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(수도권, 충청 및 강원권, 전라권, 경상권)
Author신용보증기금
URLhttps://www.data.go.kr/data/15045283/fileData.do

Alerts

연도 is highly overall correlated with 고용유발효과지수 and 2 other fieldsHigh correlation
고용증가율지수 is highly overall correlated with 고용창출기업보증지수 and 2 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 3 other fieldsHigh correlation
1인당복리후생비지수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
신보고용지수 is highly overall correlated with 고용유발효과지수 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 08:02:37.737204
Analysis finished2023-12-12 08:02:48.087516
Duration10.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
수도권
9 
충청 및 강원권
9 
전라권
9 
경상권
9 

Length

Max length8
Median length3
Mean length4.25
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수도권 9
25.0%
충청 및 강원권 9
25.0%
전라권 9
25.0%
경상권 9
25.0%

Length

2023-12-12T17:02:48.153406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:02:48.263724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도권 9
16.7%
충청 9
16.7%
및 9
16.7%
강원권 9
16.7%
전라권 9
16.7%
경상권 9
16.7%

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
2023-12-12T17:02:48.382259image/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
2023-12-12T17:02:48.512839image/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)

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.025833
Minimum77.13
Maximum104.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:48.648896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77.13
5-th percentile81.3525
Q187.2675
median92.14
Q399.5725
95-th percentile102.0425
Maximum104.61
Range27.48
Interquartile range (IQR)12.305

Descriptive statistics

Standard deviation7.1227183
Coefficient of variation (CV)0.077399118
Kurtosis-0.87779996
Mean92.025833
Median Absolute Deviation (MAD)5.25
Skewness-0.059477196
Sum3312.93
Variance50.733116
MonotonicityNot monotonic
2023-12-12T17:02:48.795610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
92.87 1
 
2.8%
101.88 1
 
2.8%
104.61 1
 
2.8%
85.69 1
 
2.8%
88.82 1
 
2.8%
95.39 1
 
2.8%
96.69 1
 
2.8%
94.0 1
 
2.8%
88.86 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
77.13 1
2.8%
80.07 1
2.8%
81.78 1
2.8%
81.92 1
2.8%
83.54 1
2.8%
85.69 1
2.8%
86.16 1
2.8%
86.88 1
2.8%
86.9 1
2.8%
87.39 1
2.8%
ValueCountFrequency (%)
104.61 1
 
2.8%
102.26 1
 
2.8%
101.97 1
 
2.8%
101.88 1
 
2.8%
100.06 1
 
2.8%
100.0 4
11.1%
99.43 1
 
2.8%
96.69 1
 
2.8%
96.5 1
 
2.8%
95.39 1
 
2.8%

고용증가율지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.28083
Minimum93.68
Maximum106.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:48.926200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93.68
5-th percentile93.975
Q198.55
median100.495
Q3102.97
95-th percentile106.7075
Maximum106.94
Range13.26
Interquartile range (IQR)4.42

Descriptive statistics

Standard deviation3.8121995
Coefficient of variation (CV)0.038015236
Kurtosis-0.60809809
Mean100.28083
Median Absolute Deviation (MAD)2.445
Skewness-0.18269289
Sum3610.11
Variance14.532865
MonotonicityNot monotonic
2023-12-12T17:02:49.121680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
103.23 1
 
2.8%
98.71 1
 
2.8%
103.01 1
 
2.8%
99.72 1
 
2.8%
94.95 1
 
2.8%
94.01 1
 
2.8%
101.63 1
 
2.8%
106.73 1
 
2.8%
103.05 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
93.68 1
2.8%
93.87 1
2.8%
94.01 1
2.8%
94.18 1
2.8%
94.95 1
2.8%
95.09 1
2.8%
95.19 1
2.8%
95.57 1
2.8%
98.07 1
2.8%
98.71 1
2.8%
ValueCountFrequency (%)
106.94 1
2.8%
106.73 1
2.8%
106.7 1
2.8%
106.53 1
2.8%
103.23 1
2.8%
103.15 1
2.8%
103.05 1
2.8%
103.01 1
2.8%
103.0 1
2.8%
102.96 1
2.8%

