Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory102.4 B

Variable types

Text1
Numeric10

Dataset

Description샘플 데이터
Author신한은행
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=320

Reproduction

Analysis started2023-12-10 14:57:40.989964
Analysis finished2023-12-10 14:58:02.753800
Duration21.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:58:02.943758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters210
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)86.7%

Sample

1st row1*0*3*1
2nd row1*0*0*9
3rd row1*0*2*5
4th row1*0*7*2
5th row1*0*5*1
ValueCountFrequency (%)
1*0*5*0 2
 
6.7%
1*0*3*0 2
 
6.7%
1*0*3*1 1
 
3.3%
1*0*2*9 1
 
3.3%
1*0*9*7 1
 
3.3%
1*0*0*3 1
 
3.3%
1*0*2*0 1
 
3.3%
1*0*7*0 1
 
3.3%
1*0*2*1 1
 
3.3%
1*0*7*8 1
 
3.3%
Other values (18) 18
60.0%
2023-12-10T23:58:03.496598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 90
42.9%
0 40
19.0%
1 37
17.6%
5 8
 
3.8%
3 7
 
3.3%
2 7
 
3.3%
7 6
 
2.9%
8 5
 
2.4%
9 5
 
2.4%
4 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
57.1%
Other Punctuation 90
42.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
33.3%
1 37
30.8%
5 8
 
6.7%
3 7
 
5.8%
2 7
 
5.8%
7 6
 
5.0%
8 5
 
4.2%
9 5
 
4.2%
4 3
 
2.5%
6 2
 
1.7%
Other Punctuation
ValueCountFrequency (%)
* 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 90
42.9%
0 40
19.0%
1 37
17.6%
5 8
 
3.8%
3 7
 
3.3%
2 7
 
3.3%
7 6
 
2.9%
8 5
 
2.4%
9 5
 
2.4%
4 3
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 90
42.9%
0 40
19.0%
1 37
17.6%
5 8
 
3.8%
3 7
 
3.3%
2 7
 
3.3%
7 6
 
2.9%
8 5
 
2.4%
9 5
 
2.4%
4 3
 
1.4%

총수신평잔_건수(DEP_TOT_AVJN_N)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95031.333
Minimum14723
Maximum179692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:03.796586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14723
5-th percentile41465.8
Q151558.25
median92361
Q3131993.75
95-th percentile177103.9
Maximum179692
Range164969
Interquartile range (IQR)80435.5

Descriptive statistics

Standard deviation48559.078
Coefficient of variation (CV)0.51097966
Kurtosis-1.0843334
Mean95031.333
Median Absolute Deviation (MAD)41695
Skewness0.36401178
Sum2850940
Variance2.3579841 × 109
MonotonicityNot monotonic
2023-12-10T23:58:04.029871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
133657 1
 
3.3%
178075 1
 
3.3%
69012 1
 
3.3%
46333 1
 
3.3%
119166 1
 
3.3%
46102 1
 
3.3%
90513 1
 
3.3%
143064 1
 
3.3%
127004 1
 
3.3%
171347 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
14723 1
3.3%
39160 1
3.3%
44284 1
3.3%
45227 1
3.3%
46102 1
3.3%
46333 1
3.3%
50211 1
3.3%
50267 1
3.3%
55432 1
3.3%
56338 1
3.3%
ValueCountFrequency (%)
179692 1
3.3%
178075 1
3.3%
175917 1
3.3%
171347 1
3.3%
157580 1
3.3%
143064 1
3.3%
138744 1
3.3%
133657 1
3.3%
127004 1
3.3%
119166 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6191537 × 1011
Minimum1.4857511 × 1011
Maximum1.5373999 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:04.304390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4857511 × 1011
5-th percentile2.044895 × 1011
Q14.4854705 × 1011
median5.4305481 × 1011
Q36.2024981 × 1011
95-th percentile9.2164404 × 1011
Maximum1.5373999 × 1012
Range1.3888248 × 1012
Interquartile range (IQR)1.7170276 × 1011

Descriptive statistics

Standard deviation2.6689032 × 1011
Coefficient of variation (CV)0.47496532
Kurtosis5.2426582
Mean5.6191537 × 1011
Median Absolute Deviation (MAD)9.3828154 × 1010
Skewness1.6343273
Sum1.6857461 × 1013
Variance7.1230441 × 1022
MonotonicityNot monotonic
2023-12-10T23:58:04.562526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
515798986015.899 1
 
