Overview

Dataset statistics

Number of variables15
Number of observations5932
Missing cells70
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory759.0 KiB
Average record size in memory131.0 B

Variable types

Numeric7
Categorical7
Text1

Dataset

Description전국 시도에 등록된 부동산개벌업체의 전년대비 전문인력 증감 현황 정보(인력구분 및 인력에 대한 상태 정보, 등급(초급, 중급, 고급, 특급))
Author국토교통부
URLhttps://www.data.go.kr/data/15063639/fileData.do

Alerts

보고_년도_해당없음_수 is highly overall correlated with 보고_년도_고급_수 and 4 other fieldsHigh correlation
보고_년도_고급_수 is highly overall correlated with 보고_년도_해당없음_수 and 3 other fieldsHigh correlation
보고_년도_특급_수 is highly overall correlated with 보고_년도_해당없음_수 and 3 other fieldsHigh correlation
전년_보고_년도_해당없음_수 is highly overall correlated with 보고_년도_해당없음_수High correlation
전년_보고_년도_고급_수 is highly overall correlated with 보고_년도_해당없음_수 and 3 other fieldsHigh correlation
전년_보고_년도_특급_수 is highly overall correlated with 보고_년도_해당없음_수 and 3 other fieldsHigh correlation
보고_년도_초급_수 is highly imbalanced (99.1%)Imbalance
보고_년도_중급_수 is highly imbalanced (97.9%)Imbalance
전년_보고_년도_초급_수 is highly imbalanced (99.3%)Imbalance
전년_보고_년도_중급_수 is highly imbalanced (98.5%)Imbalance
시군구_명 has 70 (1.2%) missing valuesMissing
보고_년도_해당없음_수 has 2330 (39.3%) zerosZeros
보고_년도_고급_수 has 4318 (72.8%) zerosZeros
보고_년도_특급_수 has 4035 (68.0%) zerosZeros
전년_보고_년도_해당없음_수 has 4495 (75.8%) zerosZeros
전년_보고_년도_고급_수 has 4675 (78.8%) zerosZeros
전년_보고_년도_특급_수 has 4466 (75.3%) zerosZeros

Reproduction

Analysis started2023-12-12 19:56:31.310316
Analysis finished2023-12-12 19:56:39.701005
Duration8.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년도
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.1984
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:39.753016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9799818
Coefficient of variation (CV)0.00098057813
Kurtosis-1.1941413
Mean2019.1984
Median Absolute Deviation (MAD)2
Skewness-0.15557099
Sum11977885
Variance3.920328
MonotonicityIncreasing
2023-12-13T04:56:39.892370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 980
16.5%
2020 935
15.8%
2022 917
15.5%
2018 868
14.6%
2019 816
13.8%
2016 769
13.0%
2017 647
10.9%
ValueCountFrequency (%)
2016 769
13.0%
2017 647
10.9%
2018 868
14.6%
2019 816
13.8%
2020 935
15.8%
2021 980
16.5%
2022 917
15.5%
ValueCountFrequency (%)
2022 917
15.5%
2021 980
16.5%
2020 935
15.8%
2019 816
13.8%
2018 868
14.6%
2017 647
10.9%
2016 769
13.0%

시도_명
Categorical

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
경기도
1714 
서울특별시
1330 
부산광역시
452 
인천광역시
331 
충청남도
300 
Other values (13)
1805 

Length

Max length7
Median length5
Mean length4.2855698
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 1714
28.9%
서울특별시 1330
22.4%
부산광역시 452
 
7.6%
인천광역시 331
 
5.6%
충청남도 300
 
5.1%
경상남도 251
 
4.2%
전라남도 189
 
3.2%
광주광역시 188
 
3.2%
경상북도 184
 
3.1%
충청북도 173
 
2.9%
Other values (8) 820
13.8%

Length

2023-12-13T04:56:40.028049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1714
28.9%
서울특별시 1330
22.4%
부산광역시 452
 
