Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory99.3 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 14:56:32.998043
Analysis finished2023-12-10 14:56:53.465409
Duration20.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T23:56:53.773722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)62.0%

Sample

1st rowG*0*2*8*3*
2nd rowG*0*0*3*0*
3rd rowG*0*1*7*6*
4th rowG*0*0*9*2*
5th rowG*0*1*6*5*
ValueCountFrequency (%)
g*0*1*4*9 3
 
3.0%
g*0*1*0*0 3
 
3.0%
g*0*2*1*0 2
 
2.0%
g*0*1*5*5 2
 
2.0%
g*0*1*7*3 2
 
2.0%
g*0*0*3*0 2
 
2.0%
g*0*0*1*7 2
 
2.0%
g*0*2*5*8 2
 
2.0%
g*0*2*8*3 2
 
2.0%
g*0*1*3*0 2
 
2.0%
Other values (70) 78
78.0%
2023-12-10T23:56:54.474804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 500
50.0%
0 159
 
15.9%
G 100
 
10.0%
1 64
 
6.4%
2 43
 
4.3%
3 25
 
2.5%
9 22
 
2.2%
5 22
 
2.2%
7 19
 
1.9%
8 18
 
1.8%
Other values (2) 28
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 500
50.0%
Decimal Number 400
40.0%
Uppercase Letter 100
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
39.8%
1 64
16.0%
2 43
 
10.8%
3 25
 
6.2%
9 22
 
5.5%
5 22
 
5.5%
7 19
 
4.8%
8 18
 
4.5%
6 16
 
4.0%
4 12
 
3.0%
Other Punctuation
ValueCountFrequency (%)
* 500
100.0%
Uppercase Letter
ValueCountFrequency (%)
G 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900
90.0%
Latin 100
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 500
55.6%
0 159
 
17.7%
1 64
 
7.1%
2 43
 
4.8%
3 25
 
2.8%
9 22
 
2.4%
5 22
 
2.4%
7 19
 
2.1%
8 18
 
2.0%
6 16
 
1.8%
Latin
ValueCountFrequency (%)
G 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 500
50.0%
0 159
 
15.9%
G 100
 
10.0%
1 64
 
6.4%
2 43
 
4.3%
3 25
 
2.5%
9 22
 
2.2%
5 22
 
2.2%
7 19
 
1.9%
8 18
 
1.8%
Other values (2) 28
 
2.8%
Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean844.94
Minimum8
Maximum5177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:54.753999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile55.2
Q1365.5
median759.5
Q31068
95-th percentile2040.55
Maximum5177
Range5169
Interquartile range (IQR)702.5

Descriptive statistics

Standard deviation724.31405
Coefficient of variation (CV)0.85723726
Kurtosis12.25646
Mean844.94
Median Absolute Deviation (MAD)365
Skewness2.6018616
Sum84494
Variance524630.84
MonotonicityNot monotonic
2023-12-10T23:56:55.036422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
796 2
 
2.0%
623 2
 
2.0%
963 1
 
1.0%
878 1
 
1.0%
826 1
 
1.0%
752 1
 
1.0%
487 1
 
1.0%
1762 1
 
1.0%
8 1
 
1.0%
478 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
8 1
1.0%
27 1
1.0%
30 1
1.0%
32 1
1.0%
40 1
1.0%
56 1
1.0%
60 1
1.0%
74 1
1.0%
103 1
1.0%
112 1
1.0%
ValueCountFrequency (%)
5177 1
1.0%
2895 1
1.0%
2229 1
1.0%
2224 1
1.0%
2203 1
1.0%
2032 1
1.0%
1935 1
1.0%
1765 1
1.0%
1762 1
1.0%
1648 1
1.0%
Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.771694 × 109
Minimum395959.68
Maximum2.6657437 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:55.313138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum395959.68
5-th percentile89947530
Q17.8117109 × 108
median2.3389246 × 109
Q34.121897 × 109
95-th percentile1.3598601 × 1010
Maximum2.6657437 × 1010
Range2.6657041 × 1010
Interquartile range (IQR)3.3407259 × 109

