Overview

Dataset statistics

Number of variables9
Number of observations385
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.8 KiB
Average record size in memory79.3 B

Variable types

Categorical3
Numeric6

Dataset

Description대전광역시 도로관리시스템에 등재된 자전거보관소 현황입니다. ※ 2022년 공공데이터 기업 매칭 지원사업으로 청년 인턴을 통해 구축·정비된 데이터입니다. 법적 효력이 없으므로 참고 목적으로만 활용하시기 바랍니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15110477/fileData.do

Alerts

지형지물부호 has constant value ""Constant
대장초기화여부 has constant value ""Constant
관리번호 is highly overall correlated with 행정읍면동High correlation
도엽번호 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
보관대수 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 도엽번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 보관대수 and 1 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 관리번호 and 4 other fieldsHigh correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:30:09.076565
Analysis finished2023-12-12 07:30:13.995319
Duration4.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
자전거보관소
385 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자전거보관소 385
100.0%

Length

2023-12-12T16:30:14.070716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:30:14.213398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자전거보관소 385
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.96623
Minimum2
Maximum394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T16:30:14.402176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile21.2
Q1102
median199
Q3298
95-th percentile374.8
Maximum394
Range392
Interquartile range (IQR)196

Descriptive statistics

Standard deviation113.68552
Coefficient of variation (CV)0.57138097
Kurtosis-1.1936919
Mean198.96623
Median Absolute Deviation (MAD)98
Skewness-0.0097206136
Sum76602
Variance12924.397
MonotonicityNot monotonic
2023-12-12T16:30:14.590872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.3%
232 1
 
0.3%
265 1
 
0.3%
216 1
 
0.3%
212 1
 
0.3%
211 1
 
0.3%
206 1
 
0.3%
205 1
 
0.3%
195 1
 
0.3%
175 1
 
0.3%
Other values (375) 375
97.4%
ValueCountFrequency (%)
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
11 1
0.3%
ValueCountFrequency (%)
394 1
0.3%
393 1
0.3%
392 1
0.3%
391 1
0.3%
390 1
0.3%
389 1
0.3%
388 1
0.3%
387 1
0.3%
386 1
0.3%
385 1
0.3%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
대전광역시 서구
241 
대전광역시 중구
92 
대전광역시 대덕구
52 

Length

Max length10
Median length9
Mean length9.1350649
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시 서구
2nd row대전광역시 서구
3rd row대전광역시 서구
4th row대전광역시 서구
5th row대전광역시 서구

Common Values

ValueCountFrequency (%)
대전광역시 서구 241
62.6%
대전광역시 중구 92
 
23.9%
대전광역시 대덕구 52
 
13.5%

Length

2023-12-12T16:30:14.739297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:30:14.856244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 385
50.0%
서구 241
31.3%
중구 92
 
11.9%
대덕구 52
 
6.8%

도엽번호
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36710065
Minimum36710017
Maximum36710089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T16:30:14.994224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36710017
5-th percentile36710030
Q136710056
median36710066
Q336710076
95-th percentile36710078
Maximum36710089
Range72
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.063325
Coefficient of variation (CV)3.5585132 × 10-7
Kurtosis2.5548277
Mean36710065
Median Absolute Deviation (MAD)10
Skewness-1.3318505
Sum1.4133375 × 1010
Variance170.65046
MonotonicityNot monotonic
2023-12-12T16:30:15.149348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
36710056 80
20.8%
36710066 66
17.1%
36710076 50
13.0%
36710077 26
 
6.8%
36710067 23
 
6.0%
36710075 17
 
4.4%
36710078 17
 
4.4%
36710058 16
 
4.2%
36710074 16
 
4.2%
36710055 15
 
3.9%
Other values (15) 59
15.3%
ValueCountFrequency (%)
36710017 2
 
0.5%
36710018 2
 
0.5%
36710019 2
 
0.5%
36710027 7
 
1.8%
36710028 7
 
1.8%
36710037 1
 
0.3%
36710047 3
 
0.8%
36710048 1
 
0.3%
36710055 15
 
3.9%
36710056 80
20.8%
ValueCountFrequency (%)
36710089 1
 
0.3%
36710084 14
 
3.6%
36710078 17
 
4.4%
36710077 26
 
6.8%
36710076 50
13.0%
36710075 17
 
4.4%
36710074 16
 
4.2%
36710068 3
 
0.8%
36710067 23
 
6.0%
36710066 66
17.1%

도로구간번호
Real number (ℝ)

Distinct295
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30449.151
Minimum7959
Maximum120786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T16:30:15.330357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7959
5-th percentile14494.2
Q117312
median27879
Q340722
95-th percentile47635.2
Maximum120786
Range112827
Interquartile range (IQR)23410

