Overview

Dataset statistics

Number of variables9
Number of observations195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory78.7 B

Variable types

Text3
Numeric6

Dataset

Description2021년 12월 31일 기준 인천광역시 각종 위원회의 전체위원수, 전체여성위원수, 전체여성비율, 위촉직위원수, 위촉직여성위원수, 위촉직여성비율을 공개합니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15103837&srcSe=7661IVAWM27C61E190

Alerts

전체위원(명) is highly overall correlated with 전체여성(명) and 2 other fieldsHigh correlation
전체여성(명) is highly overall correlated with 전체위원(명) and 2 other fieldsHigh correlation
전체여성비율 is highly overall correlated with 위촉직여성비율High 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 2 other fieldsHigh correlation
위촉직여성비율 is highly overall correlated with 전체여성비율High correlation
위원회명 has unique valuesUnique

Reproduction

Analysis started2024-01-28 07:32:22.240010
Analysis finished2024-01-28 07:32:25.666270
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위원회명
Text

UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T16:32:25.799085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length10.015385
Min length5

Characters and Unicode

Total characters1953
Distinct characters238
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)100.0%

Sample

1st rowIFEZ교통영향평가심의위원회
2nd row가축방역심의회
3rd row감정노동종사자권리보장위원회
4th row감정평가업자선정위원회
5th row건강생활실천협의회
ValueCountFrequency (%)
ifez교통영향평가심의위원회 1
 
0.5%
식품진흥기금운용심의위원회 1
 
0.5%
적극행정위원회 1
 
0.5%
인천주민참여예산위원회 1
 
0.5%
일자리위원회 1
 
0.5%
자살예방위원회 1
 
0.5%
자치경찰위원회 1
 
0.5%
자치분권협의회 1
 
0.5%
재난관리기금운용심의위원회 1
 
0.5%
재난심리회복지원단 1
 
0.5%
Other values (185) 185
94.9%
2024-01-28T16:32:26.103390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 197
 
10.1%
원 190
 
9.7%
위 174
 
8.9%
의 64
 
3.3%
심 58
 
3.0%
지 57
 
2.9%
정 38
 
1.9%
관 36
 
1.8%
시 33
 
1.7%
도 30
 
1.5%
Other values (228) 1076
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1946
99.6%
Uppercase Letter 4
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 197
 
10.1%
원 190
 
9.8%
위 174
 
8.9%
의 64
 
3.3%
심 58
 
3.0%
지 57
 
2.9%
정 38
 
2.0%
관 36
 
1.8%
시 33
 
1.7%
도 30
 
1.5%
Other values (221) 1069
54.9%
Uppercase Letter
ValueCountFrequency (%)
I 1
25.0%
F 1
25.0%
Z 1
25.0%
E 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1946
99.6%
Latin 4
 
0.2%
Common 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 197
 
10.1%
원 190
 
9.8%
위 174
 
8.9%
의 64
 
3.3%
심 58
 
3.0%
지 57
 
2.9%
정 38
 
2.0%
관 36
 
1.8%
시 33
 
1.7%
도 30
 
1.5%
Other values (221) 1069
54.9%
Latin
ValueCountFrequency (%)
I 1
25.0%
F 1
25.0%
Z 1
25.0%
E 1
25.0%
Common
ValueCountFrequency (%)
( 1
33.3%
) 1
33.3%
· 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1945
99.6%
ASCII 6
 
0.3%
None 1
 
0.1%
Compat Jamo 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 197
 
10.1%
원 190
 
9.8%
위 174
 
8.9%
의 64
 
3.3%
심 58
 
3.0%
지 57
 
2.9%
정 38
 
2.0%
관 36
 
1.9%
시 33
 
1.7%
도 30
 
1.5%
Other values (220) 1068
54.9%
ASCII
ValueCountFrequency (%)
( 1
16.7%
) 1
16.7%
I 1
16.7%
F 1
16.7%
Z 1
16.7%
E 1
16.7%
None
ValueCountFrequency (%)
· 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

전체위원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.348718
Minimum3
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-28T16:32:26.221280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q110
median14
Q320
95-th percentile50
Maximum213
Range210
Interquartile range (IQR)10

