Overview

Dataset statistics

Number of variables15
Number of observations649
Missing cells833
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.0 KiB
Average record size in memory126.2 B

Variable types

Numeric6
Categorical2
Text3
DateTime4

Dataset

Description강원특별자치도 춘천시에서 공사중인 공사장의 연번, 건축구분, 허가번호, 지목, 대지면적(㎡), 건축면적(㎡), 연면적(㎡), 증축연면적(㎡), 건폐율(%), 용적률(%), 허가일, 착공처리일, 착공예정일, 사용승인일, 데이터기준일에 대한 자료
Author강원특별자치도 춘천시
URLhttps://www.data.go.kr/data/15043049/fileData.do

Alerts

데이터기준일 has constant value ""Constant
대지면적 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
건축면적 is highly overall correlated with 대지면적 and 1 other fieldsHigh correlation
연면적 is highly overall correlated with 대지면적 and 2 other fieldsHigh correlation
건폐율 is highly overall correlated with 용적률High correlation
용적률 is highly overall correlated with 연면적 and 1 other fieldsHigh correlation
건축구분 is highly imbalanced (59.2%)Imbalance
증축연면적 has 469 (72.3%) missing valuesMissing
사용승인일 has 364 (56.1%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:51:24.904300
Analysis finished2023-12-12 14:51:30.449146
Duration5.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct649
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325
Minimum1
Maximum649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-12T23:51:30.540119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.4
Q1163
median325
Q3487
95-th percentile616.6
Maximum649
Range648
Interquartile range (IQR)324

Descriptive statistics

Standard deviation187.49444
Coefficient of variation (CV)0.57690598
Kurtosis-1.2
Mean325
Median Absolute Deviation (MAD)162
Skewness0
Sum210925
Variance35154.167
MonotonicityNot monotonic
2023-12-12T23:51:31.066560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214 1
 
0.2%
509 1
 
0.2%
494 1
 
0.2%
495 1
 
0.2%
496 1
 
0.2%
497 1
 
0.2%
498 1
 
0.2%
499 1
 
0.2%
500 1
 
0.2%
501 1
 
0.2%
Other values (639) 639
98.5%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
649 1
0.2%
648 1
0.2%
647 1
0.2%
646 1
0.2%
645 1
0.2%
644 1
0.2%
643 1
0.2%
642 1
0.2%
641 1
0.2%
640 1
0.2%

건축구분
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
신축
451 
증축
180 
대수선
 
13
재축
 
3
개축
 
1

Length

Max length9
Median length2
Mean length2.0308166
Min length2

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row증축
2nd row신축
3rd row신축
4th row증축
5th row신축

Common Values

ValueCountFrequency (%)
신축 451
69.5%
증축 180
 
27.7%
대수선 13
 
2.0%
재축 3
 
0.5%
개축 1
 
0.2%
가설건축물축조허가 1
 
0.2%

Length

2023-12-12T23:51:31.278429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:51:31.408188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 451
69.5%
증축 180
 
27.7%
대수선 13
 
2.0%
재축 3
 
0.5%
개축 1
 
0.2%
가설건축물축조허가 1
 
0.2%
Distinct581
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-12-12T23:51:31.642570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.51926
Min length14

Characters and Unicode

Total characters10721
Distinct characters43
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

Unique513 ?
Unique (%)79.0%

Sample

1st row2023-민원담당관-증축신고-15
2nd row2023-사북면-신축신고-18
3rd row2023-민원담당관-공용건축물-27
4th row2023-사북면-증축신고-8
5th row2023-사북면-신축신고-16
ValueCountFrequency (%)
2023-민원담당관-신축허가-3 2
 
0.3%
2023-민원담당관-신축허가-34 2
 
0.3%
2023-민원담당관-신축허가-61 2
 
0.3%
2023-신북읍-증축신고-2 2
 
0.3%
2023-동면-신축신고-9 2
 
0.3%
2023-동면-증축신고-4 2
 
0.3%
2023-민원담당관-공용건축물-10 2
 
0.3%
2023-민원담당관-증축허가-7 2
 
0.3%
2023-민원담당관-신축허가-35 2
 
0.3%
2023-동면-증축신고-1 2
 
0.3%
Other values (571) 629
96.9%
2023-12-12T23:51:32.063788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1947
18.2%
2 1726
16.1%
931
 
