Overview

Dataset statistics

Number of variables16
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory145.9 B

Variable types

Numeric13
Categorical3

Dataset

Description울산광역시 행정동을 기준으로 100m 격자별 국가산단지역 내 기업, 위험물, 위험물 특성 현황 통계 정보를 데이터로 제공
Author울산광역시
URLhttps://www.data.go.kr/data/15109139/fileData.do

Alerts

격자아이디 is highly overall correlated with 격자좌표(X)High correlation
격자좌표(X) is highly overall correlated with 격자아이디High correlation
위험물 수 is highly overall correlated with 상온상태(고체) 수 and 10 other fieldsHigh correlation
상온상태(고체) 수 is highly overall correlated with 위험물 수 and 9 other fieldsHigh correlation
상온상태(액체) 수 is highly overall correlated with 위험물 수 and 10 other fieldsHigh correlation
상온상태(기타) 수 is highly overall correlated with 위험물 수 and 10 other fieldsHigh correlation
색상(무색) 수 is highly overall correlated with 위험물 수 and 10 other fieldsHigh correlation
색상(적색) 수 is highly overall correlated with 위험물 수 and 9 other fieldsHigh correlation
색상(기타) 수 is highly overall correlated with 위험물 수 and 10 other fieldsHigh correlation
냄새(달콤) 수 is highly overall correlated with 위험물 수 and 8 other fieldsHigh correlation
냄새(자극) 수 is highly overall correlated with 위험물 수 and 10 other fieldsHigh correlation
냄새(기타) 수 is highly overall correlated with 위험물 수 and 8 other fieldsHigh correlation
기업 수 is highly overall correlated with 위험물 수 and 8 other fieldsHigh correlation
상온상태(가스) 수 is highly overall correlated with 위험물 수 and 8 other fieldsHigh correlation
기업 수 is highly imbalanced (68.0%)Imbalance
격자아이디 has unique valuesUnique
상온상태(고체) 수 has 16 (23.2%) zerosZeros
상온상태(액체) 수 has 7 (10.1%) zerosZeros
상온상태(기타) 수 has 24 (34.8%) zerosZeros
색상(무색) 수 has 2 (2.9%) zerosZeros
색상(적색) 수 has 31 (44.9%) zerosZeros
색상(기타) 수 has 16 (23.2%) zerosZeros
냄새(달콤) 수 has 43 (62.3%) zerosZeros
냄새(자극) 수 has 22 (31.9%) zerosZeros
냄새(기타) 수 has 7 (10.1%) zerosZeros

Reproduction

Analysis started2024-03-14 17:43:45.001356
Analysis finished2024-03-14 17:44:28.091563
Duration43.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:28.301798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.4
Q118
median35
Q352
95-th percentile65.6
Maximum69
Range68
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.062403
Coefficient of variation (CV)0.5732115
Kurtosis-1.2
Mean35
Median Absolute Deviation (MAD)17
Skewness0
Sum2415
Variance402.5
MonotonicityStrictly increasing
2024-03-15T02:44:28.599756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
45 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
46 1
 
1.4%
44 1
 
1.4%
53 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%
60 1
1.4%

격자좌표(X)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412285.32
Minimum411223
Maximum414523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:28.826409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum411223
5-th percentile411323
Q1411723
median412123
Q3412623
95-th percentile414023
Maximum414523
Range3300
Interquartile range (IQR)900

Descriptive statistics

Standard deviation791.14612
Coefficient of variation (CV)0.0019189287
Kurtosis0.4743385
Mean412285.32
Median Absolute Deviation (MAD)500
Skewness1.010591
Sum28447687
Variance625912.19
MonotonicityIncreasing
2024-03-15T02:44:29.038726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
411723 7
 
10.1%
412423 5
 
7.2%
412223 4
 
5.8%
412123 4
 
5.8%
413023 4
 
5.8%
411523 4
 
5.8%
411623 4
 
5.8%
411323 4
 
5.8%
412023 4
 
5.8%
412623 3
 
4.3%
Other values (17) 26
37.7%
ValueCountFrequency (%)
411223 1
 
1.4%
411323 4
5.8%
411423 3
4.3%
411523 4
5.8%
411623 4
5.8%
411723 7
10.1%
411823 3
4.3%
411923 3
4.3%
412023 4
5.8%
412123 4
5.8%
ValueCountFrequency (%)
414523 1
 
