Overview

Dataset statistics

Number of variables7
Number of observations2232
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.9 KiB
Average record size in memory60.1 B

Variable types

Categorical4
Numeric3

Dataset

Description경기도 비정규직 관련 근로형태별 성별 주당 근로시간
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=YA48N5I7WST4AJQ7V9M530074854&infSeq=1

Alerts

근로자수 is highly overall correlated with 주당평균근로시간High correlation
주당평균근로시간 is highly overall correlated with 근로자수 and 2 other fieldsHigh correlation
근로형태 is highly overall correlated with 주당평균근로시간High correlation
성별 is highly overall correlated with 주당평균근로시간High correlation

Reproduction

Analysis started2023-12-10 22:04:23.128695
Analysis finished2023-12-10 22:04:26.180985
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

Distinct31
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
포천시
 
72
여주시
 
72
연천군
 
72
가평군
 
72
양평군
 
72
Other values (26)
1872 

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포천시
2nd row포천시
3rd row포천시
4th row여주시
5th row여주시

Common Values

ValueCountFrequency (%)
포천시 72
 
3.2%
여주시 72
 
3.2%
연천군 72
 
3.2%
가평군 72
 
3.2%
양평군 72
 
3.2%
수원시 72
 
3.2%
성남시 72
 
3.2%
의정부시 72
 
3.2%
안양시 72
 
3.2%
부천시 72
 
3.2%
Other values (21) 1512
67.7%

Length

2023-12-11T07:04:26.240688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포천시 72
 
3.2%
군포시 72
 
3.2%
오산시 72
 
3.2%
남양주시 72
 
3.2%
구리시 72
 
3.2%
양주시 72
 
3.2%
광주시 72
 
3.2%
화성시 72
 
3.2%
김포시 72
 
3.2%
안성시 72
 
3.2%
Other values (21) 1512
67.7%

근로형태
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
임시직
744 
일용직
744 
상용직
744 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row임시직
2nd row일용직
3rd row일용직
4th row상용직
5th row상용직

Common Values

ValueCountFrequency (%)
임시직 744
33.3%
일용직 744
33.3%
상용직 744
33.3%

Length

2023-12-11T07:04:26.347588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:04:26.439045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임시직 744
33.3%
일용직 744
33.3%
상용직 744
33.3%

성별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
여성
1116 
남성
1116 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row여성
2nd row남성
3rd row여성
4th row남성
5th row여성

Common Values

ValueCountFrequency (%)
여성 1116
50.0%
남성 1116
50.0%

Length

2023-12-11T07:04:26.540525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:04:26.661014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여성 1116
50.0%
남성 1116
50.0%

조사년도
Real number (ℝ)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2013
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-12-11T07:04:26.776747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12014
median2015.5
Q32017
95-th percentile2018
Maximum2018
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7082078
Coefficient of variation (CV)0.00084753552
Kurtosis-1.2687248
Mean2015.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum4498596
Variance2.917974
MonotonicityNot monotonic
2023-12-11T07:04:26.938543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013 372
16.7%
2016 372
16.7%
2017 372
16.7%
2015 372
16.7%
2014 372
16.7%
2018 372
16.7%
ValueCountFrequency (%)
2013 372
16.7%
2014 372
16.7%
2015 372
16.7%
2016 372
16.7%
2017 372
16.7%
2018 372
16.7%
ValueCountFrequency (%)
2018 372
16.7%
2017 372
16.7%
2016 372
16.7%
2015 372
16.7%
2014 372
16.7%
2013 372
16.7%

조사반기
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
1
1116 
2
1116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1116
50.0%
2 1116
50.0%

Length

2023-12-11T07:04:27.047687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:04:27.162129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1116
50.0%
2 1116
50.0%

근로자수
Real number (ℝ)

HIGH CORRELATION 

Distinct1983
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25319.776
Minimum97
Maximum251154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-12-11T07:04:27.270071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile1004.2
Q13858
median10252
Q329439.25
95-th percentile109172.65
Maximum251154
Range251057
Interquartile range (IQR)25581.25

Descriptive statistics

Standard deviation37628.492
Coefficient of variation (CV)1.4861305
Kurtosis7.9706905
Mean25319.776
Median Absolute Deviation (MAD)8005
Skewness2.6931381
Sum56513741
Variance1.4159034 × 109
MonotonicityNot monotonic
2023-12-11T07:04:27.419801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4358 4
 
