Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells44
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory83.7 B

Variable types

Text1
Numeric8

Dataset

Description법무부에서 해외로 진출하는 우리 중소기업의 법률 자문을 무료로 지원하고  법률 지원한  결과에 대한  주제별/업종별  통계입니다.
Author법무부
URLhttps://www.data.go.kr/data/15043239/fileData.do

Alerts

2019년 건수 is highly overall correlated with 2019년 비율 and 6 other fieldsHigh correlation
2019년 비율 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2018년 건수 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2018년 비율 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2017년 건수 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2017년 비율 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2016년 건수 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2016년 비율 is highly overall correlated with 2019년 건수 and 6 other fieldsHigh correlation
2019년 건수 has 6 (16.7%) missing valuesMissing
2019년 비율 has 6 (16.7%) missing valuesMissing
2018년 건수 has 2 (5.6%) missing valuesMissing
2018년 비율 has 2 (5.6%) missing valuesMissing
2017년 건수 has 3 (8.3%) missing valuesMissing
2017년 비율 has 3 (8.3%) missing valuesMissing
2016년 건수 has 11 (30.6%) missing valuesMissing
2016년 비율 has 11 (30.6%) missing valuesMissing
업종별 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:24:08.164389
Analysis finished2023-12-12 02:24:16.959384
Duration8.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종별
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T11:24:17.214295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length17.5
Mean length13.666667
Min length3

Characters and Unicode

Total characters492
Distinct characters130
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row1차 금속 제조업
2nd row가죽, 가방 및 신발 제조업
3rd row건설업
4th row고무제품 및 플라스틱제품 제조업
5th row교육 서비스업
ValueCountFrequency (%)
및 24
 
17.1%
제조업 21
 
15.0%
서비스업 5
 
3.6%
그 3
 
2.1%
밖의 3
 
2.1%
영상 2
 
1.4%
의약품 1
 
0.7%
의복 1
 
0.7%
전자부품 1
 
0.7%
기술 1
 
0.7%
Other values (78) 78
55.7%
2023-12-12T11:24:17.742086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
21.1%
업 40
 
8.1%
제 33
 
6.7%
및 24
 
4.9%
조 21
 
4.3%
, 17
 
3.5%
품 14
 
2.8%
비 11
 
2.2%
스 10
 
2.0%
기 10
 
2.0%
Other values (120) 208
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 368
74.8%
Space Separator 104
 
21.1%
Other Punctuation 17
 
3.5%
Decimal Number 1
 
0.2%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 40
 
10.9%
제 33
 
9.0%
및 24
 
6.5%
조 21
 
5.7%
품 14
 
3.8%
비 11
 
3.0%
스 10
 
2.7%
기 10
 
2.7%
의 8
 
2.2%
서 7
 
1.9%
Other values (115) 190
51.6%
Space Separator
ValueCountFrequency (%)
104
100.0%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
74.4%
Common 124
 
25.2%
Han 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 40
 
10.9%
제 33
 
9.0%
및 24
 
6.6%
조 21
 
5.7%
품 14
 
3.8%
비 11
 
3.0%
스 10
 
2.7%
기 10
 
2.7%
의 8
 
2.2%
서 7
 
1.9%
Other values (113) 188
51.4%
Common
ValueCountFrequency (%)
104
83.9%
, 17
 
13.7%
1 1
 
0.8%
) 1
 
0.8%
( 1
 
0.8%
Han
ValueCountFrequency (%)
理 1
50.0%
修 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 365
74.2%
ASCII 124
 
25.2%
CJK 2
 
0.4%
Compat Jamo 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
83.9%
, 17
 
13.7%
1 1
 
0.8%
) 1
 
0.8%
( 1
 
0.8%
Hangul
ValueCountFrequency (%)
업 40
 
11.0%
제 33
 
9.0%
및 24
 
6.6%
조 21
 
5.8%
품 14
 
3.8%
비 11
 
3.0%
스 10
 
2.7%
기 10
 
2.7%
의 8
 
2.2%
서 7
 
1.9%
Other values (112) 187
51.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%
CJK
ValueCountFrequency (%)
理 1
50.0%
修 1
50.0%