1.4%
414223 1
 
1.4%
414123 1
 
1.4%
414023 2
2.9%
413623 1
 
1.4%
413523 1
 
1.4%
413423 1
 
1.4%
413223 1
 
1.4%
413123 1
 
1.4%
413023 4
5.8%

격자좌표(Y)
Real number (ℝ)

Distinct40
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317573.09
Minimum314447
Maximum322847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:29.356169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum314447
5-th percentile315247
Q1316547
median317047
Q3318647
95-th percentile320507
Maximum322847
Range8400
Interquartile range (IQR)2100

Descriptive statistics

Standard deviation1804.2965
Coefficient of variation (CV)0.0056815158
Kurtosis-0.15232906
Mean317573.09
Median Absolute Deviation (MAD)900
Skewness0.69957799
Sum21912543
Variance3255485.9
MonotonicityNot monotonic
2024-03-15T02:44:29.685787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
316547 6
 
8.7%
316947 5
 
7.2%
320047 4
 
5.8%
317347 4
 
5.8%
316047 3
 
4.3%
316647 3
 
4.3%
315247 2
 
2.9%
316147 2
 
2.9%
316847 2
 
2.9%
315547 2
 
2.9%
Other values (30) 36
52.2%
ValueCountFrequency (%)
314447 1
 
1.4%
314747 1
 
1.4%
314847 1
 
1.4%
315247 2
2.9%
315347 1
 
1.4%
315547 2
2.9%
315747 1
 
1.4%
315947 1
 
1.4%
316047 3
4.3%
316147 2
2.9%
ValueCountFrequency (%)
322847 1
 
1.4%
320847 1
 
1.4%
320747 1
 
1.4%
320547 1
 
1.4%
320447 1
 
1.4%
320347 2
2.9%
320147 2
2.9%
320047 4
5.8%
319947 1
 
1.4%
319847 1
 
1.4%

기업 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size680.0 B
1
61 
2
 
6
6
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row1
2nd row1
3rd row2
4th row6
5th row1

Common Values

ValueCountFrequency (%)
1 61
88.4%
2 6
 
8.7%
6 1
 
1.4%
3 1
 
1.4%

Length

2024-03-15T02:44:29.903405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:44:30.083843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 61
88.4%
2 6
 
8.7%
6 1
 
1.4%
3 1
 
1.4%

위험물 수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6086957
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:30.330652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile31
Maximum72
Range71
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.638896
Coefficient of variation (CV)1.4681546
Kurtosis11.999543
Mean8.6086957
Median Absolute Deviation (MAD)3
Skewness3.2779491
Sum594
Variance159.74169
MonotonicityNot monotonic
2024-03-15T02:44:30.764362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 12
17.4%
2 11
15.9%
3 7
10.1%
5 5
 
7.2%
4 5
 
7.2%
7 4
 
5.8%
6 4
 
5.8%
8 2
 
2.9%
15 2
 
2.9%
11 2
 
2.9%
Other values (13) 15
21.7%
ValueCountFrequency (%)
1 12
17.4%
2 11
15.9%
3 7
10.1%
4 5
7.2%
5 5
7.2%
6 4
 
5.8%
7 4
 
5.8%
8 2
 
2.9%
9 2
 
2.9%
11 2
 
2.9%
ValueCountFrequency (%)
72 1
1.4%
53 1
1.4%
51 1
1.4%
37 1
1.4%
22 1
1.4%
19 1
1.4%
18 1
1.4%
17 1
1.4%
16 1
1.4%
15 2
2.9%

상온상태(고체) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.826087
Minimum0
Maximum13
Zeros16
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:31.134933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4849933
Coefficient of variation (CV)1.3608297
Kurtosis8.7137689
Mean1.826087
Median Absolute Deviation (MAD)1
Skewness2.786567
Sum126
Variance6.1751918
MonotonicityNot monotonic
2024-03-15T02:44:31.507956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 31
44.9%
0 16
23.2%
2 8
 
