Overview

Dataset statistics

Number of variables9
Number of observations4992
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory385.3 KiB
Average record size in memory79.0 B

Variable types

Numeric7
DateTime1
Categorical1

Dataset

Description계획 관리 번호,검토일,관리기관코드,관리기관명,부지면적,대책수립면적,녹지 및 나지면적,필요대책량,설치대책량
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15648/S/1/datasetView.do

Alerts

관리기관코드 is highly overall correlated with 관리기관명High correlation
부지면적 is highly overall correlated with 대책수립면적 and 3 other fieldsHigh correlation
대책수립면적 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
녹지 및 나지면적 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
필요대책량 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
설치대책량 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
관리기관명 is highly overall correlated with 관리기관코드High correlation
부지면적 is highly skewed (γ1 = 25.29581175)Skewed
녹지 및 나지면적 is highly skewed (γ1 = 50.24384899)Skewed
설치대책량 is highly skewed (γ1 = 33.82271349)Skewed
계획 관리 번호 has unique valuesUnique
녹지 및 나지면적 has 718 (14.4%) zerosZeros

Reproduction

Analysis started2024-05-17 21:53:23.090324
Analysis finished2024-05-17 21:53:36.371385
Duration13.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

계획 관리 번호
Real number (ℝ)

UNIQUE 

Distinct4992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2709.5583
Minimum1
Maximum5386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.0 KiB
2024-05-18T06:53:36.594539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile305.55
Q11373.75
median2732.5
Q34033.25
95-th percentile5112.45
Maximum5386
Range5385
Interquartile range (IQR)2659.5

Descriptive statistics

Standard deviation1542.8014
Coefficient of variation (CV)0.56939221
Kurtosis-1.1887231
Mean2709.5583
Median Absolute Deviation (MAD)1329
Skewness-0.013954022
Sum13526115
Variance2380236.2
MonotonicityNot monotonic
2024-05-18T06:53:37.000189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4457 1
 
< 0.1%
3959 1
 
< 0.1%
2643 1
 
< 0.1%
1726 1
 
< 0.1%
1727 1
 
< 0.1%
1848 1
 
< 0.1%
2644 1
 
< 0.1%
1721 1
 
< 0.1%
2646 1
 
< 0.1%
2619 1
 
< 0.1%
Other values (4982) 4982
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 (%)
5386 1
< 0.1%
5385 1
< 0.1%
5384 1
< 0.1%
5383 1
< 0.1%
5382 1
< 0.1%
5381 1
< 0.1%
5380 1
< 0.1%
5379 1
< 0.1%
5378 1
< 0.1%
5377 1
< 0.1%

검토일
Date

Distinct1948
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
Minimum2014-02-11 00:00:00
Maximum2024-04-26 00:00:00
2024-05-18T06:53:37.411909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:37.920810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리기관코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1143.8922
Minimum1010
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.0 KiB
2024-05-18T06:53:38.304020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010
5-th percentile1030
Q11080
median1160
Q31210
95-th percentile1240
Maximum1250
Range240
Interquartile range (IQR)130

Descriptive statistics

Standard deviation70.516704
Coefficient of variation (CV)0.061646283
Kurtosis-1.1479926
Mean1143.8922
Median Absolute Deviation (MAD)60
Skewness-0.26362525
Sum5710310
Variance4972.6055
MonotonicityNot monotonic
2024-05-18T06:53:38.712174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1160 554
 
11.1%
1240 297
 
5.9%
1220 294
 
5.9%
1230 290
 
5.8%
1190 275
 
5.5%
1070 266
 
5.3%
1170 238
 
4.8%
1120 231
 
4.6%
1040 222
 
4.4%
1180 218
 
4.4%
Other values (15) 2107
42.2%
ValueCountFrequency (%)
1010 107
2.1%
1020 122
2.4%
1030 147
2.9%
1040 222
4.4%
1050 142
2.8%
1060 212
4.2%
1070 266
5.3%
1080 144
2.9%
1090 164
3.3%
1100 77
 
1.5%
ValueCountFrequency (%)
1250 182
 
3.6%
1240 297
5.9%
1230 290
5.8%
1220 294
5.9%
1210 213
 
4.3%
1200 104
 
2.1%
1190 275
5.5%
1180 218
 
4.4%
1170 238
4.8%
1160 554
11.1%

관리기관명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
강서구청
554 
송파구청
 
297
서초구청
 
294
강남구청
 
290
영등포구청
 
275
Other values (20)
3282 

Length

Max length5
Median length4
Mean length4.0971554
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용산구청
2nd row중랑구청
3rd row송파구청
4th row강북구청
5th row송파구청

