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(산업중분류별규모별재해자수) 산업안전보건에 대한 통계 자료로써 규모별, 산업중 분류별, 재해자수 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15064490/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 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-9인 and 6 other fieldsHigh correlation
구분 has unique valuesUnique
5인-9인 has unique valuesUnique
10인-19인 has unique valuesUnique
20인-29인 has unique valuesUnique
100인-299인 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 1 (3.3%) zerosZeros
50인-99인 has 1 (3.3%) zerosZeros
100인-299인 has 1 (3.3%) zerosZeros
300인-499인 has 5 (16.7%) zerosZeros
500인-999인 has 7 (23.3%) zerosZeros
1000인 이상 has 7 (23.3%) zerosZeros

Reproduction

Analysis started2023-12-12 09:14:20.053882
Analysis finished2023-12-12 09:14:32.956474
Duration12.9 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

2023-12-12T18:14:33.035533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:14:33.203285image/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
2023-12-12T18:14:33.490008image/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%
2023-12-12T18:14:33.973273image/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%
Mean1281.0667
Minimum1
Maximum11522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:34.134891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q167
median341
Q3704
95-th percentile6556.5
Maximum11522
Range11521
Interquartile range (IQR)637

Descriptive statistics

Standard deviation2637.3834
Coefficient of variation (CV)2.0587402
Kurtosis9.3720163
Mean1281.0667
Median Absolute Deviation (MAD)291.5
Skewness3.0573035
Sum38432
Variance6955791.4
MonotonicityNot monotonic
2023-12-12T18:14:34.311326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
497 2
 
6.7%
73 1
 
3.3%
725 1
 
3.3%
1 1
 
3.3%
192 1
 
3.3%
8919 1
 
3.3%
1535 1
 
3.3%
6 1
 
3.3%
866 1
 
3.3%
2696 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
1 1
3.3%
3 1
3.3%
6 1
3.3%
8 1
3.3%
24 1
3.3%
31 1
3.3%
58 1
3.3%
65 1
3.3%
73 1
3.3%
87 1
3.3%
ValueCountFrequency (%)
11522 1
3.3%
8919 1
3.3%
3669 1
3.3%
3494 1
3.3%
2696 1
3.3%
1535 1
3.3%
866 1
3.3%
725 1
3.3%
641 1
3.3%
515 1
3.3%

5인-9인
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.3
Minimum0
Maximum3680
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:34.460633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.35
Q138.25
median188.5
Q3376.25
95-th percentile2901.35
Maximum3680
Range3680
Interquartile range (IQR)338

Descriptive statistics

Standard deviation973.1873
Coefficient of variation (CV)1.8045379
Kurtosis5.3833288
Mean539.3
Median Absolute Deviation (MAD)158
Skewness2.4731314
Sum16179
Variance947093.53
MonotonicityNot monotonic
2023-12-12T18:14:34.586987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
94 1
 
3.3%
214 1
 
3.3%
0 1
 
3.3%
368 1
 
3.3%
38 1
 
3.3%
3485 1
 
3.3%
965 1
 
3.3%
2 1
 
3.3%
296 1
 
3.3%
1903 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
2 1
3.3%
5 1
3.3%
6 1
3.3%
24 1
3.3%
30 1
3.3%
31 1
3.3%
38 1
3.3%
39 1
3.3%
42 1
3.3%
ValueCountFrequency (%)
3680 1
3.3%
3485 1
3.3%
2188 1
3.3%
1903 1
3.3%
965 1
3.3%
513 1
3.3%
408 1
3.3%
379 1
3.3%
368 1
3.3%
311 1
3.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563.8
Minimum0
Maximum3993
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:34.722445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.45
Q146.25
median190.5
Q3591.5
95-th percentile2498.4
Maximum3993
Range3993
Interquartile range (IQR)545.25

Descriptive statistics

Standard deviation948.21773
Coefficient of variation (CV)1.6818335
Kurtosis5.5969762
Mean563.8
Median Absolute Deviation (MAD)165.5
Skewness2.3806412
Sum16914
Variance899116.86
MonotonicityNot monotonic
2023-12-12T18:14:34.877162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
86 1
 
3.3%
242 1
 
3.3%
0 1
 
3.3%
726 1
 
3.3%
27 1
 
3.3%
2619 1
 
3.3%
1418 1
 
3.3%
5 1
 
3.3%
221 1
 
3.3%
1791 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
2 1
3.3%
3 1
3.3%
5 1
3.3%
18 1
3.3%
23 1
3.3%
27 1
3.3%
45 1
3.3%
50 1
3.3%
81 1
3.3%
ValueCountFrequency (%)
3993 1
3.3%
2619 1
3.3%
2351 1
3.3%
1791 1
3.3%
1418 1
3.3%
726 1
3.3%
721 1
3.3%
638 1
3.3%
452 1
3.3%
275 1
3.3%

