Overview

Dataset statistics

Number of variables17
Number of observations632
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)0.5%
Total size in memory90.2 KiB
Average record size in memory146.2 B

Variable types

Categorical8
Numeric9

Dataset

Description경기주택도시공사 기금관리시스템의 기존주택 전세임대 권역별 센터정보로써 권역명, 전화번호, 권역주소 등의 정보를 포함하고 있습니다.
Author경기주택도시공사
URLhttps://www.data.go.kr/data/15119265/fileData.do

Alerts

Dataset has 3 (0.5%) duplicate rowsDuplicates
약어 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
권역주소(담당 법무법인) is highly overall correlated with 구분 and 3 other fieldsHigh correlation
전화번호 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
권역명 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
사업연도 is highly overall correlated with 계획호수 and 5 other fieldsHigh correlation
관리지역 아이디 is highly overall correlated with 이율코드 and 4 other fieldsHigh correlation
계획호수 is highly overall correlated with 사업연도 and 3 other fieldsHigh correlation
지원호수 is highly overall correlated with 사업연도 and 3 other fieldsHigh correlation
계약호수 is highly overall correlated with 사업연도 and 3 other fieldsHigh correlation
해약호수 is highly overall correlated with 계획호수 and 2 other fieldsHigh correlation
이율코드 is highly overall correlated with 사업연도 and 6 other fieldsHigh correlation
적용일 is highly overall correlated with 사업연도 and 7 other fieldsHigh correlation
적용이율 is highly overall correlated with 이율코드 and 3 other fieldsHigh correlation
구분 is highly overall correlated with 사업연도 and 10 other fieldsHigh correlation
공급지역 is highly overall correlated with 관리지역 아이디 and 2 other fieldsHigh correlation
절삭단위 is highly overall correlated with 관리지역 아이디 and 5 other fieldsHigh correlation
비고 is highly overall correlated with 이율코드 and 4 other fieldsHigh correlation
구분 is highly imbalanced (64.5%)Imbalance
절삭단위 is highly imbalanced (75.0%)Imbalance
비고 is highly imbalanced (83.8%)Imbalance
권역명 is highly imbalanced (96.7%)Imbalance
전화번호 is highly imbalanced (96.7%)Imbalance
약어 is highly imbalanced (96.7%)Imbalance
권역주소(담당 법무법인) is highly imbalanced (97.1%)Imbalance
사업연도 has 74 (11.7%) zerosZeros
관리지역 아이디 has 74 (11.7%) zerosZeros
계획호수 has 344 (54.4%) zerosZeros
지원호수 has 308 (48.7%) zerosZeros
계약호수 has 323 (51.1%) zerosZeros
해약호수 has 386 (61.1%) zerosZeros
이율코드 has 563 (89.1%) zerosZeros
적용일 has 563 (89.1%) zerosZeros
적용이율 has 577 (91.3%) zerosZeros

Reproduction

Analysis started2024-04-17 16:49:44.086401
Analysis finished2024-04-17 16:49:52.005356
Duration7.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
연도별 공급지역
558 
이율관리
69 
센터관리
 
5

Length

Max length8
Median length8
Mean length7.5316456
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연도별 공급지역
2nd row연도별 공급지역
3rd row연도별 공급지역
4th row연도별 공급지역
5th row연도별 공급지역

Common Values

ValueCountFrequency (%)
연도별 공급지역 558
88.3%
이율관리 69
 
10.9%
센터관리 5
 
0.8%

Length

2024-04-18T01:49:52.099095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:49:52.220376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연도별 558
46.9%
공급지역 558
46.9%
이율관리 69
 
5.8%
센터관리 5
 
0.4%

사업연도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.802215
Minimum0
Maximum23
Zeros74
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:52.325175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median13
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7507442
Coefficient of variation (CV)0.52731063
Kurtosis-0.7210435
Mean12.802215
Median Absolute Deviation (MAD)5
Skewness-0.39294446
Sum8091
Variance45.572547
MonotonicityNot monotonic
2024-04-18T01:49:52.467810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 74
 
