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(산업중분류별규모별질병재해자수) 산업안전보건에 대한 데이터 통계자료로써 규모별, 산업중분류별, 질병재해자수 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15064492/fileData.do

Alerts

5인 미만 is highly overall correlated with 5인-9인 and 8 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 7 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 미만 and 7 other fieldsHigh correlation
구분 has unique valuesUnique
5인 미만 has 2 (6.7%) zerosZeros
5인-9인 has 1 (3.3%) zerosZeros
10인-19인 has 2 (6.7%) zerosZeros
20인-29인 has 4 (13.3%) zerosZeros
30인-49인 has 4 (13.3%) zerosZeros
50인-99인 has 2 (6.7%) zerosZeros
100인-299인 has 2 (6.7%) zerosZeros
300인-499인 has 10 (33.3%) zerosZeros
500인-999인 has 9 (30.0%) zerosZeros
1000인 이상 has 7 (23.3%) zerosZeros

Reproduction

Analysis started2023-12-13 00:43:21.764310
Analysis finished2023-12-13 00:43:28.961465
Duration7.2 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

2023-12-13T09:43:29.013676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:43:29.112825image/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
2023-12-13T09:43:29.280000image/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%
2023-12-13T09:43:29.546927image/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 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.16667
Minimum0
Maximum981
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:29.643615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q115.75
median46.5
Q387.75
95-th percentile675.55
Maximum981
Range981
Interquartile range (IQR)72

Descriptive statistics

Standard deviation234.08429
Coefficient of variation (CV)1.8122655
Kurtosis6.6677553
Mean129.16667
Median Absolute Deviation (MAD)34
Skewness2.6596068
Sum3875
Variance54795.454
MonotonicityNot monotonic
2023-12-13T09:43:29.733627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
18 2
 
6.7%
69 2
 
6.7%
2 2
 
6.7%
0 2
 
6.7%
981 1
 
3.3%
63 1
 
3.3%
28 1
 
3.3%
676 1
 
3.3%
269 1
 
3.3%
3 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 2
6.7%
2 2
6.7%
3 1
3.3%
4 1
3.3%
10 1
3.3%
15 1
3.3%
18 2
6.7%
22 1
3.3%
28 1
3.3%
35 1
3.3%
ValueCountFrequency (%)
981 1
3.3%
676 1
3.3%
675 1
3.3%
269 1
3.3%
250 1
3.3%
148 1
3.3%
133 1
3.3%
94 1
3.3%
69 2
6.7%
63 1
3.3%

5인-9인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.133333
Minimum0
Maximum422
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:29.820930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median27
Q360.75
95-th percentile360.5
Maximum422
Range422
Interquartile range (IQR)52.75

Descriptive statistics

Standard deviation111.74347
Coefficient of variation (CV)1.6645005
Kurtosis5.381692
Mean67.133333
Median Absolute Deviation (MAD)25
Skewness2.477994
Sum2014
Variance12486.602
MonotonicityNot monotonic
2023-12-13T09:43:29.902560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 5
 
16.7%
57 1
 
3.3%
422 1
 
3.3%
0 1
 
3.3%
62 1
 
3.3%
3 1
 
3.3%
311 1
 
3.3%
126 1
 
3.3%
65 1
 
3.3%
132 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 1
 
3.3%
1 5
16.7%
3 1
 
3.3%
7 1
 
3.3%
11 1
 
3.3%
13 1
 
3.3%
14 1
 
3.3%
17 1
 
3.3%
19 1
 
3.3%
21 1
 
3.3%
ValueCountFrequency (%)
422 1
3.3%
401 1
3.3%
311 1
3.3%
132 1
3.3%
126 1
3.3%
80 1
3.3%
65 1
3.3%
62 1
3.3%
57 1
3.3%
43 1
3.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.6
Minimum0
Maximum481
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:29.999482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q17
median32
Q362.25
95-th percentile381.05
Maximum481
Range481
Interquartile range (IQR)55.25

Descriptive statistics

Standard deviation125.54285
Coefficient of variation (CV)1.6178202
Kurtosis5.7990981
Mean77.6
Median Absolute Deviation (MAD)26
Skewness2.479483
Sum2328
Variance15761.007
MonotonicityNot monotonic
2023-12-13T09:43:30.110600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 2
 
