Overview

Dataset statistics

Number of variables4
Number of observations255
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory35.5 B

Variable types

Numeric3
Categorical1

Dataset

Description인천광역시 부평구 부평지하상가 CCTV 위치 정보 데이터는 부평지하상가 내 CCTV의 위치정보(위도, 경도)를 제공하고 있습니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15118527&srcSe=7661IVAWM27C61E190

Alerts

종류 has constant value ""Constant
순번 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique

Reproduction

Analysis started2024-03-18 03:41:31.808431
Analysis finished2024-03-18 03:41:33.985403
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-18T12:41:34.048401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.7
Q164.5
median128
Q3191.5
95-th percentile242.3
Maximum255
Range254
Interquartile range (IQR)127

Descriptive statistics

Standard deviation73.756356
Coefficient of variation (CV)0.57622153
Kurtosis-1.2
Mean128
Median Absolute Deviation (MAD)64
Skewness0
Sum32640
Variance5440
MonotonicityStrictly increasing
2024-03-18T12:41:34.164077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
163 1
 
0.4%
164 1
 
0.4%
165 1
 
0.4%
166 1
 
0.4%
167 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
Other values (245) 245
96.1%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
255 1
0.4%
254 1
0.4%
253 1
0.4%
252 1
0.4%
251 1
0.4%
250 1
0.4%
249 1
0.4%
248 1
0.4%
247 1
0.4%
246 1
0.4%

종류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
CCTV
255 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CCTV 255
100.0%

Length

2024-03-18T12:41:34.267887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:41:34.362091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cctv 255
100.0%

경도
Real number (ℝ)

Distinct155
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.72419
Minimum126.72235
Maximum126.72696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-18T12:41:34.472640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72235
5-th percentile126.72282
Q1126.7232
median126.72377
Q3126.72428
95-th percentile126.72676
Maximum126.72696
Range0.00461
Interquartile range (IQR)0.00108

Descriptive statistics

Standard deviation0.0013281224
Coefficient of variation (CV)1.0480417 × 10-5
Kurtosis-0.39406435
Mean126.72419
Median Absolute Deviation (MAD)0.00056
Skewness1.0215937
Sum32314.668
Variance1.763909 × 10-6
MonotonicityNot monotonic
2024-03-18T12:41:34.586138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.72385 5
 
2.0%
126.72317 5
 
2.0%
126.72613 5
 
2.0%
126.72638 4
 
1.6%
126.72305 4
 
1.6%
126.72308 4
 
1.6%
126.72312 4
 
1.6%
126.72402 3
 
1.2%
126.72632 3
 
1.2%
126.72294 3
 
1.2%
Other values (145) 215
84.3%
ValueCountFrequency (%)
126.72235 2
0.8%
126.72243 2
0.8%
126.72259 1
0.4%
126.72266 1
0.4%
126.72268 1
0.4%
126.7227 1
0.4%
126.72275 1
0.4%
126.72276 1
0.4%
126.72278 1
0.4%
126.72281 1
0.4%
ValueCountFrequency (%)
126.72696 1
 
0.4%
126.72695 3
1.2%
126.72694 2
0.8%
126.72688 1
 
0.4%
126.72687 1
 
0.4%
126.72682 1
 
0.4%
126.72681 1
 
0.4%
126.7268 1
 
0.4%
126.72678 1
 
0.4%
126.72676 2
0.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.491898
Minimum37.48986
Maximum37.49473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-18T12:41:34.707111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.48986
5-th percentile37.48994
Q137.490495
median37.49139
Q337.49396
95-th percentile37.4946
Maximum37.49473
Range0.00487
Interquartile range (IQR)0.003465

Descriptive statistics

Standard deviation0.0016274999
Coefficient of variation (CV)4.3409375 × 10-5
Kurtosis-1.224966
Mean37.491898
Median Absolute Deviation (MAD)0.00122
Skewness0.50543193
Sum9560.434
Variance2.6487558 × 10-6
MonotonicityNot monotonic
2024-03-18T12:41:34.824757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.49138 6
 
2.4%
37.48994 6
 
2.4%
37.49128 5
 
2.0%
37.49093 4
 
1.6%
37.49006 4
 
1.6%
37.49127 4
 
1.6%
37.49129 4
 
1.6%
37.49401 4
 
1.6%
37.49149 4
 
1.6%
37.49072 4
 
1.6%
Other values (148) 210
82.4%
ValueCountFrequency (%)
37.48986 1
 
0.4%
37.48988 1
 
0.4%
37.4899 1
 
0.4%
37.48991 1
 
0.4%
37.48992 2
 
0.8%
37.48993 2
 
0.8%
37.48994 6
2.4%
37.48995 1
 
0.4%
37.48997 1
 
0.4%
37.48998 3
1.2%
ValueCountFrequency (%)
37.49473 1
0.4%
37.49472 2
0.8%
37.49471 1
0.4%
37.49468 1
0.4%
37.49467 1
0.4%
37.49465 1
0.4%
37.49464 1
0.4%
37.49463 1
0.4%
37.49462 2
0.8%
37.49461 1
0.4%

Interactions

2024-03-18T12:41:33.670404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.034393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.464445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.747043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.184725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.527729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.810091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.267756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:41:33.607677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:41:34.901995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번경도위도
순번1.0000.7550.937
경도0.7551.0000.691
위도0.9370.6911.000
2024-03-18T12:41:35.053933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번경도위도
순번1.0000.2740.868
경도0.2741.0000.296
위도0.8680.2961.000

Missing values

2024-03-18T12:41:33.892731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:41:33.956035image/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

순번종류경도위도
01CCTV126.7240237.49102
12CCTV126.7240237.49098
23CCTV126.7240337.49092
34CCTV126.7240337.49084
45CCTV126.7239937.4907
56CCTV126.7241637.49072
67CCTV126.7242137.49072
78CCTV126.7241937.49084
89CCTV126.7241737.49107
910CCTV126.7240537.49065
순번종류경도위도
245246CCTV126.7266937.49421
246247CCTV126.7266337.49418
247248CCTV126.7264337.49418
248249CCTV126.7263837.49422
249250CCTV126.7265237.49431
250251CCTV126.7265237.49435
251252CCTV126.7265637.49435
252253CCTV126.7265637.4943
253254CCTV126.7261337.49431
254255CCTV126.7261337.49433