매출액고용지수
Real number (ℝ)

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.605833
Minimum52.81
Maximum111.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:49.301217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52.81
5-th percentile55.2375
Q176.6725
median92.955
Q3100.3325
95-th percentile110.14
Maximum111.02
Range58.21
Interquartile range (IQR)23.66

Descriptive statistics

Standard deviation17.677099
Coefficient of variation (CV)0.20177993
Kurtosis-0.83998439
Mean87.605833
Median Absolute Deviation (MAD)12.955
Skewness-0.52438252
Sum3153.81
Variance312.47984
MonotonicityNot monotonic
2023-12-12T17:02:49.440724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
110.13 1
 
2.8%
92.45 1
 
2.8%
56.46 1
 
2.8%
55.54 1
 
2.8%
66.37 1
 
2.8%
78.29 1
 
2.8%
81.3 1
 
2.8%
94.6 1
 
2.8%
107.08 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
52.81 1
2.8%
54.33 1
2.8%
55.54 1
2.8%
56.46 1
2.8%
66.37 1
2.8%
66.68 1
2.8%
68.6 1
2.8%
70.72 1
2.8%
72.12 1
2.8%
78.19 1
2.8%
ValueCountFrequency (%)
111.02 1
 
2.8%
110.17 1
 
2.8%
110.13 1
 
2.8%
108.12 1
 
2.8%
107.76 1
 
2.8%
107.08 1
 
2.8%
105.39 1
 
2.8%
104.06 1
 
2.8%
101.33 1
 
2.8%
100.0 4
11.1%

고용창출기업보증지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.278333
Minimum70.37
Maximum105.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:49.571796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70.37
5-th percentile71.3
Q185.355
median93.5
Q3100
95-th percentile102.3
Maximum105.34
Range34.97
Interquartile range (IQR)14.645

Descriptive statistics

Standard deviation9.7698836
Coefficient of variation (CV)0.10703398
Kurtosis-0.38595646
Mean91.278333
Median Absolute Deviation (MAD)6.59
Skewness-0.71198984
Sum3286.02
Variance95.450626
MonotonicityNot monotonic
2023-12-12T17:02:49.727412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
82.28 1
 
2.8%
91.95 1
 
2.8%
74.86 1
 
2.8%
103.02 1
 
2.8%
105.34 1
 
2.8%
98.37 1
 
2.8%
97.4 1
 
2.8%
86.01 1
 
2.8%
81.81 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
70.37 1
2.8%
70.49 1
2.8%
71.57 1
2.8%
74.86 1
2.8%
80.78 1
2.8%
81.79 1
2.8%
81.81 1
2.8%
82.28 1
2.8%
83.39 1
2.8%
86.01 1
2.8%
ValueCountFrequency (%)
105.34 1
 
2.8%
103.02 1
 
2.8%
102.06 1
 
2.8%
101.89 1
 
2.8%
100.97 1
 
2.8%
100.34 1
 
2.8%
100.0 4
11.1%
99.25 1
 
2.8%
98.5 1
 
2.8%
98.37 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
2023-12-12T17:02:49.862212image/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
2023-12-12T17:02:49.985257image/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%
Mean113.85028
Minimum98.66
Maximum157.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:50.146361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.66
5-th percentile99.79
Q1101.4725
median107.33
Q3121.56
95-th percentile147.25
Maximum157.85
Range59.19
Interquartile range (IQR)20.0875

Descriptive statistics

Standard deviation16.516813
Coefficient of variation (CV)0.14507486
Kurtosis0.79777344
Mean113.85028
Median Absolute Deviation (MAD)6.71
Skewness1.3343998
Sum4098.61
Variance272.80512
MonotonicityNot monotonic
2023-12-12T17:02:50.281628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
99.16 1
 