11.7%
6 31
 
4.9%
7 31
 
4.9%
23 31
 
4.9%
22 31
 
4.9%
21 31
 
4.9%
20 31
 
4.9%
19 31
 
4.9%
18 31
 
4.9%
17 31
 
4.9%
Other values (9) 279
44.1%
ValueCountFrequency (%)
0 74
11.7%
6 31
4.9%
7 31
4.9%
8 31
4.9%
9 31
4.9%
10 31
4.9%
11 31
4.9%
12 31
4.9%
13 31
4.9%
14 31
4.9%
ValueCountFrequency (%)
23 31
4.9%
22 31
4.9%
21 31
4.9%
20 31
4.9%
19 31
4.9%
18 31
4.9%
17 31
4.9%
16 31
4.9%
15 31
4.9%
14 31
4.9%

관리지역 아이디
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.613924
Minimum0
Maximum61
Zeros74
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:52.602692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135
median44
Q353
95-th percentile60
Maximum61
Range61
Interquartile range (IQR)18

Descriptive statistics

Standard deviation17.024723
Coefficient of variation (CV)0.4191844
Kurtosis1.0993851
Mean40.613924
Median Absolute Deviation (MAD)9
Skewness-1.3367312
Sum25668
Variance289.8412
MonotonicityNot monotonic
2024-04-18T01:49:52.724313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 74
 
11.7%
39 18
 
2.8%
50 18
 
2.8%
61 18
 
2.8%
38 18
 
2.8%
60 18
 
2.8%
59 18
 
2.8%
58 18
 
2.8%
57 18
 
2.8%
56 18
 
2.8%
Other values (22) 396
62.7%
ValueCountFrequency (%)
0 74
11.7%
31 18
 
2.8%
32 18
 
2.8%
33 18
 
2.8%
34 18
 
2.8%
35 18
 
2.8%
36 18
 
2.8%
37 18
 
2.8%
38 18
 
2.8%
39 18
 
2.8%
ValueCountFrequency (%)
61 18
2.8%
60 18
2.8%
59 18
2.8%
58 18
2.8%
57 18
2.8%
56 18
2.8%
55 18
2.8%
54 18
2.8%
53 18
2.8%
52 18
2.8%

공급지역
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
74 
고양시
 
18
과천시
 
18
광명시
 
18
광주시
 
18
Other values (27)
486 

Length

Max length4
Median length3
Mean length2.8512658
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시

Common Values

ValueCountFrequency (%)
0 74
 
11.7%
고양시 18
 
2.8%
과천시 18
 
2.8%
광명시 18
 
2.8%
광주시 18
 
2.8%
구리시 18
 
2.8%
군포시 18
 
2.8%
김포시 18
 
2.8%
남양주시 18
 
2.8%
동두천시 18
 
2.8%
Other values (22) 396
62.7%

Length

2024-04-18T01:49:52.827544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 74
 
11.7%
고양시 18
 
2.8%
화성시 18
 
2.8%
하남시 18
 
2.8%
포천시 18
 
2.8%
평택시 18
 
2.8%
파주시 18
 
2.8%
이천시 18
 
2.8%
의정부시 18
 
2.8%
의왕시 18
 
2.8%
Other values (22) 396
62.7%

계획호수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.039557
Minimum0
Maximum320
Zeros344
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:52.921446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360
95-th percentile220
Maximum320
Range320
Interquartile range (IQR)60

Descriptive statistics

Standard deviation76.066178
Coefficient of variation (CV)1.6521918
Kurtosis2.6425604
Mean46.039557
Median Absolute Deviation (MAD)0
Skewness1.8604154
Sum29097
Variance5786.0634
MonotonicityNot monotonic
2024-04-18T01:49:53.020421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 344
54.4%
20 31
 
4.9%
50 31
 
4.9%
100 25
 
4.0%
30 24
 
3.8%
10 23
 
3.6%
200 22
 
3.5%
40 13
 
2.1%
70 13
 
2.1%
300 12
 
1.9%
Other values (27) 94
 
14.9%
ValueCountFrequency (%)
0 344
54.4%
10 23
 
3.6%
15 3
 
0.5%
20 31
 
4.9%
30 24
 
3.8%
35 1
 
0.2%
40 13
 
2.1%
50 31
 
4.9%
60 5
 
0.8%
70 13
 
2.1%
ValueCountFrequency (%)
320 2
 
0.3%
300 12
1.9%
290 2
 
0.3%
280 1
 
0.2%
270 1
 
0.2%
260 3
 
0.5%
250 4
 
0.6%
240 4
 
0.6%
230 2
 
0.3%
220 3
 
0.5%

지원호수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct152
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.571203
Minimum0
Maximum461
Zeros308
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:53.127096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q342.25
95-th percentile193
Maximum461
Range461
Interquartile range (IQR)42.25