20인-29인
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.73333
Minimum0
Maximum2201
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:35.028718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.45
Q123
median104
Q3343
95-th percentile1215.05
Maximum2201
Range2201
Interquartile range (IQR)320

Descriptive statistics

Standard deviation507.57455
Coefficient of variation (CV)1.5874934
Kurtosis5.742046
Mean319.73333
Median Absolute Deviation (MAD)92
Skewness2.3148071
Sum9592
Variance257631.93
MonotonicityNot monotonic
2023-12-12T18:14:35.150130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
80 1
 
3.3%
166 1
 
3.3%
0 1
 
3.3%
812 1
 
3.3%
8 1
 
3.3%
1165 1
 
3.3%
958 1
 
3.3%
2 1
 
3.3%
102 1
 
3.3%
823 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
2 1
3.3%
4 1
3.3%
8 1
3.3%
9 1
3.3%
15 1
3.3%
21 1
3.3%
29 1
3.3%
45 1
3.3%
ValueCountFrequency (%)
2201 1
3.3%
1256 1
3.3%
1165 1
3.3%
958 1
3.3%
823 1
3.3%
812 1
3.3%
378 1
3.3%
366 1
3.3%
274 1
3.3%
166 1
3.3%

30인-49인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.5
Minimum0
Maximum1964
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:35.269814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9
Q141.25
median114
Q3364
95-th percentile1217.95
Maximum1964
Range1964
Interquartile range (IQR)322.75

Descriptive statistics

Standard deviation486.84167
Coefficient of variation (CV)1.4597951
Kurtosis3.3814409
Mean333.5
Median Absolute Deviation (MAD)101
Skewness1.9317453
Sum10005
Variance237014.81
MonotonicityNot monotonic
2023-12-12T18:14:35.411885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 2
 
6.7%
215 2
 
6.7%
111 1
 
3.3%
1964 1
 
3.3%
1 1
 
3.3%
852 1
 
3.3%
7 1
 
3.3%
1021 1
 
3.3%
1267 1
 
3.3%
96 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
3 2
6.7%
7 1
3.3%
22 1
3.3%
25 1
3.3%
41 1
3.3%
42 1
3.3%
48 1
3.3%
58 1
3.3%
ValueCountFrequency (%)
1964 1
3.3%
1267 1
3.3%
1158 1
3.3%
1021 1
3.3%
991 1
3.3%
852 1
3.3%
426 1
3.3%
373 1
3.3%
337 1
3.3%
215 2
6.7%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.93333
Minimum0
Maximum2009
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:35.569150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.8
Q128.25
median97.5
Q3366.25
95-th percentile1396.45
Maximum2009
Range2009
Interquartile range (IQR)338

Descriptive statistics

Standard deviation501.9324
Coefficient of variation (CV)1.5121482
Kurtosis4.5232109
Mean331.93333
Median Absolute Deviation (MAD)93
Skewness2.1706375
Sum9958
Variance251936.13
MonotonicityNot monotonic
2023-12-12T18:14:35.719919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
11 2
 
6.7%
6 2
 
6.7%
135 1
 
3.3%
2009 1
 
3.3%
2 1
 
3.3%
505 1
 
3.3%
1056 1
 
3.3%
1675 1
 
3.3%
70 1
 
3.3%
825 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 1
3.3%
2 1
3.3%
6 2
6.7%
11 2
6.7%
20 1
3.3%
28 1
3.3%
29 1
3.3%
37 1
3.3%
41 1
3.3%
61 1
3.3%
ValueCountFrequency (%)
2009 1
3.3%
1675 1
3.3%
1056 1
3.3%
931 1
3.3%
825 1
3.3%
538 1
3.3%
505 1
3.3%
371 1
3.3%
352 1
3.3%
297 1
3.3%

100인-299인
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.26667
Minimum0
Maximum3466
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:35.855962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.35
Q127.25
median84.5
Q3450.25
95-th percentile1862.25
Maximum3466
Range3466
Interquartile range (IQR)423

Descriptive statistics

Standard deviation755.73627
Coefficient of variation (CV)1.7605287
Kurtosis8.9809048
Mean429.26667
Median Absolute Deviation (MAD)82
Skewness2.8551977
Sum12878
Variance571137.31
MonotonicityNot monotonic
2023-12-12T18:14:35.992964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
38 1
 