3.3%
179470739247.881 1
 
3.3%
886153870662.248 1
 
3.3%
235067995321.191 1
 
3.3%
593384838293.469 1
 
3.3%
950681454917.988 1
 
3.3%
489832115940.029 1
 
3.3%
538073066267.345 1
 
3.3%
601914328614.131 1
 
3.3%
276296098911.848 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
148575108541.393 1
3.3%
179470739247.881 1
3.3%
235067995321.191 1
3.3%
276296098911.848 1
3.3%
287305464075.62 1
3.3%
362458976536.553 1
3.3%
446780893793.269 1
3.3%
447867448486.866 1
3.3%
450585860297.208 1
3.3%
469202528992.642 1
3.3%
ValueCountFrequency (%)
1537399899622.57 1
3.3%
950681454917.988 1
3.3%
886153870662.248 1
3.3%
862853904352.134 1
3.3%
741644547716.12 1
3.3%
736647827106.907 1
3.3%
659157294088.962 1
3.3%
626361632817.664 1
3.3%
601914328614.131 1
3.3%
597191676971.774 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13195.3
Minimum3376
Maximum27436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:04.823059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3376
5-th percentile6293.5
Q18965.75
median12094
Q315874.5
95-th percentile24250.75
Maximum27436
Range24060
Interquartile range (IQR)6908.75

Descriptive statistics

Standard deviation5876.3956
Coefficient of variation (CV)0.44534006
Kurtosis0.14378673
Mean13195.3
Median Absolute Deviation (MAD)3770
Skewness0.70129465
Sum395859
Variance34532026
MonotonicityNot monotonic
2023-12-10T23:58:05.062638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
15541 1
 
3.3%
10074 1
 
3.3%
7251 1
 
3.3%
18403 1
 
3.3%
8900 1
 
3.3%
5992 1
 
3.3%
9860 1
 
3.3%
22411 1
 
3.3%
15885 1
 
3.3%
15616 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3376 1
3.3%
5992 1
3.3%
6662 1
3.3%
6687 1
3.3%
7251 1
3.3%
7638 1
3.3%
8233 1
3.3%
8900 1
3.3%
9163 1
3.3%
9860 1
3.3%
ValueCountFrequency (%)
27436 1
3.3%
25756 1
3.3%
22411 1
3.3%
20854 1
3.3%
18566 1
3.3%
18403 1
3.3%
16112 1
3.3%
15885 1
3.3%
15843 1
3.3%
15616 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5060966 × 1010
Minimum9.8430046 × 109
Maximum7.883682 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:05.340784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.8430046 × 109
5-th percentile1.1897348 × 1010
Q13.2682776 × 1010
median4.371258 × 1010
Q36.2843974 × 1010
95-th percentile7.5397202 × 1010
Maximum7.883682 × 1010
Range6.8993815 × 1010
Interquartile range (IQR)3.0161199 × 1010

Descriptive statistics

Standard deviation1.9391634 × 1010
Coefficient of variation (CV)0.43034217
Kurtosis-0.77936439
Mean4.5060966 × 1010
Median Absolute Deviation (MAD)1.5190971 × 1010
Skewness-0.051036402
Sum1.351829 × 1012
Variance3.7603547 × 1020
MonotonicityNot monotonic
2023-12-10T23:58:05.591924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
33661396321.296 1
 
