Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory92.7 B

Variable types

Categorical1
Numeric9

Dataset

Description중소기업의 고용상태를 양적 & 질적 측면에서 확인할 수 있는 지표(제조업, 건설업, 도소매업, 서비스업)
Author신용보증기금
URLhttps://www.data.go.kr/data/15045285/fileData.do

Alerts

연도 is highly overall correlated with 고용유발효과지수 and 2 other fieldsHigh correlation
고용규모지수 is highly overall correlated with 신보고용지수High correlation
고용증가율지수 is highly overall correlated with 고용유발효과지수 and 1 other fieldsHigh correlation
매출액고용지수 is highly overall correlated with 신보고용지수High correlation
고용유발효과지수 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
1인당인건비지수 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
1인당복리후생비지수 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
신보고용지수 is highly overall correlated with 고용규모지수 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 00:09:44.441042
Analysis finished2023-12-12 00:09:54.267889
Duration9.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
제조업
건설업
도소매업
서비스업, 기타

Length

Max length8
Median length6
Mean length4.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제조업 9
25.0%
건설업 9
25.0%
도소매업 9
25.0%
서비스업, 기타 9
25.0%

Length

2023-12-12T09:09:54.348186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:09:54.456695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 9
20.0%
건설업 9
20.0%
도소매업 9
20.0%
서비스업 9
20.0%
기타 9
20.0%

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009
Minimum2005
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:54.571929image/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 deviation2.6186147
Coefficient of variation (CV)0.0013034419
Kurtosis-1.2324866
Mean2009
Median Absolute Deviation (MAD)2
Skewness0
Sum72324
Variance6.8571429
MonotonicityNot monotonic
2023-12-12T09:09:54.725897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2005 4
11.1%
2006 4
11.1%
2007 4
11.1%
2008 4
11.1%
2009 4
11.1%
2010 4
11.1%
2011 4
11.1%
2012 4
11.1%
2013 4
11.1%
ValueCountFrequency (%)
2005 4
11.1%
2006 4
11.1%
2007 4
11.1%
2008 4
11.1%
2009 4
11.1%
2010 4
11.1%
2011 4
11.1%
2012 4
11.1%
2013 4
11.1%
ValueCountFrequency (%)
2013 4
11.1%
2012 4
11.1%
2011 4
11.1%
2010 4
11.1%
2009 4
11.1%
2008 4
11.1%
2007 4
11.1%
2006 4
11.1%
2005 4
11.1%

고용규모지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.816667
Minimum42.04
Maximum137.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:54.901505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.04
5-th percentile56.9825
Q189.9525
median100
Q3113.5175
95-th percentile130.1725
Maximum137.18
Range95.14
Interquartile range (IQR)23.565

Descriptive statistics

Standard deviation21.705914
Coefficient of variation (CV)0.21745781
Kurtosis0.87522965
Mean99.816667
Median Absolute Deviation (MAD)11.755
Skewness-0.77438522
Sum3593.4
Variance471.14671
MonotonicityNot monotonic
2023-12-12T09:09:55.053621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
102.66 1
 
2.8%
103.51 1
 
2.8%
106.9 1
 
2.8%
113.39 1
 
2.8%
120.86 1
 
2.8%
127.55 1
 
2.8%
133.9 1
 
2.8%
106.79 1
 
2.8%
91.21 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
42.04 1
2.8%
52.1 1
2.8%
58.61 1
2.8%
60.82 1
2.8%
84.57 1
2.8%
86.11 1
2.8%
87.08 1
2.8%
88.02 1
2.8%
88.73 1
2.8%
90.36 1
2.8%
ValueCountFrequency (%)
137.18 1
2.8%
133.9 1
2.8%
128.93 1
2.8%
127.55 1
2.8%
123.61 1
2.8%
120.86 1
2.8%
119.44 1
2.8%
117.6 1
2.8%
113.9 1
2.8%
113.39 1
2.8%

고용증가율지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.951944
Minimum91.93
Maximum106.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:55.184364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91.93
5-th percentile93.2
Q197.6475
median100
Q3103.055
95-th percentile106.7125
Maximum106.97
Range15.04
Interquartile range (IQR)5.4075

Descriptive statistics

Standard deviation4.1725845
Coefficient of variation (CV)0.041745907
Kurtosis-0.73256874
Mean99.951944
Median Absolute Deviation (MAD)3.12
Skewness-0.28725293
Sum3598.27
Variance17.410462
MonotonicityNot monotonic
2023-12-12T09:09:55.369303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
103.34 1
 