8.7%
0 733
 
6.8%
639
 
6.0%
3 538
 
5.0%
418
 
3.9%
318
 
3.0%
285
 
2.7%
285
 
2.7%
Other values (33) 2901
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5030
46.9%
Decimal Number 3744
34.9%
Dash Punctuation 1947
 
18.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
931
18.5%
639
12.7%
418
 
8.3%
318
 
6.3%
285
 
5.7%
285
 
5.7%
285
 
5.7%
285
 
5.7%
285
 
5.7%
196
 
3.9%
Other values (22) 1103
21.9%
Decimal Number
ValueCountFrequency (%)
2 1726
46.1%
0 733
19.6%
3 538
 
14.4%
1 271
 
7.2%
5 88
 
2.4%
4 85
 
2.3%
9 85
 
2.3%
6 82
 
2.2%
8 72
 
1.9%
7 64
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 1947
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5691
53.1%
Hangul 5030
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
931
18.5%
639
12.7%
418
 
8.3%
318
 
6.3%
285
 
5.7%
285
 
5.7%
285
 
5.7%
285
 
5.7%
285
 
5.7%
196
 
3.9%
Other values (22) 1103
21.9%
Common
ValueCountFrequency (%)
- 1947
34.2%
2 1726
30.3%
0 733
 
12.9%
3 538
 
9.5%
1 271
 
4.8%
5 88
 
1.5%
4 85
 
1.5%
9 85
 
1.5%
6 82
 
1.4%
8 72
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5691
53.1%
Hangul 5030
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1947
34.2%
2 1726
30.3%
0 733
 
12.9%
3 538
 
9.5%
1 271
 
4.8%
5 88
 
1.5%
4 85
 
1.5%
9 85
 
1.5%
6 82
 
1.4%
8 72
 
1.3%
Hangul
ValueCountFrequency (%)
931
18.5%
639
12.7%
418
 
8.3%
318
 
6.3%
285
 
5.7%
285
 
5.7%
285
 
5.7%
285
 
5.7%
285
 
5.7%
196
 
3.9%
Other values (22) 1103
21.9%
Distinct616
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-12-12T23:51:32.397763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length25.140216
Min length17

Characters and Unicode

Total characters16316
Distinct characters120
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

Unique585 ?
Unique (%)90.1%

Sample

1st row강원특별자치도 춘천시 삼천동 22-7 외1필지
2nd row강원특별자치도 춘천시 사북면 고탄리 102-1
3rd row강원특별자치도 춘천시 동내면 사암리 1293-97 외1필지
4th row강원특별자치도 춘천시 사북면 지촌리 78-1
5th row강원특별자치도 춘천시 사북면 고탄리 산 42-3
ValueCountFrequency (%)
강원특별자치도 649
19.5%
춘천시 649
19.5%
외1필지 135
 
4.1%
동면 91
 
2.7%
동내면 84
 
2.5%
신북읍 61
 
1.8%
서면 57
 
1.7%
남산면 55
 
1.7%
신동면 54
 
1.6%
퇴계동 42
 
1.3%
Other values (698) 1453
43.6%
2023-12-12T23:51:32.904507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2681
 
16.4%
731
 
4.5%
667
 
4.1%
665
 
4.1%
1 662
 
4.1%
651
 
4.0%
649
 
4.0%
649
 
4.0%
649
 
4.0%
649
 
4.0%
Other values (110) 7663
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10379
63.6%
Decimal Number 2767
 
17.0%
Space Separator 2681
 
16.4%
Dash Punctuation 489
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
731
 
7.0%
667
 
6.4%
665
 
6.4%
651
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
Other values (98) 3771
36.3%
Decimal Number
ValueCountFrequency (%)
1 662
23.9%
2 323
11.7%
3 301
10.9%
4 265
9.6%
5 253
 
9.1%
7 211
 
7.6%
8 208
 
7.5%
6 202
 
7.3%
9 172
 
6.2%
0 170
 
6.1%
Space Separator
ValueCountFrequency (%)
2681
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 489
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10379
63.6%
Common 5937
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
731
 
7.0%
667
 
6.4%
665
 
6.4%
651
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
Other values (98) 3771
36.3%
Common
ValueCountFrequency (%)
2681
45.2%
1 662
 
11.2%
- 489
 
8.2%
2 323
 
5.4%
3 301
 
5.1%
4 265
 
4.5%
5 253
 
4.3%
7 211
 
3.6%
8 208
 
3.5%
6 202
 
3.4%
Other values (2) 342
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10379
63.6%
ASCII 5937
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2681
45.2%
1 662
 
