Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory110.4 B

Variable types

Categorical1
Text1
Numeric10

Dataset

Description산업중분류별규모별현황분석데이터(산업중분류별규모별사고재해자수) 산업안전보건에 대한 통계 자료로써 규모별, 산업중분류별, 사고재해자수등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15084672/fileData.do

Alerts

5인 미만 is highly overall correlated with 5인-9인 and 7 other fieldsHigh correlation
5인-9인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
10인-19인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
20인-29인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
30인-49인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
50인-99인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
100인-299인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
300인-499인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
500인-999인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
1000인 이상 is highly overall correlated with 5인-9인 and 7 other fieldsHigh correlation
구분 has unique valuesUnique
5인-9인 has 1 (3.3%) zerosZeros
10인-19인 has 1 (3.3%) zerosZeros
20인-29인 has 1 (3.3%) zerosZeros
30인-49인 has 3 (10.0%) zerosZeros
50인-99인 has 1 (3.3%) zerosZeros
100인-299인 has 1 (3.3%) zerosZeros
300인-499인 has 6 (20.0%) zerosZeros
500인-999인 has 8 (26.7%) zerosZeros
1000인 이상 has 9 (30.0%) zerosZeros

Reproduction

Analysis started2023-12-12 21:15:15.453460
Analysis finished2023-12-12 21:15:24.531041
Duration9.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대업종
Categorical

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
11 
기타의사업
8 
운수·창고·통신업
3 
광 업
2 
금융및보험업
 
1
Other values (5)
5 

Length

Max length13
Median length9
Mean length4.7333333
Min length3

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row금융및보험업
2nd row광 업
3rd row광 업
4th row제조업
5th row제조업

Common Values

ValueCountFrequency (%)
제조업 11
36.7%
기타의사업 8
26.7%
운수·창고·통신업 3
 
10.0%
광 업 2
 
6.7%
금융및보험업 1
 
3.3%
전기·가스·증기·수도사업 1
 
3.3%
건설업 1
 
3.3%
임 업 1
 
3.3%
어 업 1
 
3.3%
농 업 1
 
3.3%

Length

2023-12-13T06:15:24.584211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:24.683157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 11
31.4%
기타의사업 8
22.9%
업 5
14.3%
운수·창고·통신업 3
 
8.6%
광 2
 
5.7%
금융및보험업 1
 
2.9%
전기·가스·증기·수도사업 1
 
2.9%
건설업 1
 
2.9%
임 1
 
2.9%
어 1
 
2.9%

구분
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T06:15:24.879338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length9.3666667
Min length2

Characters and Unicode

Total characters281
Distinct characters101
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row금융및보험업
2nd row석탄광업및채석업
3rd row석회석·금속·비금속광업및기타광업
4th row식료품제조업
5th row섬유및섬유제품제조업
ValueCountFrequency (%)
금융및보험업 1
 
3.3%
석탄광업및채석업 1
 
3.3%
국가및지방자치단체의사업 1
 
3.3%
부동산업및임대업 1
 
3.3%
도소매·음식·숙박업 1
 
3.3%
전문·보건·교육·여가관련서비스업 1
 
3.3%
해외파견자 1
 
3.3%
기타의각종사업 1
 
3.3%
시설관리및사업지원서비스업 1
 
3.3%
농업 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T06:15:25.198899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 32
 
11.4%
· 22
 
7.8%
제 18
 
6.4%
및 12
 
4.3%
품 11
 
3.9%
기 11
 
3.9%
조 9
 
3.2%
금 6
 
2.1%
수 6
 
2.1%
비 5
 
1.8%
Other values (91) 149
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 259
92.2%
Other Punctuation 22
 
7.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 32
 
12.4%
제 18
 
6.9%
및 12
 
4.6%
품 11
 
4.2%
기 11
 
4.2%
조 9
 
3.5%
금 6
 
2.3%
수 6
 
2.3%
비 5
 
1.9%
속 5
 
1.9%
Other values (90) 144
55.6%
Other Punctuation
ValueCountFrequency (%)
· 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 259
92.2%
Common 22
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 32
 
12.4%
제 18
 
6.9%
및 12
 
4.6%
품 11
 
4.2%
기 11
 
4.2%
조 9
 
3.5%
금 6
 
2.3%
수 6
 
2.3%
비 5
 
1.9%
속 5
 
1.9%
Other values (90) 144
55.6%
Common
ValueCountFrequency (%)
· 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 259
92.2%
None 22
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 32
 
