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

Description산업근로자현황분석데이터(산업중분류별규모별근로자수) 산업중분류별규모별근로자수에 대한 통계 자료로써 산업중분류별규모별근로자수에대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15064481/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
30인-49인 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 started2023-12-11 23:31:53.865427
Analysis finished2023-12-11 23:32:03.256836
Duration9.39 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

2023-12-12T08:32:03.358374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:32:03.493740image/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
2023-12-12T08:32:03.726079image/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%
2023-12-12T08:32:04.173898image/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%
Mean114149
Minimum44
Maximum1180626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:04.301391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile125.35
Q15199.75
median28425.5
Q356463.5
95-th percentile482508.2
Maximum1180626
Range1180582
Interquartile range (IQR)51263.75

Descriptive statistics

Standard deviation240839.83
Coefficient of variation (CV)2.1098724
Kurtosis13.569791
Mean114149
Median Absolute Deviation (MAD)24527
Skewness3.4610473
Sum3424470
Variance5.8003822 × 1010
MonotonicityNot monotonic
2023-12-12T08:32:04.433645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
26268 1
 
3.3%
48698 1
 
3.3%
106 1
 
3.3%
28375 1
 
3.3%
82350 1
 
3.3%
1180626 1
 
3.3%
553718 1
 
3.3%
4754 1
 
3.3%
249883 1
 
3.3%
300144 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
44 1
3.3%
106 1
3.3%
149 1
3.3%
1111 1
3.3%
2915 1
3.3%
3023 1
3.3%
3043 1
3.3%
4754 1
3.3%
6537 1
3.3%
7151 1
3.3%
ValueCountFrequency (%)
1180626 1
3.3%
553718 1
3.3%
395474 1
3.3%
300144 1
3.3%
249883 1
3.3%
201480 1
3.3%
82350 1
3.3%
59052 1
3.3%
48698 1
3.3%
44036 1
3.3%

5인-9인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82025.433
Minimum83
Maximum649832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:04.558033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile203.95
Q13999.75
median23227
Q353241.25
95-th percentile407908.2
Maximum649832
Range649749
Interquartile range (IQR)49241.5

Descriptive statistics

Standard deviation151732.82
Coefficient of variation (CV)1.8498264
Kurtosis7.2797823
Mean82025.433
Median Absolute Deviation (MAD)20538
Skewness2.6978148
Sum2460763
Variance2.3022847 × 1010
MonotonicityNot monotonic
2023-12-12T08:32:04.662357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
62139 1
 
3.3%
32519 1
 
3.3%
118 1
 
3.3%
40165 1
 
3.3%
20890 1
 
3.3%
649832 1
 
3.3%
477399 1
 
3.3%
3613 1
 
3.3%
141856 1
 
3.3%
322975 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
83 1
3.3%
118 1
3.3%
309 1
3.3%
1013 1
3.3%
1641 1
3.3%
1765 1
3.3%
3613 1
3.3%
3714 1
3.3%
4857 1
3.3%
8819 1
3.3%
ValueCountFrequency (%)
649832 1
3.3%
477399 1
3.3%
322975 1
3.3%
211878 1
3.3%
180776 1
3.3%
141856 1
3.3%
62139 1
3.3%
55935 1
3.3%
45160 1
3.3%
40165 1
3.3%

10인-19인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90077.067
Minimum48
Maximum533412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:04.758982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile418.85
Q16481.5
median26731
Q377527
95-th percentile445968.7
Maximum533412
Range533364
Interquartile range (IQR)71045.5

Descriptive statistics

Standard deviation146919.85
Coefficient of variation (CV)1.6310462
Kurtosis3.8577269
Mean90077.067
Median Absolute Deviation (MAD)24424.5
Skewness2.1507403
Sum2702312
Variance2.1585443 × 1010
MonotonicityNot monotonic
2023-12-12T08:32:04.855696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
141917 1
 
