Overview

Dataset statistics

Number of variables8
Number of observations55
Missing cells6
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory73.3 B

Variable types

Numeric7
Text1

Dataset

Description전북특별자치도 연도별 식품전략기업 고용 및 매출 현황13년도 해당 업체가 벌어들인 금액 등우리기관에서는 더 이상 생성 불가 데이터입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15010980/fileData.do

Alerts

13년매출(백만원) is highly overall correlated with 13년고용(명) and 2 other fieldsHigh correlation
13년고용(명) is highly overall correlated with 13년매출(백만원) and 2 other fieldsHigh correlation
14년매출(백만원) is highly overall correlated with 13년매출(백만원) and 2 other fieldsHigh correlation
14년고용(명) is highly overall correlated with 13년매출(백만원) and 2 other fieldsHigh correlation
매출성장률 is highly overall correlated with 고용성장률High correlation
고용성장률 is highly overall correlated with 매출성장률High correlation
13년매출(백만원) has 1 (1.8%) missing valuesMissing
13년고용(명) has 1 (1.8%) missing valuesMissing
14년매출(백만원) has 1 (1.8%) missing valuesMissing
14년고용(명) has 1 (1.8%) missing valuesMissing
매출성장률 has 1 (1.8%) missing valuesMissing
고용성장률 has 1 (1.8%) missing valuesMissing
순번 has unique valuesUnique
기업명 has unique valuesUnique
고용성장률 has 14 (25.5%) zerosZeros

Reproduction

Analysis started2024-03-14 20:31:52.562448
Analysis finished2024-03-14 20:32:05.880781
Duration13.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-15T05:32:06.016573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q114.5
median28
Q341.5
95-th percentile52.3
Maximum55
Range54
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.02082
Coefficient of variation (CV)0.57217214
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance256.66667
MonotonicityStrictly increasing
2024-03-15T05:32:06.399130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
31 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%
46 1
1.8%

기업명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size568.0 B
2024-03-15T05:32:07.668903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.6181818
Min length2

Characters and Unicode

Total characters309
Distinct characters138
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row두메산골(영)
2nd row드림인 영농조합
3rd row(주)아리울21식품
4th row(유)지평선누룽지
5th row들판
ValueCountFrequency (%)
영농조합 2
 
3.3%
참프레 1
 
1.7%
㈜남광푸드 1
 
1.7%
대두식품 1
 
1.7%
선호발효식품 1
 
1.7%
참바다영어조합법인 1
 
1.7%
고려자연식품 1
 
1.7%
미와미 1
 
1.7%
남원지리산허브 1
 
1.7%
깊은숲속 1
 
1.7%
Other values (49) 49
81.7%
2024-03-15T05:32:09.363695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
품 12
 
3.9%
식 12
 
3.9%
영 9
 
2.9%
푸 8
 
2.6%
드 8
 
2.6%
( 8
 
2.6%
) 8
 
2.6%
조 7
 
2.3%
산 7
 
2.3%
지 7
 
2.3%
Other values (128) 223
72.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 285
92.2%
Open Punctuation 8
 
2.6%
Close Punctuation 8
 
2.6%
Space Separator 5
 
1.6%
Decimal Number 2
 
0.6%
Other Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 12
 
4.2%
식 12
 
4.2%
영 9
 
3.2%
푸 8
 
2.8%
드 8
 
2.8%
조 7
 
2.5%
산 7
 
2.5%
지 7
 
2.5%
합 6
 
2.1%
주 6
 
2.1%
Other values (122) 203
71.2%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 286
92.6%
Common 23
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 12
 
4.2%
식 12
 
4.2%
영 9
 
3.1%
푸 8
 
2.8%
드 8
 
2.8%
조 7
 
2.4%
산 7
 
2.4%
지 7
 
2.4%
합 6
 
2.1%
주 6
 
2.1%
Other values (123) 204
71.3%
Common
ValueCountFrequency (%)
( 8
34.8%
) 8
34.8%
5
21.7%
2 1
 