6.7%
183 2
 
6.7%
0 2
 
6.7%
6 2
 
6.7%
48 2
 
6.7%
4 2
 
6.7%
23 1
 
3.3%
481 1
 
3.3%
113 1
 
3.3%
265 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
0 2
6.7%
1 1
3.3%
3 1
3.3%
4 2
6.7%
6 2
6.7%
10 2
6.7%
17 1
3.3%
22 1
3.3%
23 1
3.3%
24 1
3.3%
ValueCountFrequency (%)
481 1
3.3%
476 1
3.3%
265 1
3.3%
183 2
6.7%
113 1
3.3%
92 1
3.3%
64 1
3.3%
57 1
3.3%
48 2
6.7%
44 1
3.3%

20인-29인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.966667
Minimum0
Maximum297
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:30.203135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median20
Q351
95-th percentile211.1
Maximum297
Range297
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation74.911204
Coefficient of variation (CV)1.5298408
Kurtosis4.328449
Mean48.966667
Median Absolute Deviation (MAD)19
Skewness2.1477643
Sum1469
Variance5611.6885
MonotonicityNot monotonic
2023-12-13T09:43:30.295666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
13.3%
1 3
 
10.0%
51 2
 
6.7%
21 2
 
6.7%
15 1
 
3.3%
139 1
 
3.3%
106 1
 
3.3%
166 1
 
3.3%
77 1
 
3.3%
4 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0 4
13.3%
1 3
10.0%
2 1
 
3.3%
4 1
 
3.3%
5 1
 
3.3%
6 1
 
3.3%
8 1
 
3.3%
15 1
 
3.3%
18 1
 
3.3%
19 1
 
3.3%
ValueCountFrequency (%)
297 1
3.3%
248 1
3.3%
166 1
3.3%
139 1
3.3%
106 1
3.3%
104 1
3.3%
77 1
3.3%
51 2
6.7%
32 1
3.3%
27 1
3.3%

30인-49인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.933333
Minimum0
Maximum262
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:30.395118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median23
Q361.25
95-th percentile221.6
Maximum262
Range262
Interquartile range (IQR)57.25

Descriptive statistics

Standard deviation77.000642
Coefficient of variation (CV)1.4017107
Kurtosis1.5553219
Mean54.933333
Median Absolute Deviation (MAD)20
Skewness1.6524568
Sum1648
Variance5929.0989
MonotonicityNot monotonic
2023-12-13T09:43:30.478181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 4
 
13.3%
12 3
 
10.0%
30 2
 
6.7%
3 2
 
6.7%
4 2
 
6.7%
31 1
 
3.3%
1 1
 
3.3%
122 1
 
3.3%
103 1
 
3.3%
208 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
0 4
13.3%
1 1
 
3.3%
3 2
6.7%
4 2
6.7%
9 1
 
3.3%
12 3
10.0%
14 1
 
3.3%
22 1
 
3.3%
24 1
 
3.3%
30 2
6.7%
ValueCountFrequency (%)
262 1
3.3%
227 1
3.3%
215 1
3.3%
208 1
3.3%
122 1
3.3%
117 1
3.3%
103 1
3.3%
67 1
3.3%
44 1
3.3%
40 1
3.3%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.433333
Minimum0
Maximum311
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:30.565914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q16.25
median25
Q366
95-th percentile274.55
Maximum311
Range311
Interquartile range (IQR)59.75

Descriptive statistics

Standard deviation94.639309
Coefficient of variation (CV)1.40345
Kurtosis1.3377792
Mean67.433333
Median Absolute Deviation (MAD)22.5
Skewness1.6032461
Sum2023
Variance8956.5989
MonotonicityNot monotonic
2023-12-13T09:43:30.655737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 2
 
6.7%
0 2
 
6.7%
1 2
 
6.7%
191 1
 
3.3%
67 1
 
3.3%
164 1
 
3.3%
311 1
 
3.3%
8 1
 
3.3%
101 1
 
3.3%
2 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0 2
6.7%
1 2
6.7%
2 1
3.3%
3 2
6.7%
6 1
3.3%
7 1
3.3%
8 1
3.3%
10 1
3.3%
11 1
3.3%
13 1
3.3%
ValueCountFrequency (%)
311 1
3.3%
302 1
3.3%
241 1
3.3%
238 1
3.3%
191 1
3.3%
164 1
3.3%
101 1
3.3%
67 1
3.3%
63 1
3.3%
55 1
3.3%

100인-299인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.6
Minimum0
Maximum659
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:30.745660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q16.25
median26.5
Q373.75
95-th percentile512.95
Maximum659
Range659
Interquartile range (IQR)67.5