12.4%
제 18
 
6.9%
및 12
 
4.6%
품 11
 
4.2%
기 11
 
4.2%
조 9
 
3.5%
금 6
 
2.3%
수 6
 
2.3%
비 5
 
1.9%
속 5
 
1.9%
Other values (90) 144
55.6%
None
ValueCountFrequency (%)
· 22
100.0%

5인 미만
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1151.9
Minimum1
Maximum10541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:25.326636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q135.5
median287.5
Q3647.75
95-th percentile6142.4
Maximum10541
Range10540
Interquartile range (IQR)612.25

Descriptive statistics

Standard deviation2422.4128
Coefficient of variation (CV)2.1029715
Kurtosis9.4343065
Mean1151.9
Median Absolute Deviation (MAD)271.5
Skewness3.076174
Sum34557
Variance5868083.9
MonotonicityNot monotonic
2023-12-13T06:15:25.452742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 2
 
6.7%
55 1
 
3.3%
656 1
 
3.3%
434 1
 
3.3%
164 1
 
3.3%
8243 1
 
3.3%
1266 1
 
3.3%
3 1
 
3.3%
733 1
 
3.3%
2446 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
1 2
6.7%
2 1
3.3%
3 1
3.3%
8 1
3.3%
14 1
3.3%
18 1
3.3%
29 1
3.3%
55 1
3.3%
61 1
3.3%
94 1
3.3%
ValueCountFrequency (%)
10541 1
3.3%
8243 1
3.3%
3575 1
3.3%
2819 1
3.3%
2446 1
3.3%
1266 1
3.3%
733 1
3.3%
656 1
3.3%
623 1
3.3%
468 1
3.3%

5인-9인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.16667
Minimum0
Maximum3258
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:25.553415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q124.75
median153
Q3333
95-th percentile2549.85
Maximum3258
Range3258
Interquartile range (IQR)308.25

Descriptive statistics

Standard deviation869.56402
Coefficient of variation (CV)1.8416464
Kurtosis5.5629042
Mean472.16667
Median Absolute Deviation (MAD)137.5
Skewness2.4994426
Sum14165
Variance756141.59
MonotonicityNot monotonic
2023-12-13T06:15:25.684182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 2
 
6.7%
37 1
 
3.3%
0 1
 
3.3%
306 1
 
3.3%
35 1
 
3.3%
3174 1
 
3.3%
839 1
 
3.3%
231 1
 
3.3%
1771 1
 
3.3%
110 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 1
3.3%
1 2
6.7%
4 1
3.3%
5 1
3.3%
14 1
3.3%
17 1
3.3%
23 1
3.3%
30 1
3.3%
35 1
3.3%
37 1
3.3%
ValueCountFrequency (%)
3258 1
3.3%
3174 1
3.3%
1787 1
3.3%
1771 1
3.3%
839 1
3.3%
473 1
3.3%
375 1
3.3%
342 1
3.3%
306 1
3.3%
268 1
3.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.2
Minimum0
Maximum3512
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:25.793483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.45
Q124.25
median129
Q3539
95-th percentile2138.45
Maximum3512
Range3512
Interquartile range (IQR)514.75

Descriptive statistics

Standard deviation830.93186
Coefficient of variation (CV)1.709033
Kurtosis5.7650164
Mean486.2
Median Absolute Deviation (MAD)113.5
Skewness2.3954644
Sum14586
Variance690447.75
MonotonicityNot monotonic
2023-12-13T06:15:25.906166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 2
 
6.7%
63 1
 
3.3%
0 1
 
3.3%
613 1
 
3.3%
23 1
 
3.3%
2354 1
 
3.3%
1235 1
 
3.3%
180 1
 
3.3%
1608 1
 
3.3%
102 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
2 2
6.7%
3 1
3.3%
14 1
3.3%
17 1
3.3%
23 1
3.3%
28 1
3.3%
38 1
3.3%
63 1
3.3%
ValueCountFrequency (%)
3512 1
3.3%
2354 1
3.3%
1875 1
3.3%
1608 1
3.3%
1235 1
3.3%
677 1
3.3%
613 1
3.3%
581 1
3.3%
413 1
3.3%
227 1
3.3%

20인-29인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.76667
Minimum0
Maximum1904
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:26.030455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113.25
median73
Q3317.75
95-th percentile1036.05
Maximum1904
Range1904
Interquartile range (IQR)304.5