3.3%
377 1
 
3.3%
14 1
 
3.3%
507 1
 
3.3%
9 1
 
3.3%
2049 1
 
3.3%
1634 1
 
3.3%
4 1
 
3.3%
73 1
 
3.3%
467 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
4 1
3.3%
8 1
3.3%
9 1
3.3%
14 1
3.3%
15 1
3.3%
27 1
3.3%
28 1
3.3%
38 1
3.3%
ValueCountFrequency (%)
3466 1
3.3%
2049 1
3.3%
1634 1
3.3%
1155 1
3.3%
663 1
3.3%
554 1
3.3%
507 1
3.3%
467 1
3.3%
400 1
3.3%
377 1
3.3%

300인-499인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.93333
Minimum0
Maximum1326
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:36.135272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.25
median19.5
Q3115.75
95-th percentile541.05
Maximum1326
Range1326
Interquartile range (IQR)111.5

Descriptive statistics

Standard deviation272.04803
Coefficient of variation (CV)2.0464998
Kurtosis13.000329
Mean132.93333
Median Absolute Deviation (MAD)19.5
Skewness3.3710629
Sum3988
Variance74010.133
MonotonicityNot monotonic
2023-12-12T18:14:36.275764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 5
 
16.7%
5 2
 
6.7%
18 2
 
6.7%
6 2
 
6.7%
4 1
 
3.3%
186 1
 
3.3%
309 1
 
3.3%
368 1
 
3.3%
20 1
 
3.3%
112 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0 5
16.7%
1 1
 
3.3%
2 1
 
3.3%
4 1
 
3.3%
5 2
 
6.7%
6 2
 
6.7%
18 2
 
6.7%
19 1
 
3.3%
20 1
 
3.3%
21 1
 
3.3%
ValueCountFrequency (%)
1326 1
3.3%
645 1
3.3%
414 1
3.3%
368 1
3.3%
309 1
3.3%
186 1
3.3%
129 1
3.3%
117 1
3.3%
112 1
3.3%
107 1
3.3%

500인-999인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.33333
Minimum0
Maximum1333
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:36.408883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median16
Q386.5
95-th percentile628.45
Maximum1333
Range1333
Interquartile range (IQR)85.25

Descriptive statistics

Standard deviation283.22701
Coefficient of variation (CV)2.0474242
Kurtosis10.905875
Mean138.33333
Median Absolute Deviation (MAD)16
Skewness3.1108188
Sum4150
Variance80217.54
MonotonicityNot monotonic
2023-12-12T18:14:36.535209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 7
23.3%
2 3
 
10.0%
59 2
 
6.7%
10 1
 
3.3%
655 1
 
3.3%
341 1
 
3.3%
596 1
 
3.3%
289 1
 
3.3%
14 1
 
3.3%
70 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 7
23.3%
1 1
 
3.3%
2 3
10.0%
3 1
 
3.3%
10 1
 
3.3%
14 1
 
3.3%
15 1
 
3.3%
17 1
 
3.3%
38 1
 
3.3%
57 1
 
3.3%
ValueCountFrequency (%)
1333 1
3.3%
655 1
3.3%
596 1
3.3%
341 1
3.3%
306 1
3.3%
289 1
3.3%
127 1
3.3%
92 1
3.3%
70 1
3.3%
62 1
3.3%

1000인 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.06667
Minimum0
Maximum2183
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T18:14:36.642276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median25.5
Q3201.25
95-th percentile1424.6
Maximum2183
Range2183
Interquartile range (IQR)200

Descriptive statistics

Standard deviation526.47697
Coefficient of variation (CV)1.9139977
Kurtosis5.9940338
Mean275.06667
Median Absolute Deviation (MAD)25.5
Skewness2.4824479
Sum8252
Variance277178
MonotonicityNot monotonic
2023-12-12T18:14:37.067484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 7
23.3%
27 2
 
6.7%
17 1
 
3.3%
665 1
 
3.3%
397 1
 
3.3%
4 1
 
3.3%
106 1
 
3.3%
209 1
 
3.3%
1 1
 
3.3%
23 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0 7
23.3%
1 1
 
3.3%
2 1
 
3.3%
4 1
 
3.3%
13 1
 
3.3%
17 1
 
3.3%
19 1
 
3.3%
23 1
 
3.3%
24 1
 
3.3%
27 2
 
6.7%
ValueCountFrequency (%)
2183 1
3.3%
1475 1
3.3%
1363 1
3.3%
773 1
3.3%
665 1
3.3%
429 1
3.3%
397 1
3.3%
209 1
3.3%
178 1
3.3%
142 1
3.3%