11.6%
4 4
 
5.8%
3 4
 
5.8%
6 2
 
2.9%
5 1
 
1.4%
11 1
 
1.4%
13 1
 
1.4%
10 1
 
1.4%
ValueCountFrequency (%)
0 16
23.2%
1 31
44.9%
2 8
 
11.6%
3 4
 
5.8%
4 4
 
5.8%
5 1
 
1.4%
6 2
 
2.9%
10 1
 
1.4%
11 1
 
1.4%
13 1
 
1.4%
ValueCountFrequency (%)
13 1
 
1.4%
11 1
 
1.4%
10 1
 
1.4%
6 2
 
2.9%
5 1
 
1.4%
4 4
 
5.8%
3 4
 
5.8%
2 8
 
11.6%
1 31
44.9%
0 16
23.2%

상온상태(액체) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.942029
Minimum0
Maximum11
Zeros7
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:31.737642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5.2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.916523
Coefficient of variation (CV)0.98686634
Kurtosis9.9530904
Mean1.942029
Median Absolute Deviation (MAD)1
Skewness2.8731039
Sum134
Variance3.6730605
MonotonicityNot monotonic
2024-03-15T02:44:32.015167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 26
37.7%
2 24
34.8%
0 7
 
10.1%
3 6
 
8.7%
4 2
 
2.9%
8 1
 
1.4%
11 1
 
1.4%
9 1
 
1.4%
6 1
 
1.4%
ValueCountFrequency (%)
0 7
 
10.1%
1 26
37.7%
2 24
34.8%
3 6
 
8.7%
4 2
 
2.9%
6 1
 
1.4%
8 1
 
1.4%
9 1
 
1.4%
11 1
 
1.4%
ValueCountFrequency (%)
11 1
 
1.4%
9 1
 
1.4%
8 1
 
1.4%
6 1
 
1.4%
4 2
 
2.9%
3 6
 
8.7%
2 24
34.8%
1 26
37.7%
0 7
 
10.1%

상온상태(가스) 수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size680.0 B
0
34 
1
22 
2
10 
4
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 34
49.3%
1 22
31.9%
2 10
 
14.5%
4 2
 
2.9%
3 1
 
1.4%

Length

2024-03-15T02:44:32.225799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:44:32.430647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34
49.3%
1 22
31.9%
2 10
 
14.5%
4 2
 
2.9%
3 1
 
1.4%

상온상태(기타) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1449275
Minimum0
Maximum19
Zeros24
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:32.743287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9.2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.6673254
Coefficient of variation (CV)1.7097666
Kurtosis9.6381053
Mean2.1449275
Median Absolute Deviation (MAD)1
Skewness3.0018246
Sum148
Variance13.449275
MonotonicityNot monotonic
2024-03-15T02:44:33.104777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 24
34.8%
1 21
30.4%
2 9
 
13.0%
3 5
 
7.2%
6 2
 
2.9%
4 2
 
2.9%
10 1
 
1.4%
19 1
 
1.4%
14 1
 
1.4%
16 1
 
1.4%
Other values (2) 2
 
2.9%
ValueCountFrequency (%)
0 24
34.8%
1 21
30.4%
2 9
 
13.0%
3 5
 
7.2%
4 2
 
2.9%
6 2
 
2.9%
7 1
 
1.4%
8 1
 
1.4%
10 1
 
1.4%
14 1
 
1.4%
ValueCountFrequency (%)
19 1
 
1.4%
16 1
 
1.4%
14 1
 
1.4%
10 1
 
1.4%
8 1
 
1.4%
7 1
 
1.4%
6 2
 
2.9%
4 2
 
2.9%
3 5
7.2%
2 9
13.0%

색상(무색) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7391304
Minimum0
Maximum33
Zeros2
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:33.371640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile15.2
Maximum33
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.7921148
Coefficient of variation (CV)1.2221894
Kurtosis10.543605
Mean4.7391304
Median Absolute Deviation (MAD)2
Skewness3.0395049
Sum327
Variance33.548593
MonotonicityNot monotonic
2024-03-15T02:44:33.723179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 14
20.3%
1 14
20.3%
4 12
17.4%
3 8
11.6%
6 4
 
