Overview

Dataset statistics

Number of variables11
Number of observations153
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory95.9 B

Variable types

Numeric7
Text4

Dataset

Description대전광역시 2020년 위원회 양성 비율 현황입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111021/fileData.do

Alerts

연번 is highly overall correlated with 위촉직남성(명) and 2 other fieldsHigh correlation
전체위원(명) is highly overall correlated with 위촉직위원(명) and 2 other fieldsHigh correlation
위촉직위원(명) is highly overall correlated with 전체위원(명) and 2 other fieldsHigh correlation
위촉직여성(명) is highly overall correlated with 전체위원(명) and 1 other fieldsHigh correlation
위촉직남성(명) is highly overall correlated with 연번 and 4 other fieldsHigh correlation
위촉직여성비율(퍼센트) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
위촉직남성비율(퍼센트) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
위원회명 has unique valuesUnique
위촉직여성(명) has 4 (2.6%) zerosZeros
위촉직여성비율(퍼센트) has 4 (2.6%) zerosZeros

Reproduction

Analysis started2023-12-12 15:03:12.089891
Analysis finished2023-12-12 15:03:17.679684
Duration5.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77
Minimum1
Maximum153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:17.785656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.6
Q139
median77
Q3115
95-th percentile145.4
Maximum153
Range152
Interquartile range (IQR)76

Descriptive statistics

Standard deviation44.311398
Coefficient of variation (CV)0.5754727
Kurtosis-1.2
Mean77
Median Absolute Deviation (MAD)38
Skewness0
Sum11781
Variance1963.5
MonotonicityStrictly increasing
2023-12-13T00:03:17.950694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
106 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
105 1
 
0.7%
107 1
 
0.7%
Other values (143) 143
93.5%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
153 1
0.7%
152 1
0.7%
151 1
0.7%
150 1
0.7%
149 1
0.7%
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%

위원회명
Text

UNIQUE 

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T00:03:18.202481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length18
Mean length9.875817
Min length5

Characters and Unicode

Total characters1511
Distinct characters207
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)100.0%

Sample

1st row저수지.댐안전관리위원회
2nd row산업안전보건위원회
3rd row지역건설산업활성화협의회
4th row지하안전위원회
5th row외국인투자유치협의회
ValueCountFrequency (%)
저수지.댐안전관리위원회 1
 
0.7%
빅데이터위원회 1
 
0.7%
정보공개심의회 1
 
0.7%
농업발전기금운용심의위원회 1
 
0.7%
대전시립연정국악원자문위원회 1
 
0.7%
도서관정보서비스위원회 1
 
0.7%
도시.주거환경정비기금운용심의위원회 1
 
0.7%
무형문화재위원회 1
 
0.7%
미술작품심의위원회 1
 
0.7%
양성평등기금운용심의위원회 1
 
0.7%
Other values (143) 143
93.5%
2023-12-13T00:03:18.552866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 159
 
10.5%
원 150
 
9.9%
위 132
 
8.7%
의 60
 
4.0%
지 49
 
3.2%
심 47
 
3.1%
정 31
 
2.1%
관 23
 
1.5%
사 21
 
1.4%
시 21
 
1.4%
Other values (197) 818
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1501
99.3%
Other Punctuation 4
 
0.3%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 159
 
10.6%
원 150
 
10.0%
위 132
 
8.8%
의 60
 
4.0%
지 49
 
3.3%
심 47
 
3.1%
정 31
 
2.1%
관 23
 
1.5%
사 21
 
1.4%
시 21
 
1.4%
Other values (193) 808
53.8%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
· 2
50.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1501
99.3%
Common 10
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 159
 
10.6%
원 150
 
10.0%
위 132
 
8.8%
의 60
 
4.0%
지 49
 
3.3%
심 47
 
3.1%
정 31
 
2.1%
관 23
 
1.5%
사 21
 
1.4%
시 21
 
1.4%
Other values (193) 808
53.8%
Common
ValueCountFrequency (%)
) 3
30.0%
( 3
30.0%
. 2
20.0%
· 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1501
99.3%
ASCII 8
 
0.5%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 159
 
10.6%
원 150
 
10.0%
위 132
 
8.8%
의 60
 
4.0%
지 49
 
3.3%
심 47
 
3.1%
정 31
 
2.1%
관 23
 
1.5%
사 21
 
1.4%
시 21
 
1.4%
Other values (193) 808
53.8%
ASCII
ValueCountFrequency (%)
) 3
37.5%
( 3
37.5%
. 2
25.0%
None
ValueCountFrequency (%)
· 2
100.0%