2.8%
112.49 1
 
2.8%
100.31 1
 
2.8%
101.61 1
 
2.8%
111.58 1
 
2.8%
130.67 1
 
2.8%
151.72 1
 
2.8%
102.09 1
 
2.8%
100.66 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
98.66 1
 
2.8%
99.16 1
 
2.8%
100.0 4
11.1%
100.31 1
 
2.8%
100.66 1
 
2.8%
101.06 1
 
2.8%
101.61 1
 
2.8%
101.7 1
 
2.8%
102.09 1
 
2.8%
103.88 1
 
2.8%
ValueCountFrequency (%)
157.85 1
2.8%
151.72 1
2.8%
145.76 1
2.8%
145.68 1
2.8%
139.96 1
2.8%
130.67 1
2.8%
129.84 1
2.8%
123.2 1
2.8%
122.73 1
2.8%
121.17 1
2.8%

1인당복리후생비지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.76778
Minimum100
Maximum190.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:50.422869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1126.9725
median144.56
Q3154.285
95-th percentile180.3825
Maximum190.04
Range90.04
Interquartile range (IQR)27.3125

Descriptive statistics

Standard deviation22.8081
Coefficient of variation (CV)0.16088353
Kurtosis-0.050797727
Mean141.76778
Median Absolute Deviation (MAD)14.3
Skewness-0.075336707
Sum5103.64
Variance520.20943
MonotonicityNot monotonic
2023-12-12T17:02:50.575307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
117.97 1
 
2.8%
155.77 1
 
2.8%
147.4 1
 
2.8%
153.62 1
 
2.8%
160.59 1
 
2.8%
190.04 1
 
2.8%
178.16 1
 
2.8%
123.57 1
 
2.8%
121.94 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
100.0 4
11.1%
117.97 1
 
2.8%
119.77 1
 
2.8%
121.94 1
 
2.8%
123.57 1
 
2.8%
125.81 1
 
2.8%
127.36 1
 
2.8%
128.1 1
 
2.8%
135.32 1
 
2.8%
136.71 1
 
2.8%
ValueCountFrequency (%)
190.04 1
2.8%
187.05 1
2.8%
178.16 1
2.8%
169.59 1
2.8%
162.86 1
2.8%
160.59 1
2.8%
159.83 1
2.8%
157.89 1
2.8%
155.77 1
2.8%
153.79 1
2.8%

신보고용지수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.73806
Minimum93.86
Maximum113.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:02:50.703522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93.86
5-th percentile96.6175
Q199.8225
median100.785
Q3106.305
95-th percentile111.57
Maximum113.58
Range19.72
Interquartile range (IQR)6.4825

Descriptive statistics

Standard deviation4.9849487
Coefficient of variation (CV)0.048520956
Kurtosis-0.45399912
Mean102.73806
Median Absolute Deviation (MAD)1.865
Skewness0.6402547
Sum3698.57
Variance24.849713
MonotonicityNot monotonic
2023-12-12T17:02:50.851818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 4
 
11.1%
100.47 1
 
2.8%
106.16 1
 
2.8%
97.23 1
 
2.8%
98.81 1
 
2.8%
101.76 1
 
2.8%
109.51 1
 
2.8%
113.58 1
 
2.8%
100.64 1
 
2.8%
100.05 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
93.86 1
2.8%
95.5 1
2.8%
96.99 1
2.8%
97.23 1
2.8%
98.81 1
2.8%
99.05 1
2.8%
99.1 1
2.8%
99.19 1
2.8%
99.44 1
2.8%
99.95 1
2.8%
ValueCountFrequency (%)
113.58 1
2.8%
112.23 1
2.8%
111.35 1
2.8%
110.74 1
2.8%
110.03 1
2.8%
109.51 1
2.8%
109.12 1
2.8%
107.57 1
2.8%
106.74 1
2.8%
106.16 1
2.8%