4.3%
1 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 285
92.2%
ASCII 23
 
7.4%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
품 12
 
4.2%
식 12
 
4.2%
영 9
 
3.2%
푸 8
 
2.8%
드 8
 
2.8%
조 7
 
2.5%
산 7
 
2.5%
지 7
 
2.5%
합 6
 
2.1%
주 6
 
2.1%
Other values (122) 203
71.2%
ASCII
ValueCountFrequency (%)
( 8
34.8%
) 8
34.8%
5
21.7%
2 1
 
4.3%
1 1
 
4.3%
None
ValueCountFrequency (%)
㈜ 1
100.0%

13년매출(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)100.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean7876.4259
Minimum46
Maximum72500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-15T05:32:09.818870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile156.5
Q1594
median2717
Q37855.75
95-th percentile35731.2
Maximum72500
Range72454
Interquartile range (IQR)7261.75

Descriptive statistics

Standard deviation13894.718
Coefficient of variation (CV)1.7640892
Kurtosis9.1878402
Mean7876.4259
Median Absolute Deviation (MAD)2249
Skewness2.8721424
Sum425327
Variance1.9306318 × 108
MonotonicityNot monotonic
2024-03-15T05:32:10.484727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7941 1
 
1.8%
2914 1
 
1.8%
466 1
 
1.8%
20590 1
 
1.8%
8900 1
 
1.8%
3483 1
 
1.8%
560 1
 
1.8%
46 1
 
1.8%
160 1
 
1.8%
2255 1
 
1.8%
Other values (44) 44
80.0%
ValueCountFrequency (%)
46 1
1.8%
100 1
1.8%
150 1
1.8%
160 1
1.8%
175 1
1.8%
228 1
1.8%
240 1
1.8%
368 1
1.8%
400 1
1.8%
405 1
1.8%
ValueCountFrequency (%)
72500 1
1.8%
47000 1
1.8%
37026 1
1.8%
35034 1
1.8%
34653 1
1.8%
27300 1
1.8%
20590 1
1.8%
16000 1
1.8%
13300 1
1.8%
11568 1
1.8%

13년고용(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)61.1%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean37.574074
Minimum1
Maximum242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-15T05:32:10.887169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median18
Q331.5
95-th percentile181.75
Maximum242
Range241
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation55.746936
Coefficient of variation (CV)1.4836543
Kurtosis5.558448
Mean37.574074
Median Absolute Deviation (MAD)12
Skewness2.4597523
Sum2029
Variance3107.7208
MonotonicityNot monotonic
2024-03-15T05:32:11.319229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
30 4
 
7.3%
5 4
 
7.3%
7 3
 
5.5%
12 3
 
5.5%
6 3
 
5.5%
20 3
 
5.5%
11 3
 
5.5%
2 3
 
5.5%
22 2
 
3.6%
18 2
 
3.6%
Other values (23) 24
43.6%
ValueCountFrequency (%)
1 1
 
1.8%
2 3
5.5%
4 1
 
1.8%
5 4
7.3%
6 3
5.5%
7 3
5.5%
8 1
 
1.8%
9 1
 
1.8%
10 2
3.6%
11 3
5.5%
ValueCountFrequency (%)
242 1
1.8%
225 1
1.8%
185 1
1.8%
180 1
1.8%
130 1
1.8%
106 1
1.8%
85 1
1.8%
79 1
1.8%
71 1
1.8%
52 1
1.8%

14년매출(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)87.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean9454.0185
Minimum60
Maximum80000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-15T05:32:11.723359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile272
Q1712.5
median2897.5
Q38375
95-th percentile41850
Maximum80000
Range79940
Interquartile range (IQR)7662.5

Descriptive statistics

Standard deviation16410.042
Coefficient of variation (CV)1.7357743
Kurtosis7.6487357
Mean9454.0185
Median Absolute Deviation (MAD)2347.5
Skewness2.7054085
Sum510517
Variance2.6928948 × 108
MonotonicityNot monotonic
2024-03-15T05:32:12.280008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
550 2
 
