Overview

Dataset statistics

Number of variables16
Number of observations6530
Missing cells4776
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory854.6 KiB
Average record size in memory134.0 B

Variable types

Numeric6
Text6
DateTime3
Categorical1

Dataset

Description서울특별시 중랑구 관내의 건설현장시공정보입니다. 건설현장의 대지위치, 허가일, 착공처리일, 착공예정일, 주용도, 설계사무소명 등을 제공합니다. 참고해주시기 바랍니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15035713/fileData.do

Alerts

대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 2 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 1 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 2 other fieldsHigh correlation
최대지상층수 is highly overall correlated with 연면적(제곱미터)High correlation
주용도 is highly overall correlated with 대지면적(제곱미터)High correlation
주용도 is highly imbalanced (60.0%)Imbalance
착공예정일 has 237 (3.6%) missing valuesMissing
실제착공일 has 487 (7.5%) missing valuesMissing
사용승인일 has 264 (4.0%) missing valuesMissing
최대지하층수 has 651 (10.0%) missing valuesMissing
감리사무소명 has 119 (1.8%) missing valuesMissing
시공자사무소명 has 3002 (46.0%) missing valuesMissing
대지면적(제곱미터) is highly skewed (γ1 = 51.80382537)Skewed
건축면적(제곱미터) is highly skewed (γ1 = 20.19862798)Skewed
연면적(제곱미터) is highly skewed (γ1 = 29.26174223)Skewed
연번 has unique valuesUnique
최대지하층수 has 4555 (69.8%) zerosZeros

Reproduction

Analysis started2023-12-12 01:29:09.835252
Analysis finished2023-12-12 01:29:16.931363
Duration7.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct6530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3265.5
Minimum1
Maximum6530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T10:29:17.025436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile327.45
Q11633.25
median3265.5
Q34897.75
95-th percentile6203.55
Maximum6530
Range6529
Interquartile range (IQR)3264.5

Descriptive statistics

Standard deviation1885.193
Coefficient of variation (CV)0.57730607
Kurtosis-1.2
Mean3265.5
Median Absolute Deviation (MAD)1632.5
Skewness0
Sum21323715
Variance3553952.5
MonotonicityStrictly increasing
2023-12-12T10:29:17.193166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
4351 1
 
< 0.1%
4361 1
 
< 0.1%
4360 1
 
< 0.1%
4359 1
 
< 0.1%
4358 1
 
< 0.1%
4357 1
 
< 0.1%
4356 1
 
< 0.1%
4355 1
 
< 0.1%
4354 1
 
< 0.1%
Other values (6520) 6520
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
6530 1
< 0.1%
6529 1
< 0.1%
6528 1
< 0.1%
6527 1
< 0.1%
6526 1
< 0.1%
6525 1
< 0.1%
6524 1
< 0.1%
6523 1
< 0.1%
6522 1
< 0.1%
6521 1
< 0.1%
Distinct6456
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2023-12-12T10:29:17.443126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length38
Mean length20.98974
Min length14

Characters and Unicode

Total characters137063
Distinct characters72
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6382 ?
Unique (%)97.7%

Sample

1st row서울특별시 중랑구 중화동 318-85
2nd row서울특별시 중랑구 망우동 403-37
3rd row서울특별시 중랑구 묵동 153-17
4th row서울특별시 중랑구 면목동 185-49
5th row서울특별시 중랑구 묵동 4
ValueCountFrequency (%)
중랑구 6532
23.2%
서울특별시 6530
23.2%
면목동 2273
 
8.1%
외1필지 1335
 
4.7%
묵동 1098
 
3.9%
망우동 1051
 
3.7%
중화동 871
 
3.1%
상봉동 859
 
3.1%
신내동 378
 
1.3%
외2필지 343
 
1.2%
Other values (6125) 6840
24.3%
2023-12-12T10:29:18.300163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21582
 
15.7%
7403
 
5.4%
1 7249
 
5.3%
6597
 
4.8%
6557
 
4.8%
6557
 
4.8%
6532
 
4.8%
6531
 
4.8%
6530
 
4.8%
6530
 
4.8%
Other values (62) 54995
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77034
56.2%
Decimal Number 32091
23.4%
Space Separator 21582
 
15.7%
Dash Punctuation 6347
 
4.6%
Uppercase Letter 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7403
9.6%
6597
8.6%
6557
8.5%
6557
8.5%
6532
8.5%
6531
8.5%
6530
8.5%
6530
8.5%
6530
8.5%
2273
 
3.0%
Other values (45) 14994
19.5%
Decimal Number
ValueCountFrequency (%)
1 7249
22.6%
2 4765
14.8%
3 3989
12.4%
4 3375
10.5%
5 2598
 
8.1%
6 2280
 
7.1%
0 2052
 
6.4%
7 2032
 
6.3%
9 1880
 
5.9%
8 1871
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
H 1
33.3%
Space Separator
ValueCountFrequency (%)
21582
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6347
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77034
56.2%
Common 60024
43.8%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7403
9.6%
6597
8.6%
6557
8.5%
6557
8.5%
6532
8.5%
6531
8.5%
6530
8.5%
6530
8.5%
6530
8.5%
2273
 