3.3%
43368924591.6198 1
 
3.3%
64223242504.3384 1
 
3.3%
22519302551.0802 1
 
3.3%
12925996562.0272 1
 
3.3%
64232058777.3211 1
 
3.3%
24296662783.0917 1
 
3.3%
11055727087.0855 1
 
3.3%
61179219339.4404 1
 
3.3%
38678892345.033 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
9843004611.019 1
3.3%
11055727087.0855 1
3.3%
12925996562.0272 1
3.3%
22519302551.0802 1
3.3%
24296662783.0917 1
3.3%
26196893770.2946 1
3.3%
30797278314.0522 1
3.3%
32356568820.936 1
3.3%
33661396321.296 1
3.3%
36453833005.2189 1
3.3%
ValueCountFrequency (%)
78836819871.9227 1
3.3%
76817841988.708 1
3.3%
73660863936.1648 1
3.3%
68767629747.602 1
3.3%
64532874155.8356 1
3.3%
64232058777.3211 1
3.3%
64223242504.3384 1
3.3%
63398892798.0772 1
3.3%
61179219339.4404 1
3.3%
56135235489.1854 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17070.433
Minimum2797
Maximum32356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:05.848777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2797
5-th percentile4222.7
Q111129
median16216
Q322975
95-th percentile29978.55
Maximum32356
Range29559
Interquartile range (IQR)11846

Descriptive statistics

Standard deviation8167.0929
Coefficient of variation (CV)0.47843501
Kurtosis-0.88453361
Mean17070.433
Median Absolute Deviation (MAD)6002
Skewness0.086595902
Sum512113
Variance66701406
MonotonicityNot monotonic
2023-12-10T23:58:06.100833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
26998 1
 
3.3%
22861 1
 
3.3%
32356 1
 
3.3%
16333 1
 
3.3%
19799 1
 
3.3%
23921 1
 
3.3%
13437 1
 
3.3%
26949 1
 
3.3%
21333 1
 
3.3%
13304 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
2797 1
3.3%
3392 1
3.3%
5238 1
3.3%
6585 1
3.3%
9312 1
3.3%
10373 1
3.3%
10663 1
3.3%
10997 1
3.3%
11525 1
3.3%
12633 1
3.3%
ValueCountFrequency (%)
32356 1
3.3%
30654 1
3.3%
29153 1
3.3%
26998 1
3.3%
26949 1
3.3%
24411 1
3.3%
23921 1
3.3%
23013 1
3.3%
22861 1
3.3%
22377 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1137548 × 1010
Minimum3.4209972 × 109
Maximum7.1716869 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:06.352783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.4209972 × 109
5-th percentile1.1280747 × 1010
Q11.9335782 × 1010
median2.6893066 × 1010
Q33.8406058 × 1010
95-th percentile5.8079085 × 1010
Maximum7.1716869 × 1010
Range6.8295872 × 1010
Interquartile range (IQR)1.9070276 × 1010

Descriptive statistics

Standard deviation1.6190916 × 1010
Coefficient of variation (CV)0.51998046
Kurtosis0.058025435
Mean3.1137548 × 1010
Median Absolute Deviation (MAD)8.6892582 × 109
Skewness0.72806399
Sum9.3412643 × 1011
Variance2.6214577 × 1020
MonotonicityNot monotonic
2023-12-10T23:58:06.628851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
38817786202.2736 1
 
3.3%
32684569082.4269 1
 
3.3%
57629088101.1918 1
 
3.3%
23146564072.211 1
 
3.3%
49079789921.292 1
 
3.3%
17437782761.9685 1
 
3.3%
25949081584.1027 1
 
3.3%
47205863000.1212 1
 
3.3%
47527465767.8298 1
 
3.3%
27837051220.086 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3420997176.964 1
3.3%
9333399043.924 1
3.3%
13660839351.1294 1
3.3%
16223069785.9515 1
3.3%
17437782761.9685 1
3.3%
18532131939.234 1
3.3%
18689952943.2318 1
3.3%
19023294546.204 1
3.3%
20273243767.9357 1
3.3%
21512712368.102 1
3.3%
ValueCountFrequency (%)
71716868866.0773 1
3.3%
58447264461.1747 1
3.3%
57629088101.1918 1
3.3%
56101428011.2949 1
3.3%
49079789921.292 1
3.3%
47527465767.8298 1
3.3%
47205863000.1212 1
3.3%
38817786202.2736 1
3.3%
37170873390.2939 1
3.3%
35910648271.1733 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41443.767
Minimum10648
Maximum83624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:06.872010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10648
5-th percentile13558.15
Q126859.75
median40969
Q350288.75
95-th percentile80080.9
Maximum83624
Range72976
Interquartile range (IQR)23429

Descriptive statistics

Standard deviation19764.677
Coefficient of variation (CV)0.4769035
Kurtosis-0.29951982
Mean41443.767
Median Absolute Deviation (MAD)14070.5
Skewness0.52541584
Sum1243313
Variance3.9064247 × 108
MonotonicityNot monotonic
2023-12-10T23:58:07.125597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
59361 1
 
