.8%
1 112
 
12.4%
6 74
 
8.2%
4 37
 
4.1%
3 6
 
0.7%
8 3
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 149
91.4%
, 12
 
7.4%
/ 1
 
0.6%
· 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
m 22
46.8%
k 21
44.7%
t 4
 
8.5%
Math Symbol
ValueCountFrequency (%)
+ 75
91.5%
~ 7
 
8.5%
Space Separator
ValueCountFrequency (%)
244
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Symbol
ValueCountFrequency (%)
㎞ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1411
64.1%
Hangul 743
33.7%
Latin 48
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
톤 148
19.9%
원 130
17.5%
만 80
10.8%
미 80
10.8%
이 73
9.8%
상 73
9.8%
시 14
 
1.9%
매 14
 
1.9%
가 13
 
1.7%
없 10
 
1.3%
Other values (51) 108
14.5%
Common
ValueCountFrequency (%)
0 363
25.7%
244
17.3%
5 191
13.5%
. 149
10.6%
2 115
 
8.2%
1 112
 
7.9%
+ 75
 
5.3%
6 74
 
5.2%
4 37
 
2.6%
, 12
 
0.9%
Other values (8) 39
 
2.8%
Latin
ValueCountFrequency (%)
m 22
45.8%
k 21
43.8%
t 4
 
8.3%
K 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1457
66.2%
Hangul 743
33.7%
CJK Compat 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 363
24.9%
244
16.7%
5 191
13.1%
. 149
10.2%
2 115
 
7.9%
1 112
 
7.7%
+ 75
 
5.1%
6 74
 
5.1%
4 37
 
2.5%
m 22
 
1.5%
Other values (10) 75
 
5.1%
Hangul
ValueCountFrequency (%)
톤 148
19.9%
원 130
17.5%
만 80
10.8%
미 80
10.8%
이 73
9.8%
상 73
9.8%
시 14
 
1.9%
매 14
 
1.9%
가 13
 
1.7%
없 10
 
1.3%
Other values (51) 108
14.5%
CJK Compat
ValueCountFrequency (%)
㎞ 1
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

보관료
Text

Distinct86
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:36.672322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length56
Mean length31.575472
Min length1

Characters and Unicode

Total characters3347
Distinct characters124
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)69.8%

Sample

1st row승용·승합(소형)·화물(6.5톤미만)·특수(6.5톤미만) 30분당 700원+승합(중형·대형)·화물(6.5톤이상)·특수(6.5톤이상) 1200원+1회 보관료 한도 50만 원
2nd row30분당 700원(1일 15000원)
3rd row30분600원+30분초과시 10분당 300원(최초 1시간 면제 이후 요금 부과)
4th row없음
5th row차종별 기본 30분 700원 또는 1200원 (단 1회 보관료는 50만원 한도)
ValueCountFrequency (%)
30분당 42
 
7.1%
30분 27
 
4.6%
기본 25
 
4.2%
최대 11
 
1.9%
최초 11
 
1.9%
보관료는 10
 
1.7%
700원 9
 
1.5%
500원 9
 
1.5%
1회 9
 
1.5%
1200원 9
 
1.5%
Other values (232) 430
72.6%
2024-05-11T10:21:37.889033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 621
18.6%
488
 
14.6%
원 219
 
6.5%
1 162
 
4.8%
분 137
 
4.1%
3 129
 
3.9%
+ 96
 
2.9%
5 86
 
2.6%
당 85
 
2.5%
7 50
 
1.5%
Other values (114) 1274
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1459
43.6%
Decimal Number 1150
34.4%
Space Separator 488
 
14.6%
Math Symbol 99
 
3.0%
Other Punctuation 55
 
1.6%
Close Punctuation 48
 
1.4%
Open Punctuation 48
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 219
 
15.0%
분 137
 
9.4%
당 85
 
5.8%
일 41
 
2.8%
기 40
 
2.7%
최 38
 
2.6%
형 35
 
2.4%
대 35
 
2.4%
한 31
 
2.1%
과 30
 
2.1%
Other values (93) 768
52.6%
Decimal Number
ValueCountFrequency (%)
0 621
54.0%
1 162
 