3.3%
45632 1
 
3.3%
377 1
 
3.3%
79751 1
 
3.3%
12982 1
 
3.3%
514123 1
 
3.3%
533412 1
 
3.3%
5767 1
 
3.3%
105720 1
 
3.3%
362669 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
48 1
3.3%
377 1
3.3%
470 1
3.3%
621 1
3.3%
2144 1
3.3%
2469 1
3.3%
5767 1
3.3%
6404 1
3.3%
6714 1
3.3%
11477 1
3.3%
ValueCountFrequency (%)
533412 1
3.3%
514123 1
3.3%
362669 1
3.3%
285134 1
3.3%
226648 1
3.3%
141917 1
3.3%
105720 1
3.3%
79751 1
3.3%
70855 1
3.3%
62143 1
3.3%

20인-29인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53759.8
Minimum101
Maximum325896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:04.949020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile176.95
Q15368.25
median15300.5
Q348769
95-th percentile216019.7
Maximum325896
Range325795
Interquartile range (IQR)43400.75

Descriptive statistics

Standard deviation81369.637
Coefficient of variation (CV)1.5135778
Kurtosis3.6751797
Mean53759.8
Median Absolute Deviation (MAD)14991.5
Skewness2.0076463
Sum1612794
Variance6.6210178 × 109
MonotonicityNot monotonic
2023-12-12T08:32:05.048281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
100559 1
 
3.3%
31464 1
 
3.3%
154 1
 
3.3%
98360 1
 
3.3%
5915 1
 
3.3%
231524 1
 
3.3%
325896 1
 
3.3%
3937 1
 
3.3%
45149 1
 
3.3%
197070 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
101 1
3.3%
154 1
3.3%
205 1
3.3%
413 1
3.3%
1387 1
3.3%
2918 1
3.3%
3937 1
3.3%
5186 1
3.3%
5915 1
3.3%
6332 1
3.3%
ValueCountFrequency (%)
325896 1
3.3%
231524 1
3.3%
197070 1
3.3%
167493 1
3.3%
146472 1
3.3%
100559 1
3.3%
98360 1
3.3%
49945 1
3.3%
45241 1
3.3%
45149 1
3.3%

30인-49인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62072.4
Minimum69
Maximum411070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:05.148297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile293.25
Q17671.25
median14434
Q363558.25
95-th percentile231286.45
Maximum411070
Range411001
Interquartile range (IQR)55887

Descriptive statistics

Standard deviation94956.784
Coefficient of variation (CV)1.5297747
Kurtosis5.4999014
Mean62072.4
Median Absolute Deviation (MAD)14116.5
Skewness2.2526765
Sum1862172
Variance9.0167909 × 109
MonotonicityNot monotonic
2023-12-12T08:32:05.243026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
117247 1
 
3.3%
38546 1
 
3.3%
560 1
 
3.3%
113304 1
 
3.3%
4272 1
 
3.3%
227987 1
 
3.3%
411070 1
 
3.3%
5354 1
 
3.3%
46032 1
 
3.3%
233986 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
69 1
3.3%
75 1
3.3%
560 1
3.3%
837 1
3.3%
1184 1
3.3%
4272 1
3.3%
5354 1
3.3%
7662 1
3.3%
7699 1
3.3%
8583 1
3.3%
ValueCountFrequency (%)
411070 1
3.3%
233986 1
3.3%
227987 1
3.3%
179680 1
3.3%
162605 1
3.3%
117247 1
3.3%
113304 1
3.3%
66138 1
3.3%
55819 1
3.3%
46032 1
3.3%

50인-99인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68546.567
Minimum0
Maximum524150
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:05.336393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile148.95
Q15604.25
median19813.5
Q366260.25
95-th percentile247506.4
Maximum524150
Range524150
Interquartile range (IQR)60656

