Overview

Dataset statistics

Number of variables5
Number of observations1736
Missing cells28
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.0 KiB
Average record size in memory43.1 B

Variable types

Text2
Numeric3

Dataset

Description전라북도 군산시 관내 버스정류장 현황에 대한 세부 위치 등 1. 버스정류장 명칭 및 행정구역번호 2. 소재지 주소 3. 좌표(경도, 위도 등)
URLhttps://www.data.go.kr/data/15030105/fileData.do

Alerts

행정동코드 has 28 (1.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 12:15:44.909741
Analysis finished2023-12-12 12:15:46.606028
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류장명
Text

Distinct896
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
2023-12-12T21:15:46.840894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.2402074
Min length2

Characters and Unicode

Total characters7361
Distinct characters355
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique348 ?
Unique (%)20.0%

Sample

1st row한산
2nd row함열
3rd row현대코아사거리
4th row회선
5th row후죽
ValueCountFrequency (%)
신기 10
 
0.6%
와룡 9
 
0.5%
척동 8
 
0.5%
함열 8
 
0.5%
신어은 7
 
0.4%
석화 7
 
0.4%
원오곡 6
 
0.3%
충량마을 6
 
0.3%
창오 6
 
0.3%
신성산 6
 
0.3%
Other values (886) 1663
95.8%
2023-12-12T21:15:47.437592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 325
 
4.4%
을 298
 
4.0%
산 257
 
3.5%
리 225
 
3.1%
사 162
 
2.2%
거 162
 
2.2%
신 150
 
2.0%
원 143
 
1.9%
동 139
 
1.9%
대 135
 
1.8%
Other values (345) 5365
72.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7217
98.0%
Decimal Number 72
 
1.0%
Uppercase Letter 35
 
0.5%
Open Punctuation 14
 
0.2%
Close Punctuation 14
 
0.2%
Other Punctuation 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 325
 
4.5%
을 298
 
4.1%
산 257
 
3.6%
리 225
 
3.1%
사 162
 
2.2%
거 162
 
2.2%
신 150
 
2.1%
원 143
 
2.0%
동 139
 
1.9%
대 135
 
1.9%
Other values (321) 5221
72.3%
Uppercase Letter
ValueCountFrequency (%)
A 6
17.1%
T 6
17.1%
P 5
14.3%
S 4
11.4%
C 3
8.6%
I 2
 
5.7%
B 2
 
5.7%
L 2
 
5.7%
O 1
 
2.9%
H 1
 
2.9%
Other values (3) 3
8.6%
Decimal Number
ValueCountFrequency (%)
1 28
38.9%
3 17
23.6%
2 14
19.4%
4 6
 
8.3%
5 4
 
5.6%
6 2
 
2.8%
8 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 8
88.9%
& 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7217
98.0%
Common 109
 
1.5%
Latin 35
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 325
 
4.5%
을 298
 
4.1%
산 257
 
3.6%
리 225
 
3.1%
사 162
 
2.2%
거 162
 
2.2%
신 150
 
2.1%
원 143
 
2.0%
동 139
 
1.9%
대 135
 
1.9%
Other values (321) 5221
72.3%
Latin
ValueCountFrequency (%)
A 6
17.1%
T 6
17.1%
P 5
14.3%
S 4
11.4%
C 3
8.6%
I 2
 
5.7%
B 2
 
5.7%
L 2
 
5.7%
O 1
 
2.9%
H 1
 
2.9%
Other values (3) 3
8.6%
Common
ValueCountFrequency (%)
1 28
25.7%
3 17
15.6%
( 14
12.8%
) 14
12.8%
2 14
12.8%
. 8
 
7.3%
4 6
 
5.5%
5 4
 
3.7%
6 2
 
1.8%
8 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7217
98.0%
ASCII 144
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 325
 
4.5%
을 298
 
4.1%
산 257
 
3.6%
리 225
 
3.1%
사 162
 
2.2%
거 162
 
2.2%
신 150
 
2.1%
원 143
 
2.0%
동 139
 
1.9%
대 135
 
1.9%
Other values (321) 5221
72.3%
ASCII
ValueCountFrequency (%)
1 28
19.4%
3 17
11.8%
( 14
9.7%
) 14
9.7%
2 14
9.7%
. 8
 
