Overview

Dataset statistics

Number of variables12
Number of observations134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory107.0 B

Variable types

Numeric10
Categorical2

Dataset

Description경제활동인구 월별 현황입니다. 시도명, 생산가능인구, 경제활동인구, 취업자수, 실업자수, 비경제활동인구수, 경제활동참가율, 실업률, 고용률 등의 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=138

Alerts

승인상태(W : 대기 S : 승인) has constant value ""Constant
15세이상 생산가능인구(천명) is highly overall correlated with 경제활동인구(천명) and 4 other fieldsHigh correlation
경제활동인구(천명) is highly overall correlated with 15세이상 생산가능인구(천명) and 4 other fieldsHigh correlation
취업자수(천명) is highly overall correlated with 15세이상 생산가능인구(천명) and 4 other fieldsHigh correlation
실업자수(천명) is highly overall correlated with 15세이상 생산가능인구(천명) and 8 other fieldsHigh correlation
비경제활동인구수(천명) is highly overall correlated with 15세이상 생산가능인구(천명) and 8 other fieldsHigh correlation
경제활동참가율 is highly overall correlated with 실업자수(천명) and 5 other fieldsHigh correlation
실업률 is highly overall correlated with 실업자수(천명) and 4 other fieldsHigh correlation
고용률 is highly overall correlated with 실업자수(천명) and 5 other fieldsHigh correlation
15~64세 고용률 is highly overall correlated with 실업자수(천명) and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 15세이상 생산가능인구(천명) and 7 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 20:20:59.228154
Analysis finished2024-01-09 20:21:08.117462
Duration8.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Real number (ℝ)

Distinct67
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202037.58
Minimum201801
Maximum202307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:08.184475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201804
Q1201905.25
median202010
Q3202202.75
95-th percentile202304
Maximum202307
Range506
Interquartile range (IQR)297.5

Descriptive statistics

Standard deviation162.47884
Coefficient of variation (CV)0.00080420106
Kurtosis-1.1912877
Mean202037.58
Median Absolute Deviation (MAD)107
Skewness0.066296004
Sum27073036
Variance26399.373
MonotonicityNot monotonic
2024-01-10T05:21:08.310502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201801 2
 
1.5%
202107 2
 
1.5%
202202 2
 
1.5%
202201 2
 
1.5%
202112 2
 
1.5%
202111 2
 
1.5%
202110 2
 
1.5%
202109 2
 
1.5%
202108 2
 
1.5%
201802 2
 
1.5%
Other values (57) 114
85.1%
ValueCountFrequency (%)
201801 2
1.5%
201802 2
1.5%
201803 2
1.5%
201804 2
1.5%
201805 2
1.5%
201806 2
1.5%
201807 2
1.5%
201808 2
1.5%
201809 2
1.5%
201810 2
1.5%
ValueCountFrequency (%)
202307 2
1.5%
202306 2
1.5%
202305 2
1.5%
202304 2
1.5%
202303 2
1.5%
202302 2
1.5%
202301 2
1.5%
202212 2
1.5%
202211 2
1.5%
202210 2
1.5%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
67 
충청남도
67 

Length

Max length4
Median length2.5
Mean length2.5
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row충청남도
3rd row
4th row충청남도
5th row

Common Values

ValueCountFrequency (%)
67
50.0%
충청남도 67
50.0%

Length

2024-01-10T05:21:08.427599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:21:08.514153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
67
50.0%
충청남도 67
50.0%

15세이상 생산가능인구(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23356.802
Minimum1844.5
Maximum45399.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:08.605936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1844.5
5-th percentile1857.265
Q11886.1
median22999.4
Q344846.475
95-th percentile45338.805
Maximum45399.4
Range43554.9
Interquartile range (IQR)42960.375

Descriptive statistics

Standard deviation21551.747
Coefficient of variation (CV)0.92271822
Kurtosis-2.0297498
Mean23356.802
Median Absolute Deviation (MAD)21146.65
Skewness0.00057251488
Sum3129811.5
Variance4.644778 × 108
MonotonicityNot monotonic
2024-01-10T05:21:08.728159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1885.9 2
 