Descriptive statistics

Standard deviation67.958536
Coefficient of variation (CV)1.8087932
Kurtosis6.1527675
Mean37.571203
Median Absolute Deviation (MAD)1
Skewness2.3630979
Sum23745
Variance4618.3626
MonotonicityNot monotonic
2024-04-18T01:49:53.240930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 308
48.7%
1 30
 
4.7%
2 13
 
2.1%
3 8
 
1.3%
14 6
 
0.9%
9 5
 
0.8%
8 5
 
0.8%
110 5
 
0.8%
34 5
 
0.8%
10 5
 
0.8%
Other values (142) 242
38.3%
ValueCountFrequency (%)
0 308
48.7%
1 30
 
4.7%
2 13
 
2.1%
3 8
 
1.3%
4 3
 
0.5%
5 2
 
0.3%
6 3
 
0.5%
7 1
 
0.2%
8 5
 
0.8%
9 5
 
0.8%
ValueCountFrequency (%)
461 1
0.2%
362 1
0.2%
353 1
0.2%
349 1
0.2%
324 1
0.2%
292 1
0.2%
290 1
0.2%
276 1
0.2%
270 1
0.2%
253 1
0.2%

계약호수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.536392
Minimum0
Maximum387
Zeros323
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:53.348225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile152.9
Maximum387
Range387
Interquartile range (IQR)25

Descriptive statistics

Standard deviation54.647511
Coefficient of variation (CV)1.9845559
Kurtosis9.7892577
Mean27.536392
Median Absolute Deviation (MAD)0
Skewness2.8931921
Sum17403
Variance2986.3505
MonotonicityNot monotonic
2024-04-18T01:49:53.457180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323
51.1%
1 30
 
4.7%
7 9
 
1.4%
17 8
 
1.3%
22 8
 
1.3%
23 8
 
1.3%
19 8
 
1.3%
16 7
 
1.1%
10 7
 
1.1%
21 7
 
1.1%
Other values (123) 217
34.3%
ValueCountFrequency (%)
0 323
51.1%
1 30
 
4.7%
2 5
 
0.8%
3 4
 
0.6%
4 1
 
0.2%
5 4
 
0.6%
6 5
 
0.8%
7 9
 
1.4%
8 3
 
0.5%
9 4
 
0.6%
ValueCountFrequency (%)
387 1
0.2%
351 1
0.2%
343 1
0.2%
273 1
0.2%
271 1
0.2%
257 1
0.2%
250 1
0.2%
233 1
0.2%
226 1
0.2%
224 1
0.2%

해약호수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.03481
Minimum0
Maximum118
Zeros386
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:53.567962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile62.9
Maximum118
Range118
Interquartile range (IQR)5

Descriptive statistics

Standard deviation21.982821
Coefficient of variation (CV)2.1906564
Kurtosis6.0260611
Mean10.03481
Median Absolute Deviation (MAD)0
Skewness2.5659522
Sum6342
Variance483.24443
MonotonicityNot monotonic
2024-04-18T01:49:53.671183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386
61.1%
1 35
 
5.5%
3 20
 
3.2%
2 16
 
2.5%
4 12
 
1.9%
12 8
 
1.3%
7 7
 
1.1%
9 7
 
1.1%
8 6
 
0.9%
5 6
 
0.9%
Other values (69) 129
 
20.4%
ValueCountFrequency (%)
0 386
61.1%
1 35
 
5.5%
2 16
 
2.5%
3 20
 
3.2%
4 12
 
1.9%
5 6
 
0.9%
6 5
 
0.8%
7 7
 
1.1%
8 6
 
0.9%
9 7
 
1.1%
ValueCountFrequency (%)
118 1
 
0.2%
105 1
 
0.2%
104 1
 
0.2%
103 1
 
0.2%
98 3
0.5%
96 1
 
0.2%
95 1
 
0.2%
93 1
 
0.2%
91 2
0.3%
86 1
 
0.2%

이율코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63132911
Minimum0
Maximum15
Zeros563
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:53.762110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.147899
Coefficient of variation (CV)3.4021859
Kurtosis16.458022
Mean0.63132911
Median Absolute Deviation (MAD)0
Skewness3.9545221
Sum399
Variance4.6134702
MonotonicityNot monotonic
2024-04-18T01:49:53.850538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 563
89.1%
4 19
 