14.1%
3 129
 
11.2%
5 86
 
7.5%
7 50
 
4.3%
2 48
 
4.2%
6 24
 
2.1%
4 13
 
1.1%
8 9
 
0.8%
9 8
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 21
38.2%
, 17
30.9%
· 7
 
12.7%
/ 4
 
7.3%
: 3
 
5.5%
* 3
 
5.5%
Math Symbol
ValueCountFrequency (%)
+ 96
97.0%
~ 3
 
3.0%
Space Separator
ValueCountFrequency (%)
488
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1888
56.4%
Hangul 1459
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 219
 
15.0%
분 137
 
9.4%
당 85
 
5.8%
일 41
 
2.8%
기 40
 
2.7%
최 38
 
2.6%
형 35
 
2.4%
대 35
 
2.4%
한 31
 
2.1%
과 30
 
2.1%
Other values (93) 768
52.6%
Common
ValueCountFrequency (%)
0 621
32.9%
488
25.8%
1 162
 
8.6%
3 129
 
6.8%
+ 96
 
5.1%
5 86
 
4.6%
7 50
 
2.6%
) 48
 
2.5%
2 48
 
2.5%
( 48
 
2.5%
Other values (11) 112
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1881
56.2%
Hangul 1459
43.6%
None 7
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 621
33.0%
488
25.9%
1 162
 
8.6%
3 129
 
6.9%
+ 96
 
5.1%
5 86
 
4.6%
7 50
 
2.7%
) 48
 
2.6%
2 48
 
2.6%
( 48
 
2.6%
Other values (10) 105
 
5.6%
Hangul
ValueCountFrequency (%)
원 219
 
15.0%
분 137
 
9.4%
당 85
 
5.8%
일 41
 
2.8%
기 40
 
2.7%
최 38
 
2.6%
형 35
 
2.4%
대 35
 
2.4%
한 31
 
2.1%
과 30
 
2.1%
Other values (93) 768
52.6%
None
ValueCountFrequency (%)
· 7
100.0%

관리기관전화번호
Text

MISSING 

Distinct92
Distinct (%)89.3%
Missing3
Missing (%)2.8%
Memory size980.0 B
2024-05-11T10:21:38.611633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.873786
Min length9

Characters and Unicode

Total characters1223
Distinct characters11
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

Unique83 ?
Unique (%)80.6%

Sample

1st row02-2094-2634
2nd row051-550-4562
3rd row033-737-3546
4th row041-660-2318
5th row1544-3113
ValueCountFrequency (%)
031-310-5134 3
 
2.9%
031-321-6581 3
 
2.9%
033-550-2105 2
 
1.9%
051-440-4567 2
 
1.9%
02-360-8542 2
 
1.9%
063-859-5973 2
 
1.9%
033-250-4262 2
 
1.9%
063-239-2639 2
 
1.9%
033-737-3546 2
 
1.9%
031-550-2764 1
 
1.0%
Other values (82) 82
79.6%
2024-05-11T10:21:39.592948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 204
16.7%
0 191
15.6%
2 127
10.4%
3 124
10.1%
1 112
9.2%
5 111
9.1%
4 108
8.8%
6 91
7.4%
9 53
 
4.3%
8 52
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019
83.3%
Dash Punctuation 204
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 191
18.7%
2 127
12.5%
3 124
12.2%
1 112
11.0%
5 111
10.9%
4 108
10.6%
6 91
8.9%
9 53
 
5.2%
8 52
 
5.1%
7 50
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 204
16.7%
0 191
15.6%
2 127
10.4%
3 124
10.1%
1 112
9.2%
5 111
9.1%
4 108
8.8%
6 91
7.4%
9 53
 
4.3%
8 52
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 204
16.7%
0 191
15.6%
2 127
10.4%
3 124
10.1%
1 112
9.2%
5 111
9.1%
4 108
8.8%
6 91
7.4%
9 53
 
