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(산업중분류별규모별사업장수) 산업중분류별규모별사업장수에 대한 통계 자료로써 분류별 규모별 사업장수에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15064487/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 8 other fieldsHigh correlation
20인-29인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
30인-49인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
50인-99인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
100인-299인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
300인-499인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
500인-999인 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
1000인 이상 is highly overall correlated with 5인 미만 and 8 other fieldsHigh correlation
구분 has unique valuesUnique
5인 미만 has unique valuesUnique
5인-9인 has unique valuesUnique
10인-19인 has unique valuesUnique
20인-29인 has unique valuesUnique
100인-299인 has unique valuesUnique
50인-99인 has 2 (6.7%) zerosZeros
100인-299인 has 1 (3.3%) zerosZeros
300인-499인 has 2 (6.7%) zerosZeros
500인-999인 has 4 (13.3%) zerosZeros
1000인 이상 has 8 (26.7%) zerosZeros

Reproduction

Analysis started2023-12-12 13:21:50.778268
Analysis finished2023-12-12 13:22:01.620757
Duration10.84 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

2023-12-12T22:22:01.687844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:22:01.827836image/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
2023-12-12T22:22:02.067524image/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%
2023-12-12T22:22:02.406310image/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  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74166.033
Minimum31
Maximum782153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:02.556507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile56.5
Q13545
median16735
Q336037.75
95-th percentile320947.7
Maximum782153
Range782122
Interquartile range (IQR)32492.75

Descriptive statistics

Standard deviation158859.44
Coefficient of variation (CV)2.1419433
Kurtosis13.924689
Mean74166.033
Median Absolute Deviation (MAD)14941
Skewness3.5064255
Sum2224981
Variance2.5236321 × 1010
MonotonicityNot monotonic
2023-12-12T22:22:02.773335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14781 1
 
3.3%
33372 1
 
3.3%
34 1
 
3.3%
15660 1
 
3.3%
62297 1
 
3.3%
782153 1
 
3.3%
316969 1
 
3.3%
3611 1
 
3.3%
160101 1
 
3.3%
168699 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
31 1
3.3%
34 1
3.3%
84 1
3.3%
592 1
3.3%
1749 1
3.3%
1839 1
3.3%
2341 1
3.3%
3523 1
3.3%
3611 1
3.3%
4329 1
3.3%
ValueCountFrequency (%)
782153 1
3.3%
324203 1
3.3%
316969 1
3.3%
168699 1
3.3%
160101 1
3.3%
121979 1
3.3%
62297 1
3.3%
36258 1
3.3%
35377 1
3.3%
33372 1
3.3%

5인-9인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12630.3
Minimum13
Maximum101399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:03.215367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile29.25
Q1611.75
median3540.5
Q38106.75
95-th percentile62863.5
Maximum101399
Range101386
Interquartile range (IQR)7495

Descriptive statistics

Standard deviation23537.971
Coefficient of variation (CV)1.8636114
Kurtosis7.4857759
Mean12630.3
Median Absolute Deviation (MAD)3133.5
Skewness2.727697
Sum378909
Variance5.5403609 × 108
MonotonicityNot monotonic
2023-12-12T22:22:03.378182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8942 1
 
3.3%
5003 1
 
3.3%
18 1
 
3.3%
5966 1
 
3.3%
3304 1
 
3.3%
101399 1
 
3.3%
73623 1
 
3.3%
547 1
 
3.3%
22322 1
 
3.3%
49713 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
13 1
3.3%
18 1
3.3%
43 1
3.3%
152 1
3.3%
263 1
3.3%
267 1
3.3%
547 1
3.3%
557 1
3.3%
776 1
3.3%
1326 1
3.3%
ValueCountFrequency (%)
101399 1
3.3%
73623 1
3.3%
49713 1
3.3%
32053 1
3.3%
27571 1
3.3%
22322 1
3.3%
8942 1
3.3%
8528 1
3.3%
6843 1
3.3%
5966 1
3.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6779.3
Minimum4
Maximum39832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:03.536346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile32.25
Q1464.25
median2065.5
Q35646.75
95-th percentile34207.75
Maximum39832
Range39828
Interquartile range (IQR)5182.5

Descriptive statistics

Standard deviation11117.509
Coefficient of variation (CV)1.6399199
Kurtosis3.9328589
Mean6779.3
Median Absolute Deviation (MAD)1898.5
Skewness2.1686309
Sum203379
Variance1.2359901 × 108
MonotonicityNot monotonic
2023-12-12T22:22:03.685980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10601 1
 