2.8%
98.78 1
 
2.8%
103.44 1
 
2.8%
102.16 1
 
2.8%
95.66 1
 
2.8%
94.32 1
 
2.8%
102.56 1
 
2.8%
106.75 1
 
2.8%
103.25 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
91.93 1
2.8%
92.63 1
2.8%
93.39 1
2.8%
93.8 1
2.8%
93.93 1
2.8%
94.32 1
2.8%
95.04 1
2.8%
95.17 1
2.8%
95.66 1
2.8%
98.31 1
2.8%
ValueCountFrequency (%)
106.97 1
2.8%
106.75 1
2.8%
106.7 1
2.8%
105.67 1
2.8%
103.75 1
2.8%
103.44 1
2.8%
103.34 1
2.8%
103.32 1
2.8%
103.25 1
2.8%
102.99 1
2.8%

매출액고용지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.872222
Minimum42
Maximum152.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:55.524640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile48.465
Q182.6925
median97.44
Q3114.205
95-th percentile133.8825
Maximum152.47
Range110.47
Interquartile range (IQR)31.5125

Descriptive statistics

Standard deviation26.969481
Coefficient of variation (CV)0.28130652
Kurtosis-0.13646164
Mean95.872222
Median Absolute Deviation (MAD)17.09
Skewness-0.23929487
Sum3451.4
Variance727.35291
MonotonicityNot monotonic
2023-12-12T09:09:55.680516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
123.09 1
 
2.8%
117.87 1
 
2.8%
61.45 1
 
2.8%
88.13 1
 
2.8%
96.33 1
 
2.8%
113.88 1
 
2.8%
115.63 1
 
2.8%
98.55 1
 
2.8%
119.73 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
42.0 1
2.8%
44.94 1
2.8%
49.64 1
2.8%
50.05 1
2.8%
57.77 1
2.8%
61.45 1
2.8%
72.58 1
2.8%
75.98 1
2.8%
77.09 1
2.8%
84.56 1
2.8%
ValueCountFrequency (%)
152.47 1
2.8%
146.67 1
2.8%
129.62 1
2.8%
126.01 1
2.8%
123.09 1
2.8%
119.73 1
2.8%
117.87 1
2.8%
115.63 1
2.8%
115.18 1
2.8%
113.88 1
2.8%
Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.535278
Minimum70.56
Maximum103.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:55.814987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70.56
5-th percentile72.065
Q185.31
median92.935
Q3100
95-th percentile102.1675
Maximum103.25
Range32.69
Interquartile range (IQR)14.69

Descriptive statistics

Standard deviation9.6785974
Coefficient of variation (CV)0.10690416
Kurtosis-0.4714789
Mean90.535278
Median Absolute Deviation (MAD)7.495
Skewness-0.65629396
Sum3259.27
Variance93.675248
MonotonicityNot monotonic
2023-12-12T09:09:55.954451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
81.9 1
 
2.8%
90.51 1
 
2.8%
72.23 1
 
2.8%
103.25 1
 
2.8%
102.31 1
 
2.8%
94.11 1
 
2.8%
94.68 1
 
2.8%
85.16 1
 
2.8%
82.54 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
70.56 1
2.8%
71.57 1
2.8%
72.23 1
2.8%
72.29 1
2.8%
75.01 1
2.8%
81.9 1
2.8%
82.54 1
2.8%
83.18 1
2.8%
85.16 1
2.8%
85.36 1
2.8%
ValueCountFrequency (%)
103.25 1
 
2.8%
102.31 1
 
2.8%
102.12 1
 
2.8%
101.25 1
 
2.8%
100.72 1
 
2.8%
100.51 1
 
2.8%
100.4 1
 
2.8%
100.0 4
11.1%
95.01 1
 
2.8%
94.68 1
 
2.8%

고용유발효과지수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.3
Minimum87.18
Maximum101.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:56.067358image/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 deviation5.2600445
Coefficient of variation (CV)0.054621439
Kurtosis-1.2400036
Mean96.3
Median Absolute Deviation (MAD)2.57
Skewness-0.60979903
Sum3466.8
Variance27.668069
MonotonicityNot monotonic
2023-12-12T09:09:56.173099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
100.0 4
11.1%
101.15 4
11.1%
100.63 4
11.1%
95.38 4
11.1%
99.13 4
11.1%
101.7 4
11.1%
92.12 4
11.1%
87.18 4
11.1%
89.41 4
11.1%
ValueCountFrequency (%)
87.18 4
11.1%
89.41 4
11.1%
92.12 4
11.1%
95.38 4
11.1%
99.13 4
11.1%
100.0 4
11.1%
100.63 4
11.1%
101.15 4
11.1%
101.7 4
11.1%
ValueCountFrequency (%)
101.7 4
11.1%
101.15 4
11.1%
100.63 4
11.1%
100.0 4
11.1%
99.13 4
11.1%
95.38 4
11.1%
92.12 4
11.1%
89.41 4
11.1%
87.18 4
11.1%