4.3%
8 52
 
4.3%

관리기관명
Text

Distinct97
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:40.132605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length10.820755
Min length4

Characters and Unicode

Total characters1147
Distinct characters113
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

Unique90 ?
Unique (%)84.9%

Sample

1st row서울특별시 중랑구청
2nd row부산광역시 동래구청
3rd row강원도 원주시청
4th row충청남도 서산시 교통과
5th row강남구도시관리공단
ValueCountFrequency (%)
경기도 19
 
9.1%
서울특별시 15
 
7.2%
부산광역시 15
 
7.2%
교통행정과 5
 
2.4%
전라남도 4
 
1.9%
주차관리과 4
 
1.9%
광주광역시 4
 
1.9%
충청남도 3
 
1.4%
중구청 3
 
1.4%
시흥시청 3
 
1.4%
Other values (107) 134
64.1%
2024-05-11T10:21:41.141906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 118
 
10.3%
103
 
9.0%
청 68
 
5.9%
도 62
 
5.4%
구 58
 
5.1%
광 36
 
3.1%
공 35
 
3.1%
리 32
 
2.8%
관 32
 
2.8%
산 28
 
2.4%
Other values (103) 575
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1038
90.5%
Space Separator 103
 
9.0%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 118
 
11.4%
청 68
 
6.6%
도 62
 
6.0%
구 58
 
5.6%
광 36
 
3.5%
공 35
 
3.4%
리 32
 
3.1%
관 32
 
3.1%
산 28
 
2.7%
역 27
 
2.6%
Other values (100) 542
52.2%
Space Separator
ValueCountFrequency (%)
103
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1038
90.5%
Common 109
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 118
 
11.4%
청 68
 
6.6%
도 62
 
6.0%
구 58
 
5.6%
광 36
 
3.5%
공 35
 
3.4%
리 32
 
3.1%
관 32
 
3.1%
산 28
 
2.7%
역 27
 
2.6%
Other values (100) 542
52.2%
Common
ValueCountFrequency (%)
103
94.5%
) 3
 
2.8%
( 3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1038
90.5%
ASCII 109
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 118
 
11.4%
청 68
 
6.6%
도 62
 
6.0%
구 58
 
5.6%
광 36
 
3.5%
공 35
 
3.4%
리 32
 
3.1%
관 32
 
3.1%
산 28
 
2.7%
역 27
 
2.6%
Other values (100) 542
52.2%
ASCII
ValueCountFrequency (%)
103
94.5%
) 3
 
2.8%
( 3
 
2.8%

데이터기준일자
Date

Distinct84
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size980.0 B
Minimum2017-05-18 00:00:00
Maximum2024-03-14 00:00:00
2024-05-11T10:21:41.649715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:42.068578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Text

Distinct97
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:42.692159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters742
Distinct characters11
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

Unique91 ?
Unique (%)85.8%

Sample

1st row3060000
2nd row3300000
3rd row4191000
4th row4530000
5th row3220000
ValueCountFrequency (%)
3940000 3
 
2.8%
4010000 3
 
2.8%
4050000 3
 
2.8%
3910000 2
 
1.9%
3310000 2
 
1.9%
3270000 2
 
1.9%
4070000 1
 
0.9%
3700000 1
 
0.9%
4170000 1
 
0.9%
3550000 1
 
0.9%
Other values (87) 87
82.1%
2024-05-11T10:21:43.435638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 424
57.1%
3 84
 
11.3%
4 50
 
6.7%
1 37
 
5.0%
5 36
 
4.9%
2 27
 
3.6%
8 23
 
3.1%
9 20
 
2.7%
6 18
 
2.4%
7 17
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 736
99.2%
Uppercase Letter 6
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 424
57.6%
3 84
 
11.4%
4 50
 
6.8%
1 37
 
5.0%
5 36
 
4.9%
2 27
 
3.7%
8 23
 
3.1%
9 20
 
2.7%
6 18
 
2.4%
7 17
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
B 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 736
99.2%
Latin 6
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 424
57.6%
3 84
 