2019년 건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)56.7%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean11.3
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:17.873774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q314.5
95-th percentile39
Maximum69
Range68
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation15.335669
Coefficient of variation (CV)1.3571389
Kurtosis6.7605909
Mean11.3
Median Absolute Deviation (MAD)3
Skewness2.4636714
Sum339
Variance235.18276
MonotonicityNot monotonic
2023-12-12T11:24:17.999535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 6
16.7%
3 4
11.1%
1 3
8.3%
4 3
8.3%
7 2
 
5.6%
28 1
 
2.8%
48 1
 
2.8%
9 1
 
2.8%
13 1
 
2.8%
5 1
 
2.8%
Other values (7) 7
19.4%
(Missing) 6
16.7%
ValueCountFrequency (%)
1 3
8.3%
2 6
16.7%
3 4
11.1%
4 3
8.3%
5 1
 
2.8%
7 2
 
5.6%
9 1
 
2.8%
12 1
 
2.8%
13 1
 
2.8%
15 1
 
2.8%
ValueCountFrequency (%)
69 1
2.8%
48 1
2.8%
28 1
2.8%
26 1
2.8%
24 1
2.8%
21 1
2.8%
16 1
2.8%
15 1
2.8%
13 1
2.8%
12 1
2.8%

2019년 비율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)56.7%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean3.331
Minimum0.29
Maximum20.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:18.141100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.29
Q10.59
median1.18
Q34.2725
95-th percentile11.505
Maximum20.35
Range20.06
Interquartile range (IQR)3.6825

Descriptive statistics

Standard deviation4.5241188
Coefficient of variation (CV)1.3581864
Kurtosis6.7562591
Mean3.331
Median Absolute Deviation (MAD)0.88
Skewness2.4631466
Sum99.93
Variance20.467651
MonotonicityNot monotonic
2023-12-12T11:24:18.306723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.59 6
16.7%
0.88 4
11.1%
0.29 3
8.3%
1.18 3
8.3%
2.06 2
 
5.6%
8.26 1
 
2.8%
14.16 1
 
2.8%
2.65 1
 
2.8%
3.83 1
 
2.8%
1.47 1
 
2.8%
Other values (7) 7
19.4%
(Missing) 6
16.7%
ValueCountFrequency (%)
0.29 3
8.3%
0.59 6
16.7%
0.88 4
11.1%
1.18 3
8.3%
1.47 1
 
2.8%
2.06 2
 
5.6%
2.65 1
 
2.8%
3.54 1
 
2.8%
3.83 1
 
2.8%
4.42 1
 
2.8%
ValueCountFrequency (%)
20.35 1
2.8%
14.16 1
2.8%
8.26 1
2.8%
7.67 1
2.8%
7.08 1
2.8%
6.19 1
2.8%
4.72 1
2.8%
4.42 1
2.8%
3.83 1
2.8%
3.54 1
2.8%

2018년 건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)52.9%
Missing2
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean12.705882
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:18.432401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q316.75
95-th percentile40.1
Maximum69
Range68
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation15.64285
Coefficient of variation (CV)1.2311502
Kurtosis5.4211044
Mean12.705882
Median Absolute Deviation (MAD)5
Skewness2.2191406
Sum432
Variance244.69875
MonotonicityNot monotonic
2023-12-12T11:24:18.541981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 6
16.7%
6 5
13.9%
2 4
11.1%
11 2
 
5.6%
12 2
 
5.6%
24 2
 
5.6%
3 2
 
5.6%
18 1
 
2.8%
57 1
 
2.8%
5 1
 
2.8%
Other values (8) 8
22.2%
(Missing) 2
 
5.6%
ValueCountFrequency (%)
1 6
16.7%
2 4
11.1%
3 2
 
5.6%
5 1
 
2.8%
6 5
13.9%
8 1
 
2.8%
10 1
 
2.8%
11 2
 
5.6%
12 2
 
5.6%
13 1
 
2.8%
ValueCountFrequency (%)
69 1
2.8%
57 1
2.8%
31 1
2.8%
30 1
2.8%
25 1
2.8%
24 2
5.6%
22 1
2.8%
18 1
2.8%
13 1
2.8%
12 2
5.6%

