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

Categorical4
Text1
Numeric7

Dataset

Description산업중분류별규모별현황분석데이터(산업중분류별규모별사고사망자수) 산업안전보건에 대한 통계 자료로써 규모별, 산업중분류별, 사고사망자수 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15084674/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 6 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 6 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 7 other fieldsHigh correlation
100인-299인 is highly overall correlated with 10인-19인 and 6 other fieldsHigh correlation
대업종 is highly overall correlated with 5인 미만 and 3 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 10인-19인 and 2 other fieldsHigh correlation
300인-499인 is highly imbalanced (64.6%)Imbalance
구분 has unique valuesUnique
5인 미만 has 8 (26.7%) zerosZeros
5인-9인 has 13 (43.3%) zerosZeros
10인-19인 has 12 (40.0%) zerosZeros
20인-29인 has 17 (56.7%) zerosZeros
30인-49인 has 17 (56.7%) zerosZeros
50인-99인 has 17 (56.7%) zerosZeros
100인-299인 has 19 (63.3%) zerosZeros

Reproduction

Analysis started2023-12-12 00:30:45.786003
Analysis finished2023-12-12 00:30:51.346628
Duration5.56 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

2023-12-12T09:30:51.431843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:51.622786image/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
2023-12-12T09:30:51.871854image/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%
2023-12-12T09:30:52.308547image/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%
Mean11.4
Minimum0
Maximum187
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:52.474636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median2
Q36
95-th percentile26.75
Maximum187
Range187
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation34.126135
Coefficient of variation (CV)2.9935206
Kurtosis26.412928
Mean11.4
Median Absolute Deviation (MAD)2
Skewness5.0212856
Sum342
Variance1164.5931
MonotonicityNot monotonic
2023-12-12T09:30:52.631924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 8
26.7%
1 6
20.0%
3 3
 
10.0%
6 2
 
6.7%
2 2
 
6.7%
22 2
 
6.7%
4 1
 
3.3%
187 1
 
3.3%
10 1
 
3.3%
29 1
 
3.3%
Other values (3) 3
 
10.0%
ValueCountFrequency (%)
0 8
26.7%
1 6
20.0%
2 2
 
6.7%
3 3
 
10.0%
4 1
 
3.3%
5 1
 
3.3%
6 2
 
6.7%
8 1
 
3.3%
10 1
 
3.3%
22 2
 
6.7%
ValueCountFrequency (%)
187 1
 
3.3%
29 1
 
3.3%
24 1
 
3.3%
22 2
6.7%
10 1
 
3.3%
8 1
 
3.3%
6 2
6.7%
5 1
 
3.3%
4 1
 
3.3%
3 3
10.0%

5인-9인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7333333
Minimum0
Maximum50
Zeros13
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:52.779715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile16.3
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.6773227
Coefficient of variation (CV)2.59214
Kurtosis19.137948
Mean3.7333333
Median Absolute Deviation (MAD)1
Skewness4.1815971
Sum112
Variance93.650575
MonotonicityNot monotonic
2023-12-12T09:30:52.910625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 13
43.3%
1 7
23.3%
2 4
 
13.3%
6 2
 
6.7%
3 1
 
3.3%
13 1
 
3.3%
50 1
 
3.3%
19 1
 
3.3%
ValueCountFrequency (%)
0 13
43.3%
1 7
23.3%
2 4
 
13.3%
3 1
 
3.3%
6 2
 
6.7%
13 1
 
3.3%
19 1
 
3.3%
50 1
 
3.3%
ValueCountFrequency (%)
50 1
 
3.3%
19 1
 
3.3%
13 1
 
3.3%
6 2
 
6.7%
3 1
 
3.3%
2 4
 
13.3%
1 7
23.3%
0 13
43.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1
Minimum0
Maximum47
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:53.358041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile12.75
Maximum47
Range47
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8992444
Coefficient of variation (CV)2.1705474
Kurtosis19.70517
Mean4.1
Median Absolute Deviation (MAD)1
Skewness4.1739726
Sum123
Variance79.196552
MonotonicityNot monotonic
2023-12-12T09:30:53.494153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12
40.0%
1 5
16.7%
4 2
 
6.7%
2 2
 
6.7%
7 2
 
6.7%
3 2
 
6.7%
15 1
 
3.3%
47 1
 
3.3%
10 1
 
3.3%
9 1
 
3.3%
ValueCountFrequency (%)
0 12
40.0%
1 5
16.7%
2 2
 
6.7%
3 2
 
6.7%
4 2
 
6.7%
5 1
 
3.3%
7 2
 
6.7%
9 1
 
3.3%
10 1
 
3.3%
15 1
 
3.3%
ValueCountFrequency (%)
47 1
 
3.3%
15 1
 
3.3%
10 1
 
3.3%
9 1
 
3.3%
7 2
 
6.7%
5 1
 
3.3%
4 2
 
6.7%
3 2
 
6.7%
2 2
 
6.7%
1 5
16.7%

20인-29인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7666667
Minimum0
Maximum15
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:53.592048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10.1
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.5785119
Coefficient of variation (CV)2.0255728
Kurtosis7.2282112
Mean1.7666667
Median Absolute Deviation (MAD)0
Skewness2.7341913
Sum53
Variance12.805747
MonotonicityNot monotonic
2023-12-12T09:30:53.711814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 17
56.7%
1 4
 