11.4%
4 50
 
6.8%
1 37
 
5.0%
5 36
 
4.9%
2 27
 
3.7%
8 23
 
3.1%
9 20
 
2.7%
6 18
 
2.4%
7 17
 
2.3%
Latin
ValueCountFrequency (%)
B 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 424
57.1%
3 84
 
11.3%
4 50
 
6.7%
1 37
 
5.0%
5 36
 
4.9%
2 27
 
3.6%
8 23
 
3.1%
9 20
 
2.7%
6 18
 
2.4%
7 17
 
2.3%

제공기관명
Text

Distinct97
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size980.0 B
2024-05-11T10:21:44.009929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length8.4716981
Min length7

Characters and Unicode

Total characters898
Distinct characters90
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

Unique91 ?
Unique (%)85.8%

Sample

1st row서울특별시 중랑구
2nd row부산광역시 동래구
3rd row강원특별자치도 원주시
4th row충청남도 서산시
5th row서울특별시 강남구
ValueCountFrequency (%)
경기도 29
 
14.1%
서울특별시 20
 
9.7%
부산광역시 16
 
7.8%
전라남도 4
 
1.9%
남구 4
 
1.9%
인천광역시 4
 
1.9%
광주광역시 4
 
1.9%
중구 3
 
1.5%
강원특별자치도 3
 
1.5%
강원도 3
 
1.5%
Other values (92) 116
56.3%
2024-05-11T10:21:44.976306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 109
 
12.1%
100
 
11.1%
도 56
 
6.2%
구 56
 
6.2%
광 38
 
4.2%
경 34
 
3.8%
역 30
 
3.3%
기 29
 
3.2%
서 27
 
3.0%
산 26
 
2.9%
Other values (80) 393
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
88.9%
Space Separator 100
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 109
 
13.7%
도 56
 
7.0%
구 56
 
7.0%
광 38
 
4.8%
경 34
 
4.3%
역 30
 
3.8%
기 29
 
3.6%
서 27
 
3.4%
산 26
 
3.3%
특 26
 
3.3%
Other values (79) 367
46.0%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 798
88.9%
Common 100
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 109
 
13.7%
도 56
 
7.0%
구 56
 
7.0%
광 38
 
4.8%
경 34
 
4.3%
역 30
 
3.8%
기 29
 
3.6%
서 27
 
3.4%
산 26
 
3.3%
특 26
 
3.3%
Other values (79) 367
46.0%
Common
ValueCountFrequency (%)
100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
88.9%
ASCII 100
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 109
 