전체위원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.248366
Minimum5
Maximum237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:18.680490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.6
Q111
median14
Q318
95-th percentile32
Maximum237
Range232
Interquartile range (IQR)7

Descriptive statistics

Standard deviation20.569293
Coefficient of variation (CV)1.1925357
Kurtosis87.998819
Mean17.248366
Median Absolute Deviation (MAD)4
Skewness8.6120346
Sum2639
Variance423.0958
MonotonicityNot monotonic
2023-12-13T00:03:18.791617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15 24
15.7%
11 14
 
9.2%
10 12
 
7.8%
12 11
 
7.2%
9 11
 
7.2%
14 9
 
5.9%
13 7
 
4.6%
18 7
 
4.6%
20 6
 
3.9%
7 6
 
3.9%
Other values (23) 46
30.1%
ValueCountFrequency (%)
5 5
 
3.3%
6 3
 
2.0%
7 6
3.9%
8 1
 
0.7%
9 11
7.2%
10 12
7.8%
11 14
9.2%
12 11
7.2%
13 7
4.6%
14 9
5.9%
ValueCountFrequency (%)
237 1
0.7%
100 1
0.7%
48 1
0.7%
45 1
0.7%
44 1
0.7%
37 2
1.3%
35 1
0.7%
30 2
1.3%
29 1
0.7%
28 1
0.7%

위촉직위원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.444444
Minimum2
Maximum235
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:18.915394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median11
Q315
95-th percentile27.4
Maximum235
Range233
Interquartile range (IQR)7

Descriptive statistics

Standard deviation20.404427
Coefficient of variation (CV)1.4126142
Kurtosis92.54131
Mean14.444444
Median Absolute Deviation (MAD)3
Skewness8.9346567
Sum2210
Variance416.34064
MonotonicityNot monotonic
2023-12-13T00:03:19.040073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9 16
 
10.5%
7 13
 
8.5%
14 13
 
8.5%
10 13
 
8.5%
11 11
 
7.2%
12 11
 
7.2%
13 11
 
7.2%
15 8
 
5.2%
6 7
 
4.6%
8 6
 
3.9%
Other values (21) 44
28.8%
ValueCountFrequency (%)
2 1
 
0.7%
3 3
 
2.0%
4 3
 
2.0%
5 6
 
3.9%
6 7
4.6%
7 13
8.5%
8 6
 
3.9%
9 16
10.5%
10 13
8.5%
11 11
7.2%
ValueCountFrequency (%)
235 1
0.7%
100 1
0.7%
39 1
0.7%
36 2
1.3%
34 2
1.3%
28 1
0.7%
27 2
1.3%
25 1
0.7%
24 2
1.3%
23 1
0.7%

위촉직여성(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7385621
Minimum0
Maximum47
Zeros4
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:19.165999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q13
median5
Q37
95-th percentile11.4
Maximum47
Range47
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7748949
Coefficient of variation (CV)0.83207167
Kurtosis36.721845
Mean5.7385621
Median Absolute Deviation (MAD)2
Skewness4.7767686
Sum878
Variance22.799622
MonotonicityNot monotonic
2023-12-13T00:03:19.276342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 30
19.6%
4 25
16.3%
3 18
11.8%
2 15
9.8%
7 13
8.5%
6 12
 
7.8%
8 10
 
6.5%
9 7
 
4.6%
11 5
 
3.3%
0 4
 
2.6%
Other values (8) 14
9.2%
ValueCountFrequency (%)
0 4
 
2.6%
1 4
 
2.6%
2 15
9.8%
3 18
11.8%
4 25
16.3%
5 30
19.6%
6 12
 
7.8%
7 13
8.5%
8 10
 
6.5%
9 7
 
4.6%
ValueCountFrequency (%)
47 1
 
0.7%
20 1
 
0.7%
18 1
 
0.7%
17 2
 
1.3%
14 2
 
1.3%
12 1
 
0.7%
11 5
3.3%
10 2
 
1.3%
9 7
4.6%
8 10
6.5%

위촉직남성(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7124183
Minimum0
Maximum217
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:19.392219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q39
95-th percentile17.4
Maximum217
Range217
Interquartile range (IQR)5

Descriptive statistics

Standard deviation17.889527
Coefficient of variation (CV)2.0533365
Kurtosis122.97679
Mean8.7124183
Median Absolute Deviation (MAD)2
Skewness10.626345
Sum1333
Variance320.03517
MonotonicityNot monotonic
2023-12-13T00:03:19.846482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 23
15.0%
5 18
11.8%
7 16
10.5%
6 15
9.8%
8 15
9.8%
3 15
9.8%
2 8
 