7.6%
인천광역시 331
 
5.6%
충청남도 300
 
5.1%
경상남도 251
 
4.2%
전라남도 189
 
3.2%
광주광역시 188
 
3.2%
경상북도 184
 
3.1%
충청북도 173
 
2.9%
Other values (8) 820
13.8%

시군구_명
Text

MISSING 

Distinct197
Distinct (%)3.4%
Missing70
Missing (%)1.2%
Memory size46.5 KiB
2023-12-13T04:56:40.359688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.7773797
Min length1

Characters and Unicode

Total characters22143
Distinct characters135
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.2%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구
ValueCountFrequency (%)
중구 262
 
3.7%
용인시 234
 
3.3%
강남구 186
 
2.6%
서구 170
 
2.4%
서초구 145
 
2.0%
성남시 138
 
1.9%
영등포구 134
 
1.9%
수원시 133
 
1.9%
남구 122
 
1.7%
청주시 119
 
1.7%
Other values (196) 5436
76.8%
2023-12-13T04:56:40.842200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 3915
 
17.7%
시 2798
 
12.6%
1217
 
5.5%
남 666
 
3.0%
주 609
 
2.8%
산 597
 
2.7%
서 580
 
2.6%
성 564
 
2.5%
동 522
 
2.4%
군 499
 
2.3%
Other values (125) 10176
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20890
94.3%
Space Separator 1217
 
5.5%
Decimal Number 36
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3915
18.7%
시 2798
 
13.4%
남 666
 
3.2%
주 609
 
2.9%
산 597
 
2.9%
서 580
 
2.8%
성 564
 
2.7%
동 522
 
2.5%
군 499
 
2.4%
양 472
 
2.3%
Other values (123) 9668
46.3%
Space Separator
ValueCountFrequency (%)
1217
100.0%
Decimal Number
ValueCountFrequency (%)
0 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20890
94.3%
Common 1253
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3915
18.7%
시 2798
 
13.4%
남 666
 
3.2%
주 609
 
2.9%
산 597
 
2.9%
서 580
 
2.8%
성 564
 
2.7%
동 522
 
2.5%
군 499
 
2.4%
양 472
 
2.3%
Other values (123) 9668
46.3%
Common
ValueCountFrequency (%)
1217
97.1%
0 36
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20890
94.3%
ASCII 1253
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 3915
18.7%
시 2798
 
13.4%
남 666
 
3.2%
주 609
 
2.9%
산 597
 
2.9%
서 580
 
2.8%
성 564
 
2.7%
동 522
 
2.5%
군 499
 
2.4%
양 472
 
2.3%
Other values (123) 9668
46.3%
ASCII
ValueCountFrequency (%)
1217
97.1%
0 36
 
2.9%

인력_구분_코드명
Categorical

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
건설기술자
2186 
학사학위경력자
961 
실무경력자
530 
금융기관
518 
석사학위경력자
385 
Other values (13)
1352 

Length

Max length9
Median length5
Mean length5.3553608
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건축사
2nd row건설기술자
3rd row건설기술자
4th row자산운용전문인력
5th row자산운용전문인력

Common Values

ValueCountFrequency (%)
건설기술자 2186
36.9%
학사학위경력자 961
16.2%
실무경력자 530
 
8.9%
금융기관 518
 
8.7%
석사학위경력자 385
 
6.5%
공인중개사 346
 
5.8%
건축사 247
 
4.2%
국가.지자체 158
 
2.7%
자산운용전문인력 137
 
2.3%
자격자 95
 
1.6%
Other values (8) 369
 
6.2%

Length

2023-12-13T04:56:40.982680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건설기술자 2186
36.9%
학사학위경력자 961
16.2%
실무경력자 530
 
8.9%
금융기관 518
 
8.7%
석사학위경력자 385
 
6.5%
공인중개사 346
 
5.8%
건축사 247
 
4.2%
국가.지자체 158
 
2.7%
자산운용전문인력 137
 
2.3%
자격자 95
 
1.6%
Other values (8) 369
 
6.2%

인력_상태_코드명
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
정상
2960 
삭제
2564 
등록말소
405 
삭제_퇴사
 