13.7%
도 56
 
7.0%
구 56
 
7.0%
광 38
 
4.8%
경 34
 
4.3%
역 30
 
3.8%
기 29
 
3.6%
서 27
 
3.4%
산 26
 
3.3%
특 26
 
3.3%
Other values (79) 367
46.0%
ASCII
ValueCountFrequency (%)
100
100.0%

Interactions

2024-05-11T10:21:14.971216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:11.057311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:12.304140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:13.599843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:15.318410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:11.500810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:12.637885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:13.872369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:15.614007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:11.753913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:12.930487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:14.224637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:15.942639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:12.041767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:13.289239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:21:14.628361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T10:21:45.277707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
견인차량보관소명소재지도로명주소소재지지번주소위도경도보관소전화번호보관소면적보관가능대수견인료기본요금견인료추가요금보관료관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
견인차량보관소명1.0000.9990.9960.9950.9971.0000.9811.0000.9941.0000.9970.9980.9930.9500.0000.000
소재지도로명주소0.9991.0000.9991.0001.0001.0000.9690.9920.9280.0000.9780.9970.9970.9830.8840.884
소재지지번주소0.9960.9991.0001.0001.0001.0001.0000.9950.9260.0000.9900.9920.9930.9860.9170.917
위도0.9951.0001.0001.0000.7701.0000.3780.7010.9370.9420.9800.9980.9970.7790.9970.997
경도0.9971.0001.0000.7701.0001.0000.0000.2070.8430.7720.9051.0001.0000.9681.0001.000
보관소전화번호1.0001.0001.0001.0001.0001.0000.9840.9970.9890.9640.9960.9970.9920.9140.0000.000
보관소면적0.9810.9691.0000.3780.0000.9841.0000.7790.9460.0000.9870.9740.8600.4170.7210.721
보관가능대수1.0000.9920.9950.7010.2070.9970.7791.0000.6650.9420.9880.9850.9780.0000.0000.000
견인료기본요금0.9940.9280.9260.9370.8430.9890.9460.6651.0000.9930.9981.0000.9990.9910.9990.999
견인료추가요금1.0000.0000.0000.9420.7720.9640.0000.9420.9931.0000.9961.0001.0000.9931.0001.000
보관료0.9970.9780.9900.9800.9050.9960.9870.9880.9980.9961.0001.0000.9990.9930.9990.999
관리기관전화번호0.9980.9970.9920.9981.0000.9970.9740.9851.0001.0001.0001.0001.0000.9970.9980.998
관리기관명0.9930.9970.9930.9971.0000.9920.8600.9780.9991.0000.9991.0001.0000.9991.0001.000
데이터기준일자0.9500.9830.9860.7790.9680.9140.4170.0000.9910.9930.9930.9970.9991.0001.0001.000
제공기관코드0.0000.8840.9170.9971.0000.0000.7210.0000.9991.0000.9990.9981.0001.0001.0001.000
제공기관명0.0000.8840.9170.9971.0000.0000.7210.0000.9991.0000.9990.9981.0001.0001.0001.000
2024-05-11T10:21:45.634737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도보관소면적보관가능대수
위도1.000-0.4960.1480.156
경도-0.4961.000-0.156-0.061
보관소면적0.148-0.1561.0000.595
보관가능대수0.156-0.0610.5951.000

Missing values

2024-05-11T10:21:16.465552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T10:21:17.269939image/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.
2024-05-11T10:21:17.849723image/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