Common Values

ValueCountFrequency (%)
강서구청 554
 
11.1%
송파구청 297
 
5.9%
서초구청 294
 
5.9%
강남구청 290
 
5.8%
영등포구청 275
 
5.5%
중랑구청 266
 
5.3%
구로구청 238
 
4.8%
은평구청 231
 
4.6%
성동구청 222
 
4.4%
금천구청 218
 
4.4%
Other values (15) 2107
42.2%

Length

2024-05-18T06:53:39.140911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구청 554
 
11.1%
송파구청 297
 
5.9%
서초구청 294
 
5.9%
강남구청 290
 
5.8%
영등포구청 275
 
5.5%
중랑구청 266
 
5.3%
구로구청 238
 
4.8%
은평구청 231
 
4.6%
성동구청 222
 
4.4%
금천구청 218
 
4.4%
Other values (15) 2107
42.2%

부지면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2414
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4099.8944
Minimum0
Maximum935261
Zeros17
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size44.0 KiB
2024-05-18T06:53:39.548847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile325
Q1571
median935
Q31869.25
95-th percentile13635.85
Maximum935261
Range935261
Interquartile range (IQR)1298.25

Descriptive statistics

Standard deviation20867.993
Coefficient of variation (CV)5.0898854
Kurtosis930.21774
Mean4099.8944
Median Absolute Deviation (MAD)460.5
Skewness25.295812
Sum20466673
Variance4.3547313 × 108
MonotonicityNot monotonic
2024-05-18T06:53:40.073617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331 17
 
0.3%
0 17
 
0.3%
446 14
 
0.3%
661 14
 
0.3%
764 12
 
0.2%
939 11
 
0.2%
656 11
 
0.2%
648 11
 
0.2%
378 11
 
0.2%
799 10
 
0.2%
Other values (2404) 4864
97.4%
ValueCountFrequency (%)
0 17
0.3%
1 2
 
< 0.1%
4 1
 
< 0.1%
20 1
 
< 0.1%
29 1
 
< 0.1%
30 1
 
< 0.1%
58 1
 
< 0.1%
66 1
 
< 0.1%
108 1
 
< 0.1%
123 1
 
< 0.1%
ValueCountFrequency (%)
935261 1
< 0.1%
475835 1
< 0.1%
462771 1
< 0.1%
399742 1
< 0.1%
272228 1
< 0.1%
186166 1
< 0.1%
175795 1
< 0.1%
174972 1
< 0.1%
154627 1
< 0.1%
145772 1
< 0.1%

대책수립면적
Real number (ℝ)

HIGH CORRELATION 

Distinct2411
Distinct (%)48.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3386.9305
Minimum-4432
Maximum445425
Zeros8
Zeros (%)0.2%
Negative5
Negative (%)0.1%
Memory size44.0 KiB
2024-05-18T06:53:40.575559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4432
5-th percentile284
Q1531
median862
Q31721
95-th percentile12065
Maximum445425
Range449857
Interquartile range (IQR)1190

Descriptive statistics

Standard deviation14226.223
Coefficient of variation (CV)4.2003293
Kurtosis435.53019
Mean3386.9305
Median Absolute Deviation (MAD)438
Skewness17.657283
Sum16904170
Variance2.0238543 × 108
MonotonicityNot monotonic
2024-05-18T06:53:41.022583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
648 13
 
0.3%
531 11
 
0.2%
625 11
 
0.2%
655 10
 
0.2%
370 10
 
0.2%
660 9
 
0.2%
598 9
 
0.2%
323 9
 
0.2%
306 9
 
0.2%
367 9
 
0.2%
Other values (2401) 4891
98.0%
ValueCountFrequency (%)
-4432 1
 
< 0.1%
-1746 1
 
< 0.1%
-1729 1
 
< 0.1%
-353 1
 
< 0.1%
-56 1
 
< 0.1%
0 8
0.2%
3 1
 
< 0.1%
12 1
 
< 0.1%
27 1
 
< 0.1%
78 1
 
< 0.1%
ValueCountFrequency (%)
445425 1
< 0.1%
370250 1
< 0.1%
367914 1
< 0.1%
330649 1
< 0.1%
210745 1
< 0.1%
139142 1
< 0.1%
130875 1
< 0.1%
122841 1
< 0.1%
122197 1
< 0.1%
111384 1
< 0.1%

