Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory132.4 B

Variable types

DateTime2
Categorical3
Text3
Numeric7

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/9774c6bd-c1fb-4959-a9b9-f9f69500636f

Alerts

기준년월 has constant value ""Constant
등록일자 has constant value ""Constant
작업자명 has constant value ""Constant
하위번호 is highly overall correlated with 총기업수High correlation
총기업수 is highly overall correlated with 하위번호High correlation
총기업수 is highly imbalanced (56.1%)Imbalance
행정동명 has unique valuesUnique
도로명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
하위번호 has 6 (20.0%) zerosZeros
건물하위번호 has 21 (70.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:01:06.641811
Analysis finished2023-12-10 14:01:14.503773
Duration7.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2019-01-01 00:00:00
Maximum2019-01-01 00:00:00
2023-12-10T23:01:14.577612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:14.737090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경남
4 
강원
4 
전남
4 
전북
3 
경기
2 
Other values (9)
13 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row경남
2nd row경남
3rd row경남
4th row경기
5th row경남

Common Values

ValueCountFrequency (%)
경남 4
13.3%
강원 4
13.3%
전남 4
13.3%
전북 3
10.0%
경기 2
6.7%
인천 2
6.7%
서울 2
6.7%
부산 2
6.7%
경북 2
6.7%
충남 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:01:14.906477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경남 4
13.3%
강원 4
13.3%
전남 4
13.3%
전북 3
10.0%
경기 2
6.7%
인천 2
6.7%
서울 2
6.7%
부산 2
6.7%
경북 2
6.7%
충남 1
 
3.3%
Other values (4) 4
13.3%

시군구명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:15.188091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3
Min length2

Characters and Unicode

Total characters99
Distinct characters49
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

Unique28 ?
Unique (%)93.3%

Sample

1st row창원시 마산회원구
2nd row하동군
3rd row진주시
4th row화성시
5th row산청군
ValueCountFrequency (%)
원주시 2
 
6.5%
화순군 1
 
3.2%
완도군 1
 
3.2%
장흥군 1
 
3.2%
고창군 1
 
3.2%
청송군 1
 
3.2%
경산시 1
 
3.2%
동구 1
 
3.2%
금정구 1
 
3.2%
서구 1
 
3.2%
Other values (20) 20
64.5%
2023-12-10T23:01:15.672208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 13
 
13.1%
군 12
 
12.1%
구 8
 
8.1%
산 5
 
5.1%
원 4
 
4.0%
주 4
 
4.0%
화 3
 
3.0%
양 2
 
2.0%
창 2
 
2.0%
제 2
 
2.0%
Other values (39) 44
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98
99.0%
Space Separator 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 13
 
13.3%
군 12
 
12.2%
구 8
 
8.2%
산 5
 
5.1%
원 4
 
4.1%
주 4
 
4.1%
화 3
 
3.1%
양 2
 
2.0%
창 2
 
2.0%
제 2
 
2.0%
Other values (38) 43
43.9%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98
99.0%
Common 1
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 13
 
13.3%
군 12
 
12.2%
구 8
 
8.2%
산 5
 
5.1%
원 4
 
4.1%
주 4
 
4.1%
화 3
 
3.1%
양 2
 
2.0%
창 2
 
2.0%
제 2
 
2.0%
Other values (38) 43
43.9%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98
99.0%
ASCII 1
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 13
 
13.3%
군 12
 
12.2%
구 8
 
8.2%
산 5
 
5.1%
원 4
 
4.1%
주 4
 
4.1%
화 3
 
3.1%
양 2
 
2.0%
창 2
 
2.0%
제 2
 
2.0%
Other values (38) 43
43.9%
ASCII
ValueCountFrequency (%)
1
100.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:15.982644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.5
Min length3

Characters and Unicode

Total characters105
Distinct characters51
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 (%)100.0%

Sample

1st row구암2동
2nd row악양면
3rd row내동면
4th row동탄1동
5th row오부면
ValueCountFrequency (%)
구암2동 1
 