3.3%
3384 1
 
3.3%
30 1
 
3.3%
5786 1
 
3.3%
994 1
 
3.3%
39583 1
 
3.3%
39832 1
 
3.3%
424 1
 
3.3%
8117 1
 
3.3%
27638 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4 1
3.3%
30 1
3.3%
35 1
3.3%
50 1
3.3%
154 1
3.3%
180 1
3.3%
424 1
3.3%
457 1
3.3%
486 1
3.3%
915 1
3.3%
ValueCountFrequency (%)
39832 1
3.3%
39583 1
3.3%
27638 1
3.3%
21231 1
3.3%
16747 1
3.3%
10601 1
3.3%
8117 1
3.3%
5786 1
3.3%
5229 1
3.3%
4563 1
3.3%

20인-29인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2266.1
Minimum4
Maximum13652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:03.821788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.9
Q1222
median641.5
Q32046
95-th percentile9173.2
Maximum13652
Range13648
Interquartile range (IQR)1824

Descriptive statistics

Standard deviation3427.6304
Coefficient of variation (CV)1.512568
Kurtosis3.595364
Mean2266.1
Median Absolute Deviation (MAD)628.5
Skewness1.9993467
Sum67983
Variance11748650
MonotonicityNot monotonic
2023-12-12T22:22:03.951439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4240 1
 
3.3%
1332 1
 
3.3%
7 1
 
3.3%
4064 1
 
3.3%
252 1
 
3.3%
9841 1
 
3.3%
13652 1
 
3.3%
164 1
 
3.3%
1911 1
 
3.3%
8357 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4 1
3.3%
7 1
3.3%
9 1
3.3%
17 1
3.3%
59 1
3.3%
119 1
3.3%
164 1
3.3%
212 1
3.3%
252 1
3.3%
271 1
3.3%
ValueCountFrequency (%)
13652 1
3.3%
9841 1
3.3%
8357 1
3.3%
7085 1
3.3%
6157 1
3.3%
4240 1
3.3%
4064 1
3.3%
2091 1
3.3%
1911 1
3.3%
1898 1
3.3%

30인-49인
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.5
Minimum2
Maximum10855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:04.112112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.85
Q1201.25
median400.5
Q31667
95-th percentile6229.7
Maximum10855
Range10853
Interquartile range (IQR)1465.75

Descriptive statistics

Standard deviation2528.7185
Coefficient of variation (CV)1.5247021
Kurtosis5.2332464
Mean1658.5
Median Absolute Deviation (MAD)392
Skewness2.2140432
Sum49755
Variance6394417
MonotonicityNot monotonic
2023-12-12T22:22:04.252147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 2
 
6.7%
3174 1
 
3.3%
15 1
 
3.3%
3131 1
 
3.3%
115 1
 
3.3%
6189 1
 
3.3%
10855 1
 
3.3%
140 1
 
3.3%
1239 1
 
3.3%
6263 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
2 2
6.7%
15 1
3.3%
23 1
3.3%
31 1
3.3%
115 1
3.3%
140 1
3.3%
200 1
3.3%
205 1
3.3%
222 1
3.3%
236 1
3.3%
ValueCountFrequency (%)
10855 1
3.3%
6263 1
3.3%
6189 1
3.3%
4784 1
3.3%
4311 1
3.3%
3174 1
3.3%
3131 1
3.3%
1731 1
3.3%
1475 1
3.3%
1239 1
3.3%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005.5667
Minimum0
Maximum7674
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:04.398043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.25
Q179.75
median288
Q3958.25
95-th percentile3645.45
Maximum7674
Range7674
Interquartile range (IQR)878.5

Descriptive statistics

Standard deviation1626.0232
Coefficient of variation (CV)1.6170218
Kurtosis9.401769
Mean1005.5667
Median Absolute Deviation (MAD)285.5
Skewness2.8303294
Sum30167
Variance2643951.6
MonotonicityNot monotonic
2023-12-12T22:22:04.558840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.7%
1448 1
 
3.3%
32 1
 
3.3%
953 1
 
3.3%
68 1
 
3.3%
2779 1
 
3.3%
7674 1
 
3.3%
115 1
 
3.3%
863 1
 
3.3%
3861 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 2
6.7%
5 1
3.3%
25 1
3.3%
32 1
3.3%
52 1
3.3%
63 1
3.3%
68 1
3.3%
115 1
3.3%
148 1
3.3%
162 1
3.3%
ValueCountFrequency (%)
7674 1
3.3%
3861 1
3.3%
3382 1
3.3%
2779 1
3.3%
2310 1
3.3%
1448 1
3.3%
1308 1
3.3%
960 1
3.3%
953 1
3.3%
863 1
3.3%

100인-299인
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548.93333
Minimum0
Maximum3770
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:04.706838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.35
Q155.5
median132.5
Q3583.5
95-th percentile2379.1
Maximum3770
Range3770
Interquartile range (IQR)528