3.6%
8000 2
 
3.6%
4000 2
 
3.6%
300 2
 
3.6%
500 2
 
3.6%
2300 2
 
3.6%
2000 2
 
3.6%
7700 1
 
1.8%
28000 1
 
1.8%
9700 1
 
1.8%
Other values (37) 37
67.3%
ValueCountFrequency (%)
60 1
1.8%
180 1
1.8%
220 1
1.8%
300 2
3.6%
350 1
1.8%
400 1
1.8%
500 2
3.6%
550 2
3.6%
570 1
1.8%
580 1
1.8%
ValueCountFrequency (%)
80000 1
1.8%
63000 1
1.8%
49000 1
1.8%
38000 1
1.8%
37000 1
1.8%
30000 1
1.8%
28000 1
1.8%
21000 1
1.8%
20000 1
1.8%
14500 1
1.8%

14년고용(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)63.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean43.648148
Minimum1
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2024-03-15T05:32:12.601029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q18
median17
Q335.75
95-th percentile209.25
Maximum270
Range269
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation65.521466
Coefficient of variation (CV)1.5011282
Kurtosis4.8871471
Mean43.648148
Median Absolute Deviation (MAD)11
Skewness2.3653973
Sum2357
Variance4293.0625
MonotonicityNot monotonic
2024-03-15T05:32:12.852626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 5
 
9.1%
8 4
 
7.3%
15 3
 
5.5%
9 3
 
5.5%
30 3
 
5.5%
23 3
 
5.5%
12 2
 
3.6%
7 2
 
3.6%
24 2
 
3.6%
2 2
 
3.6%
Other values (24) 25
45.5%
ValueCountFrequency (%)
1 1
 
1.8%
2 2
 
3.6%
5 5
9.1%
6 1
 
1.8%
7 2
 
3.6%
8 4
7.3%
9 3
5.5%
10 1
 
1.8%
12 2
 
3.6%
13 2
 
3.6%
ValueCountFrequency (%)
270 1
1.8%
250 1
1.8%
245 1
1.8%
190 1
1.8%
185 1
1.8%
115 1
1.8%
108 1
1.8%
83 1
1.8%
81 1
1.8%
65 1
1.8%

매출성장률
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)63.0%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean32.296296
Minimum-5
Maximum400
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.8%
Memory size623.0 B
2024-03-15T05:32:13.181580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile2.65
Q16.25
median13
Q337.75
95-th percentile98.8
Maximum400
Range405
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation59.156215
Coefficient of variation (CV)1.8316718
Kurtosis28.93015
Mean32.296296
Median Absolute Deviation (MAD)9.5
Skewness4.9312797
Sum1744
Variance3499.4577
MonotonicityNot monotonic
2024-03-15T05:32:13.631939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 5
 
9.1%
4 4
 
7.3%
38 4
 
7.3%
7 3
 
5.5%
10 3
 
5.5%
5 3
 
5.5%
32 2
 
3.6%
51 2
 
3.6%
3 2
 
3.6%
6 2
 
3.6%
Other values (24) 24
43.6%
ValueCountFrequency (%)
-5 1
 
1.8%
1 1
 
1.8%
2 1
 
1.8%
3 2
 
3.6%
4 4
7.3%
5 3
5.5%
6 2
 
3.6%
7 3
5.5%
8 1
 
1.8%
9 5
9.1%
ValueCountFrequency (%)
400 1
 
1.8%
153 1
 
1.8%
130 1
 
1.8%
82 1
 
1.8%
71 1
 
1.8%
63 1
 
1.8%
51 2
3.6%
46 1
 
1.8%
41 1
 
1.8%
38 4
7.3%

고용성장률
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)61.1%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean17.314815
Minimum-60
Maximum150
Zeros14
Zeros (%)25.5%
Negative7
Negative (%)12.7%
Memory size623.0 B
2024-03-15T05:32:14.046761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-15.45
Q10
median10.5
Q325
95-th percentile71.55
Maximum150
Range210
Interquartile range (IQR)25

Descriptive statistics

Standard deviation33.476449
Coefficient of variation (CV)1.9333992
Kurtosis4.4444376
Mean17.314815
Median Absolute Deviation (MAD)10.5
Skewness1.3735082
Sum935
Variance1120.6726
MonotonicityNot monotonic
2024-03-15T05:32:14.455405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 14
25.5%
14 4
 