녹지 및 나지면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1020
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1105.0298
Minimum0
Maximum816394
Zeros718
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size44.0 KiB
2024-05-18T06:53:41.489613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136
median81
Q3201
95-th percentile2481.15
Maximum816394
Range816394
Interquartile range (IQR)165

Descriptive statistics

Standard deviation13127.977
Coefficient of variation (CV)11.880201
Kurtosis3013.5677
Mean1105.0298
Median Absolute Deviation (MAD)61
Skewness50.243849
Sum5516309
Variance1.7234378 × 108
MonotonicityNot monotonic
2024-05-18T06:53:42.006859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 718
 
14.4%
55 41
 
0.8%
53 39
 
0.8%
39 37
 
0.7%
45 37
 
0.7%
71 37
 
0.7%
54 37
 
0.7%
40 37
 
0.7%
64 35
 
0.7%
59 35
 
0.7%
Other values (1010) 3939
78.9%
ValueCountFrequency (%)
0 718
14.4%
1 1
 
< 0.1%
5 1
 
< 0.1%
7 3
 
0.1%
8 4
 
0.1%
9 6
 
0.1%
10 5
 
0.1%
11 8
 
0.2%
12 6
 
0.1%
13 12
 
0.2%
ValueCountFrequency (%)
816394 1
< 0.1%
248803 1
< 0.1%
154203 1
< 0.1%
115154 1
< 0.1%
107845 1
< 0.1%
87958 1
< 0.1%
74867 1
< 0.1%
74837 1
< 0.1%
66398 1
< 0.1%
64725 1
< 0.1%

필요대책량
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)4.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean18.81106
Minimum-24
Maximum2453
Zeros15
Zeros (%)0.3%
Negative4
Negative (%)0.1%
Memory size44.0 KiB
2024-05-18T06:53:42.599656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-24
5-th percentile1
Q13
median5
Q310
95-th percentile66.5
Maximum2453
Range2477
Interquartile range (IQR)7

Descriptive statistics

Standard deviation81.360108
Coefficient of variation (CV)4.3251209
Kurtosis467.27546
Mean18.81106
Median Absolute Deviation (MAD)3
Skewness18.409487
Sum93886
Variance6619.4671
MonotonicityNot monotonic
2024-05-18T06:53:43.201001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 736
14.7%
4 678
13.6%
3 666
13.3%
5 489
9.8%
6 312
 
6.2%
7 275
 
5.5%
2 267
 
5.3%
8 169
 
3.4%
9 126
 
2.5%
10 108
 
2.2%
Other values (221) 1165
23.3%
ValueCountFrequency (%)
-24 1
 
< 0.1%
-6 2
 
< 0.1%
-2 1
 
< 0.1%
0 15
 
0.3%
1 736
14.7%
2 267
 
5.3%
3 666
13.3%
4 678
13.6%
5 489
9.8%
6 312
6.2%
ValueCountFrequency (%)
2453 1
< 0.1%
2450 1
< 0.1%
2036 1
< 0.1%
1882 1
< 0.1%
1186 1
< 0.1%
810 1
< 0.1%
720 1
< 0.1%
686 1
< 0.1%
672 1
< 0.1%
627 1
< 0.1%

설치대책량
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct251
Distinct (%)5.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22.61984
Minimum0
Maximum6721
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.0 KiB
2024-05-18T06:53:43.677401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q311
95-th percentile78
Maximum6721
Range6721
Interquartile range (IQR)8

Descriptive statistics

Standard deviation128.27197
Coefficient of variation (CV)5.6707729
Kurtosis1578.1254
Mean22.61984
Median Absolute Deviation (MAD)3
Skewness33.822713
Sum112873
Variance16453.699
MonotonicityNot monotonic
2024-05-18T06:53:44.113400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 638
12.8%
3 513
 
10.3%
5 494
 
9.9%
2 454
 
9.1%
6 398
 
8.0%
1 379
 
7.6%
7 284
 
5.7%
8 229
 
4.6%
9 152
 
3.0%
10 127
 
2.5%
Other values (241) 1322
26.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 379
7.6%
2 454
9.1%
3 513
10.3%
4 638
12.8%
5 494
9.9%
6 398
8.0%
7 284
5.7%
8 229
 
