Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

DateTime1
Numeric9
Categorical3

Dataset

Description컬럼명 구분 통계년월, 시도보호기호, 시군구보호기호, 행정동보호기호, 통계지사코드, 통계소속구분, 통계보호종별코드, 통계보호유형코드, 통계정액행위구분, 통계 1000원 이상 건수, 통계 1000원 이상 금액, 통계 1000원 미만 건수, 통계 1000원 미만 금액
URLhttps://www.data.go.kr/data/15121285/fileData.do

Alerts

시도보호기호 is highly overall correlated with 시군구보호기호 and 2 other fieldsHigh correlation
시군구보호기호 is highly overall correlated with 시도보호기호 and 2 other fieldsHigh correlation
행정동보호기호 is highly overall correlated with 시도보호기호 and 2 other fieldsHigh correlation
통계지사코드 is highly overall correlated with 시도보호기호 and 2 other fieldsHigh correlation
통계보호유형코드 is highly overall correlated with 통계보호종별코드High correlation
통계 1000원 이상 건수 is highly overall correlated with 통계 1000원 이상 금액High correlation
통계 1000원 이상 금액 is highly overall correlated with 통계 1000원 이상 건수High correlation
통계 1000원 미만 건수 is highly overall correlated with 통계 1000원 미만 금액High correlation
통계 1000원 미만 금액 is highly overall correlated with 통계 1000원 미만 건수High correlation
통계보호종별코드 is highly overall correlated with 통계보호유형코드High correlation
통계소속구분 is highly imbalanced (93.3%)Imbalance
통계정액행위구분 is highly imbalanced (78.2%)Imbalance
통계 1000원 이상 건수 is highly skewed (γ1 = 20.35666101)Skewed
통계 1000원 미만 건수 is highly skewed (γ1 = 50.6214086)Skewed
통계 1000원 미만 금액 is highly skewed (γ1 = 25.47018931)Skewed
통계 1000원 이상 건수 has 2478 (24.8%) zerosZeros
통계 1000원 이상 금액 has 2478 (24.8%) zerosZeros
통계 1000원 미만 건수 has 4535 (45.4%) zerosZeros
통계 1000원 미만 금액 has 4535 (45.4%) zerosZeros

Reproduction

Analysis started2023-12-12 20:21:42.547873
Analysis finished2023-12-12 20:21:56.642887
Duration14.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-01-01 00:00:00
Maximum2021-04-01 00:00:00
2023-12-13T05:21:56.721868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:56.909085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

시도보호기호
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6343455.1
Minimum5691000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:21:57.062508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5691000
5-th percentile6110000
Q16270000
median6410000
Q36450000
95-th percentile6480000
Maximum6500000
Range809000
Interquartile range (IQR)180000

Descriptive statistics

Standard deviation131236.5
Coefficient of variation (CV)0.020688488
Kurtosis0.74745093
Mean6343455.1
Median Absolute Deviation (MAD)70000
Skewness-0.97445281
Sum6.3434551 × 1010
Variance1.7223018 × 1010
MonotonicityNot monotonic
2023-12-13T05:21:57.221063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6110000 1537
15.4%
6410000 1394
13.9%
6260000 858
8.6%
6470000 840
8.4%
6480000 733
7.3%
6460000 703
7.0%
6270000 628
 
6.3%
6450000 565
 
5.7%
6280000 514
 
5.1%
6440000 472
 
4.7%
Other values (7) 1756
17.6%
ValueCountFrequency (%)
5691000 31
 
0.3%
6110000 1537
15.4%
6260000 858
8.6%
6270000 628
6.3%
6280000 514
 
5.1%
6290000 434
 
4.3%
6300000 278
 
2.8%
6310000 126
 
1.3%
6410000 1394
13.9%
6420000 410
 
4.1%
ValueCountFrequency (%)
6500000 134
 
1.3%
6480000 733
7.3%
6470000 840
8.4%
6460000 703
7.0%
6450000 565
5.7%
6440000 472
 