3.3%
악양면 1
 
3.3%
관산읍 1
 
3.3%
상하면 1
 
3.3%
파천면 1
 
3.3%
동부동 1
 
3.3%
삼성동 1
 
3.3%
장전2동 1
 
3.3%
아미동 1
 
3.3%
대림2동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:01:16.431095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 17
 
16.2%
면 15
 
14.3%
산 5
 
4.8%
장 4
 
3.8%
구 3
 
2.9%
부 3
 
2.9%
삼 3
 
2.9%
2 3
 
2.9%
내 2
 
1.9%
양 2
 
1.9%
Other values (41) 48
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
95.2%
Decimal Number 5
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
 
17.0%
면 15
 
15.0%
산 5
 
5.0%
장 4
 
4.0%
구 3
 
3.0%
부 3
 
3.0%
삼 3
 
3.0%
내 2
 
2.0%
양 2
 
2.0%
소 2
 
2.0%
Other values (39) 44
44.0%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
95.2%
Common 5
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
 
17.0%
면 15
 
15.0%
산 5
 
5.0%
장 4
 
4.0%
구 3
 
3.0%
부 3
 
3.0%
삼 3
 
3.0%
내 2
 
2.0%
양 2
 
2.0%
소 2
 
2.0%
Other values (39) 44
44.0%
Common
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
95.2%
ASCII 5
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 17
 
17.0%
면 15
 
15.0%
산 5
 
5.0%
장 4
 
4.0%
구 3
 
3.0%
부 3
 
3.0%
삼 3
 
3.0%
내 2
 
2.0%
양 2
 
2.0%
소 2
 
2.0%
Other values (39) 44
44.0%
ASCII
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%

상위번호
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.86667
Minimum17
Maximum2180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:16.615605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile30.55
Q1116.5
median301
Q3663.5
95-th percentile1411.35
Maximum2180
Range2163
Interquartile range (IQR)547

Descriptive statistics

Standard deviation530.89825
Coefficient of variation (CV)1.077164
Kurtosis2.2471701
Mean492.86667
Median Absolute Deviation (MAD)213.5
Skewness1.591918
Sum14786
Variance281852.95
MonotonicityNot monotonic
2023-12-10T23:01:16.794399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
323 2
 
6.7%
1358 1
 
3.3%
938 1
 
3.3%
91 1
 
3.3%
291 1
 
3.3%
524 1
 
3.3%
406 1
 
3.3%
183 1
 
3.3%
312 1
 
3.3%
296 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
17 1
3.3%
22 1
3.3%
41 1
3.3%
45 1
3.3%
81 1
3.3%
84 1
3.3%
91 1
3.3%
104 1
3.3%
154 1
3.3%
173 1
3.3%
ValueCountFrequency (%)
2180 1
3.3%
1455 1
3.3%
1358 1
3.3%
1247 1
3.3%
1081 1
3.3%
1080 1
3.3%
938 1
3.3%
710 1
3.3%
524 1
3.3%
455 1
3.3%

하위번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6666667
Minimum0
Maximum43
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:16.949165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2.5
Q37.75
95-th percentile29.55
Maximum43
Range43
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation10.44966
Coefficient of variation (CV)1.5674491
Kurtosis4.9261018
Mean6.6666667
Median Absolute Deviation (MAD)2
Skewness2.265462
Sum200
Variance109.1954
MonotonicityNot monotonic
2023-12-10T23:01:17.457871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 7
23.3%
0 6
20.0%
3 4
13.3%
4 2
 
6.7%
2 2
 
6.7%
8 2
 
6.7%
7 1
 
3.3%
30 1
 
3.3%
15 1
 
3.3%
13 1
 
3.3%
Other values (3) 3
10.0%
ValueCountFrequency (%)
0 6
20.0%
1 7
23.3%
2 2
 
6.7%
3 4
13.3%
4 2
 
6.7%
7 1
 
3.3%
8 2
 
6.7%
13 1
 
3.3%
15 1
 
3.3%
16 1
 
3.3%
ValueCountFrequency (%)
43 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
16 1
 
