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(산업중분류별규모별질병사망자수) 산업안전보건에 대한 통계 자료로써 규모별, 산업중분류별, 질병사망자수 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15084675/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 7 other fieldsHigh correlation
10인-19인 is highly overall correlated with 5인 미만 and 6 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 7 other fieldsHigh correlation
500인-999인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
1000인 이상 is highly overall correlated with 20인-29인 and 4 other fieldsHigh correlation
대업종 is highly overall correlated with 300인-499인High correlation
구분 has unique valuesUnique
5인 미만 has 7 (23.3%) zerosZeros
5인-9인 has 8 (26.7%) zerosZeros
10인-19인 has 9 (30.0%) zerosZeros
20인-29인 has 13 (43.3%) zerosZeros
30인-49인 has 10 (33.3%) zerosZeros
50인-99인 has 9 (30.0%) zerosZeros
100인-299인 has 9 (30.0%) zerosZeros
300인-499인 has 19 (63.3%) zerosZeros
500인-999인 has 15 (50.0%) zerosZeros
1000인 이상 has 12 (40.0%) zerosZeros

Reproduction

Analysis started2023-12-12 13:45:30.134547
Analysis finished2023-12-12 13:45:41.481163
Duration11.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대업종
Categorical

HIGH CORRELATION 

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

2023-12-12T22:45:41.563043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:45:41.708676image/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
2023-12-12T22:45:41.946974image/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%
2023-12-12T22:45:42.343321image/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  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6666667
Minimum0
Maximum40
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:42.527037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile37.55
Maximum40
Range40
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.526753
Coefficient of variation (CV)1.6339243
Kurtosis2.4624423
Mean7.6666667
Median Absolute Deviation (MAD)2
Skewness1.9643697
Sum230
Variance156.91954
MonotonicityNot monotonic
2023-12-12T22:45:42.692848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 7
23.3%
2 5
16.7%
1 4
13.3%
3 3
10.0%
6 2
 
6.7%
4 2
 
6.7%
15 1
 
3.3%
36 1
 
3.3%
37 1
 
3.3%
9 1
 
3.3%
Other values (3) 3
10.0%
ValueCountFrequency (%)
0 7
23.3%
1 4
13.3%
2 5
16.7%
3 3
10.0%
4 2
 
6.7%
6 2
 
6.7%
9 1
 
3.3%
12 1
 
3.3%
15 1
 
3.3%
36 1
 
3.3%
ValueCountFrequency (%)
40 1
 
3.3%
38 1
 
3.3%
37 1
 
3.3%
36 1
 
3.3%
15 1
 
3.3%
12 1
 
3.3%
9 1
 
3.3%
6 2
6.7%
4 2
6.7%
3 3
10.0%

5인-9인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7666667
Minimum0
Maximum24
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:42.824612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median1
Q33.75
95-th percentile14.1
Maximum24
Range24
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation5.58127
Coefficient of variation (CV)1.4817531
Kurtosis5.3293406
Mean3.7666667
Median Absolute Deviation (MAD)1
Skewness2.2520566
Sum113
Variance31.150575
MonotonicityNot monotonic
2023-12-12T22:45:42.961334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8
26.7%
0 8
26.7%
3 5
16.7%
6 1
 
3.3%
5 1
 
3.3%
2 1
 
3.3%
24 1
 
3.3%
4 1
 
3.3%
15 1
 
3.3%
13 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0 8
26.7%
1 8
26.7%
2 1
 
3.3%
3 5
16.7%
4 1
 
3.3%
5 1
 
3.3%
6 1
 
3.3%
9 1
 
3.3%
12 1
 
3.3%
13 1
 
3.3%
ValueCountFrequency (%)
24 1
 
3.3%
15 1
 
3.3%
13 1
 
3.3%
12 1
 
3.3%
9 1
 
3.3%
6 1
 
3.3%
5 1
 
3.3%
4 1
 
3.3%
3 5
16.7%
2 1
 
3.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4333333
Minimum0
Maximum31
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:43.103135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33.75
95-th percentile23.15
Maximum31
Range31
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation7.7445569
Coefficient of variation (CV)1.7468925
Kurtosis7.0966718
Mean4.4333333
Median Absolute Deviation (MAD)2
Skewness2.7163379
Sum133
Variance59.978161
MonotonicityNot monotonic
2023-12-12T22:45:43.231221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9
30.0%
1 5
16.7%
3 4
13.3%
2 4
13.3%
7 2
 
