Overview

Dataset statistics

Number of variables7
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory66.9 B

Variable types

Text1
Numeric6

Dataset

Description전국 운전면허시험장 별 운전면허증 발급현황에 관련된 데이터 입니다. 해당 데이터를 통해서 업무별(적성검사, 재발급 등) 운전면허증 발급현황도 확인하실 수 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/3047226/fileData.do

Alerts

소계 is highly overall correlated with 면허발급 and 4 other fieldsHigh correlation
면허발급 is highly overall correlated with 소계 and 4 other fieldsHigh correlation
재발급 is highly overall correlated with 소계 and 4 other fieldsHigh correlation
적성검사 is highly overall correlated with 소계 and 4 other fieldsHigh correlation
면허갱신 is highly overall correlated with 소계 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 소계 and 4 other fieldsHigh correlation
구분 has unique valuesUnique
소계 has unique valuesUnique
면허발급 has unique valuesUnique
재발급 has unique valuesUnique
적성검사 has unique valuesUnique
면허갱신 has unique valuesUnique
기타 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:13:42.466306
Analysis finished2024-04-17 19:13:45.100188
Duration2.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-04-18T04:13:45.209282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1851852
Min length2

Characters and Unicode

Total characters59
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row강남
2nd row도봉
3rd row강서
4th row서부
5th row부산북부
ValueCountFrequency (%)
강남 1
 
3.7%
원주 1
 
3.7%
마산 1
 
3.7%
포항 1
 
3.7%
문경 1
 
3.7%
광양 1
 
3.7%
전남 1
 
3.7%
전북 1
 
3.7%
예산 1
 
3.7%
대전 1
 
3.7%
Other values (17) 17
63.0%
2024-04-18T04:13:45.476911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 6
 
10.2%
산 6
 
10.2%
주 4
 
6.8%
강 3
 
5.1%
전 3
 
5.1%
남 3
 
5.1%
대 2
 
3.4%
인 2
 
3.4%
천 2
 
3.4%
북 2
 
3.4%
Other values (25) 26
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 6
 
10.2%
산 6
 
10.2%
주 4
 
6.8%
강 3
 
5.1%
전 3
 
5.1%
남 3
 
5.1%
대 2
 
3.4%
인 2
 
3.4%
천 2
 
3.4%
북 2
 
3.4%
Other values (25) 26
44.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 6
 
10.2%
산 6
 
10.2%
주 4
 
6.8%
강 3
 
5.1%
전 3
 
5.1%
남 3
 
5.1%
대 2
 
3.4%
인 2
 
3.4%
천 2
 
3.4%
북 2
 
3.4%
Other values (25) 26
44.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 6
 
10.2%
산 6
 
10.2%
주 4
 
6.8%
강 3
 
5.1%
전 3
 
5.1%
남 3
 
5.1%
대 2
 
3.4%
인 2
 
3.4%
천 2
 
3.4%
북 2
 
3.4%
Other values (25) 26
44.1%

소계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123961.44
Minimum18132
Maximum360576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-18T04:13:45.789121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18132
5-th percentile32252.7
Q165420.5
median116273
Q3169641.5
95-th percentile252840.6
Maximum360576
Range342444
Interquartile range (IQR)104221

Descriptive statistics

Standard deviation79279.749
Coefficient of variation (CV)0.63955167
Kurtosis1.6586188
Mean123961.44
Median Absolute Deviation (MAD)55529
Skewness1.0695442
Sum3346959
Variance6.2852787 × 109
MonotonicityNot monotonic
2024-04-18T04:13:45.874728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
265860 1
 
3.7%
157253 1
 
3.7%
49059 1
 
3.7%
174912 1
 
3.7%
75438 1
 
3.7%
76328 1
 
3.7%
55403 1
 
3.7%
150722 1
 
3.7%
116518 1
 
3.7%
146649 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
18132 1
3.7%
31959 1
3.7%
32938 1
3.7%
33702 1
3.7%
49059 1
3.7%
50602 1
3.7%
55403 1
3.7%
75438 1
3.7%
76328 1
3.7%
78164 1
3.7%
ValueCountFrequency (%)
360576 1
3.7%
265860 1
3.7%
222462 1
3.7%
182235 1
3.7%
180814 1
3.7%
174912 1
3.7%
171802 1
3.7%
167481 1
3.7%
157253 1
3.7%
150722 1
3.7%

면허발급
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16923.407
Minimum1565
Maximum44254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-18T04:13:45.962698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1565
5-th percentile4110
Q19009
median16085
Q322370.5
95-th percentile36226.8
Maximum44254
Range42689
Interquartile range (IQR)13361.5

