Overview

Dataset statistics

Number of variables4
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory880.0 B
Average record size in memory40.0 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description여성인재 데이터 현황에 대한 데이터로서 분야별(구분, 인원, 비율), 직종별(구분, 인원, 비율) 현황 정보를 제공합니다.
Author여성가족부
URLhttps://www.data.go.kr/data/15009882/fileData.do

Alerts

인원(명) is highly overall correlated with 비율(퍼센트) and 1 other fieldsHigh correlation
비율(퍼센트) is highly overall correlated with 인원(명) and 1 other fieldsHigh correlation
구분 is highly overall correlated with 인원(명) and 1 other fieldsHigh correlation
직종 또는 분야 has unique valuesUnique
인원(명) has unique valuesUnique
비율(퍼센트) has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:06:50.975301
Analysis finished2023-12-12 06:06:51.684044
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
분야별
16 
직종별
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분야별 16
72.7%
직종별 6
 
27.3%

Length

2023-12-12T15:06:51.753619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:06:51.875568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분야별 16
72.7%
직종별 6
 
27.3%

직종 또는 분야
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T15:06:52.046014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length9.2272727
Min length2

Characters and Unicode

Total characters203
Distinct characters85
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row교육인(교수/연구원)
2nd row공무원
3rd row경제/기업/금융인
4th row전문직업인(법조인, 의료인, 회계사 등)
5th row공공기관 임직원
ValueCountFrequency (%)
등 2
 
7.4%
교육인(교수/연구원 1
 
3.7%
농림/해양/축산/식품 1
 
3.7%
건설/교통 1
 
3.7%
인사/정부관리 1
 
3.7%
외교/국방/경찰/소방 1
 
3.7%
홍보/언론 1
 
3.7%
경영/공정거래 1
 
3.7%
법무/사법/인권 1
 
3.7%
산업/자원/특허 1
 
3.7%
Other values (16) 16
59.3%
2023-12-12T15:06:52.432360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 34
 
16.7%
인 10
 
4.9%
교 6
 
3.0%
기 5
 
2.5%
5
 
2.5%
정 5
 
2.5%
학 4
 
2.0%
원 4
 
2.0%
문 4
 
2.0%
공 4
 
2.0%
Other values (75) 122
60.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 156
76.8%
Other Punctuation 36
 
17.7%
Space Separator 5
 
2.5%
Close Punctuation 3
 
1.5%
Open Punctuation 3
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 10
 
6.4%
교 6
 
3.8%
기 5
 
3.2%
정 5
 
3.2%
학 4
 
2.6%
원 4
 
2.6%
문 4
 
2.6%
공 4
 
2.6%
육 4
 
2.6%
경 4
 
2.6%
Other values (70) 106
67.9%
Other Punctuation
ValueCountFrequency (%)
/ 34
94.4%
, 2
 
5.6%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 156
76.8%
Common 47
 
23.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 10
 
6.4%
교 6
 
3.8%
기 5
 
3.2%
정 5
 
3.2%
학 4
 
2.6%
원 4
 
2.6%
문 4
 
2.6%
공 4
 
2.6%
육 4
 
2.6%
경 4
 
2.6%
Other values (70) 106
67.9%
Common
ValueCountFrequency (%)
/ 34
72.3%
5
 
10.6%
) 3
 
6.4%
( 3
 
6.4%
, 2
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 156
76.8%
ASCII 47
 
23.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 34
72.3%
5
 
10.6%
) 3
 
6.4%
( 3
 
6.4%
, 2
 
4.3%
Hangul
ValueCountFrequency (%)
인 10
 
6.4%
교 6
 
3.8%
기 5
 
3.2%
정 5
 
3.2%
학 4
 
2.6%
원 4
 
2.6%
문 4
 
2.6%
공 4
 
2.6%
육 4
 
2.6%
경 4
 
2.6%
Other values (70) 106
67.9%

인원(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14136.545
Minimum2081
Maximum59365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T15:06:52.607602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2081
5-th percentile2665.65
Q15689
median13129
Q316708.75
95-th percentile29046.3
Maximum59365
Range57284
Interquartile range (IQR)11019.75