1.5%
1884.2 2
 
1.5%
45181.1 1
 
0.7%
1892.2 1
 
0.7%
45123.9 1
 
0.7%
1894.4 1
 
0.7%
45148.1 1
 
0.7%
1895.7 1
 
0.7%
44072.5 1
 
0.7%
45089.9 1
 
0.7%
Other values (122) 122
91.0%
ValueCountFrequency (%)
1844.5 1
0.7%
1846.4 1
0.7%
1847.8 1
0.7%
1849.9 1
0.7%
1851.8 1
0.7%
1853.7 1
0.7%
1855.9 1
0.7%
1858.0 1
0.7%
1860.2 1
0.7%
1862.4 1
0.7%
ValueCountFrequency (%)
45399.4 1
0.7%
45383.2 1
0.7%
45379.0 1
0.7%
45371.6 1
0.7%
45366.9 1
0.7%
45354.8 1
0.7%
45352.0 1
0.7%
45331.7 1
0.7%
45317.6 1
0.7%
45302.1 1
0.7%

경제활동인구(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14794.86
Minimum1118.5
Maximum29622.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:08.836644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1118.5
5-th percentile1167.405
Q11243.525
median14278.4
Q328281.85
95-th percentile29154.24
Maximum29622.6
Range28504.1
Interquartile range (IQR)27038.325

Descriptive statistics

Standard deviation13618.843
Coefficient of variation (CV)0.92051177
Kurtosis-2.0270054
Mean14794.86
Median Absolute Deviation (MAD)13130.55
Skewness0.002512771
Sum1982511.3
Variance1.8547289 × 108
MonotonicityNot monotonic
2024-01-10T05:21:08.960131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1257.4 3
 
2.2%
1259.4 2
 
1.5%
28568.3 1
 
0.7%
1253.8 1
 
0.7%
28438.6 1
 
0.7%
1244.2 1
 
0.7%
28528.1 1
 
0.7%
28528.4 1
 
0.7%
1234.7 1
 
0.7%
28346.4 1
 
0.7%
Other values (121) 121
90.3%
ValueCountFrequency (%)
1118.5 1
0.7%
1140.3 1
0.7%
1141.5 1
0.7%
1154.2 1
0.7%
1154.4 1
0.7%
1156.1 1
0.7%
1163.7 1
0.7%
1169.4 1
0.7%
1171.4 1
0.7%
1171.9 1
0.7%
ValueCountFrequency (%)
29622.6 1
0.7%
29618.3 1
0.7%
29492.5 1
0.7%
29373.9 1
0.7%
29366.1 1
0.7%
29311.0 1
0.7%
29236.4 1
0.7%
29110.0 1
0.7%
29092.7 1
0.7%
29086.5 1
0.7%

취업자수(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14275.193
Minimum1071.4
Maximum28835.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:09.087447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1071.4
5-th percentile1127.26
Q11209
median13558.35
Q327234.375
95-th percentile28424.785
Maximum28835.2
Range27763.8
Interquartile range (IQR)26025.375

Descriptive statistics

Standard deviation13135.405
Coefficient of variation (CV)0.92015609
Kurtosis-2.0245423
Mean14275.193
Median Absolute Deviation (MAD)12479.05
Skewness0.0042852244
Sum1912875.8
Variance1.7253887 × 108
MonotonicityNot monotonic
2024-01-10T05:21:09.209796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1205.1 2
 
1.5%
1220.3 2
 
1.5%
1209.0 2
 
1.5%
1169.2 1
 
0.7%
27740.7 1
 
0.7%
1238.9 1
 
0.7%
27794.6 1
 
0.7%
1219.3 1
 
0.7%
27298.4 1
 
0.7%
26212.5 1
 
0.7%
Other values (121) 121
90.3%
ValueCountFrequency (%)
1071.4 1
0.7%
1087.2 1
0.7%
1097.7 1
0.7%
1109.5 1
0.7%
1114.3 1
0.7%
1120.0 1
0.7%
1123.1 1
0.7%
1129.5 1
0.7%
1130.0 1
0.7%
1132.1 1
0.7%
ValueCountFrequency (%)
28835.2 1
0.7%
28811.7 1
0.7%
28685.7 1
0.7%
28484.5 1
0.7%
28478.4 1
0.7%
28474.6 1
0.7%
28432.0 1
0.7%
28420.9 1
0.7%
28417.8 1
0.7%
28410.0 1
0.7%