3

Length

Max length5
Median length2
Mean length2.1380647
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row삭제
2nd row정상
3rd row삭제
4th row정상
5th row삭제

Common Values

ValueCountFrequency (%)
정상 2960
49.9%
삭제 2564
43.2%
등록말소 405
 
6.8%
삭제_퇴사 3
 
0.1%

Length

2023-12-13T04:56:41.106365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:56:41.206387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 2960
49.9%
삭제 2564
43.2%
등록말소 405
 
6.8%
삭제_퇴사 3
 
0.1%

보고_년도_해당없음_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0352326
Minimum0
Maximum163
Zeros2330
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:41.315141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum163
Range163
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.7685393
Coefficient of variation (CV)3.3256833
Kurtosis197.66324
Mean2.0352326
Median Absolute Deviation (MAD)1
Skewness12.13793
Sum12073
Variance45.813124
MonotonicityNot monotonic
2023-12-13T04:56:41.453940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2330
39.3%
1 1848
31.2%
2 806
 
13.6%
3 287
 
4.8%
4 197
 
3.3%
5 87
 
1.5%
6 71
 
1.2%
7 45
 
0.8%
8 43
 
0.7%
9 28
 
0.5%
Other values (54) 190
 
3.2%
ValueCountFrequency (%)
0 2330
39.3%
1 1848
31.2%
2 806
 
13.6%
3 287
 
4.8%
4 197
 
3.3%
5 87
 
1.5%
6 71
 
1.2%
7 45
 
0.8%
8 43
 
0.7%
9 28
 
0.5%
ValueCountFrequency (%)
163 1
< 0.1%
142 1
< 0.1%
132 1
< 0.1%
126 1
< 0.1%
115 1
< 0.1%
114 1
< 0.1%
107 1
< 0.1%
97 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%

보고_년도_초급_수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
0
5922 
1
 
5
4
 
2
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5922
99.8%
1 5
 
0.1%
4 2
 
< 0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%

Length

2023-12-13T04:56:41.575835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:56:41.661592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5922
99.8%
1 5
 
0.1%
4 2
 
< 0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%

보고_년도_중급_수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
0
5905 
1
 
17
2
 
5
3
 
4
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5905
99.5%
1 17
 
0.3%
2 5
 
0.1%
3 4
 
0.1%
6 1
 
< 0.1%

Length

2023-12-13T04:56:41.774773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:56:41.872944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5905
99.5%
1 17
 
0.3%
2 5
 
0.1%
3 4
 
0.1%
6 1
 
< 0.1%

보고_년도_고급_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83985165
Minimum0
Maximum37
Zeros4318
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:41.970201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2740818
Coefficient of variation (CV)2.7077185
Kurtosis65.541967
Mean0.83985165
Median Absolute Deviation (MAD)0
Skewness6.3732812
Sum4982
Variance5.1714482
MonotonicityNot monotonic
2023-12-13T04:56:42.081377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4318
72.8%
1 609
 
10.3%
2 378
 
6.4%
3 178
 
3.0%
4 142
 
2.4%
5 99
 
1.7%
6 52
 
0.9%
7 40
 
0.7%
8 37
 
0.6%
9 24
 
0.4%
Other values (19) 55
 
0.9%
ValueCountFrequency (%)
0 4318
72.8%
1 609
 
10.3%
2 378
 
6.4%
3 178
 
3.0%
4 142
 
2.4%
5 99
 
1.7%
6 52
 
0.9%
7 40
 
0.7%
8 37
 
0.6%
9 24
 
0.4%
ValueCountFrequency (%)
37 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
23 2
< 0.1%
22 3
0.1%
20 2
< 0.1%

보고_년도_특급_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1974039
Minimum0
Maximum50
Zeros4035
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:42.231856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum50
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0902199
Coefficient of variation (CV)2.5807665
Kurtosis55.153681
Mean1.1974039
Median Absolute Deviation (MAD)0
Skewness5.9499302
Sum7103
Variance9.5494588
MonotonicityNot monotonic
2023-12-13T04:56:42.371899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 4035
68.0%
1 638
 