13.3%
2 4
 
13.3%
3 2
 
6.7%
11 1
 
3.3%
15 1
 
3.3%
9 1
 
3.3%
ValueCountFrequency (%)
0 17
56.7%
1 4
 
13.3%
2 4
 
13.3%
3 2
 
6.7%
9 1
 
3.3%
11 1
 
3.3%
15 1
 
3.3%
ValueCountFrequency (%)
15 1
 
3.3%
11 1
 
3.3%
9 1
 
3.3%
3 2
 
6.7%
2 4
 
13.3%
1 4
 
13.3%
0 17
56.7%

30인-49인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5666667
Minimum0
Maximum21
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:53.871958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13.3
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.0833286
Coefficient of variation (CV)1.9805176
Kurtosis6.3887443
Mean2.5666667
Median Absolute Deviation (MAD)0
Skewness2.5442448
Sum77
Variance25.84023
MonotonicityNot monotonic
2023-12-12T09:30:54.010529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 17
56.7%
1 4
 
13.3%
2 2
 
6.7%
3 1
 
3.3%
10 1
 
3.3%
16 1
 
3.3%
21 1
 
3.3%
4 1
 
3.3%
8 1
 
3.3%
7 1
 
3.3%
ValueCountFrequency (%)
0 17
56.7%
1 4
 
13.3%
2 2
 
6.7%
3 1
 
3.3%
4 1
 
3.3%
7 1
 
3.3%
8 1
 
3.3%
10 1
 
3.3%
16 1
 
3.3%
21 1
 
3.3%
ValueCountFrequency (%)
21 1
 
3.3%
16 1
 
3.3%
10 1
 
3.3%
8 1
 
3.3%
7 1
 
3.3%
4 1
 
3.3%
3 1
 
3.3%
2 2
 
6.7%
1 4
 
13.3%
0 17
56.7%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6333333
Minimum0
Maximum19
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:54.134875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5.55
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.6811105
Coefficient of variation (CV)2.2537411
Kurtosis17.834086
Mean1.6333333
Median Absolute Deviation (MAD)0
Skewness3.9350241
Sum49
Variance13.550575
MonotonicityNot monotonic
2023-12-12T09:30:54.258185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 17
56.7%
1 6
 
20.0%
4 2
 
6.7%
5 1
 
3.3%
2 1
 
3.3%
19 1
 
3.3%
6 1
 
3.3%
3 1
 
3.3%
ValueCountFrequency (%)
0 17
56.7%
1 6
 
20.0%
2 1
 
3.3%
3 1
 
3.3%
4 2
 
6.7%
5 1
 
3.3%
6 1
 
3.3%
19 1
 
3.3%
ValueCountFrequency (%)
19 1
 
3.3%
6 1
 
3.3%
5 1
 
3.3%
4 2
 
6.7%
3 1
 
3.3%
2 1
 
3.3%
1 6
 
20.0%
0 17
56.7%

100인-299인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3666667
Minimum0
Maximum42
Zeros19
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T09:30:54.393899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.75
95-th percentile6.2
Maximum42
Range42
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation7.6990073
Coefficient of variation (CV)3.2531017
Kurtosis26.472009
Mean2.3666667
Median Absolute Deviation (MAD)0
Skewness5.0340364
Sum71
Variance59.274713
MonotonicityNot monotonic
2023-12-12T09:30:54.499616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 19
63.3%
3 4
 
13.3%
1 3
 
10.0%
4 1
 
3.3%
42 1
 
3.3%
8 1
 
3.3%
2 1
 
3.3%
ValueCountFrequency (%)
0 19
63.3%
1 3
 
10.0%
2 1
 
3.3%
3 4
 
13.3%
4 1
 
3.3%
8 1
 
3.3%
42 1
 
3.3%
ValueCountFrequency (%)
42 1
 
3.3%
8 1
 
3.3%
4 1
 
3.3%
3 4
 
13.3%
2 1
 
3.3%
1 3
 
10.0%
0 19
63.3%

300인-499인
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
27 
1
 
2
14
 
1

Length

Max length2
Median length1
Mean length1.0333333
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
90.0%
1 2
 
6.7%
14 1
 
3.3%

Length

2023-12-12T09:30:54.635594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:54.761962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
90.0%
1 2
 
6.7%
14 1
 
3.3%

500인-999인
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
24 
1
5 
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 24
80.0%
1 5
 