실업자수(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519.66791
Minimum9.8
Maximum1569.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:09.321614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.8
5-th percentile21.4
Q135.225
median334.3
Q3994.775
95-th percentile1233.985
Maximum1569.9
Range1560.1
Interquartile range (IQR)959.55

Descriptive statistics

Standard deviation505.54042
Coefficient of variation (CV)0.9728144
Kurtosis-1.7055932
Mean519.66791
Median Absolute Deviation (MAD)322.4
Skewness0.21488947
Sum69635.5
Variance255571.12
MonotonicityNot monotonic
2024-01-10T05:21:09.437145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 2
 
1.5%
21.4 2
 
1.5%
38.2 2
 
1.5%
35.2 2
 
1.5%
18.6 1
 
0.7%
23.8 1
 
0.7%
755.9 1
 
0.7%
9.8 1
 
0.7%
787.5 1
 
0.7%
733.8 1
 
0.7%
Other values (120) 120
89.6%
ValueCountFrequency (%)
9.8 1
0.7%
14.0 1
0.7%
15.4 1
0.7%
16.3 1
0.7%
18.6 1
0.7%
19.8 1
0.7%
21.4 2
1.5%
23.8 1
0.7%
24.1 1
0.7%
24.7 1
0.7%
ValueCountFrequency (%)
1569.9 1
0.7%
1353.3 1
0.7%
1303.0 1
0.7%
1278.4 1
0.7%
1265.4 1
0.7%
1256.9 1
0.7%
1245.1 1
0.7%
1228.0 1
0.7%
1223.7 1
0.7%
1215.4 1
0.7%

비경제활동인구수(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8561.9373
Minimum599.8
Maximum17580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:09.558298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum599.8
5-th percentile609.03
Q1641.225
median8263.15
Q316470.95
95-th percentile16924.99
Maximum17580
Range16980.2
Interquartile range (IQR)15829.725

Descriptive statistics

Standard deviation7941.5152
Coefficient of variation (CV)0.92753718
Kurtosis-2.0252589
Mean8561.9373
Median Absolute Deviation (MAD)7655.75
Skewness0.0037297815
Sum1147299.6
Variance63067664
MonotonicityNot monotonic
2024-01-10T05:21:09.670776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
653.9 2
 
1.5%
626.1 2
 
1.5%
16652.7 1
 
0.7%
638.4 1
 
0.7%
16685.3 1
 
0.7%
650.1 1
 
0.7%
16620.0 1
 
0.7%
638.3 1
 
0.7%
662.7 1
 
0.7%
651.3 1
 
0.7%
Other values (122) 122
91.0%
ValueCountFrequency (%)
599.8 1
0.7%
601.0 1
0.7%
602.8 1
0.7%
603.8 1
0.7%
606.1 1
0.7%
608.7 1
0.7%
608.9 1
0.7%
609.1 1
0.7%
614.1 1
0.7%
614.8 1
0.7%
ValueCountFrequency (%)
17580.0 1
0.7%
17269.3 1
0.7%
17255.1 1
0.7%
17104.0 1
0.7%
16990.9 1
0.7%
16965.0 1
0.7%
16928.5 1
0.7%
16923.1 1
0.7%
16868.7 1
0.7%
16863.9 1
0.7%

경제활동참가율
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.274627
Minimum59.2
Maximum68.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:09.784377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.2
5-th percentile61.6
Q162.8
median63.7
Q365.9
95-th percentile67.7
Maximum68.8
Range9.6
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.9963006
Coefficient of variation (CV)0.031058921
Kurtosis-0.72608063
Mean64.274627
Median Absolute Deviation (MAD)1.35
Skewness0.31072022
Sum8612.8
Variance3.985216
MonotonicityNot monotonic
2024-01-10T05:21:09.892573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.4 6
 