Descriptive statistics

Standard deviation10940.5
Coefficient of variation (CV)0.64647146
Kurtosis0.1407246
Mean16923.407
Median Absolute Deviation (MAD)6669
Skewness0.77397765
Sum456932
Variance1.1969454 × 108
MonotonicityNot monotonic
2024-04-18T04:13:46.046675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
36759 1
 
3.7%
22714 1
 
3.7%
6682 1
 
3.7%
18634 1
 
3.7%
10082 1
 
3.7%
5394 1
 
3.7%
9416 1
 
3.7%
24068 1
 
3.7%
16255 1
 
3.7%
20544 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1565 1
3.7%
4107 1
3.7%
4117 1
3.7%
4777 1
3.7%
5394 1
3.7%
6682 1
3.7%
8602 1
3.7%
9416 1
3.7%
10082 1
3.7%
10105 1
3.7%
ValueCountFrequency (%)
44254 1
3.7%
36759 1
3.7%
34985 1
3.7%
29736 1
3.7%
28297 1
3.7%
24068 1
3.7%
22714 1
3.7%
22027 1
3.7%
20544 1
3.7%
20329 1
3.7%

재발급
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19574.519
Minimum1237
Maximum60728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-18T04:13:46.134360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1237
5-th percentile4588.6
Q19549
median17143
Q324901.5
95-th percentile45188.4
Maximum60728
Range59491
Interquartile range (IQR)15352.5

Descriptive statistics

Standard deviation14010.631
Coefficient of variation (CV)0.71575866
Kurtosis1.8824597
Mean19574.519
Median Absolute Deviation (MAD)7732
Skewness1.2349212
Sum528512
Variance1.9629779 × 108
MonotonicityNot monotonic
2024-04-18T04:13:46.225052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
50103 1
 
3.7%
23838 1
 
3.7%
9411 1
 
3.7%
24795 1
 
3.7%
10705 1
 
3.7%
9687 1
 
3.7%
8473 1
 
3.7%
22617 1
 
3.7%
16776 1
 
3.7%
21461 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1237 1
3.7%
4576 1
3.7%
4618 1
3.7%
5045 1
3.7%
5255 1
3.7%
8473 1
3.7%
9411 1
3.7%
9687 1
3.7%
10705 1
3.7%
11465 1
3.7%
ValueCountFrequency (%)
60728 1
3.7%
50103 1
3.7%
33721 1
3.7%
32960 1
3.7%
29860 1
3.7%
29580 1
3.7%
25008 1
3.7%
24795 1
3.7%
24477 1
3.7%
23838 1
3.7%

적성검사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30023.481
Minimum2618
Maximum87699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-18T04:13:46.313091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2618
5-th percentile8637.2
Q116485.5
median27438
Q339093
95-th percentile54094.8
Maximum87699
Range85081
Interquartile range (IQR)22607.5

Descriptive statistics

Standard deviation18642.4
Coefficient of variation (CV)0.62092732
Kurtosis2.0253789
Mean30023.481
Median Absolute Deviation (MAD)12181
Skewness1.0185186
Sum810634
Variance3.4753907 × 108
MonotonicityNot monotonic
2024-04-18T04:13:46.400402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
55887 1
 
3.7%
38161 1
 
3.7%
11723 1
 
3.7%
49913 1
 
3.7%
20507 1
 
3.7%
25549 1
 
3.7%
13486 1
 
3.7%
37345 1
 
3.7%
31143 1
 
3.7%
38567 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
2618 1
3.7%
8600 1
3.7%
8724 1
3.7%
9153 1
3.7%
9307 1
3.7%
11723 1
3.7%
13486 1
3.7%
19485 1
3.7%
19846 1
3.7%
20507 1
3.7%
ValueCountFrequency (%)
87699 1
3.7%
55887 1
3.7%
49913 1
3.7%
49095 1
3.7%
44603 1
3.7%
41927 1
3.7%
39619 1
3.7%
38567 1
3.7%
38161 1
3.7%
37345 1
3.7%

면허갱신
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9812.1481
Minimum520
Maximum32697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-18T04:13:46.483664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile2124.4
Q13867
median7749
Q314599.5
95-th percentile24358.3
Maximum32697
Range32177
Interquartile range (IQR)10732.5

Descriptive statistics

Standard deviation7836.0986
Coefficient of variation (CV)0.79861194
Kurtosis1.7626375
Mean9812.1481
Median Absolute Deviation (MAD)5039
Skewness1.2994705
Sum264928
Variance61404442
MonotonicityNot monotonic
2024-04-18T04:13:46.571147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26833 1
 