0.2%
3353 3
 
0.1%
13320 3
 
0.1%
16149 3
 
0.1%
2102 3
 
0.1%
12452 3
 
0.1%
5608 3
 
0.1%
6167 3
 
0.1%
1064 3
 
0.1%
3258 3
 
0.1%
Other values (1973) 2201
98.6%
ValueCountFrequency (%)
97 1
< 0.1%
123 1
< 0.1%
142 1
< 0.1%
144 1
< 0.1%
145 1
< 0.1%
156 1
< 0.1%
157 1
< 0.1%
168 1
< 0.1%
182 1
< 0.1%
191 1
< 0.1%
ValueCountFrequency (%)
251154 1
< 0.1%
234390 1
< 0.1%
231207 1
< 0.1%
225480 1
< 0.1%
221924 2
0.1%
216942 1
< 0.1%
216329 1
< 0.1%
216098 1
< 0.1%
214129 1
< 0.1%
209088 1
< 0.1%

주당평균근로시간
Real number (ℝ)

HIGH CORRELATION 

Distinct282
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.891711
Minimum13.9
Maximum57.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-12-11T07:04:27.549599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.9
5-th percentile28.655
Q136.1
median41.4
Q344.3
95-th percentile47.545
Maximum57.7
Range43.8
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation6.0068606
Coefficient of variation (CV)0.15057916
Kurtosis0.60518807
Mean39.891711
Median Absolute Deviation (MAD)3.8
Skewness-0.86717212
Sum89038.3
Variance36.082374
MonotonicityNot monotonic
2023-12-11T07:04:27.673123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.3 31
 
1.4%
42.8 28
 
1.3%
42.2 28
 
1.3%
42.7 26
 
1.2%
43.1 26
 
1.2%
42.4 25
 
1.1%
45.1 25
 
1.1%
41.8 25
 
1.1%
46.3 24
 
1.1%
42.6 24
 
1.1%
Other values (272) 1970
88.3%
ValueCountFrequency (%)
13.9 1
< 0.1%
14.2 1
< 0.1%
16.2 2
0.1%
18.1 1
< 0.1%
18.9 1
< 0.1%
19.3 1
< 0.1%
19.9 1
< 0.1%
20.2 1
< 0.1%
20.3 2
0.1%
20.5 2
0.1%
ValueCountFrequency (%)
57.7 1
< 0.1%
52.0 1
< 0.1%
51.9 1
< 0.1%
51.6 1
< 0.1%
50.8 1
< 0.1%
50.7 1
< 0.1%
50.6 1
< 0.1%
50.4 2
0.1%
50.3 1
< 0.1%
50.0 1
< 0.1%

Interactions

2023-12-11T07:04:25.432078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:24.739094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:25.127328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:25.816400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:24.900327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:25.231553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:25.909685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:25.000455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:04:25.338022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:04:27.759150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명근로형태성별조사년도조사반기근로자수주당평균근로시간
시군명1.0000.0000.0000.0000.0000.6510.315
근로형태0.0001.0000.0000.0000.0000.6180.653
성별0.0000.0001.0000.0000.0000.2330.732
조사년도0.0000.0000.0001.0000.0000.0000.138
조사반기0.0000.0000.0000.0001.0000.0000.057
근로자수0.6510.6180.2330.0000.0001.0000.447
주당평균근로시간0.3150.6530.7320.1380.0570.4471.000
2023-12-11T07:04:27.864916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사반기성별시군명근로형태
조사반기1.0000.0000.0000.000
성별0.0001.0000.0000.000
시군명0.0000.0001.0000.000
근로형태0.0000.0000.0001.000
2023-12-11T07:04:27.961377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사년도근로자수주당평균근로시간시군명근로형태성별조사반기
조사년도1.0000.007-0.1470.0000.0000.0000.000
근로자수0.0071.0000.5270.2910.4630.1780.000
주당평균근로시간-0.1470.5271.0000.1150.5010.5720.044
시군명0.0000.2910.1151.0000.0000.0000.000
근로형태0.0000.4630.5010.0001.0000.0000.000
성별0.0000.1780.5720.0000.0001.0000.000
조사반기0.0000.0000.0440.0000.0000.0001.000

Missing values

2023-12-11T07:04:26.016383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:04:26.135858image/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

시군명근로형태성별조사년도조사반기근로자수주당평균근로시간
0포천시임시직여성20131783341.4
1포천시일용직남성20131256935.2
2포천시일용직여성20131118629.0
3여주시상용직남성201311243047.9
4여주시상용직여성20131737244.0
5여주시임시직남성20131537248.9
6여주시임시직여성20131665139.9
7여주시일용직남성20131128041.8
8여주시일용직여성20131107629.8
9연천군상용직남성20131394146.4
시군명근로형태성별조사년도조사반기근로자수주당평균근로시간
2222화성시임시직남성201811646146.3
2223화성시임시직여성201812466536.3
2224화성시일용직남성20181713645.0
2225화성시일용직여성20181336431.8
2226광주시상용직남성201816192846.7
2227광주시상용직여성201813550042.4
2228광주시임시직남성201811128841.7
2229광주시임시직여성201811448335.2
2230광주시일용직남성20181483236.1
2231광주시일용직여성20181223820.3