Descriptive statistics

Standard deviation110729.1
Coefficient of variation (CV)1.6153851
Kurtosis9.5369928
Mean68546.567
Median Absolute Deviation (MAD)19648
Skewness2.8465336
Sum2056397
Variance1.2260934 × 1010
MonotonicityNot monotonic
2023-12-12T08:32:05.432647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.7%
95186 1
 
3.3%
2241 1
 
3.3%
64203 1
 
3.3%
4787 1
 
3.3%
185695 1
 
3.3%
524150 1
 
3.3%
8056 1
 
3.3%
59808 1
 
3.3%
258418 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 2
6.7%
331 1
3.3%
1909 1
3.3%
2241 1
3.3%
3181 1
3.3%
3449 1
3.3%
4787 1
3.3%
8056 1
3.3%
10138 1
3.3%
10958 1
3.3%
ValueCountFrequency (%)
524150 1
3.3%
258418 1
3.3%
234170 1
3.3%
185695 1
3.3%
158631 1
3.3%
95186 1
3.3%
90939 1
3.3%
66946 1
3.3%
64203 1
3.3%
59808 1
3.3%

100인-299인
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88327.433
Minimum0
Maximum607160
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:05.525888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile330.5
Q18669
median20710
Q391872.25
95-th percentile392769.6
Maximum607160
Range607160
Interquartile range (IQR)83203.25

Descriptive statistics

Standard deviation144649.92
Coefficient of variation (CV)1.6376556
Kurtosis6.7714723
Mean88327.433
Median Absolute Deviation (MAD)20369
Skewness2.5864019
Sum2649823
Variance2.0923598 × 1010
MonotonicityNot monotonic
2023-12-12T08:32:05.624900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
94229 1
 
3.3%
67689 1
 
3.3%
4658 1
 
3.3%
84802 1
 
3.3%
7065 1
 
3.3%
226005 1
 
3.3%
607160 1
 
3.3%
8810 1
 
3.3%
81773 1
 
3.3%
256440 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
236 1
3.3%
446 1
3.3%
2735 1
3.3%
4658 1
3.3%
7065 1
3.3%
7727 1
3.3%
8622 1
3.3%
8810 1
3.3%
10289 1
3.3%
ValueCountFrequency (%)
607160 1
3.3%
504312 1
3.3%
256440 1
3.3%
226005 1
3.3%
189819 1
3.3%
129541 1
3.3%
97038 1
3.3%
94229 1
3.3%
84802 1
3.3%
81773 1
3.3%

300인-499인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30740.933
Minimum0
Maximum231866
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:05.742199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile136.35
Q11981.5
median8812.5
Q335458.25
95-th percentile145045
Maximum231866
Range231866
Interquartile range (IQR)33476.75

Descriptive statistics

Standard deviation55196.103
Coefficient of variation (CV)1.7955246
Kurtosis8.4948203
Mean30740.933
Median Absolute Deviation (MAD)8451
Skewness2.9097534
Sum922228
Variance3.0466098 × 109
MonotonicityNot monotonic
2023-12-12T08:32:05.842187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.7%
36486 1
 
3.3%
23316 1
 
3.3%
1940 1
 
3.3%
32375 1
 
3.3%
4921 1
 
3.3%
58383 1
 
3.3%
205759 1
 
3.3%
2820 1
 
3.3%
40267 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 2
6.7%
303 1
3.3%
323 1
3.3%
400 1
3.3%
1507 1
3.3%
1806 1
3.3%
1940 1
3.3%
2106 1
3.3%
2820 1
3.3%
3352 1
3.3%
ValueCountFrequency (%)
231866 1
3.3%
205759 1
3.3%
70839 1
3.3%
58383 1
3.3%
56605 1
3.3%
55163 1
3.3%
40267 1
3.3%
36486 1
3.3%
32375 1
3.3%
23316 1
3.3%

500인-999인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29133.833
Minimum0
Maximum210653
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:05.930607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11916.25
median5819.5
Q339967.5
95-th percentile126635.05
Maximum210653
Range210653
Interquartile range (IQR)38051.25