Descriptive statistics

Standard deviation884.07068
Coefficient of variation (CV)1.6105247
Kurtosis6.6383488
Mean548.93333
Median Absolute Deviation (MAD)130
Skewness2.5414781
Sum16468
Variance781580.96
MonotonicityNot monotonic
2023-12-12T22:22:04.855690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
616 1
 
3.3%
427 1
 
3.3%
26 1
 
3.3%
540 1
 
3.3%
42 1
 
3.3%
1415 1
 
3.3%
3770 1
 
3.3%
57 1
 
3.3%
502 1
 
3.3%
1696 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
1 1
3.3%
4 1
3.3%
20 1
3.3%
26 1
3.3%
40 1
3.3%
42 1
3.3%
55 1
3.3%
57 1
3.3%
73 1
3.3%
ValueCountFrequency (%)
3770 1
3.3%
2938 1
3.3%
1696 1
3.3%
1415 1
3.3%
1187 1
3.3%
810 1
3.3%
616 1
3.3%
598 1
3.3%
540 1
3.3%
502 1
3.3%

300인-499인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.9
Minimum0
Maximum623
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:04.992216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q15
median22.5
Q392.25
95-th percentile389.1
Maximum623
Range623
Interquartile range (IQR)87.25

Descriptive statistics

Standard deviation148.12887
Coefficient of variation (CV)1.8086553
Kurtosis8.5831481
Mean81.9
Median Absolute Deviation (MAD)21.5
Skewness2.9274516
Sum2457
Variance21942.162
MonotonicityNot monotonic
2023-12-12T22:22:05.130244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5 3
 
10.0%
1 3
 
10.0%
0 2
 
6.7%
9 2
 
6.7%
60 2
 
6.7%
94 1
 
3.3%
16 1
 
3.3%
87 1
 
3.3%
13 1
 
3.3%
158 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0 2
6.7%
1 3
10.0%
4 1
 
3.3%
5 3
10.0%
7 1
 
3.3%
9 2
6.7%
13 1
 
3.3%
16 1
 
3.3%
22 1
 
3.3%
23 1
 
3.3%
ValueCountFrequency (%)
623 1
3.3%
552 1
3.3%
190 1
3.3%
158 1
3.3%
150 1
3.3%
143 1
3.3%
104 1
3.3%
94 1
3.3%
87 1
3.3%
60 2
6.7%

500인-999인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.2
Minimum0
Maximum308
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:05.262064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9.5
Q358.75
95-th percentile193.9
Maximum308
Range308
Interquartile range (IQR)55.75

Descriptive statistics

Standard deviation73.644252
Coefficient of variation (CV)1.7047281
Kurtosis7.3454429
Mean43.2
Median Absolute Deviation (MAD)9.5
Skewness2.6730891
Sum1296
Variance5423.4759
MonotonicityNot monotonic
2023-12-12T22:22:05.385235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 4
 
13.3%
3 4
 
13.3%
23 2
 
6.7%
4 2
 
6.7%
2 2
 
6.7%
68 1
 
3.3%
12 1
 
3.3%
90 1
 
3.3%
89 1
 
3.3%
308 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 4
13.3%
1 1
 
3.3%
2 2
6.7%
3 4
13.3%
4 2
6.7%
5 1
 
3.3%
7 1
 
3.3%
12 1
 
3.3%
17 1
 
3.3%
23 2
6.7%
ValueCountFrequency (%)
308 1
3.3%
265 1
3.3%
107 1
3.3%
90 1
3.3%
89 1
3.3%
73 1
3.3%
68 1
3.3%
59 1
3.3%
58 1
3.3%
37 1
3.3%

1000인 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.033333
Minimum0
Maximum122
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T22:22:05.511026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median6
Q340.5
95-th percentile61.55
Maximum122
Range122
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation29.105614
Coefficient of variation (CV)1.3837851
Kurtosis3.5144739
Mean21.033333
Median Absolute Deviation (MAD)6
Skewness1.7642846
Sum631
Variance847.13678
MonotonicityNot monotonic
2023-12-12T22:22:05.630496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
26.7%
6 4
13.3%
1 2
 
6.7%
5 2
 
6.7%
49 1
 
3.3%
28 1
 
3.3%
51 1
 
3.3%
4 1
 
3.3%
41 1
 
3.3%
122 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
0 8
26.7%
1 2
 
6.7%
4 1
 
3.3%
5 2
 
6.7%
6 4
13.3%
7 1
 
3.3%
9 1
 
3.3%
14 1
 
3.3%
28 1
 
3.3%
39 1
 
3.3%
ValueCountFrequency (%)
122 1
3.3%
62 1
3.3%
61 1
3.3%
58 1
3.3%
51 1
3.3%
50 1
3.3%
49 1
3.3%
41 1
3.3%
39 1
3.3%
28 1
3.3%