3.3%
15 1
 
3.3%
13 1
 
3.3%
8 2
6.7%
7 1
 
3.3%
4 2
6.7%
3 4
13.3%

도로명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:17.739365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.5333333
Min length3

Characters and Unicode

Total characters136
Distinct characters70
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 (%)100.0%

Sample

1st row구암서1길
2nd row미서길
3rd row산유로488번길
4th row동탄중심상가1길
5th row오동로
ValueCountFrequency (%)
구암서1길 1
 
3.3%
미서길 1
 
3.3%
정남진로 1
 
3.3%
진암구시포로 1
 
3.3%
중평병부길 1
 
3.3%
신천길 1
 
3.3%
태전로 1
 
3.3%
장전로 1
 
3.3%
까치고개로 1
 
3.3%
디지털로37길 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:01:18.239161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 19
 
14.0%
로 15
 
11.0%
1 5
 
3.7%
8 4
 
2.9%
구 3
 
2.2%
번 3
 
2.2%
고 3
 
2.2%
장 3
 
2.2%
진 3
 
2.2%
평 3
 
2.2%
Other values (60) 75
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
89.7%
Decimal Number 14
 
10.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 19
 
15.6%
로 15
 
12.3%
구 3
 
2.5%
번 3
 
2.5%
고 3
 
2.5%
장 3
 
2.5%
진 3
 
2.5%
평 3
 
2.5%
동 3
 
2.5%
지 3
 
2.5%
Other values (54) 64
52.5%
Decimal Number
ValueCountFrequency (%)
1 5
35.7%
8 4
28.6%
4 2
 
14.3%
2 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
89.7%
Common 14
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 19
 
15.6%
로 15
 
12.3%
구 3
 
2.5%
번 3
 
2.5%
고 3
 
2.5%
장 3
 
2.5%
진 3
 
2.5%
평 3
 
2.5%
동 3
 
2.5%
지 3
 
2.5%
Other values (54) 64
52.5%
Common
ValueCountFrequency (%)
1 5
35.7%
8 4
28.6%
4 2
 
14.3%
2 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
89.7%
ASCII 14
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 19
 
15.6%
로 15
 
12.3%
구 3
 
2.5%
번 3
 
2.5%
고 3
 
2.5%
장 3
 
2.5%
진 3
 
2.5%
평 3
 
2.5%
동 3
 
2.5%
지 3
 
2.5%
Other values (54) 64
52.5%
ASCII
ValueCountFrequency (%)
1 5
35.7%
8 4
28.6%
4 2
 
14.3%
2 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%

건물상위번호
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.53333
Minimum2
Maximum1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:18.410402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.7
Q124.25
median89.5
Q3215.75
95-th percentile799.95
Maximum1016
Range1014
Interquartile range (IQR)191.5

Descriptive statistics

Standard deviation266.30534
Coefficient of variation (CV)1.3903864
Kurtosis4.6507352
Mean191.53333
Median Absolute Deviation (MAD)70.5
Skewness2.1807224
Sum5746
Variance70918.533
MonotonicityNot monotonic
2023-12-10T23:01:18.586530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127 2
 
6.7%
25 2
 
6.7%
27 2
 
6.7%
215 1
 
3.3%
1016 1
 
3.3%
22 1
 
3.3%
542 1
 
3.3%
178 1
 
3.3%
101 1
 
3.3%
8 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
2 1
3.3%
8 1
3.3%
14 1
3.3%
17 1
3.3%
21 1
3.3%
22 1
3.3%
23 1
3.3%
24 1
3.3%
25 2
6.7%
27 2
6.7%
ValueCountFrequency (%)
1016 1
3.3%
1011 1
3.3%
542 1
3.3%
442 1
3.3%
405 1
3.3%
374 1
3.3%
263 1
3.3%
216 1
3.3%
215 1
3.3%
213 1
3.3%