3.0%
Other values (45) 14994
19.5%
Common
ValueCountFrequency (%)
21582
36.0%
1 7249
 
12.1%
- 6347
 
10.6%
2 4765
 
7.9%
3 3989
 
6.6%
4 3375
 
5.6%
5 2598
 
4.3%
6 2280
 
3.8%
0 2052
 
3.4%
7 2032
 
3.4%
Other values (4) 3755
 
6.3%
Latin
ValueCountFrequency (%)
c 2
40.0%
B 2
40.0%
H 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77034
56.2%
ASCII 60029
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21582
36.0%
1 7249
 
12.1%
- 6347
 
10.6%
2 4765
 
7.9%
3 3989
 
6.6%
4 3375
 
5.6%
5 2598
 
4.3%
6 2280
 
3.8%
0 2052
 
3.4%
7 2032
 
3.4%
Other values (7) 3760
 
6.3%
Hangul
ValueCountFrequency (%)
7403
9.6%
6597
8.6%
6557
8.5%
6557
8.5%
6532
8.5%
6531
8.5%
6530
8.5%
6530
8.5%
6530
8.5%
2273
 
3.0%
Other values (45) 14994
19.5%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3569
Distinct (%)54.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean311.95088
Minimum58
Maximum80349.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T10:29:18.517879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile106.4
Q1145.1
median217
Q3309
95-th percentile699
Maximum80349.7
Range80291.7
Interquartile range (IQR)163.9

Descriptive statistics

Standard deviation1218.2008
Coefficient of variation (CV)3.9051045
Kurtosis3139.5919
Mean311.95088
Median Absolute Deviation (MAD)77.5
Skewness51.803825
Sum2036727.3
Variance1484013.2
MonotonicityNot monotonic
2023-12-12T10:29:18.758851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 123
 
1.9%
165.3 19
 
0.3%
116.0 19
 
0.3%
247.0 18
 
0.3%
126.0 18
 
0.3%
300.0 16
 
0.2%
119.0 16
 
0.2%
264.0 16
 
0.2%
132.0 15
 
0.2%
329.0 15
 
0.2%
Other values (3559) 6254
95.8%
ValueCountFrequency (%)
58.0 1
< 0.1%
60.55 1
< 0.1%
61.9 1
< 0.1%
64.12 1
< 0.1%
64.4 1
< 0.1%
65.27 1
< 0.1%
66.85 1
< 0.1%
67.5 1
< 0.1%
68.24 1
< 0.1%
71.1 1
< 0.1%
ValueCountFrequency (%)
80349.7 1
< 0.1%
45028.0 1
< 0.1%
15924.5 1
< 0.1%
12185.57 1
< 0.1%
10357.0 1
< 0.1%
7710.0 1
< 0.1%
7609.33 1
< 0.1%
6998.2 1
< 0.1%
5993.1 1
< 0.1%
5799.7 1
< 0.1%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5354
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.23673
Minimum30.24
Maximum9443.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T10:29:18.969099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.24
5-th percentile61.4745
Q184.4875
median124.61
Q3168.4275
95-th percentile348.233
Maximum9443.76
Range9413.52
Interquartile range (IQR)83.94

Descriptive statistics

Standard deviation228.32005
Coefficient of variation (CV)1.4520783
Kurtosis620.7151
Mean157.23673
Median Absolute Deviation (MAD)41.44
Skewness20.198628
Sum1026755.9
Variance52130.045
MonotonicityNot monotonic
2023-12-12T10:29:19.168894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.0 5
 
0.1%
64.5 5
 
0.1%
73.92 5
 
0.1%
99.0 5
 
0.1%
82.8 5
 
0.1%
92.4 5
 
0.1%
71.1 5
 
0.1%
74.26 5
 
0.1%
82.68 5
 
0.1%
125.58 4
 
0.1%
Other values (5344) 6481
99.2%
ValueCountFrequency (%)
30.24 1
< 0.1%
34.75 1
< 0.1%
34.77 1
< 0.1%
35.72 1
< 0.1%
36.12 1
< 0.1%
36.42 1
< 0.1%
37.8 1
< 0.1%
38.6 1
< 0.1%
38.89 1
< 0.1%
39.59 1
< 0.1%
ValueCountFrequency (%)
9443.76 1
< 0.1%
6209.83 1
< 0.1%
5562.37 1
< 0.1%
5529.72 1
< 0.1%
3677.94 1
< 0.1%
3588.0 1
< 0.1%
3404.25 1
< 0.1%
2777.88 1
< 0.1%
2388.57 1
< 0.1%
2309.66 1
< 0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6087
Distinct (%)93.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean674.46502
Minimum60.9
Maximum99871.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T10:29:19.381318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60.9
5-th percentile175.344
Q1262.41
median418.83
Q3618.27
95-th percentile1732.03
Maximum99871.25
Range99810.35
Interquartile range (IQR)355.86

