Overview

Dataset statistics

Number of variables15
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.0 KiB
Average record size in memory135.3 B

Variable types

Numeric14
Categorical1

Dataset

Description샘플 데이터
AuthorKCB(코리아크레딧뷰로)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=46

Alerts

급여소득자수(STAT_P_DONG_002) has 66 (13.2%) zerosZeros
자영업자수(STAT_P_DONG_003) has 169 (33.8%) zerosZeros
급여소득자의_평균소득(STAT_P_DONG_006) has 60 (12.0%) zerosZeros
자영업자의_평균소득(STAT_P_DONG_007) has 179 (35.8%) zerosZeros
평균대출잔액(STAT_P_DONG_010) has 6 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-10 14:55:21.502026
Analysis finished2023-12-10 14:56:01.014996
Duration39.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201960.9
Minimum201810
Maximum202105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:01.133489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201810
5-th percentile201811
Q1201904.75
median202001
Q3202009
95-th percentile202104
Maximum202105
Range295
Interquartile range (IQR)104.25

Descriptive statistics

Standard deviation85.64877
Coefficient of variation (CV)0.0004240859
Kurtosis-0.75263355
Mean201960.9
Median Absolute Deviation (MAD)94
Skewness0.016283006
Sum1.0098045 × 108
Variance7335.7118
MonotonicityNot monotonic
2023-12-10T23:56:01.386550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
202009 22
 
4.4%
201907 22
 
4.4%
201811 22
 
4.4%
202005 22
 
4.4%
201906 21
 
4.2%
201812 21
 
4.2%
202010 19
 
3.8%
201903 19
 
3.8%
202103 18
 
3.6%
201904 18
 
3.6%
Other values (22) 296
59.2%
ValueCountFrequency (%)
201810 13
2.6%
201811 22
4.4%
201812 21
4.2%
201901 17
3.4%
201902 15
3.0%
201903 19
3.8%
201904 18
3.6%
201905 14
2.8%
201906 21
4.2%
201907 22
4.4%
ValueCountFrequency (%)
202105 13
2.6%
202104 15
3.0%
202103 18
3.6%
202102 12
2.4%
202101 14
2.8%
202012 14
2.8%
202011 11
2.2%
202010 19
3.8%
202009 22
4.4%
202008 11
2.2%
Distinct299
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1113797.4
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:01.656037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102055
Q11107068.8
median1114563.5
Q31120072.2
95-th percentile1124071.2
Maximum1125074
Range24021
Interquartile range (IQR)13003.5

Descriptive statistics

Standard deviation7414.2896
Coefficient of variation (CV)0.006656767
Kurtosis-1.270798
Mean1113797.4
Median Absolute Deviation (MAD)6501.5
Skewness-0.1177634
Sum5.5689869 × 108
Variance54971691
MonotonicityNot monotonic
2023-12-10T23:56:01.885806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113065 5
 
1.0%
1107068 5
 
1.0%
1119075 5
 
1.0%
1101054 5
 
1.0%
1116071 4
 
0.8%
1115071 4
 
0.8%
1120056 4
 
0.8%
1121061 4
 
0.8%
1105055 3
 
0.6%
1120053 3
 
0.6%
Other values (289) 458
91.6%
ValueCountFrequency (%)
1101053 3
0.6%
1101054 5
1.0%
1101057 2
 
0.4%
1101061 1
 
0.2%
1101064 2
 
0.4%
1101067 1
 
0.2%
1101068 3
0.6%
1101069 2
 
0.4%
1101070 1
 
0.2%
1101071 3
0.6%
ValueCountFrequency (%)
1125074 2
0.4%
1125073 2
0.4%
1125072 1
 
0.2%
1125071 1
 
0.2%
1125063 3
0.6%
1125061 1
 
0.2%
1125058 1
 
0.2%
1125056 1
 
0.2%
1125055 1
 
0.2%
1125054 2
0.4%
Distinct297
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11428173
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:02.118718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140577
Q111283064
median11440588
Q311590550
95-th percentile11710641
Maximum11740700
Range630185
Interquartile range (IQR)307486.25