3.0%
6 10
 
1.6%
1 10
 
1.6%
5 7
 
1.1%
9 6
 
0.9%
10 5
 
0.8%
13 3
 
0.5%
3 2
 
0.3%
8 1
 
0.2%
Other values (6) 6
 
0.9%
ValueCountFrequency (%)
0 563
89.1%
1 10
 
1.6%
2 1
 
0.2%
3 2
 
0.3%
4 19
 
3.0%
5 7
 
1.1%
6 10
 
1.6%
7 1
 
0.2%
8 1
 
0.2%
9 6
 
0.9%
ValueCountFrequency (%)
15 1
 
0.2%
14 1
 
0.2%
13 3
 
0.5%
12 1
 
0.2%
11 1
 
0.2%
10 5
0.8%
9 6
0.9%
8 1
 
0.2%
7 1
 
0.2%
6 10
1.6%

적용일
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2181037.1
Minimum0
Maximum20230301
Zeros563
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:53.945531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20161001
Maximum20230301
Range20230301
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6236293
Coefficient of variation (CV)2.8593245
Kurtosis4.3394879
Mean2181037.1
Median Absolute Deviation (MAD)0
Skewness2.5143093
Sum1.3784155 × 109
Variance3.889135 × 1013
MonotonicityNot monotonic
2024-04-18T01:49:54.047164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 563
89.1%
19500101 10
 
1.6%
20180101 10
 
1.6%
20161001 9
 
1.4%
19000101 6
 
0.9%
20150601 6
 
0.9%
20150102 4
 
0.6%
20200101 3
 
0.5%
20171231 3
 
0.5%
20210101 3
 
0.5%
Other values (12) 15
 
2.4%
ValueCountFrequency (%)
0 563
89.1%
19000101 6
 
0.9%
19500101 10
 
1.6%
20150101 1
 
0.2%
20150102 4
 
0.6%
20150601 6
 
0.9%
20160101 1
 
0.2%
20160520 1
 
0.2%
20161001 9
 
1.4%
20170101 1
 
0.2%
ValueCountFrequency (%)
20230301 1
 
0.2%
20230101 1
 
0.2%
20220101 2
 
0.3%
20210121 2
 
0.3%
20210101 3
 
0.5%
20201231 1
 
0.2%
20201217 1
 
0.2%
20200101 3
 
0.5%
20190101 2
 
0.3%
20180101 10
1.6%

적용이율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8928165
Minimum0
Maximum100
Zeros577
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-04-18T01:49:54.145728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.159213
Coefficient of variation (CV)6.4238733
Kurtosis53.863508
Mean1.8928165
Median Absolute Deviation (MAD)0
Skewness7.3954764
Sum1196.26
Variance147.84646
MonotonicityNot monotonic
2024-04-18T01:49:54.236964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 577
91.3%
95.0 9
 
1.4%
6.0 6
 
0.9%
5.0 4
 
0.6%
1.0 4
 
0.6%
2.0 4
 
0.6%
5.5 3
 
0.5%
6.5 3
 
0.5%
7.0 3
 
0.5%
1.5 3
 
0.5%
Other values (15) 16
 
2.5%
ValueCountFrequency (%)
0.0 577
91.3%
0.5 1
 
0.2%
1.0 4
 
0.6%
1.5 3
 
0.5%
1.56 1
 
0.2%
1.57 1
 
0.2%
1.64 1
 
0.2%
1.68 1
 
0.2%
1.76 1
 
0.2%
1.77 1
 
0.2%
ValueCountFrequency (%)
100.0 1
 
0.2%
95.0 9
1.4%
55.0 1
 
0.2%
20.0 1
 
0.2%
10.0 1
 
0.2%
8.0 1
 
0.2%
7.0 3
 
0.5%
6.5 3
 
0.5%
6.0 6
0.9%
5.5 3
 
0.5%

절삭단위
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
591 
-1
 
32
-4
 
9

Length

Max length2
Median length1
Mean length1.0648734
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 591
93.5%
-1 32
 
5.1%
-4 9
 
1.4%

Length

2024-04-18T01:49:54.334573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:49:54.414603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 591
93.5%
1 32
 