건물하위번호
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0333333
Minimum0
Maximum46
Zeros21
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:18.733267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile23.95
Maximum46
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10.364007
Coefficient of variation (CV)2.5695885
Kurtosis9.5904825
Mean4.0333333
Median Absolute Deviation (MAD)0
Skewness3.0449234
Sum121
Variance107.41264
MonotonicityNot monotonic
2023-12-10T23:01:18.894323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 21
70.0%
1 4
 
13.3%
28 1
 
3.3%
7 1
 
3.3%
46 1
 
3.3%
19 1
 
3.3%
17 1
 
3.3%
ValueCountFrequency (%)
0 21
70.0%
1 4
 
13.3%
7 1
 
3.3%
17 1
 
3.3%
19 1
 
3.3%
28 1
 
3.3%
46 1
 
3.3%
ValueCountFrequency (%)
46 1
 
3.3%
28 1
 
3.3%
19 1
 
3.3%
17 1
 
3.3%
7 1
 
3.3%
1 4
 
13.3%
0 21
70.0%

위도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.4699
Minimum126.11204
Maximum129.08776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:19.077774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.11204
5-th percentile126.41413
Q1126.9006
median127.22278
Q3127.94746
95-th percentile129.01896
Maximum129.08776
Range2.9757132
Interquartile range (IQR)1.046859

Descriptive statistics

Standard deviation0.83164267
Coefficient of variation (CV)0.0065242277
Kurtosis-0.58384184
Mean127.4699
Median Absolute Deviation (MAD)0.51364676
Skewness0.54729729
Sum3824.0971
Variance0.69162953
MonotonicityNot monotonic
2023-12-10T23:01:19.323661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
128.5995163162 1
 
3.3%
126.7540218308 1
 
3.3%
126.1120419261 1
 
3.3%
126.9416983788 1
 
3.3%
126.4364125921 1
 
3.3%
129.0183945122 1
 
3.3%
128.7811269991 1
 
3.3%
127.4240715675 1
 
3.3%
129.0877551188 1
 
3.3%
129.0194307689 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126.1120419261 1
3.3%
126.3959056784 1
3.3%
126.4364125921 1
3.3%
126.5849778642 1
3.3%
126.7194102593 1
3.3%
126.7540218308 1
3.3%
126.7741997174 1
3.3%
126.899037234 1
3.3%
126.9052821521 1
3.3%
126.918777451 1
3.3%
ValueCountFrequency (%)
129.0877551188 1
3.3%
129.0194307689 1
3.3%
129.0183945122 1
3.3%
128.7811269991 1
3.3%
128.5995163162 1
3.3%
128.0652905125 1
3.3%
128.0152147163 1
3.3%
127.9545553479 1
3.3%
127.926163786 1
3.3%
127.8781381988 1
3.3%

경도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.148633
Minimum33.52082
Maximum38.187726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:19.547678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.52082
5-th percentile34.363467
Q135.165663
median36.089476
Q337.346367
95-th percentile37.834711
Maximum38.187726
Range4.6669063
Interquartile range (IQR)2.1807038

Descriptive statistics

Standard deviation1.2458443
Coefficient of variation (CV)0.034464493
Kurtosis-0.94465048
Mean36.148633
Median Absolute Deviation (MAD)1.0505938
Skewness-0.072891426
Sum1084.459
Variance1.552128
MonotonicityNot monotonic
2023-12-10T23:01:19.751542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.2518150004 1
 