Descriptive statistics

Standard deviation22.533856
Coefficient of variation (CV)1.1646175
Kurtosis40.486336
Mean19.348718
Median Absolute Deviation (MAD)4
Skewness5.7400464
Sum3773
Variance507.77468
MonotonicityNot monotonic
2024-01-28T16:32:26.327352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10 26
 
13.3%
9 17
 
8.7%
15 16
 
8.2%
11 11
 
5.6%
7 10
 
5.1%
12 10
 
5.1%
13 10
 
5.1%
17 9
 
4.6%
14 9
 
4.6%
18 9
 
4.6%
Other values (32) 68
34.9%
ValueCountFrequency (%)
3 1
 
0.5%
5 2
 
1.0%
6 2
 
1.0%
7 10
 
5.1%
8 6
 
3.1%
9 17
8.7%
10 26
13.3%
11 11
5.6%
12 10
 
5.1%
13 10
 
5.1%
ValueCountFrequency (%)
213 1
 
0.5%
166 1
 
0.5%
142 1
 
0.5%
60 1
 
0.5%
59 1
 
0.5%
58 1
 
0.5%
55 1
 
0.5%
53 1
 
0.5%
50 3
1.5%
49 1
 
0.5%

전체여성(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3948718
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-28T16:32:26.426261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile18.6
Maximum89
Range88
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.1880586
Coefficient of variation (CV)1.1072617
Kurtosis57.080474
Mean7.3948718
Median Absolute Deviation (MAD)2
Skewness6.539648
Sum1442
Variance67.044303
MonotonicityNot monotonic
2024-01-28T16:32:26.522994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 34
17.4%
6 27
13.8%
5 27
13.8%
3 20
10.3%
8 16
8.2%
2 13
 
6.7%
7 11
 
5.6%
9 11
 
5.6%
11 7
 
3.6%
10 6
 
3.1%
Other values (14) 23
11.8%
ValueCountFrequency (%)
1 2
 
1.0%
2 13
 
6.7%
3 20
10.3%
4 34
17.4%
5 27
13.8%
6 27
13.8%
7 11
 
5.6%
8 16
8.2%
9 11
 
5.6%
10 6
 
3.1%
ValueCountFrequency (%)
89 1
 
0.5%
57 1
 
0.5%
25 1
 
0.5%
23 1
 
0.5%
22 2
1.0%
21 1
 
0.5%
20 3
1.5%
18 1
 
0.5%
17 2
1.0%
16 2
1.0%

전체여성비율
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.489231
Minimum3.3
Maximum82.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-28T16:32:26.639014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile20.77
Q133.3
median40
Q345.85
95-th percentile61.85
Maximum82.4
Range79.1
Interquartile range (IQR)12.55

Descriptive statistics

Standard deviation12.152745
Coefficient of variation (CV)0.30014759
Kurtosis1.3150439
Mean40.489231
Median Absolute Deviation (MAD)6.7
Skewness0.38462124
Sum7895.4
Variance147.68921
MonotonicityNot monotonic
2024-01-28T16:32:26.772620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 24
 
12.3%
33.3 18
 
9.2%
50.0 14
 
7.2%
42.9 9
 
4.6%
44.4 8
 
4.1%
30.0 6
 
3.1%
38.5 5
 
2.6%
28.6 5
 
2.6%
57.1 4
 
2.1%
66.7 4
 
2.1%
Other values (62) 98
50.3%
ValueCountFrequency (%)
3.3 1
 
0.5%
12.1 1
 
0.5%
12.5 1
 
0.5%
13.5 1
 
0.5%
14.3 1
 
0.5%
18.5 1
 
0.5%
20.0 4
2.1%
21.1 1
 
0.5%
21.4 1
 
0.5%
22.2 2
1.0%
ValueCountFrequency (%)
82.4 1
 
0.5%
77.8 1
 
0.5%
75.0 1
 
0.5%
73.3 1
 
0.5%
66.7 4
2.1%
64.7 1
 
0.5%
63.6 1
 
0.5%
61.1 1
 
0.5%
60.0 4
2.1%
57.1 4
2.1%

위촉직위원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.384615
Minimum2
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-28T16:32:26.887253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median11
Q318
95-th percentile47.3
Maximum211
Range209
Interquartile range (IQR)11

Descriptive statistics

Standard deviation22.345478
Coefficient of variation (CV)1.3638085
Kurtosis43.045527
Mean16.384615
Median Absolute Deviation (MAD)4
Skewness5.9702254
Sum3195
Variance499.32038
MonotonicityNot monotonic
2024-01-28T16:32:26.990288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7 26
 