5.1%
4 9
 
1.4%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct36
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
577 
2018년 1월 1일부터 적용
 
9
총자산 합계 한도 금액
 
5
자동차 최대한도 금액
 
4
20161001 임대시작일 부터 적용 대여금 지급 규모별 임대료 이율
 
3
Other values (31)
 
34

Length

Max length38
Median length1
Mean length2.3449367
Min length1

Unique

Unique29 ?
Unique (%)4.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 577
91.3%
2018년 1월 1일부터 적용 9
 
1.4%
총자산 합계 한도 금액 5
 
0.8%
자동차 최대한도 금액 4
 
0.6%
20161001 임대시작일 부터 적용 대여금 지급 규모별 임대료 이율 3
 
0.5%
20150102 계약체결 부터 적용 임대료 이율 3
 
0.5%
20150102 계약체결 부터 적용 대여금 지급율 2
 
0.3%
2019년 기준 적용 이율 1
 
0.2%
2017년 기준 적용 이율 1
 
0.2%
2017년12월 부터 손실보정적립금 부여 안함 1
 
0.2%
Other values (26) 26
 
4.1%

Length

2024-04-18T01:49:54.507149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 577
71.2%
적용 32
 
4.0%
이율 17
 
2.1%
부터 13
 
1.6%
2018년 10
 
1.2%
금액 10
 
1.2%
1월 9
 
1.1%
1일부터 9
 
1.1%
대여금 7
 
0.9%
한도 7
 
0.9%
Other values (61) 119
 
14.7%

권역명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
627 
본사
 
1
수원권역
 
1
성남권역
 
1
고양권역
 
1

Length

Max length5
Median length1
Mean length1.0221519
Min length1

Unique

Unique5 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 627
99.2%
본사 1
 
0.2%
수원권역 1
 
0.2%
성남권역 1
 
0.2%
고양권역 1
 
0.2%
남양주권역 1
 
0.2%

Length

2024-04-18T01:49:54.834756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:49:54.920435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 627
99.2%
본사 1
 
0.2%
수원권역 1
 
0.2%
성남권역 1
 
0.2%
고양권역 1
 
0.2%
남양주권역 1
 
0.2%

전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
627 
1588-0466
 
1
031-213-1115
 
1
02-2174-2993
 
1
02-554-5302
 
1

Length

Max length12
Median length1
Mean length1.0806962
Min length1

Unique

Unique5 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 627
99.2%
1588-0466 1
 
0.2%
031-213-1115 1
 
0.2%
02-2174-2993 1
 
0.2%
02-554-5302 1
 
0.2%
02-6219-7005 1
 
0.2%

Length

2024-04-18T01:49:55.008061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:49:55.092348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 627
99.2%
1588-0466 1
 
0.2%
031-213-1115 1
 
0.2%
02-2174-2993 1
 
0.2%
02-554-5302 1
 
0.2%
02-6219-7005 1
 
0.2%

약어
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
627 
본사
 
1
수원권
 
1
성남권
 
1
고양권
 
1

Length

Max length4
Median length1
Mean length1.0158228
Min length1

Unique

Unique5 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 627
99.2%
본사 1
 
0.2%
수원권 1
 
0.2%
성남권 1
 
0.2%
고양권 1
 
0.2%
남양주권 1
 
0.2%

Length

2024-04-18T01:49:55.185765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:49:55.275904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 627
99.2%
본사 1
 
0.2%
수원권 1
 
0.2%
성남권 1
 
0.2%
고양권 1
 
0.2%
남양주권 1
 
0.2%

권역주소(담당 법무법인)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
0
628 
법무법인 서린
 
1
법무법인 코러스
 
1
법무법인 덕수
 
1
법무법인 씨케이
 
1

Length

Max length8
Median length1
Mean length1.0411392
Min length1

Unique

Unique4 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 628
99.4%
법무법인 서린 1
 
0.2%
법무법인 코러스 1
 
0.2%
법무법인 덕수 1
 
0.2%
법무법인 씨케이 1
 
0.2%

Length

2024-04-18T01:49:55.374498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:49:55.465598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 628
98.7%
법무법인 4
 