Descriptive statistics

Standard deviation49303.543
Coefficient of variation (CV)1.6923123
Kurtosis7.3521193
Mean29133.833
Median Absolute Deviation (MAD)5819.5
Skewness2.6478034
Sum874015
Variance2.4308394 × 109
MonotonicityNot monotonic
2023-12-12T08:32:06.026037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4
 
13.3%
46001 1
 
3.3%
25789 1
 
3.3%
1229 1
 
3.3%
63753 1
 
3.3%
1905 1
 
3.3%
61600 1
 
3.3%
210653 1
 
3.3%
4509 1
 
3.3%
49594 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0 4
13.3%
555 1
 
3.3%
1229 1
 
3.3%
1330 1
 
3.3%
1905 1
 
3.3%
1950 1
 
3.3%
2096 1
 
3.3%
2409 1
 
3.3%
2438 1
 
3.3%
2628 1
 
3.3%
ValueCountFrequency (%)
210653 1
3.3%
169417 1
3.3%
74346 1
3.3%
63753 1
3.3%
61600 1
3.3%
49594 1
3.3%
46001 1
3.3%
40314 1
3.3%
38928 1
3.3%
25789 1
3.3%

1000인 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53621.367
Minimum0
Maximum302795
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:32:06.119439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1264
median12570.5
Q374789.25
95-th percentile214667.3
Maximum302795
Range302795
Interquartile range (IQR)74525.25

Descriptive statistics

Standard deviation78041.011
Coefficient of variation (CV)1.4554088
Kurtosis3.3024147
Mean53621.367
Median Absolute Deviation (MAD)12570.5
Skewness1.880697
Sum1608641
Variance6.0903994 × 109
MonotonicityNot monotonic
2023-12-12T08:32:06.220196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 8
26.7%
95530 1
 
3.3%
10468 1
 
3.3%
72681 1
 
3.3%
6161 1
 
3.3%
75492 1
 
3.3%
252497 1
 
3.3%
1262 1
 
3.3%
137724 1
 
3.3%
52532 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0 8
26.7%
1056 1
 
3.3%
1262 1
 
3.3%
6161 1
 
3.3%
7524 1
 
3.3%
10468 1
 
3.3%
11570 1
 
3.3%
12026 1
 
3.3%
13115 1
 
3.3%
22126 1
 
3.3%
ValueCountFrequency (%)
302795 1
3.3%
252497 1
3.3%
168431 1
3.3%
137724 1
3.3%
133605 1
3.3%
114607 1
3.3%
95530 1
3.3%
75492 1
3.3%
72681 1
3.3%
56859 1
3.3%