5.8%
7 3
 
4.3%
11 3
 
4.3%
0 2
 
2.9%
9 2
 
2.9%
5 2
 
2.9%
Other values (5) 5
 
7.2%
ValueCountFrequency (%)
0 2
 
2.9%
1 14
20.3%
2 14
20.3%
3 8
11.6%
4 12
17.4%
5 2
 
2.9%
6 4
 
5.8%
7 3
 
4.3%
8 1
 
1.4%
9 2
 
2.9%
ValueCountFrequency (%)
33 1
 
1.4%
25 1
 
1.4%
23 1
 
1.4%
18 1
 
1.4%
11 3
4.3%
9 2
2.9%
8 1
 
1.4%
7 3
4.3%
6 4
5.8%
5 2
2.9%

색상(적색) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3768116
Minimum0
Maximum14
Zeros31
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:34.056308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4500987
Coefficient of variation (CV)1.7795454
Kurtosis13.116535
Mean1.3768116
Median Absolute Deviation (MAD)1
Skewness3.3536205
Sum95
Variance6.0029838
MonotonicityNot monotonic
2024-03-15T02:44:34.604764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 31
44.9%
1 21
30.4%
2 7
 
10.1%
3 3
 
4.3%
4 2
 
2.9%
5 2
 
2.9%
8 1
 
1.4%
14 1
 
1.4%
11 1
 
1.4%
ValueCountFrequency (%)
0 31
44.9%
1 21
30.4%
2 7
 
10.1%
3 3
 
4.3%
4 2
 
2.9%
5 2
 
2.9%
8 1
 
1.4%
11 1
 
1.4%
14 1
 
1.4%
ValueCountFrequency (%)
14 1
 
1.4%
11 1
 
1.4%
8 1
 
1.4%
5 2
 
2.9%
4 2
 
2.9%
3 3
 
4.3%
2 7
 
10.1%
1 21
30.4%
0 31
44.9%

색상(기타) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3768116
Minimum0
Maximum36
Zeros16
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:34.955734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile13.8
Maximum36
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.002699
Coefficient of variation (CV)1.7776233
Kurtosis14.665073
Mean3.3768116
Median Absolute Deviation (MAD)1
Skewness3.55302
Sum233
Variance36.032396
MonotonicityNot monotonic
2024-03-15T02:44:35.329125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 21
30.4%
0 16
23.2%
2 13
18.8%
4 5
 
7.2%
7 2
 
2.9%
5 2
 
2.9%
8 2
 
2.9%
21 1
 
1.4%
36 1
 
1.4%
3 1
 
1.4%
Other values (5) 5
 
7.2%
ValueCountFrequency (%)
0 16
23.2%
1 21
30.4%
2 13
18.8%
3 1
 
1.4%
4 5
 
7.2%
5 2
 
2.9%
6 1
 
1.4%
7 2
 
2.9%
8 2
 
2.9%
9 1
 
1.4%
ValueCountFrequency (%)
36 1
1.4%
24 1
1.4%
21 1
1.4%
15 1
1.4%
12 1
1.4%
9 1
1.4%
8 2
2.9%
7 2
2.9%
6 1
1.4%
5 2
2.9%

냄새(달콤) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86956522
Minimum0
Maximum8
Zeros43
Zeros (%)62.3%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:35.689755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4.6
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.696996
Coefficient of variation (CV)1.9515454
Kurtosis7.0167851
Mean0.86956522
Median Absolute Deviation (MAD)0
Skewness2.6458747
Sum60
Variance2.8797954
MonotonicityNot monotonic
2024-03-15T02:44:35.885945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 43
62.3%
1 16
 