Descriptive statistics

Standard deviation439.43449
Coefficient of variation (CV)1.6229268
Kurtosis5.7782883
Mean270.76667
Median Absolute Deviation (MAD)65
Skewness2.3065876
Sum8123
Variance193102.67
MonotonicityNot monotonic
2023-12-13T06:15:26.136845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
8 2
 
6.7%
1 2
 
6.7%
1904 1
 
3.3%
0 1
 
3.3%
673 1
 
3.3%
1059 1
 
3.3%
792 1
 
3.3%
81 1
 
3.3%
746 1
 
3.3%
41 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 1
3.3%
1 2
6.7%
2 1
3.3%
4 1
3.3%
8 2
6.7%
13 1
3.3%
14 1
3.3%
23 1
3.3%
27 1
3.3%
41 1
3.3%
ValueCountFrequency (%)
1904 1
3.3%
1059 1
3.3%
1008 1
3.3%
792 1
3.3%
746 1
3.3%
673 1
3.3%
357 1
3.3%
340 1
3.3%
251 1
3.3%
134 1
3.3%

30인-49인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.56667
Minimum0
Maximum1702
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:26.241428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median79.5
Q3330.75
95-th percentile1001.4
Maximum1702
Range1702
Interquartile range (IQR)311.75

Descriptive statistics

Standard deviation422.54359
Coefficient of variation (CV)1.5168491
Kurtosis3.3995532
Mean278.56667
Median Absolute Deviation (MAD)76.5
Skewness1.9217879
Sum8357
Variance178543.08
MonotonicityNot monotonic
2023-12-13T06:15:26.350114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3
 
10.0%
19 2
 
6.7%
3 2
 
6.7%
99 1
 
3.3%
191 1
 
3.3%
730 1
 
3.3%
7 1
 
3.3%
918 1
 
3.3%
1059 1
 
3.3%
74 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 3
10.0%
3 2
6.7%
7 1
 
3.3%
13 1
 
3.3%
19 2
6.7%
32 1
 
3.3%
39 1
 
3.3%
44 1
 
3.3%
53 1
 
3.3%
54 1
 
3.3%
ValueCountFrequency (%)
1702 1
3.3%
1059 1
3.3%
931 1
3.3%
918 1
3.3%
874 1
3.3%
730 1
3.3%
396 1
3.3%
342 1
3.3%
297 1
3.3%
191 1
3.3%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.5
Minimum0
Maximum1707
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:26.496567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.45
Q110.5
median66.5
Q3312.25
95-th percentile1151.6
Maximum1707
Range1707
Interquartile range (IQR)301.75

Descriptive statistics

Standard deviation423.14909
Coefficient of variation (CV)1.5998075
Kurtosis4.7165313
Mean264.5
Median Absolute Deviation (MAD)62.5
Skewness2.2051149
Sum7935
Variance179055.16
MonotonicityNot monotonic
2023-12-13T06:15:26.882807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 2
 
6.7%
93 1
 
3.3%
253 1
 
3.3%
2 1
 
3.3%
438 1
 
3.3%
10 1
 
3.3%
892 1
 
3.3%
1364 1
 
3.3%
3 1
 
3.3%
62 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
2 1
3.3%
3 1
3.3%
5 2
6.7%
9 1
3.3%
10 1
3.3%
12 1
3.3%
17 1
3.3%
31 1
3.3%
ValueCountFrequency (%)
1707 1
3.3%
1364 1
3.3%
892 1
3.3%
724 1
3.3%
693 1
3.3%
475 1
3.3%
438 1
3.3%
332 1
3.3%
253 1
3.3%
223 1
3.3%

100인-299인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.66667
Minimum0
Maximum2868
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:26.991805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.45
Q113.25
median55
Q3339.75
95-th percentile1469.45
Maximum2868
Range2868
Interquartile range (IQR)326.5

Descriptive statistics

Standard deviation615.17597
Coefficient of variation (CV)1.9365455
Kurtosis10.384354
Mean317.66667
Median Absolute Deviation (MAD)52
Skewness3.0722467
Sum9530
Variance378441.47
MonotonicityNot monotonic
2023-12-13T06:15:27.097658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
21 2
 