Descriptive statistics

Standard deviation2092.4432
Coefficient of variation (CV)3.1023748
Kurtosis1182.8017
Mean674.46502
Median Absolute Deviation (MAD)173.09
Skewness29.261742
Sum4403582.1
Variance4378318.7
MonotonicityNot monotonic
2023-12-12T10:29:19.562435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
659.98 6
 
0.1%
659.88 5
 
0.1%
659.55 5
 
0.1%
659.26 5
 
0.1%
659.85 5
 
0.1%
659.57 4
 
0.1%
659.97 4
 
0.1%
659.78 4
 
0.1%
659.76 4
 
0.1%
659.8 4
 
0.1%
Other values (6077) 6483
99.3%
ValueCountFrequency (%)
60.9 1
< 0.1%
61.0 2
< 0.1%
65.61 1
< 0.1%
75.6 1
< 0.1%
77.34 1
< 0.1%
87.85 1
< 0.1%
88.83 1
< 0.1%
97.87 1
< 0.1%
98.23 1
< 0.1%
99.16 1
< 0.1%
ValueCountFrequency (%)
99871.25 1
< 0.1%
83043.47 1
< 0.1%
37895.52 1
< 0.1%
31182.2176 1
< 0.1%
29117.67 1
< 0.1%
24370.92 1
< 0.1%
23824.618 1
< 0.1%
23243.7166 1
< 0.1%
22688.39 1
< 0.1%
19109.22 1
< 0.1%
Distinct3232
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
Minimum1990-02-28 00:00:00
Maximum2022-11-25 00:00:00
2023-12-12T10:29:19.762210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:19.973918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3356
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2023-12-12T10:29:20.360525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9998469
Min length9

Characters and Unicode

Total characters65299
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1752 ?
Unique (%)26.8%

Sample

1st row1990-05-01
2nd row1994-03-19
3rd row1993-09-05
4th row1996-04-22
5th row1996-08-22
ValueCountFrequency (%)
2003-06-30 45
 
0.7%
2003-07-01 35
 
0.5%
2003-06-27 23
 
0.4%
2009-07-21 15
 
0.2%
2003-06-28 15
 
0.2%
2017-02-01 14
 
0.2%
2003-07-02 11
 
0.2%
2002-08-28 11
 
0.2%
2017-04-14 11
 
0.2%
2012-09-03 11
 
0.2%
Other values (3346) 6339
97.1%
2023-12-12T10:29:20.910366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17838
27.3%
- 13060
20.0%
2 11876
18.2%
1 9093
13.9%
3 2578
 
3.9%
8 1883
 
2.9%
6 1861
 
2.8%
4 1860
 
2.8%
7 1826
 
2.8%
5 1774
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52239
80.0%
Dash Punctuation 13060
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17838
34.1%
2 11876
22.7%
1 9093
17.4%
3 2578
 
4.9%
8 1883
 
3.6%
6 1861
 
3.6%
4 1860
 
3.6%
7 1826
 
3.5%
5 1774
 
3.4%
9 1650
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 13060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65299
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17838
27.3%
- 13060
20.0%
2 11876
18.2%
1 9093
13.9%
3 2578
 
3.9%
8 1883
 
2.9%
6 1861
 
2.8%
4 1860
 
2.8%
7 1826
 
2.8%
5 1774
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17838
27.3%
- 13060
20.0%
2 11876
18.2%
1 9093
13.9%
3 2578
 
3.9%
8 1883
 
2.9%
6 1861
 
2.8%
4 1860
 
2.8%
7 1826
 
2.8%
5 1774
 
2.7%

착공예정일
Text

MISSING 

Distinct3473
Distinct (%)55.2%
Missing237
Missing (%)3.6%
Memory size51.1 KiB
2023-12-12T10:29:21.258955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9809312
Min length6

Characters and Unicode

Total characters62810
Distinct characters12
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

Unique1923 ?
Unique (%)30.6%

Sample

1st row1990-05-01
2nd row1994-03-20
3rd row1993-09-06
4th row1996-05-03
5th row1996-09-13
ValueCountFrequency (%)
2003-06-30 68
 
1.1%
2017-02-03 19
 
0.3%
2003-06-28 18
 
0.3%
2003-07-05 14
 
0.2%
2009-07-21 13
 
0.2%
2017-04-17 10
 
0.2%
2016-03-07 9
 
0.1%
2015-08-06 8
 
0.1%
2016-12-30 8
 
0.1%
2018-03-05 8
 
0.1%
Other values (3463) 6120
97.2%
2023-12-12T10:29:21.736066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17273
27.5%
- 12586
20.0%
2 11173
17.8%
1 8818
14.0%
3 2555
 
4.1%
5 1879
 
3.0%
6 1804
 
2.9%
8 1783
 
2.8%
4 1746
 
2.8%
7 1719
 
2.7%
Other values (2) 1474
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50222
80.0%
Dash Punctuation 12586
 