1인당인건비지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.69194
Minimum87.71
Maximum157.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:56.309159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87.71
5-th percentile93.1325
Q1100
median106.065
Q3117.935
95-th percentile141.9275
Maximum157.02
Range69.31
Interquartile range (IQR)17.935

Descriptive statistics

Standard deviation16.140513
Coefficient of variation (CV)0.1458147
Kurtosis0.7933271
Mean110.69194
Median Absolute Deviation (MAD)8.46
Skewness1.1037807
Sum3984.91
Variance260.51616
MonotonicityNot monotonic
2023-12-12T09:09:56.451955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
101.35 1
 
2.8%
127.53 1
 
2.8%
116.12 1
 
2.8%
107.66 1
 
2.8%
117.06 1
 
2.8%
137.64 1
 
2.8%
157.02 1
 
2.8%
108.12 1
 
2.8%
95.16 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
87.71 1
 
2.8%
91.64 1
 
2.8%
93.63 1
 
2.8%
95.16 1
 
2.8%
95.78 1
 
2.8%
97.02 1
 
2.8%
98.27 1
 
2.8%
98.39 1
 
2.8%
100.0 4
11.1%
100.63 1
 
2.8%
ValueCountFrequency (%)
157.02 1
2.8%
142.43 1
2.8%
141.76 1
2.8%
137.64 1
2.8%
129.06 1
2.8%
127.53 1
2.8%
125.93 1
2.8%
123.94 1
2.8%
120.56 1
2.8%
117.06 1
2.8%

1인당복리후생비지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.51806
Minimum100
Maximum230.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:56.661600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1125.0875
median138.25
Q3148.2975
95-th percentile189.7575
Maximum230.74
Range130.74
Interquartile range (IQR)23.21

Descriptive statistics

Standard deviation27.86852
Coefficient of variation (CV)0.19832697
Kurtosis3.2390803
Mean140.51806
Median Absolute Deviation (MAD)13.405
Skewness1.349789
Sum5058.65
Variance776.65438
MonotonicityNot monotonic
2023-12-12T09:09:56.815148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
123.34 1
 
2.8%
153.89 1
 
2.8%
146.94 1
 
2.8%
140.31 1
 
2.8%
142.78 1
 
2.8%
164.83 1
 
2.8%
145.82 1
 
2.8%
125.33 1
 
2.8%
115.0 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
100.0 4
11.1%
115.0 1
 
2.8%
117.71 1
 
2.8%
117.84 1
 
2.8%
123.34 1
 
2.8%
124.36 1
 
2.8%
125.33 1
 
2.8%
128.37 1
 
2.8%
130.51 1
 
2.8%
132.55 1
 
2.8%
ValueCountFrequency (%)
230.74 1
2.8%
216.87 1
2.8%
180.72 1
2.8%
167.34 1
2.8%
164.83 1
2.8%
156.39 1
2.8%
153.89 1
2.8%
152.38 1
2.8%
152.37 1
2.8%
146.94 1
2.8%

신보고용지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.28361
Minimum91.32
Maximum122.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:09:56.941865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91.32
5-th percentile94.285
Q1100
median102.205
Q3108.18
95-th percentile118.96
Maximum122.54
Range31.22
Interquartile range (IQR)8.18

Descriptive statistics

Standard deviation7.4033919
Coefficient of variation (CV)0.070992861
Kurtosis0.49083444
Mean104.28361
Median Absolute Deviation (MAD)3.25
Skewness0.78689707
Sum3754.21
Variance54.810212
MonotonicityNot monotonic
2023-12-12T09:09:57.118218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
104.81 1
 
2.8%
112.09 1
 
2.8%
100.62 1
 
2.8%
107.56 1
 
2.8%
109.48 1
 
2.8%
117.17 1
 
2.8%
121.03 1
 
2.8%
104.32 1
 
2.8%
100.56 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
91.32 1
 
2.8%
92.38 1
 
2.8%
94.92 1
 
2.8%
95.08 1
 
2.8%
97.85 1
 
2.8%
98.31 1
 
2.8%
100.0 4
11.1%
100.37 1
 
2.8%
100.56 1
 
2.8%
100.62 1
 
2.8%
ValueCountFrequency (%)
122.54 1
2.8%
121.03 1
2.8%
118.27 1
2.8%
117.17 1
2.8%
112.09 1
2.8%
112.03 1
2.8%
109.48 1
2.8%
109.47 1
2.8%
109.08 1
2.8%
107.88 1
2.8%