16.7%
7 1
 
3.3%

Length

2023-12-12T09:30:54.898295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:55.034570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
80.0%
1 5
 
16.7%
7 1
 
3.3%

1000인 이상
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
22 
1
4 
2
 
2
3
 
1
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 22
73.3%
1 4
 
13.3%
2 2
 
6.7%
3 1
 
3.3%
8 1
 
3.3%

Length

2023-12-12T09:30:55.176232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:55.298835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
73.3%
1 4
 
13.3%
2 2
 
6.7%
3 1
 
3.3%
8 1
 
3.3%

Interactions

2023-12-12T09:30:50.266622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:46.346189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.263050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.838872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.405860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.038719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.641521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.361317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:46.739327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.386760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.925788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.499401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.124211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.718456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.499366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:46.817596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.475533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.996821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.594650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.203985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.806875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.606011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:46.896602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.548141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.063090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.685500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.307971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.900419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.700350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:46.998329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.628857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.141295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.772634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.403838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.987849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.795529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.077365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.696169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.211786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.851868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.485611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.080600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.895825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.170482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:47.773024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.331612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:48.952190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:49.580337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:50.185437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:30:55.397559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.7360.4610.6850.0000.3930.5790.8140.7420.7420.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.7361.0001.0001.0000.8690.9910.9950.7880.9630.9450.9380.267
5인-9인0.4611.0001.0001.0000.9840.9280.8670.9570.8120.8130.6870.740
10인-19인0.6851.0000.8690.9841.0000.8830.8790.9681.0000.8410.7160.880
20인-29인0.0001.0000.9910.9280.8831.0000.9770.8120.9810.9950.9470.638
30인-49인0.3931.0000.9950.8670.8790.9771.0000.7660.9811.0000.9470.687
50인-99인0.5791.0000.7880.9570.9680.8120.7661.0001.0000.7170.7400.814
100인-299인0.8141.0000.9630.8121.0000.9810.9811.0001.0000.9330.9330.680
300인-499인0.7421.0000.9450.8130.8410.9951.0000.7170.9331.0000.9410.262
500인-999인0.7421.0000.9380.6870.7160.9470.9470.7400.9330.9411.0000.000
1000인 이상0.0001.0000.2670.7400.8800.6380.6870.8140.6800.2620.0001.000
2023-12-12T09:30:55.556431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
300인-499인1000인 이상500인-999인대업종
300인-499인1.0000.1840.7020.518
1000인 이상0.1841.0000.0000.000
500인-999인0.7020.0001.0000.518
대업종0.5180.0000.5181.000
2023-12-12T09:30:55.685795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인대업종300인-499인500인-999인1000인 이상
5인 미만1.0000.6660.6340.5610.5130.5810.3830.5120.7120.6930.189
5인-9인0.6661.0000.7270.7730.5200.5030.3700.1530.8060.6380.357
10인-19인0.6340.7271.0000.5920.7280.7060.6740.2970.8420.6760.528
20인-29인0.5610.7730.5921.0000.5910.5200.4640.1830.8280.7050.365
30인-49인0.5130.5200.7280.5911.0000.5830.5200.1670.9430.6780.534
50인-99인0.5810.5030.7060.5200.5831.0000.6600.2240.6770.7080.435
100인-299인0.3830.3700.6740.4640.5200.6601.0000.6080.6810.6830.628
대업종0.5120.1530.2970.1830.1670.2240.6081.0000.5180.5180.000
300인-499인0.7120.8060.8420.8280.9430.6770.6810.5181.0000.7020.184
500인-999인0.6930.6380.6760.7050.6780.7080.6830.5180.7021.0000.000
1000인 이상0.1890.3570.5280.3650.5340.4350.6280.0000.1840.0001.000

Missing values

2023-12-12T09:30:51.033989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:30:51.273511image/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
0금융및보험업금융및보험업0001000000
1광 업석탄광업및채석업1000000010
2광 업석회석·금속·비금속광업및기타광업1040311000
3제조업식료품제조업6112041011
4제조업섬유및섬유제품제조업2020201000
5제조업목재및종이제품제조업0241113000
6제조업출판·인쇄·제본업0010000000
7제조업화학및고무제품제조업43731010111
8제조업의약품·화장품·연탄·석유제품제조업0101000000
9제조업기계기구·금속·비금속광물제품제조업221315111654102
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업0100000000
21농 업농업3220000000
22기타의사업시설관리및사업지원서비스업221999730000
23기타의사업기타의각종사업5101000000
24기타의사업해외파견자0000000000
25기타의사업전문·보건·교육·여가관련서비스업2170142010
26기타의사업도소매·음식·숙박업24652200000
27기타의사업부동산업및임대업3000000000
28기타의사업국가및지방자치단체의사업1232003012
29기타의사업주한미군0000000000