6.7%
6 1
 
3.3%
4 1
 
3.3%
31 1
 
3.3%
16 1
 
3.3%
29 1
 
3.3%
ValueCountFrequency (%)
0 9
30.0%
1 5
16.7%
2 4
13.3%
3 4
13.3%
4 1
 
3.3%
6 1
 
3.3%
7 2
 
6.7%
8 1
 
3.3%
16 1
 
3.3%
29 1
 
3.3%
ValueCountFrequency (%)
31 1
 
3.3%
29 1
 
3.3%
16 1
 
3.3%
8 1
 
3.3%
7 2
 
6.7%
6 1
 
3.3%
4 1
 
3.3%
3 4
13.3%
2 4
13.3%
1 5
16.7%

20인-29인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6
Minimum0
Maximum15
Zeros13
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:43.332298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11.75
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.9531742
Coefficient of variation (CV)1.5204516
Kurtosis4.1891426
Mean2.6
Median Absolute Deviation (MAD)1
Skewness2.1133159
Sum78
Variance15.627586
MonotonicityNot monotonic
2023-12-12T22:45:43.444234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13
43.3%
3 6
20.0%
2 3
 
10.0%
1 3
 
10.0%
14 1
 
3.3%
15 1
 
3.3%
7 1
 
3.3%
9 1
 
3.3%
6 1
 
3.3%
ValueCountFrequency (%)
0 13
43.3%
1 3
 
10.0%
2 3
 
10.0%
3 6
20.0%
6 1
 
3.3%
7 1
 
3.3%
9 1
 
3.3%
14 1
 
3.3%
15 1
 
3.3%
ValueCountFrequency (%)
15 1
 
3.3%
14 1
 
3.3%
9 1
 
3.3%
7 1
 
3.3%
6 1
 
3.3%
3 6
20.0%
2 3
 
10.0%
1 3
 
10.0%
0 13
43.3%

30인-49인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7
Minimum0
Maximum32
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:43.574202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.75
95-th percentile14.55
Maximum32
Range32
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation6.6651435
Coefficient of variation (CV)1.8013901
Kurtosis11.11609
Mean3.7
Median Absolute Deviation (MAD)1
Skewness3.0918997
Sum111
Variance44.424138
MonotonicityNot monotonic
2023-12-12T22:45:43.698757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
33.3%
1 7
23.3%
2 4
 
13.3%
32 1
 
3.3%
5 1
 
3.3%
15 1
 
3.3%
14 1
 
3.3%
4 1
 
3.3%
9 1
 
3.3%
8 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0 10
33.3%
1 7
23.3%
2 4
 
13.3%
3 1
 
3.3%
4 1
 
3.3%
5 1
 
3.3%
6 1
 
3.3%
8 1
 
3.3%
9 1
 
3.3%
14 1
 
3.3%
ValueCountFrequency (%)
32 1
 
3.3%
15 1
 
3.3%
14 1
 
3.3%
9 1
 
3.3%
8 1
 
3.3%
6 1
 
3.3%
5 1
 
3.3%
4 1
 
3.3%
3 1
 
3.3%
2 4
13.3%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5
Minimum0
Maximum25
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:44.111249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33
95-th percentile18.1
Maximum25
Range25
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8870193
Coefficient of variation (CV)1.5304487
Kurtosis1.9951435
Mean4.5
Median Absolute Deviation (MAD)1.5
Skewness1.7603238
Sum135
Variance47.431034
MonotonicityNot monotonic
2023-12-12T22:45:44.221439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9
30.0%
1 6
20.0%
3 4
13.3%
2 4
13.3%
25 1
 
3.3%
16 1
 
3.3%
5 1
 
3.3%
15 1
 
3.3%
19 1
 
3.3%
17 1
 
3.3%
ValueCountFrequency (%)
0 9
30.0%
1 6
20.0%
2 4
13.3%
3 4
13.3%
5 1
 
3.3%
12 1
 
3.3%
15 1
 
3.3%
16 1
 
3.3%
17 1
 
3.3%
19 1
 
3.3%
ValueCountFrequency (%)
25 1
 
3.3%
19 1
 
3.3%
17 1
 
3.3%
16 1
 
3.3%
15 1
 
3.3%
12 1
 
3.3%
5 1
 
3.3%
3 4
13.3%
2 4
13.3%
1 6
20.0%

100인-299인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1666667
Minimum0
Maximum98
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:44.338751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q34
95-th percentile15.75
Maximum98
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.842381
Coefficient of variation (CV)2.8933591
Kurtosis26.447968
Mean6.1666667
Median Absolute Deviation (MAD)1.5
Skewness5.0292277
Sum185
Variance318.35057
MonotonicityNot monotonic
2023-12-12T22:45:44.438887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 9
30.0%
1 6
20.0%
2 4
13.3%
4 3
 
