Overview

Dataset statistics

Number of variables10
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory93.9 B

Variable types

Categorical1
Numeric9

Dataset

Description중소기업의 고용상태를 양적 & 질적 측면에서 확인할 수 있는 지표(영세, 비외감, 외감)
Author신용보증기금
URLhttps://www.data.go.kr/data/15045286/fileData.do

Alerts

연도 is highly overall correlated with 고용규모지수 and 3 other fieldsHigh correlation
고용규모지수 is highly overall correlated with 연도High correlation
고용증가율지수 is highly overall correlated with 고용창출기업보증지수 and 1 other fieldsHigh correlation
매출액고용지수 is highly overall correlated with 1인당인건비지수 and 1 other fieldsHigh correlation
고용창출기업보증지수 is highly overall correlated with 고용증가율지수High correlation
고용유발효과지수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
1인당인건비지수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
1인당복리후생비지수 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
신보고용지수 is highly overall correlated with 매출액고용지수 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 17:23:04.852110
Analysis finished2023-12-12 17:23:14.584569
Duration9.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기업규모
Categorical

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
비외감
9 
영세
9 
외감
9 

Length

Max length3
Median length2
Mean length2.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비외감
2nd row비외감
3rd row비외감
4th row비외감
5th row비외감

Common Values

ValueCountFrequency (%)
비외감 9
33.3%
영세 9
33.3%
외감 9
33.3%

Length

2023-12-13T02:23:14.670695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:23:14.818743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비외감 9
33.3%
영세 9
33.3%
외감 9
33.3%

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009
Minimum2005
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:14.911657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12007
median2009
Q32011
95-th percentile2013
Maximum2013
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6311741
Coefficient of variation (CV)0.0013096934
Kurtosis-1.2324
Mean2009
Median Absolute Deviation (MAD)2
Skewness0
Sum54243
Variance6.9230769
MonotonicityNot monotonic
2023-12-13T02:23:15.037439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2005 3
11.1%
2006 3
11.1%
2007 3
11.1%
2008 3
11.1%
2009 3
11.1%
2010 3
11.1%
2011 3
11.1%
2012 3
11.1%
2013 3
11.1%
ValueCountFrequency (%)
2005 3
11.1%
2006 3
11.1%
2007 3
11.1%
2008 3
11.1%
2009 3
11.1%
2010 3
11.1%
2011 3
11.1%
2012 3
11.1%
2013 3
11.1%
ValueCountFrequency (%)
2013 3
11.1%
2012 3
11.1%
2011 3
11.1%
2010 3
11.1%
2009 3
11.1%
2008 3
11.1%
2007 3
11.1%
2006 3
11.1%
2005 3
11.1%

고용규모지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.314074
Minimum59.64
Maximum102.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:15.202315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.64
5-th percentile63.126
Q174.51
median83.96
Q390.375
95-th percentile100
Maximum102.06
Range42.42
Interquartile range (IQR)15.865

Descriptive statistics

Standard deviation12.056085
Coefficient of variation (CV)0.14470646
Kurtosis-0.60221034
Mean83.314074
Median Absolute Deviation (MAD)9.06
Skewness-0.34394226
Sum2249.48
Variance145.34918
MonotonicityNot monotonic
2023-12-13T02:23:15.350404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
86.57 1
 
3.7%
71.35 1
 
3.7%
73.2 1
 
3.7%
74.9 1
 
3.7%
74.12 1
 
3.7%
81.94 1
 
3.7%
81.1 1
 
3.7%
83.16 1
 
3.7%
89.0 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
59.64 1
3.7%
62.97 1
3.7%
63.49 1
3.7%
64.83 1
3.7%
71.35 1
3.7%
73.2 1
3.7%
74.12 1
3.7%
74.9 1
3.7%
81.1 1
3.7%
81.94 1
3.7%
ValueCountFrequency (%)
102.06 1
 
3.7%
100.0 3
11.1%
96.17 1
 
3.7%
95.27 1
 
3.7%
91.75 1
 
3.7%
89.0 1
 
3.7%
88.99 1
 
3.7%
86.7 1
 
3.7%
86.57 1
 
3.7%
85.5 1
 
3.7%

고용증가율지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.615185
Minimum92.71
Maximum106.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:15.516199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.71
5-th percentile93.1
Q197.605
median100
Q3101.96
95-th percentile105.716
Maximum106.67
Range13.96
Interquartile range (IQR)4.355

Descriptive statistics

Standard deviation3.8878147
Coefficient of variation (CV)0.039028333
Kurtosis-0.55438913
Mean99.615185
Median Absolute Deviation (MAD)2.11
Skewness-0.23123105
Sum2689.61
Variance15.115103
MonotonicityNot monotonic
2023-12-13T02:23:16.007116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
101.79 1
 