6.7%
6 2
 
6.7%
27 1
 
3.3%
300 1
 
3.3%
13 1
 
3.3%
434 1
 
3.3%
1640 1
 
3.3%
1261 1
 
3.3%
2 1
 
3.3%
57 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
2 1
3.3%
4 1
3.3%
6 2
6.7%
10 1
3.3%
13 1
3.3%
14 1
3.3%
19 1
3.3%
21 2
6.7%
ValueCountFrequency (%)
2868 1
3.3%
1640 1
3.3%
1261 1
3.3%
749 1
3.3%
480 1
3.3%
434 1
3.3%
394 1
3.3%
353 1
3.3%
300 1
3.3%
252 1
3.3%

300인-499인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.8
Minimum0
Maximum1090
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:27.197808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median11.5
Q385
95-th percentile271.4
Maximum1090
Range1090
Interquartile range (IQR)83.75

Descriptive statistics

Standard deviation206.31386
Coefficient of variation (CV)2.3768877
Kurtosis20.468692
Mean86.8
Median Absolute Deviation (MAD)11.5
Skewness4.2757591
Sum2604
Variance42565.407
MonotonicityNot monotonic
2023-12-13T06:15:27.300218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
20.0%
5 3
 
10.0%
1 2
 
6.7%
85 2
 
6.7%
104 1
 
3.3%
4 1
 
3.3%
151 1
 
3.3%
6 1
 
3.3%
251 1
 
3.3%
284 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 6
20.0%
1 2
 
6.7%
2 1
 
3.3%
4 1
 
3.3%
5 3
10.0%
6 1
 
3.3%
9 1
 
3.3%
14 1
 
3.3%
15 1
 
3.3%
17 1
 
3.3%
ValueCountFrequency (%)
1090 1
3.3%
284 1
3.3%
256 1
3.3%
251 1
3.3%
151 1
3.3%
104 1
3.3%
90 1
3.3%
85 2
6.7%
60 1
3.3%
46 1
3.3%

500인-999인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.566667
Minimum0
Maximum511
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:27.407522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median11.5
Q349.75
95-th percentile230.2
Maximum511
Range511
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation107.98872
Coefficient of variation (CV)1.9090523
Kurtosis11.214802
Mean56.566667
Median Absolute Deviation (MAD)11.5
Skewness3.1651244
Sum1697
Variance11661.564
MonotonicityNot monotonic
2023-12-13T06:15:27.522621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 8
26.7%
2 3
 
10.0%
6 1
 
3.3%
511 1
 
3.3%
304 1
 
3.3%
1 1
 
3.3%
124 1
 
3.3%
135 1
 
3.3%
10 1
 
3.3%
58 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 8
26.7%
1 1
 
3.3%
2 3
 
10.0%
6 1
 
3.3%
8 1
 
3.3%
10 1
 
3.3%
13 1
 
3.3%
14 1
 
3.3%
30 1
 
3.3%
40 1
 
3.3%
ValueCountFrequency (%)
511 1
3.3%
304 1
3.3%
140 1
3.3%
135 1
3.3%
124 1
3.3%
111 1
3.3%
58 1
3.3%
51 1
3.3%
46 1
3.3%
45 1
3.3%

1000인 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.66667
Minimum0
Maximum2169
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:15:27.623898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20.5
Q3162.75
95-th percentile653.55
Maximum2169
Range2169
Interquartile range (IQR)162.75

Descriptive statistics

Standard deviation422.44913
Coefficient of variation (CV)2.2391296
Kurtosis17.329704
Mean188.66667
Median Absolute Deviation (MAD)20.5
Skewness3.8743906
Sum5660
Variance178463.26
MonotonicityNot monotonic
2023-12-13T06:15:27.720483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 9
30.0%
23 2
 
6.7%
11 1
 
3.3%
565 1
 
3.3%
341 1
 
3.3%
3 1
 
3.3%
94 1
 
3.3%
168 1
 
3.3%
22 1
 
3.3%
19 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 9
30.0%
1 1
 
3.3%
3 1
 
3.3%
4 1
 
3.3%
10 1
 
3.3%
11 1
 
3.3%
19 1
 
3.3%
22 1
 
3.3%
23 2
 
6.7%
46 1
 
3.3%
ValueCountFrequency (%)
2169 1
3.3%
726 1
3.3%
565 1
3.3%
514 1
3.3%
358 1
3.3%
341 1
3.3%
339 1
3.3%
168 1
3.3%
147 1
3.3%
94 1
3.3%