13.3%
8 20
 
10.3%
9 13
 
6.7%
11 12
 
6.2%
18 11
 
5.6%
10 11
 
5.6%
12 10
 
5.1%
5 9
 
4.6%
13 8
 
4.1%
14 8
 
4.1%
Other values (31) 67
34.4%
ValueCountFrequency (%)
2 3
 
1.5%
3 2
 
1.0%
4 3
 
1.5%
5 9
 
4.6%
6 7
 
3.6%
7 26
13.3%
8 20
10.3%
9 13
6.7%
10 11
5.6%
11 12
6.2%
ValueCountFrequency (%)
211 1
0.5%
166 1
0.5%
137 1
0.5%
56 1
0.5%
55 2
1.0%
51 1
0.5%
49 1
0.5%
48 2
1.0%
47 1
0.5%
40 1
0.5%

위촉직여성(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9435897
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-28T16:32:27.084624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q38
95-th percentile18.3
Maximum89
Range88
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.1798027
Coefficient of variation (CV)1.1780366
Kurtosis58.729805
Mean6.9435897
Median Absolute Deviation (MAD)2
Skewness6.6684308
Sum1354
Variance66.909173
MonotonicityNot monotonic
2024-01-28T16:32:27.176953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 43
22.1%
3 29
14.9%
5 22
11.3%
6 17
 
8.7%
8 15
 
7.7%
2 12
 
6.2%
7 12
 
6.2%
9 9
 
4.6%
10 7
 
3.6%
11 5
 
2.6%
Other values (14) 24
12.3%
ValueCountFrequency (%)
1 5
 
2.6%
2 12
 
6.2%
3 29
14.9%
4 43
22.1%
5 22
11.3%
6 17
 
8.7%
7 12
 
6.2%
8 15
 
7.7%
9 9
 
4.6%
10 7
 
3.6%
ValueCountFrequency (%)
89 1
 
0.5%
57 1
 
0.5%
24 1
 
0.5%
22 2
1.0%
21 1
 
0.5%
20 3
1.5%
19 1
 
0.5%
18 1
 
0.5%
17 1
 
0.5%
16 1
 
0.5%

위촉직여성비율
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.233846
Minimum3.3
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-28T16:32:27.278899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile32.55
Q141.7
median44.4
Q350
95-th percentile62.5
Maximum80
Range76.7
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation10.042938
Coefficient of variation (CV)0.21722047
Kurtosis3.1421918
Mean46.233846
Median Absolute Deviation (MAD)4.8
Skewness-0.14971752
Sum9015.6
Variance100.8606
MonotonicityNot monotonic
2024-01-28T16:32:27.407553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0 37
19.0%
42.9 24
 
12.3%
40.0 17
 
8.7%
44.4 17
 
8.7%
57.1 8
 
4.1%
45.5 7
 
3.6%
60.0 6
 
3.1%
41.7 6
 
3.1%
66.7 5
 
2.6%
33.3 4
 
2.1%
Other values (42) 64
32.8%
ValueCountFrequency (%)
3.3 1
 
0.5%
14.3 2
1.0%
20.8 1
 
0.5%
22.2 1
 
0.5%
25.0 2
1.0%
28.6 1
 
0.5%
30.8 2
1.0%
33.3 4
2.1%
35.7 1
 
0.5%
36.4 3
1.5%
ValueCountFrequency (%)
80.0 1
 
0.5%
76.9 1
 
0.5%
75.0 1
 
0.5%
71.4 1
 
0.5%
66.7 5
2.6%
62.5 3
 
1.5%
60.0 6
3.1%
58.8 1
 
0.5%
58.3 1
 
0.5%
57.1 8
4.1%

부서
Text

Distinct123
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T16:32:27.625473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length6.1641026
Min length3

Characters and Unicode

Total characters1202
Distinct characters173
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

Unique82 ?
Unique (%)42.1%

Sample

1st row영종청라계획과
2nd row농축산유통과
3rd row노동정책과
4th row토지정보과
5th row건강증진과
ValueCountFrequency (%)
농축산유통과 9
 