Descriptive statistics

Standard deviation189813.34
Coefficient of variation (CV)0.016609245
Kurtosis-1.2146614
Mean11428173
Median Absolute Deviation (MAD)150037.5
Skewness0.0049521278
Sum5.7140867 × 109
Variance3.6029102 × 1010
MonotonicityNot monotonic
2023-12-10T23:56:02.383473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11350710 5
 
1.0%
11560720 5
 
1.0%
11305590 5
 
1.0%
11170510 5
 
1.0%
11530790 4
 
0.8%
11680610 4
 
0.8%
11470680 4
 
0.8%
11170590 4
 
0.8%
11110630 4
 
0.8%
11590550 4
 
0.8%
Other values (287) 456
91.2%
ValueCountFrequency (%)
11110515 2
0.4%
11110530 1
 
0.2%
11110540 3
0.6%
11110560 1
 
0.2%
11110570 1
 
0.2%
11110600 3
0.6%
11110615 1
 
0.2%
11110630 4
0.8%
11110650 1
 
0.2%
11110670 1
 
0.2%
ValueCountFrequency (%)
11740700 2
0.4%
11740685 1
0.2%
11740660 2
0.4%
11740650 2
0.4%
11740640 1
0.2%
11740620 1
0.2%
11740610 1
0.2%
11740600 1
0.2%
11740590 1
0.2%
11740580 1
0.2%

성별(SEX_CD)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
256 
1
244 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 256
51.2%
1 244
48.8%

Length

2023-12-10T23:56:02.598913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:02.751399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 256
51.2%
1 244
48.8%
Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.988
Minimum24
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:02.906075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile24
Q139
median54
Q374
95-th percentile84
Maximum85
Range61
Interquartile range (IQR)35

Descriptive statistics

Standard deviation19.432114
Coefficient of variation (CV)0.35338827
Kurtosis-1.2572409
Mean54.988
Median Absolute Deviation (MAD)15
Skewness0.0095992871
Sum27494
Variance377.60707
MonotonicityNot monotonic
2023-12-10T23:56:03.095153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
44 44
 
8.8%
79 42
 
8.4%
29 39
 
7.8%
49 38
 
7.6%
64 38
 
7.6%
39 37
 
7.4%
24 37
 
7.4%
34 35
 
7.0%
54 35
 
7.0%
69 34
 
6.8%
Other values (4) 121
24.2%
ValueCountFrequency (%)
24 37
7.4%
29 39
7.8%
34 35
7.0%
39 37
7.4%
44 44
8.8%
49 38
7.6%
54 35
7.0%
59 32
6.4%
64 38
7.6%
69 34
6.8%
ValueCountFrequency (%)
85 24
4.8%
84 32
6.4%
79 42
8.4%
74 33
6.6%
69 34
6.8%
64 38
7.6%
59 32
6.4%
54 35
7.0%
49 38
7.6%
44 44
8.8%
Distinct425
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean558.448
Minimum5
Maximum3491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:03.298536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile44.9
Q1241.5
median519.5
Q3799.5
95-th percentile1197.2
Maximum3491
Range3486
Interquartile range (IQR)558

Descriptive statistics

Standard deviation398.50356
Coefficient of variation (CV)0.71359116
Kurtosis5.5625268
Mean558.448
Median Absolute Deviation (MAD)279
Skewness1.3209244
Sum279224
Variance158805.09
MonotonicityNot monotonic
2023-12-10T23:56:03.546605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
665 3
 
0.6%
5 3
 
0.6%
444 3
 
0.6%
1184 3
 
0.6%
736 3
 
0.6%
89 3
 
0.6%
10 3
 
0.6%
124 3
 
0.6%
347 3
 
0.6%
133 2
 
0.4%
Other values (415) 471
94.2%
ValueCountFrequency (%)
5 3
0.6%
6 1
 
0.2%
7 1
 
0.2%
10 3
0.6%
11 2
0.4%
13 2
0.4%
16 1
 
0.2%
20 1
 
0.2%
21 1
 
0.2%
24 2
0.4%
ValueCountFrequency (%)
3491 1
0.2%
1997 1
0.2%
1996 1
0.2%
1963 1
0.2%
1619 1
0.2%
1592 1
0.2%
1525 1
0.2%
1505 1
0.2%
1450 1
0.2%
1423 1
0.2%