Interactions

2023-12-12T09:09:52.769139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:44.747798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.795303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.809560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.126069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.002420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.844535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.845958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.832886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.868516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:44.861101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.901634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.935732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.247357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.095740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.979419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.962594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.946242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.941605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:44.976313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.997087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:47.048026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.327949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.177226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.083699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.085153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.029105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:53.038936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.090606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.130600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:47.192789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.413439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.272155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.202188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.205421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.137773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:53.139981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.188422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.245043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:47.292299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.507505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.369526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.309420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.302192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.229219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:53.252137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.303928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.360745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:47.395668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.610748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.449650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.409889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.395597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.318858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:53.349375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.436260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.487935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:47.492816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.717299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.546251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.501628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.511681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.418240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:53.451847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.540798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.604818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:47.929076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.814950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.641609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.633468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.614604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.535589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:53.545307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:45.674797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:46.707093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.027250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:48.908214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:49.740259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:50.747727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:51.726398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:09:52.659866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:09:57.245384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
업종1.0000.0000.7320.0000.5520.0000.0000.0000.0000.383
연도0.0001.0000.4240.9530.3140.9511.0000.6690.7600.456
고용규모지수0.7320.4241.0000.8000.7170.5040.4970.5850.7530.614
고용증가율지수0.0000.9530.8001.0000.6350.8600.8380.0000.7780.599
매출액고용지수0.5520.3140.7170.6351.0000.6570.4380.4160.7420.693
고용창출기업보증지수0.0000.9510.5040.8600.6571.0000.7060.6080.5340.000
고용유발효과지수0.0001.0000.4970.8380.4380.7061.0000.6020.7580.711
1인당인건비지수0.0000.6690.5850.0000.4160.6080.6021.0000.5040.705
1인당복리후생비지수0.0000.7600.7530.7780.7420.5340.7580.5041.0000.714
신보고용지수0.3830.4560.6140.5990.6930.0000.7110.7050.7141.000
2023-12-12T09:09:57.408200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수업종
연도1.0000.207-0.423-0.0190.333-0.6670.6300.6980.4790.000
고용규모지수0.2071.0000.0990.4360.131-0.2410.412-0.0310.7180.369
고용증가율지수-0.4230.0991.0000.035-0.4740.532-0.270-0.544-0.0660.000
매출액고용지수-0.0190.4360.0351.0000.093-0.2610.319-0.3250.6220.347
고용창출기업보증지수0.3330.131-0.4740.0931.000-0.1800.2070.0530.2040.000
고용유발효과지수-0.667-0.2410.532-0.261-0.1801.000-0.673-0.540-0.4860.000
1인당인건비지수0.6300.412-0.2700.3190.207-0.6731.0000.4540.7700.000
1인당복리후생비지수0.698-0.031-0.544-0.3250.053-0.5400.4541.0000.2340.000
신보고용지수0.4790.718-0.0660.6220.204-0.4860.7700.2341.0000.219
업종0.0000.3690.0000.3470.0000.0000.0000.0000.2191.000

Missing values

2023-12-12T09:09:53.960549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:09:54.183949image/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

업종연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
0제조업2005100.0100.0100.0100.0100.0100.0100.0100.0
1제조업200691.21103.25119.7382.54101.1595.16115.0100.56
2제조업200788.02106.7111.0885.41100.6397.02117.84100.37
3제조업200888.7398.31112.1189.1495.38109.71135.42103.47
4제조업200987.08102.9972.5870.5699.1391.64128.3792.38
5제조업201086.11100.9175.98100.51101.793.63137.7598.31
6제조업201190.3695.1792.83102.1292.12101.82141.13101.31
7제조업201294.893.93126.0194.5887.18116.24156.39109.08
8제조업2013100.38102.03129.6295.0189.41129.06138.24112.03
9건설업2005100.0100.0100.0100.0100.0100.0100.0100.0
업종연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
26도소매업2013133.9102.56115.6394.6889.41157.02145.82121.03
27서비스업, 기타2005100.0100.0100.0100.0100.0100.0100.0100.0
28서비스업, 기타2006102.66103.34123.0981.9101.15101.35123.34104.81
29서비스업, 기타2007106.79106.7598.5585.16100.63108.12125.33104.32
30서비스업, 기타2008113.998.6995.7388.8195.38120.56142.8107.88
31서비스업, 기타2009117.6103.3277.0972.2999.13113.94138.26103.07
32서비스업, 기타2010119.44101.5786.22101.25101.7104.47132.55106.41
33서비스업, 기타2011123.6195.04115.18100.492.12109.67130.51109.47
34서비스업, 기타2012128.9393.8146.6793.0587.18125.93152.37118.27
35서비스업, 기타2013137.18101.77152.4793.2589.41141.76135.37122.54