0.6%
서린 1
 
0.2%
코러스 1
 
0.2%
덕수 1
 
0.2%
씨케이 1
 
0.2%

Interactions

2024-04-18T01:49:50.856952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.252681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.857767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.505885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.163741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.789330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.510303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.148204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.811798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.921398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.312376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.923330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.570165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.224078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.857808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.576490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.215472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.879827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.990076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.379348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.995625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.641756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.295326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.936044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.646750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.284680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.955929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:51.057830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.444202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.068840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.710787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.363147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.007246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.719235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.357850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.028365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:51.127274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.505774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.134977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.778480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.426078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.081919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.780902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.423468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.097344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:51.202911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.579561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.210148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.858943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.497287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.177653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.854580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.501542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.552837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:51.272411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.649451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.281336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.930954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.564592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.269332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.923909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.572316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.627077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:51.358596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.718497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.360103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.010072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.641833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.353110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.995433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.646958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.708251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:51.473389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:45.790603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:46.437091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.091263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:47.724072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:48.438334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.082337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:49.730769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:49:50.786749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:49:55.553409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분사업연도관리지역 아이디공급지역계획호수지원호수계약호수해약호수이율코드적용일적용이율절삭단위비고권역명전화번호약어권역주소(담당 법무법인)
구분1.0000.9360.9400.8680.2220.2060.0810.0810.7651.0000.3500.8480.8380.9400.9400.9400.660
사업연도0.9361.0000.6980.6180.4330.4040.3630.3940.5530.9210.2790.7960.5010.0000.0000.0000.000
관리지역 아이디0.9400.6981.0001.0000.4950.5180.3640.4710.6100.9980.2590.8210.6260.1750.1750.1750.095
공급지역0.8680.6181.0001.0000.6460.6510.5550.5840.4940.9980.0000.6990.0000.0000.0000.0000.000
계획호수0.2220.4330.4950.6461.0000.7110.7980.7090.0000.2800.0000.0880.0000.0000.0000.0000.000
지원호수0.2060.4040.5180.6510.7111.0000.8470.7060.0000.1630.0000.0000.0000.0000.0000.0000.000
계약호수0.0810.3630.3640.5550.7980.8471.0000.6700.0000.1680.0000.0000.0000.0000.0000.0000.000
해약호수0.0810.3940.4710.5840.7090.7060.6701.0000.0000.1680.0000.0000.0000.0000.0000.0000.000
이율코드0.7650.5530.6100.4940.0000.0000.0000.0001.0000.9910.9420.9170.9850.0000.0000.0000.000
적용일1.0000.9210.9980.9980.2800.1630.1680.1680.9911.0000.3340.4920.9690.0000.0000.0000.000
적용이율0.3500.2790.2590.0000.0000.0000.0000.0000.9420.3341.0000.6890.9980.0000.0000.0000.000
절삭단위0.8480.7960.8210.6990.0880.0000.0000.0000.9170.4920.6891.0000.9800.0000.0000.0000.000
비고0.8380.5010.6260.0000.0000.0000.0000.0000.9850.9690.9980.9801.0000.0000.0000.0000.000
권역명0.9400.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
전화번호0.9400.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
약어0.9400.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
권역주소(담당 법무법인)0.6600.0000.0950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
2024-04-18T01:49:55.679693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
약어권역주소(담당 법무법인)구분공급지역비고전화번호절삭단위권역명
약어1.0000.9990.7030.0000.0001.0000.0001.000
권역주소(담당 법무법인)0.9991.0000.6280.0000.0000.9990.0000.999
구분0.7030.6281.0000.6730.5780.7030.5300.703
공급지역0.0000.0000.6731.0000.0000.0000.4620.000
비고0.0000.0000.5780.0001.0000.0000.8350.000
전화번호1.0000.9990.7030.0000.0001.0000.0001.000
절삭단위0.0000.0000.5300.4620.8350.0001.0000.000
권역명1.0000.9990.7030.0000.0001.0000.0001.000
2024-04-18T01:49:55.778708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업연도관리지역 아이디계획호수지원호수계약호수해약호수이율코드적용일적용이율구분공급지역절삭단위비고권역명전화번호약어권역주소(담당 법무법인)
사업연도1.0000.3110.6510.6980.7450.418-0.535-0.535-0.4720.6990.2770.5000.1870.0000.0000.0000.000
관리지역 아이디0.3111.000-0.149-0.102-0.071-0.151-0.535-0.535-0.4720.7030.9790.5040.2980.0640.0640.0640.064
계획호수0.651-0.1491.0000.9130.8980.751-0.301-0.301-0.2660.1350.2840.0510.0000.0000.0000.0000.000
지원호수0.698-0.1020.9131.0000.9810.841-0.330-0.330-0.2910.0910.3000.0000.0000.0000.0000.0000.000
계약호수0.745-0.0710.8980.9811.0000.773-0.318-0.318-0.2810.0470.2260.0000.0000.0000.0000.0000.000
해약호수0.418-0.1510.7510.8410.7731.000-0.268-0.268-0.2370.0470.2430.0000.0000.0000.0000.0000.000
이율코드-0.535-0.535-0.301-0.330-0.318-0.2681.0000.9960.8660.6380.1940.7460.9000.0000.0000.0000.000
적용일-0.535-0.535-0.301-0.330-0.318-0.2680.9961.0000.8730.9990.9360.7510.8510.0000.0000.0000.000
적용이율-0.472-0.472-0.266-0.291-0.281-0.2370.8660.8731.0000.2730.0000.6680.9570.0000.0000.0000.000
구분0.6990.7030.1350.0910.0470.0470.6380.9990.2731.0000.6730.5300.5780.7030.7030.7030.628
공급지역0.2770.9790.2840.3000.2260.2430.1940.9360.0000.6731.0000.4620.0000.0000.0000.0000.000
절삭단위0.5000.5040.0510.0000.0000.0000.7460.7510.6680.5300.4621.0000.8350.0000.0000.0000.000
비고0.1870.2980.0000.0000.0000.0000.9000.8510.9570.5780.0000.8351.0000.0000.0000.0000.000
권역명0.0000.0640.0000.0000.0000.0000.0000.0000.0000.7030.0000.0000.0001.0001.0001.0000.999
전화번호0.0000.0640.0000.0000.0000.0000.0000.0000.0000.7030.0000.0000.0001.0001.0001.0000.999
약어0.0000.0640.0000.0000.0000.0000.0000.0000.0000.7030.0000.0000.0001.0001.0001.0000.999
권역주소(담당 법무법인)0.0000.0640.0000.0000.0000.0000.0000.0000.0000.6280.0000.0000.0000.9990.9990.9991.000