4.6%
9 152
 
3.0%
ValueCountFrequency (%)
6721 1
< 0.1%
2703 1
< 0.1%
2450 1
< 0.1%
2198 1
< 0.1%
1545 1
< 0.1%
1187 1
< 0.1%
1042 1
< 0.1%
761 1
< 0.1%
747 1
< 0.1%
742 1
< 0.1%

Interactions

2024-05-18T06:53:34.053259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:24.521244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:26.183465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:27.807011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:29.422566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:31.046968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:32.683151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:34.266279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:24.719150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:26.440981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:28.059164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:29.724734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:31.327502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:32.852925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:34.443643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:24.887681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:26.653767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:28.322623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:29.901867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:31.597082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:33.020670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:34.641613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:25.113847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:26.812882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:28.521906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:30.058047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:31.769778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:33.180868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:34.888525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:25.369057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:26.975557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:28.761410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:30.277351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:31.944387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:33.349645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:35.089232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:25.651614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:27.206356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:29.035254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:30.523879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:32.143969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:33.538464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:35.302966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:25.906347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:27.535846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:29.246277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:30.777511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:32.404282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:53:33.782482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T06:53:44.485065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계획 관리 번호관리기관코드관리기관명부지면적대책수립면적녹지 및 나지면적필요대책량설치대책량
계획 관리 번호1.0000.2470.3930.0540.0750.0190.0680.042
관리기관코드0.2471.0001.0000.0230.0300.0000.0410.000
관리기관명0.3931.0001.0000.0000.0640.0000.0880.000
부지면적0.0540.0230.0001.0000.9520.9960.9410.965
대책수립면적0.0750.0300.0640.9521.0000.9870.9980.888
녹지 및 나지면적0.0190.0000.0000.9960.9871.0000.9700.911
필요대책량0.0680.0410.0880.9410.9980.9701.0000.887
설치대책량0.0420.0000.0000.9650.8880.9110.8871.000
2024-05-18T06:53:44.907566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계획 관리 번호관리기관코드부지면적대책수립면적녹지 및 나지면적필요대책량설치대책량관리기관명
계획 관리 번호1.000-0.082-0.084-0.077-0.102-0.080-0.0780.149
관리기관코드-0.0821.0000.0420.043-0.0180.0430.0450.998
부지면적-0.0840.0421.0000.9830.6190.9800.9460.000
대책수립면적-0.0770.0430.9831.0000.5870.9870.9440.025
녹지 및 나지면적-0.102-0.0180.6190.5871.0000.5850.5710.000
필요대책량-0.0800.0430.9800.9870.5851.0000.9490.035
설치대책량-0.0780.0450.9460.9440.5710.9491.0000.000
관리기관명0.1490.9980.0000.0250.0000.0350.0001.000

Missing values

2024-05-18T06:53:35.791238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T06:53:36.052279image/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.
2024-05-18T06:53:36.251404image/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

계획 관리 번호검토일관리기관코드관리기관명부지면적대책수립면적녹지 및 나지면적필요대책량설치대책량
044572024-04-26 00:00:00.01030용산구청667363445493535
153862024-04-25 00:00:00.01070중랑구청34824810011
253852024-04-19 00:00:00.01240송파구청209518693761014
353842024-04-12 00:00:00.01090강북구청4964375923
453832024-04-03 00:00:00.01240송파구청1343132333711
553732024-02-15 00:00:00.01090강북구청3723393212
653712024-02-02 00:00:00.01160강서구청231721712441215
753682024-01-23 00:00:00.01160강서구청210819173181111
853672024-01-22 00:00:00.01090강북구청1541120167979
953662024-01-18 00:00:00.01090강북구청3032792411
계획 관리 번호검토일관리기관코드관리기관명부지면적대책수립면적녹지 및 나지면적필요대책량설치대책량
4982112014-04-09 00:00:00.01170구로구청9439125159
4983132014-04-08 00:00:00.01020중구청42725329012
498452014-04-03 00:00:00.01140마포구청158681417628207779
498582014-04-03 00:00:00.01190영등포구청8004732111394057
498662014-04-02 00:00:00.01120은평구청78567019144
4987102014-04-01 00:00:00.01220서초구청97285319855
498842014-03-21 00:00:00.01140마포구청12101210077
498932014-03-12 00:00:00.01220서초구청113010847657
499022014-03-12 00:00:00.01220서초구청1491141912088
499112014-02-11 00:00:00.01020중구청9789367056