11.2%
- 489
 
8.2%
2 323
 
5.4%
3 301
 
5.1%
4 265
 
4.5%
5 253
 
4.3%
7 211
 
3.6%
8 208
 
3.5%
6 202
 
3.4%
Other values (2) 342
 
5.8%
Hangul
ValueCountFrequency (%)
731
 
7.0%
667
 
6.4%
665
 
6.4%
651
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
649
 
6.3%
Other values (98) 3771
36.3%

대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct518
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7612.6801
Minimum66
Maximum1365368.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-12T23:51:33.069756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile218.4
Q1437
median661
Q31480
95-th percentile6982.6
Maximum1365368.3
Range1365302.3
Interquartile range (IQR)1043

Descriptive statistics

Standard deviation75521.481
Coefficient of variation (CV)9.9204853
Kurtosis230.89497
Mean7612.6801
Median Absolute Deviation (MAD)317
Skewness14.791776
Sum4940629.4
Variance5.7034941 × 109
MonotonicityNot monotonic
2023-12-12T23:51:33.279160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331.0 8
 
1.2%
496.0 7
 
1.1%
330.0 7
 
1.1%
661.0 6
 
0.9%
520.0 4
 
0.6%
600.0 4
 
0.6%
1480.0 4
 
0.6%
580.0 4
 
0.6%
310.0 3
 
0.5%
571.0 3
 
0.5%
Other values (508) 599
92.3%
ValueCountFrequency (%)
66.0 1
0.2%
89.0 1
0.2%
99.0 1
0.2%
99.2 1
0.2%
100.0 1
0.2%
111.0 1
0.2%
117.0 1
0.2%
118.8 1
0.2%
125.0 1
0.2%
130.0 1
0.2%
ValueCountFrequency (%)
1365368.3 1
0.2%
975672.7 1
0.2%
889343.7 1
0.2%
217264.0 2
0.3%
109954.0 1
0.2%
99700.0 1
0.2%
37475.4 1
0.2%
32970.0 1
0.2%
31138.0 1
0.2%
29695.4 1
0.2%

건축면적
Real number (ℝ)

HIGH CORRELATION 

Distinct567
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean714.70498
Minimum18
Maximum106042.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-12T23:51:33.433960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile40.008
Q182.5
median121.2
Q3242.82
95-th percentile1402.224
Maximum106042.54
Range106024.54
Interquartile range (IQR)160.32

Descriptive statistics

Standard deviation5986.7503
Coefficient of variation (CV)8.3765336
Kurtosis295.60421
Mean714.70498
Median Absolute Deviation (MAD)57.43
Skewness16.939501
Sum463843.53
Variance35841179
MonotonicityNot monotonic
2023-12-12T23:51:33.612434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 10
 
1.5%
96.0 7
 
1.1%
50.0 6
 
0.9%
36.0 6
 
0.9%
98.0 6
 
0.9%
198.0 5
 
0.8%
84.0 5
 
0.8%
49.2 4
 
0.6%
61.2 4
 
0.6%
99.0 4
 
0.6%
Other values (557) 592
91.2%
ValueCountFrequency (%)
18.0 10
1.5%
20.79 1
 
0.2%
21.0 1
 
0.2%
24.0 2
 
0.3%
27.44 1
 
0.2%
30.0 1
 
0.2%
31.1 1
 
0.2%
31.74 1
 
0.2%
32.4 1
 
0.2%
33.1 1
 
0.2%
ValueCountFrequency (%)
106042.54 1
0.2%
106027.47 1
0.2%
21166.45 1
0.2%
12009.26 1
0.2%
8608.03 1
0.2%
7882.65 1
0.2%
7191.43 1
0.2%
6290.52 1
0.2%
5103.66 1
0.2%
4970.44 1
0.2%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct568
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1945.3515
Minimum18
Maximum399206.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-12T23:51:33.781379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile41.952
Q193.69
median149.84
Q3348.4
95-th percentile2422.494
Maximum399206.82
Range399188.82
Interquartile range (IQR)254.71

Descriptive statistics

Standard deviation22293.515
Coefficient of variation (CV)11.45989
Kurtosis310.99449
Mean1945.3515
Median Absolute Deviation (MAD)77.38
Skewness17.533268
Sum1262533.1
Variance4.9700081 × 108
MonotonicityNot monotonic
2023-12-12T23:51:33.979687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 10
 
