Overview

Dataset statistics

Number of variables5
Number of observations119
Missing cells91
Missing cells (%)15.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory45.1 B

Variable types

DateTime1
Numeric4

Dataset

Description도시재생사업활성화를 위한 정비사업자금과 관련하여 재개발 및 재건축 조합원에 승인 및 실행된 정비사업 융자 관련 현황 자료 제공. - 기간별 정비사업 추진자금 융자 실행액, 상환금액, 융자잔액
URLhttps://www.data.go.kr/data/15010503/fileData.do

Alerts

기초융자잔액 is highly overall correlated with 기말융자잔액High correlation
기말융자잔액 is highly overall correlated with 기초융자잔액High correlation
융자수탁실행금액 has 47 (39.5%) missing valuesMissing
상환금액 has 43 (36.1%) missing valuesMissing
융자수탁실행금액 has 9 (7.6%) zerosZeros
상환금액 has 7 (5.9%) zerosZeros

Reproduction

Analysis started2023-12-12 15:19:53.991868
Analysis finished2023-12-12 15:19:55.960921
Duration1.97 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

Distinct116
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2010-12-01 00:00:00
Maximum2023-07-01 00:00:00
2023-12-13T00:19:56.031240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:56.205032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기초융자잔액
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)80.5%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean97714176
Minimum320000
Maximum1.2268637 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:19:56.421570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum320000
5-th percentile50048300
Q190228090
median1.0807195 × 108
Q31.1418463 × 108
95-th percentile1.1994784 × 108
Maximum1.2268637 × 108
Range1.2236637 × 108
Interquartile range (IQR)23956540

Descriptive statistics

Standard deviation25261031
Coefficient of variation (CV)0.25851961
Kurtosis2.1007748
Mean97714176
Median Absolute Deviation (MAD)8091721
Skewness-1.5833455
Sum1.1530273 × 1010
Variance6.3811969 × 1014
MonotonicityNot monotonic
2023-12-13T00:19:56.575554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105750021 3
 
2.5%
113948381 3
 
2.5%
108135943 2
 
1.7%
90228090 2
 
1.7%
90677900 2
 
1.7%
113383380 2
 
1.7%
114098380 2
 
1.7%
117358380 2
 
1.7%
117445352 2
 
1.7%
96431353 2
 
1.7%
Other values (85) 96
80.7%
ValueCountFrequency (%)
320000 1
0.8%
13701000 1
0.8%
26698300 1
0.8%
41391300 2
1.7%
50048300 2
1.7%
53044300 1
0.8%
53967300 2
1.7%
56867300 1
0.8%
57034300 1
0.8%
60234300 1
0.8%
ValueCountFrequency (%)
122686372 1
0.8%
122542193 2
1.7%
122271193 1
0.8%
122042193 1
0.8%
120042193 1
0.8%
119931193 1
0.8%
119591393 1
0.8%
119455031 1
0.8%
119391393 1
0.8%
118658380 2
1.7%

융자수탁실행금액
Real number (ℝ)

MISSING  ZEROS 

Distinct60
Distinct (%)83.3%
Missing47
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean4330302.1
Minimum0
Maximum48301822
Zeros9
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:19:56.756350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1457500
median1478800
Q34750250
95-th percentile15190850
Maximum48301822
Range48301822
Interquartile range (IQR)4292750

Descriptive statistics

Standard deviation7164081.6
Coefficient of variation (CV)1.6544069
Kurtosis19.624423
Mean4330302.1
Median Absolute Deviation (MAD)1470000
Skewness3.7711214
Sum3.1178175 × 108
Variance5.1324065 × 1013
MonotonicityNot monotonic
2023-12-13T00:19:56.913162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
7.6%
500000 3
 
2.5%
200000 2
 
1.7%
1000000 2
 
1.7%
775170 1
 
0.8%
3570000 1
 
0.8%
4600000 1
 
0.8%
1830000 1
 
0.8%
275000 1
 
0.8%
8342930 1
 
0.8%
Other values (50) 50
42.0%
(Missing) 47
39.5%
ValueCountFrequency (%)
0 9
7.6%
150000 1
 
0.8%
200000 2
 
1.7%
229000 1
 
0.8%
275000 1
 
0.8%
280000 1
 
0.8%
300000 1
 
0.8%
320000 1
 
0.8%
330000 1
 
0.8%
500000 3
 
2.5%
ValueCountFrequency (%)
48301822 1
0.8%
20204000 1
0.8%
19129420 1
0.8%
15583000 1
0.8%
14870000 1
0.8%
13381000 1
0.8%
13050000 1
0.8%
12997300 1
0.8%
11410000 1
0.8%
10200000 1
0.8%

상환금액
Real number (ℝ)

MISSING  ZEROS 

Distinct59
Distinct (%)77.6%
Missing43
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean2679550.1
Minimum0
Maximum46301822
Zeros7
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:19:57.120023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1543750
median1625000
Q33147500
95-th percentile6789500
Maximum46301822
Range46301822
Interquartile range (IQR)2603750