2018년 비율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)52.9%
Missing2
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean2.9408824
Minimum0.23
Maximum15.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:18.650822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.23
Q10.46
median1.39
Q33.88
95-th percentile9.2835
Maximum15.97
Range15.74
Interquartile range (IQR)3.42

Descriptive statistics

Standard deviation3.6211712
Coefficient of variation (CV)1.2313213
Kurtosis5.412911
Mean2.9408824
Median Absolute Deviation (MAD)1.16
Skewness2.2172507
Sum99.99
Variance13.112881
MonotonicityNot monotonic
2023-12-12T11:24:18.764081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.23 6
16.7%
1.39 5
13.9%
0.46 4
11.1%
2.55 2
 
5.6%
2.78 2
 
5.6%
5.56 2
 
5.6%
0.69 2
 
5.6%
4.17 1
 
2.8%
13.19 1
 
2.8%
1.16 1
 
2.8%
Other values (8) 8
22.2%
(Missing) 2
 
5.6%
ValueCountFrequency (%)
0.23 6
16.7%
0.46 4
11.1%
0.69 2
 
5.6%
1.16 1
 
2.8%
1.39 5
13.9%
1.85 1
 
2.8%
2.31 1
 
2.8%
2.55 2
 
5.6%
2.78 2
 
5.6%
3.01 1
 
2.8%
ValueCountFrequency (%)
15.97 1
2.8%
13.19 1
2.8%
7.18 1
2.8%
6.94 1
2.8%
5.79 1
2.8%
5.56 2
5.6%
5.09 1
2.8%
4.17 1
2.8%
3.01 1
2.8%
2.78 2
5.6%

2017년 건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)51.5%
Missing3
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean8.3636364
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:18.882422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q312
95-th percentile28.2
Maximum40
Range39
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.7269541
Coefficient of variation (CV)1.1630054
Kurtosis3.801317
Mean8.3636364
Median Absolute Deviation (MAD)3
Skewness1.9673895
Sum276
Variance94.613636
MonotonicityNot monotonic
2023-12-12T11:24:19.032485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 6
16.7%
1 5
13.9%
4 4
11.1%
3 3
8.3%
5 2
 
5.6%
12 2
 
5.6%
23 1
 
2.8%
40 1
 
2.8%
11 1
 
2.8%
6 1
 
2.8%
Other values (7) 7
19.4%
(Missing) 3
8.3%
ValueCountFrequency (%)
1 5
13.9%
2 6
16.7%
3 3
8.3%
4 4
11.1%
5 2
 
5.6%
6 1
 
2.8%
7 1
 
2.8%
9 1
 
2.8%
11 1
 
2.8%
12 2
 
5.6%
ValueCountFrequency (%)
40 1
2.8%
36 1
2.8%
23 1
2.8%
19 1
2.8%
18 1
2.8%
16 1
2.8%
15 1
2.8%
12 2
5.6%
11 1
2.8%
9 1
2.8%

2017년 비율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)51.5%
Missing3
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3.0290909
Minimum0.36
Maximum14.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:19.167780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile0.36
Q10.72
median1.45
Q34.35
95-th percentile10.214
Maximum14.49
Range14.13
Interquartile range (IQR)3.63

Descriptive statistics

Standard deviation3.5240711
Coefficient of variation (CV)1.1634088
Kurtosis3.7981346
Mean3.0290909
Median Absolute Deviation (MAD)1.09
Skewness1.9663508
Sum99.96
Variance12.419077
MonotonicityNot monotonic
2023-12-12T11:24:19.275157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.72 6
16.7%
0.36 5
13.9%
1.45 4
11.1%
1.09 3
8.3%
1.81 2
 
5.6%
4.35 2
 
5.6%
8.33 1
 
2.8%
14.49 1
 
2.8%
3.99 1
 
2.8%
2.17 1
 
2.8%
Other values (7) 7
19.4%
(Missing) 3
8.3%
ValueCountFrequency (%)
0.36 5
13.9%
0.72 6
16.7%
1.09 3
8.3%
1.45 4
11.1%
1.81 2
 