1.5%
96.0 7
 
1.1%
198.0 7
 
1.1%
36.0 6
 
0.9%
50.0 6
 
0.9%
84.0 5
 
0.8%
61.2 4
 
0.6%
98.4 4
 
0.6%
49.2 4
 
0.6%
165.0 3
 
0.5%
Other values (558) 593
91.4%
ValueCountFrequency (%)
18.0 10
1.5%
20.79 1
 
0.2%
21.0 1
 
0.2%
24.0 2
 
0.3%
30.0 1
 
0.2%
31.1 1
 
0.2%
31.74 1
 
0.2%
32.4 1
 
0.2%
36.0 6
0.9%
36.48 1
 
0.2%
ValueCountFrequency (%)
399206.82 1
0.2%
399127.81 1
0.2%
42200.72 1
0.2%
39868.0 1
0.2%
27742.13 1
0.2%
18067.75 1
0.2%
15910.79 1
0.2%
15902.17 1
0.2%
9280.2 1
0.2%
9211.31 1
0.2%

증축연면적
Text

MISSING 

Distinct165
Distinct (%)91.7%
Missing469
Missing (%)72.3%
Memory size5.2 KiB
2023-12-12T23:51:34.458770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.3944444
Min length2

Characters and Unicode

Total characters791
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

Unique156 ?
Unique (%)86.7%

Sample

1st row24.32
2nd row728.88
3rd row30
4th row18
5th row42
ValueCountFrequency (%)
18 6
 
3.3%
84 4
 
2.2%
12 2
 
1.1%
77 2
 
1.1%
24 2
 
1.1%
40 2
 
1.1%
47.79 2
 
1.1%
47 2
 
1.1%
42 2
 
1.1%
64.32 1
 
0.6%
Other values (155) 155
86.1%
2023-12-12T23:51:35.090103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 140
17.7%
4 81
10.2%
1 79
10.0%
8 79
10.0%
2 72
9.1%
5 68
8.6%
3 64
8.1%
7 57
7.2%
6 56
 
7.1%
9 53
 
6.7%
Other values (3) 42
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 646
81.7%
Other Punctuation 144
 
18.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 81
12.5%
1 79
12.2%
8 79
12.2%
2 72
11.1%
5 68
10.5%
3 64
9.9%
7 57
8.8%
6 56
8.7%
9 53
8.2%
0 37
5.7%
Other Punctuation
ValueCountFrequency (%)
. 140
97.2%
, 4
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 140
17.7%
4 81
10.2%
1 79
10.0%
8 79
10.0%
2 72
9.1%
5 68
8.6%
3 64
8.1%
7 57
7.2%
6 56
 
7.1%
9 53
 
6.7%
Other values (3) 42
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 140
17.7%
4 81
10.2%
1 79
10.0%
8 79
10.0%
2 72
9.1%
5 68
8.6%
3 64
8.1%
7 57
7.2%
6 56
 
7.1%
9 53
 
6.7%
Other values (3) 42
 
5.3%

건폐율
Real number (ℝ)

HIGH CORRELATION 

Distinct570
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.009039
Minimum0.37
Maximum79.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-12T23:51:35.303816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile6.58384
Q112.9848
median18.63
Q325.65
95-th percentile53.62
Maximum79.55
Range79.18
Interquartile range (IQR)12.6652

Descriptive statistics

Standard deviation14.030646
Coefficient of variation (CV)0.63749471
Kurtosis1.8247991
Mean22.009039
Median Absolute Deviation (MAD)6.07
Skewness1.4580506
Sum14283.867
Variance196.85903
MonotonicityNot monotonic
2023-12-12T23:51:35.520046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.86 5
 
0.8%
19.88 4
 
0.6%
19.85 4
 
0.6%
16.1 3
 
0.5%
17.55 3
 
0.5%
19.8 3
 
0.5%
19.91 3
 
0.5%
19.99 3
 
0.5%
19.95 3
 
0.5%
19.98 3
 
0.5%
Other values (560) 615
94.8%
ValueCountFrequency (%)
0.37 1
0.2%
0.4176 1
0.2%
0.73 1
0.2%
0.82 1
0.2%
0.84 1
0.2%
1.01 1
0.2%
1.27 1
0.2%
1.6976 1
0.2%
1.86 1
0.2%
2.64 1
0.2%
ValueCountFrequency (%)
79.55 1
0.2%
78.5296 1
0.2%
69.68 1
0.2%
69.52 1
0.2%
68.25 1
0.2%
68.0725 1
0.2%
67.03 1
0.2%
63.78 1
0.2%
60.55 1
0.2%
60.37 1
0.2%