4.5%
63.1 6
 
4.5%
62.6 5
 
3.7%
63.6 4
 
3.0%
62.0 4
 
3.0%
63.0 4
 
3.0%
63.5 4
 
3.0%
66.8 4
 
3.0%
64.0 3
 
2.2%
62.7 3
 
2.2%
Other values (51) 91
67.9%
ValueCountFrequency (%)
59.2 1
 
0.7%
60.9 2
1.5%
61.1 1
 
0.7%
61.2 2
1.5%
61.6 2
1.5%
61.8 2
1.5%
61.9 2
1.5%
62.0 4
3.0%
62.1 1
 
0.7%
62.2 3
2.2%
ValueCountFrequency (%)
68.8 1
 
0.7%
68.3 1
 
0.7%
68.2 1
 
0.7%
67.9 1
 
0.7%
67.8 2
1.5%
67.7 2
1.5%
67.5 3
2.2%
67.4 2
1.5%
67.3 1
 
0.7%
67.2 1
 
0.7%

실업률
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1776119
Minimum0.8
Maximum5.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:09.994036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.7
Q12.7
median3.1
Q33.875
95-th percentile4.5
Maximum5.7
Range4.9
Interquartile range (IQR)1.175

Descriptive statistics

Standard deviation0.85097395
Coefficient of variation (CV)0.26780298
Kurtosis0.15877469
Mean3.1776119
Median Absolute Deviation (MAD)0.6
Skewness-0.11664775
Sum425.8
Variance0.72415666
MonotonicityNot monotonic
2024-01-10T05:21:10.098342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.0 12
 
9.0%
3.1 9
 
6.7%
4.0 8
 
6.0%
2.8 8
 
6.0%
2.7 6
 
4.5%
3.4 6
 
4.5%
2.4 6
 
4.5%
2.9 6
 
4.5%
3.5 6
 
4.5%
4.2 5
 
3.7%
Other values (27) 62
46.3%
ValueCountFrequency (%)
0.8 1
 
0.7%
1.1 1
 
0.7%
1.2 1
 
0.7%
1.3 1
 
0.7%
1.5 1
 
0.7%
1.6 1
 
0.7%
1.7 2
1.5%
1.9 1
 
0.7%
2.0 3
2.2%
2.1 3
2.2%
ValueCountFrequency (%)
5.7 1
 
0.7%
4.9 1
 
0.7%
4.7 1
 
0.7%
4.6 2
 
1.5%
4.5 3
 
2.2%
4.4 1
 
0.7%
4.3 4
3.0%
4.2 5
3.7%
4.1 5
3.7%
4.0 8
6.0%

고용률
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.242537
Minimum56.7
Maximum67.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:10.220438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56.7
5-th percentile59.165
Q160.425
median61.55
Q364.175
95-th percentile66.2
Maximum67.5
Range10.8
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.2931863
Coefficient of variation (CV)0.036842751
Kurtosis-0.75782173
Mean62.242537
Median Absolute Deviation (MAD)1.6
Skewness0.24543815
Sum8340.5
Variance5.2587033
MonotonicityNot monotonic
2024-01-10T05:21:10.345672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.3 6
 
4.5%
61.4 6
 
4.5%
60.4 6
 
4.5%
61.2 5
 
3.7%
59.4 4
 
3.0%
62.7 4
 
3.0%
60.0 4
 
3.0%
64.7 4
 
3.0%
61.5 4
 
3.0%
59.5 3
 
2.2%
Other values (55) 88
65.7%
ValueCountFrequency (%)
56.7 1
 
0.7%
57.4 1
 
0.7%
58.6 1
 
0.7%
58.7 1
 
0.7%
58.9 1
 
0.7%
59.1 2
1.5%
59.2 3
2.2%
59.4 4
3.0%
59.5 3
2.2%
59.6 1
 
0.7%
ValueCountFrequency (%)
67.5 1
 
0.7%
67.1 1
 
0.7%
66.6 1
 
0.7%
66.5 1
 
0.7%
66.4 1
 
0.7%
66.2 3
2.2%
65.9 1
 
0.7%
65.8 1
 
0.7%
65.6 1
 
0.7%
65.5 3
2.2%

15~64세 고용률
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.955224
Minimum64.3
Maximum71.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-01-10T05:21:10.456189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64.3
5-th percentile65.595
Q166.625
median67.85
Q369.2
95-th percentile70.635
Maximum71.8
Range7.5
Interquartile range (IQR)2.575