급여소득자수(STAT_P_DONG_002)
Real number (ℝ)

ZEROS 

Distinct331
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.502
Minimum0
Maximum1840
Zeros66
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:03.793654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153
median274.5
Q3476.25
95-th percentile839.3
Maximum1840
Range1840
Interquartile range (IQR)423.25

Descriptive statistics

Standard deviation283.77721
Coefficient of variation (CV)0.92585761
Kurtosis1.7082088
Mean306.502
Median Absolute Deviation (MAD)214
Skewness1.08391
Sum153251
Variance80529.505
MonotonicityNot monotonic
2023-12-10T23:56:04.005397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
 
13.2%
5 11
 
2.2%
81 4
 
0.8%
380 4
 
0.8%
26 4
 
0.8%
320 3
 
0.6%
460 3
 
0.6%
287 3
 
0.6%
205 3
 
0.6%
111 3
 
0.6%
Other values (321) 396
79.2%
ValueCountFrequency (%)
0 66
13.2%
5 11
 
2.2%
6 1
 
0.2%
7 2
 
0.4%
8 2
 
0.4%
9 1
 
0.2%
10 2
 
0.4%
11 2
 
0.4%
12 3
 
0.6%
13 1
 
0.2%
ValueCountFrequency (%)
1840 1
0.2%
1371 1
0.2%
1205 1
0.2%
1170 1
0.2%
1167 1
0.2%
1153 1
0.2%
1116 1
0.2%
1083 1
0.2%
1037 1
0.2%
1030 1
0.2%

자영업자수(STAT_P_DONG_003)
Real number (ℝ)

ZEROS 

Distinct122
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.994
Minimum0
Maximum270
Zeros169
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:04.215926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q355
95-th percentile137.1
Maximum270
Range270
Interquartile range (IQR)55

Descriptive statistics

Standard deviation46.307307
Coefficient of variation (CV)1.3232928
Kurtosis3.3378618
Mean34.994
Median Absolute Deviation (MAD)15
Skewness1.7554488
Sum17497
Variance2144.3667
MonotonicityNot monotonic
2023-12-10T23:56:04.423634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
33.8%
5 31
 
6.2%
6 14
 
2.8%
11 11
 
2.2%
10 8
 
1.6%
69 7
 
1.4%
27 7
 
1.4%
17 6
 
1.2%
37 6
 
1.2%
26 5
 
1.0%
Other values (112) 236
47.2%
ValueCountFrequency (%)
0 169
33.8%
5 31
 
6.2%
6 14
 
2.8%
7 3
 
0.6%
8 2
 
0.4%
10 8
 
1.6%
11 11
 
2.2%
12 5
 
1.0%
13 4
 
0.8%
15 4
 
0.8%
ValueCountFrequency (%)
270 1
0.2%
267 1
0.2%
205 1
0.2%
201 1
0.2%
188 1
0.2%
180 1
0.2%
174 1
0.2%
173 1
0.2%
164 1
0.2%
163 2
0.4%
Distinct293
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.912
Minimum0
Maximum903
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:04.666913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.95
Q167
median132.5
Q3259
95-th percentile571
Maximum903
Range903
Interquartile range (IQR)192

Descriptive statistics

Standard deviation177.87944
Coefficient of variation (CV)0.90795581
Kurtosis1.56306
Mean195.912
Median Absolute Deviation (MAD)82.5
Skewness1.4171291
Sum97956
Variance31641.094
MonotonicityNot monotonic
2023-12-10T23:56:04.883119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 6
 