3.7%
100.37 1
 
3.7%
92.71 1
 
3.7%
94.03 1
 
3.7%
98.87 1
 
3.7%
101.81 1
 
3.7%
97.16 1
 
3.7%
105.59 1
 
3.7%
101.74 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
92.71 1
3.7%
92.74 1
3.7%
93.94 1
3.7%
94.03 1
3.7%
94.23 1
3.7%
95.36 1
3.7%
97.16 1
3.7%
98.05 1
3.7%
98.3 1
3.7%
98.87 1
3.7%
ValueCountFrequency (%)
106.67 1
3.7%
105.77 1
3.7%
105.59 1
3.7%
103.16 1
3.7%
102.58 1
3.7%
102.34 1
3.7%
102.11 1
3.7%
101.81 1
3.7%
101.79 1
3.7%
101.74 1
3.7%

매출액고용지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.563704
Minimum65.12
Maximum147.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:16.165182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65.12
5-th percentile66.128
Q184.625
median100
Q3107.855
95-th percentile139.339
Maximum147.48
Range82.36
Interquartile range (IQR)23.23

Descriptive statistics

Standard deviation21.374104
Coefficient of variation (CV)0.21467767
Kurtosis0.26407247
Mean99.563704
Median Absolute Deviation (MAD)14.03
Skewness0.33209597
Sum2688.22
Variance456.85233
MonotonicityNot monotonic
2023-12-13T02:23:16.322583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
114.13 1
 
3.7%
147.48 1
 
3.7%
146.11 1
 
3.7%
104.57 1
 
3.7%
77.73 1
 
3.7%
78.82 1
 
3.7%
105.36 1
 
3.7%
106.22 1
 
3.7%
123.54 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
65.12 1
3.7%
65.87 1
3.7%
66.73 1
3.7%
70.64 1
3.7%
77.73 1
3.7%
78.82 1
3.7%
83.28 1
3.7%
85.97 1
3.7%
92.27 1
3.7%
98.15 1
3.7%
ValueCountFrequency (%)
147.48 1
3.7%
146.11 1
3.7%
123.54 1
3.7%
118.85 1
3.7%
118.25 1
3.7%
114.13 1
3.7%
109.28 1
3.7%
106.43 1
3.7%
106.22 1
3.7%
105.36 1
3.7%

고용창출기업보증지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.912593
Minimum71.15
Maximum101.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:16.501903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71.15
5-th percentile71.576
Q185.34
median93.93
Q3100
95-th percentile101.53
Maximum101.87
Range30.72
Interquartile range (IQR)14.66

Descriptive statistics

Standard deviation9.6395989
Coefficient of variation (CV)0.1060315
Kurtosis-0.36704648
Mean90.912593
Median Absolute Deviation (MAD)7.04
Skewness-0.74034945
Sum2454.64
Variance92.921866
MonotonicityNot monotonic
2023-12-13T02:23:16.680656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
83.22 1
 
3.7%
95.32 1
 
3.7%
94.15 1
 
3.7%
101.32 1
 
3.7%
99.73 1
 
3.7%
71.15 1
 
3.7%
86.89 1
 
3.7%
85.98 1
 
3.7%
81.9 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
71.15 1
3.7%
71.48 1
3.7%
71.8 1
3.7%
81.03 1
3.7%
81.9 1
3.7%
83.22 1
3.7%
84.77 1
3.7%
85.91 1
3.7%
85.98 1
3.7%
86.89 1
3.7%
ValueCountFrequency (%)
101.87 1
 
3.7%
101.62 1
 
3.7%
101.32 1
 
3.7%
101.29 1
 
3.7%
100.15 1
 
3.7%
100.0 3
11.1%
99.73 1
 
3.7%
95.32 1
 
3.7%
94.81 1
 
3.7%
94.5 1
 
3.7%

고용유발효과지수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.3
Minimum87.18
Maximum101.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:16.901114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87.18
5-th percentile87.18
Q192.12
median99.13
Q3100.63
95-th percentile101.7
Maximum101.7
Range14.52
Interquartile range (IQR)8.51

Descriptive statistics

Standard deviation5.2852727
Coefficient of variation (CV)0.054883413
Kurtosis-1.2403021
Mean96.3
Median Absolute Deviation (MAD)2.57
Skewness-0.61902605
Sum2600.1
Variance27.934108
MonotonicityNot monotonic
2023-12-13T02:23:17.008521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
100.0 3
11.1%
101.15 3
11.1%
100.63 3
11.1%
95.38 3
11.1%
99.13 3
11.1%
101.7 3
11.1%
92.12 3
11.1%
87.18 3
11.1%
89.41 3
11.1%
ValueCountFrequency (%)
87.18 3
11.1%
89.41 3
11.1%
92.12 3
11.1%
95.38 3
11.1%
99.13 3
11.1%
100.0 3
11.1%
100.63 3
11.1%
101.15 3
11.1%
101.7 3
11.1%
ValueCountFrequency (%)
101.7 3
11.1%
101.15 3
11.1%
100.63 3
11.1%
100.0 3
11.1%
99.13 3
11.1%
95.38 3
11.1%
92.12 3
11.1%
89.41 3
11.1%
87.18 3
11.1%