Interactions

2023-12-12T08:32:01.906215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.222775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.900212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.629038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.641626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.500777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.367529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.207958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.102378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.030956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.988125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.291293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.975344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.743556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.729603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.589939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.443927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.290845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.202604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.139831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.064940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.360230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.047666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.837726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.810750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.685535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.519035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.376104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.301060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.240305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.156477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.426929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.119626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.919110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.890663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.774602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.591397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.467659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.383550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.327065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.230669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.495461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.198390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.008281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.969123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.856356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.675155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.570619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.502950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.421949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.297269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.556159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.268466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.073764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.050070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.945036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.743651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.668026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.582591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.491323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.366992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.623012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.335844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.144121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.152340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.027871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.817640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.762364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.663727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.566275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.437576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.692302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.410256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.216685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.252577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.149363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.904942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.857578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.760882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.649208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.797981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.756792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.478715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.285999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.322615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.224867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.003714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.934605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.846209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.734745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:02.892566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:54.818020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:55.546294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:56.573650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:57.398575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:58.291298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:31:59.109336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.009255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:00.934204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:01.824194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:32:06.296400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
대업종1.0001.0000.0000.0000.6500.0000.4050.0000.0000.3870.5610.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5인 미만0.0001.0001.0000.9960.9050.9830.9220.8750.9010.9740.9650.835
5인-9인0.0001.0000.9961.0000.9050.9830.9020.8750.9200.9840.9690.881
10인-19인0.6501.0000.9050.9051.0000.9610.9840.8950.9760.8980.8550.800
20인-29인0.0001.0000.9830.9830.9611.0000.9860.9550.9370.9580.9170.938
30인-49인0.4051.0000.9220.9020.9840.9861.0000.9230.9770.8990.8710.822
50인-99인0.0001.0000.8750.8750.8950.9550.9231.0000.9690.8960.8160.808
100인-299인0.0001.0000.9010.9200.9760.9370.9770.9691.0000.9700.8940.875
300인-499인0.3871.0000.9740.9840.8980.9580.8990.8960.9701.0000.9850.863
500인-999인0.5611.0000.9650.9690.8550.9170.8710.8160.8940.9851.0000.876
1000인 이상0.0001.0000.8350.8810.8000.9380.8220.8080.8750.8630.8761.000
2023-12-12T08:32:06.407008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상대업종
5인 미만1.0000.9140.9040.8780.8410.8000.7540.7710.6680.6040.000
5인-9인0.9141.0000.9880.9740.9620.8790.8550.8590.7750.6510.000
10인-19인0.9040.9881.0000.9930.9840.9100.8790.8570.7950.6660.360
20인-29인0.8780.9740.9931.0000.9930.9250.8990.8630.8080.6750.000
30인-49인0.8410.9620.9840.9931.0000.9430.9250.8770.8310.6910.171
50인-99인0.8000.8790.9100.9250.9431.0000.9820.9160.9010.8240.000
100인-299인0.7540.8550.8790.8990.9250.9821.0000.9520.9310.8400.000
300인-499인0.7710.8590.8570.8630.8770.9160.9521.0000.9390.8310.163
500인-999인0.6680.7750.7950.8080.8310.9010.9310.9391.0000.8710.289
1000인 이상0.6040.6510.6660.6750.6910.8240.8400.8310.8711.0000.000
대업종0.0000.0000.3600.0000.1710.0000.0000.1630.2890.0001.000

Missing values

2023-12-12T08:32:03.023615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:32:03.185699image/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
0금융및보험업금융및보험업26268621391419171005591172479518694229364864600195530
1광 업석탄광업및채석업44834810169023632313300
2광 업석회석·금속·비금속광업및기타광업11111013214413871184331446000
3제조업식료품제조업3300533064499173285742109496995941615825150997524
4제조업섬유및섬유제품제조업32788242522862118790194431894416582335220961056
5제조업목재및종이제품제조업2117519114223751362512165101381396633705550
6제조업출판·인쇄·제본업2252315784167939992106041095811822210624380
7제조업화학및고무제품제조업44036451606214345241558195658170010156632054322126
8제조업의약품·화장품·연탄·석유제품제조업304337146404518685831878328316109091105613115
9제조업기계기구·금속·비금속광물제품제조업2014801807762266481464721626051586311898195660538928133605
대업종구분5인 미만5인-9인10인-19인20인-29인30인-49인50인-99인100인-299인300인-499인500인-999인1000인 이상
20어 업어업302316416212057500000
21농 업농업305681807114427709976993181273540000
22기타의사업시설관리및사업지원서비스업300144322975362669197070233986258418256440708397434652532
23기타의사업기타의각종사업249883141856105720451494603259808817734026749594137724
24기타의사업해외파견자4754361357673937535480568810282045091262
25기타의사업전문·보건·교육·여가관련서비스업553718477399533412325896411070524150607160205759210653252497
26기타의사업도소매·음식·숙박업1180626649832514123231524227987185695226005583836160075492
27기타의사업부동산업및임대업8235020890129825915427247877065492119056161
28기타의사업국가및지방자치단체의사업283754016579751983601133046420384802323756375372681
29기타의사업주한미군106118377154560224146581940122910468