Overview

Dataset statistics

Number of variables7
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory61.7 B

Variable types

Numeric5
Categorical1
DateTime1

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTEVNT000001

Alerts

1 is highly overall correlated with 127.01651703178224High correlation
127.01651703178224 is highly overall correlated with 1High correlation
공사 is highly imbalanced (95.4%)Imbalance
1 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:24:47.904712
Analysis finished2023-12-10 06:24:52.644070
Duration4.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:52.770509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11.9
Q151.5
median101
Q3150.5
95-th percentile190.1
Maximum200
Range198
Interquartile range (IQR)99

Descriptive statistics

Standard deviation57.590508
Coefficient of variation (CV)0.57020305
Kurtosis-1.2
Mean101
Median Absolute Deviation (MAD)50
Skewness0
Sum20099
Variance3316.6667
MonotonicityStrictly increasing
2023-12-10T15:24:52.990391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.5%
139 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
Other values (189) 189
95.0%
ValueCountFrequency (%)
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
11 1
0.5%
ValueCountFrequency (%)
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%

20200105
Real number (ℝ)

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200109
Minimum20200101
Maximum20200116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:53.219656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200101
Q120200105
median20200109
Q320200113
95-th percentile20200115
Maximum20200116
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6207689
Coefficient of variation (CV)2.2874971 × 10-7
Kurtosis-1.2292462
Mean20200109
Median Absolute Deviation (MAD)4
Skewness-0.079021882
Sum4.0198216 × 109
Variance21.351505
MonotonicityNot monotonic
2023-12-10T15:24:53.411241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20200115 18
 
9.0%
20200102 17
 
8.5%
20200106 17
 
8.5%
20200114 16
 
8.0%
20200109 14
 
7.0%
20200108 14
 
7.0%
20200101 13
 
6.5%
20200113 13
 
6.5%
20200110 12
 
6.0%
20200111 11
 
5.5%
Other values (6) 54
27.1%
ValueCountFrequency (%)
20200101 13
6.5%
20200102 17
8.5%
20200103 10
5.0%
20200104 7
3.5%
20200105 10
5.0%
20200106 17
8.5%
20200107 10
5.0%
20200108 14
7.0%
20200109 14
7.0%
20200110 12
6.0%
ValueCountFrequency (%)
20200116 6
 
3.0%
20200115 18
9.0%
20200114 16
8.0%
20200113 13
6.5%
20200112 11
5.5%
20200111 11
5.5%
20200110 12
6.0%
20200109 14
7.0%
20200108 14
7.0%
20200107 10
5.0%

1940
Real number (ℝ)

Distinct161
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1318.3618
Minimum702
Maximum2159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:53.700885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum702
5-th percentile801.9
Q11010
median1236
Q31577.5
95-th percentile2151
Maximum2159
Range1457
Interquartile range (IQR)567.5

Descriptive statistics

Standard deviation409.46796
Coefficient of variation (CV)0.31058846
Kurtosis-0.57324468
Mean1318.3618
Median Absolute Deviation (MAD)287
Skewness0.60149817
Sum262354
Variance167664.01
MonotonicityNot monotonic
2023-12-10T15:24:54.006039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1341 3
 
1.5%
1229 3
 
1.5%
1024 3
 
1.5%
2159 3
 
1.5%
2153 3
 
1.5%
2151 2
 
1.0%
828 2
 
1.0%
1246 2
 
1.0%
2124 2
 
1.0%
1042 2
 
1.0%
Other values (151) 174
87.4%
ValueCountFrequency (%)
702 1
0.5%
704 1
0.5%
705 1
0.5%
708 1
0.5%
713 1
0.5%
721 1
0.5%
743 1
0.5%
751 1
0.5%
756 1
0.5%
801 1
0.5%
ValueCountFrequency (%)
2159 3
1.5%
2156 2
1.0%
2153 3
1.5%
2152 1
 
0.5%
2151 2
1.0%
2128 1
 
0.5%
2126 1
 
0.5%
2124 2
1.0%
2122 1
 
0.5%
2111 1
 
0.5%

37.46280633538481
Real number (ℝ)

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.47666
Minimum36.646442
Maximum37.645369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:54.240707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.646442
5-th percentile37.24301
Q137.444185
median37.445389
Q337.53289
95-th percentile37.644596
Maximum37.645369
Range0.9989265
Interquartile range (IQR)0.088705288

Descriptive statistics

Standard deviation0.12038053
Coefficient of variation (CV)0.0032121466
Kurtosis10.543873
Mean37.47666
Median Absolute Deviation (MAD)0.076394546
Skewness-1.7859925
Sum7457.8553
Variance0.014491471
MonotonicityNot monotonic
2023-12-10T15:24:54.500299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
37.445389362575575 52
26.1%
37.39650377483307 27
13.6%
37.644595719405686 25
12.6%
37.24300997730297 15
 
7.5%
37.52514612405127 15
 
7.5%
37.44418516390547 13
 
6.5%
37.54637709137104 13
 
6.5%
37.524686451944895 9
 
4.5%
37.53289045185466 8
 
4.0%
37.52178390843024 8
 
4.0%
Other values (7) 14
 
7.0%
ValueCountFrequency (%)
36.64644222806543 1
 
0.5%
37.24300997730297 15
 
7.5%
37.39650377483307 27
13.6%
37.42205927512163 1
 
0.5%
37.44409439296297 1
 
0.5%
37.44418516390547 13
 
6.5%
37.445389362575575 52
26.1%
37.465456088743714 1
 
0.5%
37.52178390843024 8
 
4.0%
37.524686451944895 9
 
4.5%
ValueCountFrequency (%)
37.64536872460566 1
 
0.5%
37.644595719405686 25
12.6%
37.64175254631091 8
 
4.0%
37.55792231253848 1
 
0.5%
37.54637709137104 13
6.5%
37.53289045185466 8
 
4.0%
37.52514612405127 15
7.5%
37.524686451944895 9
 
4.5%
37.52178390843024 8
 
4.0%
37.465456088743714 1
 
0.5%

127.01651703178224
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.80401
Minimum126.65029
Maximum127.49714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:54.720551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.65029
5-th percentile126.65029
Q1126.65029
median126.71232
Q3126.87137
95-th percentile127.49714
Maximum127.49714
Range0.84685273
Interquartile range (IQR)0.22108032