Interactions

2023-12-12T22:22:00.370733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:51.167088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.457241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.442410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.359142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.265407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.036837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.327941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.404968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.341260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.483375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:51.562871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.567305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.540554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.458651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.342887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.167731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.461692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.497908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.463960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.583380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:51.643436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.652224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.618085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.540238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.409174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.246047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.586755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.588309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.553165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.710360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:51.750827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.745292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.729316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.633152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.487908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.330344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.696731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.703035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.654690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.818655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:51.852835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.847768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.830776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.739544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.566283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.423597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.799965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.799829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.752834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.911301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:51.939656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.934256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.919732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.835438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.639927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.507789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.903283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.888007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.833500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:01.015558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.036805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.031797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.010456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.927379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.716637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:56.925542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.020673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.976612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.929172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:01.110534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.126056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.131271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.098380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.021681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.790332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.022335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.114436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.057042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.041328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:01.187248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.236542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.245356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.177395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.108983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.882379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.142595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.222600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.136239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.161737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:01.270756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:52.366040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:53.347575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.272901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.189418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:55.959222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:57.237320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:58.305855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:59.233655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:00.278837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:22:05.730519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.0000.0000.3940.3850.4050.0000.0000.4630.5050.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.0001.0001.0000.9210.8880.9620.8710.8440.8380.8050.7820.840
5인-9인0.0001.0000.9211.0000.9150.9960.9020.9890.9200.9850.9820.890
10인-19인0.3941.0000.8880.9151.0000.9600.9760.9370.9790.9240.9040.964
20인-29인0.3851.0000.9620.9960.9601.0000.9830.9960.9280.9920.9780.890
30인-49인0.4051.0000.8710.9020.9760.9831.0000.9390.9770.9160.8960.945
50인-99인0.0001.0000.8440.9890.9370.9960.9391.0000.9820.9950.9860.917
100인-299인0.0001.0000.8380.9200.9790.9280.9770.9821.0000.9600.9330.977
300인-499인0.4631.0000.8050.9850.9240.9920.9160.9950.9601.0000.9960.897
500인-999인0.5051.0000.7820.9820.9040.9780.8960.9860.9330.9961.0000.870
1000인 이상0.0001.0000.8400.8900.9640.8900.9450.9170.9770.8970.8701.000
2023-12-12T22:22:05.900454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상대업종
5인 미만1.0000.9030.8890.8730.8310.7960.7580.7600.6680.6100.000
5인-9인0.9031.0000.9900.9760.9590.8760.8610.8550.7780.6850.000
10인-19인0.8890.9901.0000.9940.9820.9040.8870.8610.8010.7000.163
20인-29인0.8730.9760.9941.0000.9910.9210.9000.8580.8060.7070.139
30인-49인0.8310.9590.9820.9911.0000.9430.9300.8770.8320.7250.171
50인-99인0.7960.8760.9040.9210.9431.0000.9870.9180.8980.8310.000
100인-299인0.7580.8610.8870.9000.9300.9871.0000.9500.9250.8520.000
300인-499인0.7600.8550.8610.8580.8770.9180.9501.0000.9480.8840.216
500인-999인0.6680.7780.8010.8060.8320.8980.9250.9481.0000.9100.247
1000인 이상0.6100.6850.7000.7070.7250.8310.8520.8840.9101.0000.000
대업종0.0000.0000.1630.1390.1710.0000.0000.2160.2470.0001.000

Missing values

2023-12-12T22:22:01.396586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:22:01.555893image/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
0금융및보험업금융및보험업14781894210601424031741448616946849
1광 업석탄광업및채석업311344201120
2광 업석회석·금속·비금속광업및기타광업592152154593154000
3제조업식료품제조업19788497636541377111072236242236
4제조업섬유및섬유제품제조업2038437142113791513281113931
5제조업목재및종이제품제조업125582907166557432314890910
6제조업출판·인쇄·제본업142512424125341928416280540
7제조업화학및고무제품제조업248106843456318981475829447443014
8제조업의약품·화장품·연탄·석유제품제조업174955745721222226617629176
9제조업기계기구·금속·비금속광물제품제조업121979275711674761574311231011871505839
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업1839263509200000
21농 업농업18011278911253062055220100
22기타의사업시설관리및사업지원서비스업1686994971327638835762633861169619010728
23기타의사업기타의각종사업160101223228117191112398635021047362
24기타의사업해외파견자361154742416414011557771
25기타의사업전문·보건·교육·여가관련서비스업3169697362339832136521085576743770552308122
26기타의사업도소매·음식·숙박업7821531013993958398416189277914151588941
27기타의사업부동산업및임대업62297330499425211568421334
28기타의사업국가및지방자치단체의사업156605966578640643131953540879051
29기타의사업주한미군3418307153226526