7.3%
67 2
 
3.6%
25 2
 
3.6%
46 2
 
3.6%
20 2
 
3.6%
17 2
 
3.6%
18 1
 
1.8%
1 1
 
1.8%
5 1
 
1.8%
Other values (23) 23
41.8%
ValueCountFrequency (%)
-60 1
 
1.8%
-50 1
 
1.8%
-20 1
 
1.8%
-13 1
 
1.8%
-8 1
 
1.8%
-7 1
 
1.8%
-2 1
 
1.8%
0 14
25.5%
1 1
 
1.8%
2 1
 
1.8%
ValueCountFrequency (%)
150 1
1.8%
100 1
1.8%
80 1
1.8%
67 2
3.6%
63 1
1.8%
46 2
3.6%
45 1
1.8%
44 1
1.8%
42 1
1.8%
36 1
1.8%

Interactions

2024-03-15T05:32:02.936301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:52.919944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:54.644349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:55.939188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:57.353899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:59.078619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:00.797899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:03.196631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:53.177533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:54.793597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:56.151692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:57.505730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:59.319114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:01.049141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:03.456420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:53.434707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:54.954283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:56.309038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:57.727150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:59.644201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:01.324780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:03.696669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:53.674585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:55.128929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:56.446650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:58.000692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:59.867409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:01.536754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:03.985597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:53.943293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:55.400435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:56.675310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:58.275122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:00.110466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:01.869242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:04.240958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:54.181804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:55.625594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:56.835513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:58.527088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:00.319037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:02.250663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:04.513856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:54.444867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:55.794221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:56.995348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:31:58.748124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:00.565047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:32:02.600950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:32:14.724591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번기업명13년매출(백만원)13년고용(명)14년매출(백만원)14년고용(명)매출성장률고용성장률
순번1.0001.0000.4460.4440.2350.3950.3950.185
기업명1.0001.0001.0001.0001.0001.0001.0001.000
13년매출(백만원)0.4461.0001.0000.9760.9930.9710.0000.000
13년고용(명)0.4441.0000.9761.0000.9730.9850.2620.000
14년매출(백만원)0.2351.0000.9930.9731.0000.9720.3110.000
14년고용(명)0.3951.0000.9710.9850.9721.0000.6300.000
매출성장률0.3951.0000.0000.2620.3110.6301.0000.754
고용성장률0.1851.0000.0000.0000.0000.0000.7541.000
2024-03-15T05:32:15.039491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번13년매출(백만원)13년고용(명)14년매출(백만원)14년고용(명)매출성장률고용성장률
순번1.0000.0910.0240.0730.088-0.1210.089
13년매출(백만원)0.0911.0000.9040.9910.876-0.2730.019
13년고용(명)0.0240.9041.0000.9020.970-0.257-0.012
14년매출(백만원)0.0730.9910.9021.0000.886-0.1920.076
14년고용(명)0.0880.8760.9700.8861.000-0.1410.169
매출성장률-0.121-0.273-0.257-0.192-0.1411.0000.515
고용성장률0.0890.019-0.0120.0760.1690.5151.000

Missing values

2024-03-15T05:32:04.884566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:32:05.376487image/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.
2024-03-15T05:32:05.701230image/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

순번기업명13년매출(백만원)13년고용(명)14년매출(백만원)14년고용(명)매출성장률고용성장률
01두메산골(영)794136850060767
12드림인 영농조합298820310084-60
23(주)아리울21식품5871270012190
34(유)지평선누룽지36864007917
45들판76003080003050
56천본395645100006515344
67신화47000130630001853442
78나눔푸드13972215002270
89신덕식품25781827951880
910칠보수산에스푸드17553005710
순번기업명13년매출(백만원)13년고용(명)14년매출(백만원)14년고용(명)매출성장률고용성장률
4546청맥4220744008414
4647무주군약초(영)16802217002315
4748한풍43603046003060
4849지엠에프34653185380002701046
4950엄지식품350342423700024561
5051제너럴바이오467011500013718
5152벼이삭영농조합1800202000231115
5253(유)에녹식품330720360024920
5354전주주조2856253000235-8
5455(유)한푸드7209750940