Descriptive statistics

Standard deviation0.22986132
Coefficient of variation (CV)0.0018127291
Kurtosis3.8215491
Mean126.80401
Median Absolute Deviation (MAD)0.062033463
Skewness2.1358798
Sum25233.999
Variance0.052836227
MonotonicityNot monotonic
2023-12-10T15:24:54.954805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
126.65028795776976 52
26.1%
126.87136827759696 27
13.6%
126.7123214202789 25
12.6%
127.49714069233956 15
 
7.5%
126.66263995642196 15
 
7.5%
126.69888574704598 13
 
6.5%
126.88606805980966 13
 
6.5%
126.677911260382 9
 
4.5%
127.06019291476127 8
 
4.0%
126.80486662707574 8
 
4.0%
Other values (7) 14
 
7.0%
ValueCountFrequency (%)
126.65028795776976 52
26.1%
126.66263995642196 15
 
7.5%
126.677911260382 9
 
4.5%
126.69888574704598 13
 
6.5%
126.6995076383908 1
 
0.5%
126.70168290603132 1
 
0.5%
126.71024476221304 8
 
4.0%
126.7123214202789 25
12.6%
126.71249123570084 1
 
0.5%
126.7164934880098 1
 
0.5%
ValueCountFrequency (%)
127.49714069233956 15
7.5%
127.41928004693594 1
 
0.5%
127.06019291476127 8
 
4.0%
126.89462364512696 1
 
0.5%
126.88606805980966 13
6.5%
126.87136827759696 27
13.6%
126.80486662707574 8
 
4.0%
126.7164934880098 1
 
0.5%
126.71249123570084 1
 
0.5%
126.7123214202789 25
12.6%

공사
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
공사
198 
기타
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
공사 198
99.5%
기타 1
 
0.5%

Length

2023-12-10T15:24:55.155980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:55.351406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 198
99.5%
기타 1
 
0.5%

2020-01-05 19:40:00
Date

Distinct180
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2020-01-01 07:02:00
Maximum2020-01-16 11:34:00
2023-12-10T15:24:55.529154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:55.780715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-10T15:24:51.583444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:48.243052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:49.063169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:49.943550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:50.816527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:51.749101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:48.407787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:49.236229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:50.125586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:50.979100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:51.916214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:48.573236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:49.422405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:50.324571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:51.147016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:52.061121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:48.740930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:49.611837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:50.513751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:51.294705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:52.191552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:48.905983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:49.785221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:50.674125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:51.436885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:24:55.949170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
120200105194037.46280633538481127.01651703178224공사
11.0000.5350.0480.9320.9450.000
202001050.5351.0000.3830.0000.2560.000
19400.0480.3831.0000.2260.1470.000
37.462806335384810.9320.0000.2261.0000.9030.000
127.016517031782240.9450.2560.1470.9031.0000.000
공사0.0000.0000.0000.0000.0001.000
2023-12-10T15:24:56.119258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
120200105194037.46280633538481127.01651703178224공사
11.0000.015-0.0520.201-0.7410.000
202001050.0151.0000.0270.0610.0180.000
1940-0.0520.0271.0000.0260.0040.000
37.462806335384810.2010.0610.0261.000-0.1050.000
127.01651703178224-0.7410.0180.004-0.1051.0000.000
공사0.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T15:24:52.356827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:24:52.570188image/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

120200105194037.46280633538481127.01651703178224공사2020-01-05 19:40:00
0220200111215137.53289127.060193공사2020-01-11 21:51:00
1320200112130237.53289127.060193공사2020-01-12 13:02:00
2420200112174837.53289127.060193공사2020-01-12 17:48:00
3520200112215637.53289127.060193공사2020-01-12 21:56:00
462020011480337.53289127.060193공사2020-01-14 08:03:00
5720200114135937.53289127.060193공사2020-01-14 13:59:00
682020011580837.53289127.060193공사2020-01-15 08:08:00
7920200115105737.53289127.060193공사2020-01-15 10:57:00
81020200104124237.546377126.886068공사2020-01-04 12:42:00
91120200105112837.546377126.886068공사2020-01-05 11:28:00
120200105194037.46280633538481127.01651703178224공사2020-01-05 19:40:00
18919120200113105437.524686126.677911공사2020-01-13 10:54:00
19019220200115142437.524686126.677911공사2020-01-15 14:24:00
1911932020010292137.521784126.804867공사2020-01-02 09:21:00
19219420200102122437.521784126.804867공사2020-01-02 12:24:00
1931952020010482437.521784126.804867공사2020-01-04 08:24:00
1941962020010592237.521784126.804867공사2020-01-05 09:22:00
19519720200105122137.521784126.804867공사2020-01-05 12:21:00
19619820200105152237.521784126.804867공사2020-01-05 15:22:00
19719920200106142237.521784126.804867공사2020-01-06 14:22:00
19820020200108142037.521784126.804867공사2020-01-08 14:20:00