20.0%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17273
34.4%
2 11173
22.2%
1 8818
17.6%
3 2555
 
5.1%
5 1879
 
3.7%
6 1804
 
3.6%
8 1783
 
3.6%
4 1746
 
3.5%
7 1719
 
3.4%
9 1472
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 12586
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17273
27.5%
- 12586
20.0%
2 11173
17.8%
1 8818
14.0%
3 2555
 
4.1%
5 1879
 
3.0%
6 1804
 
2.9%
8 1783
 
2.8%
4 1746
 
2.8%
7 1719
 
2.7%
Other values (2) 1474
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17273
27.5%
- 12586
20.0%
2 11173
17.8%
1 8818
14.0%
3 2555
 
4.1%
5 1879
 
3.0%
6 1804
 
2.9%
8 1783
 
2.8%
4 1746
 
2.8%
7 1719
 
2.7%
Other values (2) 1474
 
2.3%

실제착공일
Date

MISSING 

Distinct3422
Distinct (%)56.6%
Missing487
Missing (%)7.5%
Memory size51.1 KiB
Minimum1993-09-06 00:00:00
Maximum2022-08-31 00:00:00
2023-12-12T10:29:21.944206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:22.121017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용승인일
Date

MISSING 

Distinct3402
Distinct (%)54.3%
Missing264
Missing (%)4.0%
Memory size51.1 KiB
Minimum1998-03-19 00:00:00
Maximum2023-01-20 00:00:00
2023-12-12T10:29:22.272441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:22.467830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.7753102
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T10:29:22.616398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile8
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.873994
Coefficient of variation (CV)0.39243399
Kurtosis11.742574
Mean4.7753102
Median Absolute Deviation (MAD)1
Skewness2.1362232
Sum31178
Variance3.5118536
MonotonicityNot monotonic
2023-12-12T10:29:22.739575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5 2132
32.6%
4 1580
24.2%
6 951
14.6%
3 827
 
12.7%
7 276
 
4.2%
2 270
 
4.1%
1 157
 
2.4%
8 145
 
2.2%
9 57
 
0.9%
10 42
 
0.6%
Other values (10) 92
 
1.4%
ValueCountFrequency (%)
1 157
 
2.4%
2 270
 
4.1%
3 827
 
12.7%
4 1580
24.2%
5 2132
32.6%
6 951
14.6%
7 276
 
4.2%
8 145
 
2.2%
9 57
 
0.9%
10 42
 
0.6%
ValueCountFrequency (%)
20 5
 
0.1%
19 4
 
0.1%
18 3
 
< 0.1%
17 5
 
0.1%
16 4
 
0.1%
15 15
0.2%
14 8
 
0.1%
13 11
0.2%
12 23
0.4%
11 14
0.2%

최대지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.1%
Missing651
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.25922776
Minimum0
Maximum7
Zeros4555
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-12-12T10:29:22.867976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54297094
Coefficient of variation (CV)2.094571
Kurtosis17.614553
Mean0.25922776
Median Absolute Deviation (MAD)0
Skewness3.07662
Sum1524
Variance0.29481744
MonotonicityNot monotonic
2023-12-12T10:29:22.980777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4555
69.8%
1 1185
 
18.1%
2 103
 
1.6%
3 20
 
0.3%
4 12
 
0.2%
7 2
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 651
 
10.0%
ValueCountFrequency (%)
0 4555
69.8%
1 1185
 
18.1%
2 103
 
1.6%
3 20
 
0.3%
4 12
 
0.2%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 12
 
0.2%
3 20
 
0.3%
2 103
 
1.6%
1 1185
 
18.1%
0 4555
69.8%

주용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
공동주택
3162 
단독주택
2268 
제2종근린생활시설
577 
제1종근린생활시설
 
237
업무시설
 
96
Other values (20)
 
190

Length

Max length10
Median length4
Mean length4.6597243
Min length2

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row단독주택
2nd row단독주택
3rd row단독주택
4th row단독주택
5th row문화및집회시설

Common Values

ValueCountFrequency (%)
공동주택 3162
48.4%
단독주택 2268
34.7%
제2종근린생활시설 577
 
8.8%
제1종근린생활시설 237
 
3.6%
업무시설 96
 
1.5%
노유자시설 43
 
0.7%
종교시설 31
 
0.5%
창고시설 29
 
0.4%
동.식물관련시설 20
 
0.3%
자동차관련시설 12
 
0.2%
Other values (15) 55
 
0.8%

Length

2023-12-12T10:29:23.122244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 3162
48.4%
단독주택 2268
34.7%
제2종근린생활시설 577
 
8.8%
제1종근린생활시설 237
 
3.6%
업무시설 96
 
1.5%
노유자시설 43
 
0.7%
종교시설 31
 
0.5%
창고시설 29
 
0.4%
동.식물관련시설 20
 
0.3%
자동차관련시설 12
 
0.2%
Other values (15) 55
 
0.8%
Distinct1519
Distinct (%)23.3%
Missing13
Missing (%)0.2%
Memory size51.1 KiB
2023-12-12T10:29:23.356243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length24
Mean length9.8241522
Min length2