3.3%
49083 1
 
3.3%
39231 1
 
3.3%
83624 1
 
3.3%
49194 1
 
3.3%
26734 1
 
3.3%
10648 1
 
3.3%
25508 1
 
3.3%
29427 1
 
3.3%
42707 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10648 1
3.3%
12880 1
3.3%
14387 1
3.3%
18675 1
3.3%
22024 1
3.3%
25508 1
3.3%
26734 1
3.3%
26821 1
3.3%
26976 1
3.3%
29427 1
3.3%
ValueCountFrequency (%)
83624 1
3.3%
81070 1
3.3%
78872 1
3.3%
63476 1
3.3%
60996 1
3.3%
59361 1
3.3%
57073 1
3.3%
50347 1
3.3%
50114 1
3.3%
49194 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5215549 × 1010
Minimum2.1615257 × 1010
Maximum1.7161243 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:07.395000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1615257 × 1010
5-th percentile3.4183333 × 1010
Q16.0894698 × 1010
median8.2302503 × 1010
Q31.0829584 × 1011
95-th percentile1.4790318 × 1011
Maximum1.7161243 × 1011
Range1.4999718 × 1011
Interquartile range (IQR)4.7401138 × 1010

Descriptive statistics

Standard deviation3.648276 × 1010
Coefficient of variation (CV)0.42812328
Kurtosis0.022178764
Mean8.5215549 × 1010
Median Absolute Deviation (MAD)2.4618737 × 1010
Skewness0.47228338
Sum2.5564665 × 1012
Variance1.3309918 × 1021
MonotonicityNot monotonic
2023-12-10T23:58:08.147813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
55016369053.9428 1
 
3.3%
89941406420.833 1
 
3.3%
130460761870.847 1
 
3.3%
89404793571.7482 1
 
3.3%
110395554397.034 1
 
3.3%
160334194711.747 1
 
3.3%
65437366698.958 1
 
3.3%
132709722626.618 1
 
3.3%
101996679480.327 1
 
3.3%
60351162215.175 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
21615257136.3197 1
3.3%
32056244811.2037 1
3.3%
36783108563.2211 1
3.3%
41771170070.2852 1
3.3%
43858925592.226 1
3.3%
51352366384.042 1
3.3%
55016369053.9428 1
3.3%
60351162215.175 1
3.3%
62525305756.3196 1
3.3%
65437366698.958 1
3.3%
ValueCountFrequency (%)
171612432927.853 1
3.3%
160334194711.747 1
3.3%
132709722626.618 1
3.3%
130460761870.847 1
3.3%
123107790897.779 1
3.3%
115269038717.49 1
3.3%
114364000834.577 1
3.3%
110395554397.034 1
3.3%
101996679480.327 1
3.3%
100341653514.023 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31235.867
Minimum5654
Maximum76189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:08.392272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5654
5-th percentile7789.45
Q119603
median30519.5
Q341041.25
95-th percentile61051.35
Maximum76189
Range70535
Interquartile range (IQR)21438.25

Descriptive statistics

Standard deviation17122.819
Coefficient of variation (CV)0.54817814
Kurtosis0.99716146
Mean31235.867
Median Absolute Deviation (MAD)10758.5
Skewness0.77357016
Sum937076
Variance2.9319094 × 108
MonotonicityNot monotonic
2023-12-10T23:58:08.652920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
40757 1
 
3.3%
9209 1
 
3.3%
25507 1
 
3.3%
29651 1
 
3.3%
15849 1
 
3.3%
46102 1
 
3.3%
45081 1
 
3.3%
34884 1
 
3.3%
33526 1
 
3.3%
31388 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
5654 1
3.3%
6628 1
3.3%
9209 1
3.3%
9748 1
3.3%
12719 1
3.3%
13775 1
3.3%
15849 1
3.3%
19551 1
3.3%
19759 1
3.3%
22559 1
3.3%
ValueCountFrequency (%)
76189 1
3.3%
72687 1
3.3%
46830 1
3.3%
46102 1
3.3%
45081 1
3.3%
41661 1
3.3%
41276 1
3.3%
41136 1
3.3%
40757 1
3.3%
39603 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8318591 × 1010
Minimum1.2122063 × 1010
Maximum8.7053403 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:58:08.961878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2122063 × 1010
5-th percentile1.7950581 × 1010
Q13.9607532 × 1010
median4.6429012 × 1010
Q35.9267598 × 1010
95-th percentile8.1303966 × 1010
Maximum8.7053403 × 1010
Range7.4931339 × 1010
Interquartile range (IQR)1.9660066 × 1010