Descriptive statistics

Standard deviation1.6479455
Coefficient of variation (CV)0.024250461
Kurtosis-0.80984907
Mean67.955224
Median Absolute Deviation (MAD)1.3
Skewness0.042051167
Sum9106
Variance2.7157244
MonotonicityNot monotonic
2024-01-10T05:21:10.576333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.1 8
 
6.0%
68.9 7
 
5.2%
69.6 6
 
4.5%
66.5 5
 
3.7%
69.0 5
 
3.7%
68.4 5
 
3.7%
67.0 5
 
3.7%
69.1 4
 
3.0%
65.9 4
 
3.0%
66.2 3
 
2.2%
Other values (47) 82
61.2%
ValueCountFrequency (%)
64.3 2
1.5%
64.8 1
 
0.7%
65.1 1
 
0.7%
65.2 1
 
0.7%
65.3 1
 
0.7%
65.4 1
 
0.7%
65.7 2
1.5%
65.8 3
2.2%
65.9 4
3.0%
66.0 2
1.5%
ValueCountFrequency (%)
71.8 1
0.7%
71.3 1
0.7%
71.1 1
0.7%
70.9 2
1.5%
70.8 1
0.7%
70.7 1
0.7%
70.6 1
0.7%
70.5 1
0.7%
70.4 1
0.7%
70.2 2
1.5%
Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
S
134 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
S 134
100.0%

Length

2024-01-10T05:21:10.689746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:21:10.777747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 134
100.0%

Interactions

2024-01-10T05:21:06.850181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.524064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.255154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.974221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.687350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.590863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.613430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.362405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.119789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.975436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.926670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.585769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.320033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.050805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.754544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.683232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.688921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.436741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.204126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.059056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.004607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.645914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.393853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.119578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.832188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.984799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.753571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.506291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.279687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.144524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.084530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.725690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.464238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.188415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.918608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.060876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.828416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.575920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.359496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.236719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.162209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.794047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.532943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.260444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.008692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.151006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.900345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.645221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.444873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.322397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.246859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.873756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.603983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.337340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.113311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.243823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.979548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.716307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.544295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.415275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.328547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:59.953563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.683084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.410732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.213162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.325549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.056639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.788644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.641528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.504907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.402066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.021725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.760892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.476706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.304926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.395038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.129441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.862378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.729141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.588830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.479084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.093474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.827251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.541656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.390373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.465304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.202685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.944795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.802474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.675297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:07.559290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.175233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:00.894114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:01.608519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:02.494641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:03.536068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:04.278970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.027851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:05.880977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:21:06.760037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:21:10.832606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월시도명15세이상 생산가능인구(천명)경제활동인구(천명)취업자수(천명)실업자수(천명)비경제활동인구수(천명)경제활동참가율실업률고용률15~64세 고용률
기준연월1.0000.0000.0000.0000.0000.3330.0000.6780.6500.7880.724
시도명0.0001.0001.0001.0001.0001.0001.0000.8510.4580.8150.668
15세이상 생산가능인구(천명)0.0001.0001.0001.0001.0001.0001.0000.8540.4740.8210.686
경제활동인구(천명)0.0001.0001.0001.0001.0001.0001.0000.6750.4740.8240.686
취업자수(천명)0.0001.0001.0001.0001.0001.0000.9410.8880.8390.8960.739
실업자수(천명)0.3331.0001.0001.0001.0001.0000.7920.5880.8360.6770.576
비경제활동인구수(천명)0.0001.0001.0001.0000.9410.7921.0000.6590.3610.6330.550
경제활동참가율0.6780.8510.8540.6750.8880.5880.6591.0000.7150.9800.882
실업률0.6500.4580.4740.4740.8390.8360.3610.7151.0000.8520.853
고용률0.7880.8150.8210.8240.8960.6770.6330.9800.8521.0000.930
15~64세 고용률0.7240.6680.6860.6860.7390.5760.5500.8820.8530.9301.000
2024-01-10T05:21:10.946750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월15세이상 생산가능인구(천명)경제활동인구(천명)취업자수(천명)실업자수(천명)비경제활동인구수(천명)경제활동참가율실업률고용률15~64세 고용률시도명
기준연월1.0000.4970.2800.295-0.2370.0140.143-0.4520.2290.3220.000
15세이상 생산가능인구(천명)0.4971.0000.8860.8950.6290.761-0.3500.141-0.342-0.3040.985
경제활동인구(천명)0.2800.8861.0000.9890.6230.569-0.0400.107-0.080-0.0940.985
취업자수(천명)0.2950.8950.9891.0000.5770.581-0.0540.033-0.070-0.0820.996
실업자수(천명)-0.2370.6290.6230.5771.0000.823-0.5840.814-0.704-0.7350.977
비경제활동인구수(천명)0.0140.7610.5690.5810.8231.000-0.8040.537-0.806-0.7810.996
경제활동참가율0.143-0.350-0.040-0.054-0.584-0.8041.000-0.5880.9710.8810.663
실업률-0.4520.1410.1070.0330.8140.537-0.5881.000-0.742-0.7780.340
고용률0.229-0.342-0.080-0.070-0.704-0.8060.971-0.7421.0000.9320.628
15~64세 고용률0.322-0.304-0.094-0.082-0.735-0.7810.881-0.7780.9321.0000.518
시도명0.0000.9850.9850.9960.9770.9960.6630.3400.6280.5181.000