Characters and Unicode

Total characters64024
Distinct characters405
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique937 ?
Unique (%)14.4%

Sample

1st row지원건축사사무소
2nd row세진건축사사무소
3rd row박기정건축사사무소
4th row(주)박기정종합건축사사무소
5th row신우건축사사무소
ValueCountFrequency (%)
건축사사무소 1155
 
13.5%
사무소 354
 
4.1%
동남건축사사무소 270
 
3.2%
건축사 255
 
3.0%
박노철건축사무소 251
 
2.9%
서림 211
 
2.5%
우리건축사사무소 155
 
1.8%
박노철건축사사무소 133
 
1.6%
믿음직한건축사사무소 113
 
1.3%
씨밀레건축사사무소 105
 
1.2%
Other values (1471) 5550
64.9%
2023-12-12T10:29:23.789584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12532
19.6%
6898
 
10.8%
6803
 
10.6%
6382
 
10.0%
6368
 
9.9%
2054
 
3.2%
1436
 
2.2%
) 1303
 
2.0%
( 1289
 
2.0%
874
 
1.4%
Other values (395) 18085
28.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58951
92.1%
Space Separator 2054
 
3.2%
Close Punctuation 1304
 
2.0%
Open Punctuation 1290
 
2.0%
Uppercase Letter 202
 
0.3%
Other Punctuation 95
 
0.1%
Decimal Number 71
 
0.1%
Lowercase Letter 54
 
0.1%
Dash Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12532
21.3%
6898
11.7%
6803
11.5%
6382
10.8%
6368
10.8%
1436
 
2.4%
874
 
1.5%
810
 
1.4%
809
 
1.4%
658
 
1.1%
Other values (345) 15381
26.1%
Uppercase Letter
ValueCountFrequency (%)
A 27
13.4%
G 23
11.4%
C 21
10.4%
S 15
 
7.4%
M 14
 
6.9%
N 13
 
6.4%
I 11
 
5.4%
H 11
 
5.4%
E 10
 
5.0%
O 9
 
4.5%
Other values (13) 48
23.8%
Lowercase Letter
ValueCountFrequency (%)
a 19
35.2%
i 11
20.4%
c 5
 
9.3%
t 4
 
7.4%
m 3
 
5.6%
h 3
 
5.6%
e 3
 
5.6%
r 2
 
3.7%
s 2
 
3.7%
n 1
 
1.9%
Decimal Number
ValueCountFrequency (%)
2 33
46.5%
1 33
46.5%
3 2
 
2.8%
6 1
 
1.4%
5 1
 
1.4%
9 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 62
65.3%
& 27
28.4%
, 6
 
6.3%
Close Punctuation
ValueCountFrequency (%)
) 1303
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1289
99.9%
[ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2054
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58951
92.1%
Common 4817
 
7.5%
Latin 256
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12532
21.3%
6898
11.7%
6803
11.5%
6382
10.8%
6368
10.8%
1436
 
2.4%
874
 
1.5%
810
 
1.4%
809
 
1.4%
658
 
1.1%
Other values (345) 15381
26.1%
Latin
ValueCountFrequency (%)
A 27
 
10.5%
G 23
 
9.0%
C 21
 
8.2%
a 19
 
7.4%
S 15
 
5.9%
M 14
 
5.5%
N 13
 
5.1%
I 11
 
4.3%
i 11
 
4.3%
H 11
 
4.3%
Other values (24) 91
35.5%
Common
ValueCountFrequency (%)
2054
42.6%
) 1303
27.1%
( 1289
26.8%
. 62
 
1.3%
2 33
 
0.7%
1 33
 
0.7%
& 27
 
0.6%
, 6
 
0.1%
3 2
 
< 0.1%
- 2
 
< 0.1%
Other values (6) 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58951
92.1%
ASCII 5073
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12532
21.3%
6898
11.7%
6803
11.5%
6382
10.8%
6368
10.8%
1436
 
2.4%
874
 
1.5%
810
 
1.4%
809
 
1.4%
658
 
1.1%
Other values (345) 15381
26.1%
ASCII
ValueCountFrequency (%)
2054
40.5%
) 1303
25.7%
( 1289
25.4%
. 62
 
1.2%
2 33
 
0.7%
1 33
 
0.7%
& 27
 
0.5%
A 27
 
0.5%
G 23
 
0.5%
C 21
 
0.4%
Other values (40) 201
 
4.0%

감리사무소명
Text

MISSING 

Distinct1703
Distinct (%)26.6%
Missing119
Missing (%)1.8%
Memory size51.1 KiB
2023-12-12T10:29:24.110983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length22
Mean length9.9085946
Min length2

Characters and Unicode

Total characters63524
Distinct characters411
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1043 ?
Unique (%)16.3%

Sample

1st row진영건축사사무소
2nd row박노철 건축사사무소
3rd row(주)박기정종합건축사사무소
4th row신우건축사사무소
5th row백건축사무소
ValueCountFrequency (%)
건축사사무소 1167
 