10.0%
6 2
 
6.7%
98 1
 
3.3%
5 1
 
3.3%
10 1
 
3.3%
3 1
 
3.3%
18 1
 
3.3%
ValueCountFrequency (%)
0 9
30.0%
1 6
20.0%
2 4
13.3%
3 1
 
3.3%
4 3
 
10.0%
5 1
 
3.3%
6 2
 
6.7%
10 1
 
3.3%
13 1
 
3.3%
18 1
 
3.3%
ValueCountFrequency (%)
98 1
 
3.3%
18 1
 
3.3%
13 1
 
3.3%
10 1
 
3.3%
6 2
 
6.7%
5 1
 
3.3%
4 3
10.0%
3 1
 
3.3%
2 4
13.3%
1 6
20.0%

300인-499인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2333333
Minimum0
Maximum64
Zeros19
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:44.533776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.85
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.734662
Coefficient of variation (CV)3.629277
Kurtosis27.183799
Mean3.2333333
Median Absolute Deviation (MAD)0
Skewness5.1301762
Sum97
Variance137.7023
MonotonicityNot monotonic
2023-12-12T22:45:44.626386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19
63.3%
1 4
 
13.3%
3 2
 
6.7%
64 1
 
3.3%
2 1
 
3.3%
12 1
 
3.3%
5 1
 
3.3%
4 1
 
3.3%
ValueCountFrequency (%)
0 19
63.3%
1 4
 
13.3%
2 1
 
3.3%
3 2
 
6.7%
4 1
 
3.3%
5 1
 
3.3%
12 1
 
3.3%
64 1
 
3.3%
ValueCountFrequency (%)
64 1
 
3.3%
12 1
 
3.3%
5 1
 
3.3%
4 1
 
3.3%
3 2
 
6.7%
2 1
 
3.3%
1 4
 
13.3%
0 19
63.3%

500인-999인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3666667
Minimum0
Maximum138
Zeros15
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:44.732156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile3.55
Maximum138
Range138
Interquartile range (IQR)1

Descriptive statistics

Standard deviation25.071599
Coefficient of variation (CV)4.6717265
Kurtosis29.887936
Mean5.3666667
Median Absolute Deviation (MAD)0.5
Skewness5.4624807
Sum161
Variance628.58506
MonotonicityNot monotonic
2023-12-12T22:45:44.828005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15
50.0%
1 8
26.7%
2 4
 
13.3%
138 1
 
3.3%
3 1
 
3.3%
4 1
 
3.3%
ValueCountFrequency (%)
0 15
50.0%
1 8
26.7%
2 4
 
13.3%
3 1
 
3.3%
4 1
 
3.3%
138 1
 
3.3%
ValueCountFrequency (%)
138 1
 
3.3%
4 1
 
3.3%
3 1
 
3.3%
2 4
 
13.3%
1 8
26.7%
0 15
50.0%

1000인 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5333333
Minimum0
Maximum21
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:45:44.932979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16.85
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.6307857
Coefficient of variation (CV)1.5936186
Kurtosis4.2046811
Mean3.5333333
Median Absolute Deviation (MAD)1
Skewness2.1502374
Sum106
Variance31.705747
MonotonicityNot monotonic
2023-12-12T22:45:45.026463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12
40.0%
1 4
 