Descriptive statistics

Standard deviation5.1559163 × 109
Coefficient of variation (CV)1.3670028
Kurtosis7.77522
Mean3.771694 × 109
Median Absolute Deviation (MAD)1.6440481 × 109
Skewness2.724241
Sum3.771694 × 1011
Variance2.6583473 × 1019
MonotonicityNot monotonic
2023-12-10T23:56:55.589446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259583140.088 1
 
1.0%
10096213465.414 1
 
1.0%
594943143.792 1
 
1.0%
2130565073.548 1
 
1.0%
4673410186.253 1
 
1.0%
2590697277.681 1
 
1.0%
1257065473.304 1
 
1.0%
26657437113.0 1
 
1.0%
91445073.3829 1
 
1.0%
2933827092.694 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
395959.67996 1
1.0%
48838115.464 1
1.0%
56456812.28 1
1.0%
59048729.2204 1
1.0%
61494208.719 1
1.0%
91445073.3829 1
1.0%
105351482.0 1
1.0%
129397122.4 1
1.0%
158145452.66 1
1.0%
185103952.555 1
1.0%
ValueCountFrequency (%)
26657437113.0 1
1.0%
22781927990.42 1
1.0%
22360931387.918 1
1.0%
22225139792.633 1
1.0%
18703235868.13 1
1.0%
13329936403.22 1
1.0%
11931308214.866 1
1.0%
11906975477.74 1
1.0%
10096213465.414 1
1.0%
9236857704.211 1
1.0%
Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.25
Minimum0
Maximum1552
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:55.848697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median87.5
Q3175
95-th percentile377.15
Maximum1552
Range1552
Interquartile range (IQR)175

Descriptive statistics

Standard deviation224.54184
Coefficient of variation (CV)1.6241724
Kurtosis22.725447
Mean138.25
Median Absolute Deviation (MAD)87.5
Skewness4.2473521
Sum13825
Variance50419.038
MonotonicityNot monotonic
2023-12-10T23:56:56.116129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
27.0%
6 3
 
3.0%
66 2
 
2.0%
222 2
 
2.0%
103 2
 
2.0%
178 1
 
1.0%
97 1
 
1.0%
157 1
 
1.0%
161 1
 
1.0%
203 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0 27
27.0%
6 3
 
3.0%
7 1
 
1.0%
12 1
 
1.0%
13 1
 
1.0%
24 1
 
1.0%
26 1
 
1.0%
27 1
 
1.0%
30 1
 
1.0%
31 1
 
1.0%
ValueCountFrequency (%)
1552 1
1.0%
1330 1
1.0%
637 1
1.0%
439 1
1.0%
437 1
1.0%
374 1
1.0%
366 1
1.0%
352 1
1.0%
336 1
1.0%
312 1
1.0%
Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.028114 × 108
Minimum0
Maximum3.0325355 × 109
Zeros21
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:56.813921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176441803
median2.5460939 × 108
Q35.1117194 × 108
95-th percentile1.4533029 × 109
Maximum3.0325355 × 109
Range3.0325355 × 109
Interquartile range (IQR)4.3473013 × 108

Descriptive statistics

Standard deviation5.3853223 × 108
Coefficient of variation (CV)1.336934
Kurtosis9.5710259
Mean4.028114 × 108
Median Absolute Deviation (MAD)2.255706 × 108
Skewness2.8578486
Sum4.028114 × 1010
Variance2.9001697 × 1017
MonotonicityNot monotonic
2023-12-10T23:56:57.067621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
21.0%
312663329.993 1
 
1.0%
743786670.006 1
 
1.0%
206065546.0 1
 
1.0%
320618251.009 1
 
1.0%
187375197.006 1
 
1.0%
358533218.0 1
 
1.0%
169529269.0 1
 
1.0%
83656994.0 1
 
1.0%
125534388.0 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 21
21.0%
11967565.998 1
 