13.8%
사무소 327
 
3.9%
건축사 229
 
2.7%
동남건축사사무소 223
 
2.6%
박노철건축사무소 221
 
2.6%
서림 149
 
1.8%
박노철건축사사무소 130
 
1.5%
우리건축사사무소 114
 
1.4%
박노철건축사 99
 
1.2%
믿음직한건축사사무소 97
 
1.2%
Other values (1665) 5673
67.3%
2023-12-12T10:29:24.669837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12339
19.4%
6799
 
10.7%
6697
 
10.5%
6270
 
9.9%
6255
 
9.8%
2032
 
3.2%
1455
 
2.3%
) 1313
 
2.1%
( 1291
 
2.0%
856
 
1.3%
Other values (401) 18217
28.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58421
92.0%
Space Separator 2032
 
3.2%
Close Punctuation 1313
 
2.1%
Open Punctuation 1291
 
2.0%
Uppercase Letter 228
 
0.4%
Other Punctuation 106
 
0.2%
Lowercase Letter 65
 
0.1%
Decimal Number 61
 
0.1%
Dash Punctuation 3
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12339
21.1%
6799
11.6%
6697
11.5%
6270
10.7%
6255
10.7%
1455
 
2.5%
856
 
1.5%
827
 
1.4%
826
 
1.4%
602
 
1.0%
Other values (347) 15495
26.5%
Uppercase Letter
ValueCountFrequency (%)
A 35
15.4%
C 24
10.5%
G 18
 
7.9%
S 16
 
7.0%
D 14
 
6.1%
M 14
 
6.1%
H 13
 
5.7%
J 13
 
5.7%
O 12
 
5.3%
N 11
 
4.8%
Other values (12) 58
25.4%
Lowercase Letter
ValueCountFrequency (%)
a 13
20.0%
i 7
10.8%
s 7
10.8%
c 6
9.2%
u 6
9.2%
l 5
 
7.7%
p 5
 
7.7%
t 4
 
6.2%
h 3
 
4.6%
e 3
 
4.6%
Other values (5) 6
9.2%
Decimal Number
ValueCountFrequency (%)
2 27
44.3%
1 26
42.6%
3 2
 
3.3%
5 2
 
3.3%
7 2
 
3.3%
4 1
 
1.6%
6 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 61
57.5%
& 31
29.2%
, 11
 
10.4%
/ 3
 
2.8%
Space Separator
ValueCountFrequency (%)
2032
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1313
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1291
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58422
92.0%
Common 4809
 
7.6%
Latin 293
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12339
21.1%
6799
11.6%
6697
11.5%
6270
10.7%
6255
10.7%
1455
 
2.5%
856
 
1.5%
827
 
1.4%
826
 
1.4%
602
 
1.0%
Other values (348) 15496
26.5%
Latin
ValueCountFrequency (%)
A 35
 
11.9%
C 24
 
8.2%
G 18
 
6.1%
S 16
 
5.5%
D 14
 
4.8%
M 14
 
4.8%
H 13
 
4.4%
a 13
 
4.4%
J 13
 
4.4%
O 12
 
4.1%
Other values (27) 121
41.3%
Common
ValueCountFrequency (%)
2032
42.3%
) 1313
27.3%
( 1291
26.8%
. 61
 
1.3%
& 31
 
0.6%
2 27
 
0.6%
1 26
 
0.5%
, 11
 
0.2%
- 3
 
0.1%
+ 3
 
0.1%
Other values (6) 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58421
92.0%
ASCII 5102
 
8.0%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12339
21.1%
6799
11.6%
6697
11.5%
6270
10.7%
6255
10.7%
1455
 
2.5%
856
 
1.5%
827
 
1.4%
826
 
1.4%
602
 
1.0%
Other values (347) 15495
26.5%
ASCII
ValueCountFrequency (%)
2032
39.8%
) 1313
25.7%
( 1291
25.3%
. 61
 
1.2%
A 35
 
0.7%
& 31
 
0.6%
2 27
 
0.5%
1 26
 
0.5%
C 24
 
0.5%
G 18
 
0.4%
Other values (43) 244
 
4.8%
None
ValueCountFrequency (%)
1
100.0%

시공자사무소명
Text

MISSING 

Distinct1587
Distinct (%)45.0%
Missing3002
Missing (%)46.0%
Memory size51.1 KiB
2023-12-12T10:29:25.022983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length8.4914966
Min length2

Characters and Unicode

Total characters29958
Distinct characters363
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1043 ?
Unique (%)29.6%

Sample

1st row일조종합건설(주)
2nd row석재종합주식회사
3rd row신예건설주식회사
4th row(주)도시환경종합건설
5th row금성건설(주)
ValueCountFrequency (%)
주식회사 211
 