Missing values

2024-04-18T01:49:51.631033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:49:51.873540image/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

구분사업연도관리지역 아이디공급지역계획호수지원호수계약호수해약호수이율코드적용일적용이율절삭단위비고권역명전화번호약어권역주소(담당 법무법인)
0연도별 공급지역639가평군0000000.0000000
1연도별 공급지역631고양시1031032083000.0000000
2연도별 공급지역640과천시0000000.0000000
3연도별 공급지역641광명시0000000.0000000
4연도별 공급지역642광주시0000000.0000000
5연도별 공급지역643구리시0000000.0000000
6연도별 공급지역644군포시0000000.0000000
7연도별 공급지역645김포시0000000.0000000
8연도별 공급지역632남양주시50101000.0000000
9연도별 공급지역646동두천시0000000.0000000
구분사업연도관리지역 아이디공급지역계획호수지원호수계약호수해약호수이율코드적용일적용이율절삭단위비고권역명전화번호약어권역주소(담당 법무법인)
622이율관리000000011190001011.0000000
623이율관리0000000121900010110.001월~3월 미만0000
624이율관리0000000131900010120.00회수의문0000
625이율관리0000000141900010155.003월 ~ 12월 미만0000
626이율관리00000001519000101100.0012월 이상0000
627센터관리0000000000.000본사1588-0466본사0
628센터관리0000000000.000수원권역031-213-1115수원권법무법인 서린
629센터관리0000000000.000성남권역02-2174-2993성남권법무법인 코러스
630센터관리0000000000.000고양권역02-554-5302고양권법무법인 덕수
631센터관리0000000000.000남양주권역02-6219-7005남양주권법무법인 씨케이

Duplicate rows

Most frequently occurring

구분사업연도관리지역 아이디공급지역계획호수지원호수계약호수해약호수이율코드적용일적용이율절삭단위비고권역명전화번호약어권역주소(담당 법무법인)# duplicates
0이율관리00000004201506016.0-1000002
1이율관리00000004201610016.0-1000002
2이율관리00000004201801016.0-12018년 1월 1일부터 적용00002