Descriptive statistics

Standard deviation16369.517
Coefficient of variation (CV)0.53760176
Kurtosis8.4817917
Mean30449.151
Median Absolute Deviation (MAD)10857
Skewness2.2356865
Sum11722923
Variance2.6796109 × 108
MonotonicityNot monotonic
2023-12-12T16:30:15.481208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21840 6
 
1.6%
41687 5
 
1.3%
47454 4
 
1.0%
47159 4
 
1.0%
17022 4
 
1.0%
36660 4
 
1.0%
17312 4
 
1.0%
29200 4
 
1.0%
47396 4
 
1.0%
21354 3
 
0.8%
Other values (285) 343
89.1%
ValueCountFrequency (%)
7959 1
0.3%
8357 1
0.3%
8616 1
0.3%
8741 1
0.3%
8782 2
0.5%
8844 1
0.3%
8863 1
0.3%
10183 1
0.3%
11042 1
0.3%
11956 1
0.3%
ValueCountFrequency (%)
120786 1
0.3%
113788 1
0.3%
113335 1
0.3%
111215 1
0.3%
111032 1
0.3%
104457 1
0.3%
81769 1
0.3%
72665 1
0.3%
72602 1
0.3%
72552 1
0.3%

보관대수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4909091
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T16:30:15.654431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q37
95-th percentile20
Maximum80
Range79
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.4753446
Coefficient of variation (CV)1.3614038
Kurtosis28.506873
Mean5.4909091
Median Absolute Deviation (MAD)1
Skewness4.0633949
Sum2114
Variance55.880777
MonotonicityNot monotonic
2023-12-12T16:30:15.821726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 149
38.7%
2 60
15.6%
10 50
 
13.0%
7 27
 
7.0%
5 18
 
4.7%
3 15
 
3.9%
6 12
 
3.1%
4 9
 
2.3%
20 9
 
2.3%
22 6
 
1.6%
Other values (13) 30
 
7.8%
ValueCountFrequency (%)
1 149
38.7%
2 60
15.6%
3 15
 
3.9%
4 9
 
2.3%
5 18
 
4.7%
6 12
 
3.1%
7 27
 
7.0%
8 6
 
1.6%
9 1
 
0.3%
10 50
 
13.0%
ValueCountFrequency (%)
80 1
 
0.3%
40 2
 
0.5%
36 1
 
0.3%
30 3
 
0.8%
27 2
 
0.5%
25 1
 
0.3%
22 6
1.6%
21 1
 
0.3%
20 9
2.3%
15 2
 
0.5%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
1
385 

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 (%)
1 385
100.0%

Length

2023-12-12T16:30:15.970542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:30:16.083782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 385
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct367
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.341028
Minimum36.296421
Maximum36.469685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T16:30:16.226758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.296421
5-th percentile36.30099
Q136.318604
median36.337972
Q336.353844
95-th percentile36.431118
Maximum36.469685
Range0.17326426
Interquartile range (IQR)0.03523948

Descriptive statistics

Standard deviation0.032761541
Coefficient of variation (CV)0.00090150287
Kurtosis3.8557666
Mean36.341028
Median Absolute Deviation (MAD)0.018561
Skewness1.69723
Sum13991.296
Variance0.0010733186
MonotonicityNot monotonic
2023-12-12T16:30:16.418175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3199351 3
 
0.8%
36.3220596 3
 
0.8%
36.34678377 3
 
0.8%
36.3232975 2
 
0.5%
36.3182504 2
 
0.5%
36.3342108 2
 
0.5%
36.351302 2
 
0.5%
36.44776197 2
 
0.5%
36.355709 2
 
0.5%
36.3282221 2
 
0.5%
Other values (357) 362
94.0%
ValueCountFrequency (%)
36.29642074 1
0.3%
36.296675 1
0.3%
36.2972485 1
0.3%
36.29811129 1
0.3%
36.29841486 1
0.3%
36.29842699 1
0.3%
36.29844863 1
0.3%
36.29852821 1
0.3%
36.29865093 1
0.3%
36.29881174 1
0.3%
ValueCountFrequency (%)
36.469685 1
0.3%
36.46007627 1
0.3%
36.451552 1
0.3%
36.45030638 1
0.3%
36.45013527 1
0.3%
36.45004159 1
0.3%
36.44945238 1
0.3%
36.4489848 1
0.3%
36.44891136 1
0.3%
36.44862995 1
0.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct355
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.39157
Minimum127.32909
Maximum127.47156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-12T16:30:16.602585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.32909
5-th percentile127.33963
Q1127.3783
median127.38816
Q3127.4104
95-th percentile127.43698
Maximum127.47156
Range0.1424719
Interquartile range (IQR)0.0320996