Descriptive statistics

Standard deviation12307.568
Coefficient of variation (CV)0.87062062
Kurtosis8.4676065
Mean14136.545
Median Absolute Deviation (MAD)4909.5
Skewness2.5261911
Sum311004
Variance1.5147623 × 108
MonotonicityNot monotonic
2023-12-12T15:06:53.106651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
59365 1
 
4.5%
14371 1
 
4.5%
13612 1
 
4.5%
5280 1
 
4.5%
2603 1
 
4.5%
3856 1
 
4.5%
2081 1
 
4.5%
5461 1
 
4.5%
6373 1
 
4.5%
9286 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
2081 1
4.5%
2603 1
4.5%
3856 1
4.5%
4882 1
4.5%
5280 1
4.5%
5461 1
4.5%
6373 1
4.5%
8234 1
4.5%
9286 1
4.5%
10762 1
4.5%
ValueCountFrequency (%)
59365 1
4.5%
29285 1
4.5%
24511 1
4.5%
18053 1
4.5%
17768 1
4.5%
16832 1
4.5%
16339 1
4.5%
15417 1
4.5%
14371 1
4.5%
13987 1
4.5%

비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0863636
Minimum1.3
Maximum38.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T15:06:53.266264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.74
Q13.65
median8.45
Q310.725
95-th percentile18.65
Maximum38.2
Range36.9
Interquartile range (IQR)7.075

Descriptive statistics

Standard deviation7.9187117
Coefficient of variation (CV)0.87149404
Kurtosis8.4876096
Mean9.0863636
Median Absolute Deviation (MAD)3.15
Skewness2.5296435
Sum199.9
Variance62.705996
MonotonicityNot monotonic
2023-12-12T15:06:53.420033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
38.2 1
 
4.5%
9.2 1
 
4.5%
8.8 1
 
4.5%
3.4 1
 
4.5%
1.7 1
 
4.5%
2.5 1
 
4.5%
1.3 1
 
4.5%
3.5 1
 
4.5%
4.1 1
 
4.5%
6.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.3 1
4.5%
1.7 1
4.5%
2.5 1
4.5%
3.1 1
4.5%
3.4 1
4.5%
3.5 1
4.5%
4.1 1
4.5%
5.3 1
4.5%
6.0 1
4.5%
6.9 1
4.5%
ValueCountFrequency (%)
38.2 1
4.5%
18.8 1
4.5%
15.8 1
4.5%
11.6 1
4.5%
11.4 1
4.5%
10.8 1
4.5%
10.5 1
4.5%
9.9 1
4.5%
9.2 1
4.5%
9.0 1
4.5%

Interactions

2023-12-12T15:06:51.312055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:51.109475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:51.421090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:06:51.203702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:06:53.532834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분직종 또는 분야인원(명)비율(퍼센트)
구분1.0001.0000.7500.771
직종 또는 분야1.0001.0001.0001.000
인원(명)0.7501.0001.0001.000
비율(퍼센트)0.7711.0001.0001.000
2023-12-12T15:06:53.641137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인원(명)비율(퍼센트)구분
인원(명)1.0001.0000.509
비율(퍼센트)1.0001.0000.509
구분0.5090.5091.000

Missing values

2023-12-12T15:06:51.559127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:06:51.650822image/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

구분직종 또는 분야인원(명)비율(퍼센트)
0직종별교육인(교수/연구원)5936538.2
1직종별공무원2928518.8
2직종별경제/기업/금융인2451115.8
3직종별전문직업인(법조인, 의료인, 회계사 등)1776811.4
4직종별공공기관 임직원82345.3
5직종별기타(종교인/문화예술/체육인 등)1633910.5
6분야별문화/예술/체육/관광139879.0
7분야별복지/청소년/환경/노동107626.9
8분야별교육/외국어/인문학1805311.6
9분야별정치/지방자치154179.9
구분직종 또는 분야인원(명)비율(퍼센트)
12분야별과학기술/정보통신143719.2
13분야별재정/통상/금융/회계126468.1
14분야별산업/자원/특허92866.0
15분야별법무/사법/인권63734.1
16분야별경영/공정거래54613.5
17분야별홍보/언론20811.3
18분야별외교/국방/경찰/소방38562.5
19분야별인사/정부관리26031.7
20분야별건설/교통52803.4
21분야별기타136128.8