용적률
Real number (ℝ)

HIGH CORRELATION 

Distinct607
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.487186
Minimum0
Maximum332
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-12T23:51:35.700799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.404
Q114.06
median19.86
Q335.33
95-th percentile118.356
Maximum332
Range332
Interquartile range (IQR)21.27

Descriptive statistics

Standard deviation40.567742
Coefficient of variation (CV)1.2114408
Kurtosis14.169971
Mean33.487186
Median Absolute Deviation (MAD)8.37
Skewness3.4685074
Sum21733.184
Variance1645.7417
MonotonicityNot monotonic
2023-12-12T23:51:35.869459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.08 3
 
0.5%
20.25 3
 
0.5%
17.55 3
 
0.5%
12.0 2
 
0.3%
19.95 2
 
0.3%
25.44 2
 
0.3%
17.63 2
 
0.3%
15.81 2
 
0.3%
10.08 2
 
0.3%
11.95 2
 
0.3%
Other values (597) 626
96.5%
ValueCountFrequency (%)
0.0 1
0.2%
0.45 1
0.2%
0.4611 1
0.2%
0.82 1
0.2%
0.84 1
0.2%
1.01 1
0.2%
1.11 1
0.2%
1.27 1
0.2%
1.6976 1
0.2%
1.86 1
0.2%
ValueCountFrequency (%)
332.0 1
0.2%
284.2722 1
0.2%
247.1774 1
0.2%
245.3591 1
0.2%
240.27 1
0.2%
228.66 1
0.2%
218.164 1
0.2%
210.9275 1
0.2%
199.53 1
0.2%
196.02 1
0.2%
Distinct258
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Minimum2022-08-17 00:00:00
Maximum2023-11-03 00:00:00
2023-12-12T23:51:36.098743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:36.320542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct256
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Minimum2022-08-23 00:00:00
Maximum2023-11-07 00:00:00
2023-12-12T23:51:36.518775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:36.702117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct276
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Minimum2022-08-24 00:00:00
Maximum2202-11-16 00:00:00
2023-12-12T23:51:36.885965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:37.068197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용승인일
Date

MISSING 

Distinct159
Distinct (%)55.8%
Missing364
Missing (%)56.1%
Memory size5.2 KiB
Minimum2022-09-19 00:00:00
Maximum2023-11-01 00:00:00
2023-12-12T23:51:37.248152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:37.422930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-11-07
649 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-07
2nd row2023-11-07
3rd row2023-11-07
4th row2023-11-07
5th row2023-11-07

Common Values

ValueCountFrequency (%)
2023-11-07 649
100.0%

Length

2023-12-12T23:51:37.617565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:51:37.749704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-07 649
100.0%

Interactions

2023-12-12T23:51:29.236884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:25.604584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.425847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.154939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.814705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.531731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:29.331650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:25.718024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.530042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.251906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.940230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.638282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:29.433479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:25.868356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.662237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.396284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.073635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.764193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:29.524642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.011167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.782748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.491071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.187412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.855007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:29.648601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.159221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.938072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.613456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.302998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.968153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:29.795784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:26.295975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.048121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:27.724787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:28.418683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:51:29.130805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:51:37.834888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건축구분대지면적건축면적연면적건폐율용적률
연번1.0000.1890.0000.0000.0000.2860.312
건축구분0.1891.0000.0000.0000.0000.2850.262
대지면적0.0000.0001.0000.7160.7160.1760.000
건축면적0.0000.0000.7161.0000.9420.0000.000
연면적0.0000.0000.7160.9421.0000.1850.365
건폐율0.2860.2850.1760.0000.1851.0000.827
용적률0.3120.2620.0000.0000.3650.8271.000
2023-12-12T23:51:38.330709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대지면적건축면적연면적건폐율용적률건축구분
연번1.000-0.266-0.384-0.422-0.188-0.2310.100
대지면적-0.2661.0000.7420.652-0.243-0.1670.000
건축면적-0.3840.7421.0000.9310.3580.3890.000
연면적-0.4220.6520.9311.0000.4160.5390.000
건폐율-0.188-0.2430.3580.4161.0000.8910.153
용적률-0.231-0.1670.3890.5390.8911.0000.139
건축구분0.1000.0000.0000.0000.1530.1391.000