견인차량보관소명소재지도로명주소소재지지번주소위도경도보관소전화번호보관소면적보관가능대수견인료기본요금견인료추가요금보관료관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
0면목견인보관소서울특별시 중랑구 면목천로 33서울특별시 중랑구 면목동168-237.579209127.08120702-492-8185<NA>64승용(경형 40000원+소형 45000원+중형 50000원+대형 60000원)+승합(경형 40000원+소형 60000원+중형 80000원+대형 140000원)+화물·특수(2.5톤미만 40000원+2.5톤이상 6.5톤미만 60000원+6.5톤이상 10톤미만 80000원+10톤이상 140000원)<NA>승용·승합(소형)·화물(6.5톤미만)·특수(6.5톤미만) 30분당 700원+승합(중형·대형)·화물(6.5톤이상)·특수(6.5톤이상) 1200원+1회 보관료 한도 50만 원02-2094-2634서울특별시 중랑구청2023-05-023060000서울특별시 중랑구
1제일견인(제3지구 견인관리소)부산광역시 동래구 중앙대로 1520부산광역시 동래구 온천동 1750-135.220286129.086378051-517-0507890.0402.5톤 미만 40000원+2.5톤 이상 45000원1000원(매km 추가시)30분당 700원(1일 15000원)051-550-4562부산광역시 동래구청2023-06-143300000부산광역시 동래구
2원주시 견인차사무소강원도 원주시 원일로 242강원도 원주시 학성동 469-137.357605127.943134000-0000-00002860.05020,000원~40,000원<NA>30분600원+30분초과시 10분당 300원(최초 1시간 면제 이후 요금 부과)033-737-3546강원도 원주시청2022-11-024191000강원특별자치도 원주시
3무단방치차량 견인보관소충청남도 서산시 충의로 359-33충청남도 서산시 성연면 일람리 809-336.804491126.43823041-660-23181000.02560000원없음없음041-660-2318충청남도 서산시 교통과2023-07-274530000충청남도 서산시
4강남구 견인차량보관소<NA>서울특별시 강남구 대치동 78-2937.505237127.06818302-558-72306099.0200승용(경형 40000원+소형 45000원+중형 50000원+대형 60000원)+승합(경형 40000원+소형 60000원+중형 80000원+대형 140000원)+이륜 40000원+개인형 이동장치 40000원+화물 및 특수(2.5톤 미만 40000원+2.5톤 이상 6.5톤 미만 60000원+6.5톤 이상 10톤 미만 80000원+10톤 이상 140000원)없음차종별 기본 30분 700원 또는 1200원 (단 1회 보관료는 50만원 한도)1544-3113강남구도시관리공단2023-07-263220000서울특별시 강남구
5수원시견인차량보관소<NA>경기도 수원시 권선구 대황교동 258-237.237016127.018155031-238-05602027.08030000<NA>최초 400원+30분 이후 10분 초과시 100원 가산+하루최대 3500원+월 최대 40000원031-238-0560수원도시공사 주차사업부 견인거주자팀2023-07-243740000경기도 수원시
6서구견인차량보관소인천광역시 서구 경명대로 265인천광역시 서구 경서동 374-1037.557983126.638884032-571-7714956.029경영승용차, 경형승합차(화물차) 40000원+승용차,화물차(2.5톤 미만) 50000원+승합차,화물차(2.5톤이상) 60000원<NA>기본 30분 1000원+30분 이후 15분당 500원+최초 24시간 보관료24000원+1일 경과시:10000원 추가032-560-5942인천광역시 서구청2023-07-203560000인천광역시 서구
7사상견인차량보관소부산광역시 사상구 가야대로 309부산광역시 사상구 주례동 1233-735.151033129.006434051-328-0245909.0302.5톤 미만 40000원+2.5톤 이상 6.5톤 미만 45000원+6.5톤 이상 50000원2.5톤 미만 1000원+2.5톤 이상 6.5톤 미만 1400원+6.5톤 이상 2500원30분당700원(보관일로부터 1개월까지 부과+1일 15000원을 상한으로 한다)051-310-4506부산광역시 사상구청 교통행정과 교통지도계2023-07-173390000부산광역시 사상구
8광역자동차해체재활용산업대구광역시 서구 가르뱅이로10길 20대구광역시 서구 이현동 58835.883486128.53435053-352-00501400.0500300001일 10000최고 30만원053-661-3033대구광역시 중구청2023-07-103410000대구광역시 중구
9부산광역시 사하구 견인보관소부산광역시 사하구 장림로 106(장림동)<NA>35.081668128.960803051-265-6184660.0402.5톤 미만 40000원+2.5톤 이상 6.5톤 미만 45000원+6.5톤 이상 50000원2.5톤 미만 1000원+2.5톤 이상 6.5톤 미만 1400원+6.5톤 이상 2500원30분당 700원(보관일로부터 1개월까지 부과징수, 1일 15000원을 상한으로 한다)051-220-4562부산광역시 사하구청2023-07-143340000부산광역시 사하구