1.0%
35688782.003 1
 
1.0%
48793226.004 1
 
1.0%
54796231.998 1
 
1.0%
83656994.0 1
 
1.0%
84448881.0009 1
 
1.0%
96697817.998 1
 
1.0%
125534388.0 1
 
1.0%
128840544.001 1
 
1.0%
ValueCountFrequency (%)
3032535517.959 1
1.0%
2707452375.055 1
1.0%
2223737797.941 1
1.0%
1921055949.087 1
1.0%
1867543322.053 1
1.0%
1431500736.99 1
1.0%
993746018.009 1
1.0%
955343037.999 1
1.0%
906781606.007 1
1.0%
905633744.999 1
1.0%
Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.26
Minimum0
Maximum619
Zeros33
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:57.343489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90.5
Q3174.25
95-th percentile364.8
Maximum619
Range619
Interquartile range (IQR)174.25

Descriptive statistics

Standard deviation126.88606
Coefficient of variation (CV)1.1105029
Kurtosis2.7879494
Mean114.26
Median Absolute Deviation (MAD)90.5
Skewness1.5059914
Sum11426
Variance16100.073
MonotonicityNot monotonic
2023-12-10T23:56:57.638125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
33.0%
178 2
 
2.0%
56 2
 
2.0%
174 2
 
2.0%
44 2
 
2.0%
161 2
 
2.0%
151 2
 
2.0%
230 1
 
1.0%
175 1
 
1.0%
85 1
 
1.0%
Other values (52) 52
52.0%
ValueCountFrequency (%)
0 33
33.0%
12 1
 
1.0%
22 1
 
1.0%
30 1
 
1.0%
31 1
 
1.0%
34 1
 
1.0%
44 2
 
2.0%
56 2
 
2.0%
57 1
 
1.0%
63 1
 
1.0%
ValueCountFrequency (%)
619 1
1.0%
531 1
1.0%
468 1
1.0%
430 1
1.0%
380 1
1.0%
364 1
1.0%
338 1
1.0%
278 1
1.0%
270 1
1.0%
256 1
1.0%
Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5401814 × 108
Minimum0
Maximum1.3957391 × 109
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:57.863906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5726766 × 108
Q33.7109051 × 108
95-th percentile9.1663697 × 108
Maximum1.3957391 × 109
Range1.3957391 × 109
Interquartile range (IQR)3.7109051 × 108

Descriptive statistics

Standard deviation2.9447122 × 108
Coefficient of variation (CV)1.1592527
Kurtosis2.3011384
Mean2.5401814 × 108
Median Absolute Deviation (MAD)1.5726766 × 108
Skewness1.5343284
Sum2.5401814 × 1010
Variance8.6713298 × 1016
MonotonicityNot monotonic
2023-12-10T23:56:58.160040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
26.0%
67185168.004 1
 
1.0%
347730841.0 1
 
1.0%
922123336.009 1
 
1.0%
1040590870.99 1
 
1.0%
77950071.999 1
 
1.0%
466748681.028 1
 
1.0%
1395739143.048 1
 
1.0%
21677980.0 1
 
1.0%
544322403.034 1
 
1.0%
Other values (65) 65
65.0%
ValueCountFrequency (%)
0.0 26
26.0%
9112000.0002 1
 
1.0%
17884736.001 1
 
1.0%
21067791.0 1
 
1.0%
21677980.0 1
 
1.0%
23635330.998 1
 
1.0%
28999123.002 1
 
1.0%
46372650.998 1
 
1.0%
60261221.997 1
 
1.0%
62050072.996 1
 
1.0%
ValueCountFrequency (%)
1395739143.048 1
1.0%
1079563849.943 1
1.0%
1040590870.99 1
1.0%
1027701486.0 1
1.0%
922123336.009 1
1.0%
916348212.965 1
1.0%
827257204.008 1
1.0%
763844579.984 1
1.0%
706237123.957 1
1.0%
701734211.028 1
1.0%
Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.68
Minimum0
Maximum3482
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:58.415358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1125.75
median295.5
Q3557.5
95-th percentile1015.3
Maximum3482
Range3482
Interquartile range (IQR)431.75