4.7%
6430000 343
 
3.4%
6420000 410
 
4.1%
6410000 1394
13.9%
6310000 126
 
1.3%

시군구보호기호
Real number (ℝ)

HIGH CORRELATION 

Distinct247
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4143830.8
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:21:57.406988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3080000
Q13420000
median3930000
Q34830000
95-th percentile5600000
Maximum6520000
Range3520000
Interquartile range (IQR)1410000

Descriptive statistics

Standard deviation848248.33
Coefficient of variation (CV)0.20470149
Kurtosis-0.75167171
Mean4143830.8
Median Absolute Deviation (MAD)670000
Skewness0.52558225
Sum4.1438308 × 1010
Variance7.1952522 × 1011
MonotonicityNot monotonic
2023-12-13T05:21:57.605479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3620000 129
 
1.3%
3470000 127
 
1.3%
3860000 118
 
1.2%
4800000 109
 
1.1%
3780000 107
 
1.1%
3150000 103
 
1.0%
3450000 101
 
1.0%
4490000 99
 
1.0%
3100000 99
 
1.0%
3540000 98
 
1.0%
Other values (237) 8910
89.1%
ValueCountFrequency (%)
3000000 43
0.4%
3010000 29
 
0.3%
3020000 55
0.5%
3030000 55
0.5%
3040000 49
0.5%
3050000 59
0.6%
3060000 62
0.6%
3070000 85
0.9%
3080000 69
0.7%
3090000 52
0.5%
ValueCountFrequency (%)
6520000 45
0.4%
6510000 88
0.9%
5735000 24
 
0.2%
5730000 23
 
0.2%
5725000 30
 
0.3%
5720000 29
 
0.3%
5700000 23
 
0.2%
5690000 31
 
0.3%
5680000 32
 
0.3%
5670206 30
 
0.3%

행정동보호기호
Real number (ℝ)

HIGH CORRELATION 

Distinct3173
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4179727.8
Minimum3000000
Maximum6520043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:21:57.793462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3080130
Q13420071
median3960115.5
Q34940070
95-th percentile5660017
Maximum6520043
Range3520043
Interquartile range (IQR)1519999

Descriptive statistics

Standard deviation876849.5
Coefficient of variation (CV)0.20978627
Kurtosis-0.92532608
Mean4179727.8
Median Absolute Deviation (MAD)709938.5
Skewness0.46788595
Sum4.1797278 × 1010
Variance7.6886505 × 1011
MonotonicityNot monotonic
2023-12-13T05:21:57.984746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3100133 14
 
0.1%
3470043 13
 
0.1%
5130047 12
 
0.1%
5100048 12
 
0.1%
4790029 12
 
0.1%
3540041 11
 
0.1%
5350211 11
 
0.1%
5350047 11
 
0.1%
3400085 11
 
0.1%
3620109 11
 
0.1%
Other values (3163) 9882
98.8%
ValueCountFrequency (%)
3000000 3
< 0.1%
3000042 3
< 0.1%
3000043 1
 
< 0.1%
3000044 5
0.1%
3000045 2
 
< 0.1%
3000047 4
< 0.1%
3000053 1
 
< 0.1%
3000054 1
 
< 0.1%
3000055 1
 
< 0.1%
3000056 4
< 0.1%
ValueCountFrequency (%)
6520043 4
< 0.1%
6520042 3
< 0.1%
6520041 4
< 0.1%
6520040 1
 
< 0.1%
6520039 5
0.1%
6520038 1
 
< 0.1%
6520037 1
 
< 0.1%
6520036 3
< 0.1%
6520035 4
< 0.1%
6520033 5
0.1%

통계지사코드
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.9758
Minimum101
Maximum802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:21:58.150727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile109
Q1221
median318
Q3611
95-th percentile756.05
Maximum802
Range701
Interquartile range (IQR)390