Descriptive statistics

Standard deviation1.898631 × 1010
Coefficient of variation (CV)0.39294006
Kurtosis-0.11206061
Mean4.8318591 × 1010
Median Absolute Deviation (MAD)9.4554317 × 109
Skewness0.18582232
Sum1.4495577 × 1012
Variance3.6047998 × 1020
MonotonicityNot monotonic
2023-12-10T23:58:09.244935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
55563382039.7088 1
 
3.3%
32686610020.9565 1
 
3.3%
55059406131.9896 1
 
3.3%
49103217363.2634 1
 
3.3%
45147740443.4285 1
 
3.3%
42696841126.2166 1
 
3.3%
49987083627.2213 1
 
3.3%
40477941443.142 1
 
3.3%
24630639051.2107 1
 
3.3%
60502337026.7215 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
12122063279.1232 1
3.3%
12485078744.0812 1
3.3%
24630639051.2107 1
3.3%
24956194940.4539 1
3.3%
28040218563.0409 1
3.3%
32686610020.9565 1
3.3%
36652518738.1663 1
3.3%
39344988363.3665 1
3.3%
40395162230.0971 1
3.3%
40477941443.142 1
3.3%
ValueCountFrequency (%)
87053402729.7296 1
3.3%
85653584793.1506 1
3.3%
75987765170.4404 1
3.3%
75072025748.4105 1
3.3%
69083674258.9736 1
3.3%
65343615632.0125 1
3.3%
62719073026.0103 1
3.3%
60502337026.7215 1
3.3%
55563382039.7088 1
3.3%
55521279794.6124 1
3.3%

Interactions

2023-12-10T23:58:00.185258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:41.441381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:43.009256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.730402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.889790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.809153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.699712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.524188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.582319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.906725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.392777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:41.594034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:43.185050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.270545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.068300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.015072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.877079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.716465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.780413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.079169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.599578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:41.768648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:43.339105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.448149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.263126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.228570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.058041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.012742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.982601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.278290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.783605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:41.922254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:43.481355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.606816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.438137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.392519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.218333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.403379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.166123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.442586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:00.981653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:42.099356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:43.653015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.906743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.627413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.603222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.398770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.853751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.375959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.630920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:01.155479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:42.255928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:43.859634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.072333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.821767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.777521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.568715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.267973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:56.563337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:58.855264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:01.368688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:42.420866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.045981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.234896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.992736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.961706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.762136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.589113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.133662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.117777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:01.600871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:42.561499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.222793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.407612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.184596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.150184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.977810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.902274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.339917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.366190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:01.802349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:42.700415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.413025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.552037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.379101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.348386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.165286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.165639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.557588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.652377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:01.987756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:42.837384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.552171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.710888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.591435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.507372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.333883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:55.356845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:57.726940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:59.874323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:09.458424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권코드(TRDAR_NO)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
상권코드(TRDAR_NO)1.0000.7740.9810.8590.9340.9820.8860.8270.0000.9600.839
총수신평잔_건수(DEP_TOT_AVJN_N)0.7741.0000.0000.3400.5200.6010.5780.7660.5850.5350.084
총수신평잔_총액(DEP_TOT_AVJN_TOT)0.9810.0001.0000.2170.4920.3400.4720.0000.3320.0000.000
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)0.8590.3400.2171.0000.5560.0430.6180.0000.3040.0000.000
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)0.9340.5200.4920.5561.0000.7370.5430.0000.4790.0000.000
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)0.9820.6010.3400.0430.7371.0000.7730.0370.0000.3780.000
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.8860.5780.4720.6180.5430.7731.0000.5460.3150.2610.539
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)0.8270.7660.0000.0000.0000.0370.5461.0000.0000.0000.268
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)0.0000.5850.3320.3040.4790.0000.3150.0001.0000.0000.000
신용카드_체크카드_건수(C_CARDSUM_AMT_N)0.9600.5350.0000.0000.0000.3780.2610.0000.0001.0000.000
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)0.8390.0840.0000.0000.0000.0000.5390.2680.0000.0001.000
2023-12-10T23:58:09.799422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
총수신평잔_건수(DEP_TOT_AVJN_N)1.0000.2400.020-0.048-0.0590.108-0.049-0.386-0.187-0.169
총수신평잔_총액(DEP_TOT_AVJN_TOT)0.2401.000-0.4020.176-0.0670.206-0.0580.058-0.000-0.026
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)0.020-0.4021.000-0.1370.0290.060-0.034-0.002-0.1070.142
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)-0.0480.176-0.1371.000-0.050-0.279-0.138-0.206-0.332-0.117
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)-0.059-0.0670.029-0.0501.0000.327-0.0140.4260.094-0.018
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.1080.2060.060-0.2790.3271.0000.3250.193-0.3010.055
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)-0.049-0.058-0.034-0.138-0.0140.3251.000-0.028-0.4350.261
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)-0.3860.058-0.002-0.2060.4260.193-0.0281.0000.038-0.199
신용카드_체크카드_건수(C_CARDSUM_AMT_N)-0.187-0.000-0.107-0.3320.094-0.301-0.4350.0381.0000.045
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)-0.169-0.0260.142-0.117-0.0180.0550.261-0.1990.0451.000