Descriptive statistics

Standard deviation498.47158
Coefficient of variation (CV)1.2078889
Kurtosis19.1887
Mean412.68
Median Absolute Deviation (MAD)178.5
Skewness3.8017922
Sum41268
Variance248473.92
MonotonicityNot monotonic
2023-12-10T23:56:58.699482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
7.0%
320 2
 
2.0%
674 2
 
2.0%
177 2
 
2.0%
125 2
 
2.0%
117 2
 
2.0%
52 1
 
1.0%
559 1
 
1.0%
560 1
 
1.0%
254 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0 7
7.0%
37 1
 
1.0%
45 1
 
1.0%
48 1
 
1.0%
50 1
 
1.0%
52 1
 
1.0%
64 1
 
1.0%
68 1
 
1.0%
70 1
 
1.0%
73 1
 
1.0%
ValueCountFrequency (%)
3482 1
1.0%
2851 1
1.0%
1678 1
1.0%
1266 1
1.0%
1021 1
1.0%
1015 1
1.0%
879 1
1.0%
877 1
1.0%
809 1
1.0%
787 1
1.0%
Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9303996 × 108
Minimum0
Maximum5.7428166 × 109
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:58.988613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.8143751 × 108
median4.7199242 × 108
Q38.3264174 × 108
95-th percentile1.6559905 × 109
Maximum5.7428166 × 109
Range5.7428166 × 109
Interquartile range (IQR)6.5120424 × 108

Descriptive statistics

Standard deviation6.9972619 × 108
Coefficient of variation (CV)1.1798972
Kurtosis29.422667
Mean5.9303996 × 108
Median Absolute Deviation (MAD)2.9789599 × 108
Skewness4.435643
Sum5.9303996 × 1010
Variance4.8961673 × 1017
MonotonicityNot monotonic
2023-12-10T23:56:59.257636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
34272752.0015 1
 
1.0%
633235163.9912 1
 
1.0%
923753859.0 1
 
1.0%
1202581972.062 1
 
1.0%
243025409.9836 1
 
1.0%
528242223.023 1
 
1.0%
109338036.997 1
 
1.0%
183412180.998 1
 
1.0%
913691803.0 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 8
8.0%
19408519.002 1
 
1.0%
34272752.0015 1
 
1.0%
45693733.002 1
 
1.0%
48828305.0 1
 
1.0%
86159281.998 1
 
1.0%
86524296.9865 1
 
1.0%
96885182.004 1
 
1.0%
108048295.0004 1
 
1.0%
109338036.997 1
 
1.0%
ValueCountFrequency (%)
5742816575.527 1
1.0%
2247778352.0 1
1.0%
2090279006.948 1
1.0%
1931808242.893 1
1.0%
1921188868.01 1
1.0%
1642032690.985 1
1.0%
1202581972.062 1
1.0%
1149500487.0 1
1.0%
1142348291.012 1
1.0%
1110554749.993 1
1.0%
Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.92
Minimum0
Maximum2545
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:56:59.591820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1103
median249
Q3466.5
95-th percentile1304.9
Maximum2545
Range2545
Interquartile range (IQR)363.5

Descriptive statistics

Standard deviation453.93144
Coefficient of variation (CV)1.1916713
Kurtosis6.7505548
Mean380.92
Median Absolute Deviation (MAD)161
Skewness2.377075
Sum38092
Variance206053.75
MonotonicityNot monotonic
2023-12-10T23:56:59.907953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
12.0%
103 3
 
3.0%
106 2
 
2.0%
157 2
 
2.0%
224 2
 
2.0%
1075 1
 
1.0%
389 1
 
1.0%
410 1
 
1.0%
369 1
 
1.0%
206 1
 
1.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
0 12
12.0%
14 1
 
1.0%
25 1
 
1.0%
30 1
 
1.0%
38 1
 
1.0%
54 1
 
1.0%
57 1
 
1.0%
69 1
 
1.0%
75 1
 
1.0%
88 1
 
1.0%
ValueCountFrequency (%)
2545 1
1.0%
2041 1
1.0%
1821 1
1.0%
1437 1
1.0%
1398 1
1.0%
1300 1
1.0%
1250 1
1.0%
1140 1
1.0%
1088 1
1.0%
1075 1
1.0%
Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5928467 × 108
Minimum0
Maximum1.8603387 × 109
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:57:00.218158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1056789 × 108
median2.9885719 × 108
Q34.6582742 × 108
95-th percentile1.0446252 × 109
Maximum1.8603387 × 109
Range1.8603387 × 109
Interquartile range (IQR)3.5525953 × 108