5.6%
A 6
 
4.2%
4 6
 
4.2%
T 6
 
4.2%
P 5
 
3.5%
Other values (14) 26
18.1%

소재지주소
Text

Distinct1520
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
2023-12-12T21:15:47.860793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length22.614631
Min length13

Characters and Unicode

Total characters39259
Distinct characters137
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

Unique1311 ?
Unique (%)75.5%

Sample

1st row전라북도 군산시 소룡동 1643-4번지
2nd row전라북도 익산시 함열읍 와리 551-4번지
3rd row전라북도 군산시 나운동 529-2번지
4th row전라북도 익산시 성당면 와초리 130-4번지
5th row전라북도 군산시 나포면 부곡리 265-3번지
ValueCountFrequency (%)
전라북도 1701
21.2%
군산시 1620
20.2%
옥구읍 133
 
1.7%
대야면 110
 
1.4%
임피면 107
 
1.3%
회현면 100
 
1.2%
나포면 97
 
1.2%
소룡동 96
 
1.2%
성산면 95
 
1.2%
서수면 89
 
1.1%
Other values (1611) 3882
48.3%
2023-12-12T21:15:48.481803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6305
 
16.1%
산 2103
 
5.4%
도 1882
 
4.8%
북 1783
 
4.5%
지 1778
 
4.5%
라 1727
 
4.4%
번 1722
 
4.4%
시 1718
 
4.4%
전 1701
 
4.3%
군 1657
 
4.2%
Other values (127) 16883
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24248
61.8%
Decimal Number 7150
 
18.2%
Space Separator 6305
 
16.1%
Dash Punctuation 1556
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2103
 
8.7%
도 1882
 
7.8%
북 1783
 
7.4%
지 1778
 
7.3%
라 1727
 
7.1%
번 1722
 
7.1%
시 1718
 
7.1%
전 1701
 
7.0%
군 1657
 
6.8%
리 1093
 
4.5%
Other values (115) 7084
29.2%
Decimal Number
ValueCountFrequency (%)
1 1348
18.9%
2 927
13.0%
3 813
11.4%
5 749
10.5%
6 648
9.1%
4 648
9.1%
8 530
 
7.4%
7 527
 
7.4%
9 493
 
6.9%
0 467
 
6.5%
Space Separator
ValueCountFrequency (%)
6305
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24248
61.8%
Common 15011
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2103
 
8.7%
도 1882
 
7.8%
북 1783
 
7.4%
지 1778
 
7.3%
라 1727
 
7.1%
번 1722
 
7.1%
시 1718
 
7.1%
전 1701
 
7.0%
군 1657
 
6.8%
리 1093
 
4.5%
Other values (115) 7084
29.2%
Common
ValueCountFrequency (%)
6305
42.0%
- 1556
 
10.4%
1 1348
 
9.0%
2 927
 
6.2%
3 813
 
5.4%
5 749
 
5.0%
6 648
 
4.3%
4 648
 
4.3%
8 530
 
3.5%
7 527
 
3.5%
Other values (2) 960
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24248
61.8%
ASCII 15011
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6305
42.0%
- 1556
 
10.4%
1 1348
 
9.0%
2 927
 
6.2%
3 813
 
5.4%
5 749
 
5.0%
6 648
 
4.3%
4 648
 
4.3%
8 530
 
3.5%
7 527
 
3.5%
Other values (2) 960
 
6.4%
Hangul
ValueCountFrequency (%)
산 2103
 
8.7%
도 1882
 
7.8%
북 1783
 
7.4%
지 1778
 
7.3%
라 1727
 
7.1%
번 1722
 
7.1%
시 1718
 
7.1%
전 1701
 
7.0%
군 1657
 
6.8%
리 1093
 
4.5%
Other values (115) 7084
29.2%

행정동코드
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)2.1%
Missing28
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean4.5124968 × 109
Minimum4.477025 × 109
Maximum4.580036 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-12-12T21:15:48.682168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.477025 × 109
5-th percentile4.513025 × 109
Q14.513033 × 109
median4.513038 × 109
Q34.513069 × 109
95-th percentile4.513072 × 109
Maximum4.580036 × 109
Range1.03011 × 108
Interquartile range (IQR)36000