Descriptive statistics

Standard deviation223.70413
Coefficient of variation (CV)0.56494396
Kurtosis-1.3576396
Mean395.9758
Median Absolute Deviation (MAD)185
Skewness0.3849361
Sum3959758
Variance50043.54
MonotonicityNot monotonic
2023-12-13T05:21:58.640726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242 171
 
1.7%
202 148
 
1.5%
243 134
 
1.3%
241 129
 
1.3%
222 127
 
1.3%
221 127
 
1.3%
232 126
 
1.3%
206 119
 
1.2%
651 109
 
1.1%
251 109
 
1.1%
Other values (167) 8701
87.0%
ValueCountFrequency (%)
101 88
0.9%
103 49
0.5%
104 59
0.6%
105 85
0.9%
106 99
1.0%
107 41
0.4%
108 75
0.8%
109 44
0.4%
110 92
0.9%
111 21
 
0.2%
ValueCountFrequency (%)
802 45
0.4%
801 89
0.9%
771 66
0.7%
769 42
0.4%
767 50
0.5%
765 77
0.8%
762 39
0.4%
759 39
0.4%
757 53
0.5%
756 31
 
0.3%

통계소속구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9921 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9921
99.2%
1 79
 
0.8%

Length

2023-12-13T05:21:58.861583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:21:58.975925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9921
99.2%
1 79
 
0.8%

통계보호종별코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5179 
1
4804 
6
 
12
8
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5179
51.8%
1 4804
48.0%
6 12
 
0.1%
8 5
 
0.1%

Length

2023-12-13T05:21:59.098116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:21:59.243826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5179
51.8%
1 4804
48.0%
6 12
 
0.1%
8 5
 
< 0.1%

통계보호유형코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.7951
Minimum11
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:21:59.384575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median21
Q321
95-th percentile21
Maximum49
Range38
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.979623
Coefficient of variation (CV)0.35603379
Kurtosis6.4835229
Mean16.7951
Median Absolute Deviation (MAD)0
Skewness1.5561186
Sum167951
Variance35.755892
MonotonicityNot monotonic
2023-12-13T05:21:59.597474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
21 5101
51.0%
11 3856
38.6%
13 439
 
4.4%
12 274
 
2.7%
14 102
 
1.0%
49 91
 
0.9%
16 64
 
0.6%
41 43
 
0.4%
18 9
 
0.1%
17 9
 
0.1%
Other values (6) 12
 
0.1%
ValueCountFrequency (%)
11 3856
38.6%
12 274
 
2.7%
13 439
 
4.4%
14 102
 
1.0%
16 64
 
0.6%
17 9
 
0.1%
18 9
 
0.1%
20 1
 
< 0.1%
21 5101
51.0%
31 1
 
< 0.1%
ValueCountFrequency (%)
49 91
 
0.9%
43 1
 
< 0.1%
42 1
 
< 0.1%
41 43
 
0.4%
39 5
 
0.1%
33 3
 
< 0.1%
31 1
 
< 0.1%
21 5101
51.0%
20 1
 
< 0.1%
18 9
 
0.1%

통계정액행위구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9652 
9
 
348

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9652
96.5%
9 348
 
3.5%

Length

2023-12-13T05:21:59.795114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:21:59.938097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9652
96.5%
9 348
 
3.5%

통계 1000원 이상 건수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct56
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8529
Minimum0
Maximum335
Zeros2478
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:22:00.099947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile11
Maximum335
Range335
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.9984381
Coefficient of variation (CV)2.1025757
Kurtosis971.11626
Mean2.8529
Median Absolute Deviation (MAD)1
Skewness20.356661
Sum28529
Variance35.98126
MonotonicityNot monotonic
2023-12-13T05:22:00.276553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3338
33.4%
0 2478
24.8%
2 1290
 