1.2%
65 6
 
1.2%
5 5
 
1.0%
10 5
 
1.0%
46 5
 
1.0%
117 5
 
1.0%
113 5
 
1.0%
0 4
 
0.8%
132 4
 
0.8%
82 4
 
0.8%
Other values (283) 451
90.2%
ValueCountFrequency (%)
0 4
0.8%
5 5
1.0%
7 1
 
0.2%
8 1
 
0.2%
10 5
1.0%
11 4
0.8%
12 2
 
0.4%
14 1
 
0.2%
15 2
 
0.4%
16 2
 
0.4%
ValueCountFrequency (%)
903 1
0.2%
855 1
0.2%
810 1
0.2%
783 1
0.2%
782 1
0.2%
780 1
0.2%
745 1
0.2%
727 1
0.2%
705 1
0.2%
691 1
0.2%
Distinct269
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.352
Minimum131
Maximum1105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:05.152145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile149
Q1177
median256
Q3343
95-th percentile520.05
Maximum1105
Range974
Interquartile range (IQR)166

Descriptive statistics

Standard deviation126.88706
Coefficient of variation (CV)0.45259907
Kurtosis4.9609078
Mean280.352
Median Absolute Deviation (MAD)82
Skewness1.6731594
Sum140176
Variance16100.325
MonotonicityNot monotonic
2023-12-10T23:56:05.483661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 9
 
1.8%
151 8
 
1.6%
219 7
 
1.4%
174 7
 
1.4%
150 6
 
1.2%
155 6
 
1.2%
149 6
 
1.2%
159 6
 
1.2%
148 6
 
1.2%
158 6
 
1.2%
Other values (259) 433
86.6%
ValueCountFrequency (%)
131 1
 
0.2%
139 2
 
0.4%
142 2
 
0.4%
143 1
 
0.2%
144 1
 
0.2%
145 2
 
0.4%
146 2
 
0.4%
147 5
1.0%
148 6
1.2%
149 6
1.2%
ValueCountFrequency (%)
1105 1
0.2%
865 1
0.2%
837 1
0.2%
749 1
0.2%
683 1
0.2%
676 1
0.2%
642 1
0.2%
640 1
0.2%
626 1
0.2%
611 1
0.2%
Distinct246
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.144
Minimum0
Maximum802
Zeros60
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:05.796019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1194.75
median275
Q3345
95-th percentile505.15
Maximum802
Range802
Interquartile range (IQR)150.25

Descriptive statistics

Standard deviation143.39742
Coefficient of variation (CV)0.53081846
Kurtosis0.74635021
Mean270.144
Median Absolute Deviation (MAD)72.5
Skewness0.10367416
Sum135072
Variance20562.821
MonotonicityNot monotonic
2023-12-10T23:56:06.101665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
12.0%
346 6
 
1.2%
275 5
 
1.0%
187 5
 
1.0%
186 4
 
0.8%
194 4
 
0.8%
158 4
 
0.8%
259 4
 
0.8%
281 4
 
0.8%
188 4
 
0.8%
Other values (236) 400
80.0%
ValueCountFrequency (%)
0 60
12.0%
158 4
 
0.8%
159 1
 
0.2%
162 2
 
0.4%
164 2
 
0.4%
166 1
 
0.2%
167 2
 
0.4%
170 1
 
0.2%
172 1
 
0.2%
173 1
 
0.2%
ValueCountFrequency (%)
802 1
0.2%
702 1
0.2%
700 1
0.2%
683 1
0.2%
664 1
0.2%
662 1
0.2%
646 1
0.2%
637 1
0.2%
622 1
0.2%
619 1
0.2%
Distinct219
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.036
Minimum0
Maximum1333
Zeros179
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:06.381377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median295.5
Q3369.25
95-th percentile601.25
Maximum1333
Range1333
Interquartile range (IQR)369.25

Descriptive statistics

Standard deviation214.50457
Coefficient of variation (CV)0.87540023
Kurtosis0.99369927
Mean245.036
Median Absolute Deviation (MAD)140.5
Skewness0.58578364
Sum122518
Variance46012.211
MonotonicityNot monotonic
2023-12-10T23:56:06.698838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 179
35.8%
341 5
 