10.8%
2 380
 
6.4%
3 243
 
4.1%
4 173
 
2.9%
5 115
 
1.9%
6 77
 
1.3%
7 58
 
1.0%
8 43
 
0.7%
9 35
 
0.6%
Other values (25) 135
 
2.3%
ValueCountFrequency (%)
0 4035
68.0%
1 638
 
10.8%
2 380
 
6.4%
3 243
 
4.1%
4 173
 
2.9%
5 115
 
1.9%
6 77
 
1.3%
7 58
 
1.0%
8 43
 
0.7%
9 35
 
0.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
47 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
36 2
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
31 4
0.1%
26 3
0.1%

전년_보고_년도_해당없음_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2493257
Minimum0
Maximum163
Zeros4495
Zeros (%)75.8%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:42.491901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum163
Range163
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.1558778
Coefficient of variation (CV)4.9273603
Kurtosis263.7769
Mean1.2493257
Median Absolute Deviation (MAD)0
Skewness14.034215
Sum7411
Variance37.894832
MonotonicityNot monotonic
2023-12-13T04:56:42.639551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4495
75.8%
1 507
 
8.5%
2 329
 
5.5%
3 135
 
2.3%
4 117
 
2.0%
5 64
 
1.1%
6 52
 
0.9%
7 31
 
0.5%
8 30
 
0.5%
9 24
 
0.4%
Other values (48) 148
 
2.5%
ValueCountFrequency (%)
0 4495
75.8%
1 507
 
8.5%
2 329
 
5.5%
3 135
 
2.3%
4 117
 
2.0%
5 64
 
1.1%
6 52
 
0.9%
7 31
 
0.5%
8 30
 
0.5%
9 24
 
0.4%
ValueCountFrequency (%)
163 1
< 0.1%
142 1
< 0.1%
132 1
< 0.1%
126 1
< 0.1%
115 1
< 0.1%
107 1
< 0.1%
97 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%
70 1
< 0.1%

전년_보고_년도_초급_수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
0
5925 
1
 
5
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5925
99.9%
1 5
 
0.1%
4 1
 
< 0.1%
2 1
 
< 0.1%

Length

2023-12-13T04:56:42.764059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:56:42.856885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5925
99.9%
1 5
 
0.1%
4 1
 
< 0.1%
2 1
 
< 0.1%

전년_보고_년도_중급_수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.5 KiB
0
5914 
1
 
10
2
 
3
3
 
3
6
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5914
99.7%
1 10
 
0.2%
2 3
 
0.1%
3 3
 
0.1%
6 2
 
< 0.1%

Length

2023-12-13T04:56:42.953557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:56:43.045791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5914
99.7%
1 10
 
0.2%
2 3
 
0.1%
3 3
 
0.1%
6 2
 
< 0.1%

전년_보고_년도_고급_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75168577
Minimum0
Maximum37
Zeros4675
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:43.133552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2935489
Coefficient of variation (CV)3.051207
Kurtosis68.254215
Mean0.75168577
Median Absolute Deviation (MAD)0
Skewness6.6382582
Sum4459
Variance5.2603664
MonotonicityNot monotonic
2023-12-13T04:56:43.246280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 4675
78.8%
1 375
 
6.3%
2 286
 
4.8%
3 170
 
2.9%
4 121
 
2.0%
5 100
 
1.7%
6 54
 
0.9%
7 39
 
0.7%
8 35
 
0.6%
9 20
 
0.3%
Other values (18) 57
 
1.0%
ValueCountFrequency (%)
0 4675
78.8%
1 375
 
6.3%
2 286
 
4.8%
3 170
 
2.9%
4 121
 
2.0%
5 100
 
1.7%
6 54
 
0.9%
7 39
 
0.7%
8 35
 
0.6%
9 20
 
0.3%
ValueCountFrequency (%)
37 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
28 2
< 0.1%
27 2
< 0.1%
23 2
< 0.1%
22 3
0.1%
20 2
< 0.1%
18 2
< 0.1%