1인당인건비지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.35815
Minimum81.19
Maximum150.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:17.157904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81.19
5-th percentile97.325
Q1100.08
median109.95
Q3121.69
95-th percentile140.142
Maximum150.5
Range69.31
Interquartile range (IQR)21.61

Descriptive statistics

Standard deviation16.548369
Coefficient of variation (CV)0.14598306
Kurtosis-0.1947979
Mean113.35815
Median Absolute Deviation (MAD)9.95
Skewness0.57508673
Sum3060.67
Variance273.84852
MonotonicityNot monotonic
2023-12-13T02:23:17.313605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
103.99 1
 
3.7%
140.85 1
 
3.7%
136.99 1
 
3.7%
115.62 1
 
3.7%
106.68 1
 
3.7%
81.19 1
 
3.7%
131.39 1
 
3.7%
113.92 1
 
3.7%
103.07 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
81.19 1
 
3.7%
96.53 1
 
3.7%
99.18 1
 
3.7%
99.8 1
 
3.7%
100.0 3
11.1%
100.16 1
 
3.7%
102.54 1
 
3.7%
103.07 1
 
3.7%
103.99 1
 
3.7%
106.68 1
 
3.7%
ValueCountFrequency (%)
150.5 1
3.7%
140.85 1
3.7%
138.49 1
3.7%
136.99 1
3.7%
135.52 1
3.7%
131.39 1
3.7%
122.34 1
3.7%
121.04 1
3.7%
118.78 1
3.7%
115.62 1
3.7%

1인당복리후생비지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.74407
Minimum97.22
Maximum171.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:17.494118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97.22
5-th percentile100
Q1122.185
median133.32
Q3146.4
95-th percentile166.045
Maximum171.52
Range74.3
Interquartile range (IQR)24.215

Descriptive statistics

Standard deviation20.568031
Coefficient of variation (CV)0.15378648
Kurtosis-0.57765228
Mean133.74407
Median Absolute Deviation (MAD)12.6
Skewness-0.15047158
Sum3611.09
Variance423.04389
MonotonicityNot monotonic
2023-12-13T02:23:17.653590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
121.52 1
 
3.7%
133.32 1
 
3.7%
160.2 1
 
3.7%
126.26 1
 
3.7%
124.55 1
 
3.7%
97.22 1
 
3.7%
144.44 1
 
3.7%
122.85 1
 
3.7%
117.9 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
97.22 1
 
3.7%
100.0 3
11.1%
117.9 1
 
3.7%
118.96 1
 
3.7%
121.52 1
 
3.7%
122.85 1
 
3.7%
124.55 1
 
3.7%
125.38 1
 
3.7%
126.26 1
 
3.7%
126.28 1
 
3.7%
ValueCountFrequency (%)
171.52 1
3.7%
168.55 1
3.7%
160.2 1
3.7%
154.4 1
3.7%
153.78 1
3.7%
149.73 1
3.7%
146.88 1
3.7%
145.92 1
3.7%
145.32 1
3.7%
144.91 1
3.7%

신보고용지수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.83889
Minimum86.84
Maximum114.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:23:17.822368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86.84
5-th percentile94.718
Q199
median100.78
Q3105.345
95-th percentile111.882
Maximum114.14
Range27.3
Interquartile range (IQR)6.345

Descriptive statistics

Standard deviation5.8836523
Coefficient of variation (CV)0.057774121
Kurtosis0.77934917
Mean101.83889
Median Absolute Deviation (MAD)3.9
Skewness0.0040549025
Sum2749.65
Variance34.617364
MonotonicityNot monotonic
2023-12-13T02:23:17.973529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
11.1%
101.19 1
 
3.7%
111.14 1
 
3.7%
112.2 1
 
3.7%
100.78 1
 
3.7%
96.88 1
 
3.7%
86.84 1
 
3.7%
105.59 1
 
3.7%
102.29 1
 
3.7%
102.11 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
86.84 1
 
3.7%
94.37 1
 
3.7%
95.53 1
 
3.7%
96.3 1
 
3.7%
96.88 1
 
3.7%
98.75 1
 
3.7%
98.81 1
 
3.7%
99.19 1
 
3.7%
99.34 1
 
3.7%
100.0 3
11.1%
ValueCountFrequency (%)
114.14 1
3.7%
112.2 1
3.7%
111.14 1
3.7%
108.38 1
3.7%
107.14 1
3.7%
107.12 1
3.7%
105.59 1
3.7%
105.1 1
3.7%
104.84 1
3.7%
102.29 1
3.7%