견인차량보관소명소재지도로명주소소재지지번주소위도경도보관소전화번호보관소면적보관가능대수견인료기본요금견인료추가요금보관료관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
96안산시 견인차량보관소경기도 안산시 단원구 동산로 216경기도 안산시 단원구 초지동 666-237.321062126.809843031-439-63003500.0912.5톤미만 30000원+2.5톤이상 6.5톤미만 40000원+6.5톤이상 60000원2.5톤미만 1000원+2.5톤이상 6.5톤미만 1400원+6.5톤이상 2500원기본 30분 300원+30분 초과 시 10분당 200원+1일 최대 4500원031-481-2498경기도 안산시청(안산도시공사)2023-08-073930000경기도 안산시
97강북구견인차량보관소서울특별시 강북구 삼양로 678서울특별시 강북구 우이동 99-237.663371127.01266502-944-30271707.065승용자동차(경형:40000원+소형:45000원+중형:50000원+대형:60000원)+승합자동차(경형:40000원+소형:60000원+중형:80000원+대형:140000원)+이륜자동차:40000원+화물자동차 및 특수자동차(2.5톤미만:40000원+2.5톤이상6.5톤미만:60000원+6.5톤이상10톤미만:80000원+10톤이상:140000원)<NA>30분당 700원(승합자동차중 중형과 대형, 화물 및 특수자동차중 6.5톤이상은 30분당 1200원). 단, 1회 보관료는 50만원을 한도로 한다.02-944-3024서울특별시 강북구도시관리공단2023-08-01B551282강북구도시관리공단
98성북구견인차량보관소서울특별시 성북구 동소문로 44길 44-1서울특별시 성북구 하월곡동 88-837.60595127.0303402-6925-20041006.02140000<NA>30분당 700원(승합자동차 중 중형과 대형 및 화물자동차 중 6.5톤 이상은 1200원) * 단 1회 보관료는 50만원을 한도로 한다.02-6925-2004성북구도시관리공단2023-08-173070000서울특별시 성북구
99강동구견인차량보관소서울특별시 강동구 아리수로91길 54서울특별시 강동구 665-4(강일동)37.565982127.17227802-3425-69352650.07340000원~140000원<NA>30분당 700원02-424-0101강동운수2023-08-183240000서울특별시 강동구
100양천구 견인차량보관소서울특별시 양천구 목동동로 298서울특별시 양천구 목동 91537.528132126.87652302-2643-13371855.072경형 40000원+소형 45000원+중형 50000원+대형60000원<NA>기본 30분 700원+30분당 700원02-2620-3736서울특별시 양천구청2023-08-143140000서울특별시 양천구
101부산광역시 북구 견인보관소부산광역시 북구 만덕대로65번길 83(덕천동)부산광역시 북구 덕천동 457-3835.21542129.010548051-335-7551210.0402.5톤미만 40000원+2.5톤이상 6.5톤미만 45000원+6.5톤이상 50000원2.5톤미만 1000원+2.5톤이상 6.5톤미만 1400원+6.5톤이상 2500원30분당 700원(보관일로부터 1개월까지 부과징수+1일 15000원 상한)051-309-4568부산광역시 북구청2023-08-113320000부산광역시 북구
102부산견인운수<NA>부산광역시 서구 암남동 95-1335.080601129.024312051-254-6441660.05540000매 km 증가시 1000원30분당 700원051-419-4556부산광역시 영도구청2023-08-013280000부산광역시 영도구
103금천구 견인차량보관소서울특별시 금천구 가산디지털2로 169-34서울특별시 금천구 가산동 614-3437.483304126.87604702-854-52732514.031승용차(경형 40000원+소형 45000원+중형 50000원+대형 60000원)+승합차(경형 40000원+소형 60000원+중형 80000원+대형 140000원)+화물차ㆍ특수차(2.5톤 미만 40000원+2.5톤 이상 6.5톤 미만 60000원+6.5톤 이상 10톤 미만 80000원+10톤 이상 140000원)<NA>6.5톤 미만: 30분당 700원+6.5톤 이상 : 30분 1200원 (* 단 보관료 한도 1회 500000원)02-854-5274금천구시설관리공단2023-08-073170000서울특별시 금천구
104북구 견인보관소광주광역시 북구 서방로 16-10광주광역시 북구 중흥동 280-3435.171885126.914487062-521-06066782.0132.5톤 미만 30000원+2.5톤이상 6.5톤 미만 35000원+6.5톤 이상 50000원2.5톤 미만 km당 1000원+2.5톤 이상 6.5톤 미만 km당 1400원+6.5톤 이상 km당 2500원1일 3900원+3시간 2000원062-410-8918광주광역시 북구청2023-12-213620000광주광역시 북구
105무단방치차량보관소강원특별자치도 태백시 태붐로 21강원특별자치도 태백시 황지동 244-337.164217128.986551033-550-2105758.58000033-550-2105강원특별자치도 태백시2023-12-074221000강원특별자치도 태백시