23.2%
3 2
 
2.9%
2 2
 
2.9%
4 2
 
2.9%
7 1
 
1.4%
8 1
 
1.4%
6 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 43
62.3%
1 16
 
23.2%
2 2
 
2.9%
3 2
 
2.9%
4 2
 
2.9%
5 1
 
1.4%
6 1
 
1.4%
7 1
 
1.4%
8 1
 
1.4%
ValueCountFrequency (%)
8 1
 
1.4%
7 1
 
1.4%
6 1
 
1.4%
5 1
 
1.4%
4 2
 
2.9%
3 2
 
2.9%
2 2
 
2.9%
1 16
 
23.2%
0 43
62.3%

냄새(자극) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8695652
Minimum0
Maximum11
Zeros22
Zeros (%)31.9%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:36.087172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.228908
Coefficient of variation (CV)1.1922066
Kurtosis4.6851692
Mean1.8695652
Median Absolute Deviation (MAD)1
Skewness1.9576434
Sum129
Variance4.9680307
MonotonicityNot monotonic
2024-03-15T02:44:36.276480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 22
31.9%
1 16
23.2%
2 12
17.4%
3 8
 
11.6%
4 5
 
7.2%
6 2
 
2.9%
9 1
 
1.4%
11 1
 
1.4%
8 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 22
31.9%
1 16
23.2%
2 12
17.4%
3 8
 
11.6%
4 5
 
7.2%
5 1
 
1.4%
6 2
 
2.9%
8 1
 
1.4%
9 1
 
1.4%
11 1
 
1.4%
ValueCountFrequency (%)
11 1
 
1.4%
9 1
 
1.4%
8 1
 
1.4%
6 2
 
2.9%
5 1
 
1.4%
4 5
 
7.2%
3 8
 
11.6%
2 12
17.4%
1 16
23.2%
0 22
31.9%
Distinct4
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size680.0 B
2
26 
1
21 
0
14 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
2 26
37.7%
1 21
30.4%
0 14
20.3%
3 8
 
11.6%

Length

2024-03-15T02:44:36.607810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:44:36.998181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 26
37.7%
1 21
30.4%
0 14
20.3%
3 8
 
11.6%

냄새(기타) 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5
Minimum0
Maximum31
Zeros7
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T02:44:37.320181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile17.6
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.7431883
Coefficient of variation (CV)1.3486377
Kurtosis7.5932088
Mean5
Median Absolute Deviation (MAD)2
Skewness2.6764344
Sum345
Variance45.470588
MonotonicityNot monotonic
2024-03-15T02:44:37.591702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 15
21.7%
2 12
17.4%
3 8
11.6%
0 7
10.1%
5 5
 
7.2%
6 4
 
5.8%
4 3
 
4.3%
7 3
 
4.3%
11 2
 
2.9%
31 2
 
2.9%
Other values (8) 8
11.6%
ValueCountFrequency (%)
0 7
10.1%
1 15
21.7%
2 12
17.4%
3 8
11.6%
4 3
 
4.3%
5 5
 
7.2%
6 4
 
5.8%
7 3
 
4.3%
8 1
 
1.4%
9 1
 
1.4%
ValueCountFrequency (%)
31 2
2.9%
30 1
1.4%
20 1
1.4%
14 1
1.4%
13 1
1.4%
12 1
1.4%
11 2
2.9%
10 1
1.4%
9 1
1.4%
8 1
1.4%