Descriptive statistics

Standard deviation5406971
Coefficient of variation (CV)2.0178652
Kurtosis58.118937
Mean2679550.1
Median Absolute Deviation (MAD)1125000
Skewness7.2010232
Sum2.0364581 × 108
Variance2.9235335 × 1013
MonotonicityNot monotonic
2023-12-13T00:19:57.295808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
5.9%
500000 4
 
3.4%
1000000 3
 
2.5%
1300000 2
 
1.7%
3200000 2
 
1.7%
2850000 2
 
1.7%
540000 2
 
1.7%
2700000 2
 
1.7%
1700000 2
 
1.7%
2500000 1
 
0.8%
Other values (49) 49
41.2%
(Missing) 43
36.1%
ValueCountFrequency (%)
0 7
5.9%
42877 1
 
0.8%
204300 1
 
0.8%
320000 1
 
0.8%
350000 1
 
0.8%
400806 1
 
0.8%
470000 1
 
0.8%
500000 4
3.4%
540000 2
 
1.7%
545000 1
 
0.8%
ValueCountFrequency (%)
46301822 1
0.8%
7984040 1
0.8%
7300000 1
0.8%
6950000 1
0.8%
6736000 1
0.8%
5598000 1
0.8%
5434000 1
0.8%
5300000 1
0.8%
4721000 1
0.8%
4650000 1
0.8%

기말융자잔액
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97800997
Minimum320000
Maximum1.2268637 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:19:57.449745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum320000
5-th percentile50048300
Q190228090
median1.0813594 × 108
Q31.1415588 × 108
95-th percentile1.1994229 × 108
Maximum1.2268637 × 108
Range1.2236637 × 108
Interquartile range (IQR)23927790

Descriptive statistics

Standard deviation25171503
Coefficient of variation (CV)0.25737471
Kurtosis2.1517023
Mean97800997
Median Absolute Deviation (MAD)7820000
Skewness-1.5964468
Sum1.1638319 × 1010
Variance6.3360457 × 1014
MonotonicityNot monotonic
2023-12-13T00:19:57.984288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105750021 3
 
2.5%
108135943 3
 
2.5%
113948381 3
 
2.5%
41391300 2
 
1.7%
50048300 2
 
1.7%
90677900 2
 
1.7%
113383380 2
 
1.7%
114098380 2
 
1.7%
117358380 2
 
1.7%
117445352 2
 
1.7%
Other values (85) 96
80.7%
ValueCountFrequency (%)
320000 1
0.8%
13701000 1
0.8%
26698300 1
0.8%
41391300 2
1.7%
50048300 2
1.7%
53044300 1
0.8%
53967300 2
1.7%
56867300 1
0.8%
57034300 1
0.8%
60234300 1
0.8%
ValueCountFrequency (%)
122686372 1
0.8%
122542193 2
1.7%
122271193 1
0.8%
122042193 1
0.8%
120042193 1
0.8%
119931193 1
0.8%
119591393 1
0.8%
119455031 1
0.8%
119391393 1
0.8%
118658380 2
1.7%

Interactions

2023-12-13T00:19:55.243731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.129472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.492624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.907702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:55.340757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.226180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.607744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.997546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:55.421288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.314967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.695421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:55.089102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:55.515531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.389958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:54.779734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:19:55.162759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:19:58.102187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기초융자잔액융자수탁실행금액상환금액기말융자잔액
기초융자잔액1.0000.5310.0000.925
융자수탁실행금액0.5311.0000.9290.428
상환금액0.0000.9291.0000.000
기말융자잔액0.9250.4280.0001.000
2023-12-13T00:19:58.210859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기초융자잔액융자수탁실행금액상환금액기말융자잔액
기초융자잔액1.000-0.4320.2050.917
융자수탁실행금액-0.4321.0000.133-0.147
상환금액0.2050.1331.000-0.073
기말융자잔액0.917-0.147-0.0731.000

Missing values

2023-12-13T00:19:55.637893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:19:55.746010image/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.
2023-12-13T00:19:55.893017image/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

연월기초융자잔액융자수탁실행금액상환금액기말융자잔액
02010-12-01<NA>320000<NA>320000
12011-12-0132000013381000<NA>13701000
22012-12-011370100012997300<NA>26698300
32013-12-01266983001558300089000041391300
42014-01-0141391300<NA><NA>41391300
52014-02-01413913008657000<NA>50048300
62014-03-0150048300<NA><NA>50048300
72014-04-01500483003919000<NA>53967300
82014-05-0153967300<NA><NA>53967300
92014-06-01539673002900000<NA>56867300
연월기초융자잔액융자수탁실행금액상환금액기말융자잔액
1092022-10-011175807737751700118355943
1102022-11-0111835594300118355943
1112022-12-011183559433000002700000115955943
1122023-01-011159559430540000115415943
1132023-02-0111541594302850000112565943
1142023-03-0111256594303130000109435943
1152023-04-0110943594300109435943
1162023-05-0110943594301300000108135943
1172023-06-0110813594300108135943
1182023-07-0110813594300108135943