1.0%
323 4
 
0.8%
319 4
 
0.8%
298 4
 
0.8%
331 4
 
0.8%
352 4
 
0.8%
306 3
 
0.6%
363 3
 
0.6%
292 3
 
0.6%
Other values (209) 287
57.4%
ValueCountFrequency (%)
0 179
35.8%
164 1
 
0.2%
167 1
 
0.2%
184 1
 
0.2%
188 1
 
0.2%
201 2
 
0.4%
210 1
 
0.2%
214 1
 
0.2%
224 1
 
0.2%
226 1
 
0.2%
ValueCountFrequency (%)
1333 1
0.2%
1144 1
0.2%
921 1
0.2%
846 1
0.2%
840 1
0.2%
801 1
0.2%
800 1
0.2%
792 1
0.2%
723 1
0.2%
721 1
0.2%
Distinct158
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.276
Minimum0
Maximum520
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:06.994379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile145
Q1170
median190.5
Q3241
95-th percentile282.2
Maximum520
Range520
Interquartile range (IQR)71

Descriptive statistics

Standard deviation48.589193
Coefficient of variation (CV)0.23670177
Kurtosis3.1662899
Mean205.276
Median Absolute Deviation (MAD)34
Skewness0.86233661
Sum102638
Variance2360.9096
MonotonicityNot monotonic
2023-12-10T23:56:07.731401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145 13
 
2.6%
178 13
 
2.6%
177 12
 
2.4%
170 11
 
2.2%
182 9
 
1.8%
173 9
 
1.8%
172 8
 
1.6%
175 8
 
1.6%
181 7
 
1.4%
146 7
 
1.4%
Other values (148) 403
80.6%
ValueCountFrequency (%)
0 1
 
0.2%
140 1
 
0.2%
141 1
 
0.2%
142 4
 
0.8%
143 2
 
0.4%
144 5
 
1.0%
145 13
2.6%
146 7
1.4%
147 7
1.4%
148 5
 
1.0%
ValueCountFrequency (%)
520 1
0.2%
344 1
0.2%
340 1
0.2%
338 1
0.2%
333 1
0.2%
331 1
0.2%
329 1
0.2%
321 1
0.2%
312 2
0.4%
309 1
0.2%
Distinct230
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.042
Minimum3
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:07.996111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14.95
Q162
median127.5
Q3184.25
95-th percentile247
Maximum440
Range437
Interquartile range (IQR)122.25

Descriptive statistics

Standard deviation75.954027
Coefficient of variation (CV)0.60260887
Kurtosis-0.17565346
Mean126.042
Median Absolute Deviation (MAD)60.5
Skewness0.3860537
Sum63021
Variance5769.0143
MonotonicityNot monotonic
2023-12-10T23:56:08.368456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198 6
 
1.2%
68 6
 
1.2%
113 5
 
1.0%
104 5
 
1.0%
55 5
 
1.0%
209 5
 
1.0%
67 5
 
1.0%
42 5
 
1.0%
135 5
 
1.0%
186 5
 
1.0%
Other values (220) 448
89.6%
ValueCountFrequency (%)
3 2
0.4%
5 2
0.4%
7 4
0.8%
8 2
0.4%
9 1
 
0.2%
10 3
0.6%
11 4
0.8%
12 2
0.4%
13 3
0.6%
14 2
0.4%
ValueCountFrequency (%)
440 1
0.2%
366 1
0.2%
352 1
0.2%
340 1
0.2%
320 1
0.2%
304 1
0.2%
303 1
0.2%
292 1
0.2%
290 1
0.2%
286 2
0.4%

평균대출잔액(STAT_P_DONG_010)
Real number (ℝ)

ZEROS 

Distinct474
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2874.588
Minimum0
Maximum12896
Zeros6
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:08.646017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101.45
Q11040.75
median2282
Q33967.5
95-th percentile8490.75
Maximum12896
Range12896
Interquartile range (IQR)2926.75

Descriptive statistics

Standard deviation2514.3851
Coefficient of variation (CV)0.87469408
Kurtosis1.6787505
Mean2874.588
Median Absolute Deviation (MAD)1463.5
Skewness1.3343558
Sum1437294
Variance6322132.5
MonotonicityNot monotonic
2023-12-10T23:56:08.934575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
1.2%
102 3
 