Interactions

2023-12-12T18:14:31.597058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:20.463260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.601790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.235294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.331637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.294498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.512278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.763111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.903565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.420975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.690189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:20.541052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.735105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.336014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.420928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.408192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.633177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.883170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:29.020622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.534000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.832832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:20.636420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.864057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.448534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.525440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.526278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.730204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.009387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:29.116606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.644619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.930892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:20.734821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.990074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.568650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.619790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.650244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.839577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.120767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:29.234215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.771018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:32.016271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:20.863351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:22.104905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.668516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.713633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.748496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.942410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.218979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:29.342694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.874255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:32.131294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.020037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:22.237341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.771250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.803705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.861187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.149043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.346762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:29.478075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.008223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:32.244354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.128463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:22.376141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.889433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.908160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.017131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.330180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.470041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:29.584640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.140843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:32.356297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.266458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:22.520902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:23.993224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.015097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.172338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.427728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.585471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.066857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.273328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:32.456066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.379793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:22.652220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.109938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.115141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.295869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.535054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.707685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.180316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.393797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:32.560760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:21.483079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:22.789261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:24.227221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:25.209666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:26.404864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:27.655337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:28.801897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:30.319912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:14:31.504462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:14:37.189728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.0000.0000.0000.0000.0000.0000.0000.5210.5050.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.0001.0001.0000.8850.9690.9790.8400.9330.9860.9350.7760.549
5인-9인0.0001.0000.8851.0000.9590.9190.8860.9260.8580.7030.7030.684
10인-19인0.0001.0000.9690.9591.0000.9570.9680.9910.9560.8720.7910.605
20인-29인0.0001.0000.9790.9190.9571.0000.9310.9070.9790.9510.7610.608
30인-49인0.0001.0000.8400.8860.9680.9311.0000.9750.8730.8690.7710.718
50인-99인0.0001.0000.9330.9260.9910.9070.9751.0000.9350.8460.7720.736
100인-299인0.0001.0000.9860.8580.9560.9790.8730.9351.0000.9740.8550.563
300인-499인0.5211.0000.9350.7030.8720.9510.8690.8460.9741.0000.9830.640
500인-999인0.5051.0000.7760.7030.7910.7610.7710.7720.8550.9831.0000.274
1000인 이상0.0001.0000.5490.6840.6050.6080.7180.7360.5630.6400.2741.000
2023-12-12T18:14:37.340923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상대업종
5인 미만1.0000.9610.9310.8510.7760.7650.6820.5890.5230.4390.000
5인-9인0.9611.0000.9810.9410.8830.8670.7900.7110.6310.5060.000
10인-19인0.9310.9811.0000.9670.9150.8850.8230.7140.6330.5000.000
20인-29인0.8510.9410.9671.0000.9760.9520.8990.8190.7440.5830.000
30인-49인0.7760.8830.9150.9761.0000.9730.9370.8600.8110.6550.000
50인-99인0.7650.8670.8850.9520.9731.0000.9400.8910.8550.7180.000
100인-299인0.6820.7900.8230.8990.9370.9401.0000.9170.8960.7440.000
300인-499인0.5890.7110.7140.8190.8600.8910.9171.0000.9580.7920.259
500인-999인0.5230.6310.6330.7440.8110.8550.8960.9581.0000.8530.267
1000인 이상0.4390.5060.5000.5830.6550.7180.7440.7920.8531.0000.000
대업종0.0000.0000.0000.0000.0000.0000.0000.2590.2670.0001.000

Missing values

2023-12-12T18:14:32.699414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:14:32.878708image/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
0금융및보험업금융및보험업7394868011113538221017
1광 업석탄광업및채석업5842501062151926636451333121
2광 업석회석·금속·비금속광업및기타광업873910274632952200
3제조업식료품제조업5154086383664263714001075954
4제조업섬유및섬유제품제조업30617619113411711378181519
5제조업목재및종이제품제조업497311275142104821241920
6제조업출판·인쇄·제본업116668149423728100
7제조업화학및고무제품제조업5023794522743372782585357773
8제조업의약품·화장품·연탄·석유제품제조업24304521486191181724
9제조업기계기구·금속·비금속광물제품제조업3494218823511256115893111554143061363
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업312431000000
21농 업농업32012111245581115000
22기타의사업시설관리및사업지원서비스업2696190317918239918254671127023
23기타의사업기타의각종사업866296221102967073201427
24기타의사업해외파견자6252364001
25기타의사업전문·보건·교육·여가관련서비스업15359651418958126716751634368289209
26기타의사업도소매·음식·숙박업8919348526191165102110562049309596106
27기타의사업부동산업및임대업192382787119624
28기타의사업국가및지방자치단체의사업497368726812852505507186341397
29기타의사업주한미군100012144227