Descriptive statistics

Standard deviation0.027073153
Coefficient of variation (CV)0.00021251919
Kurtosis0.02628769
Mean127.39157
Median Absolute Deviation (MAD)0.0116336
Skewness0.059886779
Sum49045.753
Variance0.00073295559
MonotonicityNot monotonic
2023-12-12T16:30:16.825151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.379377 4
 
1.0%
127.4124293 3
 
0.8%
127.4147157 3
 
0.8%
127.385181 3
 
0.8%
127.4050032 3
 
0.8%
127.3934175 2
 
0.5%
127.4298155 2
 
0.5%
127.3893205 2
 
0.5%
127.4058488 2
 
0.5%
127.390133 2
 
0.5%
Other values (345) 359
93.2%
ValueCountFrequency (%)
127.329086 1
0.3%
127.3318477 1
0.3%
127.3322646 1
0.3%
127.3322668 1
0.3%
127.3345971 1
0.3%
127.3346667 1
0.3%
127.3349559 1
0.3%
127.334976 2
0.5%
127.3350037 1
0.3%
127.335099 1
0.3%
ValueCountFrequency (%)
127.4715579 1
0.3%
127.4654679 1
0.3%
127.4594676 1
0.3%
127.4541608 1
0.3%
127.4508088 1
0.3%
127.4502797 1
0.3%
127.4500177 1
0.3%
127.4493348 1
0.3%
127.4450104 1
0.3%
127.4402103 1
0.3%

Interactions

2023-12-12T16:30:13.025543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:09.375660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.000022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.715559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.332367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:12.042345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:13.134094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:09.493828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.127246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.816919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.451708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:12.144095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:13.245124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:09.594177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.245272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.916193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.574921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:12.253955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:13.339117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:09.697315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.351654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.008086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.681321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:12.350807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:13.474136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:09.795866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.466569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.130237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.784512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:12.774861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:13.611166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:09.902070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:10.606731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.227373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:11.921720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:30:12.904158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:30:16.968976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동도엽번호도로구간번호보관대수위도경도
관리번호1.0000.8900.6530.5420.5420.6670.841
행정읍면동0.8901.0000.8770.7370.8270.9360.801
도엽번호0.6530.8771.0000.7590.3720.9780.712
도로구간번호0.5420.7370.7591.0000.4030.7510.456
보관대수0.5420.8270.3720.4031.0000.3860.473
위도0.6670.9360.9780.7510.3861.0000.723
경도0.8410.8010.7120.4560.4730.7231.000
2023-12-12T16:30:17.107755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도엽번호도로구간번호보관대수위도경도행정읍면동
관리번호1.000-0.1200.022-0.2600.047-0.3150.827
도엽번호-0.1201.000-0.3680.131-0.907-0.0110.605
도로구간번호0.022-0.3681.000-0.1210.307-0.1050.437
보관대수-0.2600.131-0.1211.0000.0830.7300.512
위도0.047-0.9070.3070.0831.0000.2790.699
경도-0.315-0.011-0.1050.7300.2791.0000.686
행정읍면동0.8270.6050.4370.5120.6990.6861.000

Missing values

2023-12-12T16:30:13.772172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:30:13.929553image/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

지형지물부호관리번호행정읍면동도엽번호도로구간번호보관대수대장초기화여부위도경도
0자전거보관소2대전광역시 서구36710065296451136.340968127.366505
1자전거보관소3대전광역시 서구36710065296971136.337972127.366728
2자전거보관소4대전광역시 서구36710065296971136.337847127.366728
3자전거보관소5대전광역시 서구36710065296972136.337289127.366765
4자전거보관소6대전광역시 서구36710076291911136.322992127.378301
5자전거보관소7대전광역시 서구36710075295151136.308977127.373685
6자전거보관소8대전광역시 서구36710075156681136.305473127.355371
7자전거보관소9대전광역시 서구36710075154241136.304703127.35226
8자전거보관소10대전광역시 서구36710075154141136.304466127.350852
9자전거보관소11대전광역시 서구36710064318501136.330292127.338986
지형지물부호관리번호행정읍면동도엽번호도로구간번호보관대수대장초기화여부위도경도
375자전거보관소373대전광역시 서구36710074153861136.302259127.338911
376자전거보관소374대전광역시 서구36710074153861136.302376127.339469
377자전거보관소375대전광역시 서구36710074153931136.301738127.338527
378자전거보관소377대전광역시 서구36710074345152136.302224127.340891
379자전거보관소385대전광역시 서구36710084344281136.297249127.341679
380자전거보관소390대전광역시 서구36710056474102136.351379127.387061
381자전거보관소391대전광역시 중구36710067150664136.328302127.404978
382자전거보관소392대전광역시 중구36710067278933136.328573127.404569
383자전거보관소393대전광역시 중구36710067278791136.328879127.40541
384자전거보관소394대전광역시 서구36710076231871136.313411127.379785