0.6%
2702 2
 
0.4%
1107 2
 
0.4%
2884 2
 
0.4%
3747 2
 
0.4%
817 2
 
0.4%
1059 2
 
0.4%
41 2
 
0.4%
4596 2
 
0.4%
Other values (464) 475
95.0%
ValueCountFrequency (%)
0 6
1.2%
5 1
 
0.2%
15 1
 
0.2%
27 1
 
0.2%
31 1
 
0.2%
37 1
 
0.2%
38 2
 
0.4%
41 2
 
0.4%
50 1
 
0.2%
61 1
 
0.2%
ValueCountFrequency (%)
12896 1
0.2%
11392 1
0.2%
11361 1
0.2%
11352 1
0.2%
11172 1
0.2%
10963 1
0.2%
10709 1
0.2%
10422 1
0.2%
10399 1
0.2%
10311 1
0.2%

Interactions

2023-12-10T23:55:57.179372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:22.527252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.571320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:27.410222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:30.589251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:32.962201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:35.954652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:39.296830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:41.912448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:44.596929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:46.982476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:49.866690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:52.380332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:54.731618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:57.362488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:22.681991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.738903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:27.636895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:30.754547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:33.244027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:36.194151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:39.495522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:42.129791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:44.762262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:47.155769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:50.049266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:52.561944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:54.903349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:57.559711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:22.851720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.911865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:27.967120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:30.912791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:33.429372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:36.413532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:39.660945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:42.301967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:44.910933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:47.322054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:50.261603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:52.744840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:55.070976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:57.802511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.011244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:25.143592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:28.247920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.088042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:33.637418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:36.614042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:39.831216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:42.496823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:45.072560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:47.533131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:50.441713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:52.906737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:55.244789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:57.992732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.145647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:25.345417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:28.586867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.239802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:33.834736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:36.826612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:40.000952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:42.705018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:45.235462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:47.718782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:50.620481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:53.081677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:55.401366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:58.197097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.308381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:25.544177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:28.921443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.394026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:34.034029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:37.061766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:40.187647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:42.959710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:45.410733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:47.894936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:50.809614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:53.230112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:55.590162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:58.387947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.456139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:26.187492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:29.204089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.553468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:34.267833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:37.292359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:40.362793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:43.169368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:45.603653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:48.068550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:50.993974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:53.381731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:55.776065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:58.563316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.622725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:26.362790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:29.399411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.697262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:34.461302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:37.498359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:40.531126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:43.362607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:45.783941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:48.244602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:51.156158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:53.524119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:55.941612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:58.732527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.765082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:26.550503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:29.560222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.828695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:34.674555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:37.687043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:40.721336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:43.545959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:45.940996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:48.398309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:51.296919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:53.685730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