Interactions

2024-03-15T02:44:23.890278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:46.088923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:48.757861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:52.763377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:55.490220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:58.488844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:01.429566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:04.058696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:07.334089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:10.752177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:14.756844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:17.434820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:20.787657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:24.163927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:46.336190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:48.958412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:53.019979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:55.751451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:58.760727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:01.581307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:04.221010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:07.624310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:10.923126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:14.927233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:17.703910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:20.941109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:24.429091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:46.507955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:49.383836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:53.162943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:56.011998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:59.112683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:01.821097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:04.472071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:07.937908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:11.384057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:15.106197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:17.966002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:21.169632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:24.702314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:46.749445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:49.628967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:53.289612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:56.344532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:59.246949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:02.046862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:04.714456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:08.195797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:11.874673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:15.280780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:18.205242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:21.392131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:24.964972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:47.193234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:49.929565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:53.438074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:56.502788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:59.443807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:02.302823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:04.895803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:08.515010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:12.216040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:15.493271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:18.468242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:21.653822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:25.238364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:47.358543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:50.427688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:53.673735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:56.666675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:59.650652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:02.513082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:05.054402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:08.781840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:12.473135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:15.646166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:18.721880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:21.904956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:25.498118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:47.503245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:50.711342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:53.923740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:56.814907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:59.896280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:02.677989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:05.203381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:09.032639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:12.861913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:15.795781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:18.974144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:22.145313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:25.753940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:47.717368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:51.008573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:54.173389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:57.067288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:00.077056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:02.879338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:05.421559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:09.310188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:13.277218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:15.971246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:19.242322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:22.408160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:26.032228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:47.931390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:51.302971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:54.412797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:57.274950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:00.308470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:03.024951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:05.751751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:09.597729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:13.606637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:16.152950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:19.564580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:22.582157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:26.201868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:48.094604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:51.607689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:54.598280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:57.520860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:00.485833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:03.175030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:06.030492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:09.854285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:13.866871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:16.329116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:19.823920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:22.941942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:26.485656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:48.256395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:51.899935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:54.766670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:57.723030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:00.683368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:03.410852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:06.302223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:10.066458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:14.090154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:16.666333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:20.091685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:23.196584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:26.858277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:48.438114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:52.178789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:55.010667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:57.991331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:00.946757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:03.671355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:06.681222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:10.331427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:14.321830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:16.935442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:20.387313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:23.497059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:27.101616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:48.583746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:52.445663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:55.248359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:43:58.235898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:01.188640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:03.914187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:07.025420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:10.590857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:14.601427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:17.181709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:20.633009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:44:23.689883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:44:37.808804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자아이디격자좌표(X)격자좌표(Y)기업 수위험물 수상온상태(고체) 수상온상태(액체) 수상온상태(가스) 수상온상태(기타) 수색상(무색) 수색상(적색) 수색상(기타) 수냄새(달콤) 수냄새(자극) 수냄새(무취) 수냄새(기타) 수
격자아이디1.0000.9680.4350.0000.2290.0000.1070.0000.0000.1750.0000.3480.0000.0000.0000.233
격자좌표(X)0.9681.0000.6390.0000.0000.0000.1170.0770.0000.1690.0000.0820.2670.0000.0000.401
격자좌표(Y)0.4350.6391.0000.0000.2480.0000.0000.0390.0000.0000.0000.0000.0000.0000.4730.000
기업 수0.0000.0000.0001.0000.8480.9210.9810.4740.9080.9810.8810.9850.8960.9210.2950.617
위험물 수0.2290.0000.2480.8481.0000.9150.8920.6590.9890.9380.8720.9580.9200.9360.5230.872
상온상태(고체) 수0.0000.0000.0000.9210.9151.0000.9470.7940.9310.9640.8920.9770.8190.8480.7950.776
상온상태(액체) 수0.1070.1170.0000.9810.8920.9471.0000.8590.9430.9740.8840.9690.9040.8930.6210.812
상온상태(가스) 수0.0000.0770.0390.4740.6590.7940.8591.0000.8770.7740.7590.7730.8070.8280.3240.531
상온상태(기타) 수0.0000.0000.0000.9080.9890.9310.9430.8771.0000.9220.9100.9340.9630.9620.6380.851
색상(무색) 수0.1750.1690.0000.9810.9380.9640.9740.7740.9221.0000.9130.9920.9150.9300.6480.885
색상(적색) 수0.0000.0000.0000.8810.8720.8920.8840.7590.9100.9131.0000.9410.8900.9220.5070.897
색상(기타) 수0.3480.0820.0000.9850.9580.9770.9690.7730.9340.9920.9411.0000.9090.9470.6540.869
냄새(달콤) 수0.0000.2670.0000.8960.9200.8190.9040.8070.9630.9150.8900.9091.0000.9610.2490.816
냄새(자극) 수0.0000.0000.0000.9210.9360.8480.8930.8280.9620.9300.9220.9470.9611.0000.4140.871
냄새(무취) 수0.0000.0000.4730.2950.5230.7950.6210.3240.6380.6480.5070.6540.2490.4141.0000.494
냄새(기타) 수0.2330.4010.0000.6170.8720.7760.8120.5310.8510.8850.8970.8690.8160.8710.4941.000
2024-03-15T02:44:38.081192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업 수냄새(무취) 수상온상태(가스) 수
기업 수1.0000.1160.400
냄새(무취) 수0.1161.0000.265
상온상태(가스) 수0.4000.2651.000
2024-03-15T02:44:38.282660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자아이디격자좌표(X)격자좌표(Y)위험물 수상온상태(고체) 수상온상태(액체) 수상온상태(기타) 수색상(무색) 수색상(적색) 수색상(기타) 수냄새(달콤) 수냄새(자극) 수냄새(기타) 수기업 수상온상태(가스) 수냄새(무취) 수
격자아이디1.0000.998-0.3480.176-0.0660.098-0.0060.1680.0690.1180.0280.1280.1950.0000.0000.000
격자좌표(X)0.9981.000-0.3830.173-0.0720.103-0.0050.1660.0600.1120.0270.1270.1970.0000.0140.000
격자좌표(Y)-0.348-0.3831.0000.1210.0920.0790.1150.1010.1590.1100.2320.1080.0730.0000.0430.302
위험물 수0.1760.1730.1211.0000.7470.8580.8100.9590.7080.8750.5610.8910.9520.7010.5150.357
상온상태(고체) 수-0.066-0.0720.0920.7471.0000.5830.7710.6890.5610.7090.2730.5710.5970.6170.6330.445
상온상태(액체) 수0.0980.1030.0790.8580.5831.0000.8220.8170.6380.7230.5340.8130.8970.7870.7340.304
상온상태(기타) 수-0.006-0.0050.1150.8100.7710.8221.0000.7440.5530.7490.5230.7570.7850.8060.7250.448
색상(무색) 수0.1680.1660.1010.9590.6890.8170.7441.0000.6850.8620.6190.8660.9280.7880.6050.323
색상(적색) 수0.0690.0600.1590.7080.5610.6380.5530.6851.0000.7940.3670.6890.6800.8060.6120.363
색상(기타) 수0.1180.1120.1100.8750.7090.7230.7490.8620.7941.0000.6050.7710.8010.8100.6040.328
냄새(달콤) 수0.0280.0270.2320.5610.2730.5340.5230.6190.3670.6051.0000.5120.6060.7830.6150.149
냄새(자극) 수0.1280.1270.1080.8910.5710.8130.7570.8660.6890.7710.5121.0000.9190.8300.6450.263
냄새(기타) 수0.1950.1970.0730.9520.5970.8970.7850.9280.6800.8010.6060.9191.0000.4640.3690.352
기업 수0.0000.0000.0000.7010.6170.7870.8060.7880.8060.8100.7830.8300.4641.0000.4000.116
상온상태(가스) 수0.0000.0140.0430.5150.6330.7340.7250.6050.6120.6040.6150.6450.3690.4001.0000.265
냄새(무취) 수0.0000.0000.3020.3570.4450.3040.4480.3230.3630.3280.1490.2630.3520.1160.2651.000