4.3%
청소년정책과 6
 
2.9%
소상공인정책과 5
 
2.4%
예산담당관실 5
 
2.4%
문화유산과 5
 
2.4%
토지정보과 4
 
1.9%
건설심사과 4
 
1.9%
보건의료정책과 4
 
1.9%
건강증진과 4
 
1.9%
산업진흥과 4
 
1.9%
Other values (114) 158
76.0%
2024-01-28T16:32:27.975262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 134
 
11.1%
정 67
 
5.6%
관 54
 
4.5%
책 53
 
4.4%
담 33
 
2.7%
당 33
 
2.7%
산 30
 
2.5%
시 23
 
1.9%
도 20
 
1.7%
소 18
 
1.5%
Other values (163) 737
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1182
98.3%
Space Separator 16
 
1.3%
Other Punctuation 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 134
 
11.3%
정 67
 
5.7%
관 54
 
4.6%
책 53
 
4.5%
담 33
 
2.8%
당 33
 
2.8%
산 30
 
2.5%
시 23
 
1.9%
도 20
 
1.7%
소 18
 
1.5%
Other values (159) 717
60.7%
Space Separator
ValueCountFrequency (%)
16
100.0%
Other Punctuation
ValueCountFrequency (%)
? 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1182
98.3%
Common 20
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 134
 
11.3%
정 67
 
5.7%
관 54
 
4.6%
책 53
 
4.5%
담 33
 
2.8%
당 33
 
2.8%
산 30
 
2.5%
시 23
 
1.9%
도 20
 
1.7%
소 18
 
1.5%
Other values (159) 717
60.7%
Common
ValueCountFrequency (%)
16
80.0%
? 2
 
10.0%
) 1
 
5.0%
( 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1182
98.3%
ASCII 20
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 134
 
11.3%
정 67
 
5.7%
관 54
 
4.6%
책 53
 
4.5%
담 33
 
2.8%
당 33
 
2.8%
산 30
 
2.5%
시 23
 
1.9%
도 20
 
1.7%
소 18
 
1.5%
Other values (159) 717
60.7%
ASCII
ValueCountFrequency (%)
16
80.0%
? 2
 
10.0%
) 1
 
5.0%
( 1
 
5.0%

연락처
Text

Distinct184
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T16:32:28.203678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.923077
Min length8

Characters and Unicode

Total characters2325
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)88.7%

Sample

1st row032-453-7593
2nd row032-440-4394
3rd row032-440-4406
4th row032-440-4597
5th row032-440-2722
ValueCountFrequency (%)
032-440-2892 2
 
1.0%
032-440-2854 2
 
1.0%
032-458-7154 2
 
1.0%
032-440-2842 2
 
1.0%
032-832-9412 2
 
1.0%
032-440-6905 2
 
1.0%
032-440-1585 2
 
1.0%
032-440-4602 2
 
1.0%
032-440-4227 2
 
1.0%
032-440-2137 2
 
1.0%
Other values (171) 175
89.7%
2024-01-28T16:32:28.545717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 467
20.1%
0 398
17.1%
- 383
16.5%
2 355
15.3%
3 310
13.3%
5 87
 
3.7%
7 83
 
3.6%
8 67
 
2.9%
6 57
 
2.5%
1 53
 
2.3%
Other values (12) 65
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1925
82.8%
Dash Punctuation 383
 
16.5%
Other Letter 11
 
0.5%
Other Punctuation 6
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 467
24.3%
0 398
20.7%
2 355
18.4%
3 310
16.1%
5 87
 
4.5%
7 83
 
4.3%
8 67
 
3.5%
6 57
 
3.0%
1 53
 
2.8%
9 48
 
2.5%
Other Letter
ValueCountFrequency (%)
원 2
18.2%
급 1
9.1%
식 1
9.1%
지 1
9.1%
센 1
9.1%
터 1
9.1%
운 1
9.1%
영 1
9.1%
위 1
9.1%
회 1
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 383
100.0%
Other Punctuation
ValueCountFrequency (%)
? 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2314
99.5%
Hangul 11
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 467
20.2%
0 398
17.2%
- 383
16.6%
2 355
15.3%
3 310
13.4%
5 87
 