:56.119657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:58.912504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:23.895904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:26.702479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:29.736518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:31.980574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:34.860706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:38.288001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:40.920393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:43.733995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:46.095158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:48.564574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:51.471779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:53.841955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:56.286336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:59.117527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.026479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:26.859652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:29.914041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:32.167516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:35.096514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:38.497862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:41.145009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:43.940579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:46.297215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:48.767378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:51.647247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:54.014135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:56.485914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:59.308299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.187449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:26.990492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:30.084750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:32.334483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:35.316144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:38.712152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:41.340696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:44.102183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:46.481118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:48.956907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:51.840470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:54.189682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:56.680767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:59.484603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.306028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:27.133870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:30.245116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:32.515062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:35.514825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:38.917779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:41.542129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:44.268061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:46.712027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:49.123828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:52.063884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:54.385995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:56.836615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:59.661405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:24.430003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:27.249604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:30.417383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:32.692718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:35.741389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:39.093292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:41.723952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:44.423491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:46.829106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:49.692326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:52.215604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:54.550095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:55:56.989604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:56:09.134509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_기준월(BS_YR_MON)통계청_행정동코드(ADM_DONG_CD)행안부_행정동코드(G_ADM_DONG_CD)성별(SEX_CD)연령구간대(AGE_RANGE)집계인구수(STAT_P_DONG_001)급여소득자수(STAT_P_DONG_002)자영업자수(STAT_P_DONG_003)기타소득자수(STAT_P_DONG_004)평균소득(STAT_P_DONG_005)급여소득자의_평균소득(STAT_P_DONG_006)자영업자의_평균소득(STAT_P_DONG_007)기타소득자의_평균소득(STAT_P_DONG_008)평균소비(STAT_P_DONG_009)평균대출잔액(STAT_P_DONG_010)
데이터_기준월(BS_YR_MON)1.0000.1680.1070.0610.0000.0000.0900.0000.0280.1420.0890.1360.0790.0690.000
통계청_행정동코드(ADM_DONG_CD)0.1681.0000.0000.0150.0000.0000.1100.0000.1000.0520.0800.0000.0000.1490.057
행안부_행정동코드(G_ADM_DONG_CD)0.1070.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.2180.1660.000
성별(SEX_CD)0.0610.0150.0001.0000.0000.0000.0000.0000.0000.0400.0000.0000.0510.0000.000
연령구간대(AGE_RANGE)0.0000.0000.0000.0001.0000.0000.1020.0910.0000.0710.0000.0000.1600.0000.000
집계인구수(STAT_P_DONG_001)0.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
급여소득자수(STAT_P_DONG_002)0.0900.1100.0000.0000.1020.0001.0000.0000.0000.0000.0000.0000.0920.0000.000
자영업자수(STAT_P_DONG_003)0.0000.0000.0000.0000.0910.0000.0001.0000.1220.0000.0000.0670.0510.0000.000
기타소득자수(STAT_P_DONG_004)0.0280.1000.0000.0000.0000.0000.0000.1221.0000.0000.0000.0000.0710.0000.000
평균소득(STAT_P_DONG_005)0.1420.0520.0000.0400.0710.0000.0000.0000.0001.0000.0330.0000.0000.0000.135
급여소득자의_평균소득(STAT_P_DONG_006)0.0890.0800.0000.0000.0000.0000.0000.0000.0000.0331.0000.0000.0710.2200.000
자영업자의_평균소득(STAT_P_DONG_007)0.1360.0000.0840.0000.0000.0000.0000.0670.0000.0000.0001.0000.0000.0000.000
기타소득자의_평균소득(STAT_P_DONG_008)0.0790.0000.2180.0510.1600.0000.0920.0510.0710.0000.0710.0001.0000.0000.077
평균소비(STAT_P_DONG_009)0.0690.1490.1660.0000.0000.0000.0000.0000.0000.0000.2200.0000.0001.0000.000
평균대출잔액(STAT_P_DONG_010)0.0000.0570.0000.0000.0000.0000.0000.0000.0000.1350.0000.0000.0770.0001.000
2023-12-10T23:56:09.479492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_기준월(BS_YR_MON)통계청_행정동코드(ADM_DONG_CD)행안부_행정동코드(G_ADM_DONG_CD)연령구간대(AGE_RANGE)집계인구수(STAT_P_DONG_001)급여소득자수(STAT_P_DONG_002)자영업자수(STAT_P_DONG_003)기타소득자수(STAT_P_DONG_004)평균소득(STAT_P_DONG_005)급여소득자의_평균소득(STAT_P_DONG_006)자영업자의_평균소득(STAT_P_DONG_007)기타소득자의_평균소득(STAT_P_DONG_008)평균소비(STAT_P_DONG_009)평균대출잔액(STAT_P_DONG_010)성별(SEX_CD)
데이터_기준월(BS_YR_MON)1.000-0.0480.078-0.0150.0270.0560.064-0.0080.0300.014-0.0900.0270.0270.0010.029
통계청_행정동코드(ADM_DONG_CD)-0.0481.000-0.047-0.0080.048-0.0750.0060.1030.031-0.049-0.080-0.0460.029-0.0640.048
행안부_행정동코드(G_ADM_DONG_CD)0.078-0.0471.000-0.0520.048-0.031-0.003-0.051-0.0490.031-0.148-0.067-0.041-0.0460.000
연령구간대(AGE_RANGE)-0.015-0.008-0.0521.000-0.0150.020-0.006-0.002-0.059-0.0140.0330.091-0.0000.0450.000
집계인구수(STAT_P_DONG_001)0.0270.0480.048-0.0151.000-0.041-0.037-0.0130.0370.021-0.0260.047-0.0050.0310.000
급여소득자수(STAT_P_DONG_002)0.056-0.075-0.0310.020-0.0411.000-0.023-0.020-0.0450.0490.029-0.000-0.0260.0420.000
자영업자수(STAT_P_DONG_003)0.0640.006-0.003-0.006-0.037-0.0231.0000.0800.045-0.0450.0570.004-0.056-0.0000.000
기타소득자수(STAT_P_DONG_004)-0.0080.103-0.051-0.002-0.013-0.0200.0801.000-0.044-0.0180.0420.051-0.0100.0370.000
평균소득(STAT_P_DONG_005)0.0300.031-0.049-0.0590.037-0.0450.045-0.0441.0000.0560.0140.114-0.034-0.0330.034
급여소득자의_평균소득(STAT_P_DONG_006)0.014-0.0490.031-0.0140.0210.049-0.045-0.0180.0561.000-0.032-0.007-0.075-0.0040.000
자영업자의_평균소득(STAT_P_DONG_007)-0.090-0.080-0.1480.033-0.0260.0290.0570.0420.014-0.0321.000-0.004-0.070-0.0430.000
기타소득자의_평균소득(STAT_P_DONG_008)0.027-0.046-0.0670.0910.047-0.0000.0040.0510.114-0.007-0.0041.000-0.0400.0570.054
평균소비(STAT_P_DONG_009)0.0270.029-0.041-0.000-0.005-0.026-0.056-0.010-0.034-0.075-0.070-0.0401.000-0.0910.000
평균대출잔액(STAT_P_DONG_010)0.001-0.064-0.0460.0450.0310.042-0.0000.037-0.033-0.004-0.0430.057-0.0911.0000.000
성별(SEX_CD)0.0290.0480.0000.0000.0000.0000.0000.0000.0340.0000.0000.0540.0000.0001.000