Missing values

2024-03-15T02:44:27.483287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:44:27.953263image/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

격자아이디격자좌표(X)격자좌표(Y)기업 수위험물 수상온상태(고체) 수상온상태(액체) 수상온상태(가스) 수상온상태(기타) 수색상(무색) 수색상(적색) 수색상(기타) 수냄새(달콤) 수냄새(자극) 수냄새(무취) 수냄새(기타) 수
014112233205471211112000111
124113233166471101001000001
234113233176472211112011011
34411323320047653581102582179331
454113233201471211002110010
564114233171471712016023214
674114233173473721111419331436811231
78411423320047217441661324211
894115233162472612214220414
9104115233169471101001010001
격자아이디격자좌표(X)격자좌표(Y)기업 수위험물 수상온상태(고체) 수상온상태(액체) 수상온상태(가스) 수상온상태(기타) 수색상(무색) 수색상(적색) 수색상(기타) 수냄새(달콤) 수냄새(자극) 수냄새(무취) 수냄새(기타) 수
59604131233160471422123010122
60614132233148471110001000010
61624134233153471311003110122
62634135233228471301002011001
636441362331734711113124010327
64654140233144471411003000123
65664140233189471302112111103
66674141233160471201102110202
67684142233147471612213220223
68694145233179471181201114856214