Missing values

2023-12-10T23:58:02.277935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:02.600847image/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

상권코드(TRDAR_NO)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
01*0*3*1133657515798986015.8989871554133661396321.2960012699838817786202.2735985936155016369053.9428024075755563382039.708801
11*0*0*944284548036549593.4929811509839665442622.0059972237737170873390.293947794171612432927.8529974113641181166685.208702
21*0*2*51759171537399899622.570068763826196893770.2946011378771716868866.0773017887271930721759.36381377536652518738.166298
31*0*7*2157580659157294088.962036668778836819871.9226991609918689952943.23181288036783108563.22113960324956194940.453899
41*0*5*150211450585860297.2080081088063398892798.0772022179724498432602.0615015034751352366384.042662875072025748.410507
51*0*5*067282560686487406.4639891420945998835982.1334991037332606826169.9059982697632056244811.2037012793440395162230.097099
61*0*4*797777549408819447.8079831068156135235489.185402931216223069785.95152202492023311989.5794071955112485078744.0812
71*0*0*5116783484960548643.6060181057744056236124.523333929333399043.9242682121615257136.3196987268755521279794.612396
81*0*7*150267447867448486.8660282575647416998100.8455963065420273243767.93569932303123107790897.7790071975947710284406.077904
91*0*1*258555446780893793.2689821584376817841988.7079931099718532131939.2340013224262525305756.3196032540328040218563.040901
상권코드(TRDAR_NO)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
201*0*4*355432626361632817.66394337639270741249.4296042301328903647554.1627015707377751303272.6166997618962719073026.0103
211*0*7*814723494340231927.874023823349168591680.086197279719023294546.20399960996115269038717.4900052255985653584793.150604
221*0*3*0171347276296098911.8480221561638678892345.0329971330427837051220.0859994270760351162215.1750033138860502337026.721497
231*0*5*0127004601914328614.1309811588561179219339.4403992133347527465767.82980329427101996679480.3269963352624630639051.210701
241*0*2*1143064538073066267.3449712241111055727087.0855012694947205863000.12120125508132709722626.6179963488440477941443.141998
251*0*7*090513489832115940.028992986024296662783.0917021343725949081584.1026991064865437366698.9584508149987083627.221298
261*0*2*046102950681454917.988037599264232058777.3210982392117437782761.96849826734160334194711.7470094610242696841126.216599
271*0*0*3119166593384838293.468994890012925996562.0272011979949079789921.29249194110395554397.0339971584945147740443.428497
281*0*9*746333235067995321.191011840322519302551.08021633323146564072.2109998362489404793571.7481992965149103217363.263397
291*0*8*569012886153870662.248047725164223242504.3384023235657629088101.19180339231130460761870.8472550755059406131.989601