Interactions

2023-12-13T02:23:13.203168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.139266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.979949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.868257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.752009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.034269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.134086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.212266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.167427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.309012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.234112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.075907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.965599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.844373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.136222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.281882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.326590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.298382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.421169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.321191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.169220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.044526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.917038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.218721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.414474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.413716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.423878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.593111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.430241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.271062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.143505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:08.007312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.326726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.531072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.509541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.552296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.694727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.513950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.363757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.238997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:08.103626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.448353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.629302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.626776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.655641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.816514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.600324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.478536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.358704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:08.259820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.593461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.747886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.726236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.762964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.915145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.692179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.597182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.457554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:08.745733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.717810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.873957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.839650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.873988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:14.024169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.792867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.700366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.556551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:08.851856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:09.866814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.994360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.943506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.987678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:14.160067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:05.891488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:06.781477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:07.666464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:08.956436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:10.011577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:11.117494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:12.064021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:23:13.109619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:23:18.090167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업규모연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
기업규모1.0000.0000.7680.0000.5250.0000.0000.2500.0000.000
연도0.0001.0000.4150.9300.6600.9511.0000.4370.7450.579
고용규모지수0.7680.4151.0000.6300.2110.3160.5490.4120.8840.734
고용증가율지수0.0000.9300.6301.0000.0910.7200.7310.2900.4730.000
매출액고용지수0.5250.6600.2110.0911.0000.6610.4220.5560.6310.709
고용창출기업보증지수0.0000.9510.3160.7200.6611.0000.8140.5430.5970.730
고용유발효과지수0.0001.0000.5490.7310.4220.8141.0000.6130.8450.640
1인당인건비지수0.2500.4370.4120.2900.5560.5430.6131.0000.4220.851
1인당복리후생비지수0.0000.7450.8840.4730.6310.5970.8450.4221.0000.812
신보고용지수0.0000.5790.7340.0000.7090.7300.6400.8510.8121.000
2023-12-13T02:23:18.272946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수기업규모
연도1.000-0.558-0.4050.0770.313-0.6670.6580.7720.3690.000
고용규모지수-0.5581.0000.3730.081-0.2450.190-0.466-0.309-0.0390.398
고용증가율지수-0.4050.3731.000-0.108-0.6160.509-0.423-0.460-0.2920.000
매출액고용지수0.0770.081-0.1081.000-0.125-0.3800.5010.0920.8280.209
고용창출기업보증지수0.313-0.245-0.616-0.1251.000-0.1470.1670.2290.0690.000
고용유발효과지수-0.6670.1900.509-0.380-0.1471.000-0.695-0.663-0.6410.000
1인당인건비지수0.658-0.466-0.4230.5010.167-0.6951.0000.6670.7790.103
1인당복리후생비지수0.772-0.309-0.4600.0920.229-0.6630.6671.0000.5180.000
신보고용지수0.369-0.039-0.2920.8280.069-0.6410.7790.5181.0000.000
기업규모0.0000.3980.0000.2090.0000.0000.1030.0000.0001.000

Missing values

2023-12-13T02:23:14.316077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:23:14.518325image/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

기업규모연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
0비외감2005100.0100.0100.0100.0100.0100.0100.0100.0
1비외감200686.57101.79114.1383.22101.15103.99121.52101.19
2비외감200783.93105.7799.3785.91100.63110.54126.28101.31
3비외감200885.3798.05118.2589.7495.38122.34144.91107.12
4비외감200983.96102.1166.7371.4899.13109.45130.7994.37
5비외감201064.8398.365.12100.15101.7109.95135.9195.53
6비외감201163.4993.9483.28101.8792.12121.04145.9299.34
7비외감201262.9792.74101.0894.587.18135.52168.55105.1
8비외감201359.64100.22102.9794.8189.41150.5154.4107.14
9영세2005100.0100.0100.0100.0100.0100.0100.0100.0
기업규모연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
17영세2013102.06102.34118.8593.9389.41138.49153.78114.14
18외감2005100.0100.0100.0100.0100.0100.0100.0100.0
19외감200689.0101.74123.5481.9101.15103.07117.9102.11
20외감200783.16105.59106.2285.98100.63113.92122.85102.29
21외감200881.197.16105.3686.8995.38131.39144.44105.59
22외감200981.94101.8178.8271.1599.1381.1997.2286.84
23외감201074.1298.8777.7399.73101.7106.68124.5596.88
24외감201174.994.03104.57101.3292.12115.62126.26100.78
25외감201273.292.71146.1194.1587.18136.99160.2112.2
26외감201371.35100.37147.4895.3289.41140.85133.32111.14