3.8%
7 83
 
3.6%
8 67
 
2.9%
6 57
 
2.5%
1 53
 
2.3%
Other values (2) 54
 
2.3%
Hangul
ValueCountFrequency (%)
원 2
18.2%
급 1
9.1%
식 1
9.1%
지 1
9.1%
센 1
9.1%
터 1
9.1%
운 1
9.1%
영 1
9.1%
위 1
9.1%
회 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2314
99.5%
Hangul 11
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 467
20.2%
0 398
17.2%
- 383
16.6%
2 355
15.3%
3 310
13.4%
5 87
 
3.8%
7 83
 
3.6%
8 67
 
2.9%
6 57
 
2.5%
1 53
 
2.3%
Other values (2) 54
 
2.3%
Hangul
ValueCountFrequency (%)
원 2
18.2%
급 1
9.1%
식 1
9.1%
지 1
9.1%
센 1
9.1%
터 1
9.1%
운 1
9.1%
영 1
9.1%
위 1
9.1%
회 1
9.1%

Interactions

2024-01-28T16:32:24.724200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:22.637468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.034091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.430457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.891835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.312737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.799967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:22.696918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.095494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.502048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.953564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.377071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:25.121157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:22.758763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.153810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.575683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.018446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.436493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:25.217737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:22.827844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.231068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.658451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.108218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.511262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:25.306240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:22.894633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.294541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.727618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.174246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.574355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:25.382607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:22.959396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.356954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:23.801136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.238053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:32:24.643342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T16:32:28.631747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체위원(명)전체여성(명)전체여성비율위촉직위원(명)위촉직여성(명)위촉직여성비율
전체위원(명)1.0000.9260.7000.9990.9310.692
전체여성(명)0.9261.0000.1940.9501.0000.264
전체여성비율0.7000.1941.0000.6920.2060.906
위촉직위원(명)0.9990.9500.6921.0000.9560.687
위촉직여성(명)0.9311.0000.2060.9561.0000.284
위촉직여성비율0.6920.2640.9060.6870.2841.000
2024-01-28T16:32:28.723845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체위원(명)전체여성(명)전체여성비율위촉직위원(명)위촉직여성(명)위촉직여성비율
전체위원(명)1.0000.830-0.1150.9210.838-0.298
전체여성(명)0.8301.0000.3950.8730.9530.058
전체여성비율-0.1150.3951.0000.0690.3080.645
위촉직위원(명)0.9210.8730.0691.0000.922-0.271
위촉직여성(명)0.8380.9530.3080.9221.0000.056
위촉직여성비율-0.2980.0580.645-0.2710.0561.000

Missing values

2024-01-28T16:32:25.492158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T16:32:25.621187image/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

위원회명전체위원(명)전체여성(명)전체여성비율위촉직위원(명)위촉직여성(명)위촉직여성비율부서연락처
0IFEZ교통영향평가심의위원회492040.8472042.6영종청라계획과032-453-7593
1가축방역심의회15533.37342.9농축산유통과032-440-4394
2감정노동종사자권리보장위원회10550.09555.6노동정책과032-440-4406
3감정평가업자선정위원회9333.37342.9토지정보과032-440-4597
4건강생활실천협의회181055.6171058.8건강증진과032-440-2722
5건축물미술작품심의위원회502142.0481939.6문화콘텐츠과032-440-3998
6건축사징계위원회9333.36350.0건축과032-440-4725
7건축위원회592237.3552240.0건축과032-440-4725
8경관위원회552341.8512141.2도시디자인단440-4784
9경제자유구역청건축위원회602236.7552240.0도시건축과032-453-7225
위원회명전체위원(명)전체여성(명)전체여성비율위촉직위원(명)위촉직여성(명)위촉직여성비율부서연락처
185하도급계약심사위원회10220.05240.0건설심사과032-440-3735
186하수도기술심의위원회10440.010440.0하수과032-440-3702
187학교밖청소년지원위원회9666.77457.1청소년정책과032-440-2854
188학교폭력대책지역위원회11763.68562.5청소년정책과032-440-2854
189항공정책위원회12433.310440.0항공과032-440-4802
190해양공간관리지역위원회14321.47342.9해양항공국 해양친수과032-458-7154
191행정심판위원회502040.0482041.7법무담당관032-440-2202
192홍보대사추천위원회10550.07457.1대변인실032-440-3044
193화장시설주변지역주민지원기금운용심의위원회9333.37342.9노인정책과032-440-2834
194환경정책위원회301240.0261142.3환경기후정책과032-440-3518