전년_보고_년도_특급_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0999663
Minimum0
Maximum50
Zeros4466
Zeros (%)75.3%
Negative0
Negative (%)0.0%
Memory size52.3 KiB
2023-12-13T04:56:43.371954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1654144
Coefficient of variation (CV)2.8777376
Kurtosis56.64994
Mean1.0999663
Median Absolute Deviation (MAD)0
Skewness6.0809633
Sum6525
Variance10.019848
MonotonicityNot monotonic
2023-12-13T04:56:43.500562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 4466
75.3%
1 373
 
6.3%
2 271
 
4.6%
3 200
 
3.4%
4 158
 
2.7%
5 110
 
1.9%
6 78
 
1.3%
7 58
 
1.0%
8 38
 
0.6%
9 35
 
0.6%
Other values (27) 145
 
2.4%
ValueCountFrequency (%)
0 4466
75.3%
1 373
 
6.3%
2 271
 
4.6%
3 200
 
3.4%
4 158
 
2.7%
5 110
 
1.9%
6 78
 
1.3%
7 58
 
1.0%
8 38
 
0.6%
9 35
 
0.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
36 2
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
31 4
0.1%
28 1
 
< 0.1%

Interactions

2023-12-13T04:56:38.274748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:32.965125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.841076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.701952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.634657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.504477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.521405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:38.387503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.069002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.965104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.834064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.782553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.666245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.627326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:38.493685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.193140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.089253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.953085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.925048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.824775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.731779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:38.590999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.298586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.213883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.099868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.045771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.966385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.850515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:38.979754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.394706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.319388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.227085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.147876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.085039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.965739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:39.115041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.576030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.459942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.369314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.269746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.231896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:38.073537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:39.235480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:33.740161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:34.584665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:35.487548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:36.404481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:37.374264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:38.180712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:56:43.588510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년도시도_명인력_구분_코드명인력_상태_코드명보고_년도_해당없음_수보고_년도_초급_수보고_년도_중급_수보고_년도_고급_수보고_년도_특급_수전년_보고_년도_해당없음_수전년_보고_년도_초급_수전년_보고_년도_중급_수전년_보고_년도_고급_수전년_보고_년도_특급_수