Interactions

2023-12-12T17:02:46.761957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.061797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.207114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.260013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.238596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.206947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.279848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.306489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:45.418919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.877865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.171173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.371820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.390511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.344788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.321951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.402196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.462831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:45.893596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.972445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.255811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.584071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.478550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.433517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.411552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.511841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.567126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:45.998682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:47.108497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.337923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.678754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.575150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.544977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.514865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.633023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.696777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.121262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:47.241385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.416341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.764649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.686007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.649409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.640013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.747070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.810885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.231370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:47.372300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.747296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.875816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.846935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.764659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.769920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.848310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.930803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.330827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:47.496394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.846622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.967229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.955308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.877328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.894919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.969277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:45.051198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.426277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:47.610447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:38.983238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.059187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.056601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.993060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.042628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.085841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:45.173542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.535717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:47.713154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:39.082814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:40.156563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:41.146464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:42.095468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:43.165957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:44.193642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:45.307172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:02:46.642956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:02:50.955462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
지역1.0000.0000.0000.0000.3800.0000.0000.0000.0000.000
연도0.0001.0000.0000.9710.6570.8631.0000.6230.5330.859
고용규모지수0.0000.0001.0000.1880.3550.2490.2520.4160.7210.650
고용증가율지수0.0000.9710.1881.0000.6610.7070.8630.5930.6050.643
매출액고용지수0.3800.6570.3550.6611.0000.5180.5180.0000.4960.617
고용창출기업보증지수0.0000.8630.2490.7070.5181.0000.8000.5380.8160.861
고용유발효과지수0.0001.0000.2520.8630.5180.8001.0000.7850.6800.838
1인당인건비지수0.0000.6230.4160.5930.0000.5380.7851.0000.8130.887
1인당복리후생비지수0.0000.5330.7210.6050.4960.8160.6800.8131.0000.642
신보고용지수0.0000.8590.6500.6430.6170.8610.8380.8870.6421.000
2023-12-12T17:02:51.102877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수지역
연도1.000-0.210-0.407-0.3080.293-0.6670.7400.8570.4790.000
고용규모지수-0.2101.0000.0770.200-0.047-0.162-0.247-0.2470.1010.000
고용증가율지수-0.4070.0771.0000.024-0.6230.573-0.411-0.578-0.4020.000
매출액고용지수-0.3080.2000.0241.000-0.185-0.0880.024-0.3520.4730.217
고용창출기업보증지수0.293-0.047-0.623-0.1851.000-0.1500.0620.2460.1580.000
고용유발효과지수-0.667-0.1620.573-0.088-0.1501.000-0.733-0.653-0.6920.000
1인당인건비지수0.740-0.247-0.4110.0240.062-0.7331.0000.7880.7580.000
1인당복리후생비지수0.857-0.247-0.578-0.3520.246-0.6530.7881.0000.5240.000
신보고용지수0.4790.101-0.4020.4730.158-0.6920.7580.5241.0000.000
지역0.0000.0000.0000.2170.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T17:02:47.873843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:02:48.028960image/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

지역연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
0수도권2005100.0100.0100.0100.0100.0100.0100.0100.0
1수도권200688.86103.05107.0881.81101.15100.66121.94100.05
2수도권200788.25106.5398.983.39100.63104.75128.1100.93
3수도권200887.3998.0793.8386.3895.38122.73153.79104.69
4수도권200986.88103.066.6870.3799.13104.24144.0195.5
5수도권201077.13100.7472.1299.25101.7103.88148.6199.19
6수도권201180.0795.0979.48100.3492.12110.79148.3999.95
7수도권201281.7893.68104.0692.9587.18129.84169.59107.57
8수도권201383.54101.67105.3992.9389.41145.68151.33110.03
9충청 및 강원권2005100.0100.0100.0100.0100.0100.0100.0100.0
지역연도고용규모지수고용증가율지수매출액고용지수고용창출기업보증지수고용유발효과지수1인당인건비지수1인당복리후생비지수신보고용지수
26전라권201396.69101.6381.397.489.41151.72178.16113.58
27경상권2005100.0100.0100.0100.0100.0100.0100.0100.0
28경상권200692.87103.23110.1382.28101.1599.16117.97100.47
29경상권200794.0106.7394.686.01100.63102.09123.57100.64
30경상권2008100.0698.72108.1289.7395.38114.08145.11106.74
31경상권2009102.26103.1568.671.5799.13101.06136.8896.99
32경상권201093.02100.8270.72100.97101.798.66136.7199.44
33경상권201196.595.1988.22101.8992.12107.35141.16102.54
34경상권201299.4393.87107.7694.0587.18123.2162.86109.12
35경상권2013101.97102.0110.1794.7189.41139.96145.51112.23