5.5%
직영 113
 
3.0%
주)우형종합건설 34
 
0.9%
주)현성종합건설 32
 
0.8%
29
 
0.8%
주)양지산업 28
 
0.7%
주)한울에이앤디종합건설 24
 
0.6%
주)서우림종합건설 21
 
0.6%
건축주직영 21
 
0.6%
주)도은이앤씨 18
 
0.5%
Other values (1547) 3284
86.1%
2023-12-12T10:29:25.591369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3370
 
11.2%
2706
 
9.0%
) 2621
 
8.7%
( 2596
 
8.7%
2530
 
8.4%
1761
 
5.9%
1716
 
5.7%
626
 
2.1%
598
 
2.0%
597
 
2.0%
Other values (353) 10837
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24366
81.3%
Close Punctuation 2622
 
8.8%
Open Punctuation 2596
 
8.7%
Space Separator 302
 
1.0%
Decimal Number 33
 
0.1%
Uppercase Letter 19
 
0.1%
Other Punctuation 12
 
< 0.1%
Other Symbol 7
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3370
 
13.8%
2706
 
11.1%
2530
 
10.4%
1761
 
7.2%
1716
 
7.0%
626
 
2.6%
598
 
2.5%
597
 
2.5%
577
 
2.4%
356
 
1.5%
Other values (328) 9529
39.1%
Uppercase Letter
ValueCountFrequency (%)
C 4
21.1%
N 2
10.5%
H 2
10.5%
P 2
10.5%
Y 2
10.5%
B 2
10.5%
E 2
10.5%
I 1
 
5.3%
L 1
 
5.3%
A 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 13
39.4%
2 13
39.4%
0 4
 
12.1%
9 1
 
3.0%
5 1
 
3.0%
3 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 7
58.3%
. 3
25.0%
& 2
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 2621
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2596
100.0%
Space Separator
ValueCountFrequency (%)
302
100.0%
Other Symbol
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24373
81.4%
Common 5566
 
18.6%
Latin 19
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3370
 
13.8%
2706
 
11.1%
2530
 
10.4%
1761
 
7.2%
1716
 
7.0%
626
 
2.6%
598
 
2.5%
597
 
2.4%
577
 
2.4%
356
 
1.5%
Other values (329) 9536
39.1%
Common
ValueCountFrequency (%)
) 2621
47.1%
( 2596
46.6%
302
 
5.4%
1 13
 
0.2%
2 13
 
0.2%
/ 7
 
0.1%
0 4
 
0.1%
. 3
 
0.1%
& 2
 
< 0.1%
9 1
 
< 0.1%
Other values (4) 4
 
0.1%
Latin
ValueCountFrequency (%)
C 4
21.1%
N 2
10.5%
H 2
10.5%
P 2
10.5%
Y 2
10.5%
B 2
10.5%
E 2
10.5%
I 1
 
5.3%
L 1
 
5.3%
A 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24366
81.3%
ASCII 5585
 
18.6%
None 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3370
 
13.8%
2706
 
11.1%
2530
 
10.4%
1761
 
7.2%
1716
 
7.0%
626
 
2.6%
598
 
2.5%
597
 
2.5%
577
 
2.4%
356
 
1.5%
Other values (328) 9529
39.1%
ASCII
ValueCountFrequency (%)
) 2621
46.9%
( 2596
46.5%
302
 
5.4%
1 13
 
0.2%
2 13
 
0.2%
/ 7
 
0.1%
C 4
 
0.1%
0 4
 
0.1%
. 3
 
0.1%
N 2
 
< 0.1%
Other values (14) 20
 
0.4%
None
ValueCountFrequency (%)
7
100.0%

Interactions

2023-12-12T10:29:15.615414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:12.317236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.093256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.729542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.315441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.918338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.725469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:12.454424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.206969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.819186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.408238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.037549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.834221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:12.585839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.321137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.925682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.509246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.156289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.929738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:12.704968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.417490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.017828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.615695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.255655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:16.034902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:12.822967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.515593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.112515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.709348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.357049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:16.142723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:12.962221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:13.623605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.217465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:14.815801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:15.490375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:29:25.742074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)최대지상층수최대지하층수주용도
연번1.0000.0260.0480.0270.4350.2280.257
대지면적(제곱미터)0.0261.0000.7780.6420.1790.2550.903
건축면적(제곱미터)0.0480.7781.0000.8680.3090.4490.797
연면적(제곱미터)0.0270.6420.8681.0000.5030.7590.695
최대지상층수0.4350.1790.3090.5031.0000.5270.640
최대지하층수0.2280.2550.4490.7590.5271.0000.497
주용도0.2570.9030.7970.6950.6400.4971.000
2023-12-12T10:29:25.909097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)최대지상층수최대지하층수주용도
연번1.0000.0780.0720.0830.383-0.0190.093
대지면적(제곱미터)0.0781.0000.9780.7910.3500.2250.728
건축면적(제곱미터)0.0720.9781.0000.8160.3700.2170.496
연면적(제곱미터)0.0830.7910.8161.0000.6750.3900.399
최대지상층수0.3830.3500.3700.6751.0000.1480.283
최대지하층수-0.0190.2250.2170.3900.1481.0000.222
주용도0.0930.7280.4960.3990.2830.2221.000