Descriptive statistics

Standard deviation3.5725925 × 108
Coefficient of variation (CV)0.99436263
Kurtosis5.0059407
Mean3.5928467 × 108
Median Absolute Deviation (MAD)1.7764913 × 108
Skewness1.9310173
Sum3.5928467 × 1010
Variance1.2763417 × 1017
MonotonicityNot monotonic
2023-12-10T23:57:00.494592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
165059946.0066 1
 
1.0%
198627299.9991 1
 
1.0%
200865070.992 1
 
1.0%
943077967.014 1
 
1.0%
102044554.0004 1
 
1.0%
889429650.015 1
 
1.0%
460959982.984 1
 
1.0%
1844421589.155 1
 
1.0%
435896187.0 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 10
10.0%
18033754.0 1
 
1.0%
18525621.0021 1
 
1.0%
27808222.9978 1
 
1.0%
35692169.0006 1
 
1.0%
45460339.0 1
 
1.0%
47088907.0032 1
 
1.0%
48810165.0002 1
 
1.0%
54090378.0006 1
 
1.0%
55166240.0 1
 
1.0%
ValueCountFrequency (%)
1860338732.035 1
1.0%
1844421589.155 1
1.0%
1244308586.0 1
1.0%
1099828153.0 1
1.0%
1052493706.936 1
1.0%
1044211098.911 1
1.0%
1022371314.993 1
1.0%
1010349569.0 1
1.0%
943077967.014 1
1.0%
889429650.015 1
1.0%

Interactions

2023-12-10T23:56:51.026238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:34.016310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:35.792939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:37.957449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:39.714607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:41.435116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:43.125754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:44.996259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:47.229712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:49.228154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:51.195860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:34.174875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:35.982263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:38.095007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:39.886133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:41.593790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:43.317942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:45.173979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:47.391001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:49.384310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:51.363592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:34.342550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:36.146882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:38.261909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:40.082312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:41.742371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:43.501738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:45.358250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:47.577572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:49.556374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:51.595739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:34.531122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:36.336868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:38.465181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:40.270941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:41.921597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:43.698708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:45.566694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:47.781963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:49.741086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:51.779427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:34.711649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:36.515510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:38.645326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:40.433041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:42.072709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:43.875995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:45.757521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:48.004881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:49.925657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:51.976915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:34.896483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:36.690377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:38.816811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:40.588163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:42.222275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:44.040324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:45.944832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:48.242229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:50.102881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:52.187873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:35.083391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:36.849627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:38.995810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:40.771733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:42.403013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:44.206334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:46.128602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:48.440015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:50.291925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:52.374757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:35.291812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:37.034505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:39.183375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:40.951787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:42.574224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:44.397332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:46.322548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:48.666750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:50.475424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:52.559343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:35.455746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:37.589591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:39.351878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:41.105643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:42.738589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:44.611664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:46.876081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:48.848825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:50.651840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:52.749680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:35.619940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:37.772281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:39.527220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:41.263790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:42.912330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:44.793168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:47.038275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:49.013222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:50.823402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:57:00.734057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
그리드코드(GRID50_ID)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)
그리드코드(GRID50_ID)1.0000.5700.0000.6260.0000.0000.9290.8430.9290.0000.000
총수신평잔_건수(DEP_TOT_AVJN_N)0.5701.0000.0000.0000.1260.0500.0000.0000.0000.0000.000
총수신평잔_총액(DEP_TOT_AVJN_TOT)0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.440
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)0.6260.0000.0001.0000.0000.0000.3520.0000.0000.5160.000
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)0.0000.1260.0000.0001.0000.0000.4040.0000.0000.0000.618
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)0.0000.0500.0000.0000.0001.0000.0520.0000.0000.0000.349
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.9290.0000.0000.3520.4040.0521.0000.0000.3680.2150.000
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)0.8430.0000.0000.0000.0000.0000.0001.0000.1720.0830.000
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)0.9290.0000.0000.0000.0000.0000.3680.1721.0000.0000.000
신용카드_체크카드_건수(C_CARDSUM_AMT_N)0.0000.0000.0000.5160.0000.0000.2150.0830.0001.0000.000
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)0.0000.0000.4400.0000.6180.3490.0000.0000.0000.0001.000
2023-12-10T23:57:01.064247image/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)1.000-0.063-0.207-0.044-0.0130.067-0.2510.043-0.022-0.106
총수신평잔_총액(DEP_TOT_AVJN_TOT)-0.0631.0000.053-0.038-0.1400.0060.090-0.0100.011-0.141
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)-0.2070.0531.0000.1370.030-0.0590.184-0.105-0.002-0.238
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)-0.044-0.0380.1371.000-0.1050.128-0.089-0.047-0.1200.024
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)-0.013-0.1400.030-0.1051.0000.0850.057-0.0140.161-0.050
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.0670.006-0.0590.1280.0851.0000.014-0.0480.158-0.115
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)-0.2510.0900.184-0.0890.0570.0141.0000.1990.088-0.144
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)0.043-0.010-0.105-0.047-0.014-0.0480.1991.000-0.0160.040
신용카드_체크카드_건수(C_CARDSUM_AMT_N)-0.0220.011-0.002-0.1200.1610.1580.088-0.0161.0000.173
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)-0.106-0.141-0.2380.024-0.050-0.115-0.1440.0400.1731.000