Descriptive statistics

Standard deviation5467265.7
Coefficient of variation (CV)0.0012115833
Kurtosis47.212742
Mean4.5124968 × 109
Median Absolute Deviation (MAD)13000
Skewness-4.4407155
Sum7.7073445 × 1012
Variance2.9890994 × 1013
MonotonicityNot monotonic
2023-12-12T21:15:48.863983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4513071000 147
 
8.5%
4513025000 133
 
7.7%
4513035000 110
 
6.3%
4513033000 106
 
6.1%
4513032000 100
 
5.8%
4513038000 97
 
5.6%
4513037000 95
 
5.5%
4513034000 89
 
5.1%
4513069000 86
 
5.0%
4513031000 77
 
4.4%
Other values (26) 668
38.5%
ValueCountFrequency (%)
4477025000 5
 
0.3%
4477025300 1
 
0.1%
4477031000 29
 
1.7%
4513025000 133
7.7%
4513031000 77
4.4%
4513032000 100
5.8%
4513033000 106
6.1%
4513034000 89
5.1%
4513035000 110
6.3%
4513036000 70
4.0%
ValueCountFrequency (%)
4580036000 1
 
0.1%
4521039000 25
 
1.4%
4514035000 8
 
0.5%
4514034000 10
 
0.6%
4514033000 26
 
1.5%
4514025000 11
 
0.6%
4513072000 70
4.0%
4513071000 147
8.5%
4513070300 67
3.9%
4513070200 17
 
1.0%

경도
Real number (ℝ)

Distinct1723
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.74259
Minimum126.43873
Maximum126.96758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-12-12T21:15:49.041813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.43873
5-th percentile126.56791
Q1126.68618
median126.73602
Q3126.81971
95-th percentile126.88175
Maximum126.96758
Range0.528851
Interquartile range (IQR)0.1335305

Descriptive statistics

Standard deviation0.09485366
Coefficient of variation (CV)0.0007483961
Kurtosis0.0058396845
Mean126.74259
Median Absolute Deviation (MAD)0.0625305
Skewness-0.32599343
Sum220025.14
Variance0.0089972168
MonotonicityNot monotonic
2023-12-12T21:15:49.252253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.729817 3
 
0.2%
126.737899 2
 
0.1%
126.829956 2
 
0.1%
126.855234 2
 
0.1%
126.75605 2
 
0.1%
126.797878 2
 
0.1%
126.705609 2
 
0.1%
126.797528 2
 
0.1%
126.78652 2
 
0.1%
126.710422 2
 
0.1%
Other values (1713) 1715
98.8%
ValueCountFrequency (%)
126.438732 1
0.1%
126.438931 1
0.1%
126.449631 1
0.1%
126.449893 1
0.1%
126.453559 1
0.1%
126.453773 1
0.1%
126.461102 1
0.1%
126.461846 1
0.1%
126.472203 1
0.1%
126.472333 1
0.1%
ValueCountFrequency (%)
126.967583 1
0.1%
126.967231 1
0.1%
126.967096 1
0.1%
126.965969 1
0.1%
126.965043 1
0.1%
126.96055 1
0.1%
126.9599 1
0.1%
126.958954 1
0.1%
126.958764 1
0.1%
126.957883 1
0.1%

위도
Real number (ℝ)

Distinct1722
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.961961
Minimum35.72905
Maximum36.080688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-12-12T21:15:49.475486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.72905
5-th percentile35.903019
Q135.937237
median35.963542
Q335.983775
95-th percentile36.034063
Maximum36.080688
Range0.351638
Interquartile range (IQR)0.04653825