기준_년도1.0000.1140.1270.1220.0540.0000.0670.0630.0510.0530.0330.0390.0420.031
시도_명0.1141.0000.3830.0640.1200.1410.1530.0960.0710.0900.1480.1250.0850.085
인력_구분_코드명0.1270.3831.0000.1010.2000.0770.0000.2790.2870.1710.0000.0000.2730.288
인력_상태_코드명0.1220.0640.1011.0000.0000.0000.0000.0000.0210.0000.0000.0190.0000.029
보고_년도_해당없음_수0.0540.1200.2000.0001.0000.1400.0000.0000.0000.9130.0000.0000.0000.000
보고_년도_초급_수0.0000.1410.0770.0000.1401.0000.1250.0000.0000.0000.4780.0000.0000.000
보고_년도_중급_수0.0670.1530.0000.0000.0000.1251.0000.2480.1930.0000.0000.0000.1760.176
보고_년도_고급_수0.0630.0960.2790.0000.0000.0000.2481.0000.8510.0000.0000.1660.8790.874
보고_년도_특급_수0.0510.0710.2870.0210.0000.0000.1930.8511.0000.0000.0000.2110.8620.861
전년_보고_년도_해당없음_수0.0530.0900.1710.0000.9130.0000.0000.0000.0001.0000.0000.0000.0000.000
전년_보고_년도_초급_수0.0330.1480.0000.0000.0000.4780.0000.0000.0000.0001.0000.0940.0000.000
전년_보고_년도_중급_수0.0390.1250.0000.0190.0000.0000.0000.1660.2110.0000.0941.0000.2990.233
전년_보고_년도_고급_수0.0420.0850.2730.0000.0000.0000.1760.8790.8620.0000.0000.2991.0000.846
전년_보고_년도_특급_수0.0310.0850.2880.0290.0000.0000.1760.8740.8610.0000.0000.2330.8461.000
2023-12-13T04:56:43.709291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인력_상태_코드명시도_명보고_년도_초급_수보고_년도_중급_수전년_보고_년도_초급_수전년_보고_년도_중급_수인력_구분_코드명
인력_상태_코드명1.0000.0350.0000.0000.0000.0150.055
시도_명0.0351.0000.0720.0780.0810.0630.098
보고_년도_초급_수0.0000.0721.0000.0470.4070.0000.039
보고_년도_중급_수0.0000.0780.0471.0000.0000.0000.000
전년_보고_년도_초급_수0.0000.0810.4070.0001.0000.0770.000
전년_보고_년도_중급_수0.0150.0630.0000.0000.0771.0000.000
인력_구분_코드명0.0550.0980.0390.0000.0000.0001.000
2023-12-13T04:56:43.808878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년도보고_년도_해당없음_수보고_년도_고급_수보고_년도_특급_수전년_보고_년도_해당없음_수전년_보고_년도_고급_수전년_보고_년도_특급_수시도_명인력_구분_코드명인력_상태_코드명보고_년도_초급_수보고_년도_중급_수전년_보고_년도_초급_수전년_보고_년도_중급_수
기준_년도1.0000.087-0.052-0.0980.133-0.040-0.0530.0550.0680.0730.0160.0410.0220.027
보고_년도_해당없음_수0.0871.000-0.641-0.7170.533-0.547-0.6050.0460.0780.0000.0590.0000.0000.000
보고_년도_고급_수-0.052-0.6411.0000.576-0.3130.6370.6540.0370.1100.0000.0000.1050.0000.069
보고_년도_특급_수-0.098-0.7170.5761.000-0.3500.6500.7130.0240.0940.0000.0000.0800.0000.084
전년_보고_년도_해당없음_수0.1330.533-0.313-0.3501.000-0.262-0.2920.0340.0660.0000.0000.0000.0000.000
전년_보고_년도_고급_수-0.040-0.5470.6370.650-0.2621.0000.7020.0320.1080.0000.0000.0740.0000.128
전년_보고_년도_특급_수-0.053-0.6050.6540.713-0.2920.7021.0000.0300.0950.0020.0000.0660.0000.098
시도_명0.0550.0460.0370.0240.0340.0320.0301.0000.0980.0350.0720.0780.0810.063
인력_구분_코드명0.0680.0780.1100.0940.0660.1080.0950.0981.0000.0550.0390.0000.0000.000
인력_상태_코드명0.0730.0000.0000.0000.0000.0000.0020.0350.0551.0000.0000.0000.0000.015
보고_년도_초급_수0.0160.0590.0000.0000.0000.0000.0000.0720.0390.0001.0000.0470.4070.000
보고_년도_중급_수0.0410.0000.1050.0800.0000.0740.0660.0780.0000.0000.0471.0000.0000.000
전년_보고_년도_초급_수0.0220.0000.0000.0000.0000.0000.0000.0810.0000.0000.4070.0001.0000.077
전년_보고_년도_중급_수0.0270.0000.0690.0840.0000.1280.0980.0630.0000.0150.0000.0000.0771.000

Missing values

2023-12-13T04:56:39.376244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:56:39.600560image/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

기준_년도시도_명시군구_명인력_구분_코드명인력_상태_코드명보고_년도_해당없음_수보고_년도_초급_수보고_년도_중급_수보고_년도_고급_수보고_년도_특급_수전년_보고_년도_해당없음_수전년_보고_년도_초급_수전년_보고_년도_중급_수전년_보고_년도_고급_수전년_보고_년도_특급_수
02016서울특별시종로구건축사삭제1000040000
12016서울특별시종로구건설기술자정상0001200002
22016서울특별시종로구건설기술자삭제0002200026
32016서울특별시종로구자산운용전문인력정상4000020000
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