Missing values

2023-12-10T23:56:52.999564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:56:53.326592image/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

그리드코드(GRID50_ID)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)
0G*0*2*8*3*963259583140.08866312663329.99361967185168.00432034272752.0015172165059946.0066
1G*0*0*3*0*237747144431.540203387746.006690.07862247778352.04641099828153.0
2G*0*1*7*6*1912970120422.128352322823101.998174464152276.991471271516023.0072545123753060.9992
3G*0*0*9*2*22243046520777.7021471431500736.990583060679.00336096885182.004406264846240.9938
4G*0*1*6*5*22031893509167.627242522449947.025134384361581.026740.00130854773.9978
5G*0*0*6*7*4702095754911.64415854796231.99811660261221.99750175513489.0011821621686349.9005
6G*0*1*5*8*297572097387.5371463032535517.95900.0213561387576.9891571244308586.0
7G*0*2*7*5*11122658757561.6190344487324.990429593649.011177190838011.98200.0
8G*0*1*3*4*74642621812.8452169581467962.0190763844579.9840480478687.03103317330819.9828
9G*0*1*2*4*15861726126589.4140236207070.0186156701734211.028125979046541.090120424029.001
그리드코드(GRID50_ID)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)
90G*0*0*2*0*28954620872282.27954505528967.0010232118251.0090153010018.007412418525621.0021
91G*0*1*9*6*2712028792642.84549328862708.993125257997784.999383268324606.0932110892100.01
92G*0*0*4*9*9716761404239.2610232765285.014222248725151.0287228225370.0113224295740225.005
93G*0*1*0*7*1765312512019.12159539752420.988468170111726.003358594505099.0331180814833179.9934
94G*0*0*3*0*62322225139792.632999437343375403.9951150.0809323611425.0113069411412.9958
95G*0*2*1*0*8593878744904.054391867543322.053178108861122.0021015716083240.9251398341210757.0
96G*0*0*1*9*10879228342471.3829996259977830.0010154233580.0002117476671460.01061044211098.911
97G*0*1*4*9*63156456812.280905633744.9990127790790.002135919978143.983275345381209.026
98G*0*2*8*3*12014930475505.51727149869025.9901017884736.001771142348291.012211251801764.9895
99G*0*0*2*2*6772864999496.8053080.0278827257204.008556306531187.02323870.0