Missing values

2023-12-12T10:29:16.310058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:29:16.563550image/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.
2023-12-12T10:29:16.804955image/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

연번대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)허가일착공처리일착공예정일실제착공일사용승인일최대지상층수최대지하층수주용도설계사무소명감리사무소명시공자사무소명
01서울특별시 중랑구 중화동 318-8599.9749.8156.581990-02-281990-05-011990-05-01<NA><NA>21단독주택지원건축사사무소진영건축사사무소<NA>
12서울특별시 중랑구 망우동 403-37113.2467.9199.311993-06-101994-03-191994-03-201994-03-192005-10-0621단독주택세진건축사사무소박노철 건축사사무소<NA>
23서울특별시 중랑구 묵동 153-17133.279.8178.071993-08-241993-09-051993-09-061993-09-062015-11-1921단독주택박기정건축사사무소<NA><NA>
34서울특별시 중랑구 면목동 185-49118.766.76179.251996-04-221996-04-221996-05-031996-05-032000-03-1521단독주택(주)박기정종합건축사사무소(주)박기정종합건축사사무소<NA>
45서울특별시 중랑구 묵동 4468.0271.17998.811996-08-221996-08-221996-09-131996-09-13<NA>31문화및집회시설신우건축사사무소신우건축사사무소일조종합건설(주)
56서울특별시 중랑구 신내동 410-52625.0364.771578.571997-07-301997-08-231997-08-231997-08-221998-03-1941공동주택백건축사무소백건축사무소석재종합주식회사
67서울특별시 중랑구 상봉동 202-1658.0394.572168.211997-07-301997-07-301997-11-04<NA><NA>51제1종근린생활시설(주)국원건축사사무소(주)국원건축사사무소신예건설주식회사
78서울특별시 중랑구 신내동 449-3 외1필지429.0245.041410.881997-08-111997-10-071997-09-1997-10-061999-01-28<NA><NA>제1종근린생활시설입체건축입체건축(주)도시환경종합건설
89서울특별시 중랑구 면목동 137-4261.8156.72514.81997-10-091998-03-04<NA>1998-03-101999-01-3031단독주택박을식건축박을식건축<NA>
910서울특별시 중랑구 면목동 137-40111.766.36175.621997-10-231998-03-04<NA>1998-03-011999-01-3021단독주택박을식건축<NA><NA>
연번대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)허가일착공처리일착공예정일실제착공일사용승인일최대지상층수최대지하층수주용도설계사무소명감리사무소명시공자사무소명
65206521서울특별시 중랑구 신내동 493-137 외1필지247.28148.25492.812022-09-262022-12-082022-12-10<NA><NA>50공동주택소요건축사사무소(주)그룹예성씨엠종합건축사사무소(주)한다종합건설
65216522서울특별시 중랑구 신내동 278-210330.0197.96231.722022-09-272022-12-012022-11-25<NA><NA>20제2종근린생활시설미성건축사사무소미성건축사사무소범주종합건설(주)
65226523서울특별시 중랑구 상봉동 279-30 외1필지109.062.06216.112022-09-292022-12-212022-12-19<NA><NA>50공동주택스튜디오포마 건축사사무소주식회사 지앤유종합건축사사무소(주)솔하임건설
65236524서울특별시 중랑구 망우동 611333.1163.7163.72022-10-172022-10-312022-10-28<NA><NA>10제2종근린생활시설(주)에스엘케이종합건축사사무소(주)에스엘케이종합건축사사무소<NA>
65246525서울특별시 중랑구 상봉동 104-37 외1필지199.22112.44662.782022-10-192022-10-312022-10-31<NA><NA>80제2종근린생활시설에스아이디건축사사무소우미건축사사무소건영종합개발(주)
65256526서울특별시 중랑구 망우동 521-3 외2필지345.07191.93690.112022-10-252022-11-182022-11-14<NA><NA>60공동주택건축사사무소 리우수현건축사사무소(주)우진탑종합건설
65266527서울특별시 중랑구 면목동 57-1 외6필지3082.91306.12497.22022-10-262022-11-212022-11-08<NA><NA>20운수시설(주)어반아지트 건축사사무소 (URBANAGIT Architects)(주)어반아지트 건축사사무소 (URBANAGIT Architects)수정종합건설주식회사
65276528서울특별시 중랑구 면목동 637-16104.861.43487.652022-10-262022-12-272023-01-02<NA><NA>81업무시설박노철건축사무소박노철건축사무소(주)가람종합건설
65286529서울특별시 중랑구 중화동 325-91 외1필지326.6195.16569.842022-11-152022-12-232022-12-26<NA><NA>50공동주택수현건축사사무소(주)씨엠파트너스 건축사사무소(주)한울에이앤디종합건설
65296530서울특별시 중랑구 면목동 365-16599.7337.611290.122022-11-252022-12-062022-11-30<NA><NA>61공동주택건축사사무소 아성업타운건축사사무소새울건설(주)