5.6%
2.17 1
 
2.8%
2.54 1
 
2.8%
3.26 1
 
2.8%
3.99 1
 
2.8%
4.35 2
 
5.6%
ValueCountFrequency (%)
14.49 1
2.8%
13.04 1
2.8%
8.33 1
2.8%
6.88 1
2.8%
6.52 1
2.8%
5.8 1
2.8%
5.43 1
2.8%
4.35 2
5.6%
3.99 1
2.8%
3.26 1
2.8%

2016년 건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)60.0%
Missing11
Missing (%)30.6%
Infinite0
Infinite (%)0.0%
Mean8.56
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:19.390393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile23.6
Maximum39
Range38
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1153716
Coefficient of variation (CV)1.0648799
Kurtosis4.1527869
Mean8.56
Median Absolute Deviation (MAD)4
Skewness1.8506757
Sum214
Variance83.09
MonotonicityNot monotonic
2023-12-12T11:24:19.513266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 5
13.9%
2 4
 
11.1%
11 2
 
5.6%
3 2
 
5.6%
13 2
 
5.6%
24 1
 
2.8%
22 1
 
2.8%
39 1
 
2.8%
14 1
 
2.8%
8 1
 
2.8%
Other values (5) 5
13.9%
(Missing) 11
30.6%
ValueCountFrequency (%)
1 5
13.9%
2 4
11.1%
3 2
 
5.6%
4 1
 
2.8%
5 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
10 1
 
2.8%
11 2
 
5.6%
12 1
 
2.8%
ValueCountFrequency (%)
39 1
2.8%
24 1
2.8%
22 1
2.8%
14 1
2.8%
13 2
5.6%
12 1
2.8%
11 2
5.6%
10 1
2.8%
9 1
2.8%
8 1
2.8%

2016년 비율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)60.0%
Missing11
Missing (%)30.6%
Infinite0
Infinite (%)0.0%
Mean3.9992
Minimum0.47
Maximum18.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:24:19.640237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.47
5-th percentile0.47
Q10.93
median2.34
Q35.61
95-th percentile11.024
Maximum18.22
Range17.75
Interquartile range (IQR)4.68

Descriptive statistics

Standard deviation4.2584269
Coefficient of variation (CV)1.0648197
Kurtosis4.1527893
Mean3.9992
Median Absolute Deviation (MAD)1.87
Skewness1.8505909
Sum99.98
Variance18.134199
MonotonicityNot monotonic
2023-12-12T11:24:19.763596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.47 5
13.9%
0.93 4
 
11.1%
5.14 2
 
5.6%
1.4 2
 
5.6%
6.07 2
 
5.6%
11.21 1
 
2.8%
10.28 1
 
2.8%
18.22 1
 
2.8%
6.54 1
 
2.8%
3.74 1
 
2.8%
Other values (5) 5
13.9%
(Missing) 11
30.6%
ValueCountFrequency (%)
0.47 5
13.9%
0.93 4
11.1%
1.4 2
 
5.6%
1.87 1
 
2.8%
2.34 1
 
2.8%
3.74 1
 
2.8%
4.21 1
 
2.8%
4.67 1
 
2.8%
5.14 2
 
5.6%
5.61 1
 
2.8%
ValueCountFrequency (%)
18.22 1
2.8%
11.21 1
2.8%
10.28 1
2.8%
6.54 1
2.8%
6.07 2
5.6%
5.61 1
2.8%
5.14 2
5.6%
4.67 1
2.8%
4.21 1
2.8%
3.74 1
2.8%