Missing values

2023-12-10T23:56:00.405378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:56:00.817052image/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

데이터_기준월(BS_YR_MON)통계청_행정동코드(ADM_DONG_CD)행안부_행정동코드(G_ADM_DONG_CD)성별(SEX_CD)연령구간대(AGE_RANGE)집계인구수(STAT_P_DONG_001)급여소득자수(STAT_P_DONG_002)자영업자수(STAT_P_DONG_003)기타소득자수(STAT_P_DONG_004)평균소득(STAT_P_DONG_005)급여소득자의_평균소득(STAT_P_DONG_006)자영업자의_평균소득(STAT_P_DONG_007)기타소득자의_평균소득(STAT_P_DONG_008)평균소비(STAT_P_DONG_009)평균대출잔액(STAT_P_DONG_010)
0202008110807611500591144901026936847227502603027
1202002110505811680656279762051091612514402031332215
2202002111107611440680144836741656567531392162181731
32020061116074115905301693481981564543613113352201862193
42020031125074112307301744105097191271468142200113
5202103111005411260680239123659238583483722621441863723
62018121122067113055902492792705628508002812072469
72020011105065112606801393475270633653172582562243070
8202011111506211740640159133524231871572842262102642639
9201907110806311500530134512579140208347028613438
데이터_기준월(BS_YR_MON)통계청_행정동코드(ADM_DONG_CD)행안부_행정동코드(G_ADM_DONG_CD)성별(SEX_CD)연령구간대(AGE_RANGE)집계인구수(STAT_P_DONG_001)급여소득자수(STAT_P_DONG_002)자영업자수(STAT_P_DONG_003)기타소득자수(STAT_P_DONG_004)평균소득(STAT_P_DONG_005)급여소득자의_평균소득(STAT_P_DONG_006)자영업자의_평균소득(STAT_P_DONG_007)기타소득자의_평균소득(STAT_P_DONG_008)평균소비(STAT_P_DONG_009)평균대출잔액(STAT_P_DONG_010)
490202003112307611740620139649460611368371780222855923
49120210211080781123065015951686609032622880213374293
4922019071101057112006702841016747172386175658260652880
49320200911010711162066518469098045219297395174247576
4942018101116071111705901741982906168267292382275992343
49520190111230651111051515959141001131901862751771635353
496201902110405511560620124751560150427486840262122529
497202103110207311110690274588281864148162270219522721
4982021011102068113806311242600132518002291481041043
4992021041108065116506602441038140291132232101673332832640