5.2%
10 7
 
4.6%
12 7
 
4.6%
9 6
 
3.9%
Other values (14) 23
15.0%
ValueCountFrequency (%)
0 1
 
0.7%
1 2
 
1.3%
2 8
 
5.2%
3 15
9.8%
4 23
15.0%
5 18
11.8%
6 15
9.8%
7 16
10.5%
8 15
9.8%
9 6
 
3.9%
ValueCountFrequency (%)
217 1
0.7%
53 1
0.7%
25 1
0.7%
22 1
0.7%
20 1
0.7%
19 2
1.3%
18 1
0.7%
17 2
1.3%
16 2
1.3%
15 2
1.3%

위촉직여성비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.039216
Minimum0
Maximum100
Zeros4
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:19.984524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.6
Q140
median44
Q350
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.62045
Coefficient of variation (CV)0.3546941
Kurtosis2.3068879
Mean44.039216
Median Absolute Deviation (MAD)6
Skewness-0.22093266
Sum6738
Variance243.99845
MonotonicityIncreasing
2023-12-13T00:03:20.130693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
50 27
17.6%
40 14
 
9.2%
43 12
 
7.8%
44 11
 
7.2%
47 9
 
5.9%
56 7
 
4.6%
42 6
 
3.9%
38 5
 
3.3%
67 5
 
3.3%
45 5
 
3.3%
Other values (32) 52
34.0%
ValueCountFrequency (%)
0 4
2.6%
6 1
 
0.7%
8 2
1.3%
13 1
 
0.7%
14 1
 
0.7%
15 1
 
0.7%
17 1
 
0.7%
21 3
2.0%
23 1
 
0.7%
25 1
 
0.7%
ValueCountFrequency (%)
100 1
 
0.7%
86 1
 
0.7%
85 1
 
0.7%
82 1
 
0.7%
75 1
 
0.7%
73 1
 
0.7%
67 5
3.3%
65 1
 
0.7%
64 1
 
0.7%
60 3
2.0%

위촉직남성비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.019608
Minimum0
Maximum100
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T00:03:20.261990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q150
median56
Q360
95-th percentile86.8
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.613536
Coefficient of variation (CV)0.27871555
Kurtosis2.3262818
Mean56.019608
Median Absolute Deviation (MAD)6
Skewness0.2167184
Sum8571
Variance243.78251
MonotonicityNot monotonic
2023-12-13T00:03:20.394833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
50 27
17.6%
60 14
 
9.2%
57 12
 
7.8%
56 11
 
7.2%
53 9
 
5.9%
44 7
 
4.6%
58 6
 
3.9%
62 5
 
3.3%
33 5
 
3.3%
55 5
 
3.3%
Other values (31) 52
34.0%
ValueCountFrequency (%)
0 1
 
0.7%
14 1
 
0.7%
15 1
 
0.7%
18 1
 
0.7%
25 1
 
0.7%
27 1
 
0.7%
33 5
3.3%
35 1
 
0.7%
36 1
 
0.7%
40 3
2.0%
ValueCountFrequency (%)
100 4
2.6%
94 1
 
0.7%
92 2
1.3%
88 1
 
0.7%
86 1
 
0.7%
85 1
 
0.7%
83 1
 
0.7%
79 3
2.0%
77 1
 
0.7%
75 1
 
0.7%

담당자
Text

Distinct146
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T00:03:20.696580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0196078
Min length2

Characters and Unicode

Total characters462
Distinct characters114
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)90.8%

Sample

1st row허미경
2nd row이나라
3rd row김은영
4th row정승용
5th row김지연
ValueCountFrequency (%)
전건호 2
 
1.3%
김미경 2
 
1.3%
이태원 2
 
1.3%
이재만 2
 
1.3%
이정갑 2
 
1.3%
한호준 2
 
1.3%
임재덕 2
 
1.3%
임승혁 1
 
0.7%
허미경 1
 
0.7%
이현경 1
 
0.7%
Other values (136) 136
88.9%
2023-12-13T00:03:21.137712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 31
 
6.7%
김 29
 
6.3%
정 23
 
5.0%
영 20
 
4.3%
경 13
 
2.8%
박 13
 
2.8%
숙 12
 
2.6%
은 12
 
2.6%
태 10
 
2.2%
현 9
 
1.9%
Other values (104) 290
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 461
99.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 31
 
