Overview

Dataset statistics

Number of variables7
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory60.7 B

Variable types

Categorical4
Numeric3

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTTOUR000001

Alerts

TRR001 has constant value ""Constant
거잠포 has constant value ""Constant
인천 중구 잠진도길 11 has constant value ""Constant
2 is highly imbalanced (60.6%)Imbalance

Reproduction

Analysis started2023-12-10 06:34:34.091961
Analysis finished2023-12-10 06:34:36.671352
Duration2.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

TRR001
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
TRR001
199 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRR001
2nd rowTRR001
3rd rowTRR001
4th rowTRR001
5th rowTRR001

Common Values

ValueCountFrequency (%)
TRR001 199
100.0%

Length

2023-12-10T15:34:36.776622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:34:36.918223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
trr001 199
100.0%

20200103
Real number (ℝ)

Distinct58
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200173
Minimum20200103
Maximum20200303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:34:37.116989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200103
5-th percentile20200105
Q120200119
median20200131
Q320200218
95-th percentile20200302
Maximum20200303
Range200
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation61.153128
Coefficient of variation (CV)3.0273567 × 10-6
Kurtosis-0.8529902
Mean20200173
Median Absolute Deviation (MAD)27
Skewness0.54465021
Sum4.0198343 × 109
Variance3739.705
MonotonicityIncreasing
2023-12-10T15:34:37.493779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200302 8
 
4.0%
20200104 7
 
3.5%
20200125 7
 
3.5%
20200119 6
 
3.0%
20200118 5
 
2.5%
20200126 5
 
2.5%
20200209 5
 
2.5%
20200131 5
 
2.5%
20200112 5
 
2.5%
20200124 5
 
2.5%
Other values (48) 141
70.9%
ValueCountFrequency (%)
20200103 1
 
0.5%
20200104 7
3.5%
20200105 4
2.0%
20200106 1
 
0.5%
20200107 5
2.5%
20200108 2
 
1.0%
20200109 3
1.5%
20200110 3
1.5%
20200111 2
 
1.0%
20200112 5
2.5%
ValueCountFrequency (%)
20200303 4
2.0%
20200302 8
4.0%
20200301 4
2.0%
20200229 4
2.0%
20200228 4
2.0%
20200227 4
2.0%
20200226 3
 
1.5%
20200225 1
 
0.5%
20200224 2
 
1.0%
20200223 4
2.0%

170000
Real number (ℝ)

Distinct20
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147286.43
Minimum10000
Maximum230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:34:37.748958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile80000
Q1120000
median150000
Q3180000
95-th percentile200000
Maximum230000
Range220000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation40021.123
Coefficient of variation (CV)0.27172308
Kurtosis0.24936701
Mean147286.43
Median Absolute Deviation (MAD)30000
Skewness-0.5472808
Sum29310000
Variance1.6016903 × 109
MonotonicityNot monotonic
2023-12-10T15:34:37.968804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
190000 30
15.1%
120000 21
10.6%
180000 21
10.6%
110000 20
10.1%
150000 17
8.5%
130000 15
7.5%
160000 14
7.0%
170000 12
 
6.0%
140000 11
 
5.5%
100000 8
 
4.0%
Other values (10) 30
15.1%
ValueCountFrequency (%)
10000 1
 
0.5%
20000 2
 
1.0%
60000 1
 
0.5%
70000 3
 
1.5%
80000 5
 
2.5%
90000 4
 
2.0%
100000 8
 
4.0%
110000 20
10.1%
120000 21
10.6%
130000 15
7.5%
ValueCountFrequency (%)
230000 1
 
0.5%
220000 1
 
0.5%
210000 6
 
3.0%
200000 6
 
3.0%
190000 30
15.1%
180000 21
10.6%
170000 12
 
6.0%
160000 14
7.0%
150000 17
8.5%
140000 11
 
5.5%

2
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
167 
2
25 
3
 
5
4
 
2

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 (%)
1 167
83.9%
2 25
 
12.6%
3 5
 
2.5%
4 2
 
1.0%

Length

2023-12-10T15:34:38.193916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:34:38.513302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 167
83.9%
2 25
 