12.9%
3 710
 
7.1%
4 477
 
4.8%
5 305
 
3.0%
6 244
 
2.4%
7 175
 
1.8%
8 146
 
1.5%
9 146
 
1.5%
Other values (46) 691
 
6.9%
ValueCountFrequency (%)
0 2478
24.8%
1 3338
33.4%
2 1290
 
12.9%
3 710
 
7.1%
4 477
 
4.8%
5 305
 
3.0%
6 244
 
2.4%
7 175
 
1.8%
8 146
 
1.5%
9 146
 
1.5%
ValueCountFrequency (%)
335 1
 
< 0.1%
106 1
 
< 0.1%
103 1
 
< 0.1%
90 1
 
< 0.1%
71 1
 
< 0.1%
68 1
 
< 0.1%
58 1
 
< 0.1%
49 2
< 0.1%
48 1
 
< 0.1%
47 4
< 0.1%

통계 1000원 이상 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct967
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1943.561
Minimum0
Maximum200040
Zeros2478
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:22:00.455458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median600
Q31780
95-th percentile9000
Maximum200040
Range200040
Interquartile range (IQR)1770

Descriptive statistics

Standard deviation4858.2548
Coefficient of variation (CV)2.4996667
Kurtosis349.47667
Mean1943.561
Median Absolute Deviation (MAD)600
Skewness12.492422
Sum19435610
Variance23602640
MonotonicityNot monotonic
2023-12-13T05:22:00.639629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2478
24.8%
10 509
 
5.1%
1000 290
 
2.9%
20 153
 
1.5%
1500 99
 
1.0%
2000 92
 
0.9%
30 89
 
0.9%
3000 71
 
0.7%
40 69
 
0.7%
500 64
 
0.6%
Other values (957) 6086
60.9%
ValueCountFrequency (%)
0 2478
24.8%
10 509
 
5.1%
20 153
 
1.5%
30 89
 
0.9%
40 69
 
0.7%
50 40
 
0.4%
60 55
 
0.5%
70 45
 
0.4%
80 37
 
0.4%
90 38
 
0.4%
ValueCountFrequency (%)
200040 1
< 0.1%
106020 1
< 0.1%
102900 1
< 0.1%
91000 1
< 0.1%
71660 1
< 0.1%
71000 1
< 0.1%
63270 1
< 0.1%
49810 1
< 0.1%
49000 1
< 0.1%
47880 1
< 0.1%

통계 1000원 미만 건수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9514
Minimum0
Maximum193
Zeros4535
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:22:00.811281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum193
Range193
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4357815
Coefficient of variation (CV)2.5602076
Kurtosis3879.3545
Mean0.9514
Median Absolute Deviation (MAD)1
Skewness50.621409
Sum9514
Variance5.9330313
MonotonicityNot monotonic
2023-12-13T05:22:00.973115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 4535
45.4%
1 3568
35.7%
2 1093
 
10.9%
3 404
 
4.0%
4 179
 
1.8%
5 81
 
0.8%
6 53
 
0.5%
7 24
 
0.2%
8 21
 
0.2%
10 9
 
0.1%
Other values (15) 33
 
0.3%
ValueCountFrequency (%)
0 4535
45.4%
1 3568
35.7%
2 1093
 
10.9%
3 404
 
4.0%
4 179
 
1.8%
5 81
 
0.8%
6 53
 
0.5%
7 24
 
0.2%
8 21
 
0.2%
9 9
 
0.1%
ValueCountFrequency (%)
193 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
28 1
 
< 0.1%
26 1
 
< 0.1%
24 1
 
< 0.1%
18 2
< 0.1%
17 1
 
< 0.1%
15 3
< 0.1%

통계 1000원 미만 금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2812
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23245.513
Minimum0
Maximum6312370
Zeros4535
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:22:01.140828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2635
Q314830
95-th percentile78573
Maximum6312370
Range6312370
Interquartile range (IQR)14830