Descriptive statistics

Standard deviation185.00617
Coefficient of variation (CV)1.6577614
Kurtosis2.752371
Mean111.6
Median Absolute Deviation (MAD)25
Skewness1.9436924
Sum3348
Variance34227.283
MonotonicityNot monotonic
2023-12-13T09:43:30.836415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11 2
 
6.7%
73 2
 
6.7%
1 2
 
6.7%
0 2
 
6.7%
2 2
 
6.7%
659 1
 
3.3%
3 1
 
3.3%
409 1
 
3.3%
373 1
 
3.3%
16 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0 2
6.7%
1 2
6.7%
2 2
6.7%
3 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
11 2
6.7%
16 1
3.3%
19 1
3.3%
ValueCountFrequency (%)
659 1
3.3%
598 1
3.3%
409 1
3.3%
406 1
3.3%
373 1
3.3%
260 1
3.3%
77 1
3.3%
74 1
3.3%
73 2
6.7%
68 1
3.3%

300인-499인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.133333
Minimum0
Maximum643
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:30.925281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q326.5
95-th percentile200.9
Maximum643
Range643
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation123.92204
Coefficient of variation (CV)2.6861713
Kurtosis19.76662
Mean46.133333
Median Absolute Deviation (MAD)5
Skewness4.2569372
Sum1384
Variance15356.671
MonotonicityNot monotonic
2023-12-13T09:43:31.011579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 10
33.3%
1 2
 
6.7%
5 2
 
6.7%
13 1
 
3.3%
236 1
 
3.3%
35 1
 
3.3%
58 1
 
3.3%
84 1
 
3.3%
27 1
 
3.3%
15 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
0 10
33.3%
1 2
 
6.7%
2 1
 
3.3%
4 1
 
3.3%
5 2
 
6.7%
7 1
 
3.3%
13 1
 
3.3%
15 1
 
3.3%
16 1
 
3.3%
17 1
 
3.3%
ValueCountFrequency (%)
643 1
3.3%
236 1
3.3%
158 1
3.3%
84 1
3.3%
58 1
3.3%
35 1
3.3%
32 1
3.3%
27 1
3.3%
25 1
3.3%
17 1
3.3%

500인-999인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.766667
Minimum0
Maximum1325
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:31.099193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q316
95-th percentile334.3
Maximum1325
Range1325
Interquartile range (IQR)16

Descriptive statistics

Standard deviation252.92205
Coefficient of variation (CV)3.0932171
Kurtosis21.677217
Mean81.766667
Median Absolute Deviation (MAD)4
Skewness4.5015443
Sum2453
Variance63969.564
MonotonicityNot monotonic
2023-12-13T09:43:31.190625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9
30.0%
1 3
 
10.0%
4 2
 
6.7%
12 2
 
6.7%
16 2
 
6.7%
8 2
 
6.7%
2 1
 
3.3%
3 1
 
3.3%
166 1
 
3.3%
1325 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
0 9
30.0%
1 3
 
10.0%
2 1
 
3.3%
3 1
 
3.3%
4 2
 
6.7%
8 2
 
6.7%
12 2
 
6.7%
15 1
 
3.3%
16 2
 
6.7%
37 1
 
3.3%
ValueCountFrequency (%)
1325 1
3.3%
472 1
3.3%
166 1
3.3%
154 1
3.3%
144 1
3.3%
52 1
3.3%
37 1
3.3%
16 2
6.7%
15 1
3.3%
12 2
6.7%

1000인 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.4
Minimum0
Maximum961
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T09:43:31.276529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5.5
Q352.25
95-th percentile537.1
Maximum961
Range961
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation213.43215
Coefficient of variation (CV)2.4702795
Kurtosis10.760397
Mean86.4
Median Absolute Deviation (MAD)5.5
Skewness3.2670514
Sum2592
Variance45553.283
MonotonicityNot monotonic
2023-12-13T09:43:31.365274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 7
23.3%
1 4
 
13.3%
4 2
 
6.7%
6 1
 
3.3%
90 1
 
3.3%
56 1
 
3.3%
12 1
 
3.3%
41 1
 
3.3%
5 1
 
3.3%
14 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
0 7
23.3%
1 4
13.3%
3 1
 
3.3%
4 2
 
6.7%
5 1
 
3.3%
6 1
 
3.3%
8 1
 
3.3%
12 1
 
3.3%
14 1
 
3.3%
15 1
 
3.3%
ValueCountFrequency (%)
961 1
3.3%
637 1
3.3%
415 1
3.3%
121 1
3.3%
100 1
3.3%
90 1
3.3%
65 1
3.3%
56 1
3.3%
41 1
3.3%
31 1
3.3%