Interactions

2023-12-13T06:15:23.685879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:15.883835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.167202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.002226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.824466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.685526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.487443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.316566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.332468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.027311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.760042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:15.970778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.242391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.078149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.918626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.761087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.559867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.388092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.401917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.096375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.827488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.081920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.327027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.163643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.025744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.854140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.644823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.456930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.470545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.164979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.889061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.193904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.431832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.267936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.118970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.950311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.724191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.786935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.556579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.231422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.955549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.594983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.520845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.345192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.199701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.027898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.798623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.862268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.632319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.301812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:24.019490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.701107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.601643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.425121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.293559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.101276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.879982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.937529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.700330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.366509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:24.086611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.805055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.679400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.500443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.366749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.176375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.962666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.010934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.777103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.442761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:24.146836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.895912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.760617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.578998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.441682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.254211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.075682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.085164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.845584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.505294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:24.206258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:16.998468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.846977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.653507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.523602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.325571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.156367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.160105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.908550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.566762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:24.262733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.076331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:17.933554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:18.738486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:19.603348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:20.401367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:21.238060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.250492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:22.967746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:23.629292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:15:27.806499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.2190.0000.0000.0000.0000.0000.1460.5770.4670.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.2191.0001.0000.9260.9480.9760.8950.9630.9880.8590.7750.717
5인-9인0.0001.0000.9261.0000.9590.9190.9050.9330.8730.6110.8740.757
10인-19인0.0001.0000.9480.9591.0000.9570.9730.9920.9690.8790.8840.731
20인-29인0.0001.0000.9760.9190.9571.0000.9430.9130.9850.9720.8660.606
30인-49인0.0001.0000.8950.9050.9730.9431.0000.9850.8890.9500.8930.720
50인-99인0.0001.0000.9630.9330.9920.9130.9851.0000.9440.8900.8490.665
100인-299인0.1461.0000.9880.8730.9690.9850.8890.9441.0000.9250.7840.670
300인-499인0.5771.0000.8590.6110.8790.9720.9500.8900.9251.0000.9290.536
500인-999인0.4671.0000.7750.8740.8840.8660.8930.8490.7840.9291.0000.728
1000인 이상0.0001.0000.7170.7570.7310.6060.7200.6650.6700.5360.7281.000
2023-12-13T06:15:27.939071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상대업종
5인 미만1.0000.9670.9330.8830.8210.8040.7570.6830.6080.4890.000
5인-9인0.9671.0000.9780.9500.9030.8840.8430.7740.7060.5450.000
10인-19인0.9330.9781.0000.9800.9470.9090.8900.7910.7220.5530.000
20인-29인0.8830.9500.9801.0000.9800.9540.9210.8320.7770.6080.000
30인-49인0.8210.9030.9470.9801.0000.9660.9420.8470.7940.6450.000
50인-99인0.8040.8840.9090.9540.9661.0000.9620.9000.8590.7450.000
100인-299인0.7570.8430.8900.9210.9420.9621.0000.9010.8800.7850.000
300인-499인0.6830.7740.7910.8320.8470.9000.9011.0000.9150.7690.322
500인-999인0.6080.7060.7220.7770.7940.8590.8800.9151.0000.8520.156
1000인 이상0.4890.5450.5530.6080.6450.7450.7850.7690.8521.0000.000
대업종0.0000.0000.0000.0000.0000.0000.0000.3220.1560.0001.000

Missing values

2023-12-13T06:15:24.348310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:15:24.480396image/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

대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
0금융및보험업금융및보험업553763659993279611
1광 업석탄광업및채석업2122014280
2광 업석회석·금속·비금속광업및기타광업1814382319510000
3제조업식료품제조업468375581340396332353905146
4제조업섬유및섬유제품제조업26014715610785875314134
5제조업목재및종이제품제조업44326822712392711051820
6제조업출판·인쇄·제본업94597144393121100
7제조업화학및고무제품제조업4513424132512972232094645358
8제조업의약품·화장품·연탄·석유제품제조업14172813445185171423
9제조업기계기구·금속·비금속광물제품제조업2819178718751008931693749256140726
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업292331000000
21농 업농업3051101024154914000
22기타의사업시설관리및사업지원서비스업244617711608746874724394855819
23기타의사업기타의각종사업73323118081746257151022
24기타의사업해외파견자3121332000
25기타의사업전문·보건·교육·여가관련서비스업12668391235792105913641261284135168
26기타의사업도소매·음식·숙박업8243317423541059918892164025112494
27기타의사업부동산업및임대업164352387106613
28기타의사업국가및지방자치단체의사업434306613673730438434151304341
29기타의사업주한미군100002134223