12.6%
3 5
 
2.5%
4 2
 
1.0%

3
Real number (ℝ)

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0703518
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:34:39.164561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13.5
median5
Q36
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.3344316
Coefficient of variation (CV)0.46040822
Kurtosis1.1511008
Mean5.0703518
Median Absolute Deviation (MAD)1
Skewness0.92485774
Sum1009
Variance5.4495711
MonotonicityNot monotonic
2023-12-10T15:34:39.720094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 50
25.1%
4 34
17.1%
3 27
13.6%
7 21
10.6%
2 18
 
9.0%
6 18
 
9.0%
8 14
 
7.0%
12 7
 
3.5%
1 5
 
2.5%
10 5
 
2.5%
ValueCountFrequency (%)
1 5
 
2.5%
2 18
 
9.0%
3 27
13.6%
4 34
17.1%
5 50
25.1%
6 18
 
9.0%
7 21
10.6%
8 14
 
7.0%
10 5
 
2.5%
12 7
 
3.5%
ValueCountFrequency (%)
12 7
 
3.5%
10 5
 
2.5%
8 14
 
7.0%
7 21
10.6%
6 18
 
9.0%
5 50
25.1%
4 34
17.1%
3 27
13.6%
2 18
 
9.0%
1 5
 
2.5%

거잠포
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
거잠포
199 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거잠포 199
100.0%

Length

2023-12-10T15:34:40.145770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:34:40.513127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거잠포 199
100.0%

인천 중구 잠진도길 11
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
인천 중구 잠진도길 11
199 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천 중구 잠진도길 11
2nd row인천 중구 잠진도길 11
3rd row인천 중구 잠진도길 11
4th row인천 중구 잠진도길 11
5th row인천 중구 잠진도길 11

Common Values

ValueCountFrequency (%)
인천 중구 잠진도길 11 199
100.0%

Length

2023-12-10T15:34:40.723064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:34:40.976542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천 199
25.0%
중구 199
25.0%
잠진도길 199
25.0%
11 199
25.0%

Interactions

2023-12-10T15:34:35.927121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:34.505114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:35.128475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:36.087173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:34.761469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:35.327604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:36.213840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:34.950021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:35.793424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:34:41.093333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020010317000023
202001031.0000.0000.2090.674
1700000.0001.0000.0000.212
20.2090.0001.0000.405
30.6740.2120.4051.000
2023-12-10T15:34:41.305011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020010317000032
202001031.0000.047-0.0630.136
1700000.0471.000-0.1100.000
3-0.063-0.1101.0000.266
20.1360.0000.2661.000

Missing values

2023-12-10T15:34:36.388068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:34:36.589976image/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

TRR0012020010317000023거잠포인천 중구 잠진도길 11
0TRR0012020010319000013거잠포인천 중구 잠진도길 11
1TRR001202001042000017거잠포인천 중구 잠진도길 11
2TRR0012020010410000017거잠포인천 중구 잠진도길 11
3TRR0012020010412000017거잠포인천 중구 잠진도길 11
4TRR0012020010414000017거잠포인천 중구 잠진도길 11
5TRR0012020010415000017거잠포인천 중구 잠진도길 11
6TRR0012020010418000017거잠포인천 중구 잠진도길 11
7TRR0012020010419000017거잠포인천 중구 잠진도길 11
8TRR001202001059000015거잠포인천 중구 잠진도길 11
9TRR0012020010510000015거잠포인천 중구 잠진도길 11
TRR0012020010317000023거잠포인천 중구 잠진도길 11
189TRR0012020030211000018거잠포인천 중구 잠진도길 11
190TRR0012020030212000018거잠포인천 중구 잠진도길 11
191TRR0012020030214000018거잠포인천 중구 잠진도길 11
192TRR0012020030218000018거잠포인천 중구 잠진도길 11
193TRR0012020030219000018거잠포인천 중구 잠진도길 11
194TRR0012020030221000018거잠포인천 중구 잠진도길 11
195TRR001202003039000015거잠포인천 중구 잠진도길 11
196TRR0012020030311000015거잠포인천 중구 잠진도길 11
197TRR0012020030315000015거잠포인천 중구 잠진도길 11
198TRR0012020030318000015거잠포인천 중구 잠진도길 11