Descriptive statistics

Standard deviation147400.84
Coefficient of variation (CV)6.3410449
Kurtosis798.42502
Mean23245.513
Median Absolute Deviation (MAD)2635
Skewness25.470189
Sum2.3245513 × 108
Variance2.1727008 × 1010
MonotonicityNot monotonic
2023-12-13T05:22:01.296090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4535
45.4%
12200 47
 
0.5%
2060 39
 
0.4%
3130 37
 
0.4%
2000 35
 
0.4%
3440 32
 
0.3%
18470 32
 
0.3%
2750 29
 
0.3%
10320 29
 
0.3%
6880 27
 
0.3%
Other values (2802) 5158
51.6%
ValueCountFrequency (%)
0 4535
45.4%
2000 35
 
0.4%
2010 6
 
0.1%
2020 3
 
< 0.1%
2030 8
 
0.1%
2040 5
 
0.1%
2050 7
 
0.1%
2060 39
 
0.4%
2070 15
 
0.1%
2080 5
 
0.1%
ValueCountFrequency (%)
6312370 1
< 0.1%
4595890 1
< 0.1%
4595030 1
< 0.1%
4529830 1
< 0.1%
4317000 1
< 0.1%
3890760 1
< 0.1%
3297480 1
< 0.1%
3020940 1
< 0.1%
2718470 1
< 0.1%
2665810 1
< 0.1%

Interactions

2023-12-13T05:21:55.243639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:45.143289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.480280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.754864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.997019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.176921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:51.236556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.605643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.854631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.374297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:45.310648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.616047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.938329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.139475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.302298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:51.384952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.739978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.979936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.480685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:45.454687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.745711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.099269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.274244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.425009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:51.499882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.886960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.156939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.593699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:45.572344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.880572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.258043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.397652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.542571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:51.608491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.022528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.320105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.714849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:45.740761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.027381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.374401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.554683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.641589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.021467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.168211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.494366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.851612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:45.898199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.173592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.503202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.690158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.750494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.136727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.300472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.650625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.968229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.037680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.288584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.641618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.812980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.871210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.245249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.424765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.784988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:56.084938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.169899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.446227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.761915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:49.943172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.987822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.367372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.549714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.972694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:56.218909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:46.336138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:47.605238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:48.880318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:50.067514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:51.115728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:52.483446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:53.696030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:55.106356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:22:01.397819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계년월시도보호기호시군구보호기호행정동보호기호통계지사코드통계소속구분통계보호종별코드통계보호유형코드통계정액행위구분통계 1000원 이상 건수통계 1000원 이상 금액통계 1000원 미만 건수통계 1000원 미만 금액
통계년월1.0000.2870.3250.3280.3480.0000.0970.1280.0630.0000.0000.0530.000
시도보호기호0.2871.0000.9180.9110.9900.2120.0440.0610.0540.0190.0290.0290.022
시군구보호기호0.3250.9181.0001.0000.8930.1710.0200.0780.0160.0230.0210.0480.000
행정동보호기호0.3280.9111.0001.0000.8870.1710.0120.0770.0160.0120.0100.0290.000
통계지사코드0.3480.9900.8930.8871.0000.3020.0400.0810.0470.0000.0230.0310.000
통계소속구분0.0000.2120.1710.1710.3021.0000.0000.0300.0000.0000.0000.0250.002
통계보호종별코드0.0970.0440.0200.0120.0400.0001.0000.7180.2370.0000.0530.0000.256
통계보호유형코드0.1280.0610.0780.0770.0810.0300.7181.0000.1490.0000.0600.0000.537
통계정액행위구분0.0630.0540.0160.0160.0470.0000.2370.1491.0000.0000.0000.0000.000
통계 1000원 이상 건수0.0000.0190.0230.0120.0000.0000.0000.0000.0001.0000.9160.7190.546
통계 1000원 이상 금액0.0000.0290.0210.0100.0230.0000.0530.0600.0000.9161.0000.7720.495
통계 1000원 미만 건수0.0530.0290.0480.0290.0310.0250.0000.0000.0000.7190.7721.0000.810
통계 1000원 미만 금액0.0000.0220.0000.0000.0000.0020.2560.5370.0000.5460.4950.8101.000
2023-12-13T05:22:01.548016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계소속구분통계보호종별코드통계정액행위구분
통계소속구분1.0000.0000.000
통계보호종별코드0.0001.0000.157
통계정액행위구분0.0000.1571.000
2023-12-13T05:22:01.672049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도보호기호시군구보호기호행정동보호기호통계지사코드통계보호유형코드통계 1000원 이상 건수통계 1000원 이상 금액통계 1000원 미만 건수통계 1000원 미만 금액통계소속구분통계보호종별코드통계정액행위구분
시도보호기호1.0000.9470.9240.987-0.024-0.132-0.145-0.0140.0060.1400.0150.036
시군구보호기호0.9471.0000.9770.962-0.022-0.140-0.148-0.0160.0060.1700.0120.016
행정동보호기호0.9240.9771.0000.938-0.019-0.133-0.139-0.0180.0040.1700.0080.016
통계지사코드0.9870.9620.9381.000-0.024-0.138-0.149-0.0130.0070.2310.0240.036
통계보호유형코드-0.024-0.022-0.019-0.0241.0000.1890.130-0.195-0.2210.0330.5890.159
통계 1000원 이상 건수-0.132-0.140-0.133-0.1380.1891.0000.881-0.186-0.2250.0000.0000.000
통계 1000원 이상 금액-0.145-0.148-0.139-0.1490.1300.8811.000-0.201-0.2380.0000.0360.000
통계 1000원 미만 건수-0.014-0.016-0.018-0.013-0.195-0.186-0.2011.0000.9250.0420.0000.000
통계 1000원 미만 금액0.0060.0060.0040.007-0.221-0.225-0.2380.9251.0000.0020.1660.000
통계소속구분0.1400.1700.1700.2310.0330.0000.0000.0420.0021.0000.0000.000
통계보호종별코드0.0150.0120.0080.0240.5890.0000.0360.0000.1660.0001.0000.157
통계정액행위구분0.0360.0160.0160.0360.1590.0000.0000.0000.0000.0000.1571.000