Descriptive statistics

Standard deviation0.042339639
Coefficient of variation (CV)0.0011773451
Kurtosis2.0143443
Mean35.961961
Median Absolute Deviation (MAD)0.022461
Skewness-0.27333389
Sum62429.964
Variance0.001792645
MonotonicityNot monotonic
2023-12-12T21:15:49.670204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.9599 3
 
0.2%
35.9639 2
 
0.1%
35.918511 2
 
0.1%
35.96056 2
 
0.1%
35.96385 2
 
0.1%
35.950344 2
 
0.1%
35.985983 2
 
0.1%
35.936012 2
 
0.1%
35.959667 2
 
0.1%
35.916891 2
 
0.1%
Other values (1712) 1715
98.8%
ValueCountFrequency (%)
35.72905 1
0.1%
35.762727 1
0.1%
35.779764 1
0.1%
35.796515 1
0.1%
35.80833 1
0.1%
35.808347 1
0.1%
35.81145 1
0.1%
35.814292 1
0.1%
35.814452 1
0.1%
35.816458 1
0.1%
ValueCountFrequency (%)
36.080688 1
0.1%
36.079219 1
0.1%
36.079005 1
0.1%
36.07895 1
0.1%
36.078717 1
0.1%
36.078561 1
0.1%
36.078533 1
0.1%
36.078083 1
0.1%
36.07792 1
0.1%
36.077709 1
0.1%

Interactions

2023-12-12T21:15:46.061503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:45.366092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:45.729165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:46.180226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:45.469416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:45.860382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:46.297832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:45.583954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:15:45.963609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:15:49.792982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드경도위도
행정동코드1.0000.3660.796
경도0.3661.0000.871
위도0.7960.8711.000
2023-12-12T21:15:50.239537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드경도위도
행정동코드1.000-0.2780.230
경도-0.2781.0000.429
위도0.2300.4291.000

Missing values

2023-12-12T21:15:46.449533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:15:46.563025image/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

정류장명소재지주소행정동코드경도위도
0한산전라북도 군산시 소룡동 1643-4번지4513071000126.59600335.957212
1함열전라북도 익산시 함열읍 와리 551-4번지4514025000126.95876436.079005
2현대코아사거리전라북도 군산시 나운동 529-2번지4513070100126.70444335.969821
3회선전라북도 익산시 성당면 와초리 130-4번지4514035000126.9356836.071161
4후죽전라북도 군산시 나포면 부곡리 265-3번지4513038000126.8273636.003254
5신오산촌마을전라북도 군산시 옥서면 옥봉리 1616-1번지4513040000126.63901635.910186
6신관동전라북도 군산시 신관동 643-3번지4513070300126.67307835.944099
7군산항6부두전라북도 군산시 오식도동 516번지4513071000126.55443335.9712
8한서울전라북도 군산시 옥구읍 오곡리 434-3번지4513025000126.68420235.902787
9시영아파트전라북도 군산시 산북동 3529-3번지4513072000126.67531135.968459
정류장명소재지주소행정동코드경도위도
1726송정써미트아파트전라북도 군산시 조촌동 산203-8번지4513065000126.7387535.960683
1727송정써미트아파트전라북도 군산시 조촌동 240-16번지4513065000126.73856735.960833
1728풍경채아파트정문전라북도 군산시 미장동 62-66번지4513069000126.7350535.963367
1729쌍용예가아파트전라북도 군산시 옥산면 당북리 430-14번지4513031000126.71538335.952267
1730쌍용예가아파트전라북도 군산시 옥산면 당북리 430-18번지4513031000126.71593335.952283
1731당북초등학교전라북도 군산시 옥산면 당북리 44-23번지4513031000126.71841735.947
1732당북초등학교전라북도 군산시 옥산면 당북리 435-17번지4513031000126.71748335.9475
1733쌍봉교차로전라북도 군산시 옥산면 옥산리 166-29번지4513031000126.74691735.947333
1734쌍봉교차로전라북도 군산시 옥산면 쌍봉리 742-47번지4513031000126.746935.9475
1735군산남고전라북도 군산시 대야면 지경리 699-228번지4513035000126.80866735.9434