13.3%
2 4
 
13.3%
7 2
 
6.7%
4 2
 
6.7%
21 1
 
3.3%
10 1
 
3.3%
5 1
 
3.3%
13 1
 
3.3%
3 1
 
3.3%
ValueCountFrequency (%)
0 12
40.0%
1 4
 
13.3%
2 4
 
13.3%
3 1
 
3.3%
4 2
 
6.7%
5 1
 
3.3%
7 2
 
6.7%
10 1
 
3.3%
13 1
 
3.3%
20 1
 
3.3%
ValueCountFrequency (%)
21 1
 
3.3%
20 1
 
3.3%
13 1
 
3.3%
10 1
 
3.3%
7 2
6.7%
5 1
 
3.3%
4 2
6.7%
3 1
 
3.3%
2 4
13.3%
1 4
13.3%

Interactions

2023-12-12T22:45:40.232577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:30.573545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.029283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.140538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.331044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.231889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.020766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.933052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.195888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.187041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.354010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:30.701009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.156837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.251368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.457490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.325002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.101329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.338881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.279988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.295423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.452748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:30.793952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.255531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.354525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.541304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.412377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.189457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.416503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.369667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.408610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.548326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.225618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.369587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.466863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.624143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.490558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.316349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.503273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.484507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.513365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.638779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.351015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.465176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.586879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.708623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.569958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.408272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.599107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.599073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.619522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.734872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.490152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.585976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.708569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.810269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.639856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.487346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.691145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.706728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.721660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.823069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.624011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.691867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.837944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.887091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.709847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.610202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.774005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.814287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.820242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.915556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.721833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.794447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.969883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.977437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.784383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.688864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.860589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.911216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.906037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:41.010943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.809851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:32.915542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.099176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.067477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.869848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.771889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:37.982404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.999223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.003841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:41.125114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:31.918190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:33.028523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:34.229343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.158322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:35.947567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:36.852018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:38.109282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:39.097291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:40.140027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:45:45.150424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.0000.1950.7120.0000.3520.0000.6140.8660.6570.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.0001.0001.0000.8420.5850.8840.7390.7800.9520.8350.8120.560
5인-9인0.1951.0000.8421.0000.8390.7700.8390.7170.7590.8470.5530.839
10인-19인0.7121.0000.5850.8391.0000.7090.8530.5940.4130.6800.0000.586
20인-29인0.0001.0000.8840.7700.7091.0000.7150.6180.8540.8960.8120.586
30인-49인0.3521.0000.7390.8390.8530.7151.0000.8260.8800.8121.0000.630
50인-99인0.0001.0000.7800.7170.5940.6180.8261.0000.9110.8011.0000.832
100인-299인0.6141.0000.9520.7590.4130.8540.8800.9111.0000.9721.0000.612
300인-499인0.8661.0000.8350.8470.6800.8960.8120.8010.9721.0001.0000.345
500인-999인0.6571.0000.8120.5530.0000.8121.0001.0001.0001.0001.0000.553
1000인 이상0.0001.0000.5600.8390.5860.5860.6300.8320.6120.3450.5531.000
2023-12-12T22:45:45.279202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상대업종
5인 미만1.0000.7180.6280.6620.6960.5820.7170.6620.6580.3270.000
5인-9인0.7181.0000.8280.7680.7670.5650.5690.5430.6180.4440.000
10인-19인0.6280.8281.0000.8630.8770.6310.6090.4750.6930.4860.318
20인-29인0.6620.7680.8631.0000.8450.7750.6580.5840.8030.6620.152
30인-49인0.6960.7670.8770.8451.0000.6590.7340.5730.7140.6250.084
50인-99인0.5820.5650.6310.7750.6591.0000.7510.5680.6850.6560.295
100인-299인0.7170.5690.6090.6580.7340.7511.0000.7830.6160.5790.384
300인-499인0.6620.5430.4750.5840.5730.5680.7831.0000.6250.4630.680
500인-999인0.6580.6180.6930.8030.7140.6850.6160.6251.0000.6680.423
1000인 이상0.3270.4440.4860.6620.6250.6560.5790.4630.6681.0000.000
대업종0.0000.0000.3180.1520.0840.2950.3840.6800.4230.0001.000

Missing values

2023-12-12T22:45:41.233336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:45:41.412997image/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
0금융및보험업금융및보험업1133130021
1광 업석탄광업및채석업1563143225986413821
2광 업석회석·금속·비금속광업및기타광업2363235100
3제조업식료품제조업3000012100
4제조업섬유및섬유제품제조업11222210010
5제조업목재및종이제품제조업3010201000
6제조업출판·인쇄·제본업2101000000
7제조업화학및고무제품제조업1542521015
8제조업의약품·화장품·연탄·석유제품제조업2210110000
9제조업기계기구·금속·비금속광물제품제조업362431151516101313
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업0000000000
21농 업농업2300000000
22기타의사업시설관리및사업지원서비스업40132999174012
23기타의사업기타의각종사업12973802324
24기타의사업해외파견자0120000000
25기타의사업전문·보건·교육·여가관련서비스업63736126444
26기타의사업도소매·음식·숙박업381286322121
27기타의사업부동산업및임대업4000011010
28기타의사업국가및지방자치단체의사업1132214012
29기타의사업주한미군0000000001