3.7%
12467 1
 
3.7%
2710 1
 
3.7%
14570 1
 
3.7%
4607 1
 
3.7%
5315 1
 
3.7%
3127 1
 
3.7%
9233 1
 
3.7%
7054 1
 
3.7%
10870 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
520 1
3.7%
2101 1
3.7%
2179 1
3.7%
2190 1
3.7%
2191 1
3.7%
2710 1
3.7%
3127 1
3.7%
4607 1
3.7%
5315 1
3.7%
5634 1
3.7%
ValueCountFrequency (%)
32697 1
3.7%
26833 1
3.7%
18584 1
3.7%
18557 1
3.7%
15023 1
3.7%
14756 1
3.7%
14629 1
3.7%
14570 1
3.7%
12467 1
3.7%
10870 1
3.7%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47627.889
Minimum12192
Maximum135198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-04-18T04:13:46.659918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12192
5-th percentile12533.4
Q127310.5
median43107
Q363330
95-th percentile93217.7
Maximum135198
Range123006
Interquartile range (IQR)36019.5

Descriptive statistics

Standard deviation29040.525
Coefficient of variation (CV)0.60973782
Kurtosis1.7440349
Mean47627.889
Median Absolute Deviation (MAD)19510
Skewness1.0810173
Sum1285953
Variance8.433521 × 108
MonotonicityNot monotonic
2024-04-18T04:13:46.750157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
96278 1
 
3.7%
60073 1
 
3.7%
18533 1
 
3.7%
67000 1
 
3.7%
29537 1
 
3.7%
30383 1
 
3.7%
20901 1
 
3.7%
57459 1
 
3.7%
45290 1
 
3.7%
55207 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
12192 1
3.7%
12351 1
3.7%
12959 1
3.7%
13090 1
3.7%
18533 1
3.7%
20901 1
3.7%
25259 1
3.7%
29362 1
3.7%
29537 1
3.7%
30383 1
3.7%
ValueCountFrequency (%)
135198 1
3.7%
96278 1
3.7%
86077 1
3.7%
72524 1
3.7%
67000 1
3.7%
65786 1
3.7%
64043 1
3.7%
62617 1
3.7%
60073 1
3.7%
57459 1
3.7%

Interactions

2024-04-18T04:13:44.588274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:42.658749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.037070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.384351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.758901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.116634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.650320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:42.724886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.097759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.449993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.819746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.194501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.711500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:42.783972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.151505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.507066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.875950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.268077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.774467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:42.847707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.211850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.573151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.933309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.351835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.840817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:42.908278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.265755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.631548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.990565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.424133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.902463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:42.973260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.325761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:43.693785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.056896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:13:44.503440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:13:46.819214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분소계면허발급재발급적성검사면허갱신기타
구분1.0001.0001.0001.0001.0001.0001.000
소계1.0001.0000.9260.9820.9620.9650.951
면허발급1.0000.9261.0000.8650.8640.8530.892
재발급1.0000.9820.8651.0000.9570.9780.880
적성검사1.0000.9620.8640.9571.0000.9480.815
면허갱신1.0000.9650.8530.9780.9481.0000.886
기타1.0000.9510.8920.8800.8150.8861.000
2024-04-18T04:13:46.903651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소계면허발급재발급적성검사면허갱신기타
소계1.0000.9490.9840.9750.9710.995
면허발급0.9491.0000.9280.9140.9040.940
재발급0.9840.9281.0000.9520.9870.979
적성검사0.9750.9140.9521.0000.9430.978
면허갱신0.9710.9040.9870.9431.0000.959
기타0.9950.9400.9790.9780.9591.000

Missing values

2024-04-18T04:13:44.986582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:13:45.068392image/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

구분소계면허발급재발급적성검사면허갱신기타
0강남2658603675950103558872683396278
1도봉1572532271423838381611246760073
2강서2224623498533721490951858486077
3서부1718022829729860360051502362617
4부산북부93913160851349220513598937834
5부산남부1162731599419690274341004843107
6대구167481297362447739619960664043
7인천1808142202729580419271475672524
8울산82242101051179119485563435227
9용인36057644254607288769932697135198
구분소계면허발급재발급적성검사면허갱신기타
17충주32938410746189153210112959
18대전111161168011714327438774942030
19예산1466492054421461385671087055207
20전북116518162551677631143705445290
21전남150722240682261737345923357459
22광양554039416847313486312720901
23문경763285394968725549531530383
24포항75438100821070520507460729537
25마산1749121863424795499131457067000
26제주490596682941111723271018533