6.7%
김 29
 
6.3%
정 23
 
5.0%
영 20
 
4.3%
경 13
 
2.8%
박 13
 
2.8%
숙 12
 
2.6%
은 12
 
2.6%
태 10
 
2.2%
현 9
 
2.0%
Other values (103) 289
62.7%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 461
99.8%
Common 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 31
 
6.7%
김 29
 
6.3%
정 23
 
5.0%
영 20
 
4.3%
경 13
 
2.8%
박 13
 
2.8%
숙 12
 
2.6%
은 12
 
2.6%
태 10
 
2.2%
현 9
 
2.0%
Other values (103) 289
62.7%
Common
ValueCountFrequency (%)
, 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 461
99.8%
ASCII 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 31
 
6.7%
김 29
 
6.3%
정 23
 
5.0%
영 20
 
4.3%
경 13
 
2.8%
박 13
 
2.8%
숙 12
 
2.6%
은 12
 
2.6%
태 10
 
2.2%
현 9
 
2.0%
Other values (103) 289
62.7%
ASCII
ValueCountFrequency (%)
, 1
100.0%

부서
Text

Distinct67
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T00:03:21.397129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length5.5228758
Min length3

Characters and Unicode

Total characters845
Distinct characters125
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)19.6%

Sample

1st row재난관리과
2nd row운영지원과
3rd row건설도로과
4th row건설도로과
5th row투자유치과
ValueCountFrequency (%)
농생명정책과 7
 
4.5%
자치분권과 7
 
4.5%
토지정보과 6
 
3.9%
교육청소년과 6
 
3.9%
보건의료과 5
 
3.2%
예산담당관 5
 
3.2%
소상공인과 5
 
3.2%
공원녹지과 4
 
2.6%
기후환경정책과 4
 
2.6%
도시경관과 4
 
2.6%
Other values (58) 102
65.8%
2023-12-13T00:03:21.794559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 122
 
14.4%
정 48
 
5.7%
관 41
 
4.9%
책 37
 
4.4%
지 26
 
3.1%
당 19
 
2.2%
담 19
 
2.2%
시 18
 
2.1%
공 17
 
2.0%
보 15
 
1.8%
Other values (115) 483
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 843
99.8%
Space Separator 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 122
 
14.5%
정 48
 
5.7%
관 41
 
4.9%
책 37
 
4.4%
지 26
 
3.1%
당 19
 
2.3%
담 19
 
2.3%
시 18
 
2.1%
공 17
 
2.0%
보 15
 
1.8%
Other values (114) 481
57.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 843
99.8%
Common 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 122
 
14.5%
정 48
 
5.7%
관 41
 
4.9%
책 37
 
4.4%
지 26
 
3.1%
당 19
 
2.3%
담 19
 
2.3%
시 18
 
2.1%
공 17
 
2.0%
보 15
 
1.8%
Other values (114) 481
57.1%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 843
99.8%
ASCII 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 122
 
14.5%
정 48
 
5.7%
관 41
 
4.9%
책 37
 
4.4%
지 26
 
3.1%
당 19
 
2.3%
담 19
 
2.3%
시 18
 
2.1%
공 17
 
2.0%
보 15
 
1.8%
Other values (114) 481
57.1%
ASCII
ValueCountFrequency (%)
2
100.0%

행정전화
Text

Distinct147
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T00:03:22.192101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length3.869281
Min length3

Characters and Unicode

Total characters592
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)92.2%

Sample

1st row5974
2nd row593
3rd row5892
4th row5931
5th row3756
ValueCountFrequency (%)
6501 2
 
1.3%
3671 2
 
1.3%
3672 2
 
1.3%
6222 2
 
1.3%
5483 2
 
1.3%
740 2
 
1.3%
3232 1
 
0.6%
3154 1
 
0.6%
5431 1
 
0.6%
7453 1
 
0.6%
Other values (138) 138
89.6%
2023-12-13T00:03:22.712175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 99
16.7%
3 85
14.4%
4 74
12.5%
2 74
12.5%
5 70
11.8%
6 61
10.3%
7 53
9.0%
8 37
 
6.2%
9 20
 
3.4%
0 15
 
2.5%
Other values (3) 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 588
99.3%
Other Punctuation 3
 
0.5%
Space Separator 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 99
16.8%
3 85
14.5%
4 74
12.6%
2 74
12.6%
5 70
11.9%
6 61
10.4%
7 53
9.0%
8 37
 
6.3%
9 20
 
3.4%
0 15
 
2.6%
Other Punctuation
ValueCountFrequency (%)
" 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 99
16.7%
3 85
14.4%
4 74
12.5%
2 74
12.5%
5 70
11.8%
6 61
10.3%
7 53
9.0%
8 37
 