3.3%
34.2006830493 1
 
3.3%
34.8291268409 1
 
3.3%
34.5624254745 1
 
3.3%
35.4455829577 1
 
3.3%
36.4583993595 1
 
3.3%
35.8095546914 1
 
3.3%
36.3377736792 1
 
3.3%
35.2276888375 1
 
3.3%
35.0995575144 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
33.5208197665 1
3.3%
34.2006830493 1
3.3%
34.5624254745 1
3.3%
34.8291268409 1
3.3%
34.9448531532 1
3.3%
35.0995575144 1
3.3%
35.1384748991 1
3.3%
35.1449876527 1
3.3%
35.2276888375 1
3.3%
35.2518150004 1
3.3%
ValueCountFrequency (%)
38.187726066 1
3.3%
37.8747856617 1
3.3%
37.7857309143 1
3.3%
37.7815505254 1
3.3%
37.6015577238 1
3.3%
37.5433413337 1
3.3%
37.4889548135 1
3.3%
37.3518877965 1
3.3%
37.3298036787 1
3.3%
37.2007450266 1
3.3%

전기사용량
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7745.3
Minimum20
Maximum137277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:19.949686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile73
Q1326
median1609.5
Q34077.75
95-th percentile14051.8
Maximum137277
Range137257
Interquartile range (IQR)3751.75

Descriptive statistics

Standard deviation24804.547
Coefficient of variation (CV)3.2025289
Kurtosis28.214175
Mean7745.3
Median Absolute Deviation (MAD)1502.5
Skewness5.2465235
Sum232359
Variance6.1526557 × 108
MonotonicityNot monotonic
2023-12-10T23:01:20.158492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
73 2
 
6.7%
326 2
 
6.7%
6946 1
 
3.3%
454 1
 
3.3%
540 1
 
3.3%
20 1
 
3.3%
11143 1
 
3.3%
1685 1
 
3.3%
2993 1
 
3.3%
1280 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
20 1
3.3%
73 2
6.7%
101 1
3.3%
113 1
3.3%
117 1
3.3%
190 1
3.3%
326 2
6.7%
454 1
3.3%
540 1
3.3%
1096 1
3.3%
ValueCountFrequency (%)
137277 1
3.3%
14185 1
3.3%
13889 1
3.3%
11143 1
3.3%
8755 1
3.3%
8213 1
3.3%
6946 1
3.3%
4145 1
3.3%
3876 1
3.3%
3513 1
3.3%

총기업수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
25 
1
3 
10
 
1
2
 
1

Length

Max length2
Median length1
Mean length1.0333333
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 25
83.3%
1 3
 
10.0%
10 1
 
3.3%
2 1
 
3.3%

Length

2023-12-10T23:01:20.360369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:20.545337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
83.3%
1 3
 
10.0%
10 1
 
3.3%
2 1
 
3.3%

등록일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-12-01 00:00:00
Maximum2020-12-01 00:00:00
2023-12-10T23:01:20.706024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:20.845583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

작업자명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
KEDSYS
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KEDSYS 30
100.0%