Missing values

2024-01-10T05:21:07.671342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:21:08.064576image/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

기준연월시도명15세이상 생산가능인구(천명)경제활동인구(천명)취업자수(천명)실업자수(천명)비경제활동인구수(천명)경제활동참가율실업률고용률15~64세 고용률승인상태(W : 대기 S : 승인)
020180144072.527232.326212.51019.816840.261.83.759.566.2S
1201801충청남도1844.51141.51097.743.9702.961.93.859.567.1S
220180244092.627348.326082.81265.416744.362.04.659.265.8S
3201802충청남도1846.41140.31087.253.0706.161.84.658.966.5S
420180344100.027811.426554.51256.916288.663.14.560.266.1S
5201803충청남도1847.81176.61130.046.6671.363.74.061.267.2S
620180444122.028028.726867.61161.116093.363.54.160.966.6S
7201804충청남도1849.91219.81181.638.2630.165.93.163.968.9S
820180544140.628184.427063.71120.715956.163.94.061.367.0S
9201805충청남도1851.81231.81200.930.9620.066.52.564.969.5S
기준연월시도명15세이상 생산가능인구(천명)경제활동인구(천명)취업자수(천명)실업자수(천명)비경제활동인구수(천명)경제활동참가율실업률고용률15~64세 고용률승인상태(W : 대기 S : 승인)
12420230345366.929062.728223.2839.516304.264.12.962.268.7S
125202303충청남도1920.41257.41222.534.9663.065.52.863.768.8S
12620230445371.629236.428432.0804.416135.164.42.862.769.0S
127202304충청남도1921.71290.31254.535.9631.467.12.865.369.6S
12820230545379.029622.628835.2787.415756.465.32.763.569.9S
129202305충청남도1923.01313.91281.032.9609.168.32.566.670.6S
13020230645383.229618.328811.7806.615764.865.02.763.569.9S
131202306충청남도1924.31324.51298.625.9599.868.82.067.571.3S
13220230745399.429492.528685.7806.815906.865.02.763.269.6S
133202307충청남도1926.31306.21274.431.8620.167.82.466.270.4S