6.2%
9 20
 
3.4%
0 15
 
2.5%
Other values (3) 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 99
16.7%
3 85
14.4%
4 74
12.5%
2 74
12.5%
5 70
11.8%
6 61
10.3%
7 53
9.0%
8 37
 
6.2%
9 20
 
3.4%
0 15
 
2.5%
Other values (3) 4
 
0.7%

Interactions

2023-12-13T00:03:16.711529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:12.562204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.497687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.151983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.791711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.429367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.084883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.810693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:12.653990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.597170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.264251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.902792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.525990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.176284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.898008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.036497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.669987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.354853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.996304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.615002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.260203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.984400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.122981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.751485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.429725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.087183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.687617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.355833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:17.081386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.224214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.850327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.527468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.165733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.771866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.447526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:17.171246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.322418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.920092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.606061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.260927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.861272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.527450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:17.262967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:13.412924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.040381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:14.698089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.350323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:15.985622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:16.624548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:03:22.817773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전체위원(명)위촉직위원(명)위촉직여성(명)위촉직남성(명)위촉직여성비율(퍼센트)위촉직남성비율(퍼센트)부서
연번1.0000.1060.1580.3630.0000.9330.9370.632
전체위원(명)0.1061.0000.9980.8650.9890.1070.1540.000
위촉직위원(명)0.1580.9981.0000.8740.9900.0000.1430.000
위촉직여성(명)0.3630.8650.8741.0000.8430.2970.2240.000
위촉직남성(명)0.0000.9890.9900.8431.0000.0000.0000.000
위촉직여성비율(퍼센트)0.9330.1070.0000.2970.0001.0000.9970.466
위촉직남성비율(퍼센트)0.9370.1540.1430.2240.0000.9971.0000.540
부서0.6320.0000.0000.0000.0000.4660.5401.000
2023-12-13T00:03:22.936138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전체위원(명)위촉직위원(명)위촉직여성(명)위촉직남성(명)위촉직여성비율(퍼센트)위촉직남성비율(퍼센트)
연번1.000-0.247-0.1950.315-0.5620.996-0.994
전체위원(명)-0.2471.0000.8980.6860.815-0.2500.250
위촉직위원(명)-0.1950.8981.0000.7960.873-0.2010.204
위촉직여성(명)0.3150.6860.7961.0000.4670.310-0.306
위촉직남성(명)-0.5620.8150.8730.4671.000-0.5690.572
위촉직여성비율(퍼센트)0.996-0.250-0.2010.310-0.5691.000-0.998
위촉직남성비율(퍼센트)-0.9940.2500.204-0.3060.572-0.9981.000

Missing values

2023-12-13T00:03:17.403770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:03:17.594037image/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

연번위원회명전체위원(명)위촉직위원(명)위촉직여성(명)위촉직남성(명)위촉직여성비율(퍼센트)위촉직남성비율(퍼센트)담당자부서행정전화
01저수지.댐안전관리위원회107070100허미경재난관리과5974
12산업안전보건위원회103030100이나라운영지원과593
23지역건설산업활성화협의회22110110100김은영건설도로과5892
34지하안전위원회109090100정승용건설도로과5931
45외국인투자유치협의회1716115694김지연투자유치과3756
56건설기술심의위원회23723518217892김성원정책기획관3072
67가축방역심의회1312111892권진석농생명정책과3812
78의료원설립추진위원회19162141388박희용복지정책과4661
89학교폭력대책지역위원회117161486홍아름교육청소년과855
910유통업상생발전협의회14132111585이태원소상공인과3671
연번위원회명전체위원(명)위촉직위원(명)위촉직여성(명)위촉직남성(명)위촉직여성비율(퍼센트)위촉직남성비율(퍼센트)담당자부서행정전화
143144도로관리심의회123216733육관수시설정비과8861
144145마약류중독자치료보호심사위원회63216733박민아보건의료과4853
145146아동여성안전지역연대22211476733허민영성인지정책담당관3172
146147청소년육성위원회1512846733박상규교육청소년과852
147148평생교육협의회1511837327박효은교육청소년과862
148149친환경무상학교급식지원심의위원회1512937525안영숙교육청소년과851
149150식생활교육위원회1511928218최경미농생명정책과3803
150151대전광역시여성가족원운영위원회14131128515정희윤여성가족원7611
151152보육정책위원회15141228614유재오가족돌봄과691
152153기록물평가심의회52201000김진희시민봉사과4214