Interactions

2023-12-13T09:43:27.920619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.124801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.718523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.320275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.905420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.546021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.434379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.043260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.653561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.314233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.983867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.181853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.773019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.377147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.964899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.839856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.498031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.102325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.721452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.373391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.045766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.237792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.824488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.431518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.023032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.899956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.553761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.157734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.782307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.431460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.110522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.296197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.891284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.486080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.088943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.962911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.610908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.217883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.852123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.491874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.176990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.355749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.949705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.546710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.157811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.023304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.673615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.277375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.915251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.553423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.234692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.412533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.007584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.606541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.219583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.087934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.732145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.339191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.976109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.615798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.296546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.476943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.064080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.666136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.287609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.148886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.794497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.399978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.040087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.675794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.362213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.538692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.128170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.727928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.356780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.212251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.856194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.464073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.106884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.740367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.421792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.600446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.189895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.788574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.420573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.287986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.919436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.530242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.185083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.801522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:28.480125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:22.658930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.253779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:23.848523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:24.484231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.364023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:25.982069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:26.592626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.252621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:43:27.863598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:43:31.430301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.4370.0000.0000.0000.0000.0000.0000.7320.1360.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.4371.0001.0000.8500.8190.8800.8910.7960.8410.9350.6100.738
5인-9인0.0001.0000.8501.0000.9730.9170.9040.8680.9220.7600.8970.139
10인-19인0.0001.0000.8190.9731.0000.9310.9000.8410.8920.6380.8600.315
20인-29인0.0001.0000.8800.9170.9311.0000.9740.9610.8750.9420.9650.679
30인-49인0.0001.0000.8910.9040.9000.9741.0000.9640.9360.8960.9860.722
50인-99인0.0001.0000.7960.8680.8410.9610.9641.0000.8740.8340.9990.690
100인-299인0.0001.0000.8410.9220.8920.8750.9360.8741.0000.8350.8270.714
300인-499인0.7321.0000.9350.7600.6380.9420.8960.8340.8351.0000.8190.876
500인-999인0.1361.0000.6100.8970.8600.9650.9860.9990.8270.8191.0000.462
1000인 이상0.0001.0000.7380.1390.3150.6790.7220.6900.7140.8760.4621.000
2023-12-13T09:43:31.541367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상대업종
5인 미만1.0000.9130.9390.9040.8490.8230.8380.7780.6900.5040.139
5인-9인0.9131.0000.9410.8890.8450.8440.7850.8040.6980.5110.000
10인-19인0.9390.9411.0000.9620.9110.8700.8420.8100.6870.4910.000
20인-29인0.9040.8890.9621.0000.9460.9030.8810.8680.7570.6010.000
30인-49인0.8490.8450.9110.9461.0000.9380.9260.8990.8130.7040.000
50인-99인0.8230.8440.8700.9030.9381.0000.9490.9230.8810.7710.000
100인-299인0.8380.7850.8420.8810.9260.9491.0000.9340.8960.7850.000
300인-499인0.7780.8040.8100.8680.8990.9230.9341.0000.9320.7970.335
500인-999인0.6900.6980.6870.7570.8130.8810.8960.9321.0000.8800.000
1000인 이상0.5040.5110.4910.6010.7040.7710.7850.7970.8801.0000.000
대업종0.1390.0000.0000.0000.0000.0000.0000.3350.0000.0001.000

Missing values

2023-12-13T09:43:28.568136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:43:28.684356image/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
0금융및보험업금융및보험업185723151242111346
1광 업석탄광업및채석업5641481042151916596431325121
2광 업석회석·금속·비금속광업및기타광업69256451442442200
3제조업식료품제조업473357263039471788
4제조업섬유및섬유제품제조업462935273226254215
5제조업목재및종이제품제조업54434819121119100
6제조업출판·인쇄·제본업227105367000
7제조업화학및고무제품제조업51373923405549712415
8제조업의약품·화장품·연탄·석유제품제조업10131784106131
9제조업기계기구·금속·비금속광물제품제조업675401476248227238406158166637
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업2100000000
21농 업농업1511104421000
22기타의사업시설관리및사업지원서비스업250132183771171017327124
23기타의사업기타의각종사업13365412122816545
24기타의사업해외파견자3131032001
25기타의사업전문·보건·교육·여가관련서비스업2691261831662083113738415441
26기타의사업도소매·음식·숙박업6763112651061031644095847212
27기타의사업부동산업및임대업28340013011
28기타의사업국가및지방자치단체의사업63621131391226773353756
29기타의사업주한미군0000101004