Interactions

2023-12-12T11:24:15.441000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:08.787506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.618401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.410263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.256123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.285345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.399252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:14.297605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.575619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:08.901160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.723789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.495303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.360845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.418043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.497295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:14.411651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.675688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.008633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.818237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.614559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.480860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.586932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.614840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:14.819710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.770851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.100079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.926398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.719507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.590370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.741265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.712862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:14.913814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.872349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.191757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.044085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.844636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.800428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.889536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.837776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.024939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:16.000014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.287669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.144456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.945029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.923806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.009239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.951074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.118566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:16.100624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.406844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.235469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.035930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.039984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.160882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:14.077323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.205878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:16.228006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:09.525958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:10.323391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:11.147380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:12.156078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:13.297230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:14.178097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:24:15.319708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:24:19.851421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종별2019년 건수2019년 비율2018년 건수2018년 비율2017년 건수2017년 비율2016년 건수2016년 비율
업종별1.0001.0001.0001.0001.0001.0001.0001.0001.000
2019년 건수1.0001.0001.0000.8910.8910.9200.9200.8720.872
2019년 비율1.0001.0001.0000.8910.8910.9200.9200.8720.872
2018년 건수1.0000.8910.8911.0001.0000.8550.8550.9370.937
2018년 비율1.0000.8910.8911.0001.0000.8550.8550.9370.937
2017년 건수1.0000.9200.9200.8550.8551.0001.0000.8710.871
2017년 비율1.0000.9200.9200.8550.8551.0001.0000.8710.871
2016년 건수1.0000.8720.8720.9370.9370.8710.8711.0001.000
2016년 비율1.0000.8720.8720.9370.9370.8710.8711.0001.000
2023-12-12T11:24:19.983228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019년 건수2019년 비율2018년 건수2018년 비율2017년 건수2017년 비율2016년 건수2016년 비율
2019년 건수1.0001.0000.8550.8550.8260.8260.8350.835
2019년 비율1.0001.0000.8550.8550.8260.8260.8350.835
2018년 건수0.8550.8551.0001.0000.8640.8640.8090.809
2018년 비율0.8550.8551.0001.0000.8640.8640.8090.809
2017년 건수0.8260.8260.8640.8641.0001.0000.8540.854
2017년 비율0.8260.8260.8640.8641.0001.0000.8540.854
2016년 건수0.8350.8350.8090.8090.8540.8541.0001.000
2016년 비율0.8350.8350.8090.8090.8540.8541.0001.000

Missing values

2023-12-12T11:24:16.420163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:24:16.614649image/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.
2023-12-12T11:24:16.823807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업종별2019년 건수2019년 비율2018년 건수2018년 비율2017년 건수2017년 비율2016년 건수2016년 비율
01차 금속 제조업72.0661.3931.0910.47
1가죽, 가방 및 신발 제조업10.29<NA><NA>41.4510.47
2건설업30.8881.8562.1710.47
3고무제품 및 플라스틱제품 제조업133.83133.0151.81115.14
4교육 서비스업41.1830.6920.7220.93
5그 밖의 기계 및 장비 제조업288.26245.56238.332411.21
6그 밖의 운송장비 제조업20.5961.3931.0920.93
7그 밖의 제품 제조업4814.165713.194014.492210.28
8금속가공제품 제조업92.65184.17113.99115.14
9금융 및 보험업<NA><NA>10.23<NA><NA><NA><NA>
업종별2019년 건수2019년 비율2018년 건수2018년 비율2017년 건수2017년 비율2016년 건수2016년 비율
26전기, 가스, 증기 및 수도사업10.2961.3910.36<NA><NA>
27전기장비 제조업247.08245.56196.88136.07
28전문, 과학 및 기술 서비스업154.42225.09124.3552.34
29전자부품, 컴퓨터, 영상, 음향 및 통신장비 제조업267.67317.18186.52125.61
30출판, 영상, 방송통신 및 정보서비스업123.54102.31155.4394.21
31코크스, 연탄 및 석유정제품 제조업<NA><NA>10.2310.36<NA><NA>
32펄프, 종이 및 종이제품 제조업20.5961.39<NA><NA><NA><NA>
33하수ㆍ폐기물 처리, 원료재생 및 환경복원업20.5910.23<NA><NA><NA><NA>
34화학물질 및 화학제품 제조업164.72306.9493.26104.67
35예술, 스포츠 및 여가 관련 서비스업<NA><NA><NA><NA>41.4520.93