Missing values

2023-12-12T23:51:29.981759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:51:30.228329image/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.
2023-12-12T23:51:30.381529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번건축구분허가번호대지위치대지면적건축면적연면적증축연면적건폐율용적률허가일착공처리일착공예정일사용승인일데이터기준일
0214증축2023-민원담당관-증축신고-15강원특별자치도 춘천시 삼천동 22-7 외1필지758.099.9299.9224.3213.1813.182023-11-032023-11-072023-11-09<NA>2023-11-07
1215신축2023-사북면-신축신고-18강원특별자치도 춘천시 사북면 고탄리 102-1809.089.789.7<NA>11.0911.092023-10-312023-11-062023-11-06<NA>2023-11-07
21신축2023-민원담당관-공용건축물-27강원특별자치도 춘천시 동내면 사암리 1293-97 외1필지283.0124.98109.25<NA>44.1638.62023-10-302023-11-072023-11-06<NA>2023-11-07
3216증축2023-사북면-증축신고-8강원특별자치도 춘천시 사북면 지촌리 78-1998.0360.92715.44728.8836.1635.84372023-10-262023-11-022023-11-02<NA>2023-11-07
4217신축2023-사북면-신축신고-16강원특별자치도 춘천시 사북면 고탄리 산 42-3652.0132.0132.0<NA>20.2520.252023-10-252023-11-012023-11-15<NA>2023-11-07
5218신축2023-신북읍-신축신고-17강원특별자치도 춘천시 신북읍 발산리 227600.098.6998.69<NA>16.4516.452023-10-252023-11-012023-10-30<NA>2023-11-07
6219신축2023-신동면-신축신고-12강원특별자치도 춘천시 신동면 정족리 59582.071.071.0<NA>12.212.22023-10-242023-11-012023-10-31<NA>2023-11-07
7220신축2023-동면-신축신고-17강원특별자치도 춘천시 동면 지내리 590334.053.5350.83<NA>16.0315.222023-10-242023-11-062023-11-06<NA>2023-11-07
8221증축2023-서면-증축신고-14강원특별자치도 춘천시 서면 안보리 449-2 외1필지996.0146.97206.553014.7620.742023-10-202023-10-262023-10-27<NA>2023-11-07
9222증축2023-동면-증축신고-7강원특별자치도 춘천시 동면 만천리 437-1 외9필지2637.0478.822640.951818.1528.862023-10-182023-11-022023-10-27<NA>2023-11-07
연번건축구분허가번호대지위치대지면적건축면적연면적증축연면적건폐율용적률허가일착공처리일착공예정일사용승인일데이터기준일
639211신축2022-민원담당관-신축허가-182강원특별자치도 춘천시 퇴계동 35-27360.070.8114.34<NA>19.6731.762022-08-192022-08-252022-08-262023-08-172023-11-07
640643증축2022-동면-증축신고-13강원특별자치도 춘천시 동면 만천리 632-20503.093.54168.7229.2218.596433.54272022-08-192022-08-292022-08-302022-10-172023-11-07
641644신축2022-동내면-신축신고-48강원특별자치도 춘천시 동내면 사암리 256 외1필지585.065.5265.52<NA>11.211.22022-08-192023-08-222023-08-21<NA>2023-11-07
642645증축2022-동산면-증축신고-3강원특별자치도 춘천시 동산면 원창리 1407-2499.097.61187.0546.3819.5637.482022-08-182022-08-262022-08-242022-09-192023-11-07
643646증축2022-신북읍-증축신고-6강원특별자치도 춘천시 신북읍 발산리 100-3563.0159.17145.974728.2425.922022-08-182022-08-302022-08-262022-10-182023-11-07
644647증축2022-신동면-증축신고-8강원특별자치도 춘천시 신동면 혈동리 751274.0433.93433.93433.9334.0634.062022-08-182022-08-262022-08-292023-07-062023-11-07
645648신축2022-동내면-신축신고-47강원특별자치도 춘천시 동내면 사암리 257208.040.540.5<NA>19.4719.472022-08-182023-08-222023-08-21<NA>2023-11-07
646212신축2022-민원담당관-신축허가-179강원특별자치도 춘천시 신북읍 율문리 491-13 외1필지1653.0173.2199.22<NA>10.4812.052022-08-172022-08-242022-08-25<NA>2023-11-07
647213신축2022-민원담당관-신축허가-180강원특별자치도 춘천시 동면 장학리 89-241120.0208.76208.76<NA>18.639318.63932022-08-172022-08-262022-08-262022-11-252023-11-07
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