Missing values

2023-12-13T05:21:56.372556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:21:56.546360image/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

통계년월시도보호기호시군구보호기호행정동보호기호통계지사코드통계소속구분통계보호종별코드통계보호유형코드통계정액행위구분통계 1000원 이상 건수통계 1000원 이상 금액통계 1000원 미만 건수통계 1000원 미만 금액
129102018-0462900003600000360002324201110110225460
797232017-0362700003430000343002322202210366017130
941012019-0661100003210000321004211101110120000
209482017-02644000045200004520105563011102194012630
917312019-07628000035400003540034237022107430014390
646642018-076410000386000038603933050111000111470
343062018-06626000033300003330031212011103201000
254282018-07648000053500005350047755022100017580
378212018-086420000427000042700264080221000134940
686432017-0164100005530000553013832102210151363000
통계년월시도보호기호시군구보호기호행정동보호기호통계지사코드통계소속구분통계보호종별코드통계보호유형코드통계정액행위구분통계 1000원 이상 건수통계 1000원 이상 금액통계 1000원 미만 건수통계 1000원 미만 금액
71962018-09626000032800003280034202011102250019730
711112017-0164100003910000391006830701110101000000
937392019-076470000509000050900337050221018601248110
223812017-026460000485000048500396670221016000
450832017-086110000302000030200401250111000124080
357412018-0964100003830000385001534002210159000
31102018-026410000405000056300083420221022150310730
619262018-03648000056701405670150767022101117000
228752017-026470000503000050300277190111066000111700
791572017-1164500004710000471009060502210210012020