Length

2023-12-10T23:01:21.017705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:21.169437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T23:01:13.084964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:07.455876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:08.752745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.557660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.312612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.166842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.028764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:13.207876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:07.587202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:08.907618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.647690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.430712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.268224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.162019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:13.344768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:07.730082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.025393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.757521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.532773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.365768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.303938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:13.467865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:07.871724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.151148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.856274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.641803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.479487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.558411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:13.599452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:08.313247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.268084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.952551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.735770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.595644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.675946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:13.731774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:08.467422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.373261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.069395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.952381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.729518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.811580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:13.859641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:08.606456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:09.452890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:10.202716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.051890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:11.869387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:12.937202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:01:21.274504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명행정동명상위번호하위번호도로명건물상위번호건물하위번호위도경도전기사용량총기업수
시도명1.0001.0001.0000.0000.6971.0000.8140.0000.8500.8230.0000.409
시군구명1.0001.0001.0000.8660.0001.0000.9510.0001.0001.0001.0000.463
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
상위번호0.0000.8661.0001.0000.3311.0000.7190.5900.4450.7640.1620.580
하위번호0.6970.0001.0000.3311.0001.0000.0000.0000.4440.3320.0000.611
도로명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
건물상위번호0.8140.9511.0000.7190.0001.0001.0000.6810.2420.1520.0000.000
건물하위번호0.0000.0001.0000.5900.0001.0000.6811.0000.3650.0000.0000.000
위도0.8501.0001.0000.4450.4441.0000.2420.3651.0000.0000.0000.000
경도0.8231.0001.0000.7640.3321.0000.1520.0000.0001.0000.5260.000
전기사용량0.0001.0001.0000.1620.0001.0000.0000.0000.0000.5261.0000.411
총기업수0.4090.4631.0000.5800.6111.0000.0000.0000.0000.0000.4111.000
2023-12-10T23:01:21.479348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명총기업수
시도명1.0000.143
총기업수0.1431.000
2023-12-10T23:01:21.610583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상위번호하위번호건물상위번호건물하위번호위도경도전기사용량시도명총기업수
상위번호1.0000.2670.1260.0550.013-0.1050.3040.0000.251
하위번호0.2671.000-0.2430.0270.0480.2130.1850.3450.525
건물상위번호0.126-0.2431.0000.252-0.0780.1090.2220.3510.000
건물하위번호0.0550.0270.2521.0000.2910.2020.2370.0000.000
위도0.0130.048-0.0780.2911.0000.065-0.1140.4900.000
경도-0.1050.2130.1090.2020.0651.0000.2670.4450.000
전기사용량0.3040.1850.2220.237-0.1140.2671.0000.0000.391
시도명0.0000.3450.3510.0000.4900.4450.0001.0000.143
총기업수0.2510.5250.0000.0000.0000.0000.3910.1431.000

Missing values

2023-12-10T23:01:14.096221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:01:14.396462image/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

기준년월시도명시군구명행정동명상위번호하위번호도로명건물상위번호건물하위번호위도경도전기사용량총기업수등록일자작업자명
02019-01경남창원시 마산회원구구암2동13584구암서1길2150128.59951635.251815694602020-12-01KEDSYS
12019-01경남하동군악양면1733미서길171127.70127835.13847532602020-12-01KEDSYS
22019-01경남진주시내동면2921산유로488번길320128.06529135.14498811702020-12-01KEDSYS
32019-01경기화성시동탄1동1047동탄중심상가1길210127.07188537.2007451418502020-12-01KEDSYS
42019-01경남산청군오부면4551오동로2130127.87813835.47099419002020-12-01KEDSYS
52019-01경기포천시내촌면452안골길1270127.24018837.785731129302020-12-01KEDSYS
62019-01강원춘천시근화동7101공지로44228127.71777837.874786387602020-12-01KEDSYS
72019-01강원원주시행구동3234꽃밭머리길1107128.01521537.351888351302020-12-01KEDSYS
82019-01강원원주시단구동145530송림길7846127.95455537.329804153412020-12-01KEDSYS
92019-01강원양구군방산면2143평화로4818번길250127.92616438.1877267302020-12-01KEDSYS
기준년월시도명시군구명행정동명상위번호하위번호도로명건물상위번호건물하위번호위도경도전기사용량총기업수등록일자작업자명
202019-01서울은평구역촌동4116진흥로1길140126.91877737.601558267102020-12-01KEDSYS
212019-01서울영등포구대림2동108143디지털로37길20126.89903737.488955821322020-12-01KEDSYS
222019-01부산서구아미동221까치고개로2161129.01943135.099558875502020-12-01KEDSYS
232019-01부산금정구장전2동29629장전로81129.08775535.227689128002020-12-01KEDSYS
242019-01대전동구삼성동3121태전로1010127.42407236.337774299302020-12-01KEDSYS
252019-01경북경산시동부동1830신천길250128.78112735.80955532602020-12-01KEDSYS
262019-01경북청송군파천면4060중평병부길17817129.01839536.458399168502020-12-01KEDSYS
272019-01전북고창군상하면5248진암구시포로5421126.43641335.4455831114302020-12-01KEDSYS
282019-01전남장흥군관산읍2913정남진로270126.94169834.5624252012020-12-01KEDSYS
292019-01전남신안군암태면910장단고길220126.11204234.82912754002020-12-01KEDSYS